Cognitive Green Communications: From Concept to Practice · Communications: From Concept to Practice Honggang ZHANG International Chair - CominLabs. Université Européenne de Bretagne

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


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.


UEB - Université Européenne de Bretagne


Global Warming – The Most Dangerous Threat ?


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.


Data Explosion - Exponential Traffic Growth


Data Explosion - Exponential Traffic Growth (2)

Source: http://bigdatadiary.com/networks-strain-to-keep-pace-with-data-explosion/internetminute/


Part I: Green Communications Paradigm Change from Coverage- & Capacity-

Driven to Energy-Efficiency Driven Era


Source: Prof. T. Aoyama, Keio University, ISCIT 2010 Keynote Speech.

Energy Crisis and Challenges


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)


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)


Architecture of Telecommunication Networks


Mobile Telecommunications Networks Power Consumption Breakdown

Energy consumption composition in Vodafone (Source: Vodafone)


Energy Consumption in Radio Access Networks


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.


Energy Consumption Reference Model for Base Station (2)

Note: Values in italic are power consumption figures in GSM system.


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.


Cognitive Green Communications Intelligence with Adaptation, Balancing & Optimization for Network Energy Saving


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.


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.


Machine Learning

Why Reinforcement Learning?


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


Processes of Reinforcement Learning


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).


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)


Once upon a Time – What was Cognitive Radio, Really? (2)


Part II: The Practice – Energy Saving for Greener Cellular Mobile Networks

“Tidal Effect” of Cellular Networks’ Traffic Flow & Loads


Representative Patterns of Traffic Loads during One Day (Cellular Networks)


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)


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)


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


Measured Traffic Loads Variation Patterns (One Week)


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.


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


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.


Network Energy Saving through BS Switching on/off (Sleep Mode)


Block Diagram of Reinforcement Learning - The learning system and the environment are both

inside the feedback loop


Reinforcement Learning: Actor-Critic Approach


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.


Stochastic BS Switching Operation with Actor-Critic Learning (2)


Base Stations’ Traffic Load State Vector


Traffic Loads and BS Power Consumption Model


Actor-Critic Learning: Markov Decision Process


Actor-Critic Learning: Markov Decision Process (2)


Actor-Critic Learning: Markov Decision Process (3)


Actor-Critic Learning Scheme for BS Power Saving


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


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.


Basics and Advantages of Transfer Learning


50

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


Basics and Features of Transfer Reinforcement Learning


Features of Transfer Actor-Critic Learning


TACT : The Transfer Learning Framework for Energy Saving Scheme


Numerical Analysis


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.


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!


Thanks!

Email: [email protected]

Documents

Cognitive Green Communications: From Concept to Practice · Communications: From Concept to Practice Honggang ZHANG International Chair - CominLabs. Université Européenne de Bretagne