Interference alignment with delayed channel state information and dynamic AR-model channel prediction in wireless networks

Abstract

Interference alignment (IA) is a promising technique that can effectively eliminate the interferences in multiuser wireless networks. However, it requires highly accurate channel state information (CSI) of the whole network at all the transmitters and receivers. In practical wireless systems, it is difficult to obtain the perfect knowledge of a dynamic channel. Particularly, the CSI at transmitters used in IA is usually delayed through feedback, which will dramatically affect the performance of IA. In this paper, the performance of IA with delayed CSI is studied. The expressions of the average signal to interference plus noise ratio and sum rate of IA networks with delayed CSI are established. To alleviate the influence of delayed CSI, an IA scheme based on dynamic autoregressive (AR)-model channel prediction is proposed, in which the parameters of AR mode are updated frequently. The CSI of the next time instant is predicted using the present and past CSI in the proposed scheme to improve the performance of IA networks. Two key factors of the scheme, window length and refresh rate are analyzed in detail. Simulation results are presented to show that the proposed IA scheme based on channel prediction can significantly improve its performance with delayed CSI.