Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn to operate in challenging communication scenarios. However, wireless receiver design poses unique challenges that fundamentally differ from those encountered in traditional deep learning domains. The main challenges arise from the limited power and computational resources of wireless devices, as well as from the dynamic nature of wireless communications, which causes continual changes to the data distribution. These challenges impair conventional AI based on highly-parameterized DNNs, motivating the development of adaptive, flexible, and light-weight AI for wireless communications, which is the focus of this article. Here, we propose that AI-based design of wireless receivers requires rethinking of the three main pillars of AI: architecture, data, and training algorithms. In terms of architecture, we review how to design compact DNNs via model-based deep learning. Then, we discuss how to acquire training data for deep receivers without compromising spectral efficiency. Finally, we review efficient, reliable, and robust training algorithms via meta-learning and generalized Bayesian learning. Numerical results are presented to demonstrate the complementary effectiveness of each of the surveyed methods. We conclude by presenting opportunities for future research on the development of practical deep receivers

翻译：人工智能（AI）被预期在未来无线技术中发挥关键作用，其中深度神经网络（DNN）使数字接收机能够在具有挑战性的通信场景中学习运行。然而，无线接收机的设计面临与传统深度学习领域截然不同的独特挑战。主要挑战源于无线设备有限的功率和计算资源，以及无线通信的动态特性——这导致数据分布持续变化。这些挑战削弱了基于高参数化DNN的传统AI的性能，从而推动开发适用于无线通信的自适应、灵活且轻量级的AI——这正是本文的研究重点。在此，我们提出基于AI的无线接收机设计需要重新审视AI的三大支柱：架构、数据与训练算法。在架构方面，我们探讨如何通过模型驱动深度学习设计紧凑型DNN。随后，讨论如何在保证频谱效率的前提下获取深度接收机的训练数据。最后，通过元学习与广义贝叶斯学习，介绍高效、可靠且稳健的训练算法。仿真结果展示了所述各类方法的互补有效性。最后，我们展望了实用化深度接收机未来的研究方向。