Joint Port Selection Based Channel Acquisition for FDD Cell-Free Massive MIMO

TL;DR

This paper introduces a novel joint port selection scheme for FDD cell-free massive MIMO that reduces CSI feedback overhead and enhances sum-rate performance using eigenvalue decomposition, greedy algorithms, and deep learning.

Contribution

It proposes a joint port selection and feedback scheme with eigenvalue decomposition, a greedy algorithm, and a deep learning approach for FDD cell-free massive MIMO systems.

Findings

The proposed schemes outperform existing methods in sum-rate.

Eigenvalue decomposition reduces feedback overhead effectively.

Deep learning enhances adaptation to fast-varying channels.

Abstract

In frequency division duplexing (FDD) cell-free massive MIMO, the acquisition of the channel state information (CSI) is very challenging because of the large overhead required for the training and feedback of the downlink channels of multiple cooperating base stations (BSs). In this paper, for systems with partial uplink-downlink channel reciprocity, and a general spatial domain channel model with variations in the average port power and correlation among port coefficients, we propose a joint-port-selection-based CSI acquisition and feedback scheme for the downlink transmission with zero-forcing precoding. The scheme uses an eigenvalue-decomposition-based transformation to reduce the feedback overhead by exploring the port correlation. We derive the sum-rate of the system for any port selection. Based on the sum-rate result, we propose a low-complexity greedy-search-based joint port selection (GS-JPS) algorithm. Moreover, to adapt to fast time-varying scenarios, a supervised deep learning-enhanced joint port selection (DL-JPS) algorithm is proposed. Simulations verify the effectiveness of our proposed schemes and their advantage over existing port-selection channel acquisition schemes.