Integrated Sensing, Communications, and Computation in Edge-Intelligent Networks: An Online Resource Management Approach

Integrated sensing, communications, and computation (ISCC) is becoming increasingly critical, particularly for enabling advanced intelligent applications. This paper proposes an ISCC framework for edge-intelligent networks, where edge intelligent devices (EIDs) cooperatively sense multiple mobile targets and simultaneously offload radar sensing data to a base station (BS) equipped with an edge server for processing. To address the time-varying nature of the network, we develop an online resource management strategy that maximizes the long-term average weighted sum rate (AWSR), subject to queue stability, average power constraints, and quality-of-service (QoS) requirements. Using the Lyapunov drift-plus-penalty framework, the original stochastic optimization problem is decomposed into a sequence of deterministic subproblems across time slots. At each time slot, sensing scheduling, transmit beamforming for both sensing and communications, receive beamforming for radar echoes, and computing resource allocation at the BS are jointly optimized through an efficient alternating optimization algorithm based on the current system state. Simulation results validate the effectiveness of the proposed online strategy, showing superior performance over baseline methods and revealing the influence of key parameters. In particular, a trade-off is observed between the AWSR and queue backlogs, which can be flexibly tuned via control parameters.