Slicing capacity-centered mode selection and resource optimization for network-assisted full-duplex cell-free distributed massive MIMO systems

Abstract

Network-assisted full-duplex (NAFD) cell-free distributed massive multiple-input multiple-output (MIMO) systems enable uplink (UL) and downlink (DL) communications within the same time-frequency resources, which potentially reduce latency by avoiding the overhead of switching UL/DL modes. However, how to choose UL/DL modes remains an important factor affecting system performance. With the dramatic increase in the number of users and access points (APs), massive access brings significant overhead in the mode selection. Additionally, the different quality of service (QoS) among users also makes the effective utilization of resources difficult. As one of the most promising technologies in sixth-generation (6G), network slicing enables the adaptive configuration of limited UL/DL resources through the resource isolation assisted NAFD technique. Therefore, we propose a slicing capacity-centered scheme. Under this scheme, APs are motivated by slicing requirements and associated slices to form different subsystems. Collaborative mode selection and resource allocation are performed within each subsystem to reduce overhead and improve resource utilization. To implement this scheme efficiently, a double-layer deep reinforcement learning (DRL) mechanism is used to realize the joint optimization of mode selection and resource allocation. Simulation results show that the slicing capacity-centered scheme can effectively improve resource utilization and reduce overhead.