In this paper, we present an AI-based novel framework for dynamic resource management in ISAC systems in 6G networks. The framework utilizes Deep Reinforcement Learning (DRL) to learn and optimize various resource control tasks such as smart beamforming, interference control, and power assignment, according to instantaneous network state and environment. The sum rate and beam pattern gain of AI-based approach are up to 45% and 50% higher than those of the static beamforming, respectively, at all scenarios. In particular, at pmax = 30 dBm and L = 64 antennas, the AI model yields a sum rate of 32 bps/Hz in rural area scenarios, 28 bps/Hz in dense smart city, and 28 bps/Hz in high-mobility urban scenario, substantially better than convex optimization (achieving 22 bps/Hz) and static beamforming (reaching at most 16 bps/Hz). Moreover, the AI model has a beam pattern gain of 32 dB in rural, 28 dB in dense and 30 dB in high-mobility urban, which leads to improved sensing accuracy through the concentration of the transmitted energy toward expected sensing directions. In an energy-efficient context, the AI-engineered model has improved energy utilization with 40% gain reduction in power compared to conventional methods for the same sum rate. It also efficiently suppresses interference with increase of up to 50% in interference suppression level thereby enabling an improvement in total system performance. These findings demonstrate the potential of the AI-based model for joint communication and sensing design for 6G ISAC systems, and provide a generalized framework for intelligent 6G wireless networks. Its capability of dynamic resource allocation, enhanced spectral efficiency, and accurate sensing in dynamic network make it a technology enabler for 6G deployment.
Aman et al. (Sun,) studied this question.