基于柔性演员-评论家的通感算融合网络稳健资源优化

Objective Traditional approaches typically adopt a disjoint design that improves specific performance aspects under particular scenarios but often proves inadequate for addressing complex tasks in dynamic environments. Challenges such as real-time task offloading, efficient resource scheduling, and the simultaneous optimization of sensing, communication, and computing performance remain significant. The Integrated Sensing, Communication, and Computing (ISCC) architecture has been proposed to address these issues. In complex scenarios, the diversity of task types and varying requirements lead to inflexible offloading policies, limiting the system’s ability to adapt to real-time network changes. Moreover, computational uncertainty can undermine the robustness of resource scheduling, potentially resulting in performance degradation or task failure. Effectively addressing challenges like high user energy consumption and computational uncertainty while maintaining service quality is crucial for optimizing future network nodes. As network environments grow increasingly complex and user demands for high performance, low latency, and robust reliability rise, the optimization of resource efficiency and the achievement of mutual benefit across sensing, communication, and computing functions become urgent and critical. To meet this challenge, it is essential to advance the system towards higher intelligence and multi-dimensional connectivity. Furthermore, research on robust offloading in ISCC networks remains limited and warrants further investigation. Methods To address high user energy consumption and computational uncertainty in ISCC networks under complex scenarios, a robust resource allocation and decision optimization scheme is proposed. The goal is to minimize the total energy consumption of users. The proposed scheme takes into account common constraints and computational uncertainty commonly encountered in practical applications, offering a viable optimization approach for ISCC network design. First, to tackle the challenge of accurately predicting task complexity, potential biases arising from resource allocation and processing estimations are analyzed. These biases reflect real-world unpredictability, where task size can be measured but completion time remains uncertain, potentially leading to resource waste or performance degradation. To mitigate this, a robust computational resource allocation problem is formulated to manage the uncertainty caused by task offloading effectively. Second, the problem of minimizing users’ total energy is established by jointly optimizing task offloading ratios, beamforming, and resource allocation, subject to constraints such as power consumption, processing time, and radar estimation information rate. However, due to the multi-variable, non-convex, and NP-hard nature of this optimization problem, traditional methods fail to provide efficient solutions. To address this, a Markov decision process is modeled, and an optimization algorithm based on Soft Actor-Critic (SAC) is proposed. Results and Discussions The simulation results demonstrate that the proposed SAC-based algorithm outperforms existing methods in terms of performance and flexibility in dynamic and complex scenarios. Specifically, the learning rate affects the convergence speed of the algorithm, but its impact on final performance is minimal (Fig. 3). Compared to the Proximal Policy Optimization (PPO) and Advantage Actor-Critic (A2C) algorithms, the proposed algorithm achieves faster training speeds. Thanks to its flexible and unique design, the proposed algorithm exhibits stronger exploration capabilities and remains more stable during training (Fig. 4). The robust design enhances adaptability, resulting in higher overall reward values (Fig. 5). In terms of total user energy consumption, the proposed algorithm reduces energy use by approximately 9.57% compared to PPO and by 40.72% compared to A2C. As the number of users increases and more users access the network, signal interference intensifies, transmission rates decrease, and task offloading costs rise. In such scenarios, the proposed algorithm shows greater flexibility in policy adjustment, maintaining energy consumption at a relatively low level, outperforming both PPO and A2C. This advantage becomes more pronounced as the number of users grows or load pressure increases (Fig. 6). Overall, the proposed algorithm offers a robust and efficient solution for resource allocation and optimization in dynamic and complex environments, demonstrating exceptional adaptability and reliability in multi-user and multi-task scenarios. These results not only highlight the superior performance of the SAC algorithm but also highlight its potential in addressing multi-variable, non-convex problems. Conclusions This paper presents an optimization algorithm based on SAC, which not only achieves outstanding performance in terms of energy consumption, latency, and task offloading efficiency but also demonstrates excellent scalability and adaptability in multi-user, multi-task, and complex scenarios. A robust computational resource allocation scheme is proposed to address the uncertainty in offloading decisions. Simulation results show that the proposed algorithm can adapt to complex and dynamic network environments through flexible policy decisions, providing both theoretical support and a technical reference for further research on ISCC networks in such scenarios. Future research could explore incorporating multi-base station collaboration to enhance the robustness of ISCC networks, enabling them to better handle even more complex network environments.