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

2025-02-10

AI Summary

In massive MIMO (mMIMO) systems, grant-free NOMA suffers from high pilot overhead and excessive access latency due to the requirement of accurate random-access channel estimation. To address this, this paper proposes a beacon-antenna-driven pre-equalization-assisted grant-free access scheme. By broadcasting pre-equalization parameters via dedicated beacon antennas, the scheme enables uplink signal pre-equalization at user equipment (UE), thereby decoupling activity detection from channel estimation for the first time. We further design a three-module iterative joint detection framework—comprising coarse detection, fine detection, and data-aided channel estimation—that breaks the conventional end-to-end channel estimation dependency. Simulation results demonstrate that, under identical access latency constraints, the proposed scheme reduces activity detection error by 42% and lowers bit error rate by one order of magnitude, significantly enhancing both detection accuracy and spectral efficiency.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements

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.

Problem

• Reduces pilot overhead in mMIMO systems
• Decreases access latency for massive NOMA
• Enhances activity detection and data demodulation

Innovation

• Pre-equalization uplink signal
• Iterative detection algorithm
• Beacon antenna channel estimation