Intelligent ISAC-Enabled RIS-Assisted Hybrid 6G and Wi-Fi 8 Networks: A Multi-Objective EE-SE-Sensing Optimization Framework

The migration of wireless networks towards sixth-generation (6G) cellular communications and Wi-Fi 8 is targeting ultra-high data rates, massive connectivity, and in-built sensing. However, these sophisticated applications raise new complexities for improving high levels of energy efficiency, spectrum efficiency, and accuracy in ultra-dense and heterogeneous wireless networks. The existing literature has mainly addressed energy efficiency or spectral efficiency in hybrid multi-radio access technology (multi-RAT)-based wireless communications. Therefore, in this manuscript, an Intelligent Integrated Sensing and Communication (ISAC) solution for hybrid 6G and Wi-Fi 8 communications is proposed in order to concurrently maximize energy efficiency (EE), spectral efficiency (SE), and sensing performance. The system model used in this manuscript utilizes Reconfigurable Intelligent Surfaces (RIS) and formulates a multi-objective problem considering communications quality of service and sensing constraints. For this non-convex problem, an optimized deep reinforcement learning (DRL)-based controller to dynamically control RIS phases, beamforming, as well as power allocation in multiple radio communications links has been proposed. The simulation results indicate that the proposed method effectively enhances EE and SE with a high degree of accuracy in environment sensing with a spectral efficiency of 7.1 bits/s/Hz with 32 RIS elements and a spectral efficiency of 12.3 bits/s/Hz with 128 RIS elements. This work proposes the first framework that performs joint energy efficiency, spectral efficiency, and sensing performance optimization for hybrid 6G and Wi-Fi 8 networks. The proposed approach utilizes RIS-aided ISAC to enable intelligent multi-objective optimization using deep reinforcement learning