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

TL;DR

This paper introduces a zero-shot semantic channel equalizer for AI-native wireless networks that aligns latent spaces of neural encoders, improving robustness against semantic noise without retraining.

Contribution

It proposes the Parseval Frame Equalizer (PFE), a novel zero-shot method for aligning encoder latent spaces, and a dynamic resource optimization strategy for multi-agent semantic communication.

Findings

PFE effectively mitigates semantic noise in simulations.

The optimization strategy balances latency, energy, and accuracy.

Approach maintains semantic consistency under diverse network conditions.

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.