Wideband Channel Estimation with A Generative Adversarial Network

TL;DR

This paper introduces a GAN-based method for wideband channel estimation in high-frequency communications, achieving accurate results with fewer pilots and low SNR, reducing pilot overhead significantly.

Contribution

The paper presents a novel GAN-based channel estimator that requires fewer pilots and performs well at low SNR, without needing retraining for different channel conditions.

Findings

Achieves the same estimation error at -5 dB SNR as traditional methods at much higher SNR.

Reduces pilot requirements by approximately 70%.

Does not require retraining despite significant channel variations.

Abstract

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.