High Dimensional Channel Estimation Using Deep Generative Networks

TL;DR

This paper introduces a deep generative network-based compressed sensing method for high-dimensional wireless channel estimation, outperforming traditional techniques and enabling effective estimation from minimal and quantized measurements.

Contribution

It proposes a novel CS approach that leverages deep generative models as priors for improved high-dimensional channel estimation, including from one-bit measurements.

Findings

Outperforms traditional CS methods like OMP and AMP in mmWave channel reconstruction.

Requires fewer pilot symbols for accurate channel estimation.

Execution time remains stable with increasing pilot measurements.

Abstract

This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network. Channel estimation using generative networks relies on the assumption that the reconstructed channel lies in the range of a generative model. Channel reconstruction using generative priors outperforms conventional CS techniques and requires fewer pilots. It also eliminates the need of a priori knowledge of the sparsifying basis, instead using the structure captured by the deep generative model as a prior. Using this prior, we also perform channel estimation from one-bit quantized pilot measurements, and propose a novel optimization objective function that attempts to maximize the correlation between the received signal and the generator's channel estimate while minimizing the rank of the channel estimate. Our approach significantly outperforms sparse signal recovery methods such as Orthogonal Matching Pursuit (OMP) and Approximate Message Passing (AMP) algorithms such as EM-GM-AMP for narrowband mmWave channel reconstruction, and its execution time is not noticeably affected by the increase in the number of received pilot symbols.