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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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