Leveraging Support Vector Machine for Optimization in 5G Cellular Systems Waveforms

The advent of 5G technology promises a paradigm shift in wireless communication networks, catering to the exponentially growing demand for high data rates, low latency, and massive connectivity. In this context, Support Vector Machine (SVM) emerges as a potent tool for enhancing the performance and efficiency of 5G cellular systems. This abstract explores the integration of SVM techniques into various facets of 5G networks, ranging from resource allocation and interference mitigation to predictive maintenance and security enhancement. SVM-based approaches offer significant advantages in optimizing radio resource management (RRM) in 5G networks. By leveraging SVM for channel estimation and prediction, network operators can efficiently allocate spectrum resources, minimize interference, and maximize throughput. Moreover, SVM classifiers can enhance the accuracy of beamforming algorithms, enabling precise directional transmission and reception, thereby improving coverage and reliability. Furthermore, SVM-based anomaly detection techniques play a crucial role in ensuring the security and integrity of 5G networks. By analyzing vast amounts of network data, SVM models can detect and mitigate various security threats, including intrusion attempts, malware propagation, and denial-of-service attacks. Additionally, SVM-based predictive maintenance algorithms facilitate proactive network management by identifying potential faults and failures in advance, thereby minimizing downtime and optimizing resource utilization. SVM enhances Bit Error Rate (BER) in 5G by optimizing resource allocation, interference mitigation, and predictive maintenance, thereby improving signal quality and reliability.