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

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Introduction
Detailed Research Questions and Contributions
Summary
Battery Lifetime-Aware Base Station Sleeping
Control with M2M/H2H Coexistence
A Battery Lifetime-Aware Cellular Network Design Framework
Amin Azari, Guowang Miao
CoS Department, ICT School
KTH 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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Introduction
Summary
Outline
1 Introduction
Background and Motivation
Paper Focus and High-Level Research Questions
State of the Art
2 Detailed Research Questions and Contributions
Battery lifetime Assessment
Performance Tradeoﬀ Analysis in Single Cell Scenario
Performance Tradeoﬀ Analysis in Multi Cell Scenario
Simulation Results and Findings
3 Summary

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Introduction
Summary
State of the Art
Outline
1 Introduction
State of the Art
3 Summary

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Introduction
Summary
State of the Art
Telecommunications
Yesterday, Today, Tomorrow
Internet of Things: Everything that beneﬁts from being
connected will be connected.

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Introduction
Summary
State of the Art
IoT over Cellular Networks
Regarding unique characteristics of cellular networks like
ubiquitous coverage, cellular-based M2M will be a key enabler
of 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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Introduction
Summary
State of the Art
Massive M2M Communications
Main challenges in enabling Massive M2M :
Scalability: up to one million simultaneous connections per
square kilometera.
Energy eﬃciency: over 10 years battery lifetime
10 times more bit-per-joule energy eﬃciencyb
.
Battery lifetime → Maintenance cost
a
Samsung. 5G vision. Tech. rep. 2015.
b
Nokia. Looking ahead to 5G. . Tech. rep. 2014.

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Introduction
Summary
State of the Art
Outline
1 Introduction
State of the Art
3 Summary

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Introduction
Summary
State of the Art
Paper Focus
Paper Focus
To incorporate battery lifetime-awareness into the design of 5G
cellular networks
High-Level Research Questions
Identify deployment and operational solutions enabling serving a
massive number of energy-limited devices:
with minimum increase in CAPEX and OPEX,
without degrading human-type users perceived QoS.

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Introduction
Summary
State of the Art
Outline
1 Introduction
State of the Art
3 Summary

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Introduction
Summary
State of the Art
State of the Art on BS Sleeping (1/2)
BS sleeping and its impacts on downlink communications have
been investigated. Optimal density of macro and micro BSs
have been founda.
BS sleeping with constraint on transmit power of users has
been investigatedb.
a
Hina Tabassum et al. “Downlink performance of cellular systems with base
station sleeping, user association, and scheduling”. In: IEEE TWC (2014),
Jyri Hämäläinen et al. “A Novel Multiobjective Cell Switch-Off Method with
Low Complexity for Realistic Cellular Deployments”. In: arXiv (2015),
Sheng Soh et al. “Energy efficient heterogeneous cellular networks”. In: IEEE
JSAC (2013), Dongxu Cao et al. “Optimal combination of base station densities
for energy-efficient two-tier heterogeneous cellular networks”. In: IEEE TWC
(2013).
b
Jinlin Peng et al. “Stochastic analysis of optimal base station energy saving
in cellular networks with sleep mode”. In: IEEE Commun. Lett. (2014).

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Introduction
Summary
State 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 battery
lifetime modeling for MTC,
battery lifetime-aware deployment and operation design
approaches 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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Introduction
Summary
Outline
1 Introduction
State of the Art
3 Summary

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Introduction
Summary
RQs and Contributions (1/3)
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 and
network battery lifetimes?

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Introduction
Summary
Energy consumption → a semi-regenerative process
Reg. 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)
DutyCycle
ReportingPeriod
(Wake up)
Data gathering
(Wake up)
)!!.
time
Power
Sleep
Data
gathering/pr
ocessing
Listening
to
eNodeB
Scheduled
transmission
Connection
establishme
nt
Reporting period

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Introduction
Summary
∗ Expected lifetime of node i
=
Energy storage at time t
Energy consumption per reporting period
× Reporting period
=
Ei (t)
Ei
perperiod
Ti ,
Eperpacket = Estatic + Edynamic,
Edynamic =
Di
Ri
(Pc + αPt),
Estatic = K(tDRX PDRX + tsyncPsync + tactPact) + tsyncPsync,
K = Number of active intervals per reporting period.
* Network lifetime: Average of individual lifetimes.

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Introduction
Summary
Outline
1 Introduction
State of the Art
3 Summary

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Introduction
Summary
Consider a massive M2M/H2H deployment in a single-cell
scenario.
We are interested in coupling between optimizing BS
operation for:
improving battery lifetime of MTC devices,
decreasing energy/cost of the access network,
improving QoS of non-MTC traﬃc.

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Introduction
Summary
Consider a massive M2M/H2H deployment in a single-cell
scenario.
We are interested in coupling between optimizing BS
operation 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 and
lifetime-aware cellular network design in the operation phase?
What is the optimal BS sleeping strategy w.r.t. batetry
lifetime of devices?

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Introduction
Summary
What is BS Sleeping?
Imapct on uplink communications is absent in literature.

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Introduction
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 bounded
transmit power, and need for long battery lifetime.
A number of human users (P1), with non-preemptive priority
over P2, require low delay.
Consider ACB for MTC:

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Introduction
Summary
Consider uplink communication of a green BS in a single cell
Massive number of deployed sensors (P2), with bounded
transmit power, and need for long battery lifetime.
A number of human users (P1), with non-preemptive priority
over P2, require low delay.
Consider ACB for MTC:
For P1 devices, when the BS is busy, they are queued to be
served, based on processor sharing, with non-preemtive priority.
For P2 devices, when the BS is asleep or busy, P2 devices retry
after a random backoﬀ time which is exponentially distributed
with rate α. When the BS is asleep, keep listening to ﬁnd the
BS available and send their data.

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Introduction
Summary

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Introduction
Summary
We use M/M/1 queuing model with processor sharing service
discipline
Sleeping time: General distribution
Listening time: Exponential distribution
Uplink service requirement: Exponential distribution
Power control: Channel inversion, ﬁxed SINR requirement for
H2H and M2M

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Introduction
Summary
Results (1/2):
Derive closed-form expressions for energy consumption of the
BS, experienced delay by users and machines, and expected
battery lifetime of machine devices.
Introduce the fundamental tradeoffs, and explore the impact
of system and traffic parameters on the introduced tradeoffs.

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Introduction
Summary
Results (2/2): Example of derived expressions:
Eb
cons = ρPs +
1 − ρ
1 + µ¯v
(Pl + µ¯vPsl + 2µEsw )
DP1 =
¯u1 + µP3(1)ˆv/2 + λ2 ¯u2
2
1 − ¯u1λ1
LP2 =
E0T
Pcατ
/
λ2
∑
m
E(N
(m)
2 ) +
[
[Pc + η ¯Pt2 ] + Est
]

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Introduction
Summary
Outline
1 Introduction
State of the Art
3 Summary

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Introduction
Summary
RQ3: What are the tradeoﬀs between green and lifetime-aware
cellular network design in the deployment phase?
What is the optimal density of BSs w.r.t. batetry lifetime of
devices?

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Introduction
Summary
BS Sleeping in a Multi-cell Scenario:
Imapct on uplink communications is absent in literature1.
1
Hina Tabassum et al. “Downlink performance of cellular systems with base
station sleeping, user association, and scheduling”. In: IEEE TWC (2014).

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Introduction
Summary
Results: using a similar methodology as for RQ2, the following
results are derived:
Given a density of BSs, we model the operation of BSs in
serving mixed M2M and H2H traffic.
Derive closed-form expressions for energy consumption of the
BSs, experienced delay by users and machines, and expected
battery lifetime of machine devices.
Introduce the fundamental tradeoffs, and explore the impact
of system and traffic parameters on the introduced tradeoffs.

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Introduction
Summary
Outline
1 Introduction
State of the Art
3 Summary

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Introduction
Summary
Analytical and Simulation Results
Enery consumption for BS/Delay for HoC and MTC
1 10 100 1000
60
70
80
90
100
110
Energy(Joule)
Econs
b
, simulation
Econs
b
, analytic
D2
, simulation
D2
, analytic
D1
, simulation
D1
, analytic
0
14
28
42
56
70
Delay(sec)
Mean listening time (sec)

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Introduction
Summary
Enery consumption for BS/EE for MTC
100
101
102
103
Mean listening time (sec)
60
70
80
90
100
110
Energy(Joule)
0.5
1.2
1.9
2.6
3.3
4
EnergyEfficiency(bpj)
×107
Econs
b
for the BS
Energy efiiciency for P2
devices

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Introduction
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
EmpricalCDFoflifetimes
Mean lis. time=714 sec
Mean lis. time==1 sec

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Introduction
Summary
Findings:
Signiﬁcant impact of the BSs’ energy saving strategies
BS sleeping
BS deployment density
on the UEs’ battery lifetimes has been presented.
Promote revisiting traditional energy saving strategies to cope
with the ever increasing number of connected machine-type
devices in cellular networks.

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Introduction
Summary
Summary
Providing scalable yet energy-efficient small data
communications 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 the
impact of MTC on existing services as well as resource
allocation for MTC on MTC battery lifetime.

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Introduction
Summary
Summary
Providing scalable yet energy-efficient small data
communications 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 the
impact of MTC on existing services as well as resource
allocation for MTC on MTC battery lifetime.
More on battery lifetime-aware network design:
Licentiate Thesis: Amin Azari, Energy Efficient Machine-Type
Communications 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

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