发帖
雷达卡
摘要翻译：
本文研究了采用线性最大比合并检测器的多小区、大容量、多输入、多输出系统的频谱效率。本文首先给出了有效信干噪比(SINR)的最小二乘法和最小均方误差(MMSE)估计方法的闭式表达式，它们分别适用于任意数目的基站天线M$和任意rican$k$因子。另外，当Ricean$k$-因子等于零时，所得结果可以在瑞利衰落条件下具体说明。本文在高m和高k因子条件下，导出了有效信干噪比的新的精确渐近表达式。相应的分析表明，当考虑无穷m$和无穷Ricean$k$因子时，导频污染被MMSE估计器去除，而在其余情况下导频污染现象仍然存在。通过Monte-Carlo模拟验证了所有理论结果。
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英文标题：
《Spectral Efficiency Analysis of Multi-Cell Massive MIMO Systems with
Ricean Fading》
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作者：
Pei Liu, Kai Luo, Da Chen, Tao Jiang, Michail Matthaiou
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最新提交年份：
2018
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Information Theory 信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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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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一级分类：Mathematics 数学
二级分类：Information Theory 信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
This paper investigates the spectral efficiency of multi-cell massive multiple-input multiple-output systems with Ricean fading that utilize the linear maximal-ratio combining detector. We firstly present closed-form expressions for the effective signal-to-interference-plus-noise ratio (SINR) with the least squares and minimum mean squared error (MMSE) estimation methods, respectively, which apply for any number of base-station antennas $M$ and any Ricean $K$-factor. Also, the obtained results can be particularized in Rayleigh fading conditions when the Ricean $K$-factor is equal to zero. In the following, novel exact asymptotic expressions of the effective SINR are derived in the high $M$ and high Ricean $K$-factor regimes. The corresponding analysis shows that pilot contamination is removed by the MMSE estimator when we consider both infinite $M$ and infinite Ricean $K$-factor, while the pilot contamination phenomenon persists for the rest of cases. All the theoretical results are verified via Monte-Carlo simulations.
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PDF链接：
https://arxiv.org/pdf/1808.08519
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