Recently, a novel coded compressed sensing (CCS) approach was proposed in [1] for dealing with the scalability problem for large sensing matrices in massive machine-type communications. The approach is to divide the compressed sensing (CS) problem into smaller CS sub-problems. However, such an approach requires stitching the results from the sub-problems to recover the result in the original CS problem. For this stitching problem, we propose a hierarchical stitching algorithm that is easier to implement in hardware for parallelization than the tree coding algorithm in [1]. For our algorithm, we also derive an upper bound on the probability of recovery errors.
翻译：最近,在[1]中提议采用新颖的编码压缩遥感(CCS)方法,处理大规模机器型通信中大型遥感矩阵的可缩放性问题。这种方法是将压缩遥感(CS)问题分为较小的CS子问题。然而,这种方法需要缝合子问题的结果,以恢复原CS问题的结果。对于这个缝合问题,我们建议采用一种等级缝合算法,在硬件中比[1]中的树编码算法更容易执行。对于我们的算法,我们还需要对回收误差的概率有一个上限。
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