In the design of wireless receivers, DNNs can be combined with traditional model-based receiver algorithms to realize modular hybrid model-based/data-driven architectures that can account for domain knowledge. Such architectures typically include multiple modules, each carrying out a different functionality. Conventionally trained DNN-based modules are known to produce poorly calibrated, typically overconfident, decisions. This implies that an incorrect decision may propagate through the architecture without any indication of its insufficient accuracy. To address this problem, we present a novel combination of Bayesian learning with hybrid model-based/data-driven architectures for wireless receiver design. The proposed methodology, referred to as modular model-based Bayesian learning, results in better calibrated modules, improving accuracy and calibration of the overall receiver. We demonstrate this approach for the recently proposed DeepSIC MIMO receiver, showing significant improvements with respect to the state-of-the-art learning methods.
翻译:在无线接收器的设计中,DNN可以与传统的基于模型的接收器算法相结合,实现模块化混合基于模型/数据驱动的架构,可以考虑领域知识。这样的架构通常包括多个模块,每个模块执行不同的功能。已知,传统的训练基于DNN的模块会生成粗略的预测,这些预测通常过于自信,表示错误的决策可能会在整个架构中传播而没有任何指示其不足的准确性。为了解决这个问题,我们提出了一种新颖的基于贝叶斯学习的混合模型/数据驱动架构,用于无线接收器的设计。所提出的方法称为模块化基于模型的贝叶斯学习,可以得到更好的标定模块,提高整个接收器的准确性和校准性。我们展示了该方法用于最近提出的DeepSIC MIMO接收器,证明了相对于现有学习方法的显着改进。