Dynamic spectrum access based on deep reinforcement learning for multiple access in cognitive radio

With the increasing shortage of spectrum resources, dynamic spectrum access (DSA) technology is proposed to maximize the spectrum resources utilization. Traditional DSA solutions can no longer meet the requirements of high throughput and low interference in large-scale access scenarios of cognitive radio (CR). Therefore, in this paper, we propose a DSA scheme based on deep reinforcement learning (DRL) combined with multiple access methods to maximize the system throughput. In the DSA network, the access strategy adopted by the secondary user (SU) will directly affect the performance of the entire system, so we introduce DRL to help the SU learn the best access strategy in a dynamic environment. The trained SU can intelligently access the appropriate channel to avoid interference to the primary user (PU) and other SUs. By combining deep Q-network (DQN) into two multiple access methods: Frequency Division Multiple Access (FDMA) and Non-orthogonal Multiple Access (NOMA), DQN-based FDMA scheme and DQN-based NOMA scheme are designed, respectively, which can find the best DSA strategy to avoid collisions with PU or other SU and improve system throughput. Simulation results show that the DQN-based NOMA scheme has better performance than the DQN-based FDMA scheme. (c) 2022 Elsevier B.V. All rights reserved.