Imran, AliManalastas, Marvin2020-05-072020-05-072020-05-08https://hdl.handle.net/11244/324307The ever-increasing demand for mobile data traffic along with new use cases are set to make the current cellular network technology obsolete and give rise to a newer and better one in the form of 5G. This arising technology is coming with a promise of massive capacity, ultra-high reliability and close to zero latency, however, coming alongside is additional complexity. 5G is expected to carry along with it more than 5000 confi guration and optimization parameters (COPs). These COPs are the backbone of a network as most of the Key Performance Indicators (KPIs) relies on the proper settings of these COPs. To set these parameters optimally, it is imperative that the relationship between COPs and KPIs be understood. However, to date, this relationship between COPs and KPIs is known to some extend but is not fully realized. But mining the COP-KPI relationship is not a dead end. Machine Learning (ML) can be leveraged to learn KPI behavior with changes in COPs. Yet, ML's full potential is bounded by the lack of representative data in the wireless community to effectively train these models. Gathering these data is, in itself, a challenge. Real data from live network is abundant, yet not representative. Although simulator is a promising source of data, its performance lies on how realistic and detailed the modeling and implementation of its functions are. In this thesis paper, we have presented a realistic and comprehensive modeling of one of the most important functions of a wireless network: the handover function. In line with 3GPP standards, we have modeled and implemented more than 20 handover related COPs. The model is incorporated in a python-based simulator to generate data. Validation and evaluation are done to prove the model accuracy and its effectiveness in capturing real handover procedure. Use cases are also presented to show its capability to simulate different COP settings and show the effects on KPIs. This thesis paper is presented as an initial step in generating representative dataset to train machine learning to model COP-KPI relationship.Attribution-NonCommercial-NoDerivatives 4.0 InternationalHandover5GSimulatorWireless communication systems--Technological innovationsBroadband communication systems--Technological innovationsMobile communication systems--Technological innovations