iRSL: Intelligent RAT selection framework for beyond 5G networks

Future networks are shifting towards more diversified and exemplary applications for societal benefits. To realise the functionalities of these advanced applications and services, Next-generation heterogeneous networks (HetNets) emerged as an exemplary connectivity solution, enabling each user device (UD) to associate with a radio access technology (RAT) in accordance with the demanded service. However, the design of an intelligent RAT association scheme for Beyond 5G (B5G) networks is becoming a crucial challenge due to the rapid growth of network heterogeneity in terms of RATs and personalised service requirements. Lately, considerable research endeavour has been noted in this direction but they are inadequate in maintaining Quality of Service (QoS) levels and RAT constraints simultaneously. Motivated by the gaps in the existing literature, an intelligent multi-connectivity approach has been proposed that facilitates agile differentiated service provisioning on the premise of ensuring the RAT capacity constraints. In particular, the proposed approach leverages the synergetic integration of matching game theory (MGT) and double deep reinforcement learning (DDRL) to attain a fine-grained UD-RAT association policy. Eventually, the proposed approach corroborated through the rigorous simulations demonstrated a substantial improvement in fairness, robustness and system satisfaction with the increase in network size.