Cross-layer Optimized Networking for Next-Generation 5G Ad Hoc Networks A Dissertation Presented by Jithin Jagannath to The Department of Electrical and Computer Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering Northeastern University Boston, Massachusetts August 2019
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Cross-layer Optimized Networking for Next-Generation 5G Ad Hoc
Networks
A Dissertation Presented
by
Jithin Jagannath
to
The Department of Electrical and Computer Engineering
in partial fulfillment of the requirements
for the degree of
Doctor of Philosophy
in
Electrical Engineering
Northeastern University
Boston, Massachusetts
August 2019
To my family for all the support and all the sacrifice they have made to empower me to achieve
2.4 Performance Evaluation Through Simulation . . . . . . . . . . . . . . . . . . . . 172.4.1 Scenario 1: Network performance as the number of session increases (All
sessions started at random time) . . . . . . . . . . . . . . . . . . . . . . . 172.4.2 Scenario 2: Network performance as the data rate of the sessions increase . 192.4.3 Scenario 3: Examining the effect of different components of DRS . . . . . 20
OFDMA Orthogonal Frequency Division Multiple Access
OLSR Optimized Link State Routing
OOC Optical Orthogonal Codes
O-OFDMA Optical Orthogonal Frequency Division Multiple Access
O-OFDM-IDMA Optical Orthogonal Frequency Division Multiplexing Interleave Division Multi-ple Access
OOK On-Off Keying
OWC Optical Wireless Communication
OWMAC Optical wireless MAC
PD Photon Detector
PHR PHY Header
PHY Physical
PRO-OFDM Polarity Reversed Optical OFDM
QoS Quality of Service
RA Random Access
RES Reserve Sectors
RF Radio Frequency
ROC Random Optical Codes
RPI Raspberry Pi
RRS Route Reliability Score
x
RSSI Received Signal Strength Indication
SACW Self-Adaptive minimum Contention Window
SDR Software Defined Radio
SH Static HELPER
S-IDLE Synchronous Idle State
SINR Signal-to-Interference-plus-Noise power Ratio
SNR Signal-to-Noise power Ratio
SWaP (Size, Weight, and Power)
TDD Time Division Duplex
TDMA Time Division Multiple Access
THP Tomlinson-Harashima Precoding
TIA Telecommunication Industry Association
TR Transceiving State
UHD Universal Hardware Driver
USRP Universal Software Radio Peripheral
UV Ultraviolet
UVC Ultraviolet Communication
VANET Vehiclular Ad Hoc Network
VHF Very High Frequency
VLC Visible Light Communication
VLN Visible Light Node
V2I Vehicle to Infrastructure
V2V Vehicle to Vehicle
VQ Virtual Queue
VQL Virtual Queue Length
Web App Website Application
WEA Wireless Emergency Alerts
xi
WiFi Wireless Fidelity
WiMAX Worldwide Interoperability for Microwave Access
WSN Wireless Sensor Network
ZF Zero Forcing
xii
Acknowledgments
Being a non-traditional graduate student working full-time, pursuing a doctoral degreewould have been impossible without the immense support and consideration of several people whohave played a crucial role along the way. Firstly, I would like to express my sincere gratitude tomy advisor Dr. Tommaso Melodia for his continuous support of my doctoral study and relatedresearch work. He has been a source of inspiration since the time I joined his group during my M.Sat University at Buffalo. Well-aware of his busy schedule, I am grateful for his patience, motivation,and immense knowledge that has guided me through this process. I have always felt more motivatedand determined after each of our meetings and discussions. I cannot imagine having a better advisorand mentor for my entire graduate studies. Similarly, I would like to express my sincere gratitudeto my supervisor, Mr. Andrew Drozd, who motivated me to pursue graduate school while workingat his company. He has been extremely flexible and accommodating with my requests and needsfor any requirement that I have put forth in this regards. I sincerely thank him for all his support,guidance and inspiration which was a key factor that has helped me achieve this goal.
Besides my advisor, I would like to thank the rest of my committee members; Dr. StefanoBasagni and Dr. Kaushik Roy Chowdhury, for their feedback, comments, and encouragement ateach stage of this process. I want to especially thank them for their time and consideration toaccommodate my request for appointments through their busy schedule even when it was out of theregular schedule.
I would also like to thank my labmates at Wireless Networks and Embedded Systems(WINES) Lab and at ANDRO Computational Solutions for stimulating discussions and workingon projects together. In particular, I would like to thank Sean Furman who was a key member ofmy team at ANDRO. He served as a co-author on a couple of papers that were published duringthis period. I also would like to thank Jessica Griffin and Ramona Smith of our HR department forproctoring my exams for all my remote courses during this period.
I am beyond grateful to my parents and my sister for supporting throughout my life andvalue their immense sacrifices to ensure success in my life. I would not have reached my goal withoutthe constant motivation, support and sacrifice they have made for years at every step of my life.Last but not the least, I would not have started, continued and completed this process without theconstant encouragement and support by my wife, Anu Jagannath, who has been a pillar of supportthroughout this academic pursuit. Being a scientist herself, she has not only been my strength andsupport system but also my colleague and co-author on several of my publications. I will cherish theextended discussions we had at work, in our car, and at home that has led to some interesting ideasand solutions. I am grateful for everything she has done to make this process as easy as possible forme. This would have been unattainable without her reinforcing attitude throughout this process.
xiii
Abstract of the Dissertation
Cross-layer Optimized Networking for Next-Generation 5G Ad Hoc
Networks
by
Jithin Jagannath
Doctor of Philosophy in Electrical Engineering
Northeastern University, August 2019
Dr. Tommaso Melodia, Advisor
The exponential growth of devices that rely on wireless communication to operate hasintroduced significant stress on the limited resources. Additionally, to offset the cost of installingnew infrastructure and to maximize revenue, 5G (5th Generation) network providers are expectedto extend their services beyond traditional cellular communication to support Internet-of-Things(IoT) and machine-to-machine ad hoc communication. To accommodate all these devices over thenext decade, there are two key research directions that need to be adopted; (i) optimizing the use ofavailable resources to meet the application-specific quality of service (QoS) requirements and (ii)develop technology that enables the utilization of unexplored and unlicensed parts of the spectrum tocomplement the current radio frequency (RF) based networks. This work focuses on how cross-layeroptimized algorithms can be the answer to both these requirements for the next-generation of 5Gnetwork.
First, cross-layer optimization is employed to enable both tactical and emergency ad hocnetworks to meet their specific requirements. To this end, a Deadline-based cross-layer Routingand Spectrum allocation (DRS) algorithm is proposed for tactical ad hoc networks to handle theheterogeneous nature of traffic. This work also puts forth a cross-layer architecture that will enableimplementation and evaluation of such cross-layer optimized routing algorithms. The proposedsolution is evaluated on a software-defined radio (SDR) testbed and shown to outperform state-of-the-art routing algorithms. Next, to aid modern emergency response, a low cost Heterogeneous EfficientLow PowEr Radio (HELPER) network is designed and developed to provide complete end-to-endconnectivity for both survivors and first responders. This is realized by designing an energy-aware routing algorithm that aims to maximize network lifetime. The operational feasibility of the
xiv
proposed HELPER network is demonstrated by developing HELPER prototype using Commercial-Off-The-Shelf (COTS) components. Thereafter, extensive quantitative evaluation is performed onthe developed HELPER testbed.
Visible Light Communication (VLC) is envisioned as a major 5G technology that canbe complementary to RF and help mitigate the congestion in the RF spectrum. Visible Light AdHoc Networks (LANET) have the potential to offer capabilities to satisfy growing industrial andmilitary requirements, including low-latency, high bandwidth communication under high networkdensity. The challenges imposed by hidden nodes, deafness and blockage are unique to LANETand influence the network differently from traditional Mobile Ad Hoc Networks (MANET) dueto directionality and Line of Sight (LOS) requirements. Therefore, networking protocols have tobe redesigned with careful consideration of these challenges. These unique challenges demandconsideration of networking problems from the cross-layer perspective. As a significant step inrealizing LANETs, this work first proposes an opportunistic Medium Access Control (MAC) protocoldesigned specifically to mitigate challenges due to deafness, hidden node problem and maximizethe utilization of full-duplex communication. Next, to advance the development of LANETs, adistributed cross-layer routing protocol (VL-ROUTE) that interacts closely with the MAC layerto maximize the throughput of LANET is proposed. In this manner, a LANET with a cross-layeroptimized link and network layer has been successfully designed and evaluated for bolstering thefuture 5G ad hoc networks.
xv
Chapter 1
Introduction
As the relevance of devices relying on wireless communication keeps gaining momentum
in all walks of our modern life, the demand for scarce resources will increase exponentially as the
deployment of 5th Generation (5G) networks progresses. It is estimated that by 2020, over 50 billion
devices will be absorbed into the Internet, generating a global network of “things” of dimensions
never seen before [1]. Given that only a few radio spectrum bands are available to the wireless carriers
[2], battling spectrum congestion while ensuring Quality of Service (QoS) will become the prime
goal in the upcoming decade. Additionally, the reliance on wireless communication has rendered it
an unavoidable component of critical applications including military missions, emergency response,
and medical services. To ensure these applications can sustain the growing number of devices,
novel networking paradigms such as cross-layer optimization need to be exploited to maximize the
utilization of the available spectrum and expand to the unutilized parts of the spectrum.
To this end, in Chapter 2, the Deadline-based cross-layer Routing and Spectrum allocation
(DRS) algorithm is proposed for a tactical ad hoc network. In a tactical ad hoc network, there
exists a constant tension between available resources and the required QoS performance. Nodes
in the network have to deal with severe interference, spectrum crunch, adversarial jamming and
changing network topologies. Additionally, a typical tactical network is required to handle various
traffic classes including regular sampling data, voice, surveillance video, threat alert, among others.
Each of these traffic classes has substantially different QoS based deadline requirements. In these
scenarios, it becomes important to examine the interaction between spectrum management, routing
and session management to develop cross-layer optimized algorithms capable of maximizing the
effective throughput of the network. The proposed solution is extensively evaluated using simulations
and on a Software Defined Radio (SDR) based testbed. The work in this chapter has been presented
1
CHAPTER 1.
at IEEE Conference on Military Communications held on November 2016 at Baltimore, MD, and
at IEEE Global Communications Conference, Washington D.C. on December 2016. The results
and insight obtained from the maturation of this work were also published in IEEE Transactions on
Mobile Computing in 2018.
In the past years, several lives have been devastated by hurricanes, tsunami, floods, earth-
quakes, and other natural disasters. Similar natural and man-made disasters are undesirable but
sometimes unavoidable [3, 4]. In these scenarios, one of the most critical infrastructures affected is
often communication networks [5, 6]. Clearly, wireless communication is an essential component
to maintain connectivity for such alert systems, Emergency Responders (ERs) as well as affected
individuals when traditional infrastructures like cell towers are affected or unavailable. Therefore, in
Chapter 3, an end-to-end solution, Heterogeneous Efficient Low PowEr Radio (HELPER) network is
proposed for emergency ad hoc network to connect survivors and first responders in the aftermath of
disasters. The initial stages of this work were presented at IEEE Consumer Communications and
Networking Conference held at Las Vegas, NV, in January 2019. A comprehensive article discussing
the development of the prototype, experiments, and demonstration of the HELPER network was
published in Ad Hoc Networks (Elsevier) in March 2019.
Visible Light Communication (VLC) has come to the forefront with the advent of modern
Light Emitting Diode (LED) technology that consumes low power and has a short response time. The
unexploited, unregulated spectrum (400 to 800 THz) is a promising candidate to alleviate the Radio
Frequency (RF) spectrum crunch. The exploration of VLC has been limited to various point-to-point
applications including setting up Light Fidelity (Li-Fi) [7] networks using smart lights, among others.
In this context, several topologies such as peer-to-peer, star and broadcast have been considered to
design Medium Access Control (MAC) protocols. This work concentrates on exploiting VLC for ad
hoc networking in military and civilian applications. Visible Light Ad Hoc Networkss (LANETs)
are envisioned to contribute significantly to the upcoming Internet of Things (IoT) revolution in both
indoor and outdoor spaces. Some indoor applications of LANETs include Device-to-Device (D2D)
communication to support IoT technology, enabling indoor positioning system, and aiding the
traditional RF based networks [8, 9]. A simple example is the devices (TV, thermostat among others)
in a smart home forming a LANET. In outdoor scenarios, one of the most promising applications
for LANET is related to vehicular communications. LANETs can also be used for air, ground
and underwater tactical missions such as Intelligence, Surveillance, and Reconnaissance (ISR)
missions entailing deployment of ships, soldiers, and unmanned surface vehicles. LANETs can also
be used in high-security military areas where RF communication is prone to eavesdropping or is
2
CHAPTER 1.
extremely congested. Therefore, as an initial step towards making LANETs a reality, Chapter 4 and
Chapter 5 propose novel MAC and routing protocols that are designed specifically to overcome the
challenges posed by LANETs. Some portion of Chapter 4 was also published in Ad Hoc Networks
(Elsevier) in 2018, introduced the overall concept of LANETs to the community. The MAC and
routing protocols were presented at the International Conference on Computing, Networking and
Communications during March 2018; and IEEE Symposium on a World of Wireless, Mobile, and
Multimedia Networks in June 2019 respectively. Finally, the conclusion of this work is presented in
Chapter 6.
3
Chapter 2
DRS: Deadline Based Routing and
Spectrum Allocation for Tactical Ad Hoc
Network
In a tactical ad hoc network, there exists a constant tension between available resources
and the required QoS performance. Nodes in the network have to deal with severe interference,
spectrum crunch, adversarial jamming and changing network topologies. Additionally, a typical
tactical network as depicted in Fig. 2.1 is required to handle various traffic classes including regular
sampling data, voice, surveillance video, threat alert, among others. Each of these traffic classes has
substantially different QoS based deadline requirements. For example, periodic surveillance data
might have looser deadline constraints when compared to a video or threat alert message. In these
delay-intolerant networks, only packets that arrive at the destination within the specified deadline are
viable and contribute to the overall network throughput. In these scenarios, it becomes important to
examine the interaction between spectrum management, routing and session management to develop
cross-layer control algorithms capable of maximizing the effective throughput of the network. In this
chapter, only the packets that arrive at the destination within the specified deadline is considered in
the computation of effective throughput.
Cognitive radio technology along with various dynamic spectrum access (DSA) techniques
have been proposed to improve the spectrum utilization of the network by enabling opportunistic
access of free spectrum chunks. In previous work [10], an optimization algorithm (ROSA) was
proposed to jointly select route and spectrum such that overall network throughput is maximized. This
4
CHAPTER 2.
Satellite Links
Unmanned Aerial Vehicles (UAVs)Airborne Relays
Vehicular Relays
Dismount Soldiers UsingRifleman Radios
Figure 2.1: Tactical ad hoc network.
algorithm combines the idea of backpressure algorithm [11] with channel-dependent opportunistic
routing. Simulations show that ROSA outperforms the traditional algorithms that use either dynamic
spectrum allocation with fixed route or dynamic routing with fixed spectrum allocation. In this
chapter, ROSA’s weakness has been realized to significantly extended the formulation to examine
the network performance in terms of effective throughput and reliability for multiple sessions with
different deadline constraints. Proposed solutions enable such routing and resource allocation
algorithms to handle heterogeneous QoS based traffic classes efficiently. Accordingly, a distributed
deadline-based optimization algorithm is developed for tactical ad hoc networks. Some of the
challenges in designing a deadline-based algorithm are as follows:
• Each node has to carefully manage multiple sessions to meet the deadline requirements.
For example, sessions with longer backlogs and larger deadlines can be held back while
• Adopting an effective resource allocation procedure that would negotiate the access of medium
and choose optimal transmission parameters. In a large network, the spectrum occupancy
varies based on location and time, thus nodes may have to use different parts of the spectrum
in order to route a session in the most effective manner.
• Choosing appropriate routes to meet the needs of each session belonging to different traffic
5
CHAPTER 2.
classes while adapting to broken routes or failed nodes by choosing alternate paths.
• The design should be scalable, reduce communication overhead and yet enable the network to
adjust dynamically to the available resources. Therefore, it is critical to design a distributed
approach that is feasible on a practical network.
Therefore, the overall objective of this chapter is to design and evaluate a distributed
algorithm that utilizes the available resources to determine optimal route, session, and spectrum to
deliver the maximum number of packets to their intended destination within the specified deadline.
The weighted virtual queue (VQ) used in cross-layer optimization ensures proper management of
the sessions. The virtual queue length (VQL) takes into account deadlines associated with each
packet. The joint routing and spectrum allocation aspects of the algorithm provide optimal resource
allocation and enable opportunistic routing. The distributed nature of the proposed algorithm along
with forward progress based routing helps the network to recover from broken routes or failed nodes.
These features are critical in any delay-intolerant applications and will be especially useful in tactical
ad hoc networks where the delayed delivery of critical information in a multihop network can be
fatal.
2.1 Related Work
Dynamic spectrum allocation has been widely investigated with the objective to maxi-
mize spectrum utilization and is mainly divided into centralized [12, 13] and distributed [10, 14]
approaches. While spectrum allocation techniques are designed to improve spectrum utility-based
QoS [15, 16, 17], queue length based backpressure (Q-BP) scheduling algorithm was first proposed
in [11] and was shown to be throughput optimal in terms of achieving network stability under any
feasible load. It is well known that the Q-BP algorithm suffers from high computational complexity
and the last packet problem. To reduce the computational delay for practical implementation, a
greedy maximum scheduling (GMS) algorithm is studied in [18, 19, 20]. This algorithm first chooses
the link l with maximum weight from the set of all links S and eliminates links that interfere with l
from the set S . Next, it again picks the link with maximum weight among the remaining links of
set S and eliminates the link causing interference to it. This process is repeated until all links have
been considered. The trade-off here is the reduced network capacity. In [21], the authors solve a
centralized network throughput maximization problem that uses the backpressure algorithm. The
study also implements the solution on hardware to perform the evaluation. Even though the network
6
CHAPTER 2.
achieves throughput improvement, the network may be prone to last packet problem which is a
crucial hindrance for tactical networks. The last packet problem of Q-BP algorithm arises because of
the assumption that flows have an infinite amount of data packets being injected into the network.
Instead, in practical networks, the flows may be finite with some flows terminating and new flows
emerging. When a finite flow has the last packet in the queue, it may be stagnant for an extended
period of time because of the presence of other queues with a larger backlog. This is referred to as the
last packet problem. It has been shown that in these cases, queue length based schemes may not be
throughput optimal [22]. Accordingly, there has been considerable work on delay-based scheduling
[23, 24, 25, 26, 27, 28, 29] to improve the delay performance of the network and eliminate the last
packet problem.
In [23], the authors use a shadow queuing architecture so that each node maintains only one
queue per neighbor (irrespective of sessions) to reduce the complexity of the queuing structure and
improve the delay performance at the cost of throughput. Each node still has to maintain a separate
shadow queue (a counter) for every flow going through the node. The backpressure algorithm is
executed using the shadow queue counters and these counters are updated according to the optimal
number of shadow packets chosen to be transmitted over each link. The key point here is that the
number of shadow packets is like a permit to transmit on the given link from the real queue but not
associated with the flow of the shadow packet itself. The packet injection rate of the shadow queue is
kept slightly higher than the actual packet injection rate. The rates are designed as follows: if the
packet injection rate of the shadow queue is xt(t), the rate of the real queue is given by βxt(t), where
β is a positive real number smaller than one. Therefore, if the number of real packets in the queue is
less than the number of shadow packets to be transmitted, all the real packets in the corresponding
queue are transmitted. The authors show that the real queue length decreases uniformly at every node
as the value of β decreases, thus leading to lower delays by Little’s law. This decrease in the delay is
accompanied by reduced throughput performance. Maintaining a single queue per neighbor is only
beneficial in scenarios where the number of flows through a node is much greater than the number of
neighbors. Authors propose a self-regulated MaxWeight scheduling algorithm in [30], where each
node estimates the aggregated link rate. They prove that the self-regulated MaxWeight scheduling is
throughput-optimal (i.e. stabilize any traffic that can be stabilized by any other algorithm) when the
traffic flows are associated with fixed routes and the packet arrivals follow some statistical property.
Both [23] and [30] are designed for fixed route scenarios, thus lacking the improvement that could
be achieved by opportunistic routing.
In [24], the authors propose a delay-limiting algorithm to control the burstiness and delays.
7
CHAPTER 2.
They adapt the upper limit for the physical queues to ensure an upper per-hop delay limit at the
expense of throughput. To ensure that nodes in the network remain operational, a lower bound has to
be set on the upper queue limit. If the traffic reduces to a point such that lower bound comes into
play, the delay-limiting approach becomes ineffective. There is also a trade-off between delay and
the degree of multipath and opportunism. As the traffic is spread spatially to utilize multiple routes,
the lower bound on the queue may again render the delay control ineffective.
A cross-layer design is proposed in [25] using VQ structures to provide finite buffer size or
worst-case delay performance. In [26], the authors design a delay-aware joint flow control, routing,
and scheduling algorithm for a multihop network to maximize network utilization. However, due
to their ([26], [25]) centralized nature and high complexity they are not well suited for practical
distributed implementation [31]. In [27], a throughput optimal scheduling algorithm is proposed using
the largest weighted delay first algorithm. The idea is to serve the queue j for which γ jWj(t)r j(t) is
maximal, where Wj( j) is the weighted delay and r j(t) the achievable capacity for link j. Although
this algorithm is an easy and distributed way to achieve throughput optimality, this formulation does
not take into account the dynamic routing possibilities or queuing dynamics of multihop traffic. Since
[27] fails to capture the queuing dynamics of multihop traffic, a new delay metric is defined in [28]
to establish a linear relation between queue length and delay. The authors also propose a greedy
algorithm that is similar to GSM discussed earlier but uses delay differential rather than queue length.
Simulations show that the average queue length of the network is similar in Q-BP and delay-based
backpressure (D-BP) but the tail of the delay distribution is much longer for Q-BP. This implies that
some queues are stagnant over extended periods of time in Q-BP whereas D-BP reduces this problem.
Unlike the proposed algorithm, D-BP is designed for fixed routes and does not consider dynamic
routing.
In [29], a delay-driven MaxWeight scheduler is presented that gets around the last packet
problem and addresses instability of the queue length based algorithms caused by rate variations.
However, it has been shown in [32, 33] that there are other factors that contribute to the inefficiency
of the back-pressure algorithm including, inefficient spatial reuse, failure to opportunistically exploit
better link rates, underutilized link capacity and inefficient routing because of insufficient path
information.
Deadline-based routing has been recently studied in [34, 35] and [36]. In [34], an utility-
based algorithm is proposed for cyclic mobile social networks under the assumption that nodes
follow cyclic mobility, periodically encountering each other with high probability. It is difficult to
extend [34] to tactical ad hoc networks without apriori knowledge of the encounter probability. To
8
CHAPTER 2.
increase the packet delivery ratio, [35] adopts an epidemic based routing algorithm and [36] proposes
a capacity-constrained routing algorithm that decides which packets have to be replicated. The
replication strategies proposed in [35] and [36] to improve the packet delivery ratio may adversely
affect the achievable throughput. The major contributions of the work presented in this chapter can
be outlined as follows,
• A novel deadline-based joint routing and spectrum allocation algorithm is proposed for tactical
ad hoc networks to meet the deadline requirements of multiple sessions. To the best of the
author’s knowledge, this is the first work that combines the interaction of opportunistic routing,
spectrum allocation and deadline constraints to maximize the effective throughput of tactical
ad hoc networks.
• The proposed algorithm is able to adapt to the needs of a dynamic network by managing
multiple sessions with variable QoS. This is accomplished by making an optimal choice about
the session, route, spectrum and power allocation used to maximize the utilization of available
resources.
• A distributed approach is formulated to enable the implementation of the proposed algorithm
in a scalable manner.
• Performance of the proposed algorithm is extensively evaluated under various simulated
scenarios.
• Another major contribution of this chapter is the development of a cross-layer experimental
framework using SDR.
• Finally, to prove the practicality of the proposed algorithm, the deadline-based joint routing and
spectrum allocation algorithm is successfully implemented on this cross-layer SDR testbed.
The rest of the chapter is organized as follows. In Section 2.2, the system model is
described in detail. The design of the deadline-based routing algorithm is discussed in Section
2.3. Next in Section 2.4, a 49 node ad hoc network is simulated to evaluate the performance of the
proposed algorithm. The design and configuration of the cross-layer testbed are described in Section
2.5. The experimental evaluation of the proposed algorithm on a SDR based testbed is discussed in
Section 2.6. Finally, the summary is provided in Section 2.7.
9
CHAPTER 2.
2.2 System Model
Consider a multihop tactical ad hoc network with M primary users and N secondary users
modeled as a directed connectivity graph G(U,E), where U = {u0,u1, ...,uN+M} is a finite set of
wireless transceiver (nodes), and (i, j) ∈ E represents unidirectional wireless link from node ui to
node u j (for simplicity, they are referred to as node i and node j). G is assumed to be link symmetric,
i.e., if (i, j) ∈ E , then ( j, i) ∈ E . The nodes from the subset P U = {u1, ...,uM} are designated as
primary users, and nodes from the subset SU = {uM+1, ...,uM+N} are designated as secondary users.
The secondary network is composed of cognitive nodes capable of adapting to the current spectrum
usage. The primary users hold the license for the specific spectrum bands and have full access to
the spectrum without interference from any other users. In relevant scenarios, the primary user can
also be a non-cooperative node (the adversary). Since the entire spectrum is not always used by
primary users, the aim of the secondary user in a cooperative scenario is to maximize spectrum utility
while ensuring no interference to primary users. Thus, a secondary user has to use the spectrum
holes [10] to maximize the spectrum usage. The secondary network will also allocate resources such
that it maximizes the number of packets delivered at the destination within their respective deadline.
Only packets that reach the destination within the specified deadline contribute towards the effective
throughput computation. The set of neighbors for node i is given by N B i , { j : (i, j) ∈ E}.The secondary users are equipped with cognitive radios capable of scanning the available
spectrum to reconfigure their transceivers on-the-fly. The entire available spectrum is given by BW .
The cognitive transceiver is capable of tuning to a set of contiguous frequency bands [ f , f +∆B],
where ∆B is the bandwidth of the cognitive radio and ∆B < BW . It is assumed that the transmit
power can be varied to exploit any available spectrum opportunity. The spectrum opportunity is
defined as the limited availability of spectrum that might currently be used by nodes (primary or
secondary users) but can be further exploited by adjusting the transmit power such that it does not
violate the Bit Error Rate (BER) constraint of the existing transmission. This work is intended for
any general physical layer but it is assumed that multiple transmissions can occur concurrently on
the same frequency band, e.g., with different spreading codes.
The total spectrum, BW is divided into separate channels, a Common Control Channel
(CCC), and a data channel. All secondary nodes use CCC to share local information for spectrum
negotiation and data channel is used exclusively for data communication. The data channel is divided
into discrete set of carriers { fmin, fmin+1, ..., fmin−1, fmax}, each of bandwidth b and identified by a
unique discrete index. The cognitive radio of the secondary user can tune into a consecutive set of
10
CHAPTER 2.
carriers from [ fmin, fmax]. Let the traffic in the network consist of multiple sessions characterized
by the source-destination pair and the application generating the session. The arrival rates of each
session si ∈ Si at node i is given by λsi (t), and characterized by vector of arrival rates Λ.
2.3 Deadline-Based Routing and Spectrum Allocation
2.3.1 Network Utility Function
Consider that the tactical ad hoc network is assumed to operate over a time slotted channel.
The spectrum utility function is calculated by node i for every time slot t when node i is backlogged
and not already transmitting or receiving packets. Each node i maintains a separate VQ for each
session. Qsi (t) is defined as the VQL formed by packets of session s in node i at time slot t. Unlike
traditional queue length, the VQL gets inflated as time passes to penalize the node for holding packets
whose deadline is approaching. More details about the design of VQL is discussed below. For each
packet qsi ∈ Qs
i (t), that belongs to session s and stored at node i, a set of fields are defined, including,
• L(qsi ) is the length of the packet in bits,
• Tr(qsi ) is the remaining life time of the packet, which is based on the deadline D(qs
i ) assigned
to the packet at the source node,
• Td(qsi ) is the time to the destination as estimated at node i.
Based on these parameters, a weight wqsi[L(qs
i ),Td(qsi ),Tr(qs
i )] can be defined for each packet qsi ∈
Qsi (t) as follows,
wqsi(L(qs
i ),Td(qsi ),Tr(qs
i )) =L(qs
i )
max(Tr(qsi ),τ)max(Tr(qs
i )−Td(qsi ),τ)
(2.1)
Examining (2.1), it can be seen that the weight wqsi
assigned to each packet is directly
proportional to L (for simplicity, qsi is removed from these notations) and inversely proportional
to Tr and Td . The τ in (2.1) is a very small value used to avoid negative and infinite weights. The
parameter Tr helps to get rid of the well-known last packet problem since Tr will increase the VQL
as time elapses. This can be interpreted as the holding penalty imposed for packets being stagnant in
the queue for an extended period of time. Since Tr is dependent on the assigned deadline, it helps the
nodes to manage different sessions by pushing critical packets faster even if the actual queue length
is comparatively smaller. Considering just the deadlines alone will not help in cases where there
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CHAPTER 2.
are two sessions with the same deadline but one is farther away from the destination than the other.
In such cases, Td will ensure that the session farther away from the destination moves through the
network at a faster rate compared to similar sessions closer to the destination. Therefore, Td can be
considered as a variable that either amplifies or diminishes the effect of Tr depending on the time
required to reach the destination. Td also encourages packets to take shorter routes if all other factors
like queue length and spectrum are the same for two different routes. The rationale will become
more evident when the network utility function used for the proposed algorithm is discussed.
Among these three parameters, the exact value of Td is not available at each node and has
to be estimated at each hop. For a centralized network, assuming global knowledge of the network,
Td can be estimated using average queuing delays, transmission rate, propagation delays and using
the knowledge of average delays experienced previously by packets with the same destination.
Estimating Td becomes further challenging in a distributed network where each node is required to
make decisions without global knowledge of the network. One solution is to estimate Td by using
queuing delay experienced by the session in the node itself. This information is used to slightly
overestimate the delay by assuming that the packet has to route through more than one node within
its transmission range itself. Underestimating Td would increase the risk of packets not reaching
the destination within the specified deadline. Therefore, Td is slightly overestimated according to
the characteristics of the network. Since Td is updated at every hop, the estimation error/margin
decreases as the packet moves closer to the destination. This method does not lead to any error
propagation since the value is updated at each hop. A simple way to estimate Td is based on distance
to destination (d), communication range (estimated based on maximum transmit power) of the nodes
deployed (R) and average time spent by the packet during each hop (Th) (estimated based processing
delay, queuing delay, transmission delay and propagation delay). The idea is to assume that a hop is
required every half range of a node and is given by α = R/2. Accordingly, an estimate of how much
time is required to reach the destination can be given as,
Td =d Th
α=
2d Th
R. (2.2)
The value of α can be varied according to the density of the network. Now from the
definition of weights, it can be seen that higher value is assigned to packets with more bits to transmit,
lower Tr and which are farther away from the destination. Accordingly, Virtual Queue Length (VQL)
of a session s in node i is defined as follows,
12
CHAPTER 2.
Qsi (t) = ∑
qsi∈Qs
i (t)wqs
i(L,Td ,Tr). (2.3)
Now, let a(qsi , j, t) = 1 represent a packet qs
i ∈ Qsi (t) is transmitted to node j at time slot t,
and a(qsi , j, t) = 0 otherwise. The routing profile of node i is defined as as
i (t) = [a(qsi , j, t)] j∈∈SU/i
qsi∈Qs
i (t),
and A represents the vector of routing profile asi (t) of all nodes in the network at instant t. The
transmission rate on link (i, j) during time slot t is defined as rsi j(t), and R as the vector of rates.
Then, the VQL of node i can be updated as,
Qsi (t +1) =
[Qs
i (t)+ ∑j∈N /i
∑qs
j∈Qsj(t)
wqsj(L,Td ,Tr)a(qs
j, i, t)− ∑j∈N /i
∑qs
i∈Qsi (t)
wqsi(L,Td ,Tr)a(qs
i , j, t)]+
.
(2.4)
Accordingly, the network link utility function Ui j for link (i, j) ∈ E for session s can be
defined as,
Ui j(asi (t)) =Ci j[Qs
i (t)−Qsj(t)]
+, (2.5)
where [Qsi (t)−Qs
j(t)]+ represents the differential VQL and Ci j is the achievable channel capacity
of the link (i, j) ∈ E at time slot t for a selected frequency ( f ) and the transmission strategy can be
given by,
Ci j( f ,Pi( f )), ∑f∈[ fi, fi+∆ fi ]
b. log2
[1+
Pi( f )PLi j( f )GN j( f )+ I j( f )
](2.6)
In the above equation, Pi( f ) represents the transmit power of node i on the frequency
f , PLi j( f ) is defined as the transmission loss due to path loss (can be computed based on the
chosen path loss model) from i to j, G represents the processing gain, which would be the length
of the spreading code when applicable, N j( f ) is the receiver noise on frequency f and I j( f ) is the
interference experienced by the receiving node j. Assuming a quasi-static channel, i.e. channel
conditions remain constant for the duration in between sensing and transmission of a packet. This
can be achieved with an efficient sensing mechanism and having a dedicated receiver that performs
sensing in parallel to the regular transceiver. As shown in (2.6), the achievable capacity primarily
depends on selected frequency F = [ fi, fi+∆ fi ], power allocation P = [Pi( f )], ∀i ∈ SU, , ∀ f and
the scheduling policy. Therefore, the overall notion of this network utility function is to couple
13
CHAPTER 2.
the constraints of packet deadline to the traditional queue length used in the differential backlog
algorithm. This is then weighted by the dynamic spectrum availability information to provide a joint
routing and spectrum allocation decision. Moreover, algorithms like ROSA [37, 38] does not handle
the QoS requirements of different traffic classes. Since this is essential for improving the reliability
of tactical ad hoc networks, the redefining of the queue length to form the new VQL is where the
proposed algorithm enhances the state-of-the-art.
2.3.2 Distributed Deadline-based Routing and Spectrum Allocation Algorithm
The overall optimization problem is to maximize the utility function discussed in (2.5).
The BER guarantees required for primary and secondary users are denoted as BERP U and BERSU
respectively. Accordingly, the required Signal-to-Interference-plus-Noise power Ratio (SINR)
thresholds required to achieve the target BER for the secondary and primary user can be represented
as SINRthP U and SINRth
P U respectively. Thus, the global objective of the optimization problem is to
find the optimal global vectors R, F, A and P that will maximize the sum of the network utilities,
under the power and BER constraints. The formulation of the optimization problem is as follows,
P1 : Given: G(U,E), PBgt , Qsi , BERSU , BERP U
Find: R, F, P, A
Maximize : ∑i∈SU
∑j∈SU/i
Ui j(asi (t)) (2.7)
subject to :
∑s∈S
rsi j ≤Ci j,∀i ∈ SU, ∀ j ∈ SU (2.8)
SINRk ≥ SINRthP U(BERP U),∀k ∈ P U,∀ f (2.9)
SINRl ≥ SINRthSU(BERSU),∀l ∈ SU,∀ f (2.10)
∑f∈[ fi, fi+∆ fi ]
Pi( f )≤ PBgti ,∀i ∈ SU (2.11)
In the above formulation, the objective is to maximize the network utility of all the active
links. The constraint (2.8) restricts the total amount of traffic in link (i, j) to be lower than or equal to
the physical link capacity. Constraint (2.9) and (2.10) imposes that any transmission by the secondary
user should guarantee the required BER for the active primary users and secondary user respectively.
14
CHAPTER 2.
Finally, PBgti is the instantaneous power available at the cognitive radio. Since solving the overall
optimization problem needs global knowledge of feasible rates and the worst-case complexity of this
centralized problem is exponential, it necessitates the need to design a distributed algorithm that is
scalable for practical implementation.
The resource allocation of the proposed algorithm consists of spectrum and power alloca-
tion. A spectrum opportunity for link (i, j) is a set of contiguous subbands where Oi j( f )≥ 0, when
Oi j( f ) is given by,
Oi j( f ) = Pmaxi ( f )−Pmin
i ( f ), (2.12)
where Pmaxi ( f ) is defined as the maximum power that can be used by the secondary node i on the
frequency f such that it satisfies the BER constraints of primary and secondary users. It is important
to note that Pmaxi ( f ) will be constrained by the maximum transmit power of the wireless radio used in
the network. On the other hand, Pmini ( f ) denotes the minimum power required to reach the required
SINRthSU at the intended secondary receiver. In other words, Pmin
i ( f ) and Pmaxi ( f ) provide the lower
bound and upper bound of transmit power respectively for node i on frequency f . The Pmini ( f ) and
Pmaxi ( f ) values are determined by a node i by gathering spectrum and resource allocation information
from its neighbors. This information is gathered using Collaborative Virtual Sensing (CVS) using the
control packets in the network. The details about resource allocation, CVS and the MAC protocol
employed as it is similar to that in ROSA.
Accordingly, the distributed DRS is proposed to maximize the throughput of a tactical ad
hoc network. In the distributed network, each node makes an adaptive decision to choose optimal
session, next hop, power allocation and spectrum to use during the next time slot based on the
information gathered from the neighbors using CVS. This decision will be different from traditional
ROSA [10] because the network utility defined here is a function of VQL and not the actual queue
lengths. Once a backlogged node senses an idle CCC, it performs the Algorithm 1 to obtain the
optimal resource allocation decision:
1. The proposed algorithm assumes that the location of the intended destination node is known
to the source node. This information is carried by the packet through the intermediate nodes.
Each node selects a feasible set of next hops for each backlogged session j ∈ (us1,u
s2, ...,u
sk),
which are neighbors with a positive advance towards the intended destination.
2. The maximum capacity for each node is calculated by considering all possible spectrum
opportunities. The maximum capacity of each feasible neighbor is used along with the
15
CHAPTER 2.
Algorithm 1 Deadline-based Resource Allocation1: t = 1, ∆ = ∞, Ci j = 0, U∗i j = 02: for si ∈ Si do3: for j ∈ u1,u2, ...uk do4: for fi ∈ [ fmin, ..., fmax−∆ fi ] do5: Calculate Pt
i ( f ) similar to [10]6: Calculate Ctemp as in (2.6)7: if Ctemp >Ci j then8: Ci j =Ctemp
9: [ f ∗i, j,P∗i,j]=[ fi,Pti]
10: end if11: end for12: U s
i j =Ci j ∗ [Qsii −Qsi
j ]13: if U s
i j >U∗i j then14: U∗i j =U s
i j
15: [ f opti ,Popt
i ,sopti , jopt]=[ f ∗i, j,P∗i,j,si, j]
16: end if17: end for18: end for19: Return [ f opt
i ,Popti ,sopt
i , jopt]
corresponding differential VQL to determine the network utility U si j. The optimal decision is
taken such that,
(sopt , jopt) = arg max(U si j). (2.13)
As seen earlier, the network utility function comprises of differential VQL and achievable
capacity. The differential VQL is a function of deadline and estimation of Td . Thus, the
sessions that have smaller deadlines or are further away from the intended destination will be
scheduled more often if the available spectrum for all sessions is comparable. The adaptive
routing will also provide most traffic to VQs that are lightly backlogged.
3. The optimal frequency and power allocation ( f opti ,Popt
i ) correspond to the values that provide
maximum Shannon capacity Ci j over the wireless link (i, jopt), where jopt is the best next hop.
Here, a contention-based MAC protocol is used in the control channel before transmitting
the packet on the selected data channel. In the contention-based MAC protocol, the probability of
accessing the medium is calculated based on the U∗i j. Nodes generate a backoff counter from the
range [0,2CW−1], where CW is the contention window. The CW is a decreasing function of U∗i j. This
will ensure that heavily backlogged VQs with more spectrum resources will have a higher probability
16
CHAPTER 2.
of transmission.
The computational complexity of the DRS algorithm at a node i is directly proportional
to the number of neighbors, number of channels and number of active sessions. Therefore, for a
constant number of channels and sessions in a network the computational complexity for node i is
given as O(|N B i|).
2.4 Performance Evaluation Through Simulation
In this section, the performance of DRS is compared with ROSA in a multihop tactical
ad hoc network. To evaluate DRS, an object-oriented packet-level discrete-event simulator is used,
which implements the features described in the earlier sections of this paper. The metric used for this
evaluation is effective throughput (η) and reliability (ρ) of the network. Effective throughput was
defined based on the number of packets received within the deadline. The reliability is defined as the
ratio of packets received at the destination within the specified deadline with respect to the number
of packets generated at the source node. The evaluation is conducted on a grid topology in a 6000 m
x 6000 m area. The sessions are initiated between disjoint random source-destination pairs and the
packet size of the packets are set at 2500 bytes and the number of packets transmitted per session is
set to 500. The total available spectrum (BW ) is set to be 54 MHz-72 MHz The bandwidth usable by
cognitive radios are restricted to be 2, 4 and 6 MHz. The bandwidth of the common control channel
is set as 2 MHz. Each result was obtained by averaging the values obtained from 50 random seeds
unless specified differently. In all figures except Fig. 2.6, the blue lines represent the performance of
DRS and red lines denote the performance of ROSA.
2.4.1 Scenario 1: Network performance as the number of session increases (All ses-sions started at random time)
In scenario 1, the network performance is evaluated as the number of active sessions in the
network increase. The parameters used during the two experiments for scenario 1 are listed in Table
2.1. The only difference between the two experiments are the deadlines assigned to different sessions.
In experiment 1, all the sessions have a deadline of 2s, which represents a highly constrained network.
Instead, in experiment 2 the odd-numbered sessions have a deadline of 1.5 s and even-numbered
sessions have a deadline of 10 s. Experiment 2 can be considered as a scenario where one session
carries periodic weather monitoring data through the network. These sessions are delay tolerant to an
17
CHAPTER 2.
Table 2.1: Parameters of scenario 1.
Parameter Experiment 1 Experiment 2
Source Rate 2 Mbits/s 2 Mbits/sSession duration 5 s 5 s
The parameters used in the evaluation are depicted in Table 3.4. As discussed earlier,
62
CHAPTER 3.
LoRa consumes extremely low power. This means that for a realistic battery to drain completely,
the evaluation may have to be run over multiple days. To save time and yet without loss of rigor,
a virtual energy level is used to evaluate the HELPER network so that one can see the network’s
behavior in experiments lasting less than 120 min. Each node is assumed to start at a total energy of
25 J and is depleted as each packet (control or data) is transmitted.
In the first experiment, F is set as the destination (would represent ERC in a real-life
scenario) and HELPER A and B are the source nodes. As shown in Table 3.4, packets are generated at
the source node at a constant rate and it has to choose appropriate routes to reach the destination. The
first metric evaluated is the minimum residual energy (Eminr ) among all HELPERs in the network. In
other words, at any given time instant t, the residual energy value of the HELPER that has consumed
the highest energy is plotted. The second metric under evaluation is the normalized throughput of the
network calculated with respect to observed point-to-point link throughput (T hl) and can be referred
to as,
T hnet =T hnet
T hl(3.8)
First, let’s look at the initial 14 minutes of the experiments. As you can see in Fig. 3.18,
the Eminr in both cases are the same since SEEK operates similarly to the greedy algorithm in this
stage even after gathering information from immediate neighbors. This is because at the beginning
most of the possible next hops have similar parameters including backlog length and residual energy.
Additionally, it can be seen from Fig. 3.19 that during the same period, the greedy algorithm seems
to marginally outperform SEEK. This can be attributed to the overhead involved in SEEK to compute
the optimal next hop from the gathered information. This marginal superiority is short-lived as SEEK
starts learning about the environment and begins to exploit spatial diversity to choose multiple paths
to the destination. This provides HELPER network with two advantages, (i) the energy consumption
is evenly spread between nodes and (ii) higher throughput is achieved. Accordingly, from Fig. 3.19,
it is evident that the death of the first node in the aggressive greedy algorithm happens much earlier
than the death of the first node in SEEK. This provides a proof-of-concept that SEEK can be applied
to maximize the network lifetime in a distributed manner.
Next, to extend the experiments further, the performance of SEEK is evaluated while
increasing the number of sessions in the network to 4. This is to evaluate if SEEK can adapt to
multiple traffic partners in the network which is expected behavior in a large distributed network.
These sessions include A→ F , B→C, C→ E and F → A and are chosen to ensure no source in a
session has it’s destination via a direct link (i.e. destination is not the source’s immediate neighbor).
63
CHAPTER 3.
Time (min)0 20 40 60 80 100 120
Min
imu
m r
esid
ual
en
erg
y (J
ou
les)
0
5
10
15
20
25
Greedy Geographic RoutingSEEK
First greedy nodedies
First node of SEEKdies
SEEK starts to share traffic load tomaximize network lifetime
Figure 3.18: Maximum energy consumed by anode.
Time (min)0 20 40 60 80 100 120
No
rmal
ized
th
rou
gh
pu
t
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Greedy Geographic RoutingSEEK
First greedy node dies
First node of SEEK dies
28% Improvement
Figure 3.19: Normalized throughput of thenetwork.
Each source in the session is set up to generate packets at a constant rate as mentioned in Table 3.4.
Both residual energy of each node and packets received are constantly monitored. First, the network
lifetime is analyzed which is defined as the duration of operation until the first node in the network
dies. This is important for such emergency networks as the death of a node would imply unconnected
users. Figure 3.20 shows how SEEK outperforms the greedy algorithm regardless of the number of
sessions. The experiments show an improvement of up to 53% in terms of network lifetime. One
interesting finding is that the network lifetime seemed to increase with the increase in sessions which
might be counter-intuitive at first sight. Further evaluation using Fig. 3.21 will reveal that the small
network is saturated even with two sessions in the network as portrayed by the throughput decline.
This implies that more collision may occur at the MAC layer leading to a larger backoff and lower
throughput as the number of sessions increase. In a saturated network, the overall throughput even
while operating for a longer period of time is better for SEEK compared to the greedy algorithm. To
further substantiate the importance of network lifetime, the percentage increase in packets delivered
by SEEK as compared to the greedy algorithm is depicted in Fig 3.22. This keeps increasing as the
number of sessions in the network grows which can be related to delivering critical information from
survivors to the ERC during the aftermath of the disaster.
Finally, the average delay per packets is evaluated as the number of sessions in the network
increases. To accomplish this, each session is set to transmit 100 packets while keeping the rest of
the setting similar to the earlier experiment. As expected, the delay per packet of both the schemes
increases as the number of sessions in the network increases due to congestion. The more critical
64
CHAPTER 3.
observation from Fig. 3.23 is that the delay incurred by packets serviced using SEEK is up to 40%
less than greedy algorithm especially when the traffic increases (3 sessions). This is because SEEK
is able to use multiple paths to distribute traffic spatially among nodes to reduce congestion at the
bottleneck nodes. This is further substantiated by the fact that the advantage in terms of lower delay
diminishes as the network saturates (4 sessions) since all the nodes are involved in either case (greedy
and SEEK) leaving no extra nodes for SEEK to distribute traffic load.
Number of sessions1 2 3 4
Net
wo
rk li
feti
me
(min
)
0
20
40
60
80
100
120
SEEKGreedy Geographic Routing
53% improvement
Figure 3.20: Network lifetime vs No. ofsessions.
Number of sessions1 2 3 4
No
rmal
ized
th
rou
gh
pu
t
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
SEEKGreedy Geographic Routing
Figure 3.21: Normalized throughput vs No. ofsessions.
Number of sessions1 2 3 4
Per
cen
tag
e o
f in
crea
se in
pac
kets
del
iver
ed b
y S
EE
K
0
10
20
30
40
50
60
70
80
90
Figure 3.22: Analysis of packet delivery.
Number of sessions1 2 3 4
Ave
rag
e D
elay
(s)
0
5
10
15
20
25
30
35
SEEKGreedy Geographic Routing
40% decrease
Figure 3.23: Average delay vs No. of sessions.
Overall, the experiments showed how nodes in SEEK share information among each other
using the control packets which is then used to perform cross-layer optimization to choose optimal
65
CHAPTER 3.
routes that ensure all nodes share the load of the traffic to maximize the network lifetime. The
improvement in the performance may be more significant on a larger network consisting of hundreds
of nodes. Here, HELPER has been prototyped and a small yet effective testbed has been set up with
a limited number of nodes to perform extensive testing. The results provide proof-of-concept that the
proposed HELPER network can be deployed in the near future to enable off-the-grid connectivity.
3.6 Summary
In this chapter, a complete end-to-end solution to enhance and enable public safety commu-
nication systems has been proposed, prototyped and demonstrated to establish the proof-of-concept.
The proposed HELPER uses heterogeneous wireless communication techniques; (i) WiFi which en-
ables EU to connect to the HELPER like any WiFi access point thereby ensuring easy and widespread
adoption, and (ii) LoRa, that provides extremely low power, long-range wireless link to implement
the ad hoc operation. The HELPER network is used to set up a completely self-sustained network
that does not require the support of any traditional communication infrastructure like cell towers or
satellite. The HELPER network is designed to serve a dual purpose; (i) enable affected individuals to
stay connected and maintain situational awareness, and (ii) equip authorities to remotely monitor the
situation, provide assistance and warnings in an efficient manner.
The proposed solution provides connected EU with live map updates to share the location
of known resources. It enables text messages between community members and equips EU with an
alternative to traditional 9-1-1 like emergency calls. Similarly, it provides ERC with the capability
to monitor the network connectivity, manage resource sharing information and send out ALERT
messages to connected users. Additionally, numerical evaluations using HELPER testbed showed
up to 53% improvement in network lifetime and up to 28% improvement in network throughput as
compared to a greedy scheme that routes using shortest path. All these demonstrated capabilities will
enhance the state-of-the-art public safety response system.
66
Chapter 4
VL-MAC: Opportunistic MAC Protocol
for Visible Light ad Hoc Network
The proliferation of wireless entities including IoT devices, multimedia devices among
others is causing significant growth in demand for bandwidth and spectrum resources. While new
portions of the RF electromagnetic spectrum are being made available and are increasingly leveraged
to meet this demand, RF communications inevitably suffer from problems including spectrum crunch,
co-channel interference, vulnerability to eavesdroppers, among others [101, 102] in the new 5G era.
Moreover, RF-based communications are not always permitted because of the potentially dangerous
effect of Electromagnetic Interference (EMI), which occurs when an external device generates
radiations that affect electrical circuits through electromagnetic induction, electrostatic coupling, or
conduction. For example, cellular and WiFi emissions are prohibited in airplanes during takeoff and
landing because electromagnetic radiations can interfere with onboard radios and radars; electronic
equipment can emit unintentional signals that allow eavesdroppers to reconstruct processed data at a
distance by means of directional antennas and wideband receivers.
Optical communications have attracted significant attention as a valid alternative over
legacy RF-based wireless communications. Optical communications are classified into two main
categories, fiber-based and Optical Wireless Communications (OWCs). Fiber-based systems are
frequently employed in the backbone network cabling because of their robustness, reliability, and
high-rate in delivering large amounts of data. OWCs are rapidly growing in popularity as an emerging
and promising wireless technology capable of high-speed data transfer over short distances [103]
[104]. An optical wireless-based system relies on optical radiation to deliver information in free
67
CHAPTER 4.
space, with wavelengths included in the Infrared Radiation (IR), visible-light, and Ultraviolet (UV)
bands. In the last decades, OWCs have been deployed in medium to long communication distance
environments, e.g., OWC has been applied for inter-chip connection as short-range transmission
while VLC found applications in medium-range indoor wireless access. Moreover, inter-building
connections can be established using IR communications whereas ultraviolet communications have
been recently adopted in outdoor non-line-of-sight scenarios and specifically for ad hoc and Wireless
Sensor Networks (WSNs). Recently, satellite communications and deep-space applications based on
OWC have been demonstrated, especially for military applications [105]. In particular, the recent
rapid increase in the use of LEDs for lightning has paved the way for the development of new
communication systems based on leveraging visible light as a communication medium. That is,
LEDs can act as illumination devices as well as information transmitters at the same time, thus
delivering data by digitally modulating the emitted light beam intensity at a very fast rate [106].
The exploration of VLC has been limited to various point-to-point applications including
setting up Li-Fi [7] networks using smart lights, among others. In this context, several topologies
such as peer-to-peer, star and broadcast have been considered to design networking protocols. In
this work, the emphasis is on enabling the use of VLC for ad hoc networking in military and civilian
applications. LANETs is expected to contribute considerably to the upcoming IoT revolution in
both indoor and outdoor spaces and this chapter explores how this can be achieved. The major
contributions of this chapter can be summarized as follows,
• The vision of using LANET for military and civilian applications requiring short-range,
low-latency, high-data rate links have been discussed in great details and the crucial role of
cross-layer technology in realizing it has been established.
• Accordingly, this process has been initiated by developing a mechanism to perform neighbor
discovery for LANETs taking into account the challenges introduced by directionality.
• A novel multi-utility based opportunistic MAC protocol, VL-MAC is designed to maximize
the throughput of LANET improving the probability of establishing links and promoting
the percentage of full-duplex communication in the network. The proposed MAC protocol
overcomes deafness, blockage and hidden node problem.
• Extensive simulations are performed to demonstrate improvements achieved by the proposed
VL-MAC protocol.
68
CHAPTER 4.
The rest of this chapter is organized as follows. In Section 4.1, the concept of LANET has
been defined and the envisioned applications have been discussed in detail. Section 4.1 also provides
a high-level comparison between LANETs and traditional Mobile Ad Hoc Networks (MANETs)
and discusses the major design challenges that need to be overcome. As a first step in the designing
LANET to support upcoming 5G networks, this chapter provides the first MAC protocol developed
for LANETs. To accomplish this, some of the recent efforts to design MAC protocol for VLC and
their shortcoming are discussed in Section 4.2. A neighbor discovery scheme for LANETs has
been presented in Section 4.3. Next, the detailed design of the proposed MAC protocol for LANET,
VL-MAC is provided in Section 4.4. Thereafter, the effectiveness of the proposed solution is proven
using extensive simulation in Section 4.5. Finally, Section 4.6 provides the summary of the chapter.
4.1 LANET: Visible-Light Ad Hoc Networks
LANETs refer to an infrastructure-less mobile ad hoc network where Visible Light Nodes
(VLNs) are wirelessly connected using multi-hop visible light links, capable of configuring their
protocol stacks in a cross-layer, online and software-defined manner, adapting to various networking
environments and demands.
4.1.1 Envisioned Applications
LANETs have a great potential for enabling a rich set of new civilian and military applica-
tions, as illustrated in Fig. 4.1, ranging from low-latency high-bandwidth indoor communications and
outdoor intelligent transportation networking, to highly secure Lower Probability of Intercept/Lower
Probability of Detection (LPI/LPD) operations under high network density and jamming conditions,
among others. Some examples of these applications are discussed below.
Intelligent Transport Systems. One of the most promising outdoor applications of
LANETs is for ad hoc vehicular communications [107] [108], including Vehicle to Infrastructure
(V2I), Infrastructure to Vehicle (I2V) and Vehicle to Vehicle (V2V) communications. LANETs can be
employed to design intelligent transport systems with better road safety. For V2V, a communication
link can be established using head and tail lights or photo-diodes and image sensors at the receiver
side, while for V2I the urban infrastructures (e.g., traffic lights, street lights) can be utilized for
transmitting useful information related to current circulation of traffic including vehicle safety, traffic
information broadcast and accident signaling. Additionally, in Vehiclular Ad Hoc Network (VANET),
69
CHAPTER 4.
Figure 4.1: LANETs employed for civilian and military applications.
the network topology is highly dynamic and often large-scale. This makes realizing visible-light
VANETs more challenging because of the limited FoV, and the relatively short transmission ranges
[109]. Moreover, different from legacy RF-VANETs, the quality of visible links can be significantly
degraded by weather conditions, including fog and rain, among others.
Internet of Things. The vision of IoT anticipates that large amounts of mobile embedded
devices and/or low-cost resource-constrained sensors will communicate with each other via the
Internet. To allow networking among a massive number of devices, the communication system
must be ubiquitous, low-cost, and bandwidth and energy-efficient. Infrastructure-less LANETs are a
promising choice for communication in the IoT because of its inherent advantages as discussed in
Section 4.1.2, e.g., orders of magnitude available bandwidth, reusing ubiquitously existing lighting
infrastructure, low-cost front-end devices, among others. Therefore, LANETs can easily enable
a wide range of IoT services, such as localization, smart home, smart city, air/land/navy defense,
70
CHAPTER 4.
among others.
D2D Communications. D2D communications are rapidly emerging in recent years [110]
and is expected to be one of the key services provided by 5G service providers to offset the cost of
deploying additional infrastructures. Beyond the crowded RF spectrum, LANETs are a promising
candidate to support D2D communications. VLC-D2D applications [111] can use LEDs and Photon
Detectors (PDs) or Liquid Crystal Display (LCD) screens and camera sensors. The ubiquitous pres-
ence of LCD screens and surveillance cameras in urban environments creates numerous opportunities
for practical D2D applications since information can, for example, be encoded in display screens
while camera sensors can record and decode data using image processing techniques [112].
Indoor Positioning. Recently proposed VLC-based indoor localization schemes have
shown improved performance, in terms of accuracy, given the higher density of LEDs as compared
to Wi-Fi access points [113]. To set up a light-weight indoor positioning network, LANET-enabled
sensors can be organized to form an ad hoc network with a tree-like structure (i.e., having a sensor
connected to a Local Area Network (LAN) as the root node) and a simplified protocol stack only
providing basic data transfer and routing functionalities that can be run on devices with limited
resources.
RF-Suppressed Applications. LANETs can provide a reliable and accurate solution
for data transmission in scenarios where RF communications are suppressed or prohibited, like
hospital and climbing/landing airplanes. For example, wireless technology is applied in hospitals for
updating information related to patient records, collecting data in a real-time way from handheld
patient devices, detecting changes in a patient’s condition, and also for observing medical images
via medical equipment (e.g. ultrasound). There, security and safety are essential to maintain the
confidentiality of patient records and to ensure that only authorized personnel has access to the data
being transferred wirelessly while limiting the interference to those interference sensitive medical
devices like EMI.
Military Applications.
In the last decades, the most common optical/visible light communication for military
applications employ IR short-range transmissions [114]. In recent years, the emerging of VLC has
shown promising advancements making possible the extensive deployment of VLC for military
communication strategies [115]. The use of VLC is turned out to be beneficial in the tactical field
with enhanced network capacity and better resistance against adversary jamming, and the research
is focused in this direction by military organizations and defense companies. Novel and advanced
visible light-based military applications include personal area networks, warfighter-to-warfighter
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communication, vehicular networks, underwater networks, and space applications including inter-
satellite and deep-space links. For example, in underwater, autonomous vehicles will be able to
self-organize in a LANET to exchange high-data rate traffic via visible light carriers as a high-rate
short-range alternative to acoustics; in ground, marine soldiers can self-organize in a LANET in case
of RF interference and be connected to command; finally, in air/space LANETs, nanosats can be
connected to a satellite station via VLC and be relay-assisted by other nanosats when in proximity in
a delay-tolerant ad hoc network.
4.1.2 LANETs vs Traditional MANETs
Similar to traditional RF-based MANETs, LANETs also have the ability to self-organize,
self-heal, and self-configure. Because of the unique characteristics of visible light compared to RF
signals, in LANETs visible light point-to-point links require mutual alignment of transmitters and
receivers given the directivity of light signal propagation, which is not easy to obtain with mobile
nodes; communication links in LANETs can be easily interrupted by intermittent blockage since
light does not propagate through opaque materials. Table 4.1 summarizes the differences between
LANETs and MANETs, in terms of critical aspects including transmitter and receiver, network
capacity, channel modeling, efficiency, and security, among others.
Transmitter and Receiver. In MANETs, the front-end components of each node are
typically antenna-based, operating at high frequency. In contrast, simple LED luminaires and
PDs or imaging sensors are typically adopted as transmitters and receivers in LANET. They are
relatively simple and inexpensive devices that operate in the baseband and do not require frequency
or sophisticated algorithms for the correction of RF impairments, e.g., phase noise and IQ imbalance
Property MANET LANET
Power Consumption Medium LowBandwidth Regulated, Limited Unlimited (400nm∼ 700nm)Infrastructure Access Point Illumination/Signaling LEDEMI Yes NoSecurity Reduced HigherMobility High ReducedLine of Sight Not required Strictly requiredTechnology Mature Early stageCoverage - Range Medium - Long Narrow - Short
Table 4.1: Comparison between LANETs and MANETs.
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[116]. As a consequence, SWaP (size, weight, and power) and cost of front-end components involved
in LANET systems are often lower than equivalent MANET systems.
Spectrum Regulation. The visible light spectrum is mostly unused for delivering infor-
mation, which implies potential high throughput and an opportunity to alleviate spectrum congestion,
particularly evident in the ISM band. The bandwidth available in the visible light portion of the
electromagnetic spectrum is considerably larger than the radio frequency bandwidth, which ranges
from 3 kHz to 300 GHz. The availability of this mostly unused portion of the spectrum provides
the opportunity to achieve high data rates through low-cost multi-user broadband communication
systems. VLC solutions could be complementary to traditional RF systems and alleviate the spectrum
congestion that especially impacts the ISM band.
Network Capacity. In MANETs, all the nodes usually operate in a shared wireless
channel with a single radio at each node, where the number of channels, the operating frequency, and
maximum transmit power are stringently regulated [117], and consequently, the network capacity is
unavoidably limited and affected by co-located networks. LANETs, instead, can rely on a substantial
portion of the unlicensed and currently unregulated spectrum as described above, which have the
potential to make significant capacity available for networked operations.
Spatial Reuse. Visible light cannot pass through opaque objects, thus resulting in low
penetration. Moreover, in contrast to omnidirectional RF communications, because of predefined
limited Field Of View (FOV) of LEDs, visible light links are typically directional. This provides
a higher degree of spatial reuse with respect to omnidirectional transmissions typically used in RF.
For example, since light cannot propagate outside of a closed room, there is no interference from
VLC signals in adjacent rooms. Because of this unique characteristic of VLC, most existing MAC
and network layer MANET protocols cannot be directly applied to LANETs and hence need to be
redesigned, including neighbor discovery and route selection, among others.
Security. Since they operate in dynamic distributed infrastructure-less configurations
without centralized control, MANETs are vulnerable to various kinds of attacks, ranging from
passive attacks such as eavesdropping to active attack such as jamming [118]. Differently, in
LANETs, the inherent security property that stems from the spatial confinement (low penetration and
restricted FOVs) of light beams, will enable secure communications since jammers or eavesdroppers
can be easily spotted than in legacy RF communication.
Costs. As discussed above, LANETs are more cost-efficient than MANETs because
of much simpler front-end devices (e.g., LEDs, PDs) compared to RF solutions for transmitting,
sampling and data processing. Moreover, nodes in MANETs are usually battery-powered to enable
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communications in the absence of a fixed infrastructure. The sensing unit, the digital processing
unit and the radio transceiver unit are the main consumers of the battery energy, and therefore
more sophisticated energy-efficient algorithms, e.g., energy-efficient MAC or routing schemes [119]
[120], are needed, which are however challenging in such resource-limited and infrastructure-less
MANETs. Differently, LEDs used as transmitters in LANETs highlight themselves by high energy
efficiency, longevity, and environment-friendly factor enabled by recent tremendous advances in
LED technologies [116]. Moreover, VLC manifests its low-power baseband processing property,
which further results in low-cost LED devices compared to high-frequency passband RF front-end
antennas.
4.1.3 Main Design Challenges
VLC has found many applications in short-, medium-, as well as long-range communi-
cations in the last decade. These include inter-chip connections, indoor wireless access, as well as
satellite and deep-space applications, among others [116, 105]. However, while there has been a sig-
nificant advancement in understanding efficient physical layer design for visible-light point-to-point
links, the core problem of developing efficient networking technology specialized for visible-light
networks is substantially unaddressed. One of the main challenges is that VLC relies on optical
radiations to deliver information in free space through a substantial portion of the unregulated spec-
trum between 400 and 800 THz, with corresponding wavelengths in the IR, visible light, and UV
bands [116]. This makes VLC substantially different from RF-based communications in terms of
communication range, transmission alignment and shadowing effect, ambient light interference and
receiver noise, and VLC ad hoc networking, among others.
Short Communication Range. Because of the limited propagation range of short-
wavelength signals, the transmission range of VLC is relatively short (typically a few meters),
compared to tens of meters for WiFi [121, 122]. When increasing the link distance, for a given
desired level of reliability the achievable data rate decays sharply, thus limiting the number of
applications where VLC high data rate transmissions can be employed.
Transmission Alignment and Shadowing Effect. Because of the low penetration of light,
while visible light signals in adjacent rooms do not interfere with each other, this also presents several
limitations. First, the transmitter and the receiver must be aligned to each other, especially for Line
of Sight (LOS) short distance communications with small FOVs, and this is challenging especially if
LANET nodes are moving [123]. Second, VLC link quality can be significantly degraded because of
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shadowing effects caused by obstructing objects, e.g., mobile human bodies [124].
Ambient Light Interference and Receiver Noise. Noise and interference in VLC are
mainly caused by exposure of the receiver to direct sunlight and by the presence of other sources of
illumination (i.e. other LED sources, fluorescent and bulb lamps) [125] [126] that cause shot noise
and consequently decrease the SNR. In turn, the receiver can be affected by thermal noise caused by
the pre-amplification chain.
Lack of Well-established Channel Models. Factors that affect the performance of visible
light links include free space loss, absorption, scattering, scintillation noise induced by atmospheric
turbulence and alignment between transmitters and receivers, among others [127]. Different from RF,
channel modeling for visible light links is still largely based on preliminary empirical measurements,
especially for outdoor Non-Line-Of-Sight (NLOS) environments [128, 129]. The applicability of
existing theoretical channel models in the design of LANETs still needs to be verified and tested in
different transmission media [130].
VLC Ad Hoc Networking. Existing work on VLC mostly focuses on increasing the
data rate for a single VLC link using advanced modulation schemes [131, 132, 133, 134, 135,
136]. However, VLC ad hoc networking with a large number of densely co-located VLC links
(i.e., LANETs) is still substantially unexplored because of the unique characteristics of VLC,
including intense modulation/direct detection (IM/DD) channel model, FOV based directionality, low-
penetration, among others. To the best of the author’s knowledge, there are no existing architectures
and protocols designed specifically for LANETs..
As mentioned earlier, this work focuses on exploiting VLC for ad hoc networking in
military and civilian applications that have been discussed in detail in the previous section. As an
initial step towards making LANETs a reality, this chapter provides a design of a LANET-specific
MAC that mitigates specific challenges put forth by LANETs.
A key distinguishing feature of VLC is directionality. While it enables better spatial
re-use, directionality is the direct reason for some of the major challenges experienced in LANETs.
The classical challenges like hidden node problem are amplified by transmission directionality,
since the control packets such as Clear-to-send (CTS) transmitted by a receiver may not be received
by nodes because of limited FOV. When a receiver is oriented towards a certain spatial sector and is
therefore unable to receive from all the remaining sectors, it is referred to as deafness. Thus, a node
may try to initiate communication with its neighbor who is experiencing deafness with respect to the
node, leading to additional delays during the contention phase. Another unique challenge of LANET
is the sudden communication discontinuity which may happen during the contention phase; thus,
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trying to access one particular neighbor may not be the most efficient way to forward packets. This
problem is referred to as blockage. Some challenges of LANET are similar to the ones experienced
by directional RF networks. The list of instantaneous neighboring nodes may change depending on
the FOV. Unlike typical RF transceiver systems equipped with a single antenna to transmit or receive,
VLC devices are usually equipped with a LED for transmission and PD for reception making these
devices inherently capable of full-duplex communication. Therefore, network protocols designed
for LANETs should be able to take advantage of full-duplex links to improve network throughput.
The unique characteristics of VLC impose the need for cross-layer design as shown in Fig.
4.2 to address these challenges [9]. Here, the process is initiated by designing a novel opportunistic
MAC protocol that optimizes the throughput of LANETs using a divide-and-conquer approach aimed
at achieving the following objectives:
• Maximize the probability of establishing a link in a given direction while overcoming chal-
lenges caused by the hidden node problem, deafness, and blockage;
• Improve the probability of full-duplex communication;
• Maximize the amount of spatial re-use.
Cross-Layer Controller
ApplicationLayer
TransportLayer
NetworkLayer
Data LinkLayer
LANET PROTOCOL STACK
Firmware
Custom Logic
Interp DACDUCLED
Driver
Decim ADCDDC TIA
Signal Processing Chain
LANET HARDWARE
LED
PDPhysical
Layer
Figure 4.2: Architecture of a LANET node.
4.2 Related Works
The study of MAC solutions for VLC is still in its infancy and even more limited is the
attention given to MAC in the context of ad hoc networks. Unfortunately, the few existing MAC
schemes designed for point-to-point VLC are not easily extendable to LANETs as they do not
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consider some unique challenges and opportunities related to VLC. Some of the existing MAC
protocols are discussed below.
CSMA-based Channel Access [137, 138, 139]. In [137], the authors propose a full-
duplex MAC protocol with Self-Adaptive minimum Contention Window (SACW) that delivers
higher throughput from the central node to the terminal nodes in a star topology. The proposed
algorithm still uses the basic slotted CSMA/CA mechanism as in [140] with adaptive contention
window. The objective of SACW MAC is to allow the central node to monitor the data traffic to
increase the probability of full-duplex operation. The authors of [138] also propose a high-speed
full-duplex MAC protocol based on Carrier Sense Multiple Access/Collision Detection (CSMA/CD)
by considering a start topology with Access Point (AP) at the center and multiple terminal nodes
trying to communicate with the AP. Another example of VLC using CSMA/CA is in [139], which
uses LED to transmit and receive to reduce hardware cost and size. This work uses LED charged in
reverse bias to receive the incoming light.
Cooperative MAC [141]. A cooperative MAC protocol is proposed in [141] to reduce
latency and for on-demand error correction. The sender and receiver will initiate a cooperative
mechanism to find relay nodes when the direct link does not provide the required bandwidth to meet
the QoS requirement. Once the cooperative mode is initiated, the sender broadcasts a RelayRequest.
Nodes within range save the sender’s identification number. Next, the destination broadcasts a
RelayRequest. Nodes that receive both RelayRequests will broadcast its information to sender and
destination if the node decides to be a relay. The relay overhears the sender’s packets and saves them
till an Acknowledgment (ACK) is received from the destination. If the ACK is not received, the relay
cently, the OFDM used in the Physical (PHY) layer of VLC has been extended to enable multi-user
access through Orthogonal Frequency Division Multiple Access (OFDMA). In [142], authors com-
pare the BER performance, receiver complexity and power efficiency of two multicarrier-based
multiple access schemes namely, Optical Orthogonal Frequency Division Multiplexing Interleave
Division Multiple Access (O-OFDM-IDMA) and Optical Orthogonal Frequency Division Multiple
Access (O-OFDMA). The authors of [143] evaluate a self-organizing interference management pro-
tocol implemented inside an aircraft cabin. The goal of the work is to allocate time-frequency slots
(referred to as chunks) for transmitting data in an Intensity-Modulation Direct-Detection (IM/DD)-
based OFDMA-Time Division Duplex (TDD) systems. Another OFDMA technique for indoor
VLC cellular networks is analyzed in [144] using Direct-Current Optical OFDM (DCO-OFDM)
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as multi-user access scheme. In [145], the authors propose a heuristic subcarrier reuse and power
redistribution algorithm to improve the BER performance of conventional Multiple Access Discrete
Multi-Tones (MA-DMT) used for VLC.
Code Division Multiple Access (CDMA) [146, 147, 148, 149, 150, 151]. There have
been several contributions aimed at employing CDMA in VLC. A system using Multi-carrier
CDMA (MC-CDMA) along with OFDM platform is proposed in [146]. The proposed design uses
Polarity Reversed Optical OFDM (PRO-OFDM) to overcome the inherent light-dimming problem
associated with using CDMA with visible light. In this design, a unipolar signal is either added
or subtracted to the minimum or maximum current respectively in the LED’s linear current range
to provide various levels of dimming. In [147], the authors discuss how Gold sequences and
Wash-Hadamard sequences can be adapted for VLC. Optical Orthogonal Codes (OOC) [148]
comprising of sequences of 0s and 1s have also been explored as a prime candidate to establish
Optical Code-Division Multiple Access (OCDMA) for visible light communication. Since as the
number of users increases in the system, it becomes challenging to generate OOC for each user,
Random Optical Codes (ROC) has been proposed as an alternative, even though they do not provide
optimal performance [149, 150]. There have also been efforts to combine Color-Shift Keying (CSK)
modulation and OCDMA to enable simultaneous transmission to multiple users [151].
QoS-Based MAC. In [152], the authors propose a QoS based slot allocation to enhance
the broadcasting MAC of IEEE 802.15.7 standard. They use a super frame structure similar to the
standard. When a new channel wants to join the AP, it sends a traffic request to the access point
along with its QoS parameters (data rate, maximum burst traffic, delay requirements, and buffer
capacity). Optical wireless MAC (OWMAC) [153] is a Time Division Multiple Access (TDMA)
based approach aimed to avoid collision, retransmission, and overhead due to control packets. In
OWMAC, each node reserves a time slot and advertises the reservation using a beacon packet.
OWMAC also employs Error-Correction Code (ECC) in their ACK to ensure that retransmission are
reduced to corrupted ACK packets. This protocol is designed to handle start like topologies.
MU-MIMO [154, 155, 156, 157, 158, 159, 160]. An alternative method uses multiple
LED arrays as transmitters to serve multiple users simultaneously [154, 155]. In contrast to the RF
counterpart, the VLC signal is inherently non-negative leading to the necessity of modifying the
design of the Zero Forcing (ZF) precoding matrix. In [155], a ZF precoder is chosen in the form
of the specific generalized inverse of the channel matrix known as the pseudo-inverse. The authors
of [154] recognize that the pseudo-inverse may not be the optimal precoder. Accordingly, they
design an optimal ZF precoding matrix for both the max-min fairness and the sum-rate maximization
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problems. Block Diagonalization (BD) algorithm [156] has also been used to design the precoding for
Multi-User Multiple-Input Multiple-Output (MU-MIMO) VLC system [157] to eliminate Multi-User
Interference (MUI) and its performance has been evaluated in [158]. Finally, Tomlinson-Harashima
Precoding (THP) [159] has been utilized in [160] to achieve better BER performance compared to
the block diagonalization algorithm in VLC systems.
MAC protocols [137, 138, 142, 143, 144, 145] that are designed for centralized operation
in a star topology are not easily extensible to LANETs. Cooperative operations like in [141] can be
employed in LANETs but cannot be the primary MAC protocol used to negotiate reliable medium
access. Techniques based on CDMA or MU-MIMO are suitable for centralized networks as it may
be complex to negotiate different codes for each link in a distributed network. Similarly, QoS-based
techniques can be used to improve a stable MAC protocol that has been primarily designed to
overcome inherent problems of LANETs such as deafness, blockage and hidden node problem.
4.3 Neighbor discovery
Consider a multihop LANET with N static VLNs modeled as a directed connectivity graph
G(U,E), where U = {u0,u1, ...,uN} is a finite set of VLN of the graph, and (i, j) ∈ E represents
a feasible unidirectional wireless link from node ui to node u j (for simplicity, they are referred
to as node i and node j) representing neighboring relationships, i.e., there is a feasible link if the
nodes are close enough. In LANET, each node consists of LED luminaires and PDs adopted as
transmitters and receivers, respectively. Since the transmissions are directional, the directions to
which the FOV of each node can be set to are represented by Ns equal sectors s ∈ S . The FOVs of
typical LEDs and PDs can vary from ±10◦ to ±60◦ [161, 162], e.g. Vishay TSHG8200, OSRAM
LCW W5SM Golden Dragon and Vishay PD TESP5700. Here, for the sake of simplicity, but without
loss of generality, the FOV for both LED and PD is chosen to be ±22.5◦, leading to eight sectors.
This can be easily extended according to the FOV of the hardware available on specific VLN. It
is also assumed that a VLN is capable of directing its FOV to all the Ns sectors when required for
transmission and reception. This is possible with multiple LEDs and PDs, that can be used depending
on which sector the nodes want to access (only one sector of a node is activated at any given time).
In non-ideal scenarios, interference mitigation techniques [163] can be employed to reduce the
interference between sectors. The neighbors are grouped into sectors based on their location which
can be provided when the network is deployed or learned by exchanging of control packets. Thus,
the superset of neighbors for node i consists of the set of neighbors in each sector represented as
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CHAPTER 4.
Table 4.2: Summary of MAC protocols for VLC.
MAC Protocol Medium AccessMethod
Topology/Operation Modes Other Comments
IEEE 802.15.7 [140] CSMA/CAPeer-to-peer, star,broadcast Standardization for VLC
SACW MAC [137] CSMA/CA Star Full-duplexLin et al [138] CSMA/CD Star Full-duplexSchmid et al [139] CSMA/CA Peer-to-peer LED-to-LEDCooperative MAC [141] CSMA/CA Peer-to-peer Cooperative relay
Broadcasting MAC [152] TDMA BroadcastFrame synchronization andsupports QoS
OWMAC [153] TDMAStar, with unicast,broadcast, & multicast 84 Mb/s data rates
Dang et al [142] OFDMA StarComparison of O-OFDMA& O-OFDM-IDMA
Ghimire et al [143] OFDMA-TDD StarSelf-organizinginterference management
Chen et al [144] DCO-OFDMIndoor downlinktransmission
Spectral efficiency of5.9 bits/s/Hz
Bykhovsky et al [145] DMT StarInterference-constrainedsubcarrier reuse
Shoreh et al [146]MC-CDMA withPRO-OFDM Star
Handles dimming usingPRO-OFDM
He et al [147] OCDMA with OOC Peer-to-peer, starBipolar-to-Unipolarencoding and decoding
Gonzalez et al [149] OCDMA with ROC Peer-to-peer, starSpecific design of OOC,higher complexity
Chen et al [151] OCDMA with CSK Peer-to-peer, starMobile phone cameraused as receiver
Yu et al [155] MU-MISO Cooperative broadcast ZF algorithm usinggeneralized inverse
Pham et al [154] MU-MISO Cooperative broadcast ZF algorithm usingoptimal precoding
MU-MIMO (BD) [157] MU-MIMO StarPrecoding using BDalgorithm
MU-MIMO (THP) [160] MU-MIMO StarPrecoding using THPalgorithm
N B i ∈ {N B i1,N B i
2, ...,N B iNs}, where N B i
s , { j : (i, j) ∈ E} is the neighbor of node i in sector s.
Let the traffic in the network consist of multiple sessions q = 1,2, ...,Q, characterized by
the source-destination pairs. In this chapter, the feasible next hop for a session is defined as any
neighbor that is closer to the destination and is termed as forward progress. In this context, each
session q in node i belongs to one or more sector queue sets q ∈ Q si (q can be a component of more
than one sector queue sets) such that the sector contains neighbors that ensure forward progress for
packets in a queue. This information will be used by the VLN while choosing an optimal sector to
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forward packets. The arrival rate of each session q ∈ Q si at node i is given by λi
q(t), and characterized
through the vector of arrival rates Λ. The VLNs in the network are assumed to be synchronized with
each other using techniques like GPS based clock synchronization. The time spent listening to each
sector is called sector duration (tsec) and this forms a sector slot as shown in Fig. 4.3. The sector
slot is further divided into multiple Control Micro-Slots (CMS). Control packets are transmitted
only at the beginning of a CMS. The duration of a CMS is set such that transmission of a control
packet can be completed in one CMS. A set of Ns sector slots forms a super-slot. VLNs have two
operational states; Synchronous Idle State (S-IDLE) and Transceiving State (TR). In S-IDLE, nodes
sequentially listen in each sector following a fixed pattern. In this way, a VLN that has to transmit in
a given sector knows the appropriate sector slot when the idle neighbors (in the given sector) will be
listening, thus mitigating the effect of deafness. The channels used by the LANET are divided into
CCC and Data Channel (DC) using, for example, orthogonal CDMA codes.
Super-slot
Sector slot NsSector slot 1
Control micro-slot (CMS)
. . .
Figure 4.3: Super-slot structure.
Neighbor discovery is critical for any ad hoc network. Due to the unique characteristics
of VLC, a neighbor discovery mechanism has to be designed specifically for LANETs. Each VLN
needs to know the neighbors that correspond to each sector. Unlike some RF directional network,
LANETs do not have the option to operate in omni-directional mode when required. This is true even
when LEDs and PDs are available in each sector since they may not have a dedicated receive and
transmit circuitry (due to cost) corresponding to each LED/PD. During the neighbor discovery phase,
it is challenging for nodes to communicate with each other due to deafness. This also invokes the
need to cooperatively share information among nodes to enable faster neighbor discovery. Therefore,
synchronization and cooperation among neighbors are used to overcome these challenges and perform
neighbor discovery.
This section describes how LANETs perform neighbor discovery using two mechanisms
namely; synchronized and random. Regardless of the mechanism followed, there is a common
message exchange procedure that takes place during the neighbor discovery phase. The nodes in the
network can be divided into two kinds of nodes; seeking nodes and responding nodes. The seeking
nodes initiate the neighbor discovery process during the first half of the sector slot using Hello
packets and responding nodes reply to the seeking nodes using Hello ACK packets during the second
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half of the sector slot. Consider the situation where nodes A and B are in the neighbor discovery
phase. Let A be the seeking node and B be the responding node. Accordingly, A activates its FOV
to a sector and chooses a random backoff within the first half of the sector slot and broadcasts a
Hello packet in a CMS. The Hello packet consists of the Node ID, location, and A’s neighbor list and
their corresponding location. When B receives the Hello packet, it adds A to its neighbor list for the
corresponding sector. B also uses the Node IDs and location of A’s neighbors to evaluate if any of
A’s neighbor has the potential of being B’s neighbor based on their location. Accordingly, B will
add them as its tentative neighbors for the corresponding sector estimated according to the node’s
location. The procedure is repeated as B transmits Hello ACK packet to A in the second half of the
sector duration. In this way, nodes collaboratively help each other to discover the neighbors. The
tentative neighbors are confirmed in the neighbor list once it receives any control packets from the
tentative neighbors. It is important to note that a seeking node may receive a Hello packet during the
backoff period from other seeking nodes. In such cases, the seeking node uses the received Hello
packet and responds with Hello ACK packet like a responding node. The two schemes to perform
neighbor discovery are discussed below.
Random Scheme. In this scheme, the responding nodes are called static nodes as they
randomly pick one sector and remains in the same sector during the neighbor discovery phase. In
contrast, the seeking nodes randomly pick a sector to start from and then sequentially activates
the FOV in a counter-clockwise direction such that it covers all the eight sectors one-by-one. The
performance of this scheme is plotted in Fig. 4.4 (dotted lines) assuming that the sector duration is
large enough for nodes to exchange neighbor information without collision using random backoff.
The parameters used for simulation are tabulated in Table 4.3. The percentage of neighbors discovered
(including tentative neighbors) after one super-slot (eight sector slots in this case) is used to evaluate
the performance of neighbor discovery scheme. It can be observed from Fig. 4.4 (dotted lines)
that as the ratio of seeking nodes (γ) in the network increases, the performance of the random
neighbor discovery scheme increases. This implies that the best approach is to have no static nodes
(γ = 1) during the random scheme of neighbor discovery. As expected, the performance of neighbor
discovery also increases with the increase in density of nodes in the network. This can be seen from
the increased performance as the number of nodes increases from 25 to 200. The advantage of this
scheme is that it does not require synchronization.
Synchronous Scheme. In contrast to the earlier scheme, here there are no static nodes.
Instead, the responding nodes synchronously activate FOV in different directions such that it covers
all eight sectors sequentially, listening to one sector at a time. These nodes are referred to as
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Ratio of seeking nodes0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9