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

2024-06-13

AI Summary

To address the severe pilot overhead explosion and channel aging in high-mobility TDD mmWave massive MIMO systems, this paper proposes a joint spatial-frequency-temporal channel extrapolation framework. Methodologically, we introduce the first knowledge- and data-driven Spatial-Frequency Channel Extrapolation Network (KDD-SFCEN), and design a generative-AI-based Time-Domain Uplink-Downlink Channel Extrapolation Network (TUDCEN), which tightly integrates physical channel priors, deep neural networks, and TDD channel reciprocity constraints. The framework enables collaborative modeling across all three domains and cross-domain extrapolation. Experimental results demonstrate a 16× reduction in pilot training overhead, significantly improved channel tracking accuracy and spectral efficiency under high-speed mobility, and superior overall performance compared to state-of-the-art channel estimation and extrapolation methods.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• mmWave TDD
• large MIMO systems
• signal prediction

Innovation

• High Mobility mmWave MIMO
• KDD-SFCEN and TUDCEN Networks
• Pilot Overhead Reduction