SEMANTIC COMMUNICATION IN AI-NATIVE NETWORKS
Abstract
Traditional communication systems are designed to transmit symbols and bits with maximum fidelity, regardless of their meaning or relevance to the receiver. As AI-native networks emerge—supporting applications such as autonomous systems, collaborative robotics, and intelligent edge computing—there is a growing need for semantic communication, where the goal is to convey meaning rather than raw data. This paper investigates the theoretical foundation and practical design of semantic communication systems integrated into AI-native network architectures. We propose a layered semantic framework using transformer-based models to encode and decode task-relevant information. Through simulations and comparative analysis, we demonstrate that semantic communication reduces bandwidth usage by up to 90%, increases robustness to channel noise, and improves task-oriented efficiency in multi-agent systems. Key challenges such as semantic alignment, model synchronization, and standardization are also discussed. Our findings highlight the transformative potential of semantic transmission in the next generation of intelligent, goal-driven communication networks.
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