Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systems

TL;DR

This paper introduces a deep generative model approach to efficiently estimate downlink channels in FDD massive MIMO systems by leveraging uplink information and learning channel parameter distributions.

Contribution

It proposes a novel DGM-based method that exploits uplink-downlink reciprocity and learns channel parameter distributions for improved estimation with minimal downlink training.

Findings

Outperforms conventional methods in SNR range

Achieves near-optimal performance with few downlink pilots

Utilizes learned distributions for better channel estimation

Abstract

It is well accepted that acquiring downlink channel state information in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems is challenging because of the large overhead in training and feedback. In this paper, we propose a deep generative model (DGM)-based technique to address this challenge. Exploiting the partial reciprocity of uplink and downlink channels, we first estimate the frequency-independent underlying channel parameters, i.e., the magnitudes of path gains, delays, angles-of-arrivals (AoAs) and angles-of-departures (AoDs), via uplink training, since these parameters are common in both uplink and downlink. Then, the frequency-specific underlying channel parameters, namely, the phase of each propagation path, are estimated via downlink training using a very short training signal. In the first step, we incorporate the underlying distribution of the channel parameters as a prior into our channel estimation algorithm. We use DGMs to learn this distribution. Simulation results indicate that our proposed DGM-based channel estimation technique outperforms, by a large gap, the conventional channel estimation techniques in practical ranges of signal-to-noise ratio (SNR). In addition, a near-optimal performance is achieved using only few downlink pilot measurements.