Yihenew Wondie (PhD)Tiblets Kinfe2023-12-142023-12-142023-09http://etd.aau.edu.et/handle/123456789/947In the last few years, fourth generation (4G) mobile networks have become much more complex due to deployments of macro, micro, pico, and femtocells to boost network capacity and quality of Services(QoS). Long-Term Evolution Advanced (LTE-A) system is a 4G wireless communication technology that offers mobile devices high-speed data transmission, increased capacity, and improved network performance. While LTE-A offers improved capabilities, it also poses unique resource allocation problems. The following are some difficulties in allocating LTE-A resources: heterogeneous network deployment, diverse QoS requirements and dynamic traffic patterns. Traditional resource management approaches usually use static strategies or predefined rules and are insensitive to the current demand on the network. To allocate resources effectively based on the current network conditions, resource allocation must be adaptable and dynamic. Adaptive resource allocation methods that can handle the dynamic nature of LTE-A networks can be developed with the help of machine learning and artificial intelligence. This thesis evaluates the performance of machine learning-based resource allocation strategies in LTE-A networks using realistic data. Four different machine learning-based resource allocation strategies are compared: Long Short Term Memory (LSTM), Conventional Neural Network and Long Short Term Memory (CNN-LSTM), Random Forest (RF) and K Nearest Neighbor(KNN) algorithm. Performance measures include the accuracy of resource allocation, Root Mean Square Error (RMSE), Mean Absolute Error(MAE) and speed of convergence( running time). The results show that LSTM is the model with higher accuracy score of resource allocation that is 98.56% and in terms RMSE and MAE random forest has lowest value of 0.294 and 0.08. In the case of running time KNN takes shortest time of 1.58 second.en-USLTE, LTE-Advanced, Machine Learning, Radio Resource Allocation, Channel quality indicatorThesis