In this paper, a semantic communication framework is proposed for textual data transmission. In the studied model, a base station (BS) extracts the semantic information from textual data, and transmits it to each user. The semantic information is modeled by a knowledge graph (KG) that consists of a set of semantic triples. After receiving the semantic information, each user recovers the original text using a graph-to-text generation model. To measure the performance of the considered semantic communication framework, a metric of semantic similarity (MSS) that jointly captures the semantic accuracy and completeness of the recovered text is proposed. Due to wireless resource limitations, the BS may not be able to transmit the entire semantic information to each user and satisfy the transmission delay constraint. Hence, the BS must select an appropriate resource block for each user as well as determine and transmit part of the semantic information to the users. As such, we formulate an optimization problem whose goal is to maximize the total MSS by jointly optimizing the resource allocation policy and determining the partial semantic information to be transmitted. To solve this problem, a proximal-policy-optimization-based reinforcement learning (RL) algorithm integrated with an attention network is proposed. The proposed algorithm can evaluate the importance of each triple in the semantic information using an attention network and then, build a relationship between the importance distribution of the triples in the semantic information and the total MSS. Compared to traditional RL algorithms, the proposed algorithm can dynamically adjust its learning rate thus ensuring convergence to a locally optimal solution. Simulation results show that the proposed framework can reduce by 41.3% data that the BS needs to transmit and improve by two-fold the total MSS compared to a standard communication network without using semantic communication techniques.

中文翻译：

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语义通信的性能优化：基于注意力的强化学习方法


本文提出了一种用于文本数据传输的语义通信框架。在所研究的模型中，基站（BS）从文本数据中提取语义信息，并将其传输给每个用户。语义信息由由一组语义三元组组成的知识图（KG）建模。在接收到语义信息后，每个用户使用图到文本的生成模型恢复原始文本。为了衡量所考虑的语义通信框架的性能，提出了一种语义相似度（MSS）度量，该度量联合捕获恢复文本的语义准确性和完整性。由于无线资源的限制，BS可能无法向每个用户发送完整的语义信息并满足传输延迟约束。因此，BS必须为每个用户选择合适的资源块，并确定并向用户发送部分语义信息。因此，我们提出了一个优化问题，其目标是通过联合优化资源分配策略和确定要传输的部分语义信息来最大化总MSS。为了解决这个问题，提出了一种与注意力网络集成的基于近端策略优化的强化学习（RL）算法。该算法可以使用注意力网络评估语义信息中每个三元组的重要性，然后建立语义信息中三元组的重要性分布与总MSS之间的关系。与传统的强化学习算法相比，该算法可以动态调整学习率，从而确保收敛到局部最优解。 仿真结果表明，与不使用语义通信技术的标准通信网络相比，所提出的框架可以减少BS需要传输的41.3％的数据，并将总MSS提高两倍。