Xcelerate5G: Optimizing Resource Allocation Strategies for 5G Network Using ML

As we embrace the transformative era of 5G technology, promising unprecedented data rates, minimal latency, and extensive device connectivity, the need for effective resource allocation becomes paramount. This research delves into the realm of machine learning, specifically exploring linear regression, support vector machines (SVM), and k-nearest neighbor (KNN) models to optimize resource allocation in 5G networks. Examining previous research, we uncover a focus on training models to assess incoming traffic and predict network slices for unknown device types using key performance indicators (KPIs) [1]. To enhance resource utilization, our study introduces and compares three machine learning models: linear regression, SVM, and KNN. These models forecast optimal resource allocation based on past network data and user trends. While linear regression offers simplicity, SVM and KNN present more sophisticated and adaptive models. In the dynamic conditions of 5G networks, machine learning-based resource allocation outperforms traditional methods, excelling in bandwidth efficiency, user satisfaction, noise reduction, and signal strength. Key considerations include accuracy, scalability, and resource distribution for various application types. This study underscores the significance of machine learning techniques, contributing to a deeper understanding of resource allocation in 5G networks. It provides comprehensive insights into the advantages and limitations of linear regression, SVM, and KNN models, empowering network operators and researchers to make informed decisions that enhance the overall performance and efficiency of evolving 5G networks across diverse use cases.