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Research Paper#Integrated Sensing and Communications, Beamforming, OFDM, Angle Estimation🔬 ResearchAnalyzed: Jan 3, 2026 06:27

Beam-Squint-Aided Hierarchical Sensing for Integrated Sensing and Communications

Published:Dec 31, 2025 08:42
1 min read
ArXiv

Analysis

This paper introduces a novel hierarchical sensing framework for wideband integrated sensing and communications using uniform planar arrays (UPAs). The key innovation lies in leveraging the beam-squint effect in OFDM systems to enable efficient 2D angle estimation. The proposed method uses a multi-stage sensing process, formulating angle estimation as a sparse signal recovery problem and employing a modified matching pursuit algorithm. The paper also addresses power allocation strategies for optimal performance. The significance lies in improving sensing performance and reducing sensing power compared to conventional methods, which is crucial for efficient integrated sensing and communication systems.
Reference

“The proposed framework achieves superior performance over conventional sensing methods with reduced sensing power.”

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Research Paper#Spiking Neural Networks, Adversarial Robustness, Machine Learning🔬 ResearchAnalyzed: Jan 3, 2026 16:26

Reliable Adversarial Robustness Evaluation for Spiking Neural Networks

Published:Dec 27, 2025 08:43
1 min read
ArXiv

Analysis

This paper addresses the challenge of evaluating the adversarial robustness of Spiking Neural Networks (SNNs). The discontinuous nature of SNNs makes gradient-based adversarial attacks unreliable. The authors propose a new framework with an Adaptive Sharpness Surrogate Gradient (ASSG) and a Stable Adaptive Projected Gradient Descent (SA-PGD) attack to improve the accuracy and stability of adversarial robustness evaluation. The findings suggest that current SNN robustness is overestimated, highlighting the need for better training methods.
Reference

“The experimental results further reveal that the robustness of current SNNs has been significantly overestimated and highlighting the need for more dependable adversarial training methods.”

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