摘要翻译：
考虑到massive MIMO中昂贵的射频链路、巨大的训练开销和反馈负担问题，本文提出了一种在自适应部分连接射频预编码结构(PRPS)下的混合时间尺度分组混合预编码(MPHP)方案，其中射频预编码器使用自适应连接网络(ACN)和M个模拟移相器(APSs)，其中M是基站(BS)的天线数。利用混合时间级信道状态信息(CSI)结构，将ACN和APSs的联合设计问题归结为统计信噪比(SSLNR)最大化问题，并提出了一种启发式群射频预编码(GRFP)算法来提供近似最优解。仿真结果表明，与传统的混合预编码方案相比，该方案具有更好的能量效率(EE)和更低的硬件成本、CSI信令开销和计算复杂度。
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英文标题：
《Mixed-timescale Per-group Hybrid Precoding for Multiuser Massive MIMO
  Systems》
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作者：
Yinglei Teng, Min Wei, An Liu, Vincent Lau and Yong Zhang
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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：
  Considering the expensive radio frequency (RF) chain, huge training overhead and feedback burden issues in massive MIMO, in this letter, we propose a mixed-timescale per-group hybrid precoding (MPHP) scheme under an adaptive partially-connected RF precoding structure (PRPS), where the RF precoder is implemented using an adaptive connection network (ACN) and M analog phase shifters (APSs), where M is the number of antennas at the base station (BS). Exploiting the mixed-time stage channel state information (CSI) structure, the joint-design of ACN and APSs is formulated as a statistical signal-to-leakage-and-noise ratio (SSLNR) maximization problem, and a heuristic group RF precoding (GRFP) algorithm is proposed to provide a near-optimal solution. Simulation results show that the proposed design advances at better energy efficiency (EE) and lower hardware cost, CSI signaling overhead and computational complexity than the conventional hybrid precoding (HP) schemes.
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PDF链接：
https://arxiv.org/pdf/1803.07731