A Cognitive scheme for Radio Admission Control in LTE systems

In order to provide QoS requirements in high speed future communication networks, such as LTE, the operator has to provide a Radio Admission Control algorithm which will guarantee the QoS of different service types (e.g. voice, data, video, ftp) while maximizing radio resource utilization. In this paper we propose a Cognitive Radio Admission Control Scheme based on a Multilayer Feed-forward Neural Network. According to our scheme, the eNodeB performs Radio Admission Control using a cognitive engine that learns from the past experience how the admission of a new session would affect the QoS of all sessions in the future. We implemented our Radio Admission Control Scheme using a LTE-EPC simulator. Since our scheme is based on learning from the past experience, we expect that it will be able to satisfy QoS requirements, in a variety of realistic scenarios, which can not be accounted for in analytical models.