Performance Optimization for Semantic Communications: An Attention-Based Reinforcement Learning Approach
Performance Optimization for Semantic Communications: An Attention-Based Reinforcement Learning Approach
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DOI：
10.1109/jsac.2022.3191112
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发表时间：
2022-08
影响因子：
16.4
通讯作者：
Yining Wang;Mingzhe Chen;Tao Luo;W. Saad;D. Niyato;H. Poor;Shuguang Cui
中科院分区：
文献类型：
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作者：
Yining Wang;Mingzhe Chen;Tao Luo;W. Saad;D. Niyato;H. Poor;Shuguang Cui
本文提出了一个文本数据传输的语义通信框架。在所研究的模型中，一个基站（BS）从文本数据中提取语义信息，并将其发送给每个用户。语义信息由一组语义三元组组成的知识图（KG）建模。在接收到语义信息之后，每个用户使用图到文本生成模型来恢复原始文本。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）的度量，该度量共同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整个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适当的资源块，以及确定并向用户发送部分语义信息。因此，我们制定了一个优化问题，其目标是最大限度地提高总MSS，通过共同优化的资源分配政策，并确定部分语义信息进行传输。为了解决这个问题，提出了一种基于近似策略优化的强化学习（RL）算法与注意力网络。该算法利用注意力网络评估语义信息中每个三元组的重要性，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，该算法可以动态地调整学习速率，从而确保收敛到局部最优解。仿真结果表明，所提出的框架可以减少41.3%的数据，BS需要传输和提高了两倍的总MSS相比，一个标准的通信网络，而不使用语义通信技术。
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.