The central challenge in massive machine-type communications (mMTC) is to connect a large number of uncoordinated devices through a limited spectrum. The typical mMTC communication pattern is sporadic, with short packets. This could be exploited in grant-free random access in which the activity detection, channel estimation, and data recovery are formulated as a sparse recovery problem and solved via compressed sensing algorithms. This approach results in new challenges in terms of high computational complexity and latency. We present how data-driven methods can be applied in grant-free random access and demonstrate the performance gains. Variations of neural networks for the problem are discussed, as well as future challenges and potential directions.
@article{arxiv.2209.05951,
title = {Data-Driven Compressed Sensing for Massive Wireless Access},
author = {Yanna Bai and Wei Chen and Feifei Sun and Bo Ai and Petar Popovski},
journal= {arXiv preprint arXiv:2209.05951},
year = {2022}
}
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in IEEE Communication Magazine vol:60, iss:11, 2022