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Found 6 papers, 1 papers with code
T-PRIME: Transformer-based Protocol Identification for Machine-learning at the Edge
Spectrum sharing allows different protocols of the same standard (e. g., 802. 11 family) or different standards (e. g., LTE and DVB) to coexist in overlapping frequency bands.
TRACTOR: Traffic Analysis and Classification Tool for Open RAN
5G and beyond cellular networks promise remarkable advancements in bandwidth, latency, and connectivity.
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).
Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard
Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguration.
Machine Learning on Camera Images for Fast mmWave Beamforming
Perfect alignment in chosen beam sectors at both transmit- and receive-nodes is required for beamforming in mmWave bands.
ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks
This paper describes the architecture and performance of ORACLE, an approach for detecting a unique radio from a large pool of bit-similar devices (same hardware, protocol, physical address, MAC ID) using only IQ samples at the physical layer.
