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可与传统的基于模型的接收器算法相结合,以实现模块混合模式/数据驱动的结构,这些结构可以考虑到域知识,这些结构通常包括多个模块,每个模块都具有不同的功能。据了解,经过《公约》培训的DNN的模块会产生校准不当、通常过于自信的决定。这意味着不正确的决定可能通过该架构传播,而没有任何迹象表明其不够准确。为解决这一问题,我们提出了一种新型组合,将巴伊西亚人的学习与基于模型/数据驱动的无线接收器设计的混合结构结合起来。拟议方法称为模块模型的Bayesian学习,在更精确的模块中取得成果,提高整个接收器的准确性和校准。我们为最近提议的深思基海事组织接收器演示了这一方法,显示在最先进的学习方法方面有了重大改进。