A joint sensing and transmission power control policy for RF energy harvesting cognitive radio networks. (10th June 2018)

• Record Type: Journal Article
• Authors: Yan, Feiyu; Zhao, Jihong; Qu, Hua; Xu, Xiguang
• Is Part Of: International journal of communication systems. Volume 31:Number 12(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-06-10
• Subjects: cognitive radio -- POMDP -- RF energy harvesting -- spectrum sensing -- transmission power control; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.3715
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: We consider a radio frequency energy harvesting cognitive radio network in which a secondary user (SU) can opportunistically access channel to transmit packets or harvest radio frequency energy when the channel is idle or occupied by a primary user. The channel occupancy state and the channel fading state are both modeled as finite state Markov chains. At the beginning of each time slot, the SU should determine whether to harvest energy for future use or sense the primary channel to acquire the current channel occupancy state. It then needs to select an appropriate transmission power to execute the packet transmission or harvest energy if the channel is detected to be idle or busy, respectively. This sequential decision‐making, done to maximize the SU's expected throughput, requires to design a joint spectrum sensing and transmission power control policy based on the amount of stored energy, the retransmission index, and the belief on the channel state. We formulate this as a partially observable Markov decision process and use a computationally tractable point‐based value iteration algorithm. Section5 illustrate the significant outperformance of the proposed optimal policy compared with the optimal fixed‐power policy and the greedy fixed‐power policy. Abstract : In this paper, we propose a joint spectrum sensing and transmission power control policy in an RF energy harvesting cognitive radio network. The partially observable Markov decision process framework is applied to solve this optimization problem based on the amount of stored energy, the retransmission index and the belief on the channel occupancy state and the channel fading state. Simulation results show that the proposed optimal policy outperforms the optimal fixed‐power policy and the greedy fixed‐power policy significantly.