Parameter estimation and channel reconstruction based on compressive sensing for ultra-wideband MB-OFDM systems

highlights•The ultra-wideband channel measured in a laboratory environment can be modelled as sparse.•The proposed method accurately estimates the parameters of ultra-wideband channel.•With the estimated parameters of channel, the proposed estimator outperforms the state-of-the-art estimators.•The proposed estimator has lower complexity than minimum mean-squared error estimator. Multi-band orthogonal frequency-division multiplexing (MB-OFDM) is an important transmission technique for ultra-wideband (UWB) communication. One of the challenges for practical realization of these UWB MB-OFDM systems is the estimation of the channel. In UWB MB-OFDM, the channel can be modelled as sparse, and channel estimation (CE) based on compressed sensing (CS) can be used. However, the existing techniques all require prior knowledge of some channel parameters, which are not known in practice, e.g. the dictionary size, corresponding to the effective duration of the channel impulse response (CIR), and the sparsity of the CIR. Therefore, in this paper, we propose a CS-based channel parameter estimation method to estimate the dictionary size and the sparsity based on a pilot preamble of which the duration is shorter than the total duration of the CIR. Using the resulting parameter estimates, we reconstruct the CIR with the compressive sampling matching pursuit (CoSaMP) method. We show that the proposed algorithm is able to accurately estimate the sparsity and the dictionary size, and can effectively reconstruct the CIR for channels that are either based on a mathematical model or real, measured channels. Moreover, as the algorithm has acceptable complexity, the proposed method is suitable for practical use.