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

Minhoe Kim1, Woongsup Lee2

  • 1Computer and Information Science Department, Korea University, Sejong-ro, Sejong 2511, Republic of Korea.

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|April 28, 2023
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Summary
This summary is machine-generated.

We introduce a novel deep spread multiplexing (DSM) scheme utilizing deep neural network (DNN) encoder and decoder systems. This research explores optimal training procedures for enhanced multiplexing performance across diverse communication conditions.

Keywords:
SCMAautoencoderdeep learningdeep spread multiplexing

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Traditional multiplexing schemes face limitations in dynamic communication environments.
  • Deep learning offers potential for advanced signal processing techniques.
  • Autoencoder structures are emerging as powerful tools in deep learning applications.

Purpose of the Study:

  • To propose and investigate a deep spread multiplexing (DSM) scheme.
  • To explore deep neural network (DNN) based encoder and decoder training procedures.
  • To enhance multiplexing performance for multiple orthogonal resources.

Main Methods:

  • Designing a multiplexing scheme using an autoencoder structure derived from deep learning.
  • Investigating various training procedures for the DNN-based encoder and decoder system.
  • Evaluating performance across different channel models, signal-to-noise (SNR) levels, and noise types.

Main Results:

  • The proposed DSM scheme demonstrates effective multiplexing capabilities.
  • Training procedures significantly impact the performance of the DNN-based encoder and decoder.
  • Simulation results validate the performance under various tested conditions.

Conclusions:

  • Deep learning-based encoder-decoder systems offer a promising approach for advanced multiplexing.
  • Optimized training strategies are crucial for maximizing the benefits of DSM.
  • The developed DSM scheme shows potential for improving communication system efficiency.