Performance Optimization for Semantic Communications: An Attention-based Learning Approach
Performance Optimization for Semantic Communications: An Attention-based Learning Approach
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DOI：
10.1109/globecom46510.2021.9685056
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Yining Wang;Mingzhe Chen;W. Saad;Tao Luo;Shuguang Cui;H. Poor
中科院分区：
文献类型：
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作者：
Yining Wang;Mingzhe Chen;W. Saad;Tao Luo;Shuguang Cui;H. Poor
本文提出了一种面向无线网络的语义通信框架。在该框架中，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。该语义信息由知识图(KG)建模，因此，该语义信息由一组语义三元组组成。每个用户在接收到语义信息后，使用图形到文本生成模型来恢复原始文本。为了衡量所研究的语义通信系统的性能，提出了一种语义相似度度量(MSS)，该度量联合捕获了恢复文本的语义准确性和完备性。由于无线资源的限制，BS只能向每个用户发送部分语义信息，以满足传输时延约束。因此，BS必须为每个用户选择适当的资源块，并确定要发送的部分语义信息。该问题被描述为一个优化问题，其目标是通过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移动台数量。针对这一问题，提出了一种与注意力网络相结合的基于策略梯度的强化学习(RL)算法。该算法利用注意力网络对语义信息中的每个三元组的重要性进行评估，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。仿真结果表明，与不考虑语义通信的标准通信网络相比，提出的语义通信框架可以将BS需要发送的数据量减少高达46%，并使总移动台数量提高两倍。
In this paper, a semantic communication framework is proposed for wireless networks. In the proposed framework, a base station (BS) extracts the semantic information from textual data, and, transmits it to each user. This semantic information is modeled by a knowledge graph (KG) and hence, the semantic information 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 studied semantic communication system, 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 can only transmit partial semantic information to each user so as to satisfy the transmission delay constraint. Hence, the BS must select an appropriate resource block for each user and determine partial semantic information to be transmitted. This problem is formulated as an optimization problem whose goal is to maximize the total MSS by optimizing the resource allocation policy and determining the partial semantic information to be transmitted. To solve this problem, a policy gradient-based reinforcement learning (RL) algorithm integrated with the 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. Simulation results demonstrate that the proposed semantic communication framework can reduce the size of data that the BS needs to transmit by up to 46% and yield a two-fold improvement in the total MSS compared to a standard communication network that does not consider semantic communications.