# RSS map-assisted MIMO channel estimation in the upper mid-band under pilot constraints

arXiv: https://arxiv.org/abs/2603.00112 · HTML: https://mlsanity.com/paper/2603.00112/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 48 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
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- [Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets](https://mlsanity.com/paper/2103.10974/) (2021)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [Generative Diffusion Models for High Dimensional Channel Estimation](https://mlsanity.com/paper/2408.10501/) (2024)
- [Cellular Wireless Networks in the Upper Mid-Band](https://mlsanity.com/paper/2309.03038/) (2023)
- [Upper Mid-Band Spectrum for 6G: Vision, Opportunity and Challenges](https://mlsanity.com/paper/2502.17914/) (2025)
- [Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information](https://mlsanity.com/paper/2505.06900/) (2025)
- [Toward Real-Time Digital Twins of EM Environments: Computational Benchmark of Ray Launching Software](https://mlsanity.com/paper/2406.05042/) (2024)
- [Enabling 6G Performance in the Upper Mid-Band by Transitioning From Massive to Gigantic MIMO](https://mlsanity.com/paper/2407.05630/) (2024)
- [Reducing Pilots in Channel Estimation with Predictive Foundation Models](https://mlsanity.com/paper/2512.15562/) (2025)
- [Multidimensional orthogonal matching pursuit: theory and application to high accuracy joint localization and communication at mmWave](https://mlsanity.com/paper/2208.11600/) (2022)
- [Full-Duplex Millimeter Wave MIMO Channel Estimation: A Neural Network Approach](https://mlsanity.com/paper/2402.03886/) (2024)
- [Separable multidimensional orthogonal matching pursuit and its application to joint localization and communication at mmWave](https://mlsanity.com/paper/2210.17450/) (2022)
- [Ultra-wideband Double-Directionally Resolved Channel Measurements of Line-of-Sight Microcellular Scenarios in the Upper Mid-band](https://mlsanity.com/paper/2412.12306/) (2024)
- [AI-based Environment-Aware XL-MIMO Channel Estimation with Location-Specific Prior Knowledge Enabled by CKM](https://mlsanity.com/paper/2507.06066/) (2025)
- [Digital Twin Channel-Aided CSI Prediction: An Environment-Based Subspace Extraction Approach for Achieving Low Overhead and High Robustness](https://mlsanity.com/paper/2508.05142/) (2025)
- [Environment-Aware MIMO Channel Estimation in Pilot-Constrained Upper Mid-Band Systems](https://mlsanity.com/paper/2511.05771/) (2025)
- [Enhancing Diffusion Model Guidance through Calibration and Regularization](https://mlsanity.com/paper/2511.05844/) (2025)

## Abstract (arXiv)

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 modules and cross-attention mechanisms to fuse initial channel estimates with 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 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. We further extend the decoder for multi-step temporal prediction, enabling accurate forecasting of several future channel snapshots from a single estimate, useful for proactive beamforming and scheduling in mobile scenarios. The proposed framework maintains practical computational complexity, making it viable for massive MIMO systems in upper-mid band frequencies. Unlike black-box neural approaches, the physics-informed design provides a more interpretable channel estimation method.