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摘要翻译：
在多输入多输出(MIMO)干扰广播信道(IBC)中，使加权和速率(WSR)最大化的最优波束形成器(BFs)一直是一个重要的研究领域。在实际情况下，由于发射机(CSIT)只有部分信道状态信息可用，这一问题变得更加复杂。因此，最优化度量的典型选择是期望加权和率(EWSR)。然而，期望算子的存在使得优化成为一项艰巨的任务。另一方面，对于massive MIMO（MaMIMO）的特殊但重要的特殊情况，EWSR收敛于基于期望信号协方差的期望干扰协方差WSR（ESEI-WSR）,该度量更易于优化。最近，[1]考虑了多用户多输入单输出(MISO)场景，提出用ESEI-WSR近似EWSR。然后，他们导出了这个近似的一个常数界。本文对有限天线尺寸下EWSR和ESEI-WSR准则之间的差距进行了细致的分析。
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英文标题：
《A Refined Analysis of the Gap between Expected Rate for Partial CSIT and
the Massive MIMO Rate Limit》
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作者：
Kalyana Gopala, Dirk Slock
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最新提交年份：
2017
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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：
Optimal BeamFormers (BFs) that maximize the Weighted Sum Rate (WSR) for a Multiple-Input Multiple-Output (MIMO) interference broadcast channel (IBC) remains an important research area. Under practical scenarios, the problem is compounded by the fact that only partial channel state information at the transmitter (CSIT) is available. Hence, a typical choice of the optimization metric is the Expected Weighted Sum Rate (EWSR). However, the presence of the expectation operator makes the optimization a daunting task. On the other hand, for the particular, but significant, special case of massive MIMO (MaMIMO), the EWSR converges to Expected Signal covariance Expected Interference covariance based WSR (ESEI-WSR) and this metric is more amenable to optimization. Recently, [1] considered a multi-user Multiple-Input Single-Output (MISO) scenario and proposed approximating the EWSR by ESEI-WSR. They then derived a constant bound for this approximation. This paper performs a refined analysis of the gap between EWSR and ESEI-WSR criteria for finite antenna dimensions.
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PDF链接：
https://arxiv.org/pdf/1710.09572
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