Joint Active User Estimation and Resource Allocation in Cell-Free Networks for 6G

The upcoming sixth generation (6G) wireless networks introduce the scenario of massive ultra-reliable low-latency communication (mURLLC), which aims to support a large number of real-time services with millisecond-level latency and high reliability. Cell-free (CF) systems have emerged as a promising solution for enabling mURLLC by leveraging grant-free random access (GFRA) schemes with K-repetition, which significantly enhance communication reliability under stringent latency constraints. However, due to the random selection of subchannels by users in the K-repetition approach, transmission collisions are inevitable, leading to access failures. A key strategy to mitigate collisions and improve the access success rate is to accurately estimate the number of active users in the CF system and allocate network resources accordingly. In this paper, we propose an efficient active user estimation scheme tailored for CF networks, which estimates the number of active users by observing the occupancy states of sub-channels in mURLLC scenarios. Building on this estimation, we further develop a dynamic resource allocation scheme that adapts to varying network load conditions. Simulation results show that the proposed estimation method outperforms baseline schemes in terms of estimation accuracy, and the proposed adaptive resource allocation strategy maintains a lower access failure probability, effectively satisfying the Quality of Service (QoS) requirements of users in CF systems.