Dynamic Spectrum Access with Reinforcement Learning for Unlicensed Access in 5G and Beyond

Dynamic spectrum access (DSA) to achieve spectrum sharing in unlicensed bands is a promising approach for meeting the growing demands of forthcoming and deployed wireless networks, such as long-term evolution license-assisted access (LTE-LAA) and IEEE 802.11 Wi-Fi systems. In this paper, we consider a coexistence scenario where multiple LAA and Wi-Fi links compete for spectrum sharing subchannel access. We introduce a reinforcement-learning-based subchannel selection technique which allows access points (APs) and eNBs to select best subchannel distributively considering their medium access control (MAC) channel access protocols along with the physical layer parameters. The performance of this scheme is investigated through simulations, including the convergence property and sum throughput. Numerical results show that the proposed reinforcement-learning scheme converges fast and the sum throughput of the LAA and Wi-Fi systems is reasonably close to the result based on exhaustive search.