Energy Efficient MAC for Cellular-Based M2M Communications

KTH ROYAL INSTITUTEOF TECHNOLOGY

Energy Efficient MAC for Cellular-BasedM2M Communications

Amin Azari and Guowang Miao

KTH Royal Institute of Technology

GlobalSIP Conference, 2014, Atlanta, USA

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Contents:

• Introduction• System model and problem formulation• Proposed MAC design• Simulation Results• Conclusion

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Motivation

Future wireless access (5G) • Key challenges

• Continued traffic growth in terms of volume• Continued traffic growth in terms of number of devices• Spectral & Enrgy efficient system design

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M2M communication

• M2M communications: Communication of smart devices without human intervention.

• Some characteristics:• Large number of short-lived sessions• (usually) low-payload• Vastly diverse characteristics (e.g. battery capacity)• Vastly diverse QoS requirements (e.g. delay)

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M2M Communication Enablers

ReliabilityA

vaila

bilit

y

Cellular-based M2M

Proprietary Cellular

Low-power WLAN

Zigbee-like

Low-power Bluetooth

• Reliability = resilience to interference, throughput and outage guarantees

Reference: GREEN NETWORK TECHNOLOGIES FOR MTC IN 5G, Jesus Alonso-Zarate, EIT/ICT Summer school presentation

• Availability = coverage, roaming, mobility

CoverageMobility & RoamingInterference ControlEnergy Efficiency ?

☑

☑

☑

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Contents:

• Introduction• System model and problem formulation• Proposed MAC design• Simulation Results• Conclusion

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System model

• Single Cell• N machine nodes

• Battery-driven nodes• Long battery-life is desired

• Specific resource allocation for M2M (no cellular user)• Event-driven traffic (Poisson packet arrival)

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Problem formulation

• Clustering design• Complete, partial or no-clustering? • Number of clusters• Cluster-head selection & reselection

• Communication Protocol• Intra-cluster communication protocol• Inter-cluster communication protocol

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Problem formulation

• Clustering design• Presented in

Energy-Efficient Clustering Design for M2M Communications,

G. Miao and P. Zhang, GlobalSIP 2014

• Communication protocol design• In this work

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Contents:

• Introduction• System model and problem formulation• Proposed MAC design

• Clustering for cellular-based M2M• Intra-cluster communication• Inter-cluster communication

• Simulation Results• Conclusion

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Proposed MAC design: Clustering

• Clustering• Given desired receive SNR• Calculate transmission power at ith node,

• If – node i is to be clustered

• In each cluster the node with the lowest will be CH.

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Proposed MAC design: Intra-cluster Communication

• Intra-cluster communication• Low traffic load

• CSMA/CA has good performance in low-load regime• Scalable, low signaling overhead, and acceptable EE

• The EE, delay, and user capacity analysis:

Details

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Proposed MAC design: Multi-Phase CSMA

• Even more energy efficiency• Multi-phase CSMA for intra-cluster communication • Enables close-to-zero power wastage• Needs local synchronization (tradeoff)

Analytical performance evaluation is presented to verify performance improvment.

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Proposed MAC design: Inter-cluster

• Inter-cluster communication• Heterogeneous system

• Length of data packet (CH and CM)• State: delay critical, queue status and residual energy

• Interference to the cellular users must be avoided.

THEN• Reservation-based protocols (e.g. dynamic TDMA)• Moderate scalability and energy-saving

• Analytical results are omitted from the paper due to the page limit.

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Proposed MAC: Communication frame

Inter-clusterIntra-cluster

Multi-phase CSMA

Reservation

Notification

Transmission

Notification

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Contents:

• Introduction• System model and problem formulation• Proposed MAC design• Simulation Results• Conclusion

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Simulation Results: System Model

• Single cell with LTE base station• Uplink transmission of battery-driven machine nodes• 4-phase CSMA for intra-cluster communication• Dynamic TDMA for inter-cluster communication• Poisson packet arrival at nodes• Clustering threshold: varied

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Simulation Results_1

Partial clustering

Delay and energy performance evaluation

No clustering

Complete clustering

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Simulation Results Analysis

• Clustering is not always (for all nodes) EE • However, it always eases the massive access problem

• Partial clustering outperforms non- and all-clustering• Delay performance is sacrificed for getting EE• Tradeoff delay/energy efficiency

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Simulation Results_2

Battery lives of cluster heads (CH) and members (CM) for proposed MACand dynamic TDMA

Cluster member in proposed MAC

Cluster head in proposed MAC

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Simulation Results Analysis

• Proposed MAC has extended the battery life of nodes.

• The extension is 500% on average and 800% at some points.

• The battery life of cluster heads is sacrificed by 50%.

• Cluster-head reselection scheme

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Conclusion

• Key requirement for enabling M2M communication over cellular networks• Providing efficiency

• Energy efficient massive access can prolong the lifetime• Clustering for all nodes is not EE• Using CH reselection algorithms, one can prolong the

overall network lifetime

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Future works

• Revisiting design principles of cellular networks to address massive access problem in an efficient way• Considering heterogeneous characteristics of machine

nodes• Considering heterogeneous QoS of machine nodes

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Thanks for your participation.

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Supporting Materials

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Cellular-based M2M

M2M communications supported by cellular networks• Direct or through gateway

Advantages:• Ubiquitous Coverage• Mobility & Roaming• Interference Control

Disadvantages:• Designed and optimized for small number of long-lived sessions

• Massive access problem• Energy inefficiency

• Attaching to the network is contention-based, etc.• Physical layer inefficiency

• Not optimized for small data payload

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Problem formulation• Access schemes

• Contention-free schemes– Not scalable (High signaling)– High average packet delay– High energy efficiency

• Contention-based schemes– Scalable and distributed– Low-delay in low-load/ High-delay in high-load– Energy wasting in medium- to high-load regime

• Reservation-based schemes– Contention-based in reservation, -free in transmission

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Details of the derived performance analyses

: aggregated traffic arrival rate

ps: probability of successful transmission

+ : packet length Round trip time from transmission to acknowledgement.

Back

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Energy Efficient System Design

• Energy Saving ≠ Energy Efficiency• Complete Saving of Energy = Shut down network

completely to save the most energy• Not desired!

• Purpose of energy-efficient wireless network design• Not to save energy• Make the best/efficient use of energy!

• Energy saving w/o losing service quality• Bit-per-Joule design metric