Related papers: RIS-Based On-the-Air Semantic Communications -- a Diffractional Deep Neural Network Approach

RIS-Based On-the-Air Semantic Communications -- a Diffractional Deep Neural Network Approach

URL: http://arxiv.org/abs/2312.00535v1
Date: Fri, 1 Dec 2023 12:15:49 GMT
Title: RIS-Based On-the-Air Semantic Communications -- a Diffractional Deep Neural Network Approach
Authors: Shuyi Chen, Yingzhe Hui, Yifan Qin, Yueyi Yuan, Weixiao Meng, Xuewen Luo, Hsiao-Hwa Chen
Abstract summary: Current AI-based semantic communication methods require digital hardware for implementation. RIS-based semantic communications offer appealing features, such as light-speed computation, low computational power requirements, and the ability to handle multiple tasks simultaneously.
Score: 10.626169088908867
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: 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.

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