Joint Constellation and Labeling Design for Gaussian Interference Channels via Autoencoders
Razavi Pour, Seyed Reza; Vaezi, Mojtaba
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https://hdl.handle.net/2142/130298
Description
Title
Joint Constellation and Labeling Design for Gaussian Interference Channels via Autoencoders
Author(s)
Razavi Pour, Seyed Reza
Vaezi, Mojtaba
Issue Date
2025-09-17
Keyword(s)
Interference channel
Deep learning
Constellation design
Abstract
We propose a learning-based modulation design and symbol labeling scheme for the two-user interference channel (IC) with arbitrary channel gains. To this end, we develop autoencoders (AEs) with trainable mappers and demappers, jointly optimized in an end-to-end manner. We show that initialization of the transmitter constellations plays a key role in achieving stable convergence and improving performance. Our analysis shows that the proposed AEs can learn effective modulation schemes across a wide range of interference levels and signal-to-noise ratios by jointly performing bitwise clustering through suitable labeling and maximizing the minimum symbol distances to reduce the impact of interference in the IC. We further incorporate low-density parity-check (LDPC) channel codes and demonstrate that the proposed AE-IC with LDPC outperforms existing methods in terms of bit error rates.
Publisher
Allerton Conference on Communication, Control, and Computing
Series/Report Name or Number
2025 61st Allerton Conference on Communication, Control, and Computing Proceedings
ISSN
2836-4503
Type of Resource
Text
Genre of Resource
Conference Paper/Presentation
Language
eng
Handle URL
https://hdl.handle.net/2142/130298&&
Copyright and License Information
Copyright 2025 is held by Seyed Reza Razavi Pour and Mojtaba Vaezi.
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