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：在无线接收机设计中，深度神经网络可与传统基于模型的接收机算法相结合，构建能够利用领域知识的模块化混合模型/数据驱动架构。此类架构通常包含多个模块，每个模块执行不同功能。传统训练的基于深度神经网络的模块会产生校准不良（通常表现为过度自信）的判决结果。这意味着当某个模块的判决出现错误时，可能因缺乏精度不足的指示信号而沿架构传播。为解决该问题，我们提出一种贝叶斯学习与混合模型/数据驱动架构相结合的新型无线接收机设计方法。该方法被称为"模块化基于模型的贝叶斯学习"，能够生成校准更优的模块，从而提升整体接收机的精度与校准性能。我们以近期提出的DeepSIC MIMO接收机为例验证该方法，结果表明其相较现有最优学习方法具有显著改进。