为6G设计可编码语义的波形，提升通信效率与鲁棒性。
Semantic Waveforms for AI-Native 6G Networks
- 提出正交语义序列复用技术，实现语义信息直接嵌入波形。
- 相比传统OFDM，频谱效率提升且语义保真度更高。
- 适合追求智能通信与资源优化的研究者参考。
本文提出一种面向AI原生6G网络的语义感知波形设计框架，联合优化物理层资源利用与语义通信效率和鲁棒性，并显式考虑射频链路的硬件约束。所提方法称为正交语义序列复用（OSSDM），采用可参数化、正交基的波形设计，实现无线信号可控降质，以保留语义关键内容并最小化资源消耗。实验表明，OSSDM不仅增强对信道损伤的语义鲁棒性，还通过在波形层面直接编码有意义信息，提升语义频谱效率。大量数值实验显示，其在频谱效率和语义保真度上均优于传统OFDM波形。该语义波形协同设计为AI原生智能通信系统开辟了新方向，支持通过波形级语义编码实现意义感知的物理信号构造。
原文摘要 · Abstract (English)
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.
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