Year: 2025
Type: article
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.
Authors: Rajesh Shrestha, Mingjie Shao, Mingyi Hong, Wing-Kin Ma, Xiao Fu
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G311665065, G3265829877, Collaborative Research: CIF: Small: Inverse Reinforcement Learning with Heterogeneous Data: Estimation Algorithms with Finite Time and Sample Guarantees, CIF: Small: Latent Neural Factor Models for Radio Cartography From Bits, Bi-Level Optimization for Hierarchical Machine Learning Problems: Models, Algorithms and Applications
OpenAlex ID: W4412836718