QoE-Aware Self-Tuning of Service Priority Factor for Resource Allocation Optimization in LTE Networks

Long Term Evolution (LTE) brings to the theory of advanced leading-edge technologies which guarantees ubiquitous broadband access. As a result, the continuous increment in the number of user terminals (UT) and their expectation leads to the importance of managing and updating the networks as per the expectation of users. In this paper, the concept of self-tuning is used for adjusting the service priority factor in the scheduling algorithms to allocate the resource blocks (RBs) in order to give the appropriate Quality of Service (QoS) based on Quality of Experience (QoE). To access QoE-aware, a QoE model is created by using the Artificial Neural Network (ANN) algorithm to estimate the QoE score by using the QoS parameters. We propose the Particle Genetic Algorithm (PGA) to find the optimal parameter of service priority factors, and the proposed algorithm works efficiently by increasing the average QoE of the network along with maintaining the QoE threshold for each of the multi-service. The detailed comparison is presented between the proposed and reference algorithms to highlight the significance of the proposed algorithm and the simulation and analytical results show that the proposed algorithm outperforms the existing ones in terms of improving the allocation of resources under the environment of limited resources.