Semantic Edge Computing (SEC) and Semantic Communications (SemComs) have been proposed as viable approaches to achieve real-time edge-enabled intelligence in sixth-generation (6G) wireless networks. On one hand, SemCom leverages the strength of Deep Neural Networks (DNNs) to encode and communicate the semantic information only, while making it robust to channel distortions by compensating for wireless effects. Ultimately, this leads to an improvement in the communication efficiency. On the other hand, SEC has leveraged distributed DNNs to divide the computation of a DNN across different devices based on their computational and networking constraints. Although significant progress has been made in both fields, the literature lacks a systematic view to connect both fields. In this work, we fulfill the current gap by unifying the SEC and SemCom fields. We summarize the research problems in these two fields and provide a comprehensive review of the state of the art with a focus on their technical strengths and challenges.

翻译：语义边缘计算与语义通信已被提出作为在第六代无线网络中实现实时边缘智能的可行途径。一方面，语义通信利用深度神经网络的优势，仅对语义信息进行编码与传输，并通过补偿无线信道效应使其对信道失真具有鲁棒性。这最终提升了通信效率。另一方面，语义边缘计算利用分布式深度神经网络，根据各设备的计算与网络约束条件，将深度神经网络的计算任务分配到不同设备上执行。尽管这两个领域均已取得显著进展，现有文献仍缺乏将两者联系起来的系统性视角。本研究通过统一语义边缘计算与语义通信领域填补了这一空白。我们总结了两大领域的研究问题，并聚焦其技术优势与挑战，对前沿进展进行了全面综述。