arxivJul 2

Deep Learning-Driven Black-Box Doherty Power Amplifier with Pixelated Output Combiner and Extended Efficiency Range

arXiv:2603.16565v2 Announce Type: replace-cross Abstract: This article presents a deep learning-driven inverse design methodology for Doherty power amplifiers (PA) with multi-port pixelated output combiner networks. A deep convolutional neural network (CNN) is developed and trained as an electromagn

DE1 model #deep-learning #signal-processing #hardware-architecture Read on arxiv →

arxivJun 16

Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data

arXiv:2410.16089v2 Announce Type: replace Abstract: The unique cost, flexibility, speed, and efficiency of modern UAVs make them an attractive choice in many applications in contemporary society. This, however, causes an ever-increasing number of reported malicious or accidental incidents, rendering

DECO2 models #uav-detection #deep-learning #signal-processing Read on arxiv →

arxivMay 25bullish

Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling

arXiv:2603.01655v2 Announce Type: replace Abstract: Ray tracing has become a standard for accurate radio propagation modeling, but suffers from exponential computational complexity, as the number of candidate paths scales with the number of objects raised to the interaction order. This bottleneck li

GE1 model #machine-learning #signal-processing #optimization Read on arxiv →

arxivMay 21bullish

Cross-Subject Intracranial EEG Reconstruction from Scalp Recordings Using Multi-Scale Cross-Attention Transformers

arXiv:2605.18897v1 Announce Type: cross Abstract: Intracranial EEG (iEEG) provides high-fidelity neural recordings essential for clinical and brain-computer interface applications, but acquiring these signals requires invasive surgery. While recent studies have attempted to estimate iEEG from non-in

CA1 model #neural-recordings #brain-computer #machine-learning Read on arxiv →

arxivMay 8bullish

PPO-Based Dynamic Positioning of HAPS-BS in Wind-Disturbed Stratospheric Maritime Networks

arXiv:2605.05240v1 Announce Type: cross Abstract: High-Altitude Platform Stations (HAPS) offer a promising solution for wide-area wireless coverage in maritime regions lacking terrestrial infrastructure. However, maintaining reliable performance is challenging due to dynamic ship mobility and atmosp

PR1 model #wireless-coverage #reinforcement-learning #maritime-networks Read on arxiv →

arxivMay 6bullish

Adaptive 3D-RoPE: Physics-Aligned Rotary Positional Encoding for Wireless Foundation Models

arXiv:2605.00968v1 Announce Type: cross Abstract: Positional encoding plays a pivotal role in determin?ing the extrapolation and generalization performance of wireless foundation models for channel state information (CSI) modeling, latent characterization, and task-specific prediction. However, exis

#wireless #channel-state #positional-encoding Read on arxiv →

arxivMay 6

Data driven approach for Outdoor Channel Prediction in 5G and Beyond

arXiv:2605.01777v1 Announce Type: cross Abstract: An evolution of Wireless Communications towards 5G and beyond provides improved user experience in terms of quality of services. Understanding and estimating Channel information plays crucial role in providing better user experience. Traditional meth

LISUDE3 models #wireless-communications #5g #machine-learning Read on arxiv →

arxivApr 10

Introducing Echo Networks for Computational Neuroevolution

arXiv:2604.08204v1 Announce Type: new Abstract: For applications on the extreme edge, minimal networks of only a few dozen artificial neurons for event detection and classification in discrete time signals would be highly desirable. Feed-forward networks, RNNs, and CNNs evolved through evolutionary

ECNERN4 models · +1 #machine-learning #neural-networks #evolutionary-computing Read on arxiv →