Adaptive cognitive radio energy-harvesting scheme using sequential game approach

Sungwook Kim

doi:10.1186/s13638-016-0787-x

Abstract

Radio frequency (RF) energy transfer and harvesting techniques have recently become alternative methods to overcome the barriers that prevent the real-world wireless device deployment. For the next-generation wireless networks, they can be key techniques. In this study, we develop a novel energy-harvesting scheme for the cognitive radio (CR) network system. Using the sequential game model, data transmission and energy harvesting in each device are dynamically scheduled. Our approach can capture the wireless channel state while considering multiple device interactions. In a distributed manner, individual devices adaptively adjust their decisions based on the current system information while maximizing their payoffs. When the channel selection collision occurs, our sequential bargaining process coordinates this problem to optimize a social fairness. Finally, we have conducted extensive simulations. The results demonstrate that the proposed scheme achieves an excellent performance for the energy efficiency and spectrum utilization. The main contribution of our work lies in the fact that we shed some new light on the trade-off between individual wireless devices and CR network system.

Highlights

Wireless communication network is becoming more and more important and has recently attracted a lot of research interest
2 Cognitive radio network energy-harvesting algorithm we present an energy-harvesting game model for the CRN-Radio frequency (RF) system
We focus on the problem of channel selection problem for dynamic spectrum access in a multi-channel cognitive radio network with RF energy (CRN-RF) system

Summary

Introduction

Wireless communication network is becoming more and more important and has recently attracted a lot of research interest. Wireless devices must identify spectrum holes for opportunistic data transmission and search for occupied spectrum band to harvest RF energy Such RF signals could be from nearby non-battery powered base stations or access points. The major contributions of our proposed scheme are (i) the adjustable dynamics considering the current CRN-RF system environments, (ii) the ability to strike an appropriate trade-off in harvesting energy and transferring data, (iii) practical approach to effectively reach a desirable solution, (iv) dynamic interactive process in a distributed fashion, and (v) the ability to maximize the total system performance by incorporating the social fairness. If SUs want to harvest the energy, they try to find the active data transferring channels of PUs. In this study, we assume a practical energy consumption and harvesting model for wireless networks. N pi;k are the total number of packets in the kth frame of the player i

Cognitive radio sensing mechanism

Conclusions