Paper Reading AI Learner

Variational Autoencoders for Studying the Manifold of Precoding Matrices with High Spectral Efficiency

2021-11-23 11:45:45
Evgeny Bobrov (1 and 2), Alexander Markov (3), Dmitry Vetrov (3) ((1) Moscow Research Center, Huawei Technologies, Russia, (2) M. V. Lomonosov Moscow State University, Russia, (3) National Research University Higher School of Economics, Russia)
arXiv_AI
       
Abstract

In multiple-input multiple-output (MIMO) wireless communications systems, neural networks have been employed for channel decoding, detection, channel estimation, and resource management. In this paper, we look at how to use a variational autoencoder to find a precoding matrix with a high Spectral Efficiency (SE). To identify efficient precoding matrices, an optimization approach is used. Our objective is to create a less time-consuming algorithm with minimum quality degradation. To build precoding matrices, we employed two forms of variational autoencoders: conventional variational autoencoders (VAE) and conditional variational autoencoders (CVAE). Both methods may be used to study a wide range of optimal precoding matrices. To the best of our knowledge, the development of precoding matrices for the spectral efficiency objective function (SE) utilising VAE and CVAE methods is being published for the first time.

Abstract (translated)

URL

https://arxiv.org/abs/2111.15626

PDF

https://arxiv.org/pdf/2111.15626.pdf

Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot