Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Early-Exiting DNN MIMO Detector Design with Fast Signal Estimation Process

Authors
Hua, D.-T.Lee, D.Tran, A.T.Lakew, D.S.Do, Q.T.Cho, S.
Issue Date
2023
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
deep convolutional neural network; deep neural network; early-exiting model; Multiple input multiple output; signal detector
Citation
5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023, pp 505 - 507
Pages
3
Journal Title
5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
Start Page
505
End Page
507
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67583
DOI
10.1109/ICAIIC57133.2023.10067114
ISSN
2831-6991
Abstract
Multi-input multi-output (MIMO) receiver design has been continuously investigated in order to satisfy the massive requirements of six-generation (6G) wireless networks regarding the accuracy-centric signal estimation under the dynamic channel state information. In addtion, the architectures MIMO detector is a pioneering work for applying artificial intelligence techniques. Eventhough the DL-based MIMO detector can be able to provide accurate signal estimation, huge execution time and energy consumption of immensely large deep neural network (DNN) models in size has not been studied. To this end, we introduce a concept of early-exiting DNN MIMO detector and fast signal estimation, which outputting the estimated transmitted signal without a full forward and back propagation computation of the conventional DNN model. The early-exiting model and the fast signal estimation concept is believed to be suitable for scenarios of practical time sensitive and resouce limited applications. © 2023 IEEE.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Software > School of Computer Science and Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Cho, Sung Rae photo

Cho, Sung Rae
소프트웨어대학 (소프트웨어학부)
Read more

Altmetrics

Total Views & Downloads

BROWSE