Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO Systems Under High-Mobility Scenarios

TL;DR

This paper introduces a 3D channel extrapolation framework for TDD mmWave massive MIMO systems that significantly reduces pilot overhead and enhances spectral efficiency in high-mobility scenarios using knowledge-driven and AI-powered neural networks.

Contribution

It proposes a novel 3D channel extrapolation framework combining knowledge-driven and AI methods to reduce pilot overhead in high-mobility massive MIMO systems.

Findings

Reduces pilot training overhead by 16 times.

Improves spectral efficiency under high-mobility conditions.

Outperforms existing channel estimation/extrapolation methods.

Abstract

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge-and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system's spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods.