Bit Loading Using Imperfect CSIT for Prediction-Based Resource Allocation in Mobile OFDMA

Abstract

We present a prediction-based resource allocation algorithm (RA) for orthogonal frequency-division multiple-access (OFDMA) downlink, where inaccuracies in the wireless channel predictions are accounted for in the problem formulation. As the prediction quality significantly degrades with the prediction horizon, we propose a solution based on the histogram of the prediction error. This characterization also enables different mobile stations (MSs) to use different channel predictors as it does not rely on a specific prediction scheme. Using this characterization of the prediction error and based on classical resource allocation strategies, we derive an algorithm that incorporates imperfect channel prediction information of future time slots. We evaluate the proposed algorithm using a practical low-complexity channel predictor suitable for implementation at the MSs. Simulation results show that the proposed algorithm outperforms previous prediction-based RA strategies without the characterization of the prediction error, and the system throughput is comparable with the case with perfect channel state information in the transmitter (CSIT).