Deep Joint Source-Channel Coding for Two-Way Relay Channels

This paper proposes a deep joint source-channel coding (DeepJSCC) scheme for two-way relay channels (TWRC) where two source nodes exchange image samples by means of a cooperative relay node. Unlike conventional DeepJSCC approaches developed for one-way relay channels (OWRC), the proposed DeepJSCC-TWRC facilitates self-interference cancellation at source nodes, thereby utilizing time resources more efficiently. We propose to share identical encoder and decoder models at source nodes so that they can observe a number of image samples during the training step. The effectiveness of the proposed method is demonstrated through numerical results.

Authors

Institutions

Publication Details

Published
2025-01-15
DOI
https://doi.org/10.1109/icoin63865.2025.10992784
Primary Topic
Cooperative Communication and Network Coding
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Deep Joint Source-Channel Coding for Two-Way Relay Channels

Hoon Lee, Han S. Uhm, Doyun Lee
Cooperative Communication and Network Coding
article

Deep Joint Source-Channel Coding for Two-Way Relay Channels

Hoon Lee, Han S. Uhm, Doyun Lee
article en

Abstract

This paper proposes a deep joint source-channel coding (DeepJSCC) scheme for two-way relay channels (TWRC) where two source nodes exchange image samples by means of a cooperative relay node. Unlike conventional DeepJSCC approaches developed for one-way relay channels (OWRC), the proposed DeepJSCC-TWRC facilitates self-interference cancellation at source nodes, thereby utilizing time resources more efficiently. We propose to share identical encoder and decoder models at source nodes so that they can observe a number of image samples during the training step. The effectiveness of the proposed method is demonstrated through numerical results.

Ulsan National Institute of Science and Technology (KR)
Defense Acquisition Program Administration
Openalex Percentile: Top 90%
Cooperative Communication and Network Coding
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.