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eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Overcoming BS Down-Tilt for Air-Ground ISAC Coverage: Ant...
Lingyi Zhu, Zhongxiang Wei, Fan Liu, Jianjun Wu, Xiao-Wei Tang, · 2026-01-18 · via eess.SP updates on arXiv.org

Integrated sensing and communication holds great promise for low-altitude economy applications. However, conventional downtilted base stations primarily provide sectorized forward lobes for ground services, failing to sense air targets due to backward blind zones. In this paper, a novel antenna structure is proposed to enable air-ground beam steering, facilitating simultaneous full-space sensing and communication (S&C). Specifically, instead of inserting a reflector behind the antenna array for backlobe mitigation, an omni-steering plate is introduced to collaborate with the active array for omnidirectional beamforming. Building on this hardware innovation, sum S&C mutual information (MI) is maximized, jointly optimizing user scheduling, passive coefficients of the omni-steering plate, and beamforming of the active array. The problem is decomposed into two subproblems: one for optimizing passive coefficients via Riemannian gradient on the manifold, and the other for optimizing user scheduling and active array beamforming. Exploiting relationships among S&C MI, data decoding MMSE, and parameter estimation MMSE, the original subproblem is equivalently transformed into a sum weighted MMSE problem, rigorously established via the Lagrangian and first-order optimality conditions. Simulations show that the proposed algorithm outperforms baselines in sum-MI and MSE, while providing 360 sensing coverage. Beampattern analysis further demonstrates effective user scheduling and accurate target alignment.