Enhancing 5G LTE Communications: A Novel LDPC Decoder for Next-Generation Systems
Journal Title: Information Dynamics and Applications - Year 2024, Vol 3, Issue 1
Abstract
The advent of fifth-generation (5G) long-term evolution (LTE) technology represents a critical leap forward in telecommunications, enabling unprecedented high-speed data transfer essential for today’s digital society. Despite the advantages, the transition introduces significant challenges, including elevated bit error rate (BER), diminished signal-to-noise ratio (SNR), and the risk of jitter, undermining network reliability and efficiency. In response, a novel low-density parity check (LDPC) decoder optimized for 5G LTE applications has been developed. This decoder is tailored to significantly reduce BER and improve SNR, thereby enhancing the performance and reliability of 5G communications networks. Its design accommodates advanced switching and parallel processing capabilities, crucial for handling complex data flows inherent in contemporary telecommunications systems. A distinctive feature of this decoder is its dynamic adaptability in adjusting message sizes and code rates, coupled with the augmentation of throughput via reconfigurable switching operations. These innovations allow for a versatile approach to optimizing 5G networks. Comparative analyses demonstrate the decoder’s superior performance relative to the quasi-cyclic low-density check code (QCLDC) method, evidencing marked improvements in communication quality and system efficiency. The introduction of this LDPC decoder thus marks a significant contribution to the evolution of 5G networks, offering a robust solution to the pressing challenges faced by next-generation communication systems and establishing a new standard for high-speed wireless connectivity.
Authors and Affiliations
Divyashree Yamadur Venkatesh, Komala Mallikarjunaiah, Mallikarjunaswamy Srikantaswamy, Ke Huang
Keywords
Related Articles
- EP ID EP744285
- DOI https://doi.org/10.56578/ida030104
- Views 43
- Downloads 1
Optimizing Energy Storage and Hybrid Inverter Performance in Smart Grids Through Machine Learning
The effective integration of renewable energy sources (RES), such as solar and wind power, into smart grids is essential for advancing sustainable energy management. Hybrid inverters play a pivotal role in the conversio...
Enhanced Defect Detection in Insulator Iron Caps Using Improved YOLOv8n
To address the challenges in detecting surface defects on insulator iron caps, particularly due to the complex backgrounds that hinder accurate identification, an improved defect detection algorithm based on YOLOv8n, w...
An Improved TextRank Keyword Extraction Method Based on the Watts-Strogatz Model
Traditional methods for keyword extraction predominantly rely on statistical relationships between words, neglecting the cohesive structure of the extracted keyword set. This study introduces an enhanced method for keywo...
Multi-Channel Scheduling for Short-Range Wireless Communication Networks Using a Q-Learning Feedback Mechanism
The traditional channel scheduling methods in short-range wireless communication networks are often constrained by fixed rules, resulting in inefficient channel resource utilization and unstable data communication. To...
An Enhanced Convolutional Neural Network for Accurate Classification of Grape Leaf Diseases
Grape leaf diseases can significantly reduce grape yield and quality, making accurate and efficient identification of these diseases crucial for improving grape production. This study proposes a novel classification meth...