Enabling mURLLC in Network-Assisted Full-Duplex Cell-Free Networks by Dual Time-Scale Resource Scheduling

Network-assisted full-duplex (NAFD) cell-free (CF) network emerges as a promising solution for enabling massive ultra-reliable and low-latency communications (mURLLC). In the massive Internet-of-Things (mIoT) scenarios where users' active statuses change, the existing NAFD resource scheduling schemes require frequent invocation of optimization algorithms, causing significant energy loss and operational delays. So they are not conducive to mURLLC. This paper proposes a dual time-scale resource scheduling scheme, which combines the improved long-term AP duplex mode optimization method with the short-term power allocation optimization method to further enhance the mURLLC ability of NAFD CF networks. In the proposed long-term AP duplex mode optimization method, we first derive the closed-form expressions of active users' time overflow (TO) probability as the service latency indicator. Operating on a superframe as a large time-scale unit, the improved long-term AP duplex mode optimization method initially employs a long-term active user prediction algorithm to forecast active users in an upcoming superframe and then leverages the long-term AP duplex mode optimization algorithm based on multi-agent deep reinforcement learning to achieve optimal long-term AP mode selection which minimizes the TO probability. In the proposed short-term power allocation optimization method, we design a heuristic algorithm to ensure active users in each coherence time can receive high-reliable and low-latency service. Simulation results demonstrate the effectiveness of the proposed scheme. Compared with the short-term AP mode and power joint optimization methods, the dual time-scale resource scheduling scheme achieves similar spectral efficiency and a much lower TO probability, while also avoiding the frequent AP mode optimization and switching, making it more suitable for mURLLC.