Environment-Aware MIMO Channel Estimation in Pilot-Constrained Upper Mid-Band Systems

TL;DR

This paper introduces a physics-informed neural network framework that enhances MIMO channel estimation in pilot-constrained environments by combining model-based methods with deep learning, leading to significant accuracy improvements.

Contribution

It proposes a novel PINN approach with an enhanced U-Net architecture that fuses initial estimates with RSS maps, improving performance in complex urban environments.

Findings

Over 5 dB NMSE gain compared to state-of-the-art methods

Strong performance in pilot-limited scenarios

Robustness across frequencies and environments

Abstract

Accurate multiple-input multiple-output (MIMO) channel estimation is critical for next-generation wireless systems, enabling enhanced communication and sensing performance. Traditional model-based channel estimation methods suffer, however, from performance degradation in complex environments with a limited number of pilots, while purely data-driven approaches lack physical interpretability, require extensive data collection, and are usually site-specific. This paper presents a novel physics-informed neural network (PINN) framework that combines model-based channel estimation with a deep network to exploit prior information about the propagation environment and achieve superior performance under pilot-constrained scenarios. The proposed approach employs an enhanced U-Net architecture with cross-attention mechanisms to fuse initial channel estimates with received signal strength (RSS) maps to provide refined channel estimates. Comprehensive evaluation using realistic ray-tracing data from urban environments demonstrates significant performance improvements, achieving over 5 dB gain in normalized mean squared error (NMSE) compared to state-of-the-art methods, with particularly strong performance in pilot-limited scenarios and robustness across different frequencies and environments with only minimal fine-tuning. The proposed framework maintains practical computational complexity, making it viable for massive MIMO systems in upper mid-band frequencies.