1. KTH ROYAL INSTITUTE
OF TECHNOLOGY
Energy Efficient MAC for Cellular-Based
M2M Communications
Amin Azari and Guowang Miao
KTH Royal Institute of Technology
GlobalSIP Conference, 2014, Atlanta, USA

2. Contents:
• Introduction
• System model and problem formulation
• Proposed MAC design
• Simulation Results
• Conclusion
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3. 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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4. 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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5. M2M Communication Enablers
ReliabilityAvailability
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
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Coverage
Mobility & Roaming
Interference Control
Energy Efficiency ?
☑
☑
☑

6. Contents:
• Introduction
• System model and problem formulation
• Proposed MAC design
• Simulation Results
• Conclusion
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7. 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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8. 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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9. 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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10. 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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11. Proposed MAC design: Clustering
• Clustering
• Given desired receive SNR
• Calculate transmission power at ith node, 𝑃𝑖
• If 𝑃𝑖 > 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑
– node i is to be clustered
• In each cluster the node with the lowest 𝑃𝑖 will be CH.
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12. 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:
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13. 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)
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Analytical performance evaluation is presented to
verify performance improvment.

14. 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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15. Proposed MAC: Communication frame
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Inter-clusterIntra-cluster
Multi-phase
CSMA
Reservation
Notification
Transmission
Notification

16. Contents:
• Introduction
• System model and problem formulation
• Proposed MAC design
• Simulation Results
• Conclusion
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17. Simulation Results: System Model
• Single cell with LTE base station
• Uplink transmission of 𝑁 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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18. Simulation Results_1
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Partial clustering
Delay and energy performance evaluation
No clustering
Complete
clustering

19. Simulation Results Analysis
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• 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

20. Simulation Results_2
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Battery lives of cluster heads (CH) and members (CM) for proposed MAC
and dynamic TDMA
Cluster
member in
proposed
MAC
Cluster head
in proposed
MAC

21. Simulation Results Analysis
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• 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

22. 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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23. 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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24. Thanks for your participation.
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25. Supporting Materials
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26. 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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27. 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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28. Details of the derived performance analyses
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𝑔: aggregated traffic arrival rate
ps: probability of successful transmission
𝜏 𝑠 = 𝜏 𝑝+ 𝜏 𝑟
𝜏 𝑝: packet length
𝜏 𝑟: Round trip time from transmission to acknowledgement.

29. 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
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