Abstract
In this paper, the estimation of overspread, i.e., doubly spread underwater acoustic (UWA) channels of strong dispersion is considered. We show that although the UWA channel dispersion causes the degeneration of channel sparsity, it leads to a low-rank structure especially when the channel delay-Doppler-spread function is separable in delay and Doppler domain. Therefore, we introduce the low-rank criterion to estimate the UWA channels, which can help to improve the estimation performance in the case of strong dispersion. The estimator is based on the discrete delay-Doppler-spread function representation of channel, and is formulated as a low-rank matrix recovery problem which can be solved by the singular value projection technique. Simulation examples are carried out to demonstrate the effectiveness of the proposed low-rank-based channel estimator.
Highlights
Due to multi-path propagation and time-varying nature, the underwater acoustic (UWA) channel is known to be doubly spread in delay and Doppler domain [1,2,3,4], which is referred to as doubly selective in the literature [5,6,7]
By formulating the channel in a discrete matrix with respect to the delay-Doppler-spread function (called the delay-Doppler-spread matrix (DDSM) in this paper), we show that the channel dispersion gives rise to a useful low-rank structure of DDSM, especially when the channel delay-Doppler-spread function is separable in delay and Doppler domain
The time-varying UWA channel is generated with statistical underwater acoustic channel model proposed in [1]
Summary
Due to multi-path propagation and time-varying nature, the underwater acoustic (UWA) channel is known to be doubly spread in delay and Doppler domain [1,2,3,4], which is referred to as doubly selective in the literature [5,6,7]. To combat the effects of delay-Doppler spread in time-varying UWA channels, accurate estimation of the multi-path delay, Doppler frequency, and the channel gain is needed, which is a challenging task for high- speed UWA communications [8,9]. Channel estimation is usually performed with the aid of training signals. In [2], a sparse channel estimation technique is developed based on the delay-Doppler-spread function representation of the channel. In [3], a computationally
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