Adaptive Feedback-Aided Hybrid Random Access for mURLLC Service Over Cell-Free Networks

Abstract

As dominating 6G-standard service, massive ultrareliable low-latency communications (mURLLC) strive to meet the strict requirements of massive users on latency and error rate. Compared with the traditional grant-based random access (GBRA), the grant-free random access (GFRA) allows users to directly transmit data, which avoid heavy signaling overhead and reduce delay. However, pilot collision interference resulting from uncoordinated resource selection in GFRA leads to serious transmission failure, especially in mURLLC scenarios. Therefore, this article proposes an adaptive feedback-aided hybrid random access mechanism based on the advantages of GBRA and GFRA in cell-free networks. In the proposed mechanism, the feedback factors inserted between pilot and data not only make different access policies for massive users in real time, but also achieve an acceptable tradeoff among signaling overhead, access success probability and access delay. The spatial sparsity of cell-free networks is further utilized to solve the pilot collision and improve the successful access probability. The simulation results demonstrate that the proposed hybrid random access mechanism can improve access throughout with lower signaling overhead and better meet the requirements of mURLLC.