Deep learning (DL) has been recognized as an instrumental tool for the design of future communication systems. Since it is still not clear whether a fully data-driven end-to-end communication learning approach would eventually outperform the traditional ones in terms of performance and complexity, it is argued that the optimal design needs to be tackled by taking the benefits of both model-based and data-driven approaches and by leveraging the concept of transfer learning. However, the grand question lies in how this can be implemented efficiently. As such, this paper proposes an efficient end-to-end OFDM based receiver learning approach based on distributed data-driven and model-based approaches. The approach relies mainly on augmenting a typical OFDM receiver’s processing blocks with a shallow neural network as a data-driven stub to improve its performance. Relying on a two-phases training approach, the last receiver’s processing stage benefits from the transfer learning approach to improve its performance. Limiting the scope to a typical OFDM transmission where the DL-based methods fail, the proposed model-aided shallow learning receiver shows performance improvements compared to the baseline structure.

深度学习（DL）被公认为是未来通信系统设计的工具。由于仍不清楚完全数据驱动的端到端通信学习方法在性能和复杂性方面是否最终会优于传统方法，因此有人认为，必须通过兼顾两者的优势来解决最佳设计问题。基于模型和数据驱动的方法，并利用转移学习的概念。但是，最大的问题在于如何有效地实现这一点。因此，本文提出了一种基于分布式数据驱动和基于模型的有效的基于端到端OFDM的接收机学习方法。该方法主要依赖于用浅景深扩展典型的OFDM接收机的处理块。神经网络作为数据驱动的存根来提高其性能。依靠两阶段的培训方法，最后一个接收者的处理阶段将从迁移学习方法中受益，以提高其性能。限于基于DL的方法失败的典型OFDM传输的范围，与基线结构相比，所提出的模型辅助浅学习接收机显示出性能上的改进。