HIGHLIGHTS
- who: Access and collaborators from the (UNIVERSITY) have published the paper: Deep Learning-based Activity Detection for Grant-free Random Access, in the Journal: (JOURNAL)
- what: Considering these promising results in this paper the authors develop two deep learning (DL) sparse support recovery algorithms to detect active devices in mMTC random Unlike previous works the authors investigate the impact of the type of sequences for preamble design on the activity detection accuracy. Besides the authors demonstrate that the DL algorithms achieve activity detection accuracy comparable to state-of-the-art techniques with extremely low computational . . .
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