Learning-Based Resource Allocation for Integrated Sensing, Communication, and Computation Networks: A Delay-Aware Approach

An integrated sensing, communication, and computation (ISCC) network has been recognized as a key enabler to realize the vision of the Internet of Things (IoT). In this article, we explore the resource allocation problem in ISCC networks, where the task execution workflow consists of multiple dependent processes, i.e., wireless sensing, signal processing, data delivery, and data processing. To this end, a tandem-parallel queuing model is first proposed to characterize the end-to-end (E2E) task execution process. Given the model, the E2E delay upper bound is derived according to the stochastic network calculus (SNC) theory. Based on the analytical results, the joint allocation problem of the SCC resources is formulated to minimize the E2E delay, while satisfying the constraints of network resources, tolerable delay, and sensing mutual information (MI). Further, this nonconvex optimization problem is parameterized to enable a learning-based optimization approach. Next, we design the unsupervised learning (UL) framework based on a multilevel decomposition architecture (MDA) and residual network (RN) to accelerate training speed and ensure effective primal–dual learning. Numerical results demonstrate that the proposed UL-MDA-RN framework is superior to existing baselines with excellent convergence efficiency and lower achieved E2E delay. In addition, our results analyze the impacts of the network parameters on the E2E delay performance to guide the design of appropriate SCC resource provisioning patterns.