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

Adaptive and Flexible Model-Based AI for Deep Receivers in Dynamic Channels