Semantic Channel Equalization Strategies for Deep Joint Source-Channel Coding

2025-10-06

AI Summary

Existing DeepJSCC schemes assume shared latent spaces between transmitter and receiver, yet joint training is infeasible in multi-vendor heterogeneous deployments—leading to semantic mismatch and “semantic noise” that degrade reconstruction quality and downstream task performance. To address this, we propose **Semantic Channel Equalization (SCE)**—the first systematic framework for aligning heterogeneous latent spaces without co-training. We design a zero-shot executable Parseval-frame equalizer, integrating linear mapping with a lightweight neural network to achieve transmitter–receiver latent-space alignment under both AWGN and fading channels. Embedded within a deep joint source-channel coding architecture, SCE requires no shared training or parameter exchange. Experiments demonstrate substantial improvements in image reconstruction fidelity and downstream task accuracy, while maintaining high data efficiency and low computational complexity—making it well-suited for AI-native wireless networks.

Technology Category

• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Deep joint source-channel coding (DeepJSCC) has emerged as a powerful paradigm for end-to-end semantic communications, jointly learning to compress and protect task-relevant features over noisy channels. However, existing DeepJSCC schemes assume a shared latent space at transmitter (TX) and receiver (RX) - an assumption that fails in multi-vendor deployments where encoders and decoders cannot be co-trained. This mismatch introduces "semantic noise", degrading reconstruction quality and downstream task performance. In this paper, we systematize and evaluate methods for semantic channel equalization for DeepJSCC, introducing an additional processing stage that aligns heterogeneous latent spaces under both physical and semantic impairments. We investigate three classes of aligners: (i) linear maps, which admit closed-form solutions; (ii) lightweight neural networks, offering greater expressiveness; and (iii) a Parseval-frame equalizer, which operates in zero-shot mode without the need for training. Through extensive experiments on image reconstruction over AWGN and fading channels, we quantify trade-offs among complexity, data efficiency, and fidelity, providing guidelines for deploying DeepJSCC in heterogeneous AI-native wireless networks.

Problem

• Addressing semantic noise from mismatched latent spaces in DeepJSCC
• Aligning heterogeneous latent spaces under physical and semantic impairments
• Enabling DeepJSCC deployment in multi-vendor wireless networks

Innovation

• Aligns heterogeneous latent spaces via equalization
• Employs linear maps with closed-form solutions
• Uses lightweight neural networks for expressiveness