An Effective Double Density Dual-Tree Complex Wavelet Transform Based Denoising for MIMO-OFDM

Abstract

Nowadays, modern mobile telecommunication systems use multiple input multiple output (MIMO) with orthogonal frequency division multiplexing (OFDM) because of its robustness and huge spectrum efficiency. The most significant issues in MIMO-OFDM are noises occurred during communication and precise channel estimation. In this paper, the double density dual-tree complex wavelet transform (DDDTCWT) based denoising is proposed for eliminating noises occurred over the MIMO-OFDM. The fast independent component analysis using negentropy (FICAN) is used for performing blind channel estimation according to the statistics obtained from the received signal. Therefore, an effective denoising and channel estimation accomplished in the MIMO-OFDM is used to reduce the errors during the transmission. The performance of DDDTCWT-FICAN is analyzed using mean square error (MSE), bit error rate (BER) and symbol error rate (SER). The existing research used to evaluate the efficiency of DDDTCWT-FICAN are pilot-based interpolation (PBI) technique, adaptive optimized fast blind channel estimation (AOFBCE) and discrete fourier transform (DFT) with denoise model based least square (LS) Wiener namely DFT-LS-WIENER. The BER of the DDDTCWT-FICAN for 10 dB of SNR is 0.0053, it is less when compared to the PBI.