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摘要翻译：
超密集网络(UDN)是支持5G移动系统的最有前途的技术之一。通过在固定区域内部署更多的小小区，可以显著减小用户和接入点之间的平均距离，从而可以利用密集的空间频率重用。然而，严重的干扰是UDN的主要障碍。大多数贡献依靠合作博弈论来处理干扰。近年来，随着云计算技术的发展，本文提倡将以用户为中心的密集C-RAN思想应用于UDN。在密集C-RAN下，可以调用集中的信号处理来支持CoMP传输。我们总结了密集的以用户为中心的C-RAN的主要挑战。其中最具挑战性的问题是全球CSI对于合作传输的要求。我们只依赖于部分CSI，即集群间大规模CSI来研究这一需求。此外，还考虑了簇内CSI的估计，包括导频分配和鲁棒传输。最后，本文重点介绍了实现以用户为中心的密集C-RAN的几个有希望的研究方向，特别强调了“大数据”技术的应用。
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英文标题：
《User-centric C-RAN Architecture for Ultra-dense 5G Networks: Challenges
and Methodologies》
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作者：
Cunhua Pan, Maged Elkashlan, Jiangzhou Wang, Jinhong Yuan and Lajos
Hanzo
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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：
Ultra-dense networks (UDN) constitute one of the most promising techniques of supporting the 5G mobile system. By deploying more small cells in a fixed area, the average distance between users and access points can be significantly reduced, hence a dense spatial frequency reuse can be exploited. However, severe interference is the major obstacle in UDN. Most of the contributions deal with the interference by relying on cooperative game theory. This paper advocates the application of dense user-centric C-RAN philosophy to UDN, thanks to the recent development of cloud computing techniques. Under dense C-RAN, centralized signal processing can be invoked for supporting CoMP transmission. We summarize the main challenges in dense user-centric C-RANs. One of the most challenging issues is the requirement of the global CSI for the sake of cooperative transmission. We investigate this requirement by only relying on partial CSI, namely, on inter-cluster large-scale CSI. Furthermore, the estimation of the intra-cluster CSI is considered, including the pilot allocation and robust transmission. Finally, we highlight several promising research directions to make the dense user-centric C-RAN become a reality, with special emphasis on the application of the `big data' techniques.
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PDF链接：
https://arxiv.org/pdf/1710.0079
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