Deep learning is envisioned to play a key role in the design of future wireless receivers. A popular approach to design learning-aided receivers combines deep neural networks (DNNs) with traditional model-based receiver algorithms, realizing hybrid model-based data-driven architectures. Such architectures typically include multiple modules, each carrying out a different functionality dictated by the model-based receiver workflow. Conventionally trained DNN-based modules are known to produce poorly calibrated, typically overconfident, decisions. Consequently, incorrect decisions may propagate through the architecture without any indication of their insufficient accuracy. To address this problem, we present a novel combination of Bayesian deep learning with hybrid model-based data-driven architectures for wireless receiver design. The proposed methodology, referred to as modular Bayesian deep learning, is designed to yield calibrated modules, which in turn improves both accuracy and calibration of the overall receiver. We specialize this approach for two fundamental tasks in multiple-input multiple-output (MIMO) receivers - equalization and decoding. In the presence of scarce data, the ability of modular Bayesian deep learning to produce reliable uncertainty measures is consistently shown to directly translate into improved performance of the overall MIMO receiver chain.

翻译：深度学习被预期将在未来无线接收机设计中发挥关键作用。一种流行的学习辅助接收机设计方法将深度神经网络与传统基于模型的接收机算法相结合，实现混合型模型驱动与数据驱动架构。此类架构通常包含多个模块，每个模块执行由基于模型的接收机工作流程所规定的不同功能。传统训练的基于深度神经网络的模块会产生校准不良（通常过度自信）的决策，导致错误决策可能在缺乏准确性指示的情况下在架构中传播。针对该问题，我们提出一种将贝叶斯深度学习与混合型模型驱动数据驱动架构相结合的新型无线接收机设计方案。所提出的方法被称为模块化贝叶斯深度学习，其设计目标是生成经过校准的模块，从而提升整个接收机的准确性与校准性能。我们将该方法具体应用于多输入多输出接收机的两个基本任务——均衡与解码。在数据稀缺的情况下，模块化贝叶斯深度学习生成可靠不确定性度量的能力被证明能直接转化为整体MIMO接收机链路性能的提升。