Deep Learning-Aided Distributed Transmit Power Control for Underlay Cognitive Radio Network
- Lee, Woongsup; Lee, Kisong
- Issue Date
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Power control; Interference; Cognitive radio; Transceivers; Receivers; Neural networks; Transmitters; Deep neural network; transmit power control; underlay cognitive radio network; distributed operation
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.70, no.4, pp.3990 - 3994
- Journal Title
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Start Page
- End Page
- In this paper, we investigate deep learning-aided distributed transmit power control in the context of an underlay cognitive radio network (CRN). In the proposed scheme, the fully distributed transmit power control strategy of secondary users (SUs) is learned by means of a distributed deep neural network (DNN) structure in an unsupervised manner, such that the average spectral efficiency (SE) of the SUs is maximized whilst allowing the interference on primary users (PUs) to be regulated properly. Unlike previous centralized DNN-based strategies that require complete channel state information (CSI) to optimally determine the transmit power of SU transceiver pairs (TPs), in our proposed scheme, each SU TP determines its own transmit power based solely on its local CSI. Our simulation results verify that the proposed scheme can achieve a near-optimal SE comparable with a centralized DNN-based scheme, with a reduced computation time and no signaling overhead.
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- 해양과학대학 > 지능형통신공학과 > Journal Articles
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