Latency-Aware Resource Allocation for Integrated Communications, Computation, and Sensing in Cell-Free mMIMO Systems
In this paper, we investigate a cell-free massive multiple-input and multiple-output (MIMO)-enabled integration communication, computation, and sensing (ICCS) system, aiming to minimize the maximum computation latency to guarantee the stringent sensing requirements. We consider a two-tier offloading framework, where each multi-antenna terminal can optionally offload its local tasks to either multiple mobile-edge servers for distributed computation or the cloud server for centralized computation while satisfying the sensing requirements and power constraint. The above offloading problem is formulated as a mixed-integer programming and non-convex problem, which can be decomposed into three sub-problems, namely, distributed offloading decision, beamforming design, and execution scheduling mechanism. First, the continuous relaxation and penalty-based techniques are applied to tackle the distributed offloading strategy. Then, the weighted minimum mean square error (WMMSE) and successive convex approximation (SCA)-based lower bound are utilized to design the integrated communication and sensing (ISAC) beamforming. Finally, the other resources can be judiciously scheduled to minimize the maximum latency. A rigorous convergence analysis and numerical results substantiate the effectiveness of our method. Furthermore, simulation results demonstrate that multi-point cooperation in cell-free massive MIMO-enabled ICCS significantly reduces overall computation latency, in comparison to the benchmark schemes.
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