Uplink Multiuser Scheduling in Cognitive Radio Network using Group-based SVM Scheduling and Comparing with a Linear Classifier

Aim: To study the uplink multiuser scheduling in cognitive radio networks using group-based SVM scheduling and compare it with a linear classifier utilizing the machine learning approach.The performance analysis for improving accuracy inuplink multiuser scheduling in cognitive radio network using the Novel group-based SVM scheduling (N=10) compared with a linear classifier (N=10). Calculation is used to calculate the sample size. G power of 0.80 with α=0.05.The mean throughput of the groupbased SVM scheduling, at 89.33%, is significantly higher thanthat of the linear classifier Algorithm, at 81.52%. Despite the insignificance, the accuracy and loss rate were 0.77 (p>0.05). The novel group-based SVM scheduling algorithm outperforms the linear classifier Algorithm in terms of mean throughput. Cognitive Radio Network, Signal-to-Noise Ratio, Novel group-based SVM scheduling, Spectrum Sensing, Channel Capacity, Machine learning.