Semantic Communication Meets Edge Intelligence: Semantic-Relay-Aided Text Transmissions

Semantic communication (SemCom) has emerged as a promising technology to improve the spectrum efficiency of next-generation wireless networks, by extracting meaningful content from the data and transmitting relevant semantic information only. However, the existing research usually overlooks the limited computing and storage resources on the mobile devices, which may make it unaffordable to implement resource-demanding deep learning (DL)-based semantic encoders/decoders. Moreover, besides the end-to-end SemCom framework, cooperative SemCom has not been well studied in the existing works, which can further enhance the communication performance. To address these issues, we propose a new architecture in this article, called semantic relay (SemRelay), which acts as an edge server to provide DL-enabled SemCom (DeepSC) services for two categories of edge users, called semantic users (SemUsers) with rich computing resources and conventional users (ConUsers) with limited resources. Two new transmission protocols are proposed for enabling text transmissions from the base station to the SemUsers and ConUsers, respectively, via the SemRelay (edge server). Moreover, an optimization problem is formulated to jointly design the SemRelay transmit power allocation and system bandwidth allocation to maximize the weighted sum-rate of all the users. Although this problem is nonconvex and hence difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution by applying the block coordinate descent and successive convex approximation techniques. Finally, the numerical results demonstrate the effectiveness of our proposed algorithm and the superior performance of the proposed SemRelay as compared to the traditional decode-and-forward relays, especially in the small bandwidth regime.