Using k-means clustering with transfer and Q learning for spectrum, load and energy optimization in opportunistic mobile broadband networks

In this paper, we investigate the use of an integrated machine learning algorithm to jointly optimize the spectrum allocation, load balancing and energy saving aspects in the opportunistic mobile broadband network for temporary event and disaster relief scenarios. A novel k-means algorithm has been developed to dynamically partition the users in a cell into clusters, to improve interference mitigation and spectrum reuse. It is integrated with a Q learning algorithm for resource allocation and transfer learning algorithm for cell selection. Topology management is developed using Q learning to improve BS placement and sleep mode operation. System simulation is carried out using a practical Ljubljana scenario. Compared to the classical LTE resource allocation and cell selection approach, clustered Q learning and transfer learning achieves significant QoS improvement in terms of spectrum and load optimization. With topology management, the learning algorithms show an effective balance between energy saving and QoS.