摘要翻译：
针对大规模多输入多输出(MIMO)系统，研究了一种基于期望传播(EP)算法的turbo软检测器。对于高阶调制和/或大量天线，MIMO系统中的最佳检测在计算上变得不可行。在这种情况下，线性最小均方误差(LMMSE)表现出低复杂度和良好的性能，但远未达到最优。为了提高性能，可以使用EP算法。在本文中，我们回顾了以往的基于EP的检测器，并从复杂度和性能方面改进了它们的估计。具体来说，我们通过将一致先验替换为非一致先验来改善自迭代EP级的收敛性，从而更好地表征turbo过程开始时解码器返回的信息。我们还回顾了EP参数，以避免使用高阶调制时的不稳定性和降低计算复杂度。仿真结果表明，与以往文献中发现的方法相比，这种新型检测器具有较强的鲁棒性和更高的性能。实验结果还表明，在信道状态信息不完全的情况下，该检测器具有较好的鲁棒性。
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英文标题：
《Self and turbo iterations for MIMO receivers and large-scale systems》
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作者：
Irene Santos and Juan Jos\'e Murillo-Fuentes
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最新提交年份：
2019
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  We investigate a turbo soft detector based on the expectation propagation (EP) algorithm for large-scale multiple-input multiple-output (MIMO) systems. Optimal detection in MIMO systems becomes computationally unfeasible for high-order modulations and/or large number of antennas. In this situation, the linear minimum mean square error (LMMSE) exhibits a low-complexity with a good performance, however far from optimal. To improve the performance, the EP algorithm can be used. In this paper, we review previous EP-based detectors and enhance their estimation in terms of complexity and performance. Specifically, we improve the convergence of the self-iterated EP stage by replacing the uniform prior by a non-uniform one, which better characterizes the information returned by the decoder once the turbo procedure starts. We also review the EP parameters to avoid instabilities when using high-order modulations and to reduce the computational complexity. Simulation results illustrate the robustness and enhanced performance of this novel detector in comparison with previous approaches found in the literature. Results also show that the proposed detector is robust in the presence of imperfect channel state information (CSI).
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PDF链接：
https://arxiv.org/pdf/1805.05065