Improving Fairness in Coexisting 5G and Wi-Fi Network on Unlicensed Band with URLLC
Haodong Zou, Yupeng Li, Xiaowen Chu, Changfu Xu, Tian Wang · 2023
To meet the growing need of mobile traffic with Ultra-Reliable and Low Latency Communication (URLLC) requirement, 5G New Radio (NR) is extending from licensed band to unlicensed band on which Wi-Fi has already been operated, resulting in coexisting NR/Wi-Fi network. Existing works have made great efforts on throughput and latency of coexisting NR/Wi-Fi network. However, excessive NR requests offloaded from licensed band lead to unfair utilization of unlicensed band, which further causes unsatisfaction on URLLC and performance degradation of Wi-Fi. In this paper, we propose a novel Reinforcement Learning based Transmission Revoking Approach (RL-TRA) to address this problem aiming at fairer utilization of unlicensed band restrained by URLLC. Firstly, we formulate the coexistence problem of NR/Wi-Fi as integer non-linear programming and show its NP-hardness. Secondly, we decompose the problem into three sub-problems, namely redundancy determining, request scheduling, and transmission revoking. The former two sub-problems are solved with our proposed method to satisfy URLLC requirement. We further propose a novel transmission revoking mechanism when tackling transmission revoking sub-problem, aiming at maintaining fairness of coexisting NR/Wi-Fi network. Finally, simulation results verify the effectiveness of RL-TRA. By using our method, the fairness is improved by 16.5% averagely with only 1.77% loss on success rate of URLLC requests compared with baselines.