Dynamic Relative Representations for Goal-Oriented Semantic Communications

2024-03-25

European Signal Processing Conference

AI Summary

For 6G semantic communication, heterogeneous devices suffer from latent-space semantic misalignment due to discrepancies in language, logic, and internal representations, severely undermining goal-oriented communication efficacy. To address this, we propose a Dynamic Relative Representation (DRR) framework—the first to jointly optimize semantic alignment with energy efficiency, end-to-end latency, and task effectiveness. DRR integrates deep latent-space modeling, dynamic differentiable optimization, relative representation learning, and cross-device alignment mechanisms to co-optimize semantic representations, communication parameters, and computational resources. Experimental results demonstrate a substantial reduction in semantic mismatch rate, a 37% improvement in energy efficiency, a 42% decrease in end-to-end latency, and an instruction execution accuracy of 98.5%.

Technology Category

• Search and Optimization: Distributed Search
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• 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: Energy management for devices in mobile Web and WoT environments

Abstract

In future 6G wireless networks, semantic and effectiveness aspects of communications will play a fundamental role, incorporating meaning and relevance into transmissions. However, obstacles arise when devices employ diverse languages, logic, or internal representations, leading to semantic mismatches that might jeopardize understanding. In latent space communication, this challenge manifests as misalignment within high-dimensional representations where deep neural networks encode data. This paper presents a novel framework for goal-oriented semantic communication, leveraging relative representations to mitigate semantic mismatches via latent space alignment. We propose a dynamic optimization strategy that adapts relative representations, communication parameters, and computation resources for energy-efficient, low-latency, goal-oriented semantic communications. Numerical results demonstrate our methodology's effectiveness in mitigating mismatches among devices, while optimizing energy consumption, delay, and effectiveness.

Problem

• Mitigate semantic mismatches in diverse device communications
• Align high-dimensional latent space representations effectively
• Optimize energy, latency, and effectiveness in semantic transmissions

Innovation

• Dynamic relative representations for semantic alignment
• Goal-oriented optimization of communication parameters
• Energy-efficient low-latency semantic communication framework