Semantic Waveforms for AI-Native 6G Networks

2026-02-10

AI Summary

This work addresses the challenge of simultaneously achieving high physical-layer resource efficiency and robust semantic communication in AI-native 6G networks. To this end, the authors propose a semantic-aware waveform design framework that directly embeds semantic information into the physical-layer waveform. By leveraging parameterizable orthogonal bases, the framework introduces an Orthogonal Semantic Sequence Division Multiplexing (OSSDM) mechanism to enable semantic-driven signal generation with controllable degradation. The approach jointly optimizes the physical and semantic layers while explicitly accounting for radio-frequency hardware constraints. Experimental results demonstrate that OSSDM significantly outperforms conventional OFDM in both semantic fidelity and spectral efficiency, thereby enhancing the robustness and resource utilization of semantic communications.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Agent Communication

Application Category

• 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: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

In this paper, we propose a semantic-aware waveform design framework for AI-native 6G networks that jointly optimizes physical layer resource usage and semantic communication efficiency and robustness, while explicitly accounting for the hardware constraints of RF chains. Our approach, called Orthogonal Semantic Sequency Division Multiplexing (OSSDM), introduces a parametrizable, orthogonal-base waveform design that enables controlled degradation of the wireless transmitted signal to preserve semantically significant content while minimizing resource consumption. We demonstrate that OSSDM not only reinforces semantic robustness against channel impairments but also improves semantic spectral efficiency by encoding meaningful information directly at the waveform level. Extensive numerical evaluations show that OSSDM outperforms conventional OFDM waveforms in spectral efficiency and semantic fidelity. The proposed semantic waveform co-design opens new research frontiers for AI-native, intelligent communication systems by enabling meaning-aware physical signal construction through the direct encoding of semantics at the waveform level.

Problem

• semantic communication
• waveform design
• 6G networks
• spectral efficiency
• hardware constraints

Innovation

• semantic waveform
• AI-native 6G
• Orthogonal Semantic Sequency Division Multiplexing
• semantic spectral efficiency
• physical-layer semantic encoding