摘要翻译:
发射天线数目的盲计数和多输入多输出(MIMO)方案的盲辨识是MIMO信号识别的两个关键步骤,无论在军事还是商业应用中都是如此。传统的方法把它们看作两个独立的问题,分别是源数枚举和空时冗余的存在性检测。本文提出了一种同时确定发射天线数目和MIMO方案的联合盲辨识算法。通过重构接收信号,基于信号子空间秩推导出三个子空间秩特征,用于确定发射天线数目和识别空时冗余。然后,采用基于Gerschgorin半径的方法和前馈神经网络来计算这三个特征,并利用最小加权范数-1距离度量进行决策。特别地,我们的方法可以识别其他的MIMO方案,这是大多数以前的工作没有考虑的,并且兼容于单载波和正交频分复用(OFDM)系统。仿真结果验证了该方法在单载波和OFDM系统中的可行性,并证明了该方法在较短的观测周期内和可接受的复杂度下具有良好的识别性能。
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英文标题:
《Joint Blind Identification of the Number of Transmit Antennas and MIMO
Schemes Using Gerschgorin Radii and FNN》
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作者:
Mingjun Gao, Yongzhao Li, Octavia A. Dobre, Naofal Al-Dhahir
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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:
Blind enumeration of the number of transmit antennas and blind identification of multiple-input multiple-output (MIMO) schemes are two pivotal steps in MIMO signal identification for both military and commercial applications. Conventional approaches treat them as two independent problems, namely the source number enumeration and the presence detection of space-time redundancy, respectively. In this paper, we develop a joint blind identification algorithm to determine the number of transmit antennas and MIMO scheme simultaneously. By restructuring the received signals, we derive three subspace-rank features based on the signal subspace-rank to determine the number of transmit antennas and identify space-time redundancy. Then, a Gerschgorin radii-based method and a feed-forward neural network are employed to calculate these three features, and a minimal weighted norm-1 distance metric is utilized for decision making. In particular, our approach can identify additional MIMO schemes, which most previous works have not considered, and is compatible with both single-carrier and orthogonal frequency division multiplexing (OFDM) systems. Simulation results verify the viability of our proposed approach for single-carrier and OFDM systems and demonstrate its favorable identification performance for a short observation period with acceptable complexity.
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PDF链接:
https://arxiv.org/pdf/1803.10849