5G Network Planning Tool Based on Machine Learning
5G is becoming a strategic priority for telecom operators. The deployment of 5G networks will necessarily demand the installation of new Base Station equipment to support the requirements of next-generation mobile services. This research presents a smart 5G network planning software that exploits Machine Learning techniques. The proposed approach is able to predict the number of needed base stations required to meet 5G network coverage and capacity requirements, based on the measurement history of the network. Coverage planning includes link budget analysis for obtaining the maximum allowable path loss on the radio link and then determining cell radius using proper RF propagation models. The capacity planning part deals with analyzing the requirements in 5G network to meet the needed data speeds for different services. We use the service models and traffic models approach to estimate the single user average throughput during the network busy hour, then we calculate the cell average throughput. Finally we end up with the estimated number of 5G cells for the required quality of service. The functionality of the tool is invertible to predict the quality of service provided by a specified 5G resources.
It is evident that computers retain some cleverness and we owe this to all the programs that we consider them useful, but we are still very far reaching the real points (making a machine intelligent). However, there are many things which animals and humans can do easily, but they remain out of the grasp of computers. We use Artificial Intelligence doing those tasks involve knowledge that is currently implicit, but we have information about them via data and examples acquisition. We always think, how to get machines to gain that kind of intelligence using data and examples built in it as an operational knowledge system. At this point, we may introduce deep learning which is machine learning research work having a motion to its main aims, with an Artificial Intelligence label and its branches.
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