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雷达卡
摘要翻译：
提出了一种低复杂度的时域半盲算法来估计和跟踪时变MIMO OFDM信道。首先针对训练模式提出了基于最小均方(LMS)的算法，然后结合决策方向(DD)或自适应Bussgang算法(ABA)技术对算法进行了扩展，适用于盲操作模式。在盲模式下，由于判决误差的存在，LMS算法考虑了较小的步长，并进行了多次信道估计以提高其精度。在盲模式下的每一轮估计中，步长被减小以形成某种退火。仿真了DD LMS和ABA LMS两种方法，并将其与全训练情况进行了比较，以信道估计误差的MSE作为比较准则。结果表明，对于2×4 DD LMS和4×4 ABA LMS算法存在接近全训练情况的估计误差。当然，在某些情况下，前一种所提出的技术性能更好，而在其他情况下，后者更好，因此，在所有信道条件下，它的结合都是非常有趣的。
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英文标题：
《Low Complexity Time Domain Semi-Blind MIMO-OFDM Channel Estimation Using
Adaptive Bussgang Algorithm》
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作者：
Ebrahim Karami and Markku Juntti
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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：
In this paper, a low complexity time domain semi-blind algorithm is proposed to estimate and track the time varying MIMO OFDM channels. First, the proposed least mean squares (LMS) based algorithm is developed for the training mode and then is extended for the blind mode of the operation by combining with the decision direction (DD) or adaptive Bussgang algorithm (ABA) techniques. In the blind mode, because of decision errors, a smaller step size is considered for the LMS algorithm and the channel estimation is run a few times to improve its precision. In each round of the estimation in the blind mode, the step size is decreased to form some kind of annealing. Both DD LMS and ABA LMS techniques are simulated and compared to the full training case and MSE of channel estimation error is considered as comparison criterion. It is shown for 2x4 DD LMS and for 4x4 ABA LMS algorithms present near full training case estimation error. Of course in some scenarios the former proposed technique performs better and in other scenarios the latter is better and therefore combine of it can be very interesting in all channel conditions.
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PDF链接：
https://arxiv.org/pdf/1802.00114
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