Downlink MIMO Channel Estimation from Bits: Recoverability and Algorithm

TL;DR

This paper introduces a novel feedback and estimation framework for downlink MIMO channel estimation in FDD systems, combining compression, dithering, and a specialized ADMM algorithm with harmonic retrieval to improve accuracy and efficiency.

Contribution

It proposes a new feedback scheme with compression and dithering, and develops an ADMM-based algorithm with harmonic retrieval for maximum likelihood estimation of MIMO channels.

Findings

The proposed method accurately recovers MIMO channels under the double directional model.

The compression schemes differ in overhead and computational complexity, offering trade-offs.

Numerical experiments demonstrate the effectiveness of the approach.

Abstract

In frequency division duplex (FDD) massive MIMO systems, a major challenge lies in acquiring the downlink channel state information}\ (CSI) at the base station (BS) from limited feedback sent by the user equipment (UE). To tackle this fundamental task, our contribution is twofold: First, a simple feedback framework is proposed, where a compression and Gaussian dithering-based quantization strategy is adopted at the UE side, and then a maximum likelihood estimator (MLE) is formulated at the BS side. Recoverability of the MIMO channel under the widely used double directional model is established. Specifically, analyses are presented for two compression schemes -- showing one being more overhead-economical and the other computationally lighter at the UE side. Second, to realize the MLE, an alternating direction method of multipliers (ADMM) algorithm is proposed. The algorithm is carefully designed to integrate a sophisticated harmonic retrieval (HR) solver as subroutine, which turns out to be the key of effectively tackling this hard MLE problem.Extensive numerical experiments are conducted to validate the efficacy of our approach.