We propose a Bayesian deep learning framework for model driven online sparse channel estimation task in Multi-user MIMO systems. Tools from Bayesian neural network and stochastic variational Bayesian Inference are utilized to capture aleatoric and epistemic uncertainty estimates. We treat the network prediction as an auxiliary variable to allow inference performance to be unaffected by the stage of training of the network. In addition to providing uncertainty estimates, being Bayesian, the framework enables us the possibility to marginalize over penalty parameters and is well suited for online scenario with changing environments. Our simulations show that the framework is robust to model mismatch, and efficiently captures uncertainty in the predictions.

中文翻译：

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使用贝叶斯神经网络的毫米波系统在线下行链路多用户信道估计


我们提出了一种贝叶斯深度学习框架，用于多用户 MIMO 系统中模型驱动的在线稀疏信道估计任务。利用贝叶斯神经网络和随机变分贝叶斯推理的工具来捕获任意和认知不确定性估计。我们将网络预测视为辅助变量，以使推理性能不受网络训练阶段的影响。除了提供不确定性估计之外，作为贝叶斯框架，该框架使我们能够边缘化惩罚参数，并且非常适合环境不断变化的在线场景。我们的模拟表明，该框架对于模型失配具有鲁棒性，并且有效地捕获了预测中的不确定性。