Online Learning of Modular Bayesian Deep Receivers: Single-Step Adaptation with Streaming Data
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914595590897664 |
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| author | Gusakov, Yakov Simeone, Osvaldo Routtenberg, Tirza Shlezinger, Nir |
| author_facet | Gusakov, Yakov Simeone, Osvaldo Routtenberg, Tirza Shlezinger, Nir |
| contents | Deep neural network (DNN)-based receivers offer a powerful alternative to classical model-based designs for wireless communication, especially in complex and nonlinear propagation environments. However, their adoption is challenged by the rapid variability of wireless channels, which makes pre-trained static DNN-based receivers ineffective, and by the latency and computational burden of online stochastic gradient descent (SGD)-based learning. In this work, we propose an online learning framework that enables rapid low-complexity adaptation of DNN-based receivers. Our approach is based on two main tenets. First, we cast online learning as Bayesian tracking in parameter space, enabling a single-step adaptation, which deviates from multi-epoch SGD . Second, we focus on modular DNN architectures that enable parallel, online, and localized variational Bayesian updates. Simulations with practical communication channels demonstrate that our proposed online learning framework can maintain a low error rate with markedly reduced update latency and increased robustness to channel dynamics as compared to traditional gradient descent based method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06045 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online Learning of Modular Bayesian Deep Receivers: Single-Step Adaptation with Streaming Data Gusakov, Yakov Simeone, Osvaldo Routtenberg, Tirza Shlezinger, Nir Signal Processing Information Theory Deep neural network (DNN)-based receivers offer a powerful alternative to classical model-based designs for wireless communication, especially in complex and nonlinear propagation environments. However, their adoption is challenged by the rapid variability of wireless channels, which makes pre-trained static DNN-based receivers ineffective, and by the latency and computational burden of online stochastic gradient descent (SGD)-based learning. In this work, we propose an online learning framework that enables rapid low-complexity adaptation of DNN-based receivers. Our approach is based on two main tenets. First, we cast online learning as Bayesian tracking in parameter space, enabling a single-step adaptation, which deviates from multi-epoch SGD . Second, we focus on modular DNN architectures that enable parallel, online, and localized variational Bayesian updates. Simulations with practical communication channels demonstrate that our proposed online learning framework can maintain a low error rate with markedly reduced update latency and increased robustness to channel dynamics as compared to traditional gradient descent based method. |
| title | Online Learning of Modular Bayesian Deep Receivers: Single-Step Adaptation with Streaming Data |
| topic | Signal Processing Information Theory |
| url | https://arxiv.org/abs/2511.06045 |