An Overview of Uplink Access Techniques in Machine-Type Communications

• Published in: IEEE network Vol. 35; no. 3; pp. 246 - 251
• Main Authors: El-Tanab, Manal, Hamouda, Walaa
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3GPP; Cellular communication; Cellular networks; Data communication; Delays; Internet of Things; Machine learning; Network latency; Proposals; Quality of service; Random access; Uplink
• ISSN: 0890-8044; 1558-156X
• DOI: 10.1109/MNET.011.2000513

The bright future of smart cities relies on an effective deployment of IoT technologies. Machine-type communications (MTC) is a major backbone technology that supports connectivity for the Internet of things (IoT). Cellular networks are known to be cost-effective, with ubiquitous coverage that ease the deployment of MTC. However, cellular networks were originally designed for human-centric services with high-cost devices and ever-increasing rate requirements. In contrast, MTC services need to support low-cost, low-energy, massive number of devices. This poses a number of challenges toward the adaptation of current cellular networks to accommodate MTC. This article gives an overview of the conventional random access (RA) scheme of cellular networks and its variants in the literature. However, without discounting the efforts of optimizing the RA scheme, we show that due to the increased collisions and prohibitive overhead, it falls short to support MTC with reduced latency and guaranteed reliability. Alternatively, we discuss different uplink access techniques that are found promising in tackling massive connectivity while avoiding the shortcomings of the conventional RA. Moreover, we discuss how to utilize different future 5G and beyond technologies to efficiently handle massive MTC while pointing out the promising role of machine learning techniques.