Minimizing Quantized Semantic Age of Information (QSAoI) in Foundation Model-Based Semantic Communications

2026-06-30

AI Summary

This work addresses the mismatch between semantic and physical layers in 6G semantic communications under finite blocklength (FBL) low-latency transmission, which hinders the simultaneous optimization of semantic freshness and transmission efficiency. To bridge this gap, the paper introduces, for the first time, a novel metric termed Quantized Semantic Age of Information (QSAoI) and develops a foundation-model-based cross-layer joint optimization framework that dynamically co-designs mixed-precision quantization strategies and physical blocklengths. Leveraging fixed-point analysis and bisection search, a low-complexity algorithm is devised to solve the resulting nonlinear optimization problem, enabling adaptive block-level resource allocation. Experimental results demonstrate that the proposed method intelligently adjusts quantization precision according to channel conditions and significantly reduces the expected QSAoI across diverse scenarios, outperforming existing baselines.

Abstract

The emerging techniques of semantic communications and edge computing in 6G networks necessitate a paradigm shift toward co-designed semantic-aware and adaptive resource allocation for short-packet transmissions. However, there is a fundamental gap between the semantic layer and the physical layer under low-latency finite blocklength (FBL) effects. To bridge this gap, we introduce the Quantized Semantic Age of Information (QSAoI), a novel metric that rigorously captures the trade-offs among freshness and semantic efficiency of high-level features in real-time communication in the FBL regime. Guided by this metric, we propose a novel foundation model-based efficient co-designed framework to minimize the expected QSAoI over wireless fading channels in latency-constrained semantic communication. Specifically, we formulate a non-linear joint optimization problem to dynamically optimize the block-wise mixed-precision quantization (MPQ) strategy and the physical blocklength. To efficiently resolve this complex problem, we develop a high-efficiency low-complexity algorithm based on fixpoint inspection and bisection search. Extensive simulations validate that our proposed algorithm dynamically adapts the semantic quantization precision to varying channel conditions, effectively minimizing the expected QSAoI compared to baselines.

Problem

• Semantic Age of Information
• Finite Blocklength
• Semantic Communications
• Resource Allocation
• Quantization

Innovation

• Quantized Semantic Age of Information
• Foundation Model
• Mixed-Precision Quantization
• Finite Blocklength
• Semantic Communications