This research aimed to introduce a novel approach for non-intrusive objective
measurement of voice Quality of Service (QoS) in LTE networks. While achieving this aim, the thesis established a thorough knowledge of how voice traffic is
handled in LTE networks, the LTE network architecture and its similarities and
differences to its predecessors and traditional ground IP networks and most
importantly those QoS affecting parameters which are exclusive to LTE environments. Mean Opinion Score (MOS) is the scoring system used to measure
the QoS of voice traffic which can be measured subjectively (as originally intended). Subjective QoS measurement methods are costly and time-consuming,
therefore, objective methods such as Perceptual Evaluation of Speech Quality
(PESQ) were developed to address these limitations. These objective methods
have a high correlation with subjective MOS scores. However, they either require individual calculation of many network parameters or have an intrusive
nature that requires access to both the reference signal and the degraded signal
for comparison by software. Therefore, the current objective methods are not
suitable for application in real-time measurement and prediction scenarios.
A major contribution of the research was identifying LTE-specific QoS affecting parameters. There is no previous work that combines these parameters to
assess their impacts on QoS.
The experiment was configured in a hardware in the loop environment. This
configuration could serve as a platform for future research which requires simulation of voice traffic in LTE environments.
The key contribution of this research is a novel non-intrusive objective method
for QoS measurement and prediction using neural networks. A comparative
analysis is presented that examines the performance of four neural network
algorithms for non-intrusive measurement and prediction of voice quality over
LTE networks. In conclusion, the Bayesian Regularization algorithm with 4 neurons in the hidden layer and sigmoid symmetric transfer function was identified as the best solution with a Mean Square Error (MSE) rate of 0.001 and
regression value of 0.998 measured for the testing data set