摘要翻译：
由于采用了适当设计的脉冲成形原型滤波器，滤波器组多载波(FBMC)系统可以获得较低的带外(OoB)发射，并且对信道和同步误差具有鲁棒性。然而，这是以较长的滤波尾为代价的，当块大小较小时，这可能会显著降低频谱效率。滤波器输出截断(FOT)可以通过丢弃滤波器尾部来降低系统开销，但也可能通过在接收信号中引入载波间干扰(ICI)和符号间干扰(ISI)项来严重破坏FBMC系统的正交性。结果，信干比(SIR)下降。此外，本征干扰项的存在也是FBMC与多输入多输出(MIMO)相结合的一个障碍。本文从理论上分析了FOT对MIMO-FBMC系统的影响。首先，我们推导了MIMO-FBMC系统的矩阵模型，并利用矩阵模型分析了有限滤波器长度和FOT对系统性能的影响。分析表明FOT可以避免时域开销，但也会在接收符号中引入额外的干扰。为了消除干扰项，我们提出了一种补偿算法，将奇偶重叠因素作为两种不同的情况，在这种情况下，信号以不同的方式受到截断干扰。该补偿算法的一般形式可以补偿MIMO-FBMC块中的所有符号，并可以提高每个符号的SIR值，以便在接收端更好地检测。实验还表明，该算法不需要开销，并且仍然可以获得与无滤波器截断情况相当的误码率性能。
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英文标题：
《Spectrum Efficient MIMO-FBMC System using Filter Output Truncation》
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作者：
Adnan Zafar, Lei Zhang, Pei Xiao and Muhammad Ali Imran
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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：
  Due to the use of an appropriately designed pulse shaping prototype filter, filter bank multicarrier (FBMC) system can achieve low out of band (OoB) emissions and is also robust to the channel and synchronization errors. However, it comes at a cost of long filter tails which may reduce the spectral efficiency significantly when the block size is small. Filter output truncation (FOT) can reduce the overhead by discarding the filter tails but may also significantly destroy the orthogonality of FBMC system, by introducing inter carrier interference (ICI) and inter symbol interference (ISI) terms in the received signal. As a result, the signal to interference ratio (SIR) is degraded. In addition, the presence of intrinsic interference terms in FBMC also proves to be an obstacle in combining multiple input multiple output (MIMO) with FBMC. In this paper, we present a theoretical analysis on the effect of FOT in an MIMO-FBMC system. First, we derive the matrix model of MIMO-FBMC system which is subsequently used to analyze the impact of finite filter length and FOT on the system performance. The analysis reveals that FOT can avoid the overhead in time domain but also introduces extra interference in the received symbols. To combat the interference terms, we then propose a compensation algorithm that considers odd and even overlapping factors as two separate cases, where the signals are interfered by the truncation in different ways. The general form of the compensation algorithm can compensate all the symbols in a MIMO-FBMC block and can improve the SIR values of each symbol for better detection at the receiver. It is also shown that the proposed algorithm requires no overhead and can still achieve a comparable BER performance to the case with no filter truncation.
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PDF链接：
https://arxiv.org/pdf/1711.08842