Machine Learning Methods for Allocating Resources in Wireless Communication

The need for wireless communication services is growing daily. It consequently raised the demand for efficient spectrum management. Spectrum resources may be underutilized as a result of traditional static allocation techniques. Dynamic and adaptive spectrum allocation is made possible by machine learning (ML) techniques, which provide real-time modifications in response to shifting demand and usage patterns. Here, based on the machine learning framework and evaluation metrics, different algorithms like, Machine Linear Regression (MLR), Support Vector Machine (SVM), Gradient Boosting Regression (GBR), Decision Tree (DT), and K-nearest neighbors (KNN)—are examined and compared with each another. The KNN model has the lowest MSE 0.0084 and a high r2 score of 0.8835 as compared to other models indicating lowest error with high precision. Further, it is used for prediction of resource allocation of wireless communication with signal strength, latency and required bandwidth of different users which are the major constraint of resource allocation.