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Found 3 papers, 1 papers with code
Learning from the Best: Active Learning for Wireless Communications
We evaluate the performance of different active learning algorithms on a publicly available multi-modal dataset with different modalities including image and LiDAR.
Neural Network-based OFDM Receiver for Resource Constrained IoT Devices
Here, ML blocks replace the individual processing blocks of an OFDM receiver, and we specifically describe this swapping for the legacy channel estimation, symbol demapping, and decoding blocks with Neural Networks (NNs).
PRONTO: Preamble Overhead Reduction with Neural Networks for Coarse Synchronization
The L-STF occupies upto 40% of the preamble length and takes upto 32 us of airtime.
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