Packet Scheduling Using SVM Models in Wireless Communication Networks

Packet Scheduling Using SVM Models in Wireless Communication Networks

This paper addresses a support vector machine (SVM) model-based packet scheduling in wireless communication networks. The linear SVM, as well as the polynomial SVM model-based proportional fair scheduling (SVMPFS), are suggested. Moreover, their performances are analyzed for various system and machine learning parameters such as the average window, signal to interference ratio (SIR) and the degree of a polynomial. From the computer simulations, the performance measurements of PFS, such as, user selection fairness and user throughput are calculated, which proves that performance of SVM-PFS approaches to that of the conventional metric-based PFS in most cases, while the simulation results show that the polynomial SVM-PFS outperforms the linear SVM-PFS. It is also shown that the SVM-PFS performs better under higher SIR or larger average window.