Dynamic Preamble-Resource Partitioning for Critical MTC in Massive MIMO Systems

Abstract

Preamble resources are scarce and precious in random access (RA), which need to be efficiently utilized to support critical machine-type communication (MTC) with stringent access requirements. In this article, we study a grant-free RA scenario for critical MTC in the context of massive multiple-input–multiple-output (MIMO). To enhance the access reliability of delay-sensitive devices within a predefined latency budget, two dynamic preamble-resource partitioning (DPP) schemes are proposed. Particularly, by leveraging massive MIMO, we first analytically investigate the feasibility of DPP in the considered RA scenario and demonstrate its performance superiority to the conventional scheme. Based on the analytical results, we then propose a greedy DPP scheme that performs locally optimal preamble-resource partitioning in each RA slot. To find the globally optimal DPP solution, we further propose a reinforcement learning (RL)-based scheme by modeling the considered RA scenario as a Markov decision process. Simulation results show the practicality and effectiveness of the proposed DPP schemes. In particular, to achieve a target access failure rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 10^{-2}$ </tex-math></inline-formula> , the proposed RL-based scheme is able to enhance the RA traffic load by 42% and improve the preamble resource utilization by over 25% compared to the conventional baseline scheme.