Joint Communication and Computing Resource Allocation for Cell-Free-Enabled MEC Networks

This paper investigates a cell-free massive multiple-input multiple-output (CF-mMIMO)-enabled mobile edge computing (MEC) network, aiming at satisfying stringent quality-of-service (QoS) requirements of computation-intensive services. We design a partial offloading strategy, where users execute local computing and simultaneously perform task offloading through reliable cell-free links. A joint communication and computing resource allocation (JCCRA) problem is formulated to maximize the sum computational bits of the system. The optimization approach jointly optimizes the transmit power, the local computing frequency, as well as the time allocation for local computing and computing offloading of each user. To tackle the non-convex problem, we develop an iterative algorithm based on fractional programming (FP) theory and block coordinate ascent (BCA) method. The original objective function is first reformulated using FP technique, and then it is decomposed into multiple sub-problems by BCA method. For each sub-problem, we derive the optimal closed-form solutions by leveraging Lagrangian dual transform and Karush-KuhnTucker (KKT) conditions. Numerical results demonstrate that the proposed scheme outperforms other benchmark schemes in terms of improving the sum computational bits.