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雷达卡
摘要翻译：
基于奇异值分解(SVD)的波束形成与滤波器组多载波偏移正交幅度调制（FBMC/OQAM）相结合的方法至今尚未成功。这种组合的困难在于，即使在中等频率选择性的信道下，波束形成器也可能经历相邻子信道之间的显著变化，从而破坏FBMC/OQAM实值符号之间的正交性。本文从两个方面来解决这个问题：（1）基于扩频FBMC（FS-FBMC）结构，采用SVD-FS-FBMC结构来支持更细粒度的频域波束形成，即在FS-FBMC音调上进行波束形成，而不是在子信道上进行波束形成；ii）提出了一种基于SVD-FS-FBMC的信道平滑准则和方法，改进的波束形成和平滑方法大大提高了波束形成的平滑性，有效地抑制了ICI/ISI泄漏，并在IEEE 802.11n无线局域网环境下进行了仿真，结果表明，在频率选择性信道下，SVD-FS-FBMC系统的误码率性能与正交频分复用(OFDM)系统相近。
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英文标题：
《Smoothed SVD-based Beamforming for FBMC/OQAM Systems Based on Frequency
Spreading》
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作者：
Yu Qiu, Daiming Qu, Da Chen, and Tao Jiang
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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：
The combination of singular value decomposition (SVD)-based beamforming and filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) has not been successful to date. The difficulty of this combination is that, the beamformers may experience significant changes between adjacent subchannels, therefore destroy the orthogonality among FBMC/OQAM real-valued symbols, even under channels with moderate frequency selectivity. In this paper, we address this problem from two aspects: i) an SVD-FS-FBMC architecture is adopted to support beamforming with finer granularity in frequency domain, based on the frequency spreading FBMC (FS-FBMC) structure, i.e., beamforming on FS-FBMC tones rather than on subchannels; ii) criterion and methods are proposed to smooth the beamformers from tone to tone. The proposed finer beamforming and smoothing greatly improve the smoothness of beamformers, therefore effectively suppress the leaked ICI/ISI. Simulations are conducted under the scenario of IEEE 802.11n wireless LAN. Results show that the proposed SVD-FS-FBMC system shares close BER performance with its orthogonal frequency division multiplexing (OFDM) counterpart under the frequency selective channels.
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PDF链接：
https://arxiv.org/pdf/1806.06994
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