Design and Implementation of a Light-Weight Channel Vector Classifier Based on Support Vector Machine for Real-Time 5G Beam Index Detection

Abstract

Machine Learning (ML) is recently considered a key technology for bringing outstanding performance to wireless communications. Conventional research has highlighted the potential of Support Vector Machines (SVMs), which train their model based on optimization theory, to enhance the performance of wireless communications. However, there are practical issues that makes SVM difficult to apply to a wireless communication system. SVM generally entails a heavy training process with high computational complexity, and the model requires a significant amount of time for training. Also, the entire dataset needs to be trained at once, requiring a substantial amount of memory for data storage. To enable SVM in wireless communications, we propose Real-Time Channel Vector Classifier (RTCVC), which employs a light-weight SVM model capable of training and processing incoming data in real-time. A novel input data pre-processing technique is implemented to reduce the computational overhead associated with calculating non-linear functions. The rearranged formulation of the original problem also allows each SVM sub-model to be trained distributively over time based on incremental parameters. For performance evaluation, we implement the RTCVC inter-operating with 5G beam index detection, whose detection probability has been theoretically proven to be significantly enhanced by SVM. The software modules of the RTCVC are based on LibSVM, a well-known open-source library for implementing SVM sub-models. The experimental results confirm that RTCVC significantly reduces training time while maintaining suitable performance for 5G beam index detection.