Timing Advance Estimation and Beamforming of Random Access Response in Crowded TDD Massive MIMO Systems

TL;DR

This paper introduces a beamforming method in massive MIMO systems to improve random access response decoding, reducing latency and power requirements in high-density user scenarios.

Contribution

It proposes a novel joint grouping and beamforming approach for RA requests in massive MIMO, enhancing collision resolution and efficiency.

Findings

RA latency is significantly reduced compared to LTE.

Power requirements decrease by approximately 1.5 dB with each doubling of antennas.

The method improves decoding reliability in high user density scenarios.

Abstract

Timing advance (TA) estimation at the base station (BS) and reliable decoding of random access response (RAR) at the users are the most important steps in the initial random access (RA) procedure. However, due to the limited availability of physical resources dedicated for RA, successful completion of RA requests would become increasingly difficult in high user density scenarios, due to contention among users requesting RA. In this paper, we propose to use the large antenna array at the massive MIMO BS to jointly group RA requests from different users using the same RA preamble. We then beamform the common RAR of each detected user group onto the same frequency resource, in such a way that most users in the group can reliably decode the RAR. The proposed RAR beamforming therefore automatically resolves the problem of collision between multiple RA requests on the same RA preamble, which reduces the RA latency significantly as compared to LTE. Analysis and simulations also reveal that for a fixed desired SINR of the received RAR, both the required per-user RA preamble transmission power and the total RAR beamforming power can be decreased roughly by 1.5 dB with every doubling in the number of BS antennas.