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

中文翻译：

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可分离的联合盲解卷积和解混

盲解卷积和解混是从卷积的总和中重建卷积信号和核的问题。在诸如盲MIMO的许多应用中会出现此问题。这项工作提出了一种通过凸优化实现盲解卷积和解混的可分离方法。与以前的工作不同，我们的公式允许将问题分解为较小的优化问题，从而显着提高了复杂性。我们开发了与原始不可分问题相符的恢复保证，并在几种归一化约束条件下证明了该方法的性能。