Deep Learning for SWIPT: Optimization of Transmit-Harvest-Respond in Wireless-Powered Interference Channel
- Lee, Woongsup; Lee, Kisong; Choi, Hyun-Ho; Leung, Victor C. M.
- Issue Date
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Wireless communication; Optimization; Training; Resource management; Protocols; Interference channels; Interference; Deep learning; neural network; energy harvesting; simultaneous wireless information and power transmission; optimization; interference channel
- IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, v.20, no.8, pp.5018 - 5033
- Journal Title
- IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
- Start Page
- End Page
- In this paper, we consider a wireless-powered two-way communication, called transmit-harvest-respond, with co-channel interference. The two-way communication considered here comprises three steps: i) transmitters send data signals, ii) receivers decode information and harvest energy simultaneously from the received signals using a policy of time switching (TS) or power splitting (PS), and iii) receivers transmit responses back to transmitters using this harvested energy. We aim to find the transmit power and energy harvesting ratios that maximize the sum rate of the forward links while ensuring a minimum rate requirement for each backward link. Due to the non-convexity and NP hardness of the optimization problem considered here, we first derive suboptimal solutions using an iterative algorithm (IA) on the basis of asymptotic strong duality. In view of the high computation time of the IA, we then design an efficient deep neural network (DNN) framework and novel training strategy as a means of combining supervised and unsupervised training. Specifically, DNNs are pre-trained using the suboptimal solutions obtained by the IA in a supervised manner, as a means of initialization; further training is then applied to DNNs using a well-designed loss function in an unsupervised manner to enhance performance. Simulation results reveal that the pre-training technique using IA solutions is beneficial for improving the performance of the DNN. The proposed hybrid scheme thus achieves near-optimal performances with a lower computation time, compared with the use of IA or DNN alone.
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- 해양과학대학 > 지능형통신공학과 > Journal Articles
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