AI-driven dynamic resource allocation for ISAC systems in…

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, inte…

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 (sim 32) bps/Hz in rural area scenarios, (sim 28) bps/Hz in dense smart city, and (sim 28) bps/Hz in high-mobility urban scenario, substantially better than convex optimization (achieving (sim 22) bps/Hz) and static beamforming (reaching at most (sim 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.

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https://doi.org/10.1109/OJCOMS.2024.3515933 (2024). Saikia, P., Jee, A., Singh, K., Huang, W.-J., Boulogeorgos, A.-A. A. & Tsiftsis, T. A. Hybrid-ris empowered uav-assisted ISAC systems: Transfer learning-based DRL, IEEE Transactions on Communications. https://doi.org/10.1109/TCOMM.2025.3548797. Ghani, Madeeha and Zubair conceived of the presented idea. Ali and Alfakeeh implemented the approach and carried out the experiments.

Madeeha and Omar were involved in supervising the project and helped designing the experiments. All authors discussed the results and contributed to the final manuscript. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.

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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Aman, M., Rehman, G.U., Zubair, M. et al. AI-driven dynamic resource allocation for ISAC systems in 6G networks: intelligent beamforming, interference management, and power allocation. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42247-y

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This report covers the latest developments in artificial intelligence. The information presented highlights key changes and updates that are relevant to those following this topic.

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