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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
Liquid Intelligent Metasurface for Fluid Antennas-Assiste...
Li-Hsiang Shen · 2025-07-22 · via eess.SP updates on arXiv.org

This paper proposes a novel liquid intelligent metasurface (LIM)-assisted downlink multi-user multiple-input single-output (MISO) system, wherein both the base station (BS) and the metasurface are respectively equipped with fluid antennas (FA) and liquid elements. Unlike conventional reconfigurable metasurface-assisted systems with static geometries, the proposed architecture enables joint electromagnetic and spatial reconfigurability by allowing both the FA-empowered BS (FAS) and LIM to dynamically adjust their small-scale positions in addition to beamforming and phase-shift controls. We formulate a sum-rate maximization problem that jointly optimizes the BS beamforming, LIM phase-shifts, and the positions of fluid antennas and liquid elements. The problem is highly non-convex due to coupling between variables, fractional expressions, unit-modulus constraints as well as spatial correlation functions. To address these challenges, we adopt alternating optimization and introduce auxiliary variables and employ Lagrangian dual and quadratic transformation, successive convex approximation (SCA) as well as the penalty convex-concave procedure (PCCP) to solve the respective subproblems. Simulation results have demonstrated that the proposed FAS-LIM architecture significantly outperforms benchmark methods employing conventional fixed metasurface and fixed antenna arrays in terms of various parameter settings.