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Deep Learning and Artificial Intelligence in Wireless Communications Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (25 May 2023) | Viewed by 1417

Special Issue Editors


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Guest Editor
Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University, Tongyeong 53064, Korea
Interests: deep learning; wireless communications; cognitive radio; smart grid; data science
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Defense ICT Convergence Research Section, Electronics and Telecommunications Research Institute (ETRI), Daejeon, Korea
Interests: wireless communication; game theory; reinforcement learning; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ever since Guglielmo Marconi succeeded in transmitting data over a wireless channel, the inherent convenience of wireless communication technology has enabled it to exert an ever-increasing impact on our daily lives. The increasing popularity of wireless communication technology has resulted in the rapid advance of wireless technologies, including the fifth generation (5G) communication, fog networking, molecular communication, and millimeter wave communication. These new technologies for wireless communication are likely to have diverse service requirements, such as extremely low delay, and complex system models, which makes it harder to properly manage them using conventional approaches. This leads to a new research paradigm, i.e., deep-learning- and artificial-intelligence-based wireless communications, which have gained a lot popularity due to their remarkable performance compared to traditional schemes. These new data-driven approaches have changed the paradigm of research to a learning-based approach where the scheme is designed autonomously observing data. This new research paradigm is more applicable to  recent wireless technologies, i.e., future wireless communication systems, since they can provide near-optimal performance without relying on the specific mathematically tractable system model, yet lower computational complexity is required. This Special Issue encourages the submission of high-quality, innovative, and original contributions covering topics regarding the application of artificial intelligence and deep learning in wireless communication systems.

Dr. Woongsup Lee
Dr. Byungchang Chung
Guest Editors

Manuscript Submission Information

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Keywords

  • deep learning
  • artificial intelligence
  • future wireless communiation
  • big data
  • machine learning

Published Papers (1 paper)

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Research

11 pages, 434 KiB  
Communication
Deep Spread Multiplexing and Study of Training Methods for DNN-Based Encoder and Decoder
by Minhoe Kim and Woongsup Lee
Sensors 2023, 23(8), 3848; https://doi.org/10.3390/s23083848 - 10 Apr 2023
Viewed by 898
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 [...] Read more.
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. Full article
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