Data-Driven Compressed Sensing for Massive Wireless Access

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.

Automated summary

The central challenge in massive machine-type communications is to connect a large number of uncoordinated devices through a limited spectrum. Data-driven methods and variations of neural networks are discussed for grant-free random access, with the demonstration of performance gains. Future challenges and potential directions are also mentioned.