
We propose the first non-trivial generic decoding algorithm for codes in the sum-rank metric. The new method combines ideas of well-known generic decoders in the Hamming and rank metric. For the same code parameters and number of errors, the new generic decoder has a larger expected complexity than the known generic decoders for the Hamming metric and smaller than the known rank-metric decoders. Furthermore, we give a formal hardness reduction, providing evidence that generic sum-rank decoding is computationally hard. As a by-product of the above, we solve some fundamental coding problems in the sum-rank metric: we give an algorithm to compute the exact size of a sphere of a given sum-rank radius, and also give an upper bound as a closed formula; and we study erasure decoding with respect to two different notions of support.
FOS: Computer and information sciences, Computer Science - Information Theory, Information Theory (cs.IT), Probabilistic Hardness Reduction, Sum-Rank Metric, Generic Decoding, Decisional Sum-Rank Syndrome Decoding Problem, Sum-Rank-Metric Codes
FOS: Computer and information sciences, Computer Science - Information Theory, Information Theory (cs.IT), Probabilistic Hardness Reduction, Sum-Rank Metric, Generic Decoding, Decisional Sum-Rank Syndrome Decoding Problem, Sum-Rank-Metric Codes
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