A Versatile Pilot Design Scheme for FDD Systems Utilizing Gaussian Mixture Models

TL;DR

This paper introduces a GMM-based pilot design scheme for FDD MIMO systems that improves channel estimation efficiency and adaptability, reducing pilot overhead and enhancing performance in multi-user scenarios.

Contribution

The paper presents a novel GMM-based pilot design framework for FDD MIMO systems, enabling offline training, codebook construction, and adaptive pilot optimization without online re-training.

Findings

Superior performance over existing methods in simulations

Reduces the number of pilots needed for effective estimation

Adapts seamlessly to various SNR levels and user configurations

Abstract

In this work, we propose a Gaussian mixture model (GMM)-based pilot design scheme for downlink (DL) channel estimation in single- and multi-user multiple-input multiple-output (MIMO) frequency division duplex (FDD) systems. In an initial offline phase, the GMM captures prior information during training, which is then utilized for pilot design. In the single-user case, the GMM is utilized to construct a codebook of pilot matrices and, once shared with the mobile terminal (MT), can be employed to determine a feedback index at the MT. This index selects a pilot matrix from the constructed codebook, eliminating the need for online pilot optimization. We further establish a sum conditional mutual information (CMI)-based pilot optimization framework for multi-user MIMO (MU-MIMO) systems. Based on the established framework, we utilize the GMM for pilot matrix design in MU-MIMO systems. The analytic representation of the GMM enables the adaptation to any signal-to-noise ratio (SNR) level and pilot configuration without re-training. Additionally, an adaption to any number of MTs is facilitated. Extensive simulations demonstrate the superior performance of the proposed pilot design scheme compared to state-of-the-art approaches. The performance gains can be exploited, e.g., to deploy systems with fewer pilots.