Access Strategy in Super WiFi Network Powered by Solar Energy Harvesting: A POMDP Method

The recently announced Super Wi-Fi Network proposal in United States is aiming to enable Internet access in a nation-wide area. As traditional cable-connected power supply system becomes impractical or costly for a wide range wireless network, new infrastructure deployment for Super Wi-Fi is required. The fast developing Energy Harvesting (EH) techniques receive global attentions for their potential of solving the above power supply problem. It is a critical issue, from the user's perspective, how to make efficient network selection and access strategies. Unlike traditional wireless networks, the battery charge state and tendency in EH based networks have to be taken into account when making network selection and access, which has not been well investigated. In this paper, we propose a practical and efficient framework for multiple base stations access strategy in an EH powered Super Wi-Fi network. We consider the access strategy from the user's perspective, who exploits downlink transmission opportunities from one base station. To formulate the problem, we used Partially Observable Markov Decision Process (POMDP) to model users' observations on the base stations' battery situation and decisions on the base station selection and access. Simulation results show that our methods are efficacious and significantly outperform the traditional widely used CSMA method.

[1]  Dusit Niyato,et al.  A game theoretic analysis of service competition and pricing in heterogeneous wireless access networks , 2008, IEEE Transactions on Wireless Communications.

[2]  Matthijs T. J. Spaan,et al.  Partially Observable Markov Decision Processes , 2010, Encyclopedia of Machine Learning.

[3]  Mani B. Srivastava,et al.  Design considerations for solar energy harvesting wireless embedded systems , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[4]  Zhu Han,et al.  Network Association Strategies for an Energy Harvesting Aided Super-WiFi Network Relying on Measured Solar Activity , 2016, IEEE Journal on Selected Areas in Communications.

[5]  K. J. Ray Liu,et al.  Graphical Evolutionary Game for Information Diffusion Over Social Networks , 2013, IEEE Journal of Selected Topics in Signal Processing.

[6]  M. Collares-Pereira,et al.  TAG: A time-dependent, autoregressive, Gaussian model for generating synthetic hourly radiation , 1992 .

[7]  Alagan Anpalagan,et al.  Decision-Theoretic Distributed Channel Selection for Opportunistic Spectrum Access: Strategies, Challenges and Solutions , 2013, IEEE Communications Surveys & Tutorials.

[8]  K. J. Ray Liu,et al.  Renewal-theoretical dynamic spectrum access in cognitive radio network with unknown primary behavior , 2011, IEEE Journal on Selected Areas in Communications.

[9]  K. J. Ray Liu,et al.  Joint Spectrum Sensing and Access Evolutionary Game in Cognitive Radio Networks , 2013, IEEE Transactions on Wireless Communications.

[10]  K. J. Ray Liu,et al.  Data-Driven Stochastic Models and Policies for Energy Harvesting Sensor Communications , 2014, IEEE Journal on Selected Areas in Communications.

[11]  Terence D. Todd,et al.  The need for access point power saving in solar powered WLAN mesh networks , 2008, IEEE Network.

[12]  K. J. Ray Liu,et al.  Game theory for cognitive radio networks: An overview , 2010, Comput. Networks.

[13]  Pascal Poupart,et al.  Partially Observable Markov Decision Processes , 2010, Encyclopedia of Machine Learning.

[14]  Mohamed A. El-Sharkawi Electric energy : an introduction , 2004 .

[15]  K. J. Ray Liu,et al.  Wireless Access Network Selection Game with Negative Network Externality , 2013, IEEE Transactions on Wireless Communications.

[16]  Wei Hwang,et al.  High efficiency power management system for solar energy harvesting applications , 2010, 2010 IEEE Asia Pacific Conference on Circuits and Systems.

[17]  L. Wosinska,et al.  Mobile backhaul in heterogeneous network deployments: Technology options and power consumption , 2012, 2012 14th International Conference on Transparent Optical Networks (ICTON).

[18]  Hsiao-Hwa Chen,et al.  Energy-efficient non-cooperative cognitive radio networks: micro, meso, and macro views , 2014, IEEE Communications Magazine.

[19]  Ekram Hossain,et al.  Opportunistic Access to Spectrum Holes Between Packet Bursts: A Learning-Based Approach , 2011, IEEE Transactions on Wireless Communications.

[20]  Ananthram Swami,et al.  Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework , 2007, IEEE Journal on Selected Areas in Communications.