Preamble Allocation and Access Delay Reduction for Improved Massive Random Access in 5G Beamforming Networks

In 5th generation (5G) beamforming networks, beam alignment is conducted in the random access process, where user equipment (UE) in different beam regions share the preambles according to the network configuration. The limited number of preambles results in an increased collision probability and access delay, which can become large when the number of contending UEs is significant, as in massive machine type communication (mMTC) scenarios. In this article, a novel method to allocate preambles to each region and adjust the physical random access channel (PRACH) occasion duration is proposed to solve the random access problem in beamforming networks within massive communication scenarios. An analytical model for the backoff process is derived through a Markov chain to define the probability of successful preamble transmission and the access delay considering retransmissions. Based on the model, an optimization problem is proposed to derive the optimal preamble allocation and PRACH occasion duration considering the access rate in each region under the uniform traffic model. Subsequently, a delay-aware preamble and PRACH allocation (DPPA) algorithm is developed to solve the problem. Simulation results demonstrate that the proposed method outperforms the existing methods in terms of preamble efficiency, access delay, and access success probability.