Abstract
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 $5^{th}$ generation (5G) technology focuses on achieving ultra-reliable low latency communication (URLLC) services. TI applications require an extraordinary degree of reliability and latency. The $3^{rd}$ 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.
Highlights
Tactile internet (TI) emerged as a novel technology to move Internet from the Internet of Things (IoT) to real-time interactive techniques with a portfolio of engineering, social and commercial use cases, which will revolutionize most aspects of the future communication technologies [1], [2]
In this survey paper, we have comprehensively summarized the researches related to the advancement of the 5th generation (5G)/beyond 5G (B5G) communication systems, especially focusing on the concept of 5G new radio (5G New Radio (NR)) and ultra-reliable low latency communication (URLLC) requirements
This survey briefly covers the research in physical layer (PHY) layer, MAC layer resource allocation, PHY-MAC cross-layer MAC-random access (RA) mechanisms, and the implications of one of the recently emerged machine learning (ML) techniques, that is, federated reinforcement learning (FRL) in wireless communication systems
Summary
Tactile internet (TI) emerged as a novel technology to move Internet from the Internet of Things (IoT) to real-time interactive techniques with a portfolio of engineering, social and commercial use cases, which will revolutionize most aspects of the future communication technologies [1], [2]. RANDOM ACCESS CHANNEL MECHANISM One of the most basic sources of latency in 5G systems is the underlying link association with the assistance of an random access channel (RACH) method that causes several milliseconds delays [41] This turns out to be challenging for TI applications because of inconsistent communications by the devices with URLLC requirements with a small data packets and contending for a fixed number of preambles. RA improvements include implementation of short transmission slots of 5G numerology, quicker transmission of UL data packets, efficient back-off times for QoS-based traffic, and dedicated resource allocation for URLLC applications to reduce the channel access latency.
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