Handover prediction for long-term window scheduling based on SINR maps

In this paper we propose a handover prediction algorithm based on signal-to-interference-and-noise-ratio (SINR) maps. Because SINR values at a user are dependent on geographical positions if the transmission powers of base stations (BS) are known, we introduce the concept of a SINR map which is a geographical map with overlay average SINR values at users at map pixels. The handover position is predicted by maximizing the probability function of positions along the street with handover thresholds based on the SINR map with considering fast fading characteristics while the handover timing is predicted by mobility prediction. The result of handover prediction is shown to be consistent with the simulated real handover very well. Combined with handover prediction and SINR maps the near-future user and channel conditions can be predicted, which enable the long-term window scheduling algorithm with proportional fairness constraint among users in multicell multiuser orthogonal frequency division multiplexing (OFDM) systems with handover management. We show that the proposed algorithm outperforms previous instantaneous scheduling algorithms in terms of sum rate significantly.