Semantic communication has emerged as a promising technology to break the Shannon limit by extracting the meaning of source data and sending relevant semantic information only. However, some mobile devices may have limited computation and storage resources, which renders it difficult to deploy and implement the resource-demanding deep learning based semantic encoder/decoder. To tackle this challenge, we propose in this paper a new semantic relay (SemRelay), which is equipped with a semantic receiver for assisting text transmission from a resource-abundant base station (BS) to a resource-constrained mobile device. Specifically, the SemRelay first decodes the semantic information sent by the BS (with a semantic transmitter) and then forwards it to the user by adopting conventional bit transmission, hence effectively improving the text transmission efficiency. We formulate an optimization problem to maximize the achievable (effective) bit rate by jointly designing the SemRelay placement and bandwidth allocation. Although this problem is non-convex and generally difficult to solve, we propose an efficient penalty-based algorithm to obtain a high-quality suboptimal solution. Numerical results show the close-to-optimal performance of the proposed algorithm as well as significant rate performance gain of the proposed SemRelay over conventional decode-and-forward relay.

翻译：语义通信通过提取源数据的含义并仅传输相关语义信息，已成为突破香农极限的一项前景广阔的技术。然而，部分移动设备可能面临计算和存储资源受限的问题，这使得部署和实现需要大量资源的深度学习语义编码器/解码器变得困难。为应对这一挑战，本文提出一种新型语义中继（SemRelay），该中继配备语义接收器，用于辅助资源丰富的基础站（BS）向资源受限的移动设备传输文本。具体而言，SemRelay首先解码BS（通过语义发射器）发送的语义信息，然后采用传统比特传输方式将其转发给用户，从而有效提升文本传输效率。我们通过联合设计SemRelay的部署位置和带宽分配，构建了一个最大化可达（有效）比特率的优化问题。尽管该问题非凸且求解困难，我们提出了一种高效的基于惩罚的算法，以获得高质量的次优解。数值结果表明，所提算法具有接近最优的性能，且与传统的解码转发中继相比，所提SemRelay在速率性能上实现了显著提升。