Super-Resolution Blind Channel Modeling

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We consider the problem of extracting a wide-band channel model when only measurements in parts of this band are available, specifically in disjoint frequency sub-bands. Conventional channel modeling techniques cannot model at all those parts of the band where no sounding signals are available; or, if they use conventional interpolation, suffer from poor performance. To circumvent this obstacle, we develop in this paper a three-step super-resolution blind algorithm. First, the path delays are estimated by exploiting super-resolution algorithms such as MUSIC or ESPRIT based on the transfer function of each sub-band, separately. Exploiting such a set of delay estimates, the proposed algorithm performs blind (i.e., without training signal) channel estimation over the unmeasured sub-bands, and subsequently derives the frequency response over the whole wide-band channel. Finally, estimates derived from different sub-bands are combined via a soft combining technique. Computer simulations show that the proposed super-resolution blind algorithm can achieve a significant performance gain over conventional methods.