摘要翻译：
本稿件已于2017年9月7日提交至《通信交易》；2018年1月10日、2018年3月27日修订；并于2018年4月25日被接受，我们提出了一种新的滤波器型均衡器来改进线性最小均方误差(LMMSE)turbo均衡器的解，其计算复杂度被限制为滤波器长度的二次型。当使用高阶调制和/或大存储器信道时，由于其计算复杂性，最佳BCJR均衡器是不可用的。在这种情况下，滤波器型LMMSE turbo均衡表现出良好的性能相比于其他近似。在本文中，我们证明了在估计后验概率时使用期望传播(EP)可以显著地改进该解。首先，它产生了将发送到信道解码器的外在分布的更精确的估计。其次，与其他基于EP的算法相比，该算法的计算复杂度被限制为有限冲激响应(FIR)长度的二次型。此外，我们回顾了以前的基于EP的turbo均衡实现。我们利用解码器的输出，而不是考虑默认的一致先验。仿真结果表明，无论是否采用turbo均衡，这种新的基于EP的滤波器的性能都明显优于以前EP算法的turbo方法，并改进了LMMSE算法。
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英文标题：
《Turbo EP-based Equalization: a Filter-Type Implementation》
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作者：
Irene Santos, Juan Jos\'e Murillo-Fuentes, Eva Arias-de-Reyna, and
  Pablo M. Olmos
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最新提交年份：
2018
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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：
  This manuscript has been submitted to Transactions on Communications on September 7, 2017; revised on January 10, 2018 and March 27, 2018; and accepted on April 25, 2018   We propose a novel filter-type equalizer to improve the solution of the linear minimum-mean squared-error (LMMSE) turbo equalizer, with computational complexity constrained to be quadratic in the filter length. When high-order modulations and/or large memory channels are used the optimal BCJR equalizer is unavailable, due to its computational complexity. In this scenario, the filter-type LMMSE turbo equalization exhibits a good performance compared to other approximations. In this paper, we show that this solution can be significantly improved by using expectation propagation (EP) in the estimation of the a posteriori probabilities. First, it yields a more accurate estimation of the extrinsic distribution to be sent to the channel decoder. Second, compared to other solutions based on EP the computational complexity of the proposed solution is constrained to be quadratic in the length of the finite impulse response (FIR). In addition, we review previous EP-based turbo equalization implementations. Instead of considering default uniform priors we exploit the outputs of the decoder. Some simulation results are included to show that this new EP-based filter remarkably outperforms the turbo approach of previous versions of the EP algorithm and also improves the LMMSE solution, with and without turbo equalization.
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PDF链接：
https://arxiv.org/pdf/1711.08188