Sparse Multipath Channels Research Articles

Delay-spread estimation using cyclic-prefix in wireless OFDM systems

A novel cyclic-prefix based delay-spread estimation technique for wireless OFDM systems is proposed. In particular, the authors propose a technique for estimating the delays and powers of multipath components when the channel is sparse, i.e. a few strong multipaths distantly spaced in time, and a technique for estimating the RMS delay-spread when the channel has a large number of sample-spaced multipath components. The proposed techniques are based on a multiple-argument correlation function which exhibits change of gradient according to the delay path arrival pattern, i.e delay times and powers. Numerical results demonstrate the accuracy of the proposed techniques in estimating the relative timing and the power of delay paths for a sparse multipath channel, and the RMS delay-spread for a sample-spaced multipath channel. Moreover, the RMS delay-spread estimation can be used adaptively to operate the MMSE channel estimation process in the OFDM receiver at near optimum.

Channel estimation for OFDM transmission in multipath fading channels based on parametric channel modeling

We present an improved channel estimation algorithm for orthogonal frequency-division multiplexing mobile communication systems using pilot subcarriers. This algorithm is based on a parametric channel model where the channel frequency response is estimated using an L-path channel model. In the algorithm, we employ the ESPRIT (estimation of signal parameters by rotational invariance techniques) method to do the initial multipath time delays acquisition and propose an interpath interference cancellation delay locked loop to track the channel multipath time delays. With the multipath time delays information, a minimum mean square error estimator is derived to estimate the channel frequency response. It is demonstrated that the use of the parametric channel model can effectively reduce the signal subspace dimension of the channel correlation matrix for the sparse multipath fading channels and, consequently, improve the channel estimation performance.

Blind estimation of multipath channel parameters: a modal analysis approach

We propose a novel approach to efficiently estimate multipath channel parameters, which is particularly useful in sparse multipath channels. Conventional methods do not fully exploit the inherent structure present in the combined channel response; the excess number of parameters to be estimated by conventional methods makes the identification difficult. By utilizing a priori knowledge of the transmission data pulse, the channel identification problem is transformed into the mode estimation problem. Then, the parameters directly related to the multipath propagation are extracted in the the modal analysis framework, and hence, the number of estimation parameters are significantly reduced. Finally, the multipath channel parameters are obtained by inverse-transforming the mode parameters. Simulation results show significant improvement in the normalized mean square error over existing approaches.