基于矢量-近似-信息传递的 MIMO-OFDM 水下声学通信信道估计

IF 3.8 2区 工程技术 Q1 ENGINEERING, CIVIL
Wenxuan Chen;Jun Tao;Lu Ma;Gang Qiao
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引用次数: 0

摘要

准确的信道估计(CE)对正交频分复用(OFDM)水下声学(UWA)通信的性能至关重要,尤其是在多输入多输出(MIMO)情况下。在本文中,我们探讨了矢量近似消息传递(VAMP)与期望最大化(EM)的结合,以获得 MIMO OFDM UWA 通信的 CE。EM-VAMP-CE 方案是通过对信道脉冲响应采用伯努利-高斯(BG)先验分布来开发的,并通过 EM 算法学习 BG 先验分布的超参数。通过合成数据和两次海上 UWA 通信实验中收集的真实数据,对 EM-VAMP-CE 的性能进行了评估。结果表明,与现有的 CE 方法相比,EM-VAMP-CE 实现了更好的性能-复杂性权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vector-Approximate-Message-Passing-Based Channel Estimation for MIMO-OFDM Underwater Acoustic Communications
Accurate channel estimation (CE) is critical to the performance of orthogonal frequency-division multiplexing (OFDM) underwater acoustic (UWA) communications, especially under multiple-input multiple-output (MIMO) scenarios. In this article, we explore vector approximate message passing (VAMP) coupled with expectation-maximization (EM) to obtain CE for MIMO OFDM UWA communications. The EM-VAMP-CE scheme is developed by employing a Bernoulli–Gaussian (BG) prior distribution for the channel impulse response, and hyperparameters of the BG prior distribution are learned via the EM algorithm. The performance of the EM-VAMP-CE is evaluated through both synthesized data and real data collected in two at-sea UWA communication experiments. It is shown that the EM-VAMP-CE achieves better performance–complexity tradeoff compared with the existing CE methods.
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来源期刊
IEEE Journal of Oceanic Engineering
IEEE Journal of Oceanic Engineering 工程技术-工程:大洋
CiteScore
9.60
自引率
12.20%
发文量
86
审稿时长
12 months
期刊介绍: The IEEE Journal of Oceanic Engineering (ISSN 0364-9059) is the online-only quarterly publication of the IEEE Oceanic Engineering Society (IEEE OES). The scope of the Journal is the field of interest of the IEEE OES, which encompasses all aspects of science, engineering, and technology that address research, development, and operations pertaining to all bodies of water. This includes the creation of new capabilities and technologies from concept design through prototypes, testing, and operational systems to sense, explore, understand, develop, use, and responsibly manage natural resources.
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