Joint Multiservice Resource Optimization for Integrated Sensing, Communication, and Computing Networks
Shenhu Zhang, Shi Yan, Zilong Tang, Dong Wang, Mugen Peng · IEEE Internet of Things Journal · 2025
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 multi-service 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 multi-agent reinforcement learning to effectively coordinate service resource competition. Subsequently, a one-to-one matching game is developed for radio resource blocks 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-Tucker 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.