Dynamic bandwidth allocation for OFDMA-PONs using Hidden Markov Model

Abstract

Abstract: Accurate prediction of traffic conditions on OFDMA-PONs is important because of its vital role in network resource management and efficient bandwidth allocation. Given the dynamic and stochastic nature of network traffic, our proposed algorithm conducts a probabilistic approach by using the Hidden Markov Model (HMM). The HMM defines traffic states with two parameters: the mean and contrast of the bandwidth request observations. Simulation results demonstrate the performance comparison between with and without the prediction method in terms of throughput and end-to-end delay. As a result, the throughput improves 15% and the saturation offered load of the delay for the prediction and non-prediction is 0.8 and 0.7, respectively.