Machine-Learning-Based-Cell-Association
This was my Bachelor's Thesis project carried out during my final year at the Vellore Institute of Technology, India
With the advent of 5G communication networks, the number of devices on the core 5G network significantly increases. A 5G network is a cloud native, massively connected IoT platform with a huge number of devices hosted on the network as compared to prior generation networks. Previously known Machine Type Communication (MTC), it is now known as massive Machine Type Communication (mMTC) and it plays a pivotal role in the new network scenario with a larger pool of devices. As ultra-low latency is the key metric in developing 5G communication, a proper cell association scheme is now required to meet the load and traffic needs of the new network, as compared to the earlier cell association schemes which were based only on the Reference Signal Received Power (RSRP). The eNodeB with the highest RSRP may not always be optimal for cell association to provide the lowest latency. This research proposes an unsupervised machine learning algorithm, namely Hidden Markov Model (HMM) learning on the network’s telemetry data, which is used to learn network parameters and select the best eNodeB for cell association, with the objective of ultimate ultralow latency. The proposed model uses an HMM learning followed by decoding for selecting the optimal cell for association.