Blind Deconvolution using Modulated Inputs

This paper considers the blind deconvolution of multiple modulated signals, and an arbitrary filter. Multiple inputs $\boldsymbol{s}_1, \boldsymbol{s}_2, \ldots, \boldsymbol{s}_N =: [\boldsymbol{s}_n]$ are modulated (pointwise multiplied) with random sign sequences $\boldsymbol{r}_1, \boldsymbol{r}_2, \ldots, \boldsymbol{r}_N =: [\boldsymbol{r}_n]$, respectively, and the resultant inputs $(\boldsymbol{s}_n \odot \boldsymbol{r}_n) \in $\mathbb{C}$^Q,
n = [N]$areconvolvedagainstanarbitraryinput$\boldsymbol{h} \in $\mathbb{C}$^M$toyieldthemeasurements$\boldsymbol{y}_n = (\boldsymbol{s}_n\odot \boldsymbol{r}_n)\circledast \boldsymbol{h}, \ n = [N] := 1,2,\ldots,N,$where$\odot$,and$\circledast$ denote pointwise multiplication, and circular convolution. Given $[{\mathit{y}}_n]$, we want to recover the unknowns $[{\mathit{s}}_n]$, and $\mathit{h}$. We make a structural assumption that unknown $[{\mathit{s}}_n]$ are members of a known $K$-dimensional (not necessarily random) subspace, and prove that the unknowns can be recovered from sufficiently many observations using an alternating gradient descent algorithm whenever the modulated inputs $\boldsymbol{s}_n \odot \boldsymbol{r}_n$arelongenough,i\mathrm{.}e,$Q \gtrsim KN+M$ (to within log factors and signal dispersion/coherence parameters).