发表机构
Ericsson Canada; Concordia University（爱立信加拿大公司; 康考迪亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对语义通信评估碎片化问题，从评估视角综述文本与图像语义通信的关键性能指标，分类梳理各类指标，分析现存挑战并展望未来研究方向。
AI 中文摘要
语义通信将通信系统的目标从准确的符号重建转向意义保留、任务完成及高效信息交换。然而，其评估在电信、自然语言处理、计算机视觉和机器学习领域仍呈碎片化，尚无单一指标可跨模态、跨任务、跨信道条件表征语义质量。本文从统一的、以评估为中心的视角，综述了基于文本和图像的语义通信系统的关键性能指标（KPIs）。与以往主要围绕架构、应用或传输策略组织的综述不同，本研究聚焦于应如何定义和衡量语义成功。现有KPIs按通信目标、源模态、接收端输出、参考可用性、评估层级及信道或资源约束进行分类。该综述回顾了基于重建、面向任务、无参考、表征层级、感知及信道感知的指标，并对其作用、优势与局限性开展跨模态比较。此外，分析了未解决的语义-KPI挑战如何影响监控、质量保证、资源优化、故障诊断及标准化。关键开放问题包括缺乏通用语义成功标准和标准化语义真值、语义漂移、无参考评估受限、机器学习指标与通信约束的整合薄弱，以及缺乏关系层级和多模态KPIs。最后，勾勒了面向标准化、可解释、自适应、任务感知及通信感知评估框架的未来研究方向。
英文摘要
Semantic communication shifts the objective of communication systems from accurate symbol reconstruction toward meaning preservation, task accomplishment, and efficient information exchange. However, its evaluation remains fragmented across telecommunications, natural language processing, computer vision, and machine learning, and no single metric can characterize semantic quality across modalities, tasks, and channel conditions. This article surveys key performance indicators (KPIs) for text- and image-based semantic communication systems from a unified, evaluation-centered perspective. Unlike prior surveys primarily organized around architectures, applications, or transmission strategies, this work focuses on how semantic success should be defined and measured. Existing KPIs are classified according to communication goal, source modality, receiver output, reference availability, evaluation level, and channel or resource constraints. The survey reviews reconstruction-based, task-oriented, reference-free, representation-level, perceptual, and channel-aware metrics, and presents a cross-modality comparison of their roles, strengths, and limitations. It further analyzes how unresolved semantic-KPI challenges affect monitoring, quality assurance, resource optimization, fault diagnosis, and standardization. Key open problems include the absence of universal semantic success criteria and standardized semantic ground truth, semantic drift, limited reference-free evaluation, weak integration of machine-learning metrics with communication constraints, and the lack of relation-level and multimodal KPIs. Finally, future research directions are outlined toward standardized, interpretable, adaptive, task-aware, and communication-aware evaluation frameworks.
