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Found 7 papers, 0 papers with code
Self-supervised Contrastive Learning for 6G UM-MIMO THz Communications: Improving Robustness Under Imperfect CSI
This paper investigates the potential of contrastive learning in 6G ultra-massive multiple-input multiple-output (UM-MIMO) communication systems, specifically focusing on hybrid beamforming under imperfect channel state information (CSI) conditions at THz.
A Fast Effective Greedy Approach for MU-MIMO Beam Selection in mm-Wave and THz Communications
This paper addresses the beam-selection challenges in Multi-User Multiple Input Multiple Output (MU-MIMO) beamforming for mm-wave and THz channels, focusing on the pivotal aspect of spectral efficiency (SE) and computational efficiency.
Beyond Traditional Beamforming: Singular Vector Projection Techniques for MU-MIMO Interference Management
The numerical results demonstrate the superiority of the SVBS algorithm over the existing algorithms, with the IOSVB offering near-identical SE and the DR-IOSVB balancing the performance and computational efficiency.
Real-time Seismic Intensity Prediction using Self-supervised Contrastive GNN for Earthquake Early Warning
In this paper, we propose a novel deep learning approach, Seismic Contrastive Graph Neural Network (SC-GNN), for highly accurate seismic intensity prediction using a small portion of initial seismic waveforms from a few seismic stations.
Automated Level Crossing System: A Computer Vision Based Approach with Raspberry Pi Microcontroller
A Raspberry Pi microcontroller detects impending trains using computer vision on live video, and the intersection is closed until the incoming train passes unimpeded.
A CNN based Multifaceted Signal Processing Framework for Heart Rate Proctoring Using Millimeter Wave Radar Ballistocardiography
The recent pandemic has refocused the medical world's attention on the diagnostic techniques associated with cardiovascular disease.
A CNN-LSTM-based Fusion Separation Deep Neural Network for 6G Ultra-Massive MIMO Hybrid Beamforming
Simulation results indicate that the proposed system can attain almost the same level of SE as that of the numerical iterative algorithms, while incurring a substantial reduction in computational cost.
