RIS-Based On-the-Air Semantic Communications -- a Diffractional Deep
  Neural Network Approach
Shuyi Chen Yingzhe Hui Yifan Qin Yueyi Yuan Weixiao Meng Xuewen Luo Hsiao-Hwa Chen
Semantic communication has gained significant attention recently due to its
advantages in achieving higher transmission efficiency by focusing on semantic
information instead of bit-level information. However, current AI-based
semantic communication methods require digital hardware for implementation.
With the rapid advancement on reconfigurable intelligence surfaces (RISs), a
new approach called on-the-air diffractional deep neural networks (D$^2$NN) can
be utilized to enable semantic communications on the wave domain. This paper
proposes a new paradigm of RIS-based on-the-air semantic communications, where
the computational process occurs inherently as wireless signals pass through
RISs. We present the system model and discuss the data and control flows of
this scheme, followed by a performance analysis using image transmission as an
example. In comparison to traditional hardware-based approaches, RIS-based
semantic communications offer appealing features, such as light-speed
computation, low computational power requirements, and the ability to handle
multiple tasks simultaneously.