Cooperation and Crosslayer Design in Wireless Networks Andrea Goldsmith Stanford University DAWN ARO MURI Program Review U.C. Santa Cruz September 12, 2006
Jan 04, 2016
Cooperation and Crosslayer Design
in Wireless Networks
Andrea GoldsmithStanford University
DAWN ARO MURI Program Review
U.C. Santa CruzSeptember 12, 2006
Wireless Multimedia Networks In Military Operations
•Command/Control•Data, Images, Video
•Delay Constraints•Energy Constraints
Challenges to meeting network performance
requirements
Wireless channels are a difficult and capacity-limited broadcast communications medium
Fundamental capacity limits of wireless networks are unknown and, worse yet, poorly defined.
Wireless network protocols are generally ad-hoc and based on layering, which can be highly suboptimal
Energy and delay constraints change fundamental design principles
No single layer in the protocol stack can guarantee QoS: cross-layer design needed
Cooperation in Wireless Networks
Many possible cooperation strategies. Transmitter and receiver clusters can form virtual MIMO links. Cooperating nodes can be used as relays, possibly with
conferencing.
We investigate which forms of cooperation are effective. We consider dirty paper coding (DPC), relaying (DF and CF),
one-shot and iterative conferencing. Capacity gain from cooperation depends on network topology,
CSI, number of cooperating nodes, and SNR.
Virtual MIMO
• TX1 sends to RX1, TX2 sends to RX2• TX1 and TX2 cooperation leads to a MIMO BC• RX1 and RX2 cooperation leads to a MIMO MAC• TX and RX cooperation leads to a MIMO channel• Power and bandwidth spent for cooperation
TX1
TX2
RX1
RX2
Capacity Gain with Cooperation (2x2)
TX cooperation needs large cooperative channel gain to approach broadcast channel bound
MIMO bound unapproachable
TX1x1
x2
GG
Joint work with N. Jindal and U. Mitra
Capacity Gainvs Network Topology
Cooperative DPC best
Cooperative DPC worst
RX2
y2
TX1x1
x2
x1
d=1
d=r<1
Joint work with C. Ng
Optimal cooperation coupled with access and routing
Relative Benefits ofTX and RX Cooperation
Two possible CSI models: Each node has full CSI (synchronization between Tx and relay). Receiver phase CSI only (no TX-relay synchronization).
Two possible power allocation models: Optimal power allocation: Tx has power constraint aP, and
relay (1-a)P ; 0≤a≤1 needs to be optimized. Equal power allocation (a = ½).
Joint work with C. Ng
Transmitter vs. Receiver Cooperation
Capacity gain only realized with the right cooperation strategy
With full CSI, Tx co-op is superior.
With optimal power allocation and receiver phase CSI, Rx co-op is superior.
With equal power allocation and Rx phase CSI, cooperation offers no capacity gain.
Similar observations in Rayleigh fading channels.
Multiple-Antenna Relay Channel
Full CSIPower per transmit antenna: P/M.
Single-antenna source and relay
Two-antenna destination SNR > PU: No multiplexing gain;
can’t exceed SIMO channel capacity (Host-Madsen’05)
SNR < PL: MIMO GainJoint work with C. Ng and N. Laneman
Conferencing Relay Channel
Willems introduced conferencing for MAC (1983)Transmitters conference before sending message
We consider a relay channel with conferencing between the relay and destination
The conferencing link has total capacity C which can be allocated between the two directions
Joint work with C. Ng, I. Maric, S. Shamai, and R. Yates
Iterative vs. One-shot Conferencing
Weak relay channel: the iterative scheme is disadvantageous. Strong relay channel: iterative outperforms one-shot
conferencing for large C.
One-shot: DF vs. CF Iterative vs. One-shot
One-Shot Iterative
Crosslayer Design in Ad-Hoc Wireless
Networks
ApplicationNetworkAccessLink
Hardware
Substantial gains in throughput, efficiency, and end-to-end
performance from cross-layer design
Joint Compression andChannel Coding with
MIMOUse antennas for multiplexing:
Use antennas for diversity
High-RateQuantizer
ST CodeHigh Rate Decoder
Error Prone
Low Pe
Low-RateQuantizer
ST CodeHigh
DiversityDecoder
How should antennas be used?
Joint with T. Hollidayand H. V. Poor
Depends on end-to-end metric.
End-to-End Tradeoffs
kRu Index
Assignment
s bits
i)Channel Encoder
s bits
i
MIMO Channel
Channel Decoder
Inverse Index Assignment j)
s bits
j
s bits
Increased rate heredecreases source
distortion
But permits less diversity
here
Resulting in more errors
SourceEncoder
SourceDecoder
And maybe higher total distortion
A joint design is needed
vj
Antenna Assignment vs. SNR
Diversity-Multiplexing-ARQ
Suppose we allow ARQ with incremental redundancyARQ is a form of diversity [Caire/El
Gamal/Damen’05]Comes at the cost of delay
0
2
4
6
8
10
12
14
16
0 1 2 3 4
ARQ Window
Size L=1
L=2 L=3
L=4
d
r
Minimum Distortion under Delay Constraints
Delay/Throughput/Robustness across
Multiple Layers
Multiple routes through the network can be used for multiplexing or reduced delay/loss
Application can use single-description or multiple description codes
Can optimize optimal operating point for these tradeoffs to minimize distortion
A
B
Application layer
Network layer
MAC layer
Link layer
Cross-layer protocol design for real-time
media
Capacity assignment
for multiple service classes
Capacity assignment
for multiple service classes
Congestion-distortionoptimizedrouting
Congestion-distortionoptimizedrouting
Adaptivelink layer
techniques
Adaptivelink layer
techniques
Loss-resilientsource coding
and packetization
Loss-resilientsource coding
and packetization
Congestion-distortionoptimized
scheduling
Congestion-distortionoptimized
scheduling
Traffic flows
Link capacities
Link state information
Transport layer
Rate-distortion preamble
Joint with T. Yoo, E. Setton, X. Zhu, and B. Girod
Video streaming performance
3-fold increase
5 dB
100
s
(logarithmic scale)
1000
Energy-Constrained Nodes
Each node can only send a finite number of bits.Energy minimized by sending each bit very slowly. Introduces a delay versus energy tradeoff for each
bit.
Short-range networks must consider both transmit and processing/circuit energy.Sophisticated techniques not necessarily energy-
efficient. Long transmission times not necessarily optimalMultihop routing not necessarily optimal
Changes everything about the network design:Bit allocation must be optimized across all protocols.Delay vs. throughput vs. node/network lifetime
tradeoffs.Optimization of node cooperation.
Cross-Layer Optimization Model
The cost function f0(.) is energy consumption.
The design variables (x1,x2,…) are parameters that affect energy consumption, e.g. transmission time.
fi(x1,x2,…)0 and gj(x1,x2,…)=0 are system constraints, such as a delay or rate constraints.
If not convex, relaxation methods can be used. We focus on TD systems
Min ,...),( 210 xxf
s.t. ,0,...),( 21 xxfi Mi ,,1 Kj ,,1 ,0,...),( 21 xxg j
Joint work with S. Cui
Minimum Energy Routing
Transmission and Circuit Energy
4 3 2 1
0.3
(0,0)
(5,0)
(10,0)
(15,0)
Multihop routing may not be optimal when circuit energy consumption is considered
bits
RR
ppsR
100
0
60
32
1
Red: hub nodeBlue: relay onlyGreen: relay/source
Relay Nodes with Data to Send
Transmission energy only
4 3 2 10.115
0.515
0.185
0.085
0.1Red: hub nodeGreen: relay/source
ppsR
ppsR
ppsR
20
80
60
3
2
1
(0,0)
(5,0)
(10,0)
(15,0)
• Optimal routing uses single and multiple hops
• Link adaptation yields additional 70% energy savings
Virtual MIMO with Routing
Double String Topology with Alamouti Cooperation
Alamouti 2x1 diversity coding schemeAt layer j, node i acts as ith antennaSynchronization needed, but no cluster communication
Optimize link (constellation); MAC (transmission time), routing (which hops to use), scheduling
Goal is to optimize energy/delay tradeoff curve
Total Energy versus Delay
Cooperative Compression
Source data correlated in space and time
Nodes should cooperate in compression as well as communication and routing Joint source/channel/network codingWhat is optimal: virtual MIMO vs. relaying
Conclusions Cooperation in wireless networks is essential
Leads to significant capacity gains The appropriate form of cooperation depends on the
environment and CSI assumptions Many forms of cooperation are still unexplored
End-to-end performance requires a cross-layer design that exploits tradeoffs at each layer by higher layer protocols Cross-layer design leads to increased throughput, efficiency,
and end-to-end performance Cross-layer design requires new design and analysis tools Cross-layer design under energy constraints yields atypical
protocols Care must be used to avoid negative interactions and maintain
simplicity and scalability.
Plans for the Coming Year
Cooperative CommunicationsConferencing with multiple iterationsLayered broadcast coding approachesMultiple relays with multiple antennasCooperation for cognitive radios
Cross-layer DesignExtend diversity/multiplexing/ARQ
tradeoff analysis to wireless networksBroader the notion of source/channel
separation to include channel outage/error
Incorporate network coding into cross-layer design (w/ T. Ephremides and M. Medard)
Joint Source/Channel/Network Coding
SourceSourceCodingCodingSourceSourceCodingCoding
InformationInformationTheoreticTheoretic
RateRateRegionsRegions
InformationInformationTheoreticTheoretic
RateRateRegionsRegions
NetworkNetworkCodingCoding
ororRoutingRouting
NetworkNetworkCodingCoding
ororRoutingRouting
ConvexConvexOptimizationOptimization
(Minimum(MinimumDistortion)Distortion)
ConvexConvexOptimizationOptimization
(Minimum(MinimumDistortion)Distortion)
SSSSSSSS
D(·)D(·)D(·)D(·)
Separate Separate DesignDesign OptimalOptimal
Separate Separate DesignDesign OptimalOptimal
Separate Separate Design Design
Optimal?Optimal?
Separate Separate Design Design
Optimal?Optimal?