Efficient cloud-based cellular planning algorithms for 3G and 4G networks - Cortesão, Rodrigo Ramalho de Lobato (2021)
In mobile network deployments of growing size, the optimum and fast planning of radio resources are a key task. Cloud services enable efficient and scalable implementation of procedures and algorithms. In this dissertation, a cloud-based planning system for 3G and 4G networks is presented, using Amazon Web Services (AWS) for cloud implementation. It extracts configuration and performance data from the network, enabling to accurately estimate cells coverage, identify neighbouring cells and optimally plan Scrambling Codes (SC) in an UMTS network and Physical Cell Identity (PCI) in LTE networks. This system is integrated in a Software-as-a-Service monitoring and planning tool Metric, owned by Multivision, allowing for an easy and efficient allocation of the network resources. The system operation is demonstrated in a small canonical scenario for SCs, a small realistic scenario of PCIs cluster planning, taking less than 0,6 seconds to perform the planning. For a realistic 3G scenario with 12 484 unplanned cells, the planning of SCs is efficiently achieved, taking less than 8 seconds, and guaranteeing no collisions between first order neighbouring cells.