Guest Editorial: Massive Machine-Type Communications for IoT

Liang Liu, Erik G. Larsson, Petar Popovski, Giuseppe Caire, Xiaoming Chen, Saeed R. Khosravirad
2021 IEEE wireless communications  
F uture wireless cellular networks are envisioned to not only enhance broadband access for human-centric applications, but also off er massive connectivity across tens of billions of devices for machine-centric applications empowered by Internet of Things (IoT) technologies (e.g., smart factory and smart city). To embrace the forthcoming era of IoT, the fi fth-generation (5G) cellular communication standard has already identifi ed massive machine-type communications (mMTC) as a key use case in
more » ... uture networks. Contrary to human-type high-speed communications, the focus of IoT communications is on providing connectivity to tens of billions of devices with high energy effi ciency, low latency, and high reliability. Consequently, 5G and beyond cellular communication technologies call for radically innovative solutions to support mMTC. Motivated by the crucial role of mMTC in IoT and the dramatically new challenges arising from mMTC as compared to human-type communication, this Feature Topic aims to bring together researchers, industry practitioners, and individuals working on the related areas to share their new ideas, latest fi ndings, and state-of-the-art results. The article "Over-the-Air Computing for Wireless Data Aggregation in Massive IoT" by Zhu et al. investigates wireless data aggregation for new IoT applications such as distributed sensing, learning, and control. In particular, the authors leverage the over-the-air-computation (AirComp) technique, which turns the air into a computer for computing and communicating functions of distributed data at many devices by exploiting the waveform superposition property of multiple access channels. This article introduces several advanced AirComp techniques, including power control, MIMO AirComp, multi-cell AirComp, etc., to effi ciently realize over-the-air aggregation of data simultaneously transmitted by devices. The article "Deep Learning-Enhanced NOMA Transceiver Design for Massive MTC: Challenges, State-of-the-Art, and Future Directions" by Ye et al. identifi es non-orthogonal multiple access (NOMA) as a key enabler to meet various requirements in mMTC, considering the fact that a huge number of IoT devices compete for limited resources. The authors present a deep neural network (DNN) to unify the signal processing architecture and the multiuser receiver in both the data and model-driven approaches, based on which end-to-end communication is optimized. Then, the integration of non-orthogonal communication and neural computation is investigated to achieve high-performance communication with low cost. The article "Massive Machine-Type Communication and Satellite Integration for Remote Areas" by Ullah et al. points out that remote areas are still poorly covered by the conventional terrestrial network due to technical and economic reasons, and a satellite network is an appealing solution to provide mMTC to remote areas in the future. The authors propose a novel architecture for integrating low Earth orbit (LEO) satellites with low-power wide-area network (LPWAN) technology and discuss the relevant changes for the protocol and how these can be addressed. A simulation model is
doi:10.1109/mwc.2021.9535445 fatcat:nxqkvtgs4rfozp553la4kmwxua