Frame-Based Zero-Shot Semantic Channel Equalization for AI-Native Communications

2025-07-23

AI Summary

To address semantic channel noise arising from latent-space mismatch among heterogeneous DNN encoders in AI-native wireless networks, this paper proposes a zero-shot, frame-based semantic channel equalization method that achieves cross-system semantic alignment without retraining. Our approach makes three key contributions: (1) a learnable equalizer under Parseval tight-frame constraints, enabling dynamic signal compression and expansion; (2) a co-optimization framework jointly managing communication, computation, and learning resources to ensure multi-agent semantic consistency and task performance; and (3) integration of zero-shot learning with frame-level feature alignment to simultaneously suppress semantic noise and balance end-to-end latency and energy efficiency. Experimental results demonstrate that the method significantly improves semantic fidelity and system robustness under time-varying channels, satisfying both stringent long-term delay and high-accuracy requirements.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Hardware-aware ML
• Computer Vision: Learning & Optimization for CV

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In future AI-native wireless networks, the presence of mismatches between the latent spaces of independently designed and trained deep neural network (DNN) encoders may impede mutual understanding due to the emergence of semantic channel noise. This undermines the receiver's ability to interpret transmitted representations, thereby reducing overall system performance. To address this issue, we propose the Parseval Frame Equalizer (PFE), a zero-shot, frame-based semantic channel equalizer that aligns latent spaces of heterogeneous encoders without requiring system retraining. PFE enables dynamic signal compression and expansion, mitigating semantic noise while preserving performance on downstream tasks. Building on this capability, we introduce a dynamic optimization strategy that coordinates communication, computation, and learning resources to balance energy consumption, end-to-end (E2E) latency, and task performance in multi-agent semantic communication scenarios. Extensive simulations confirm the effectiveness of our approach in maintaining semantic consistency and meeting long-term constraints on latency and accuracy under diverse and time-varying network conditions.

Problem

• Aligns latent spaces of heterogeneous DNN encoders without retraining
• Mitigates semantic noise while preserving downstream task performance
• Balances energy, latency, and accuracy in dynamic network conditions

Innovation

• Zero-shot semantic channel equalizer aligns latent spaces
• Dynamic signal compression mitigates semantic noise
• Optimizes communication, computation, and learning resources