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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
RF Lens-Embedded Antenna Array for mmWave MIMO: Design an...
Yae Jee Cho, Gee-Yong Suk, Byoungnam Kim, Dong Ku Kim, Chan-Byou · 2018-01-23 · via eess.SP updates on arXiv.org

The requirement of high data-rate in the fifth generation wireless systems (5G) calls for the ultimate utilization of the wide bandwidth in the mmWave frequency band. Researchers seeking to compensate for mmWave's high path loss and to achieve both gain and directivity have proposed that mmWave multiple-input multiple-output (MIMO) systems make use of beamforming systems. Hybrid beamforming in mmWave demonstrates promising performance in achieving high gain and directivity by using phase shifters at the analog processing block. What remains a problem, however, is the actual implementation of mmWave beamforming systems; to fabricate such a system is costly and complex. With the aim of reducing such cost and complexity, this article presents actual prototypes of the lens antenna as an effective device to be used in the future 5G mmWave hybrid beamforming systems. Using a lens as a passive phase shifter enables beamforming without the heavy network of active phase shifters, while gain and directivity are achieved by the energy-focusing property of the lens. Proposed in this article are two types of lens antennas, one for static and the other for mobile usage. Their performance is evaluated using measurements and simulation data along with link-level analysis via a software defined radio (SDR) platform. Results show the promising potential of the lens antenna for its high gain and directivity, and its improved beam-switching feasibility compared to when a lens is not used. System-level evaluations reveal the significant throughput enhancement in both real indoor and outdoor environments. Moreover, the lens antenna's design issues are also discussed by evaluating different lens sizes.