A stochastic geometry analysis of cooperative wireless networks powered by energy harvesting

A large wireless network with energy harvesting transmitters is considered, where a group of K transmitters form a cluster to cooperatively serve a desired user. Using stochastic geometry, simple closed-form expressions are derived to characterize the outage performance as a function of important parameters such as the energy harvesting rate, buffer size and cluster size for a given cluster geometry. The developed framework also allows the K in-cluster transmitters to have different energy harvesting capabilities. A comparison with simulation results reveals that the derived expressions closely model the signal-to-interference-and-noise ratio distribution at the receiver, particularly in the low-outage regime. Lastly, the developed framework is used to investigate the impact of different parameters such as cluster and buffer size on outage performance.

[1]  Jeffrey G. Andrews,et al.  Fundamental Limits of Cooperation , 2012, IEEE Transactions on Information Theory.

[2]  Rui Zhang,et al.  MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer , 2011, IEEE Transactions on Wireless Communications.

[3]  Martin Haenggi,et al.  On distances in uniformly random networks , 2005, IEEE Transactions on Information Theory.

[4]  Jeffrey G. Andrews,et al.  What Will 5G Be? , 2014, IEEE Journal on Selected Areas in Communications.

[5]  D. Katabi,et al.  JMB: scaling wireless capacity with user demands , 2012, CCRV.

[6]  Jing Yang,et al.  Transmission with Energy Harvesting Nodes in Fading Wireless Channels: Optimal Policies , 2011, IEEE Journal on Selected Areas in Communications.

[7]  Sivarama Venkatesan,et al.  Coordinating Base Stations for Greater Uplink Spectral Efficiency in a Cellular Network , 2007, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications.

[8]  Purushottam Kulkarni,et al.  Energy Harvesting Sensor Nodes: Survey and Implications , 2011, IEEE Communications Surveys & Tutorials.

[9]  Gerhard Fettweis,et al.  Coordinated Multi-Point in Mobile Communications: From Theory to Practice , 2011 .

[10]  Robert W. Heath,et al.  Base station cooperation with dynamic clustering in super-dense cloud-RAN , 2013, 2013 IEEE Globecom Workshops (GC Wkshps).

[11]  Rui Zhang,et al.  Optimal Energy Allocation for Wireless Communications With Energy Harvesting Constraints , 2011, IEEE Transactions on Signal Processing.

[12]  Jeffrey G. Andrews,et al.  Fundamentals of Heterogeneous Cellular Networks with Energy Harvesting , 2013, IEEE Transactions on Wireless Communications.

[13]  Aylin Yener,et al.  Sum-rate optimal power policies for energy harvesting transmitters in an interference channel , 2011, Journal of Communications and Networks.

[14]  Kaibin Huang,et al.  Spatial Throughput of Mobile Ad Hoc Networks Powered by Energy Harvesting , 2011, IEEE Transactions on Information Theory.

[15]  Deniz Gündüz,et al.  Designing intelligent energy harvesting communication systems , 2014, IEEE Communications Magazine.

[16]  Martin Haenggi,et al.  Stochastic Geometry for Wireless Networks , 2012 .

[17]  Rahul Vaze Transmission capacity of wireless ad hoc networks with energy harvesting nodes , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[18]  Jeffrey G. Andrews,et al.  A Tractable Approach to Coverage and Rate in Cellular Networks , 2010, IEEE Transactions on Communications.

[19]  Kaibin Huang,et al.  Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks , 2013, IEEE Transactions on Wireless Communications.

[20]  Ho Van Khuong,et al.  General expression for pdf of a sum of independent exponential random variables , 2006, IEEE Commun. Lett..