Today, wireless networks are becoming responsible for serving intelligent applications, such as extended reality and metaverse, holographic telepresence, autonomous transportation, and collaborative robots. Although current fifth-generation (5G) networks can provide high data rates in terms of Gigabytes/second, they cannot cope with the high demands of the aforementioned applications, especially in terms of the size of the high-quality live videos and images that need to be communicated in real-time. Therefore, with the help of artificial intelligence (AI)-based future sixth-generation (6G) networks, the semantic communication concept can provide the services demanded by these applications. Unlike Shannon's classical information theory, semantic communication urges the use of the semantics (meaningful contents) of the data in designing more efficient data communication schemes. Hence, in this paper, we model semantic communication as an energy minimization framework in heterogeneous wireless networks with respect to delay and quality-of-service constraints. Then, we propose a sub-optimal solution to the NP-hard combinatorial mixed-integer nonlinear programming problem (MINLP) by utilizing efficient techniques such as discrete optimization variables' relaxation. In addition, AI-based autoencoder and classifier are trained and deployed to perform semantic extraction, reconstruction, and classification services. Finally, we compare our proposed sub-optimal solution with different state-of-the-art methods, and the obtained results demonstrate its superiority.

翻译：如今，无线网络正承担起服务智能应用（如扩展现实与元宇宙、全息远程呈现、自主交通及协作机器人）的责任。尽管当前第五代（5G）网络能以吉字节/秒量级提供高数据速率，但仍无法应对上述应用的高需求，尤其是在需实时通信的高质量直播视频与图像的数据量方面。因此，基于人工智能（AI）的未来第六代（6G）网络将借助语义通信概念，为这些应用提供所需服务。与香农经典信息论不同，语义通信主张利用数据中的语义（有意义内容）来设计更高效的数据通信方案。基于此，本文在异构无线网络中，考虑时延与服务质量约束，将语义通信建模为能量最小化框架。随后，我们通过采用离散优化变量松弛等高效技术，针对NP难组合混合整数非线性规划问题（MINLP）提出一种次优解方案。此外，我们训练并部署了基于AI的自编码器与分类器，用于执行语义提取、重构与分类服务。最后，将所提出的次优解与多种现有方法进行对比，实验结果表明其具有优越性。