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arXiv · 2209.05951

Data-Driven Compressed Sensing for Massive Wireless Access

Yanna Bai·Wei Chen·Feifei Sun·Bo Ai·Petar Popovski
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Abstract

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.

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Yanna Bai, Wei Chen, Feifei Sun, Bo Ai, Petar Popovski. 2022-09-13. Data-Driven Compressed Sensing for Massive Wireless Access. https://arxiv.org/abs/2209.05951

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