Computer and Information Science Department, Korea University, Sejong-ro, Sejong 2511, Republic of Korea.
Graduate School of Information, Yonsei University, Seoul 03722, Republic of Korea.
Sensors (Basel). 2023 Apr 10;23(8):3848. doi: 10.3390/s23083848.
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.
我们提出了一种基于深度神经网络的编解码器的深度扩展复用（DSM）方案，并研究了基于 DNN 的编解码器系统的训练过程。使用基于自动编码器结构的多路复用多个正交资源，该结构源于深度学习技术。此外，我们研究了可以利用各种方面（例如信道模型、训练信号噪声比（SNR）水平和噪声类型）的性能的训练方法。通过训练基于 DNN 的编解码器并通过仿真结果验证，评估了这些因素的性能。
