摘要翻译：
在时分双工(TDD)系统中，大规模多输入多输出(MIMO)依赖信道互易性获得下行(DL)信道状态信息(CSI)和上行(UL)信道状态信息(CSI)。实际上，在基站(BS)不同天线之间的射频(RF)模拟电路中的不匹配破坏了端到端的UL和DL信道互易性。为了避免massive MIMO带来的严重性能下降，必须对天线进行校准。在TDD massive MIMO系统中，为了补偿RF增益失配和恢复信道互易性，有许多校准方案。本文重点研究了不同BS天线通过硬件传输线互连的内部自校准方案。首先，我们研究了任意互连策略下的校准性能。其次，我们得到了在只有(M-1)条传输线的BS上每个互连策略的闭式Cramer-Rao下界(CRLB)表达式，M表示BS天线的总数。在此基础上，我们进一步证明了星型互连策略对于内部自校准是最优的，因为它的CRLB最小。此外，我们还提出了有效的递归算法来推导所有校准系数的相应最大似然估计。数值模拟结果也证实了我们的理论分析和结果。
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英文标题：
《Interconnection Strategies for Self-Calibration of Large Scale Antenna
  Arrays》
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作者：
Hanyu Zhu, Fuqian Yang, Zhaowei Zhu, and Xiliang Luo
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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：
  In time-division duplexing (TDD) systems, massive multiple-input multiple-output (MIMO) relies on the channel reciprocity to obtain the downlink (DL) channel state information (CSI) with the uplink (UL) CSI. In practice, the mismatches in the radio frequency (RF) analog circuits among different antennas at the base station (BS) break the end-to-end UL and DL channel reciprocity. Antenna calibration is necessary to avoid the severe performance degradation with massive MIMO. Many calibration schemes are available to compensate the RF gain mismatches and restore the channel reciprocity in TDD massive MIMO systems. In this paper, we focus on the internal self-calibration scheme where different BS antennas are interconnected via hardware transmission lines. First, we study the resulting calibration performance for an arbitrary interconnection strategy. Next, we obtain closed-form Cramer-Rao lower bound (CRLB) expressions for each interconnection strategy at the BS with only (M-1) transmission lines and M denotes the total number of BS antennas. Basing on the derived results, we further prove that the star interconnection strategy is optimal for internal self-calibration due to its lowest CRLB. In addition, we also put forward efficient recursive algorithms to derive the corresponding maximum-likelihood (ML) estimates of all the calibration coefficients. Numerical simulation results are also included to corroborate our theoretical analyses and results.
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PDF链接：
https://arxiv.org/pdf/1709.07206