Massive Uncoordinated Multiple Access for Beyond 5G

• Published in: IEEE transactions on wireless communications 2022-05, Vol.21 (5), p.2969-2986
• Main Authors: Mohammadkarimi, Mostafa, Dobre, Octavia A., Win, Moe Z.
• Format: Article
• Language: English
• Subjects: Algorithms; beyond 5G; Channel estimation; Codes; Communications traffic; Electronic devices; Internet of Things; Internet-of-Things (IoT); Machine learning; massive machine-type communications (mMTC); Matching pursuit algorithms; Multiple access; multiple antennas; Multiuser detection; nonlinear multiuser detection; Packet transmission; Signal reconstruction; sparse signal reconstruction; sporadic transmission; Uplink; uplink multiple access; Uplinking; Wireless communication; Wireless communication systems
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2021.3117256

Existing wireless communication systems have been mainly designed to provide substantial gain in terms of data rates. However, 5G and Beyond will depart from this scheme, with the objective not only to provide services with higher data rates. One of the main goals is to support massive machine-type communications (mMTC) in the Internet-of-Things (IoT) applications. Supporting massive uplink communications for devices with sporadic traffic pattern and short-packet size, as it is in many mMTC use cases, is a challenging task, particularly when the control signaling is not negligible in size compared to the payload. In addition, channel estimation becomes challenging for sporadic and short-packet transmission due to the limited number of employed pilots. In this paper, a new uplink multiple access (MA) scheme is proposed for mMTC, which can support a large number of uncoordinated IoT devices with short-packet and sporadic traffic. The proposed uplink MA scheme removes the overheads associated with the device identifier as well as pilots and preambles related to channel estimation. An alternative mechanism for device identification (DI) is employed, where a unique spreading code is dedicated to each IoT device as identifier. This unique code is simultaneously used for the spreading purpose and DI. Two IoT DI algorithms which employ sparse signal reconstruction methods are proposed to determine the active IoT devices prior to data detection. Specifically, the Bayesian information criterion model order selection method is employed to develop an IoT DI algorithm for unknown and time-varying activity rate. Our proposed MA scheme benefits from a new non-coherent nonlinear multiuser detection algorithm designed on the basis of unsupervised machine learning techniques to enable data detection without a priori knowledge on channel state information. For performance improvement, an extension to multiple receive antennas through hard decision combining is proposed. The effectiveness of the proposed MA scheme for known and unknown activity rate and high overloading factor is supported by simulation results.