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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
On the Response of Basic Walfisch-Ikegami and Walfisch-Be...
Ayotunde Ayorinde, Hisham Muhammed, Ike Mowete · 2023-02-24 · via eess.SP updates on arXiv.org

This paper systematically examines the response to Quasi Moment Method (QMM) calibration, of the basic COST231 Walfisch Ikegami, ITUR Walfisch Ikegami, and Walfisch-Bertoni models. First, it is demonstrated that the component parameters of the models are suitable candidates for use as expansion or testing functions with QMM pathloss model calibration schemes; and thereafter, the basic models are subjected to calibration, using measurement data available in the open literature. Computational results reveal that the COST231 Walfisch Ikegami and ITU Walfisch Ikegami models have virtually identical QMM calibration root mean square error (RMSE) responses; and that the Walfisch Bertoni model has better RMSE responses than both of them. A particular attribute revealed by the simulation results is that all QMM calibrated Walfisch type basic models have excellent mean prediction error (MPE) metrics (in general, less than 0.001dB). In addition to pathloss prediction profiles, the paper also presents profiles of disaggregated net pathloss, in terms of contributions by component parameters, including roof-top-to-street diffraction and scatter loss and multiscreen diffraction loss.