Low-complexity blind channel estimation algorithm for UWB communication systems

A low-complexity blind channel estimation algorithm is proposed for ultra-wideband (UWB) wireless communication systems in dense multipath environments. To accurately estimate channel impulse response (CIR), the proposed algorithm exploits an unconventional denoising fast Fourier transform (FFT) to perform deconvolution. The denoising FFT employs the discrete wavelet transform (DWT) to compute discrete Fourier transform (DFT), meanwhile suppressing the noise. Theoretical analysis and simulation results both show that the proposed approach greatly improves the estimation accuracy of CIR, compared with the FFT-based algorithm. However, their computational complexities are on the same order. In particular, the proposed method can be used for identification of more realistic channel models, in which signal echoes along different propagation paths undergo different frequency-selective fading.