Distributed Reinforcement Learning–based Seamless Multi-Connectivity Solution for Heterogeneous 5GNR and Wi-Fi Networks

Abstract

The integration of 5G New Radio (5G-NR) and Wi-Fi networks in heterogeneous environments holds significant promise for delivering critical services with relatively high reliability and low latency. In this paper, we tackle the critical challenge of establishing robust multi-connectivity within heterogeneous systems integrating 5G NR and Wi-Fi systems. In this context, our focus is on optimizing a new, distributed multi-Connectivity solution, with a main emphasis on identifying the ideal base stations (BSs) and Wi-Fi access points (APs) with their respective spectrum associations. This solution is essential for ensuring low-latency and reliable communication required, particularly for mission-critical services. The simulation results demonstrate the significant advantages of the proposed algorithm compared to reference schemes. It leads to network performance improvements by enhancing system utility function, reducing service access time, and increasing packet success rates, through high-quality connectivity with relatively low interference probability. In particular, for mission-critical services, the proposed algorithm leads to a 16 % and 41% increase in effective network throughput when the offered load is 190 Mbps, which outperforms the benchmark approaches. Furthermore, it improves the utility function by approximately 0.12, and 0.19 compared to the reference approaches. This reflects a substantial improvements in success delivery rates for mission-critical services by 10 % and 18 %, respectively, when compared to the reference approaches, particularly evident at an offered load of approximately 190Mbps.