AI 中文总结
该研究开发了感知开销的分析框架，量化不同通信场景下语义通信的频谱与能量成本，推导盈亏平衡条件，为其实际部署提供设计指导。
AI 中文摘要
语义通信（SemCom）承诺通过传递任务相关意义而非原始比特来减少传输有效载荷。但实际的SemCom也会产生语义元数据、控制信令、反馈、模型或知识库同步以及神经计算成本，可能抵消语义压缩带来的收益。本文开发了一种感知开销的分析框架，用于在任务效用相等的情况下量化SemCom的频谱资源和能量成本。该框架涵盖点对点传输、用户设备（UE）到下一代NodeB（gNB）的上行链路，以及单个gNB下的UE到UE通信，并推导了关于有效载荷大小、语义压缩因子、模型复用、协议开销和计算能量的闭式盈亏平衡条件。仿真结果显示，SemCom仅在有效载荷足够大时才具有频谱优势，而能量增益因处理和同步开销需要更大的有效载荷；多用户下行链路尤其有利，因为共享的语义开销可在多个UE间分摊。这些发现为实际SemCom评估和面向标准化的部署提供了设计指导。
英文摘要
Semantic communication (SemCom) promises to reduce transmitted payloads by conveying task-relevant meaning instead of raw bits. However, practical SemCom also incurs semantic metadata, control signaling, feedback, model or knowledge-base synchronization, and neural computation costs, which may offset semantic compression gains. This paper develops an overhead-aware analytical framework for quantifying the spectral-resource and energy costs of SemCom under equal task utility. The framework covers point-to-point transmission, user equipment (UE)-to-next-generation NodeB (gNB) uplink, and UE-to-UE communication under a single gNB, and derives closed-form break-even conditions with respect to payload size, semantic compression factor, model reuse, protocol overhead, and computation energy. Simulation results show that SemCom becomes spectrally beneficial only for sufficiently large payloads, while energy gains require larger payloads due to processing and synchronization overheads. The results also show that multi-user downlink is particularly favorable, as shared semantic overheads can be amortized across multiple UEs. These findings provide design guidance for realistic SemCom evaluation and standardization-oriented deployment.
CommentsAccepted for publication in IEEE CSCN 2026
