Nonlinear energy harvesting and clustering cooperation in WPCNs

Abstract

The increasing demand for data and the rapid increase in the number of wireless connected devices make the shortage of energy and spectrum resources more serious. This paper considers a wireless powered communication network (WPCN) composed of N wireless devices (WDs) installed with single-antenna and a hybrid access point (HAP) equipped with multi-antenna, where HAP sends wireless energy to WDs in the downlink and receives information transmission from WDs in the uplink. To overcome “double near and far” problem, this paper adopts a clustering cooperative transmission method to enhance some WDs’ throughput performance far from the HAP, i.e., one of N WDs is selected as a cluster head (CH) and the remaining (N − 1) WDs as cluster members (CMs), and the CH helps relay CMs’ information to transmit. However, because the CH needs to transmit N WDs’ information, its energy consumed during information transmission will be the bottleneck of the system performance. To achieve a tradeoff between energy and data rate, this paper adopts multi-antenna energy beamforming technology to concentrate more energy to transmit the CH, so as to balance the energy consumption among all WDs. Considering the influence of in-phase/orthogonal imbalance,nonlinear amplification amplitude and phase noise on those physical transceivers of low-cost sensor nodes, nonlinear energy harvesting technology is employed to improve throughput performance of the WPCN system. Particularly, the proposed scheme’s throughput performance is derived, and simulation results demonstrate that this scheme can be effective to increase the WPCN system’s throughput fairness and spectral efficiency.

Performance optimization of cognitive wireless powered communication network

“…Bi et al studied the fault-tolerant optimization problem of online computing in mobile edge computing networks, proposed the use of deep reinforcement learning for stable online computing task allocation, and used the Lyapunov method to ensure the stability and robustness of the algorithm [15]. Yuan et al studied the problem of nonlinear energy harvesting and swarm cooperation in WPCNs, improved the network performance by optimizing energy harvesting and swarm cooperation, and considered the influence of nonlinear effects on energy harvesting [16]. In general, they focused on the application and optimization of block-chain technology, deep reinforcement learning, nonlinear energy harvesting and swarm cooperation in WPCNs, respectively, and provided new ideas and methods for the research of future wireless communication networks [14][15][16].…”

“…Yuan et al studied the problem of nonlinear energy harvesting and swarm cooperation in WPCNs, improved the network performance by optimizing energy harvesting and swarm cooperation, and considered the influence of nonlinear effects on energy harvesting [16]. In general, they focused on the application and optimization of block-chain technology, deep reinforcement learning, nonlinear energy harvesting and swarm cooperation in WPCNs, respectively, and provided new ideas and methods for the research of future wireless communication networks [14][15][16].…”