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Enabling Grant-Free URLLC: An Overview of Principle and Enhancements by Massive MIMO

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posted on 2021-08-23, 21:55 authored by Jie DingJie Ding, Mahyar Nemati, Shiva Pokhrel, Ok-Sun Park, Jinho Choi, Fumiyuki Adachi
Enabling ultra-reliable low-latency communication (URLLC) with stringent requirements for transmitting data packets (e.g., 99.999% reliability and 1 millisecond latency) presents considerable challenges in uplink transmissions. For each packet transmission over dynamically allocated network radio resources, the conventional random access protocols are based on a request-grant scheme. This induces excessive latency and necessitates reliable control signalling, resulting overhead. To address these problems, grant-free (GF) solutions are proposed in the fifth-generation (5G) new radio (NR). In this paper, an overview and vision of the state-of-the-art in enabling GF URLLC are presented. In particular, we first provide a comprehensive review of NR specifications and techniques for URLLC, discuss underlying principles, and highlight impeding issues of enabling GF URLLC. Furthermore, we briefly explain two key phenomena of massive multiple-input multiple-output (mMIMO) (i.e., channel hardening and favorable propagation) and build several deep insights into how celebrated mMIMO features can be exploited to enhance the performance of GF URLLC. Moving further ahead, we examine the potential of cell-free (CF) mMIMO and analyze its distinctive features and benefits over mMIMO to resolve GF URLLC issues. Finally, we identify future research directions and challenges in enabling GF URLLC with CF mMIMO.

A new version of the paper has been updated on 21/08/2021


This work was supported by the Institute for Information and Communications Technology Promotion (IITP) funded by the Korea Government through the Ministry of Science, ICT and Future Planning (MSIT) through the Development of Beyond 5G Mobile Communication Technologies (Ultra- Reliable, Low-Latency, and Massive Connectivity) and Combined Access Technologies for Cellular Based Industrial Automation Systems under Grant 2017-0-00724.


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