!!!!!Main azari

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Introduction
Battery lifetime Assessment
Performance Tradeoff Analysis in Split M2M/H2H Traffic Serving
Performance Tradeoff Analysis in Multiplexing Joint M2M/H2H
Fundamental Tradeoffs in Serving Massive MTC
Services over Cellular Networks
Amin Azari, Guowang Miao
CoS Department, ICT School
KTH Royal Institute of Technology
May 23, 2017
Amin Azari, Guowang Miao Fundamental Tradeoffs in Serving Massive MTC Services over Cellular Networks 1 / 34

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Introduction
Outline
Battery
lifetime and
reliability for
MTC
Energy/cost
saving for the
access
network
QoS for human
oriented
communications

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Introduction
Outline
1 Introduction
Motivation
Focus and High-Level Research Questions
2 Battery lifetime Assessment
3 Performance Tradeoff Analysis in Split M2M/H2H Traffic
Serving
Problem Formulation
Research Methodology
Simulation Results and Findings
4 Performance Tradeoff Analysis in Multiplexing Joint M2M/H2H
Performance Tradeoff Analysis in Single/Multi Cell Scenario
More Reading

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Introduction
Motivation
Outline
1 Introduction
Motivation
Serving
Problem Formulation
More Reading

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Introduction
Motivation
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
Motivation
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
Motivation
Outline
1 Introduction
Motivation
Serving
Problem Formulation
More Reading

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Introduction
Motivation
Research Focus
Research 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
RQs and Contributions (1/4)
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
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
∗ 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
Experimental results versus analytical results
0 1 3 5 7 9 11 13 15 17 19 21 23
Time (hours)
7500
8000
8500
9000
9500
Remainingbatteryenergy(Joule)
Expriment
Proposed model

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Introduction
Problem Formulation
Outline
1 Introduction
Motivation
Serving
Problem Formulation
More Reading

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Introduction
Problem Formulation
Performance Tradeoff Analysis
RQ2: What are the consequences of allocating radio resources
to massive MTC services on
energy consumption of the BSs,
network spectral efficiency,
experienced delay of non-MTC traffic, and
battery lifetime of machine-type devices?

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Introduction
Problem Formulation
Research Questions (2/4)
Split Resource Allocation to MTC and HoC over RACH and
PUSCH
!"(sec)
#M
!"# (sec)
PUSCH resource (H C)
PUSCH resource (MTC)
PRACH resource (H C)
PRACH resource (MTC)
!
time
frequency
"M (Hz)
! (Hz)
"# (sec)

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Introduction
Problem Formulation
Outline
1 Introduction
Motivation
Serving
Problem Formulation
More Reading

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Introduction
Problem Formulation
Energy and delay analysis for IoT traﬃc
Need to model energy consumption of machine devices in
random access and data transmission.
Energy consumption: [reservation over RACH+potential
collision(s)]+[data transmissio over PUSCH+potential
retranmsnissiopn(s)]
Retransmission in RACH is possible due to collision and in
PUSCH because of limited transmit power of IoT devices and
available resources for IoT.
We have developed a Markov chain to investigate the
probability of collision over RACH, outage over PUSCH, and
average number of present users in RACH and PUSCH stages
as a function of traﬃc load for IoT communications.

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Introduction
Problem Formulation
Delay analysis for HoC traﬃc
Experienced delay in data transmission: Delay over RACH
+Delay over PUSCH.
Collision over RACH increases the access delay.
Lack of available resources over PUSCH and bounded transmit
power increase the delay.
We have developed a Markov chain to investigate the
probability of collision over RACH, outage over PUSCH, and
average number of present users in RACH and PUSCH stages
as a function of traﬃc load for H2H communications.

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Introduction
Problem Formulation
Using these Markov chains,
battery lifetime of IoT devices,
energy consumption of BS
experienced delay
spectral efficiency
has been modeled as a function of traffic and resource
allocation parameters.
Performance tradeoffs have been highlighted.

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Introduction
Problem Formulation
Outline
1 Introduction
Motivation
Serving
Problem Formulation
More Reading

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Introduction
Problem Formulation
Simulation Results
Simulation parameters

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Introduction
Problem Formulation
Simulation Results
Optimized operation points w.r.t. EE, Energy Consumption BSs,
Delay, SE.
EnergyEfficiency(bits/joule)
α: RACH alloc. to MTCβ: PUSCH alloc. to MTC
0
100
1
5
×105
0.8
0.610-2
10
0.4
0.2
0
Max energy efficiency
β: PUSCH alloc. to MTCα: RACH alloc. to MTC
110
1
0.5
10-3
10-2
10-1
120
0
100
Avg.cons.energy
perunittime(J)
130
X: 0.582
Y: 0.07
Z: 112
Min Energy
ExpriencedDelay(sec)
β:PUSCHalloc.toMTC
0
100
10-1
10-2
α: RACH alloc. to MTC
10.80.610-3
0.40.20
1
2
Min delay
β: PUSCH alloc. to MTC α: RACH alloc. to MTC
0
100
0.5
1
SpectralEfficiency(bits/sec/Hz)
1
0.8
10-2
0.6
1.5
0.4
0.2
10-4 0
Maximum
spectral efficiency
X: 0.92
Y: 0.004
Z: 1.311

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Introduction
Problem Formulation
Simulation Results
Optimized Operation Points.
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
x 10
5
0
0.2
0.4
0.6
0.8
1
Time (× reporting period)
CDFofindividuallifetimes
α=28%, β=0.5%
α=28%, β=5%
α=37%, β=0.5%
α=37%, β=5%
α=74%, β=0.5%
α=74%, β=5%

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Introduction
Problem Formulation
Simulation Results
10-3
10-2
10-1
100
β: PUSCH alloc. to IoT
110
120
130
Ave.cons.energy
perunittime(J)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
α: RACH alloc. to IoT
110
120
130
Ave.cons.energy
perunittime(J)
Min Energy
Min Energy
β=0.07
α=0.582

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Introduction
Problem Formulation
Simulation Results
0.6
0.8
1
β:PUSCHalloc.toMTC
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
α: RACH alloc. to MTC
10-2
Max energy efficiency for MTC
EE, EC, and ED
increase; SE decrease
EC increases;
EE and ED decrease
Min BS energy consumption
EC and SE
increase
Min Delay for HoC
Max uplink spectral
efficiency (HoC+MTC)

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Introduction
Problem Formulation
Simulation Results
Findings
Significant impacts of uplink resource provisioning on the
battery lifetime of energy-limited devices, energy/spectral
efficiency of the network, and experienced delay in uplink
communications have been presented.
The derived results figure out the ways in which scarce radio
and energy resources for the BS and QoS for human-oriented
communications could be preserved while coping with the ever
increasing number of energy-limited MTC-type devices in
cellular networks.

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Introduction
More Reading
Outline
1 Introduction
Motivation
Serving
Problem Formulation
More Reading

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Introduction
More Reading
RQs and Contributions (3-4/4)
Performance Tradeoff Analysis in Single Cell Scenario
Consider a massive M2M/H2H deployment in a single-cell
scenario.
RQ3: 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?
RQ4: 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
More Reading
Outline
1 Introduction
Motivation
Serving
Problem Formulation
More Reading

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Introduction
More Reading
Analytical and Simulation Results
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
More Reading
Analytical and Simulation Results
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
More Reading
Outline
1 Introduction
Motivation
Serving
Problem Formulation
More Reading

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Introduction
More Reading
More Reading
Thesis: Amin Azari, Energy Eﬃcient Machine-Type
Communications over Cellular Networks: A Battery
Lifetime-Aware Cellular Network Design Framework, KTH
Royal Institute of Technology, 2016.
G. Miao, A. Azari, and T. Hwang, E2-MAC: energy eﬃcient
medium access for massive M2M communications, IEEE
Transactions on Communications, no. 99, 2016.
G. Miao, A. Azari, Battery Lifetime-Aware Base Station
Sleeping Control with M2M/H2H Coexistence, IEEE
Globecom 2016

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Appendix Thanks
Questions
Thanks for your attention.

!!!!!Main azari

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