波域语义均衡：利用具有强互耦的实用动态超表面天线

中文摘要 AI 辅助

异构网络中独立训练的AI原生智能体之间的语义失配会损害语义通信。混合模拟-数字语义均衡可以在不重新训练语义收发器的情况下对齐不兼容的潜在表示。我们研究了基于所制造的动态超表面天线（DMA）的该方法的实际实现，DMA是一种新兴的低成本、低功耗、超紧凑的混合模拟-数字波束成形技术。我们使用多端口网络理论（MNT）对DMA辅助信道进行建模，考虑了互耦（MC）、结构散射和二元有损调谐状态。我们使用了所制造的具有强互耦的19 GHz DMA原型的实验估计MNT参数。我们在参考接收机几何形状下联合优化数字预均衡器和后均衡器以及DMA，以实现潜在空间对齐，然后在接收机移动后冻结数字级并仅调整DMA。在我们的接收机CIFAR-10图像分类任务中，联合优化系统达到94.5%的准确率，而仅DMA自适应在接收机移动后恢复了90.3%的中位准确率。我们的语义感知波域自适应显著优于语义不知情的基准。我们进一步观察到，在不同信道使用中改变DMA配置对语义感知或语义不知情优化几乎没有额外好处。

英文摘要

Semantic mismatch between independently trained AI-native agents in heterogeneous networks can impair semantic communications. Hybrid analog-digital semantic equalization can align the incompatible latent representations without retraining the semantic transceivers. We study a practical realization of this approach based on a fabricated dynamic metasurface antenna (DMA), an emerging low-cost, low-power, ultracompact technology for hybrid analog-digital beamforming. We model the DMA-assisted channel using multiport-network theory (MNT), accounting for mutual coupling (MC), structural scattering, and binary lossy tuning states. We use the experimentally estimated MNT parameters of our fabricated 19-GHz DMA prototype with strong MC. We jointly optimize digital pre- and post-equalizers and the DMA at a reference receiver geometry for latent-space alignment, then freeze the digital stages and adapt only the DMA after receiver motion. In our CIFAR-10 image-classification task at the receiver, the jointly optimized system achieves 94.5% accuracy, while DMA-only adaptation restores a median accuracy of 90.3% after receiver motion. Our semantic-aware wave-domain adaptation substantially outperforms semantic-unaware benchmarks. We further observe that varying the DMA configuration across channel uses provides little additional benefit for either semantic-aware or semantic-unaware optimization.

发表机构

• Univ Rennes, CNRS, IETR - UMR 6164（雷恩大学）
• CEA Leti, Univ Grenoble Alpes（格勒诺布尔阿尔卑斯大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

5 pages with 3 figures