Cell Edge Throughput Enhancement in V2X Communications Using Graph-Based Advanced Deep Learning Scheduling Algorithms

Abstract

Optimizing downlink coordinated multipoint (CoMP) performance through advanced scheduling algorithms enhances V2X communication technology, enabling efficient resource allocation, minimizing interference, and maximizing data rates for reliable and synchronized communication between vehicles and infrastructure. In this paper, several scheduling algorithms were compared, including support vector machine (SVM) linear, SVM Radial Basis Function (RBF), SVM Sigmoid, Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Graph Convolutional Networks (GCN). The performance metrics used in this comparison included CoMP decision, throughput, and cell edge throughput. The results showed that the out-rated GCN algorithm had the best-triggering composition for 5G radio networks, outperforming the other algorithms in terms of CoMP decision accuracy and overall throughput. In particular, the GCN algorithm demonstrated significant improvements in cell edge throughput, which is critical for ensuring reliable communication in areas with weaker signal strength. The reported results proves that the integration of advanced scheduling algorithms in the downlink CoMP framework enhances the efficiency of V2X communication, enabling optimized resource allocation, interference mitigation, and maximized throughput, thereby improving system efficiency, reducing latency, and ensuring reliable and seamless information exchange for connected vehicles, smart cities, and industrial automation.

KEYWORDS:

• Cell edge throughput
• Coordinated Multipoint (CoMP)
• Data rates
• Graph convolutional networks (GCN)
• Resource allocation
• Scheduling algorithms
• V2X communication

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

P. Reshma

P Reshma received her BTech degree in electronics and communication engineering from Andhra University College of Engineering, Visakhapatnam, India, in 2016 and MTech degree in communication systems in National Institute of Technology (NIT), Puducherry, India, in 2021. She is currently working towards her PhD degree in National Institute of Technology (NIT), Tiruchirappalli, Tamil Nadu, India, from 2021. Her research interests include vehicular communication, tera hertz communication, channel estimation, machine and deep learning for wireless communication.

V. Sudha

V Sudha received her BE degree in electronics and communication engineering from Bharathidasan University, India, in 2000 and the ME degree in communication systems from Anna University, India, in 2006. She completed her PhD in the area of wireless communication systems at National Institute of Technology, Tiruchirappalli, in 2017. She has 22 years of teaching experience. Currently, she is working as Associate Professor in the Department of Electronics and Communication Engineering, National Institute of Technology (NIT), Tiruchirappalli, India. Her research interests include multicarrier wireless systems, digital communication, wireless optical communication, and wireless networks. She has published several papers in international journals and conferences. She is a life member of ISTE. Email: [email protected]