Separable Joint Blind Deconvolution and Demixing

Blind deconvolution and demixing is the problem of reconstructing convolved signals kernels from sum their convolutions. This arises in many applications, such as blind MIMO. work presents a separable approach to via convex optimization. Unlike previous works, our formulation allows separation into smaller optimization problems, which significantly improves complexity. We develop recovery guarantees, comply with those original non-separable problem, demonstrate method performance under several normalization constraints.