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arXiv · 2504.15514

Learning-Based Two-Way Communications: Algorithmic Framework and Comparative Analysis

David R. Nickel·Anindya Bijoy Das·David J. Love·Christopher G. Brinton
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Abstract

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 for each other 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 framework. We compare such schemes against their 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.

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David R. Nickel, Anindya Bijoy Das, David J. Love, Christopher G. Brinton. 2025-04-22. Learning-Based Two-Way Communications: Algorithmic Framework and Comparative Analysis. https://arxiv.org/abs/2504.15514

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