Energy Efficient Machine-Type Communications over

Introduction Research Questions and Contributions Summary, Future Works, and Publications

Energy Efficient Machine-Type Communications over Cellular Networks A Battery Lifetime-Aware Cellular Network Design Framework

Amin Azari CoS Department, ICT School KTH Royal Institute of Technology

Licentiate Thesis Seminar 02-12-2016 . . .

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Energy Efficient Machine-Type Communications over Cellular Networks

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Outline 1

Introduction Background and Motivation Thesis Focus and High-Level Research Questions State of the Art

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Research Questions and Contributions Battery lifetime Assessment MAC Design for Clustered MTC MAC Design for for Direct MTC Performance Tradeoff Analysis

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Summary, Future Works, and Publications Summary Future Works Overview of Publications . . .

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Background and Motivation Thesis Focus and High-Level Research Questions State of the Art

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Telecommunications Yesterday, Today, Tomorrow

Internet of Things: Everything that benefits from being . . . . . . . . . . . connected will be connected. . . . . . . . . . . . . Amin Azari


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IoT over Cellular Networks

Regarding unique characteristics of cellular networks like ubiquitous coverage, machine-type communications (MTC) 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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IoT over Cellular Networks Technology drivers for 5G Internet-of-Things Massive MTC Mission-critical MTC

Mobile Broadband

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Massive Machine-Type Communications

Characteristics of Massive MTC Large numbers of short-lived sessions Usually short payload size Battery-driven Vastly diverse QoS requirements delay requirement from msec to hours

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Massive Machine-Type Communications

Main challenges in enabling Massive MTC : 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 b

Samsung. 5G vision. Tech. rep. 2015. Nokia. Looking ahead to 5G. . Tech. rep. 2014.

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Thesis Focus

Thesis 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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State of the Art (1/3) Scalability issues in serving MTC have been investigateda . Capillary networking for connecting a massive number of dumb devices to cellular networks through gateways has been proposedb . Energy efficiency of LTE for small data communications has been exploredc . a

Jermyn et al. “Scalability of M2M systems and the IoT on LTE”. . In: IEEE WoWMoM. 2015. b Sachs et al. “Capillary networks–a smart way to get things connected”. In: Ericsson Review (2014). c Wang et al. “Energy-efficiency of LTE for small data M2M communications”. In: IEEE ICC. 2013. . . .

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State of the Art (2/3) Battery lifetime preserving solutions like Long-DRX for cellular MTC have been proposeda . Evolutionary solutions like ACB for compensating the massive access problem have been proposedb . Revolutionary solutions like LTE-M for accommodating MTC traffic in cellular networks have been proposedc . a

Tirronen et al. “Reducing energy consumption of LTE devices for M2M communication”. In: IEEE GC. 2012. b Laya et al. “Is the random access channel of LTE and LTE-A suitable for M2M?”. In: IEEE Commun. Sur. & Tut. (2014). c Nokia. LTE-M – Optimizing LTE for the IoT. . Tech. rep. 2015. . . .

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State of the Art (3/3) 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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Battery lifetime Assessment MAC Design for Clustered MTC MAC Design for for Direct MTC Performance Tradeoff Analysis

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RQs and Contributions (1/4) 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 and network battery lifetimes?

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RQs and Contributions (1/4) Energy consumption → a semi-regenerative process Reg. point → end of each successful data transmission epoch. BS

UE

Data gathering (Turn radio on) !"#

+

time

Cell Info

$!%

&',(

Duty Cycle

Reporting Period

)!!.

&',$

)*-

(Sleep) (Wake up)

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Reporting period Power

(Wake up)

PRACH: Random Access Request (RN, BSR, Cause, PDCCH CC) PDCCH: Uplink Assignment (RACH reference, PUSCH allocation, BS VR = 0, CRNTI 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)

Listening to eNodeB

Sleep

Data gathering/pr ocessing

Connection establishme nt

Scheduled transmission

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∗ Expected lifetime of node i Energy storage at time t = × Reporting period Energy consumption per reporting period Ei (t) = i Ti , Eperperiod Eperpacket = Estatic + Edynamic , Di Edynamic = (Pc + αPt ), Ri Estatic = K (tDRX PDRX + tsync Psync + tact Pact ) + tsync Psync , K = Number of active intervals per reporting period. * Network lifetime: SIL, LIL, and AIL. Amin Azari

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Comparision of simulation and analytical results confirms that by tuning our proposed models, network battery lifetime can be predicted effectively. CDF of individual lifetimes

1 0.8 0.6 0.4

(1,200) E2−MAC

0.2

(1,100) E2−MAC Analytic. (1,100) Analytic. (1,200)

0 0 Amin Azari

200

400

600 800 Time (× TRA)

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1000

1200

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Contribution Present more realistic energy consumption, individual and network battery lifetime models for MTC services over cellular networks that can be used by other researchers to evaluate impact of their proposed techniques on the network battery lifetime, and hence, maintenance costs.

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RQs and Contributions (2/4) Battery Lifetime-Aware Solutions in MAC Design for Clustered MTC

Model: a massive machine deployment in a multi-cell scenario without strict delay requirement (best effort). No dedicated gateway. Application: connected sensors for data gathering, e.g. temperature/humidity/presence monitoring in an area. Goal: to minimize the amount of consumed energy at terminals per bit of received data at the BS. To address the massive concurrent access issue and energy saving −→ cluster-based MTC where feasible. . . .

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If clustered MTC, RQ2: What is the optimal cluster-size? RQ3: What is the optimal cluster-head (CH) selection scheme? RQ4: Where should clustering be used? RQ5: Which communications protocols must be used inside and outside the clusters? RQ6: What is the impact of underlying intra cluster communications on primary communications? . . .

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Methodology Introduce analytical models for energy consumptions of nodes in CH, CM, and direct connectivity modes. Formulate clustering design problems including cludter size, CH selection scheme and its frequency, and MAP indide clusters as network battery lifetime maximization problems. Finding the cluster size, CH selection scheme, and CH reselection period that maximize the network lifetime. Propose a load-adaptive multiple access scheme for intre-cluster communications in order to provide a tunable tradeoff between energy efficiency and delay. . . .

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Solution: (z ∗ , n∗ ) E 2 -MAC z ∗ → optimized cluster size; n∗ → optimized number of phases for n-phase CSMA; Allocated frames for MTC

TRA

1 frame

time

Intra-cluster Commun.

Inter-cluster Commun. PRBPs

Blank frames: reserved for Intra-cluster communications.

1 Subframes 1 Slot: 0.5ms

180 KHz

Resource Block

Resource blocks are allocated to Minimum allocatable CHs for uplink transmission. resource: 2 slots=1 PRBP

PRBPs

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Orthogonal resources are allocated to neighbor clusters for interference management. . . . . . . Amin Azari


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300

2 E2−MAC E2−MAC E −MAC

250

2

E −MAC

E2−MAC

200 150 100 50 0

E2−MACn

cMAC (1,200) (1,100) (1,100) (2,100) (3,100) (1,50)

Max exprienced delay (× TRA)

Min individual lifetime ( × TRA )

Simulation results: Comparison of RACH, non-optimized clustering, and optimized clustering

6

E2−MAC

2

4

E −MAC E2−MAC E2−MAC 2 E −MACn

2

0

cMAC (1,200) (1,100) (1,100) (2,100) (3,100) (1,50)

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E2−MAC

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Findings In massive MTC deployment, clustering may increase the EE and battery lifetime. The battery lifetime performance of clustered MTC significantly improves by choosing an appropriate cluster size. While CHs consumes more energy, by intelligent CH reselection at well-designed periods network battery lifetime can be prolonged significantly (e.g. SIL:340/25). Further batetry lifetime improvement is achievable by using n-phase CSMA, which in turn sacrifies the delay performance. . . .

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RQs and Contributions (3/4) Battery Lifetime-Aware Solutions in MAC Design for Direct MTC

Model: a massive MTC deployment in a multi-cell scenario with direct data transmission to the BSs. Devices pass through the RACH, sends reservation over PUCCH, receive response over PDCCH, send scheduled data over PUSCH. Existing access reservation, scheduling, and scheduled data transmission procedures have been designed for human-oriented communications (HoC). → bottlenecks for MTC. . . .

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If direct MTC: RQ7: How does energy consumption of devices in access reservation scale with the number of devices served per BS? RQ8: How can access reservation performance be improved with introducing less complexity and cost to the network? RQ9: What is the theoretical model describing coupling between scheduling and network battery lifetime? I RQ10: How can we design a network lifetime-aware scheduler suitable for serving massive MTC devices? I . . .

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Methodology Define network lifetime as a function of individual lifetimes of nodes: SIL, LIL, AIL, and ξSIL, w.r.t. the MTC application. Formulate uplink scheduling and transmit power control as a network lifetime maximization problem. Find the battery lifetime-aware scheduling solutions. Explore impact of control parameters and highlight the tradeoffs. . . .

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Solutions Present sophisticated uplink scheduling algorithms for MTC traffic over SC-FDMA systems (e.g. Algorithm 1 in paper B). Present low-complexity scheduling algorithms with limited feedback requirement (e.g. Algorithm 4 in paper B). Present MTC scheduler for existing LTE systems.

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Simulation results: Lifetime-aware, State-of-the-Art 13

Jain index of ind. lifetimes

SIL network lifetime ( × Ti )

4

x 10

3 2 1 0

Sch. 1

Sch. 2

Sch. 3

Sch. 4

Sch. 5

Sch. 6

Jain index Variance

0.8 0.6

6

0.4

4

0.2

2

0

Sch. 1

Sch. 2

Sch. 3

Sch. 4

Sch. 5

Sch. 6

0

Scheduling schemes

Scheduling schemes

Battery Lifetime Analysis

8

Variance of ind. lifetimes

x 10 10

1 4

Lifetime Fairness Analysis

Sch. 1: Lifetime-aware; Sch. 2: Low-com. lifetime-aware; Sch. 4: Round Robin; Sch. 5: Channel-aware; Sch. 6: Energy-efficient uplink resource allocation in LTE networks with M2M/H2H co-existence under statistical QoS guarantees, IEEE Transactions on Communications, vol. 62, no. 7, pp. 23532365, July 2014. . . .

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Findings Modeling energy consumption of MTC, and designing respective scheduling schemes can significantly prolong the network lifetime. Uplink scheduling based on the max-min fairness let machine nodes to last for a long time and die approximately at the same time, and hence, contributes significantly in network’s maintenance costs reduction.

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Contribution to Industry Filling a patent on “Lifetime-Aware Quality Class Identifier Definition for Machine-Type Communications over Cellular Networks” with Ericsson (IvD has been submitted).

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RQs and Contributions (4/4) Performance Tradeoff Analysis

Model: a massive machine deployment in a multi-cell scenario with direct data transmission to the BSs. Devices pass through the RACH, send reservation over PUCCH, receive response over PDCCH, send scheduled data over PUSCH. We are interested in coupling between optimizing cellular networks for: improving battery lifetime of MTC devices, decreasing energy/cost of the access network, improving QoS of non-MTC traffic. . . .

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RQ11: What are the tradeoffs between green and lifetime-aware cellular network design in deployment and operation phases? In a single cell scenarion, what is the optimal BS sleeping strategy w.r.t. batetry lifetime of devices? In a multi-cell scenario, what is the optimal density of BSs w.r.t. batetry lifetime of devices?

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BS Sleeping

Imapct on uplink communications is absent in. literature. . . . . . . . . . . . . .

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Methodology Develop a tractable framework to model the operation of a green BS which serves mixed MTC and HoC traffic, and saves energy by going to the sleep mode. 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. Extend the results to the multi-cell scneario. BS sleeping → Lower density of BSs. . . .

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Research Questions (4/4) Performance Tradeoff Analysis

Analytical and Simulation Results

110

70

90

simulation

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analytic

D2 , simulation

80

D2 , analytic

28

D1 , simulation D1 , analytic

70

Energy (Joule)

56 E bcons , E bcons ,

Delay (sec)

Energy (Joule)

100

110

×10 7 4

100

3.3

90

2.6

80

1.9 E bcons for the BS

70

Energy efiiciency for P2 devices

14

60 10 0 60 1

10 100 Mean listening time (sec)

10 1

0 1000

1.2 0.5 10 3

10 2

Mean listening time (sec)

Enery saving for BS/Delay for HoC/ Battery lifetime for MTC . . .

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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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RQ12: 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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frequency

Split Resource Allocation to MTC and HoC over RACH and PUSCH

!"# (sec) !

"# (sec)

! "(sec)

#M ! (Hz)

"M (Hz) time PRACH resource (HŽC) PRACH resource (MTC)

PUSCH resource (HŽC) PUSCH resource (MTC) . . .

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Research Questions (4/4)

×10 5

Avg. cons. energy per unit time (J)

Energy Efficiency (bits/joule)

Simulation Results: Optimized operation points w.r.t. EE, Energy Consumption BSs, Delay, SE. Max energy efficiency

10

5

0 10 0 10 -2

β: PUSCH alloc. to MTC

0

0.2

0.4

0.6

0.8

Min Energy

130 120 X: 0.582 Y: 0.07 Z: 112

110 1

0.5

1

0

Spectral Efficiency (bits/sec/Hz)

Exprienced Delay (sec)

α : RACH alloc. to MTC

2 1 0 10 0

Min delay

Ha USC

β: P

10 -1

10 -2

. to

lloc MTC

10

10

10 -1

0


X: 0.92 Y: 0.004 Z: 1.311

1.5

1

0.5 Maximum spectral efficiency

0 10 0 -2

-3

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0.2

0.4

0.6

. to MTC α: RACH alloc

0.8

10 -3

10 -2

α : RACH alloc. to MTC

1

10 β : PU SCH alloc

. . .

. to M

10 -4

0

0.2

0.8

0.6

0.4

. T.C . . . . . α.: R.A . . . . . . . . . . . . . . . . . . . . . . . . .


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TC

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lloc CH a

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Optimized Operation Points.


1

Max energy efficiency for MTC

0.8 EE, EC, and ED increase; SE decrease

0.6

Min BS energy consumption

10 -2

EC and SE increase

EC increases; EE and ED decrease

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

α : RACH alloc. to MTC Min Delay for HoC Amin Azari

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0.9

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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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Summary Future Works Overview of Publications

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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. MAC and scheduling design problems have been formulated as network battery lifetime maximizing optimization problems. 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. Promising lifetime improvement evidences have been presented using simulations in the context of LTE. . . .

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Future works The way in which our work can be extended The proposed framework can be extended to other cellular network design problems: security and authentication, automatic retransmissions, handover, and etc. My planned work Mission-critical MTC as another key driver of 5G Developing a revolutionary scheme for realizing ultra-high battery lifetime . . .

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Overview of Publications (1/4) Battery Lifetime-Aware Solutions in MAC Design for Clustered MTC

Paper 1 (Paper A in the appendix): G. Miao; A. Azari; T. Hwang, “E 2 -MAC: Energy Efficient Medium Access For Massive M2M Communications,” published in IEEE Transactions on Communications, 2016 Paper 2: A. Azari and G. Miao, “Energy efficient MAC for cellular-based M2M communications,” Signal and Information Processing (GlobalSIP), 2014 IEEE Global Conference on, Atlanta, GA, 2014, pp. 128-132.

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Overview of Publications (2/4) Battery Lifetime-Aware Solutions in MAC Design for Direct MTC (Access reservation)

Paper 3: Amin Azari, Mohammad Istiak Hossain, and Jan I Markendahl, “RACH Dimensioning for Reliable MTC over Cellular Networks,” Submitted to 2017 IEEE VTC. Paper 4: Mohammad Istiak Hossein, Amin Azari, and Jens Zander, “DERA: Augmented Random Access for Cellular Networks with Dense H2H-MTC Mixed Traffic,” IEEE Globecom Workshops, 2016 Paper 5: Mohammad Istiak Hossain, Amin Azari, Jan Markendahl, and Jens Zander, “Enhanced Random Access: Initial Access Load Balance in Highly Dense LTE-A Networks for Multiservice (H2H-MTC) Traffic,” Submitted to 2017 IEEE ICC. . . . . . . . . . . . . . . . . . . . .

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Overview of Publications (3/4) Battery Lifetime-Aware Solutions in MAC Design for Direct MTC (Scheduling)

Paper 6 (Paper B in the appendix): A. Azari and G. Miao, “Network Lifetime Maximization for Cellular-Based M2M Networks,” IEEE Transactions on Wireless Communications, Under major revision, 2016. Paper 7: A. Azari and G. Miao, “Lifetime-aware scheduling and power control for cellular-based M2M communications,” IEEE WCNC, 2015. Paper 8: Amin Azari; “Energy-efficient scheduling and grouping for machine-type communications over cellular networks,” in Ad Hoc Networks, vol. 43, June 2016, pp 16-29. Paper 9: A. Azari and G. Miao, “Lifetime-Aware Scheduling and Power Control for M2M Communications in LTE Networks,” 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), Glasgow, 2015, pp. 1-5. . .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. Amin Azari


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Overview of Publications (4/4) Analysis of the Performance Tradeoffs

Paper 10 (Paper C in the appendix): A. Azari and G. Miao, “Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence,” In 2016 IEEE Globecom, Washington DC, 2016, pp. 1-5. Paper 11 (Paper D in the appendix): A. Azari and G. Miao, “Fundamental Tradeoffs in Resource Provisioning for IoT Services over Cellular Networks,” Submitted to 2017 IEEE ICC.

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Appendix

Thanks and Question

Questions

Thanks for your attention.

Questions?

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