The Price of Meaning: Quantifying Semantic Communication Overheads in Practice

2026-07-29

AI Summary

This work addresses the practical limitations of semantic communication, where metadata overhead, control signaling, model synchronization, and computational costs often offset its theoretical compression gains, with insufficient systematic evaluation of overall resource and energy expenditure. The paper presents the first analytical framework that explicitly accounts for these multidimensional overheads, quantifying the actual spectral and energy efficiency benefits of semantic communication under equal task utility. Through analytical modeling, closed-form derivations, and simulations across point-to-point, uplink, and user-to-user scenarios, it demonstrates that semantic communication achieves spectral efficiency gains only at relatively large payload sizes, while energy efficiency requires even higher loads to amortize processing overheads. Notably, multiuser downlink configurations exhibit the most favorable performance due to shared overhead distribution.

Abstract

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.

Problem

• Semantic Communication
• Communication Overhead
• Spectral Efficiency
• Energy Consumption
• Task Utility

Innovation

• semantic communication
• overhead-aware framework
• spectral efficiency
• energy cost
• break-even analysis