AI 中文总结
研究针对无人机通信系统中DOA估计难题，提出电磁神经网络（EMNN），由堆叠智能超表面和全连接层构成，并开发分层估计框架。经训练，EMNN在双信号场景下比传统波束成形方法分类误差降低约13 dB，成本和功耗更低。
AI 中文摘要
准确且实时的到达角（DOA）估计对无人机通信系统中的波束成形至关重要。然而，现有的高精度DOA估计算法在受机载信号处理限制的无人机上实现时计算复杂度高。为解决此问题，开发了用于DOA估计的电磁神经网络（EMNN），它仅基于幅度观测就能生成入射信号的角谱。EMNN由安装在无人机上的堆叠智能超表面（SIM）和级联的全连接层组成。还开发了分层DOA估计框架，分粗、细两个阶段进行估计。仿真结果验证了在双信号场景中，EMNN比传统波束成形（CBF）方法在分类误差降低方面有约13 dB的增益，且成本和射频相关功耗更低。
英文摘要
Accurate and real-time direction of arrival (DOA) estimation is crucial for beamforming in unmanned aerial vehicle (UAV) communication systems. However, the existing high-precision DOA estimation algorithms encounter high computational complexity when implemented on a UAV with on-board signal processing constraints. To tackle this issue, an electromagnetic neural network (EMNN) is developed for DOA estimation, which is capable of generating the angular spectrum of the incident signal based solely on amplitude observation. Specifically, the proposed EMNN consists of two components: a stacked intelligent metasurfaces (SIM) is mounted on the UAV, and each meta-atom is an artificial neuron that can process signals in the electromagnetic domain with low energy consumption and ultra-fast computing speed. Furthermore, a fully connected layer is cascaded to process the received amplitude signal, enhancing the non-linear extraction and representational ability of EMNN. Moreover, to reduce the computational complexity and observation snapshots required for high-resolution DOA estimation, we develop a hierarchical DOA estimation framework, which involves two stages for conducting coarse and fine DOA estimation, respectively. For each stage, EMNN is trained on randomly generated training samples and their corresponding spectra to achieve the desired estimation goal. Finally, the simulation results validate that the proposed EMNN achieves approximately 13 dB gain in classification error reduction over the conventional beamforming (CBF) method in dual-signal scenarios, albeit its lower cost and radio frequency (RF)-related power consumption.
Comments16 pages, 13 figures, 4 tables, accepted by IEEE TWC
