Optimal channel selection for simultaneous RF energy harvesting and data transmission in cognitive radio networks. Issue 3 (8th February 2018)

• Record Type: Journal Article
• Authors: Hooshiary, Akram; Azmi, Paeiz; Mokari, Nader; Maleki, Sina
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 29:Issue 3(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-02-08
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.3291
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: In this paper, a radio frequency–powered cognitive radio network is considered, in which the secondary users are powered by a radio frequency energy harvester (rectenna). Unlike most existing works, we consider a realistic rectenna characteristic function and derive the actual amount of harvested energy and thus obtain the resulting actual energy level of the secondary users. We consider a system architecture at which simultaneous energy harvesting and data transmission for each secondary user is possible. We introduce a strategy to manage the challenge of network throughput decreasing because of the lack of the secondary users' energy via selecting the best possible channels for energy harvesting and, simultaneously, by allocating the best channels for data transmission. Therefore, we implement cognition in spectrum utilization and in energy harvesting. We show that the amount of harvested energy affects the available energy of the secondary user and, consequently, the throughput; therefore, the channel selection to maximize energy harvesting affects the network throughput. To maximize the network throughput, the Hungarian algorithm is employed, and then, an algorithm with lower complexity based on the matching theory is proposed. Finally, we compare our proposed approach with some existing benchmarks and show its high performance in energy harvesting and system throughput. Abstract : In RF powered cognitive radio network, we consider a realistic Rectenna characteristic function and derive actual amount of harvested energy and the resulting actual energy level of the secondary users. We implement cognition in spectrum utilization and in energy harvesting. We show that the amount of harvested energy affects the available energy of the secondary user and consequently the throughput. We compare our proposed approach with some existing benchmarks and show its high performance in energy harvesting and system throughput.