In this paper, we present a probability graph-based semantic information compression system for scenarios where the base station (BS) and the user share common background knowledge. We employ probability graphs to represent the shared knowledge between the communicating parties. During the transmission of specific text data, the BS first extracts semantic information from the text, which is represented by a knowledge graph. Subsequently, the BS omits certain relational information based on the shared probability graph to reduce the data size. Upon receiving the compressed semantic data, the user can automatically restore missing information using the shared probability graph and predefined rules. This approach brings additional computational resource consumption while effectively reducing communication resource consumption. Considering the limitations of wireless resources, we address the problem of joint communication and computation resource allocation design, aiming at minimizing the total communication and computation energy consumption of the network while adhering to latency, transmit power, and semantic constraints. Simulation results demonstrate the effectiveness of the proposed system.

翻译：本文提出了一种基于概率图的语义信息压缩系统，适用于基站与用户共享共同背景知识的场景。我们采用概率图来表示通信双方共享的知识。在传输特定文本数据时，基站首先从文本中提取语义信息，并以知识图的形式表示。随后，基站根据共享的概率图省略部分关系信息以减小数据量。用户接收到压缩后的语义数据后，可借助共享的概率图及预设规则自动恢复缺失信息。该方法在有效降低通信资源消耗的同时，带来了额外的计算资源消耗。考虑到无线资源的局限性，我们研究了联合通信与计算资源分配的设计问题，目标是在满足时延、发射功率及语义约束的前提下，最小化网络的总通信与计算能耗。仿真结果验证了所提系统的有效性。