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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
Movable Antennas Meet Intelligent Reflecting Surface: Whe...
Xin Wei, Weidong Mei, Qingqing Wu, Boyu Ning, Zhi Chen · 2024-08-28 · via eess.SP updates on arXiv.org

Intelligent reflecting surface (IRS) and movable antenna (MA)/fluid antenna (FA) techniques have both received increasing attention in the realm of wireless communications due to their ability to reconfigure and improve wireless channel conditions. In this paper, we investigate the integration of MAs/FAs into an IRS-assisted wireless communication system. In particular, we consider the downlink transmission from a multi-MA base station (BS) to a single-antenna user with the aid of an IRS, aiming to maximize the user's received signal-to-noise ratio (SNR), by jointly optimizing the BS/IRS active/passive beamforming and the MAs' positions. Due to the similar capability of MAs and IRS for channel reconfiguration, we first conduct theoretical analyses of the performance gain of MAs over conventional fixed-position antennas (FPAs) under the line-of-sight (LoS) BS-IRS channel and derive the conditions under which the performance gain becomes more or less significant. Next, to solve the received SNR maximization problem, we propose an alternating optimization (AO) algorithm that decomposes it into two subproblems and solve them alternately. Numerical results are provided to validate our analytical results and evaluate the performance gains of MAs over FPAs under different setups.