A Fair Multi-User Traffic Matching Strategy for Transmission and Computing in IRS-Assisted Cell-Free MEC Networks

The data processing capability of intelligent reflective surfaces (IRS)-assisted cell-free mobile edge computing (MEC) is constrained by both the offloaded data traffic during wireless transmission and the computed data traffic in the MEC server. The effectiveness of wireless transmission is adversely affected by channel estimation errors. Additionally, fairness is a critical consideration in multi-user scenarios. However, achieving fair multi-user traffic matching in IRS-assisted cell-free MEC networks with imperfect channel state information (CSI) remains an unsolved issue. To tackle this challenge, this paper proposes a max-min multi-user traffic matching (M3TM) strategy for transmission and computing, which mitigates the impact of channel estimation errors through stochastic analysis. The proposed strategy applies a sequential transmission paired with immediate sequential computing mode to ensure IRS gain for each user and improve computing resource utilization efficiency. A cell-free wireless transmission sub-model is proposed with a stochastic analysis of channel estimation errors. Based on the transmission sub-model, a transmission-computing traffic matching model, representing the processed data volume for each user, is proposed, which comprehensively accounts for the mutual constraints between wireless transmission and edge computing. Subsequently, a max-min processed data volume optimization problem is formulated, leveraging the max-min criterion to ensure fairness among users. To address the non-convex formulated problem and alleviate the adverse impact of channel estimation errors, a variable grouping optimization algorithm is proposed, which combines a block coordinate descent (BCD)-based method with a proof-by-contradiction approach to optimize different groups of variables. Simulation results validate the superiority of the proposed strategy.