Deep Learning Transceiver for Terahertz Band Communication System with 1-bit ADC and Oversampling

Metasebia D. Gemeda, Min S. Han, Ameha T. Abebe, Chung G. Kang

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

In this study, with the aim of reducing power consumption for terahertz band wireless communication, we present a deep learning-based solution for transceiver design with 1-bit quantization and oversampling at the receiver, and Faster- than-Nyquist transmission. Our simulation results illustrate that the studied system with 1-bit quantization achieves bit-error- rate performance comparable to that of an end-to-end channel autoencoder without the constraint of 1-bit quantization subject to the same spectral efficiency. It is also demonstrated that reliable communication can be achieved at rates exceeding 5.3 bits/sec/Hz which corresponds to 80% of the achievable capacity at adequate SNR.

Original languageEnglish
Pages (from-to)515-516
Number of pages2
JournalProceedings - IEEE Consumer Communications and Networking Conference, CCNC
DOIs
Publication statusPublished - 2022
Event19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022 - Virtual, Online, United States
Duration: 2022 Jan 82022 Jan 11

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • autoencoder
  • deep learning
  • one-bit-quantization
  • oversampling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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