Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt

TL;DR

This paper introduces a geometry-aided framework for robust channel acquisition in MIMO-OFDM systems, effectively combining geometric features and neural networks to improve accuracy and resilience under sparse pilot conditions.

Contribution

It proposes a novel GCD method that leverages environmental geometry and neural networks to enhance channel estimation, outperforming existing pilot-based and pilot-free approaches.

Findings

Achieves leading accuracy in sparse pilot scenarios

Demonstrates strong generalization in new and dynamic environments

Shows robustness against position errors and environmental uncertainties

Abstract

Channel state information (CSI) is critical for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Pilot-based channel estimation methods suffer from high pilot overhead and low channel acquisition quality, while pilot-free approaches typically impose impractical demands on positional or environmental information precision. This paper proposes geometry-aided channel deduction (GCD), which leverages readily available geometric information to assist channel acquisition. The environmental map and base station position together constitute the scenario geometry, which can provide geometric channel features through ray tracing. To obtain the complete channel, the user first retrieves approximate geometric features by performing neighborhood searching within a pre-extracted geometric feature set, and then converts them into pseudo channels through a priori designed feature alignment. These pseudo channels serve as contextual prompt, providing supplementary channel features beyond those derived from pilot-based estimate. Finally, a neural network fuses these pseudo channels with partial estimate to generate the complete channel. Comprehensive experiments validate the superiority of our method, which achieves the leading accuracy in channel acquisition under sparse pilot conditions, demonstrates strong generalization capabilities in new scenarios and dynamic environments, and exhibits robust resilience against user position errors and non-ideal environmental information.