Joint Multiservice Resource Optimization for Integrated Sensing, Communication, and Computing Networks

To meet the multidimensional extreme performance requirements of intelligent services in sixth-generation mobile (6G) networks, it is crucial to implement the joint management of sensing, communication, and computation resources. However, the competition between services and the inherent conflicts among multidimensional resources result in a prominent contradiction between the efficiency of joint resource management and its high complexity. To address the challenges, a multiservice coexistence model is proposed, incorporating sensing, communication, and computing requirements. The optimization problem is decomposed to enable a low-complexity solution. Initially, a service resource management and mode selection algorithm is proposed, leveraging attention-assisted multiagent reinforcement learningmultiagent reinforcement learning (MARL) to effectively coordinate service resource competition. Subsequently, a one-to-one matching game is developed for radio resource blocksresource blocks (RB) and users, ensuring stable maximization of joint sensing and communication performance while optimizing radio resource reuse. Finally, a computing resource management algorithm is designed using the Lagrange multiplier method and Karush-Kuhn-TuckerKarush–Kuhn–Tucker (KKT) conditions to enhance computing performance. Theoretical analysis and numerical simulations validate the proposed schemes in terms of low complexity and high effectiveness, achieving approximately 20% overall performance improvement over baseline schemes.