Compensating for CQI aging by channel prediction: The LTE downlink

In the downlink of Long Term Evolution (LTE) systems, feedback and processing delays cause a mismatch between the current channel state and the Channel Quality Information (CQI) at the base station. This CQI aging leads to inaccurate channel adaptation and can, thus, highly degrade the cell capacity. To compensate for this performance loss, we study several CQI predictors under realistic delay and channel assumptions. Our results on cell throughput show that linear prediction with Stochastic Approximation provides at least the performance gains of the computationally more complex covariance-based linear predictors and Kalman filters. This surprising result points to Stochastic Approximation as a powerful and practical technique to increase downlink performance with limited channel knowledge.