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.

翻译：在频分双工（FDD）无小区大规模MIMO系统中，由于多个协作基站（BS）下行链路信道训练与反馈所需开销巨大，信道状态信息（CSI）的获取极具挑战性。针对具有部分上下行信道互易性、且平均导频功率与导频系数相关性存在变化的通用空间域信道模型，本文提出了一种基于联合导频选择的CSI获取与反馈方案，用于采用迫零预编码的下行传输。该方案通过基于特征值分解的变换，利用导频相关性降低反馈开销。我们推导了任意导频选择下系统的和速率，并基于该和速率结果提出了一种低复杂度的贪心搜索联合导频选择（GS-JPS）算法。此外，为适应快时变场景，提出了一种基于监督深度学习的增强型联合导频选择（DL-JPS）算法。仿真验证了所提方案的有效性，以及相较于现有导频选择信道获取方案的性能优势。