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摘要翻译：
物理层网络编码(PNC)已经被研究用来服务于无线网络MIMO系统，其回程负载比云无线接入网（Cloud-RAN）和协调多点(CoMP)等方法低得多。本文针对5G无线局域网的高用户密度要求，提出了一种工程应用PNC的设计原则。与适用于双向中继信道的计算转发和PNC设计准则不同，该准则是针对网络MIMO（N-MIMO）系统的上行链路设计的。我们证明了所提出的设计准则保证：1）整个系统运行在二元系统之上；2）在每个接入点使用的PNC功能克服所有奇异衰落状态；3）目的地可以明确地恢复所有源消息，同时总的回程负载保持在最低水平。然后，我们提出了一个两阶段搜索算法来识别最优的PNC映射函数，大大降低了实时计算复杂度。本文研究了不同QAM调制方案下信道估计信息和减少奇异衰落状态数的影响。此外，提出了一种基于查找表机制的次优搜索方法，在性能损失有限的情况下进一步降低计算复杂度。数值结果表明，所提出的方案在减少回程负荷的情况下，实现了较低的停运概率。
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英文标题：
《Wireless Network Coding in Network MIMO: A New Design for 5G and Beyond》
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作者：
Tong Peng, Yi Wang, Alister G. Burr and Mohammad Shikh-Bahaei
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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：
Physical layer network coding (PNC) has been studied to serve wireless network MIMO systems with much lower backhaul load than approaches such as Cloud Radio Access Network (Cloud-RAN) and coordinated multipoint (CoMP). In this paper, we present a design guideline of engineering applicable PNC to fulfil the request of high user densities in 5G wireless RAN infrastructure. Unlike compute-and-forward and PNC design criteria for two-way relay channels, the proposed guideline is designed for uplink of network MIMO (N-MIMO) systems. We show that the proposed design criteria guarantee that 1) the whole system operates over binary system; 2) the PNC functions utilised at each access point overcome all singular fade states; 3) the destination can unambiguously recover all source messages while the overall backhaul load remains at the lowest level. We then develop a two-stage search algorithm to identify the optimum PNC mapping functions which greatly reduces the real-time computational complexity. The impact of estimated channel information and reduced number of singular fade states in different QAM modulation schemes is studied in this paper. In addition, a sub-optimal search method based on lookup table mechanism to achieve further reduced computational complexity with limited performance loss is presented. Numerical results show that the proposed schemes achieve low outage probability with reduced backhaul load.
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PDF链接：
https://arxiv.org/pdf/1801.07061
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