Context-Aware Semantic Communication for the Wireless Networks

TL;DR

This paper introduces CaSemCom, a context-aware semantic communication framework utilizing LLMs and MoE architectures to adaptively encode high-impact features, significantly enhancing efficiency and fidelity in next-generation wireless networks.

Contribution

It presents a novel, adaptive semantic communication framework that incorporates context-awareness and multimodal processing, outperforming existing static models and RL-based methods.

Findings

Improves reconstructed image fidelity in wireless transmission.

Reduces bandwidth usage compared to traditional methods.

Outperforms baseline approaches in convergence speed and semantic accuracy.

Abstract

In next-generation wireless networks, supporting real-time applications such as augmented reality, autonomous driving, and immersive Metaverse services demands stringent constraints on bandwidth, latency, and reliability. Existing semantic communication (SemCom) approaches typically rely on static models, overlooking dynamic conditions and contextual cues vital for efficient transmission. To address these challenges, we propose CaSemCom, a context-aware SemCom framework that leverages a Large Language Model (LLM)-based gating mechanism and a Mixture of Experts (MoE) architecture to adaptively select and encode only high-impact semantic features across multiple data modalities. Our multimodal, multi-user case study demonstrates that CaSemCom significantly improves reconstructed image fidelity while reducing bandwidth usage, outperforming single-agent deep reinforcement learning (DRL) methods and traditional baselines in convergence speed, semantic accuracy, and retransmission overhead.