Deep Spread Multiplexing and Study of Training Methods for DNN-Based Encoder and Decoder.

基于 DNN 的编译码器的深度扩展复用及训练方法研究 [J].

• 影响指数: 3.847
• 发表时间: 2023 Apr 10
• 来源期刊: Sensors (Basel)
• DOI: 10.3390/s23083848
• PMID: 37112189
• 文章类型: Journal Article
• 中科院分区: Q2;Q3;Q1
• 作者列表: Kim M,Lee W

关键词: SCMA; autoencoder; deep learning; deep spread multiplexing

Abstract

We propose a deep spread multiplexing (DSM) scheme using a DNN-based encoder and decoder and we investigate training procedures for a DNN-based encoder and decoder system. Multiplexing for multiple orthogonal resources is designed with an autoencoder structure, which originates from the deep learning technique. Furthermore, we investigate training methods that can leverage the performance in terms of various aspects such as channel models, training signal-to-noise (SNR) level and noise types. The performance of these factors is evaluated by training the DNN-based encoder and decoder and verified with simulation results.

摘要

我们提出了一种使用基于DNN的编码器和解码器的深度扩展复用（DSM）方案，并研究了基于DNN的编码器和解码器系统的训练过程。采用自动编码器结构设计了多个正交资源的多路复用，这源于深度学习技术。此外，我们研究了可以在渠道模型等各个方面利用性能的训练方法，训练信噪比(SNR)水平和噪声类型。通过训练基于DNN的编码器和解码器来评估这些因素的性能，并通过仿真结果进行验证。