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Learning-Based Two-Way Communications: Algorithmic Framework and Comparative Analysis
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AI
DOI：10.1109/LCOMM.2025.3588133.png)
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Abstract
En 中文
Machine learning (ML)-based feedback channel coding has garnered significant research interest in the past few years. However, there has been limited research exploring ML approaches in the so-called two-way setting where two users jointly encode messages and feedback over a shared channel. In this work, we present a general architecture for ML-based two-way feedback coding, and show how several popular one-way schemes can be converted to the two-way setting through our algorithmic framework. We compare such schemes against one-way counterparts, revealing error-rate benefits of ML-based two-way coding in certain signal-to-noise ratio (SNR) regimes. We then analyze the tradeoffs between error performance and computational overhead for three state-of-the-art neural network coding models instantiated in the two-way paradigm.
Keywords:
Vectors
Symbols
Decoding
Binary sequences
Signal to noise ratio
Feature extraction
Channel coding
Training
Optimization
Transformers
AI/ML for communications
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W
