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IntroductionDetailed Research Questions and Contributions
Summary
Battery Lifetime-Aware Base Station SleepingControl with M2M/H2H Coexistence
A Battery Lifetime-Aware Cellular Network Design Framework
Amin Azari, Guowang Miao
CoS Department, ICT SchoolKTH Royal Institute of Technology
December 5, 2016
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 1 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Outline
1 IntroductionBackground and MotivationPaper Focus and High-Level Research QuestionsState of the Art
2 Detailed Research Questions and ContributionsBattery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
3 Summary
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 2 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Background and MotivationPaper Focus and High-Level Research QuestionsState of the Art
Outline
1 IntroductionBackground and MotivationPaper Focus and High-Level Research QuestionsState of the Art
2 Detailed Research Questions and ContributionsBattery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
3 Summary
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 3 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Background and MotivationPaper Focus and High-Level Research QuestionsState of the Art
TelecommunicationsYesterday, Today, Tomorrow
Internet of Things: Everything that benefits from being
connected will be connected.Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 4 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Background and MotivationPaper Focus and High-Level Research QuestionsState of the Art
IoT over Cellular Networks
Regarding unique characteristics of cellular networks likeubiquitous coverage, cellular-based M2M will be a key enablerof IoT.
In 1G to 4G:
high-capacity high-throughput low-latency infrastructure,forgotten about large-scale small-data communications,forgotten about mission-critical communications.
Need for evolutionary and revolutionary changes.
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 5 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Background and MotivationPaper Focus and High-Level Research QuestionsState of the Art
Massive M2M Communications
Main challenges in enabling Massive M2M :
Scalability: up to one million simultaneous connections persquare kilometera.
Energy efficiency: over 10 years battery lifetime
10 times more bit-per-joule energy efficiencyb.Battery lifetime → Maintenance cost
aSamsung. 5G vision. Tech. rep. 2015.bNokia. Looking ahead to 5G. . Tech. rep. 2014.
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 6 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Background and MotivationPaper Focus and High-Level Research QuestionsState of the Art
Outline
1 IntroductionBackground and MotivationPaper Focus and High-Level Research QuestionsState of the Art
2 Detailed Research Questions and ContributionsBattery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
3 Summary
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 7 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Background and MotivationPaper Focus and High-Level Research QuestionsState of the Art
Paper Focus
Paper Focus
To incorporate battery lifetime-awareness into the design of 5Gcellular networks
High-Level Research Questions
Identify deployment and operational solutions enabling serving amassive number of energy-limited devices:
with minimum increase in CAPEX and OPEX,
without degrading human-type users perceived QoS.
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 8 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Background and MotivationPaper Focus and High-Level Research QuestionsState of the Art
Outline
1 IntroductionBackground and MotivationPaper Focus and High-Level Research QuestionsState of the Art
2 Detailed Research Questions and ContributionsBattery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
3 Summary
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 9 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Background and MotivationPaper Focus and High-Level Research QuestionsState of the Art
State of the Art on BS Sleeping (1/2)
BS sleeping and its impacts on downlink communications havebeen investigated. Optimal density of macro and micro BSshave been founda.
BS sleeping with constraint on transmit power of users hasbeen investigatedb.
aHina Tabassum et al. “Downlink performance of cellular systems with basestation sleeping, user association, and scheduling”. In: IEEE TWC (2014),Jyri Hamalainen et al. “A Novel Multiobjective Cell Switch-Off Method withLow Complexity for Realistic Cellular Deployments”. In: arXiv (2015),Sheng Soh et al. “Energy efficient heterogeneous cellular networks”. In: IEEEJSAC (2013), Dongxu Cao et al. “Optimal combination of base station densitiesfor energy-efficient two-tier heterogeneous cellular networks”. In: IEEE TWC(2013).
bJinlin Peng et al. “Stochastic analysis of optimal base station energy savingin cellular networks with sleep mode”. In: IEEE Commun. Lett. (2014).
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 10 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Background and MotivationPaper Focus and High-Level Research QuestionsState of the Art
State of the Art (1/2)
Summary of literature study
To the best of our knowledge,
accurate energy consumption, individual and network batterylifetime modeling for MTC,
battery lifetime-aware deployment and operation designapproaches for cellular networks, and
study of tradeoffs between optimizing cellular network for:
improving battery lifetime of MTC,decreasing energy/cost of access network,improving QoS of non-MTC
are absent in literature.
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 11 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
Outline
1 IntroductionBackground and MotivationPaper Focus and High-Level Research QuestionsState of the Art
2 Detailed Research Questions and ContributionsBattery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
3 Summary
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 12 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (1/3)Battery lifetime Assessment
The initial problem faced in lifetime-aware cellular network design:→ lack of a methodology to model the network battery lifetime.
RQ1: How to derive a low-complexity model of individual andnetwork battery lifetimes?
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 13 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (1/3)
Energy consumption → a semi-regenerative processReg. point → end of each successful data transmission epoch.
UE BS
Cell Info
PRACH: Random Access
Request (RN, BSR, Cause,
PDCCH CC)
PDCCH: Uplink Assignment
(RACH reference, PUSCH
allocation, BS VR = 0, C-
RNTI assignment)
PUSCH: Data transfer
(TLLI/S-TMSI, MS VS = 0,
last = true, data)
PDCCH: Uplink Ack
(TLLI/S-TMSI, C-RNTI
confirmation, BS VR=1)
!"#+ $!%
&',(
&',$
)*-
(Turn radio on)
(Sleep)
Du
ty C
ycle
Re
po
rting
Pe
riod
(Wake up)
Data gathering
(Wake up)
)!!.
time
Po
we
r
Sleep
Data
gathering/pr
ocessing
Listening
to
eNodeB
Scheduled
transmission
Connection
establishme
nt
Reporting period
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 14 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (1/3)Battery lifetime Assessment
∗ Expected lifetime of node i
=Energy storage at time t
Energy consumption per reporting period× Reporting period
=Ei (t)
E iperperiod
Ti ,
Eperpacket = Estatic + Edynamic ,
Edynamic =Di
Ri(Pc + αPt),
Estatic = K (tDRXPDRX + tsyncPsync + tactPact) + tsyncPsync ,
K = Number of active intervals per reporting period.
* Network lifetime: Average of individual lifetimes.Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 15 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
Outline
1 IntroductionBackground and MotivationPaper Focus and High-Level Research QuestionsState of the Art
2 Detailed Research Questions and ContributionsBattery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
3 Summary
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 16 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (2/3)Performance Tradeoff Analysis in Single Cell Scenario
Consider a massive M2M/H2H deployment in a single-cellscenario.
We are interested in coupling between optimizing BSoperation for:
improving battery lifetime of MTC devices,decreasing energy/cost of the access network,improving QoS of non-MTC traffic.
RQ2: What are the tradeoffs between green andlifetime-aware cellular network design in the operation phase?
What is the optimal BS sleeping strategy w.r.t. batetrylifetime of devices?
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 17 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (2/3)Performance Tradeoff Analysis in Single Cell Scenario
Consider a massive M2M/H2H deployment in a single-cellscenario.
We are interested in coupling between optimizing BSoperation for:
improving battery lifetime of MTC devices,decreasing energy/cost of the access network,improving QoS of non-MTC traffic.
RQ2: What are the tradeoffs between green andlifetime-aware cellular network design in the operation phase?
What is the optimal BS sleeping strategy w.r.t. batetrylifetime of devices?
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 17 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (2/3)Performance Tradeoff Analysis in Single Cell Scenario
What is BS Sleeping?
Imapct on uplink communications is absent in literature.
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 18 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (2/3)Performance Tradeoff Analysis in Single Cell Scenario
How do we model the problem (1/3):
Consider uplink communication of a green BS in a single cell
Massive number of deployed sensors (P2), with boundedtransmit power, and need for long battery lifetime.
A number of human users (P1), with non-preemptive priorityover P2, require low delay.
Consider ACB for MTC:
For P1 devices, when the BS is busy, they are queued to beserved, based on processor sharing, with non-preemtive priority.For P2 devices, when the BS is asleep or busy, P2 devices retryafter a random backoff time which is exponentially distributedwith rate α. When the BS is asleep, keep listening to find theBS available and send their data.
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 19 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (2/3)Performance Tradeoff Analysis in Single Cell Scenario
How do we model the problem (1/3):
Consider uplink communication of a green BS in a single cell
Massive number of deployed sensors (P2), with boundedtransmit power, and need for long battery lifetime.
A number of human users (P1), with non-preemptive priorityover P2, require low delay.
Consider ACB for MTC:
For P1 devices, when the BS is busy, they are queued to beserved, based on processor sharing, with non-preemtive priority.For P2 devices, when the BS is asleep or busy, P2 devices retryafter a random backoff time which is exponentially distributedwith rate α. When the BS is asleep, keep listening to find theBS available and send their data.
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 19 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (2/3)Performance Tradeoff Analysis in Single Cell Scenario
How do we model the problem (2/3):
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 20 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (2/3)Performance Tradeoff Analysis in Single Cell Scenario
How do we model the problem (3/3):
We use M/M/1 queuing model with processor sharing servicediscipline
Sleeping time: General distribution
Listening time: Exponential distribution
Uplink service requirement: Exponential distribution
Power control: Channel inversion, fixed SINR requirement forH2H and M2M
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 21 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (2/3)Performance Tradeoff Analysis in Single Cell Scenario
Results (1/2):
Derive closed-form expressions for energy consumption of theBS, experienced delay by users and machines, and expectedbattery lifetime of machine devices.
Introduce the fundamental tradeoffs, and explore the impactof system and traffic parameters on the introduced tradeoffs.
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 22 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (2/3)Performance Tradeoff Analysis in Single Cell Scenario
Results (2/2): Example of derived expressions:
E bcons = ρPs +
1− ρ
1 + µv(Pl + µvPsl + 2µEsw )
DP1 =u1 + µP3(1)v/2 + λ2u
22
1− u1λ1
LP2 =E0T
Pcατ/λ2
∑m
E(N(m)2 ) +
[[Pc + ηPt2 ] + Est
]
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 23 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
Outline
1 IntroductionBackground and MotivationPaper Focus and High-Level Research QuestionsState of the Art
2 Detailed Research Questions and ContributionsBattery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
3 Summary
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 24 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (3/3)Performance Tradeoff Analysis in Multi Cell Scenario
RQ3: What are the tradeoffs between green and lifetime-awarecellular network design in the deployment phase?
What is the optimal density of BSs w.r.t. batetry lifetime ofdevices?
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 25 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (3/3)Performance Tradeoff Analysis in Multi Cell Scenario
BS Sleeping in a Multi-cell Scenario:
Imapct on uplink communications is absent in literature1.
1Hina Tabassum et al. “Downlink performance of cellular systems with basestation sleeping, user association, and scheduling”. In: IEEE TWC (2014).
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 26 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
RQs and Contributions (3/3)Performance Tradeoff Analysis in Multi Cell Scenario
Results: using a similar methodology as for RQ2, the followingresults are derived:
Given a density of BSs, we model the operation of BSs inserving mixed M2M and H2H traffic.
Derive closed-form expressions for energy consumption of theBSs, experienced delay by users and machines, and expectedbattery lifetime of machine devices.
Introduce the fundamental tradeoffs, and explore the impactof system and traffic parameters on the introduced tradeoffs.
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 27 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
Outline
1 IntroductionBackground and MotivationPaper Focus and High-Level Research QuestionsState of the Art
2 Detailed Research Questions and ContributionsBattery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
3 Summary
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 28 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
Simulation Results and FindingsAnalytical and Simulation Results
Enery consumption for BS/Delay for HoC and MTC
1 10 100 100060
70
80
90
100
110
Ene
rgy
(Jou
le)
Econs
b , simulation
Econs
b , analytic
D2, simulation
D2, analytic
D1, simulation
D1, analytic
0
14
28
42
56
70
Del
ay (
sec)
Mean listening time (sec)
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 29 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
Simulation Results and FindingsAnalytical and Simulation Results
Enery consumption for BS/EE for MTC
100 101 102 103
Mean listening time (sec)
60
70
80
90
100
110
Ene
rgy
(Jou
le)
0.5
1.2
1.9
2.6
3.3
4
Ene
rgy
Effi
cien
cy (
bpj)
×107
Econs
b for the BS
Energy efiiciency for P2 devices
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 30 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
Simulation Results and FindingsAnalytical and Simulation Results
Enery consumption for BS/Battery Lifetime for MTC
0 2 4 6 8 10 12 14 16
Time (× T) ×105
0
0.2
0.4
0.6
0.8
1
Em
pric
al C
DF
of l
ifetim
es
Mean lis. time=714 secMean lis. time=100 secMean lis. time=10 secMean lis. time=2 secMean lis. time==1 sec
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 31 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings
Simulation Results and FindingsAnalytical and Simulation Results
Findings:
Significant impact of the BSs’ energy saving strategies
BS sleepingBS deployment density
on the UEs’ battery lifetimes has been presented.
Promote revisiting traditional energy saving strategies to copewith the ever increasing number of connected machine-typedevices in cellular networks.
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 32 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Summary
Providing scalable yet energy-efficient small datacommunications is a key requirement for realization of IoT.
To realize long lasting MTC services over cellular networks,different aspects of cellular networks must be optimized.
Performance tradeoffs have been explored to control theimpact of MTC on existing services as well as resourceallocation for MTC on MTC battery lifetime.
More on battery lifetime-aware network design:Licentiate Thesis: Amin Azari, Energy Efficient Machine-TypeCommunications over Cellular Networks, KTH University,2016, Available Online.
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 33 / 34
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IntroductionDetailed Research Questions and Contributions
Summary
Summary
Providing scalable yet energy-efficient small datacommunications is a key requirement for realization of IoT.
To realize long lasting MTC services over cellular networks,different aspects of cellular networks must be optimized.
Performance tradeoffs have been explored to control theimpact of MTC on existing services as well as resourceallocation for MTC on MTC battery lifetime.
More on battery lifetime-aware network design:Licentiate Thesis: Amin Azari, Energy Efficient Machine-TypeCommunications over Cellular Networks, KTH University,2016, Available Online.
Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 33 / 34