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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
Joint Transmitter and Receiver Design for Movable Antenna...
Ying Gao, Qingqing Wu, Wen Chen · 2024-04-18 · via eess.SP updates on arXiv.org

Movable antenna (MA) is an emerging technology that utilizes localized antenna movement to achieve better channel conditions for enhancing communication performance. In this paper, we study the MA-enhanced multicast transmission from a base station equipped with multiple MAs to multiple groups of single-MA users. Our goal is to maximize the minimum weighted signal-to-interference-plus-noise ratio (SINR) among all the users by jointly optimizing the position of each transmit/receive MA and the transmit beamforming. To tackle this challenging problem, we first consider the single-group scenario and propose an efficient algorithm based on the techniques of alternating optimization and successive convex approximation. Particularly, when optimizing transmit or receive MA positions, we construct a concave lower bound for the signal-to-noise ratio (SNR) of each user using only the second-order Taylor expansion, which simplifies the problem-solving process compared to the existing two-step approximation method. The proposed design is then extended to the general multi-group scenario. Simulation results show that the proposed algorithm converges faster than the existing two-step approximation method, achieving a 3.4% enhancement in max-min SNR. Moreover, it can improve the max-min SNR/SINR by up to 22.5%, 181.7%, and 343.9% compared to benchmarks employing only receive MAs, only transmit MAs, and both transmit and receive FPAs, respectively.