Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning with Deep Unfolding

The year 2019 witnessed the rollout of the 5G standard, which promises to offer significant data rate improvement over 4G. While 5G is still in its infancy, there has been an increased shift in the research community for communication technologies beyond 5G. The recent emergence of machine learning approaches for enhancing wireless communications and empowering them with much-desired intelligence holds immense potential for redefining wireless communication for 6G. The evolving communication systems will be bottlenecked in terms of latency, throughput, and reliability by the underlying signal processing at the physical layer. This article presents the service requirements and the key challenges posed by the envisioned 6G communication architecture. We sketch the deficiencies of the traditional algorithmic principles and data-hungry deep learning (DL) approaches in the context of 6G networks. Specifically, we present model-driven DL approaches as a key enabler towards provisioning the intelligent physical layer for 6G AI radio. Finally, we conclude by presenting promising directions to motivate future 6G research.