Task Load Modelling for LTE Baseband Signal Processing with Artificial Neural Network Approach

Abstract: This thesis gives a research on developing an automatic or guided-automatic tool to predict the hardware (HW) resource occupation, namely task load, with respect to the software (SW) application algorithm parameters in an LTE base station. For the signal processing in an LTE base station it is important to get knowledge of how many HW resources will be used when applying a SW algorithm on a specic platform. The information is valuable for one to know the system and platform better, which can facilitate a reasonable use of the available resources. The process of developing the tool is considered to be the process of building a mathematical model between HW task load and SW parameters, where the process is dened as function approximation. According to the universal approximation theorem, the problem can be solved by an intelligent method called articial neural networks (ANNs). The theorem indicates that any function can be approximated with a two-layered neural network as long as the activation function and number of hidden neurons are proper. The thesis documents a work ow on building the model with the ANN method, as well as some research on data subset selection with mathematical methods, such as Partial Correlation and Sequential Searching as a data pre-processing step for the ANN approach. In order to make the data selection method suitable for ANNs, a modication has been made on Sequential Searching method, which gives a better result. The results show that it is possible to develop such a guided-automatic tool for prediction purposes in LTE baseband signal processing under specic precision constraints. Compared to other approaches, this model tool with intelligent approach has a higher precision level and a better adaptivity, meaning that it can be used in any part of the platform even though the transmission channels are dierent.