超稠密网络的广义稀疏低秩优化
Generalized Sparse and Low-Rank Optimization for Ultra-Dense Networks
摘要：
超密集网络（UDN）是一种有前途的技术，可以进一步发展无线网络并满足5G网络的各种性能要求。UDN拥有丰富的接入点，每个接入点都有通信，计算和存储资源，带来了前所未有的好处，包括显著提高网络频谱效率和能源效率，大大降低延迟以实现新颖的移动应用，以及为物联网（IoT）设备提供大规模接入的能力。然而，如此巨大的希望伴随着巨大的研究挑战。要设计和运作这种拥有各类资源的复杂网络，就需要采用高效和创新的方法。这促使最近引入高度结构化和概括性的网络优化模型。在这篇文章中，我们提出了一些最近提出的大规模稀疏和低秩框架，用于优化UDN，并得到各种激励应用程序的支持。一个特别注意的算法方法来处理非凸目标函数和约束，以及计算的可扩展性。
英文摘要：
Ultra-dense network (UDN) is a promising technology to further evolve wireless networks and meet the diverse performance requirements of 5G networks. With abundant access points, each with communication, computation and storage resources, UDN brings unprecedented benefits, including significant improvement in network spectral efficiency and energy efficiency, greatly reduced latency to enable novel mobile applications, and the capability of providing massive access for Internet of Things (IoT) devices. However, such great promises come with formidable research challenges. To design and operate such complex networks with various types of resources, efficient and innovative methodologies will be needed. This motivates the recent introduction of highly structured and generalizable models for network optimization. In this article, we present some recently proposed large-scale sparse and low-rank frameworks for optimizing UDNs, supported by various motivating applications. A special attention is paid on algorithmic approaches to deal with nonconvex objective functions and constraints, as well as computational scalability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
