Learning-Based Two-Way Communications: Algorithmic Framework and Comparative Analysis
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913934645133312 |
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| author | Nickel, David R. Das, Anindya Bijoy Love, David J. Brinton, Christopher G. |
| author_facet | Nickel, David R. Das, Anindya Bijoy Love, David J. Brinton, Christopher G. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15514 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning-Based Two-Way Communications: Algorithmic Framework and Comparative Analysis Nickel, David R. Das, Anindya Bijoy Love, David J. Brinton, Christopher G. Signal Processing 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. |
| title | Learning-Based Two-Way Communications: Algorithmic Framework and Comparative Analysis |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.15514 |