Interference alignment based on channel prediction with delayed channel state information

Interference alignment (IA) is a promising technique that can eliminate the interference in multi-user communication networks effectively. However, it requires highly accurate and real-time channel state information (CSI) at both transmitters and receivers. In practical systems, it is difficult to obtain the perfect knowledge of a dynamic channel due to channel estimation errors, communication latency and capacity constraints. Particularly, transmitters in IA systems usually get imperfect CSI fed back from receivers with a delay, which will greatly affect the performance of IA. In this paper, the performance of IA with delayed CSI is studied, and the decrease of the total network capacity due to the delayed CSI is analyzed. To mitigate the influence of the delayed CSI, an IA scheme based on channel prediction is proposed using two easy-to-implement and practical channel predictors, minimum mean square estimate (MMSE) and weighted least squares error (WLSE) predictors. The CSI of the next time instant is predicted using the present and past CSI. Simulation results are presented to show the effectiveness of the channel prediction IA schemes with the delayed channel knowledge.