Conference Proceedings

Deep Reinforcement Learning-Based Power Control in Full-Duplex Cognitive Radio Networks

Xiangyue Meng, Hazer Inaltekin, Brian Krongold

2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) | IEEE | Published : 2018

Abstract

This paper considers the use of full-duplex technology in cognitive radio networks to allow secondary users to sense the presence of primary users and transmit data simultaneously. This is the main advantage over half-duplex radios. In such networks, the so-called sensing-throughput trade-off exists due to the fact that while a higher transmit power results in higher secondary network throughput, sensing performance is degraded by the self-interference at the full-duplex transceiver. This paper presents a novel deep reinforcement learning-based joint spectrum sensing and power control algorithm for downlink communications in a cognitive small cell. The proposed algorithm can adapt to the unk..

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