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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
Blocker-Aware Beamforming and Dynamic Power Allocation fo...
Abdulahi Abiodun Badrudeen, Nakyung Lee, Adam Dubs, Sunwoo Kim · 2025-11-09 · via eess.SP updates on arXiv.org

This paper proposes a blocker-aware multicarrier integrated sensing and communication (ISAC)-non orthogonal multiple access (NOMA) system, leveraging hybrid beamforming and dynamic power allocation to enhance spectrum efficiency in 6G networks. Recognizing the performance degradation caused by environmental blockers, the system introduces a joint waveform design that ensures robust operation under varying channel conditions. A channel switching mechanism is deployed to reroute communication through alternative non-line-of-sight paths when the primary line-of-sight links are obstructed. Moreover, a dynamic power allocation strategy enforces a minimum rate constraint for the weak NOMA user, ensuring consistent quality of service. Extensive simulations over multiple blockage scenarios and signal to noise (SNR) conditions validate the effectiveness of the proposed solution. Notably, under severe blockage, the system achieves up to a 400% sensing rate enhancement at 15 dB SNR, with only a 20% reduction in communication rate. These results corroborate the system's ability to adapt and optimize joint sensing-communication performance in practical deployment environments.