Optimizing Data Transmission in 5G Networks for Low Latency and High Reliability
TL;DR
This paper conducts a comprehensive literature survey to examine existing optimization techniques and proposes an improved methodology leveraging AI-driven resource allocation and dynamic spectrum sharing that demonstrates the effectiveness of the proposed approach in reducing end-to-end latency and improving network reliability.
Abstract
The rapid advancement of wireless communication has led to the emergence of the fifth-generation (5G) network, which aims to provide ultra-reliable low-latency communication (URLLC) while ensuring high data transmission rates. Optimizing data transmission in 5G networks is critical for supporting real-time applications such as autonomous vehicles, telemedicine, industrial automation, and smart cities. This paper explores various techniques and strategies to enhance data transmission efficiency, minimize latency, and improve reliability in 5G networks. We analyze the key performance indicators (KPIs) that influence data transmission, including bandwidth utilization, network slicing, and multiple access techniques. Furthermore, we discuss the role of edge computing, artificial intelligence (AI)-driven network management, and adaptive modulation techniques in optimizing data transmission. The paper also highlights the impact of interference management, energy efficiency considerations, and security protocols on 5G network performance. We conduct a comprehensive literature survey to examine existing optimization techniques and propose an improved methodology leveraging AI-driven resource allocation and dynamic spectrum sharing. Through simulation and analytical results, we demonstrate the effectiveness of the proposed approach in reducing end-to-end latency and improving network reliability. The findings contribute to the ongoing efforts in optimizing 5G networks and lay the foundation for future research in beyond-5G (B5G) and sixth-generation (6G) communication systems.