cognitive green communications: from concept to practice · communications: from concept to...
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1 UEB - Université Européenne de Bretagne & Supélec
Cognitive Green Communications: From Concept
to Practice Honggang ZHANG
International Chair - CominLabs Université Européenne de Bretagne (UEB) &
Supélec/IETR
Supélec SCEE Seminar March 21, 2013 – Rennes, France
2 UEB - Université Européenne de Bretagne & Supélec
Outline
Part I – the Concept: Energy-efficient Cognitive Green Radio Communications
Part II – the Practice: Cognitive Green Communications for Achieving Energy Saving within Cellular Mobile Networks
ACKNOWLEDGEMENT:
This presentation is supported by the International Chair Program, CominLabs Excellence Center, Université Européenne de Bretagne (UEB) and SUPELEC/IETR. (GREAT: Green Cognitive Radio for Energy-Aware wireless communication Technologies evolution) Also, thanks to Prof. Jacques Palicot (SUPELEC), Dr. Tao Chen (VTT), Dr. Xianfu Chen (VTT), Mr. Rongpeng Li (ZJU), and Mr. Xuan Zhou (ZJU) for their supporting materials.
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UEB - Université Européenne de Bretagne
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Global Warming – The Most Dangerous Threat ?
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Terrible Climate Change: Trans-Arctic Shipping Routes Navigable 21st-midcentury
Source: Laurence C. Smith and Scott R. Stephenson, “New Trans-Arctic Shipping Routes Navigable by Midcentury,” PNAS, January 2013.
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Data Explosion - Exponential Traffic Growth
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Data Explosion - Exponential Traffic Growth (2)
Source: http://bigdatadiary.com/networks-strain-to-keep-pace-with-data-explosion/internetminute/
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Part I: Green Communications Paradigm Change from Coverage- & Capacity-
Driven to Energy-Efficiency Driven Era
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Source: Prof. T. Aoyama, Keio University, ISCIT 2010 Keynote Speech.
Energy Crisis and Challenges
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ICT Sector Commitments to Targets and Deadlines for CO2 and Greenhouse Gas Emissions and Energy
Efficiency/Consumption (European Commission 2009/03/12)
Energy Crisis and Challenges (2)
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ICT Sector Commitments to Targets and Deadlines for CO2 and Greenhouse Gas Emissions and Energy
Efficiency/Consumption (European Commission 2009/03/12)
Energy Crisis and Challenges (3)
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Architecture of Telecommunication Networks
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Mobile Telecommunications Networks Power Consumption Breakdown
Energy consumption composition in Vodafone (Source: Vodafone)
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Energy Consumption in Radio Access Networks
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Energy Consumption Reference Model for Base Station
2400 500 300 150 110
Source: Tao Chen, et al., “Network Energy Saving Technologies for Green Wireless Access Networks”IEEE Wireless Communications Magazine, 2011.
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Energy Consumption Reference Model for Base Station (2)
Note: Values in italic are power consumption figures in GSM system.
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Network-wide Energy Saving Strategies & Techniques
Increasing bandwidth can also save energy, depending on context
Source: Tao Chen, et al., “Network Energy Saving Technologies for Green Wireless Access Networks”IEEE Wireless Communications Magazine, 2011.
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Cognitive Green Communications Intelligence with Adaptation, Balancing & Optimization for Network Energy Saving
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Features & Key Functionalities of Cognitive Radio (Cognitive Cycle)
Source: Gurkan Gur and Fatih Alagoz, “Green Wireless Communications via Cognitive Dimension: An Overview”, IEEE Network, March 2011.
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Embedded Intelligence in a General Cognitive Radio Transceiver
Cognitive Radio Node
PHY Layer
MAC Layer
Network Layer
Application Layer
Source: Xianfu Chen, Zhifeng Zhao, and Honggang Zhang, “Stochastic Power Adaptation with Multi-agent Reinforcement Learning for Cognitive Wireless Mesh Networks,” IEEE Transactions on Mobile Computing, Q4 2012. Xianfu Chen, Zhifeng Zhao, Honggang Zhang, and Tao Chen, “Conjectural variations in multi-agent reinforcement learning for energy-efficient cognitive wireless mesh networks,” in Proceedings of IEEE WCNC 2012, Paris, France, Apr. 2012.
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Machine Learning
Why Reinforcement Learning?
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Basics of Reinforcement Learning
Policy: What to do Reward: What is good Value: What is good
because it predicts reward Model: What follows what
Policy
Reward
Value
Model of environment
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Processes of Reinforcement Learning
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Workflow of Energy Saving Mechanism Enabled by Cognitive Process/Cycle
Source: Oliver Blume, et al. “Energy Savings in Mobile Networks Based on Adaptation to Traffic Statistics,” Bell Labs Technical Journal 15(2), 77–94 (2010).
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Once upon a Time – What was Cognitive Radio, Really?
Joe Mitola’s Cognitive Radio (1999) Simon Haykin’s Cognitive Radio (2005)
DySPAN’s Cognitive Radio (2007)
Cognitive Radio (G. Gur and F. Alagoz, 2011)
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Once upon a Time – What was Cognitive Radio, Really? (2)
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Part II: The Practice – Energy Saving for Greener Cellular Mobile Networks
“Tidal Effect” of Cellular Networks’ Traffic Flow & Loads
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Representative Patterns of Traffic Loads during One Day (Cellular Networks)
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Normalized load of three different cell sectors over 3 weeks. The moving average of each cell over one second has been plotted. The cells show high load (Top), varying
load (Middle), and low load (Bottom). Source: Daniel Willkomm et al., “Primary User Behavior in Cellular Networks and Implications for Dynamic Spectrum Access”.
Representative Patterns of Traffic Loads during 3 Weeks (Cellular Networks)
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E-commerce website: 292 production web servers over 5 days. (Traffic varies by day/weekend, power doesn’t.)
Representative Patterns of Traffic Load during 5 Days (Core Networks/Internet)
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Base Stations’ Traffic Loads Measurement Campaigns in Zhejiang (China)
Source: Xuan Zhou, Zhifeng Zhao, Rongpeng Li, Yifan Zhou, and Honggang Zhang, “The Predictability of Cellular Networks Traffic,” IEEE ISCIT2012, October 2012.
Traffic records from 9 MSCs and SGSNs with about 7000 BSs with coverage of 780 km2
Both GSM and UMTSBSs traffic from January to December in 2012, serving about 3 million subscribers
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Measured Traffic Loads Variation Patterns (One Week)
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Typical Examples of Measured Base Stations’ Traffic Loads in Zhejiang (China)
Source: Rongpeng Li, Zhifeng Zhao, Yan Wei, Xuan Zhou, and Honggang Zhang, “GM-PAB: a grid-based energy saving scheme with predicted traffic load guidance for cellular networks,” in Proceedings of IEEE ICC 2012, Ottawa, Canada, Jun. 2012.
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Sensing and Prediction of Cellular Networks’ Traffic Flows & Loads
,t,t,t xxy 321 BS1
BS3
BS2 Router
route 1
route 3
route 2 link 2
link 1
link 3
6,3
6,2
6,1
5,3
5,2
5,1
4,13,32,3
4,13,22,2
4,13,12,1
1,3
1,2
1,1
x
x
x
x
x
x
xxx
xxx
xxx
x
x
x
X
Interpolation: fill in the missing data from incomplete and/or indirect measurements of the Traffic Matrices
Future Anomaly Missing
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Sensing and Prediction of Cellular Networks’ Traffic Flows &Loads (2)
Source: Rongpeng Li, Zhifeng Zhao, Xuan Zhou, and Honggang Zhang, “Energy savings scheme in radio access networks via compressive sensing-based traffic load prediction,” European Transactions on Emerging Telecommunications Technologies (ETT), Nov. 2012.
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Network Energy Saving through BS Switching on/off (Sleep Mode)
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Block Diagram of Reinforcement Learning - The learning system and the environment are both
inside the feedback loop
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Reinforcement Learning: Actor-Critic Approach
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Stochastic BS Switching Operation with Actor-Critic Learning
Source” Rongpeng Li, Zhifeng Zhao, Xian Chen, and Honggang Zhang, “Energy Saving through a Learning Framework in Greener Cellular Radio Access Networks,” in Proceedings of IEEE Globecom 2012, Anaheim, USA, Dec. 2012.
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Stochastic BS Switching Operation with Actor-Critic Learning (2)
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Base Stations’ Traffic Load State Vector
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Traffic Loads and BS Power Consumption Model
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Actor-Critic Learning: Markov Decision Process
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Actor-Critic Learning: Markov Decision Process (2)
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Actor-Critic Learning: Markov Decision Process (3)
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Actor-Critic Learning Scheme for BS Power Saving
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Parameter description Value Simulation area 1.5km * 1.5km Maximum transmission power Macro BS 20W
Micro BS 1W Maximum operational power Macro BS 865W
Micro BS 38W Height Macro BS 32m
Micro BS 12.5m Intra-cell interference factor 0.01 Channel bandwidth 1.25MHz File requests Arrival rate
File size 100kbyte Constant power percentage
Numerical Analysis
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Energy Saving by Actor-Critic Learning (BS Switching & Sleep Mode)
Performance comparison between Actor-Critic learning framework (LF) based energy saving scheme and the state-of-the-art (SOTA) scheme
(JSAC, Sept. 2012) under various static/variant traffic arrival rates.
Source” Rongpeng Li, Zhifeng Zhao, Xian Chen, and Honggang Zhang, “Energy Saving through a Learning Framework in Greener Cellular Radio Access Networks,” in Proceedings of IEEE Globecom 2012, Anaheim, USA, Dec. 2012.
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Basics and Advantages of Transfer Learning
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Xuan Zhou, Zhifeng Zhao, R. Li, Y. Zhou, J. Palicot, and Honggang Zhang, “TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks,” arXiv:1211.6616, November 2012.
Stochastic BS Switching Operation with Transfer Reinforcement Learning
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Basics and Features of Transfer Reinforcement Learning
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Features of Transfer Actor-Critic Learning
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TACT : The Transfer Learning Framework for Energy Saving Scheme
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Numerical Analysis
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Performance impact of the transfer rate factor θ to the TACT scheme when λ = 5× 10−6
Energy Saving by Transfer Actor-Critic Learning (BS Switching & Sleep Mode)
Performance comparison among classical AC scheme, TACT scheme and SOTA scheme under various
homogeneous traffic arrival rates when the transfer rate θ = 0.1
Xuan Zhou, Zhifeng Zhao, R. Li, Y. Zhou, J. Palicot, and Honggang Zhang, “TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks,” arXiv:1211.6616, November 2012.
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Summary & Conclusion
Environmental-friendly Green Communications: – A paradigm change from traditional coverage- & capacity-driven
to energy-efficiency driven communications and networks (Smart, sustainable, and self-harmonized greener ICT).
Cognitive Green Radio Communications: – Besides spectrum and energy, intelligence is the THIRD kind of
resource, but without limitation of scarcity. – Learning and decision making algorithms under green constraint
can play a significant role in enabling energy- and spectral-efficient greener future communications.
– Effective energy saving can be realized by using various learning approaches in mobile cellular networks.
Cognitive Green Communications:
From Concept to Reality!