Spectrum Sharing between Cellular and Wi-Fi Networks based on Deep Reinforcement Learning

Abstract

Recently, mobile traffic is growing rapidly and spectrum resources are becoming scarce in wireless networks. Due to this, the wireless network capacity will not meet the traffic demand. To address this problem, using cellular systems in an unlicensed spectrum emerged as an effective solution. In this case, cellular systems need to coexist with Wi-Fi and other systems. For that, we propose an efficient channel assignment method for Wi-Fi AP and cellular NB, based on the DRL method. To train the DDQN model, we implement an emulator as an environment for spectrum sharing in densely deployed NB and APs in wireless heterogeneous networks. Our proposed DDQN algorithm improves the average throughput from 25.5% to 48.7% in different user arrival rates compared to the conventional method. We evaluated the generalization performance of the trained agent, to confirm channel allocation efficiency in terms of average throughput under the different user arrival rates.