Super-Resolution Blind Channel Modeling

In this work, we propose a super-resolution blind channel modeling algorithm to characterize wide-band channels comprised of disjoint frequency subbands. Since sounding signals are not available over the frequency guard bands separating adjacent subbands, conventional channel modeling methods suffer from poor performance in modeling the channel frequency response over the guard bands. To circumvent this obstacle, a three-step super-resolution blind algorithm is developed. First, the path delays are estimated by exploiting super-resolution algorithms such as MUSIC or ESPRIT. After that, the proposed algorithm performs blind channel estimation over the guard bands and subsequently derive the frequency response over the whole wideband channel. Finally, estimates derived from different subbands are combined via a soft combining technique. It is shown by computer simulations that the proposed superresolution blind algorithm can achieve a significant performance gain over conventional methods.