2023/12/01 by Shuyi Chen, Yingzhe Hui, Chen, Shuyi +11 · 1 citation
Computer Science · Engineering · Materials Science · #Advanced Memory and Neural Computing #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Metamaterials and Metasurfaces Applications #Neural Networks and Reservoir Computing #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2312.00535
openalex publication_date 2023/12/01 · openalex created_date 2023/12/05 · openalex updated_date 2026/07/28
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 (D2NN) 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.