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.
翻译:2019年推出了5G标准,该标准有望在4G的基础上大大改善数据率。 5G标准虽然仍处于初创阶段,但在通信技术研究界中,超越5G的通信技术出现了更大的转变。 最近出现了加强无线通信和赋予无线通信权能的机器学习方法,这为6G重新定义无线通信提供了巨大潜力。 不断演变的通信系统将因物理层的基本信号处理而在延缓度、吞吐量和可靠性方面受到阻碍。在本立场文件中,我们提出需要重新设计迭代信号处理算法,利用深层开发的技术满足6G网络的物理层要求。为此,我们首先介绍6G所设想的通信结构所提出的服务要求和所构成的主要挑战。我们概述了6G网络中传统算法原则和数据饥饿深度学习方法的缺陷。具体地说,通过勾画域知识与DL之间的相互作用来进行深入的信号处理。本文章中审查的深度演练方法明确放在了为未来一代硬件网络实现真正高效化的硬件网络所施加的高度要求的背景下。