摘要翻译：
海量连通性和低时延是物联网（IoT）实现其所服务的众多设备所需的服务质量（QoS）的两个重要挑战。针对这些挑战，本文提出了一种新的毫米波非正交多址（mmWave-NOMA）传输方案，该方案是为蜂窝机对机(M2M)通信系统设计的，适用于物联网应用。它由一个基站(BS)和许多在蜂窝通信环境中工作的多机类型通信(MTC)设备组成。我们考虑了它的下行链路性能，并假设多个MTC设备共享所提出的mmWave-NOMA传输方案提供的相同通信资源，从而可以支持大规模连接。针对该系统，提出了一种新的MTC配对方案，该方案基于基站与MTC设备之间的距离来设计，旨在降低系统在大规模连接和延迟方面的总开销。特别地，我们考虑了三种不同的MTC设备配对方案，即i）随机近和随机远MTC设备(RNRF)；ii）最近近和最近远MTC设备(NNNF)；iii）最近近和最远MTC设备(NNFF)，对这三种配对方案的性能进行了分析，推导了它们的中断概率和总速率的闭式表达式，并对三种MTC设备配对方案的性能进行了比较研究。
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英文标题：
《Millimeter-Wave NOMA Transmission in Cellular M2M Communications for
  Internet of Things》
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作者：
Tiejun Lv and Yuyu Ma and Jie Zeng and P. Takis Mathiopoulos
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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：
  Massive connectivity and low latency are two important challenges for the Internet of Things (IoT) to achieve the Quality of Service (QoS) provisions required by the numerous devices it is designed to service. Motivated by these challenges, in the paper we introduce a new millimeter-wave non-orthogonal multiple access (mmWave-NOMA) transmission scheme designed for cellular machine-to-machine (M2M) communication systems for IoT applications. It consists of one base station (BS) and numerous multiple machine type communication (MTC) devices operating in a cellular communication environment. We consider its down-link performance and assume that multiple MTC devices share the same communication resources offered by the proposed mmWave-NOMA transmission scheme, which can support massive connectivity. For this system, a novel MTC pairing scheme is introduced the design of which is based upon the distance between the BS and the MTC devices aiming at reducing the system overall overhead for massive connectivity and latency. In particular, we consider three different MTC device pairing schemes, namely i) the random near and the random far MTC devices (RNRF); ii) the nearest near and the nearest far MTC devices (NNNF); and iii) the nearest near and the farthest far MTC device (NNFF). For all three pairing schemes, their performance is analyzed by deriving closed-form expressions of the outage probability and the sum rate. Furthermore, performance comparison studies of the three MTC device pairing schemes have been carried out.
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PDF链接：
https://arxiv.org/pdf/1805.12281