# Deep Residual Learning-Assisted Channel Estimation in Ambient Backscatter Communications

arXiv: https://arxiv.org/abs/2009.07468 · HTML: https://mlsanity.com/paper/2009.07468/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [Adversarial Score-Based Generative Models for MMSE-achieving AmBC Channel Estimation](https://mlsanity.com/paper/2309.05776/) (2023)
- [3D Dynamic Radio Map Prediction Using Vision Transformers for Low-Altitude Wireless Networks](https://mlsanity.com/paper/2511.19019/) (2025)
- [AI-Empowered UAV-Assisted Backscatter Localization and ISAC for Zero-Energy IoT: A Comprehensive Survey](https://mlsanity.com/paper/2606.23125/) (2026)

## Abstract (arXiv)

Channel estimation is a challenging problem for realizing efficient ambient backscatter communication (AmBC) systems. In this letter, channel estimation in AmBC is modeled as a denoising problem and a convolutional neural network-based deep residual learning denoiser (CRLD) is developed to directly recover the channel coefficients from the received noisy pilot signals. To simultaneously exploit the spatial and temporal features of the pilot signals, a novel three-dimension (3D) denoising block is specifically designed to facilitate denoising in CRLD. In addition, we provide theoretical analysis to characterize the properties of the proposed CRLD. Simulation results demonstrate that the performance of the proposed method approaches the performance of the optimal minimum mean square error (MMSE) estimator with perfect statistical channel correlation matrix.