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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
Resource Allocation via Backscatter-Aware Transmit Antenn...
Rahul Gulia, Ashish Sheikh, Feyisayo Favour Popoola, Serisha Vad · 2025-12-31 · via eess.SP updates on arXiv.org

This paper addresses a fundamental physical layer conflict in hybrid Wireless Sensor Networks (WSNs) between high-throughput primary communication and the stringent power envelope requirements of passive backscatter sensors. We propose a Backscatter-Constrained Transmit Antenna Selection (BC-TAS) framework, a per-subcarrier selection strategy for multi-antenna illuminators operating within a Multi-Dimensional Orthogonal Frequency Division Multiplexing (MD-OFDM) architecture. Unlike conventional signal-to-noise ratio (SNR) centric selection schemes, BC-TAS employs a multi-objective cost function that jointly maximizes desired link reliability, stabilizes the incident RF energy envelope at passive Surface Acoustic Wave (SAW) sensors, and suppresses interference toward coexisting victim receivers. By exploiting the inherent sparsity of MD-OFDM, the proposed framework enables dual-envelope regulation, simultaneously reducing the transmitter Peak-to-Average Power Ratio (PAPR) and the Backscatter Crest Factor (BCF) observed at the tag. To enhance robustness under imperfect Channel State Information (CSI), a Kalman-based channel smoothing mechanism is incorporated to maintain selection stability in low-SNR regimes. Numerical results using IEEE 802.11be dispersive channel models and a nonlinear Rapp power amplifier demonstrate that BC-TAS achieves orders-of-magnitude improvement in outage probability and significant gains in energy efficiency compared to conventional MU-MIMO baselines, while ensuring spectral mask compliance under reduced power amplifier back-off. These results establish BC-TAS as an effective illuminator-side control mechanism for enabling reliable and energy-stable sensing and communication coexistence in dense, power-constrained wireless environments.