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We consider a cognitive heterogeneous network (HetNet), in which multiplepairs of secondary users adopt sensing-based approaches to coexist with a pairof primary users on a certain spectrum band. Due to imperfect spectrum sensing,secondary transmitters (STs) may cause interference to the primary receiver(PR) and make it difficult for the PR to select a proper modulation and/orcoding scheme (MCS). To deal with this issue, we exploit deep reinforcementlearning (DRL) and propose an intelligent MCS selection algorithm for theprimary transmission. To reduce the system overhead caused by MCS switchings,we further introduce a switching cost factor in the proposed algorithm.Simulation results show that the primary transmission rate of the proposedalgorithm without the switching cost factor is 90 percent to 100 percent of theoptimal MCS selection scheme, which assumes that the interference from the STsis perfectly known at the PR as prior information, and is 30 percent to 100percent higher than those of the benchmark algorithms. Meanwhile, the proposedalgorithm with the switching cost factor can achieve a better balance betweenthe primary transmission rate and system overheads than both the optimalalgorithm and benchmark algorithms.

Deep Reinforcement Learning based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks