AI-Native Communications

Hankyul Baek; Haemin Lee; Soohyun Park; Hyunsoo Lee; Jihong Park; Joongheon Kim

doi:10.1007/978-3-031-37920-8_21

AI-Native Communications

Hankyul Baek
, Haemin Lee
, Soohyun Park
, Hyunsoo Lee
, Jihong Park
, Joongheon Kim^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter

1 Citation (Scopus)

Abstract

The emergence of artificial intelligence (AI)-based methods evolving from 5G to 6G is accelerating. Therefore, to optimize the communication system in the 6G era, it is essential to adapt several AI-based optimization methods according to each environment. In this chapter, we introduce two general AI-based optimization methods, named AI-aided and AI-native. In addition, we describe the pros and cons of each method. Finally, by illustrating previous studies on AI in communication, we aim to speed up the development of both AI-based and AI-native optimization methods in line with the upcoming 6G era.

Original language	English
Title of host publication	Signals and Communication Technology
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	563-571
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-031-37920-8_21
Publication status	Published - 2024

Publication series

Name	Signals and Communication Technology
Volume	Part F1944
ISSN (Print)	1860-4862
ISSN (Electronic)	1860-4870

Bibliographical note

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

AI-aided networks
AI-native communications
Autoencoder
PHY layer applications
Semantic communications

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications
Electrical and Electronic Engineering

Access to Document

10.1007/978-3-031-37920-8_21

Cite this

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title = "AI-Native Communications",

abstract = "The emergence of artificial intelligence (AI)-based methods evolving from 5G to 6G is accelerating. Therefore, to optimize the communication system in the 6G era, it is essential to adapt several AI-based optimization methods according to each environment. In this chapter, we introduce two general AI-based optimization methods, named AI-aided and AI-native. In addition, we describe the pros and cons of each method. Finally, by illustrating previous studies on AI in communication, we aim to speed up the development of both AI-based and AI-native optimization methods in line with the upcoming 6G era.",

keywords = "AI-aided networks, AI-native communications, Autoencoder, PHY layer applications, Semantic communications",

author = "Hankyul Baek and Haemin Lee and Soohyun Park and Hyunsoo Lee and Jihong Park and Joongheon Kim",

note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.",

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doi = "10.1007/978-3-031-37920-8\_21",

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series = "Signals and Communication Technology",

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AU - Baek, Hankyul

AU - Lee, Haemin

AU - Park, Soohyun

AU - Lee, Hyunsoo

AU - Park, Jihong

AU - Kim, Joongheon

PY - 2024

Y1 - 2024

N2 - The emergence of artificial intelligence (AI)-based methods evolving from 5G to 6G is accelerating. Therefore, to optimize the communication system in the 6G era, it is essential to adapt several AI-based optimization methods according to each environment. In this chapter, we introduce two general AI-based optimization methods, named AI-aided and AI-native. In addition, we describe the pros and cons of each method. Finally, by illustrating previous studies on AI in communication, we aim to speed up the development of both AI-based and AI-native optimization methods in line with the upcoming 6G era.

AB - The emergence of artificial intelligence (AI)-based methods evolving from 5G to 6G is accelerating. Therefore, to optimize the communication system in the 6G era, it is essential to adapt several AI-based optimization methods according to each environment. In this chapter, we introduce two general AI-based optimization methods, named AI-aided and AI-native. In addition, we describe the pros and cons of each method. Finally, by illustrating previous studies on AI in communication, we aim to speed up the development of both AI-based and AI-native optimization methods in line with the upcoming 6G era.

KW - AI-aided networks

KW - AI-native communications

KW - Autoencoder

KW - PHY layer applications

KW - Semantic communications

UR - https://www.scopus.com/pages/publications/85179885543

U2 - 10.1007/978-3-031-37920-8_21

DO - 10.1007/978-3-031-37920-8_21

M3 - Chapter

AN - SCOPUS:85179885543

T3 - Signals and Communication Technology

SP - 563

EP - 571

BT - Signals and Communication Technology

PB - Springer Science and Business Media Deutschland GmbH

ER -

AI-Native Communications

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

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