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.
Nonorthogonal multiple access (NOMA) and energy harvesting technologies are two promising approaches to increase spectral and energy efficiency, respectively. In this paper, a pattern division multiple access (PDMA) uplink transmission network in which users are able to harvest energy from the received radio frequency power is considered. In order to maximize the energy efficiency, we perform resource allocation in the power, subcarrier, and time domains. Also, for each time slot, the pattern matrix is determined dynamically. To enhance the detection reliability, successive interference cancellation and maximal ratio combining at each receiver are utilized. For resource allocation, the actual harvested and available energies of the users are considered. Moreover, in order to perform a comprehensive analysis, further to PDMA, the energy efficiency of the power domain NOMA (PD-NOMA) is also investigated and the results are compared. It is shown that the energy efficiency and the average total transferred data of PDMA are higher than that of PD-NOMA.
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