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Downlink Precoding for DP-UPA FDD Massive MIMO via Multi-Dimensional Active Channel Sparsification
IEEE Transactions on Wireless Communications ( IF 10.4 ) Pub Date : 2022-02-24 , DOI: 10.1109/twc.2022.3152002
Han Yu , Xinping Yi , Giuseppe Caire

In this paper, we consider user selection and downlink precoding for an over-loaded single-cell massive multiple-input multiple-output (MIMO) system in frequency division duplexing (FDD) mode, where the base station is equipped with a dual-polarized uniform planar array (DP-UPA) and serves a large number of single-antenna users. Due to the absence of uplink-downlink channel reciprocity and the high-dimensionality of channel matrices, it is extremely challenging to design downlink precoders using closed-loop channel probing and feedback with limited spectrum resource. To address these issues, a novel methodology – active channel sparsification (ACS) – has been proposed recently in the literature for uniform linear array (ULA) to design sparsifying precoders, which substantially reduces channel feedback overhead. Pushing forward this line of research, we aim to facilitate the potential deployment of ACS in practical FDD massive MIMO systems, by extending it from ULA to DP-UPA with explicit user selection and making the current ACS implementation simplified. To this end, by leveraging Toeplitz matrix theory, we start with the spectral properties of channel covariance matrices from the lens of their matrix-valued spectral density function. Inspired by these properties, we extend the original ACS using scalar-weight bipartite graph representation to the matrix-weight counterpart. Building upon such matrix-weight bipartite graph representation, we propose a multi-dimensional ACS (MD-ACS) method, which is a generalization of original ACS formulation and is more suitable for DP-UPA antenna configurations. The nonlinear integer program formulation of MD-ACS can be classified as a generalized multi-assignment problem (GMAP), for which we propose a simple yet efficient greedy algorithm to solve it. Simulation results demonstrate the performance improvement of the proposed MD-ACS with greedy algorithm over the state-of-the-art methods based on the QuaDRiGa channel models.

中文翻译:

基于多维主动信道稀疏化的 DP-UPA FDD Massive MIMO 下行链路预编码

在本文中,我们考虑了频分双工(FDD)模式下过载单小区大规模多输入多输出(MIMO)系统的用户选择和下行链路预编码,其中基站配备了双极化统一平面阵列(DP-UPA)并服务于大量单天线用户。由于不存在上下行信道互易性和信道矩阵的高维性,使用有限频谱资源的闭环信道探测和反馈设计下行预编码器极具挑战性。为了解决这些问题,最近在文献中提出了一种新颖的方法——主动信道稀疏化 (ACS),用于均匀线性阵列 (ULA) 来设计稀疏预编码器,这大大减少了信道反馈开销。推进这一研究方向,我们的目标是促进 ACS 在实际 FDD 大规模 MIMO 系统中的潜在部署,方法是通过明确的用户选择将其从 ULA 扩展到 DP-UPA,并简化当前的 ACS 实施。为此,通过利用 Toeplitz 矩阵理论,我们从通道协方差矩阵的矩阵值谱密度函数的透镜的谱特性开始。受这些属性的启发,我们将使用标量权二分图表示的原始 ACS 扩展到矩阵权重对应物。基于这种矩阵权重二分图表示,我们提出了一种多维 ACS(MD-ACS)方法,它是原始 ACS 公式的推广,更适合 DP-UPA 天线配置。MD-ACS的非线性整数规划公式可以归类为广义多分配问题(GMAP),为此我们提出了一种简单而有效的贪心算法来解决它。仿真结果证明了所提出的 MD-ACS 与基于 QuaDRiGa 通道模型的最新方法相比具有贪婪算法的性能改进。
更新日期:2022-02-24
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