Spectrum-Efficient Distributed Compressed Sensing Based Channel Estimation for OFDM Systems Over Doubly Selective Channels

In this paper, we deal with channel estimation (CE) for high-mobility orthogonal frequency division multiplexing (OFDM) systems. To make the numerous (unknown) estimation for the high-mobility OFDM systems practicable, the channels are assumed to be time- and frequency-selective-or doubly selective (DS) and approximated by a basis expansion model (BEM). As the DS channel requires the distributed acquisition of multiple correlated signals in the delay-Doppler channel domain, we proceed to estimate jointly sparse BEM coefficient vectors over a DS channel as against numerous channel coefficients. On account of channel time-variation, the resulting channel matrix in the frequency domain exhibits (approximately banded) pseudo-circular structure, which gives rise to a diagonally dominant yet full matrix rather than a diagonal matrix and thus induces inter-channel interference (ICI). On the premise of this observation, we propose a new pilot design scheme that identifies the optimal pilot placement and values for each pilot cluster to combat ICI. Furthermore, to obtain a channel estimator consistent with the jointly sparse delay-Doppler [i.e., two dimensional (2D)] channel model, an algorithm namely, distributed compressed sensing (DCS)-based stage determined matching pursuit (DCS-SdMP), is proposed. Our claims are supported by simulation results, which are obtained considering Jakes' channels with fairly high Doppler spreads, which show the superiority of the proposed schemes over other different methods of CE.