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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
Location Sensing and Beamforming Design for IRS-Enabled M...
Zhouyuan Yu, Xiaoling Hu, Chenxi Liu, Mugen Peng, Caijun Zhong · 2022-08-10 · via eess.SP updates on arXiv.org

This paper explores the potential of the intelligent reflecting surface (IRS) in realizing multi-user concurrent communication and localization, using the same time-frequency resources. Specifically, we propose an IRS-enabled multi-user integrated sensing and communication (ISAC) framework, where a distributed semi-passive IRS assists the uplink data transmission from multiple users to the base station (BS) and conducts multi-user localization, simultaneously. We first design an ISAC transmission protocol, where the whole transmission period consists of two periods, i.e., the ISAC period for simultaneous uplink communication and multi-user localization, and the pure communication (PC) period for only uplink data transmission. For the ISAC period, we propose a multi-user location sensing algorithm, which utilizes the uplink communication signals unknown to the IRS, thus removing the requirement of dedicated positioning reference signals in conventional location sensing methods. Based on the sensed users' locations, we propose two novel beamforming algorithms for the ISAC period and PC period, respectively, which can work with discrete phase shifts and require no channel state information (CSI) acquisition. Numerical results show that the proposed multi-user location sensing algorithm can achieve up to millimeter-level positioning accuracy, indicating the advantage of the IRS-enabled ISAC framework. Moreover, the proposed beamforming algorithms with sensed location information and discrete phase shifts can achieve comparable performance to the benchmark considering perfect CSI acquisition and continuous phase shifts, demonstrating how the location information can ensure the communication performance.