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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
Gradient Ascent Algorithm for Enhancing Secrecy Rate in W...
Neji Mensi, Danda B. Rawat, Elyes Balti · 2021-08-12 · via eess.SP updates on arXiv.org

The emerging Internet of Things (IoT) and bidirectional communications in smart grid are expected to improve smart grid capabilities and electricity management. Because of massive number of IoT devices in smart grid, size of the data to be transmitted increases, that demands a high data rate to meet the real-time smart grid communications requirements. Sub-6 GHz, millimeter-wave (mmWave) technologies, and massive multiple-input multiple-output (MIMO) technologies can meet high data rate demands. However, IoT enabled smart grid is still subject to various security challenges such as eavesdropping, where attackers attempt to overhear the transmitted signals and the jamming attack, where the attacker perturbs the received signals at the receiver. In this paper, our goal is to investigate jamming and eavesdropping attacks while improving secrecy capacity for smart grid communications. Specifically, we propose to employ a hybrid beamforming design for wireless communications in smart energy grid. In previous works, the secrecy capacity is increased by randomly augmenting the source power or setting the system combiners. Unlike state-of-the-art, we design and evaluate the Gradient Ascent algorithm to search for the best combiners/waveform that maximizes the secrecy capacity in smart grid communications. We also study two different optimization scenarios by considering both fixed and variable transmit power. Numerical results are used for performance evaluation and supporting our formal analysis.