URLLC in 5G & Beyond: Specifications and Technologies

With the ability to communicate between machines and people, the Tactile Internet (TI) represents a substantial advancement over the Internet of Things (IoT). TI wants to make it easier to develop real-time interactive applications in a variety of fields, including as engineering, social engagement, and business. The future 5th Generation (5G) technology is focused on offering Ultra-Reliable Low Latency Communication (URLLC) services to satisfy the demanding needs of TI applications. According to 3GPP specifications, URLLC may transmit a single 32-byte packet with a latency of less than one millisecond and achieve 99.99% dependability. In order to accomplish these objectives, the 3GPP presents 5G New Radio (5G NR), a flexible orthogonal frequency division multiplexing (OFDM) approach, as a new radio access technology (RAT). With the introduction of this new RAT, it is clear that we need next-generation solutions, especially ones that include network intelligence. To fulfil the URLLC needs of 5G NR, machine learning (ML) methods are anticipated to be crucial in building intelligent network resource distribution algorithms. This review analyses the possible use of Federated reinforcement learned, an ML approach, to satisfy the needs of 5G NR URLLC and lists the pertinent URLLC accomplishments. Plane channel entry techniques that support URLLC in 5G NR for TI workloads are covered in great detail in this study. It also pinpoints seven crucial upcoming situations where FRL might function as a possible facilitator for URLLC in 5G NR.