摘要翻译：
下一代无线网络的目标是在频谱效率(SE)和能量效率(EE)方面提供实质性的改善。Massive MIMO已经被证明是一种可行的技术，它通过使用多个基站天线对多个用户进行空间复用来实现这些目标。Massive MIMO在多小区系统中的一个潜在限制是导频污染，导频污染是信道估计过程中由相邻小区重复使用导频引起的干扰引起的。减少导频污染的一种标准方法，称为规则导频(RP)，是在分离传输数据和导频符号的同时调整导频序列的长度。另一种称为叠加导频(SP)的方法发送导频和数据符号的叠加。这允许使用更长的飞行员，反过来，减少飞行员污染。考虑了多小区Massive MIMO网络的上行链路，采用最大比合并检测，并从SE和EE两个方面对RP和SP进行了比较。为此，我们导出了在实际随机BS部署下具有SP的严格闭式可达速率。我们证明了附加的相干和非相干干扰抵消了SP对导频污染的影响。数值结果表明，当这两种方法都被优化后，在实际情况下，RP达到了与SE和EE相类似的SE和EE。
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英文标题：
《Spectral and Energy Efficiency of Superimposed Pilots in Uplink Massive
  MIMO》
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作者：
Daniel Verenzuela, Emil Bj\"ornson, Luca Sanguinetti
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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：
  Next generation wireless networks aim at providing substantial improvements in spectral efficiency (SE) and energy efficiency (EE). Massive MIMO has been proved to be a viable technology to achieve these goals by spatially multiplexing several users using many base station (BS) antennas. A potential limitation of Massive MIMO in multicell systems is pilot contamination, which arises in the channel estimation process from the interference caused by reusing pilots in neighboring cells. A standard method to reduce pilot contamination, known as regular pilot (RP), is to adjust the length of pilot sequences while transmitting data and pilot symbols disjointly. An alternative method, called superimposed pilot (SP), sends a superposition of pilot and data symbols. This allows to use longer pilots which, in turn, reduces pilot contamination. We consider the uplink of a multicell Massive MIMO network using maximum ratio combining detection and compare RP and SP in terms of SE and EE. To this end, we derive rigorous closed-form achievable rates with SP under a practical random BS deployment. We prove that the reduction of pilot contamination with SP is outweighed by the additional coherent and non-coherent interference. Numerical results show that when both methods are optimized, RP achieves comparable SE and EE to SP in practical scenarios.
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PDF链接：
https://arxiv.org/pdf/1709.07722