Prediction Based Bandwidth Allocation for Cognitive LTE Network

In this paper we present a novel dynamic bandwidth allocation technique in which different base stations share the total available spectrum to maximize the quality of service (QoS) in the network, and show the implementation of this technique in a cognitive 3rd Generation Partnership Project Long Term Evolution (3GPP LTE) network. Assuming, that each base station is characterized by a concave increasing utility and a positive weight, we conduct a weighted utility maximization framework, and develop a simple prediction-based bandwidth allocation algorithm. To deal with heterogeneous network applications we propose to deploy the approach used in optimal flow and congestion control (OFC) where the resources are assigned based on speed of load increase. Using the appropriate load indictors, the algorithm first identifies the base stations with increasing (decreasing) load, and then decrease (increase) the channel utilization of base stations with increased (decreased) load using weighted proportional fairness criterion.