Sparse Adaptive Channel Estimation Based on Discrete Fourier Transform
Ge Tao, Yang Shouyi, Zhang Aihua
Signals and Telecommunication Journal, Vol. 3, No. 1, 2014.
DOI: 
Link: http://www.dline.info/stj/fulltext/v3n1/4.pdf
Abstract:
In communications, compressive sensing is largely accepted for sparse channel estimation and its variants. The key advantage of compressed sensing lies in its reconstruction algorithm recovering the original high-dimensional sparse data from low dimensional data. However, the algorithms will be in week effectiveness when the sparsity is unknown. In this article, a novel sparse adaptive channel estimation algorithm based on discrete Fourier transform (DFT) was proposed. It performed preliminary estimation by using discrete Fourier transform matrix as the observation matrix and inverse discrete Fourier transform to the received data at the receiver. It is found that the local peak indexes of this preliminary estimation results correspond with the non-zero taps of the sparse channel. After the non-zero taps were located, least square (LS) algorithm was used to estimate the channel impulse response (CIR). Mathematical analysis and simulation results have shown that the proposed algorithm (discrete Fourier transform based-least square, DFT-LS) outperform than existed multiple tracking algorithms such as sparsity adaptive matching pursuit (SAMP) and orthogonal matching pursuit (OMP), and are with lower complexity as well.