Capacity of Fading Gaussian Channel with an Energy Harvesting Sensor Node

Network life time maximization is becoming an important design goal in wireless sensor networks. Energy harvesting has recently become a preferred choice for achieving this goal as it provides near perpetual operation. We study such a sensor node with an energy harvesting source and compare various architectures by which the harvested energy is used. We find its Shannon capacity when it is transmitting its observations over a fading AWGN channel with perfect/no channel state information provided at the transmitter. We obtain an achievable rate when there are inefficiencies in energy storage and the capacity when energy is spent in activities other than transmission.

[1]  R. Gray.  Entropy and Information Theory , 1990, Springer New York.

[2]  Pramod Viswanath,et al.  Information capacity of energy harvesting sensor nodes , 2011, 2011 IEEE International Symposium on Information Theory Proceedings.

[3]  Vinod Sharma,et al.  Efficient energy management policies for networks with energy harvesting sensor nodes , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[4]  Rui Zhang,et al.  Optimal energy allocation for wireless communications powered by energy harvesters , 2010, 2010 IEEE International Symposium on Information Theory.

[5]  József Balogh,et al.  On k−coverage in a mostly sleeping sensor network , 2008, Wirel. Networks.

[6]  József Balogh,et al.  On k-coverage in a mostly sleeping sensor network , 2004, MobiCom '04.

[7]  Mani B. Srivastava,et al.  An environmental energy harvesting framework for sensor networks , 2003, ISLPED '03.

[8]  Vinod Sharma,et al.  Optimal Sleep-Wake Policies for an Energy Harvesting Sensor Node , 2009, 2009 IEEE International Conference on Communications.

[9]  Vinod Sharma,et al.  Optimal energy management policies for energy harvesting sensor nodes , 2008, IEEE Transactions on Wireless Communications.

[10]  Shlomo Shamai,et al.  Fading Channels: Information-Theoretic and Communication Aspects , 1998, IEEE Trans. Inf. Theory.

[11]  Anantha Chandrakasan,et al.  Bounding the lifetime of sensor networks via optimal role assignments , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[12]  Vijay K. Bhargava,et al.  Wireless sensor networks with energy harvesting technologies: a game-theoretic approach to optimal energy management , 2007, IEEE Wireless Communications.

[13]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[14]  Mani B. Srivastava,et al.  Power management in energy harvesting sensor networks , 2007, TECS.

[15]  Upendra Dave,et al.  Applied Probability and Queues , 1987 .

[16]  David E. Culler,et al.  Perpetual environmentally powered sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[17]  Eitan Altman,et al.  Taxation for green communication , 2010, 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks.

[18]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[19]  Kannan Ramchandran,et al.  Distributed compression in a dense microsensor network , 2002, IEEE Signal Process. Mag..

[20]  Anantha Chandrakasan,et al.  Dynamic Power Management in Wireless Sensor Networks , 2001, IEEE Des. Test Comput..

[21]  Sennur Ulukus,et al.  Information-theoretic analysis of an energy harvesting communication system , 2010, 2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops.

[22]  Gaurav S. Sukhatme,et al.  Studying the feasibility of energy harvesting in a mobile sensor network , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[23]  Deborah Estrin,et al.  An energy-efficient MAC protocol for wireless sensor networks , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[24]  U. Rieder,et al.  Markov Decision Processes , 2010 .

[25]  M. Raginsky,et al.  On the information capacity of Gaussian channels under small peak power constraints , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[26]  Shlomo Shamai,et al.  The capacity of average and peak-power-limited quadrature Gaussian channels , 1995, IEEE Trans. Inf. Theory.

[27]  Vinod Sharma,et al.  Joint power control, scheduling and routing for multicast in multihop energy harvesting sensor networks , 2009, 2009 International Conference on Ultra Modern Telecommunications & Workshops.

[28]  Koushik Kar,et al.  Rechargeable sensor activation under temporally correlated events , 2007, 2007 5th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks and Workshops.

[29]  Pravin Varaiya,et al.  Capacity of fading channels with channel side information , 1997, IEEE Trans. Inf. Theory.

[30]  Pai H. Chou,et al.  AmbiMax: Autonomous Energy Harvesting Platform for Multi-Supply Wireless Sensor Nodes , 2006, 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks.

[31]  Joel G. Smith,et al.  The Information Capacity of Amplitude- and Variance-Constrained Scalar Gaussian Channels , 1971, Inf. Control..

[32]  Mani B. Srivastava,et al.  Emerging techniques for long lived wireless sensor networks , 2006, IEEE Communications Magazine.

[33]  Gustavo de Veciana,et al.  Minimizing energy consumption in large-scale sensor networks through distributed data compression and hierarchical aggregation , 2004, IEEE Journal on Selected Areas in Communications.