Non-stationary wiener filter design for channel estimation of PS-OFDM cognitive radio using time-reversal communication: A locally stationary approach

Abstract

Time-reversal communication is considered as a potential Green wireless communications scheme for cognitive radio networks. The network base station utilizes time-reversal communication to exploit multi-path propagation, in order to provide spatial focusing at an intended cognitive radio. This reduces interference to other radios in the network. However, time-reversal spatial focusing performance is dependent on robust channel estimation. Under time-varying channel conditions or imperfect channel estimation, the performance of time-reversal communication deteriorates immensely. To ameliorate this deterioration, we design a non-stationary time-varying non-causal Wiener filter based on the time-varying spectrum. The time-varying spectrum is obtained by first modeling the time-varying channel as a locally stationary process. Which means that over small time intervals the channel is approximately stationary, and correlated inside these stationary intervals. Consequently, the time-varying spectrum can be easily calculated by estimating the covariance of the Wigner-Ville distribution of each locally stationary process. Based on that premise, the goal of this paper is to investigate through simulation, the performance of the proposed Wiener filter versus the conventional optimal Wiener filter when the time-varying channel is modeled as a locally stationary process. The performance is derived by computing the symbol error rate (SER), minimum mean square error (MMSE) and the output versus input signal-to-noise ratio (SNR).