Adaptive Hybrid Access Scheme for 5G URLLC using Machine Learning and Fountain Codes

Abstract

Ultra-reliable low-latency communication (URLLC) is a pivotal aspect of 5G mobile networks, necessitating robust mechanisms for secure and dependable data transfer. With the rise of applications requiring strict performance standards, especially in industrial automation and healthcare, it is crucial to adopt sophisticated methods like machine learning and novel coding techniques to effectively tackle these challenges (Indoonundon & Fowdur, 2021). Machine learning integration aids in optimizing resource distribution and channel allocation, allowing networks to quickly adapt to changing conditions and user needs, thus boosting performance and reliability in URLLC contexts (Niknam et al., 2019). This flexibility is especially vital due to the swift increase in data traffic and the intricacy of contemporary wireless environments, prompting a transition from conventional model-driven methods to data-driven strategies that utilize the extensive data within the network (Niknam et al., 2019). Such a shift promotes more knowledgeable decision-making that can adapt to real-time situations, enhancing the system's capability to sustain low latency and high dependability for essential applications (Savazzi et al., 2020; Chen et al., 2018). Present studies are delving into the use of sophisticated channel coding patterns and error control techniques, which are crucial for addressing the distinct challenges presented by URLLC application demands, ensuring effective transmission within the strict latency and reliability parameters (Indoonundon & Fowdur, 2021). Additionally, the exploration of innovative methods like fountain codes offers the promise of dependable transmission without the necessity for retransmission, thus resolving the core issues of URLLC in scenarios requiring utmost reliability and minimal latency