语义波形用于AI原生6G网络
Semantic Waveforms for AI-Native 6G Networks
AI总结：
本文提出OSSDM波形设计方法，通过在波形层面直接编码语义，提升6G网络的频谱效率和语义保真度。
摘要：
本文提出了一种面向AI原生6G网络的语义感知波形设计框架，旨在联合优化物理层资源使用和语义通信效率与鲁棒性，同时明确考虑射频链路的硬件约束。我们的方法称为正交语义序列分乘复用（OSSDM），引入了一种可参数化的正交基波形设计，能够有控制地降级无线传输信号以保留语义重要的内容，同时最小化资源消耗。我们证明OSSDM不仅增强了语义鲁棒性以对抗信道损伤，还通过在波形层面直接编码有意义信息提高了语义频谱效率。广泛的数值评估显示，OSSDM在频谱效率和语义保真度方面均优于传统OFDM波形。所提出的语义波形联合设计通过在波形层面直接编码语义，为AI原生、智能通信系统开辟了新的研究前沿。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
