期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 69, 期 3, 页码 3500-3504出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2020.2972596
关键词
Neural network; energy efficiency; energy harvesting; time switching; optimization
资金
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [2019R1A2C4070466, 2019R1F1A1058587]
- National Research Foundation of Korea [22A20152213084, 2019R1A2C4070466, 2019R1F1A1058587] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
In this paper, we consider a wireless-powered network with co-channel interference where the transmitters control their transmit power and receivers harvest wireless energy using a time switching policy. Considering the interference channels among multiple nodes, we jointly optimize the transmit power and energy harvesting time to maximize the energy efficiency of the network. To solve this non-convex optimization problem, we first design an iterative algorithm based on a typical optimization technique, and then, propose a learning algorithm based on a neural network with a proper loss function. Simulation results show that the proposed learning algorithm can achieve a near-optimal energy efficiency with reducing the computational complexity, compared to an iterative algorithm with a suboptimal performance.
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