URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

The tactile internet (TI) is believed to be the prospective advancement of the internet of things (IoT), comprising human-to-machine and machine-to-machine communication. TI focuses on enabling real-time interactive techniques with a portfolio of engineering, social, and commercial use cases. For this purpose, the prospective 5th generation (5G) technology focuses on achieving ultra-reliable low latency communication (URLLC) services. TI applications require an extraordinary degree of reliability and latency. The 3rd generation partnership project (3GPP) defines that URLLC is expected to provide 99 :99% reliability of a single transmission of 32 bytes packet with a latency of less than one millisecond. 3GPP proposes to include an adjustable orthogonal frequency division multiplexing (OFDM) technique, called 5G new radio (5G NR), as a new radio access technology (RAT). Whereas, with the emergence of a novel physical layer RAT, the need for the design for prospective next-generation technologies arises, especially with the focus of network intelligence. In such situations, machine learning (ML) techniques are expected to be essential to assist in designing intelligent network resource allocation protocols for 5G NR URLLC requirements. Therefore, in this survey, we present a possibility to use the federated reinforcement learning (FRL) technique, which is one of the ML techniques, for 5G NR URLLC requirements and summarizes the corresponding achievements for URLLC. We provide a comprehensive discussion of MAC layer channel access mechanisms that enable URLLC in 5G NR for TI. Besides, we identify seven very critical future use cases of FRL as potential enablers for URLLC in 5G NR.

Automated summary

The tactile internet (TI) is considered a promising advancement in the Internet of Things (IoT), focusing on real-time interactive techniques and ultra-reliable low latency communication services. The introduction of the 5G new radio (5G NR) technology by the 3rd Generation Partnership Project (3GPP) aims to enhance network intelligence and reliability, with machine learning techniques playing a crucial role in designing intelligent network resource allocation protocols for 5G NR URLLC requirements. The use of federated reinforcement learning (FRL) is proposed as a potential solution for achieving URLLC in 5G NR, with identified future use cases for enabling URLLC in 5G NR.