Search Results for author:
Found 12 papers, 5 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.
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.
Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming
To achieve this, we go beyond instantiating a single twin and propose the 'Multiverse' paradigm, with several possible digital twins attempting to capture the real world at different levels of fidelity.
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.
Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network
Beam selection for millimeter-wave links in a vehicular scenario is a challenging problem, as an exhaustive search among all candidate beam pairs cannot be assuredly completed within short contact times.
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.
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.
Open-World Class Discovery with Kernel Networks
We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples.
Learn-Prune-Share for Lifelong Learning
In lifelong learning, we wish to maintain and update a model (e. g., a neural network classifier) in the presence of new classification tasks that arrive sequentially.
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.
