Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence

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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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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


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Summary

Background and MotivationPaper Focus and High-Level Research QuestionsState of the Art

Outline



3 Summary


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Summary


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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Summary


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.


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Summary


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.


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Summary


Outline



3 Summary


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Summary


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.


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Summary


Outline



3 Summary


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Summary


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).


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Summary


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.


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Summary

Battery lifetime AssessmentPerformance Tradeoff Analysis in Single Cell ScenarioPerformance Tradeoff Analysis in Multi Cell ScenarioSimulation Results and Findings

Outline



3 Summary


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Summary


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?


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Summary


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


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Summary


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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Summary


Outline



3 Summary


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Summary


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?


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Summary



What is BS Sleeping?

Imapct on uplink communications is absent in literature.


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Summary



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.


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Summary





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Summary




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


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Summary



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.


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Summary



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

]


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Summary


Outline



3 Summary


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Summary


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?


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Summary



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).


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Summary



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.


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Summary


Outline



3 Summary


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Summary


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)


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Summary



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


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Summary



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


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Summary



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.


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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.


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Appendix Thanks and Question

Questions

Thanks for your attention.

Questions?


Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence

Engineering

Transcript of Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence