aBRSL: AI based bilateral RAT selection framework for next-generation wireless networks

Next-generation wireless networks (NGWNs) are moving towards a more advanced and exemplary environment encompassing data-intensive, delay-sensitive and energy-efficient services. To accommodate the stringent requirements of these radical and multifarious services, Next-generation wireless heterogeneous networks (NGWHNs) have been envisioned as an exemplary connectivity solution which enables flow based association among user devices (UDs) and radio access technologies (RATs). However, designing an intelligent RAT association scheme for NGWHNs is a significant challenge as the network heterogeneity tends to intensify in terms of access technologies and niche Quality of Service (QoS) requirements. Recently, substantial research endeavours have been carried out in this line of work but they are insufficient in sustaining adequate service levels and RAT capacity constraints concurrently. Inspired by the pitfalls in the pertinent work, an intelligent bilateral RAT selection framework has been developed. The proposed solution facilitates QoS provisioning while adhering to the RAT capacity limitations through well-defined preference functions. Within this paradigm, the proposal explores the diversity of matching game theory and double deep reinforcement learning (DDRL) that facilitates faster convergence to stable and balanced RAT selection policy. Finally, the proposed solution validated with the help of extensive simulations exhibited a significant gain of 42% and 46.35% respectively in terms of system utility and robustness in comparison to other schemes. Eventually, the performance assessment underlines the supremacy of the proposed solution by 22% in terms of system satisfaction with the varying network size.