Performance Optimization of 5G Networks for Ultra-Reliable Low-Latency Communication (URLLC)
DOI:
https://doi.org/10.15662/IJARCST.2023.0605001Keywords:
5G, URLLC, latency optimization, reliability, network slicing, edge computing, HARQ, short blocklength codes.arXivAbstract
The advent of 5G technology has ushered in a new era of wireless communication, characterized by Ultra-Reliable Low-Latency Communication (URLLC). URLLC is pivotal for applications demanding stringent latency and reliability, such as industrial automation, autonomous vehicles, and remote surgery. This paper delves into the performance optimization of 5G networks tailored for URLLC, focusing on strategies to meet the stringent requirements of sub-1ms latency and 99.999% reliability. Tom's Hardware+4Router Freak+4Financial Times+4IJSREM+1
One of the primary challenges in URLLC is the inherent trade-off between latency and reliability. Traditional retransmission mechanisms, while enhancing reliability, introduce delays that are detrimental to latency-sensitive applications. To address this, advanced coding techniques, such as short block-length codes and hybrid automatic repeat request (HARQ) enhancements, are explored. These methods aim to achieve high reliability without compromising on latency. Router Freak+1arXiv+1
Furthermore, the paper examines the role of network slicing and edge computing in optimizing URLLC performance. Network slicing allows for the creation of dedicated virtual networks tailored to specific application requirements, ensuring that URLLC services receive prioritized resources. Edge computing, on the other hand, reduces latency by processing data closer to the source, minimizing the need for long-distance data transmission.
Simulation results presented in this study demonstrate the efficacy of these strategies in enhancing the performance of 5G networks for URLLC. The findings underscore the importance of integrated approaches that combine advanced coding, network slicing, and edge computing to meet the demanding requirements of URLLC applications. Router Freak
References
1. Thota, J., & Aijaz, A. (2019). On Performance Evaluation of Random Access Enhancements for 5G uRLLC. arXiv:1901.07006. arXiv
2. Yang, H., Xiong, Z., Zhao, J., Niyato, D., Yuen, C., & Deng, R. (2020). Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications. arXiv:2002.08743. arXiv
3. Anand, A., & de Veciana, G. (2018). Resource Allocation and HARQ Optimization for URLLC Traffic in 5G Wireless Networks. arXiv:1804.09201. arXiv
4. Esswie, A. A., & Pedersen, K. I. (2018). Multi-User Preemptive Scheduling for Critical Low Latency Communications in 5G Networks. arXiv:1806.04588. arXiv
5. González, J., & García, R. (2021). Latency Reduction for Narrowband URLLC Networks: A Performance Evaluation. Wireless Networks, 27(7), 2577–2593.


