An Outlook on the Interplay of AI and Software-Defined Metasurfaces

Recent advances in programmable metasurfaces, also dubbed as software-defined metasurfaces (SDMs), are envisioned to offer a paradigm shift from uncontrollable to fully tunable and customizable wireless propagation environments, enabling a plethora of new applications and technological trends. Therefore, in view of this cutting edge technological concept, we first review the architecture and electromagnetic waves manipulation functionalities of SDMs. We then detail some of the recent advancements that have been made towards realizing these programmable functionalities in wireless communication applications. Furthermore, we elaborate on how artificial intelligence (AI) can address various constraints introduced by real-time deployment of SDMs, particularly in terms of latency, storage, energy efficiency, and computation. A review of the state-of-the-art research on the integration of AI with SDMs is presented, highlighting their potentials as well as challenges. Finally, the paper concludes by offering a look ahead towards unexplored possibilities of AI mechanisms in the context of SDMs.

[1]  Ian F. Akyildiz,et al.  A New Wireless Communication Paradigm through Software-Controlled Metasurfaces , 2018, IEEE Communications Magazine.

[2]  Harald Haas,et al.  Distributed Spatial Modulation: A Cooperative Diversity Protocol for Half-Duplex Relay-Aided Wireless Networks , 2016, IEEE Transactions on Vehicular Technology.

[3]  Chau Yuen,et al.  Indoor Signal Focusing Improvement via Deep Learning Configured Intelligent Metasurfaces , 2019 .

[4]  Sotiris Ioannidis,et al.  An Interpretable Neural Network for Configuring Programmable Wireless Environments , 2019, 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[5]  Qiang Cheng,et al.  Space-time-coding digital metasurfaces , 2018, Nature Communications.

[6]  Ian F. Akyildiz,et al.  A Novel Communication Paradigm for High Capacity and Security via Programmable Indoor Wireless Environments in Next Generation Wireless Systems , 2018, Ad Hoc Networks.

[7]  Osvaldo Simeone,et al.  A Very Brief Introduction to Machine Learning With Applications to Communication Systems , 2018, IEEE Transactions on Cognitive Communications and Networking.

[8]  Polina Kapitanova,et al.  Smart Table Based on a Metasurface for Wireless Power Transfer , 2018, Physical Review Applied.

[9]  Mehdi Bennis,et al.  A Speculative Study on 6G , 2019, IEEE Wireless Communications.

[10]  Shi Jin,et al.  Wireless communications with programmable metasurface: Transceiver design and experimental results , 2018, China Communications.

[11]  Mérouane Debbah,et al.  Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both? , 2019, IEEE Transactions on Communications.

[12]  Chau Yuen,et al.  Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication , 2018, IEEE Transactions on Wireless Communications.

[13]  Hamed Haddadi,et al.  Deep Learning in Mobile and Wireless Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.