Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System

TL;DR

This work tackles the high pilot overhead and access latency of grant-free massive access in mMIMO mMTC by introducing a beacon-assisted pre-equalization framework. An iterative detector is developed, consisting of coarse data detection, data-aided channel estimation in the virtual angular domain, and fine data detection, leveraging uplink/downlink reciprocity and multi-antenna diversity. The approach extends pre-equalization to mMIMO via a semi-blind, three-model pipeline (MMV sparse reconstruction, CS in angular domain, and LMMSE), with iterative CE-DD refinements that yield improvements over state-of-the-art schemes under the same latency. Simulation results on a large-scale setting demonstrate BER/NMSE gains, and the authors provide reproducible code at the linked repository, highlighting practical impact for low-latency massive access in 5G/6G networks.

Abstract

The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of random access channels to perform activity detection and the following coherent data demodulation, which suffers from excessive pilot overhead and access latency. To address this, we propose a pre-equalization aided grant-free massive access scheme for mMIMO systems, where an iterative detection scheme is conceived. Specifically, the base station (BS) firstly activates one of its antennas (i.e., beacon antenna) to broadcast a beacon signal, which facilitates the user equipment (UEs) to perform downlink channel estimation and pre-equalize the uplink random access signal with respect to the channels associated with the beacon antenna. During the uplink transmission stage, the BS detects UEs' activity and data by using the proposed iterative detection algorithm, which consists of three modules: coarse data detection (DD), data-aided channel estimation (CE), and fine DD. In the proposed algorithm, the joint activity and DD is firstly performed based on the signals received by the beacon antenna. Subsequently, the DD is further refined by iteratively performing data-aided CE module and fine DD module using signals received by all BS antennas. Our simulation results demonstrate that the proposed scheme outperforms state-of-the-art mMIMO-based grant-free massive NOMA schemes with the same access latency. Simulation codes are provided to reproduce the results in this article: https://github.com/owenwang517/tvt-2025.

Figures (5)

• Figure 1: The proposed pre-equalization aided grant-free massive access scheme.
• Figure 2: The BER and NMSE performance of different schemes with transmit power $\rho = 7\textrm{dBm}$.
• Figure 3: Comparison of ADEP performance for different schemes versus time slot overhead $T$ with $M=56, \rho = 7 \textrm{dBm}$.
• Figure 4: Comparison of NMSE performance for different schemes versus time slot overhead $T$ with $M=60, \rho = 7 \textrm{dBm}$.
• Figure 5: The BER performance of different schemes with $M=60$.