Distributed Massive MIMO With Low Resolution ADCs for Massive Random Access

IF 8.7 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Yuhui Song;Zijun Gong;Yuanzhu Chen;Cheng Li
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引用次数: 0

Abstract

Massive machine-type communications (mMTC), an essential fifth-generation (5G) usage scenario, aims to provide services for a large number of users that intermittently transmit small data packets in smart cities, manufacturing, and agriculture. Massive random access (MRA) emerges as a promising candidate for multiple access in mMTC characterized by the sporadic data traffic. Despite the use of massive multiple-input multiple-output (mMIMO) in MRA to achieve spatial division multiple access and mitigate small-scale fading, existing research endeavors overlook the near-far effect of large-scale fading by assuming perfect power control. In this paper, we present a cost-efficient, effective, and fully distributed solution for MRA to combat large-scale fading, wherein distributed access points (APs) cooperatively detect and serve active users. Each AP is equipped with low resolution analog-to-digital converters (ADCs) for energy-efficient system implementation. Specifically, we derive a rigorous closed-form expression for the uplink achievable rate, considering the impact of non-orthogonal pilots and low resolution ADCs. We also propose a scalable distributed algorithm for user activity detection under flat fading channels, and further adapt it to handle frequency-selective fading in popular orthogonal frequency division multiplexing (OFDM) systems. The proposed solution is fully distributed, since most processing tasks, such as activity detection, channel estimation, and data detection, are localized at each AP. Simulation results demonstrate the significant advantage of distributed systems over co-located systems in accommodating more users while achieving higher activity detection accuracy, and quantify performance loss resulting from the use of low resolution ADCs.
基于低分辨率adc的大规模随机接入分布式大规模MIMO
mMTC (Massive machine-type communications)是5G必不可少的使用场景,旨在为智慧城市、制造业、农业等领域大量用户间歇性传输小数据包提供服务。海量随机接入(MRA)是具有零星数据流量特点的多址通信(mMTC)中一种很有前途的多址接入方式。尽管在MRA中使用了大规模多输入多输出(mMIMO)来实现空分多址和缓解小规模衰落,但现有的研究通过假设完美的功率控制而忽略了大规模衰落的近远效应。在本文中,我们提出了一种经济、有效和完全分布式的MRA解决方案来对抗大规模衰落,其中分布式接入点(ap)协同检测和服务活跃用户。每个AP都配备了低分辨率模数转换器(adc),以实现节能系统。具体来说,考虑到非正交导频和低分辨率adc的影响,我们推导了上行可达速率的严格封闭表达式。我们还提出了一种可扩展的分布式算法用于平坦衰落信道下的用户活动检测,并进一步将其应用于处理常见的正交频分复用(OFDM)系统中的频率选择性衰落。所提出的解决方案是完全分布式的,因为大多数处理任务,如活动检测、信道估计和数据检测,都定位在每个AP上。仿真结果表明,分布式系统在容纳更多用户的同时,实现了更高的活动检测精度,并量化了使用低分辨率adc造成的性能损失。
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来源期刊
IEEE Journal of Selected Topics in Signal Processing
IEEE Journal of Selected Topics in Signal Processing 工程技术-工程:电子与电气
CiteScore
19.00
自引率
1.30%
发文量
135
审稿时长
3 months
期刊介绍: The IEEE Journal of Selected Topics in Signal Processing (JSTSP) focuses on the Field of Interest of the IEEE Signal Processing Society, which encompasses the theory and application of various signal processing techniques. These techniques include filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals using digital or analog devices. The term "signal" covers a wide range of data types, including audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and others. The journal format allows for in-depth exploration of signal processing topics, enabling the Society to cover both established and emerging areas. This includes interdisciplinary fields such as biomedical engineering and language processing, as well as areas not traditionally associated with engineering.
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