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.

翻译：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，该框架在显式考虑射频链路硬件约束的同时，联合优化物理层资源利用与语义通信的效率和鲁棒性。我们提出的正交语义序分复用方法引入了一种参数化正交基波形设计，通过对无线传输信号进行可控降质来保留语义显著性内容，同时最小化资源消耗。我们证明OSSDM不仅能增强语义内容对抗信道损伤的鲁棒性，还能通过在波形层面直接编码有意义信息来提升语义频谱效率。大量数值评估表明，OSSDM在频谱效率和语义保真度方面均优于传统OFDM波形。所提出的语义波形协同设计通过波形层面对语义的直接编码实现意义感知的物理信号构建，为AI原生智能通信系统开辟了新的研究前沿。