# Wideband Channel Estimation with A Generative Adversarial Network

arXiv: https://arxiv.org/abs/2012.12063 · HTML: https://mlsanity.com/paper/2012.12063/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 16 of 16)

- [Generative Diffusion Models for High Dimensional Channel Estimation](https://mlsanity.com/paper/2408.10501/) (2024)
- [Generative AI for Physical Layer Communications: A Survey](https://mlsanity.com/paper/2312.05594/) (2023)
- [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024)
- [Machine Learning in NextG Networks via Generative Adversarial Networks](https://mlsanity.com/paper/2203.04453/) (2022)
- [Leveraging Variational Autoencoders for Parameterized MMSE Estimation](https://mlsanity.com/paper/2307.05352/) (2023)
- [Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation](https://mlsanity.com/paper/2205.12445/) (2022)
- [Diffusion Models for Accurate Channel Distribution Generation](https://mlsanity.com/paper/2309.10505/) (2023)
- [Can Stochastic Gradient Langevin Dynamics Provide Differential Privacy for Deep Learning?](https://mlsanity.com/paper/2110.05057/) (2021)
- [End-to-End Autoencoder Communications with Optimized Interference Suppression](https://mlsanity.com/paper/2201.01388/) (2021)
- [Deep OFDM Channel Estimation: Capturing Frequency Recurrence](https://mlsanity.com/paper/2401.05436/) (2024)
- [A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning](https://mlsanity.com/paper/2507.01976/) (2025)
- [Generative AI Meets Wireless Sensing: Towards Wireless Foundation Model](https://mlsanity.com/paper/2509.15258/) (2025)
- [EVT-Based Generative AI for Tail-Aware Channel Estimation](https://mlsanity.com/paper/2604.25008/) (2026)
- [Federated Multi-Task Learning for THz Wideband Channel and DoA Estimation](https://mlsanity.com/paper/2207.06017/) (2022)
- [RADIANCE: Radio-Frequency Adversarial Deep-learning Inference for Automated Network Coverage Estimation](https://mlsanity.com/paper/2308.10584/) (2023)
- [Sampling-Free Diffusion Transformers for Low-Complexity MIMO Channel Estimation](https://mlsanity.com/paper/2602.02202/) (2026)

## Abstract (arXiv)

Communication at high carrier frequencies such as millimeter wave (mmWave) and terahertz (THz) requires channel estimation for very large bandwidths at low SNR. Hence, allocating an orthogonal pilot tone for each coherence bandwidth leads to excessive number of pilots. We leverage generative adversarial networks (GANs) to accurately estimate frequency selective channels with few pilots at low SNR. The proposed estimator first learns to produce channel samples from the true but unknown channel distribution via training the generative network, and then uses this trained network as a prior to estimate the current channel by optimizing the network's input vector in light of the current received signal. Our results show that at an SNR of -5 dB, even if a transceiver with one-bit phase shifters is employed, our design achieves the same channel estimation error as an LS estimator with SNR = 20 dB or the LMMSE estimator at 2.5 dB, both with fully digital architectures. Additionally, the GAN-based estimator reduces the required number of pilots by about 70% without significantly increasing the estimation error and required SNR. We also show that the generative network does not appear to require retraining even if the number of clusters and rays change considerably.