RSS map-assisted MIMO channel estimation in the upper mid-band under pilot constraints
Alireza Javid, Nuria Gonz\'alez-Prelcic

TL;DR
This paper introduces a physics-informed neural network framework that combines model-based and data-driven methods to improve MIMO channel estimation in the upper mid-band, especially under pilot constraints, with enhanced accuracy and interpretability.
Contribution
It proposes a novel PINN architecture with transformer modules for improved channel estimation using RSS maps, extending to multi-step prediction, and demonstrating superior performance in realistic urban scenarios.
Findings
Achieves over 5 dB NMSE gain compared to state-of-the-art methods.
Performs robustly across different frequencies and environments with minimal fine-tuning.
Enables accurate multi-step channel prediction for proactive beamforming.
Abstract
Accurate wireless channel estimation is critical for next-generation wireless systems, enabling precise precoding for effective user separation, reduced interference across cells, and high-resolution sensing, among other benefits. 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 synergistically combines model-based channel estimation with a deep network to exploit prior information about environmental propagation characteristics and achieve superior performance under pilot-constrained scenarios. The proposed approach employs an enhanced U-Net architecture with transformer…
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Taxonomy
TopicsMillimeter-Wave Propagation and Modeling · Advanced MIMO Systems Optimization · Wireless Signal Modulation Classification
