Reliable recovery of hierarchically sparse signals and application in machine-type communications

We examine and propose a solution to the problem of recovering a block sparse signal with sparse blocks from linear measurements. Such problems naturally emerge in the context of mobile communication, in settings motivated by desiderata of a 5G framework. We introduce a new variant of the Hard Thresholding Pursuit (HTP) algorithm [1] referred to as HiHTP. For the specific class of sparsity structures, HiHTP performs significantly better in numerical experiments compared to HTP. We provide both a proof of convergence and a recovery guarantee for noisy Gaussian measurements that exhibit an improved asymptotic scaling in terms of the sampling complexity in comparison with the usual HTP algorithm.