  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. In this position paper, we motivate the need
to redesign iterative signal processing algorithms by leveraging deep unfolding
techniques to fulfill the physical layer requirements for 6G networks. To this
end, we begin by presenting the service requirements and the key challenges
posed by the envisioned 6G communication architecture. We outline the
deficiencies of the traditional algorithmic principles and data-hungry deep
learning (DL) approaches in the context of 6G networks. Specifically, deep
unfolded signal processing is presented by sketching the interplay between
domain knowledge and DL. The deep unfolded approaches reviewed in this article
are positioned explicitly in the context of the requirements imposed by the
next generation of cellular networks. Finally, this article motivates open
research challenges to truly realize hardware-efficient edge intelligence for
future 6G networks.
