摘要翻译：
在本文中，我们研究了一个无授权的大规模设备多址(MaDMA)系统中的多用户检测(MUD)问题，其中大量单天线用户设备向多天线基站(BS)发送零星数据。具体来说，我们分别针对时隙和非时隙无授权MaDMA系统提出了两种MUD方案，即随机稀疏学习多用户检测（RSL-MUD）和结构化稀疏学习多用户检测（SSL-MUD）。在时隙RSL-MUD方案中，活动用户生成和传输具有随机稀疏性的数据包。在非时隙SSL-MUD方案中，我们引入了一个基于滑动窗口的检测框架，每个观察窗口中的用户信号自然呈现结构化稀疏性。我们表明，通过利用嵌入在用户信号中的稀疏性，我们可以在单个阶段恢复用户活动状态、信道和用户数据，而无需使用导频信号进行信道估计和/或活动用户识别。为此，我们提出了一种基于消息传递的统计推断框架，用于基站在不知道用户身份和信道状态信息的情况下盲目检测用户数据。仿真结果表明，我们的RSL-MUD和SSL-MUD方案在降低传输开销和改善系统误码性能方面都明显优于同类方案。
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英文标题：
《Sparsity Learning Based Multiuser Detection in Grant-Free Massive-Device
  Multiple Access》
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作者：
Tian Ding, Xiaojun Yuan, and Soung Chang Liew
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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：
  In this work, we study the multiuser detection (MUD) problem for a grant-free massive-device multiple access (MaDMA) system, where a large number of single-antenna user devices transmit sporadic data to a multi-antenna base station (BS). Specifically, we put forth two MUD schemes, termed random sparsity learning multiuser detection (RSL-MUD) and structured sparsity learning multiuser detection (SSL-MUD) for the time-slotted and non-time-slotted grant-free MaDMA systems, respectively. In the time-slotted RSL-MUD scheme, active users generate and transmit data packets with random sparsity. In the non-time-slotted SSL-MUD scheme, we introduce a sliding-window-based detection framework, and the user signals in each observation window naturally exhibit structured sparsity. We show that by exploiting the sparsity embedded in the user signals, we can recover the user activity state, the channel, and the user data in a single phase, without using pilot signals for channel estimation and/or active user identification. To this end, we develop a message-passing based statistical inference framework for the BS to blindly detect the user data without any prior knowledge of the identities and the channel state information (CSI) of the active users. Simulation results show that our RSL-MUD and SSL-MUD schemes significantly outperform their counterpart schemes in both reducing the transmission overhead and improving the error behavior of the system.
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PDF链接：
https://arxiv.org/pdf/1807.10911