Prediction of Accuracy in Wireless Networks by Power Control Algorithm with Multi Agent Q-Learning Method

The design of power control algorithms for wireless networks has long been a challenging problem for researchers. Previous research has focused mainly on optimizing the delivery of data in order to increase network throughput and reduce interference. However., prediction of the accuracy of these algorithms in different scenarios is an open problem which has received little attention in the literature. This paper proposes the use of a Support Vector Machine (SVM) for predicting the accuracy of a given power control algorithm's performance in a wireless network. The algorithm was trained using data from a large pool of transmissions over many different wireless networks., and a cross-validation procedure was used to ensure that the results are not biased. The performance of the SVM model is compared to traditional algorithms and shows significant improvements in accuracy. Additionally., the results illustrate that different scenarios and networks may require different power control algorithms and that the SVM can accurately predict which algorithm is best suited for each scenario. The results suggest that the improved accuracy of the SVM model should be taken into account when designing power control algorithms for wireless networks.