用轻量化融合与多分支网络提升6G大规模双层天线定位精度
Multi-Branch DNN and CRLB-Ratio-Weight Fusion for Enhanced DOA Sensing via a Massive H$^2$AD MIMO Receiver
- 基于天线数倒数近似克拉美罗下界，免实时计算降低复杂度
- 在-15dB低信噪比下，方向估计误差比传统方法降低一个数量级
- 适合对低信噪比场景下高精度定位有需求的6G通信系统
作为绿色MIMO结构，大规模双层天线（massive H²AD）被视为未来6G无线网络的潜在技术。针对该结构中不同子阵列组间目标方向值融合时复杂度高、性能难保障且依赖先验知识的问题，提出一种轻量级克拉美罗下界（CRLB）比值加权融合（WF）方法，通过用天线数倒数近似各子阵列的逆CRLB，避免实时计算CRLB，显著降低复杂度和对先验知识的依赖，同时保持融合性能。此外，构建多分支深度神经网络（MBDNN），利用多个子阵列的候选角度，通过子阵列专用分支与共享回归模块结合，有效消除伪解并融合真实角度。仿真结果表明，所提CRLB比值-WF方法性能接近基于CRLB的方法，但大幅减少对先验知识依赖；更显著的是，所提MBDNN在低信噪比条件下表现优异，在SNR = -15 dB时，估计精度相比CRLB比值-WF方法提升一个数量级。
原文摘要 · Abstract (English)
As a green MIMO structure, massive H$^2$AD is viewed as a potential technology for the future 6G wireless network. For such a structure, it is a challenging task to design a low-complexity and high-performance fusion of target direction values sensed by different sub-array groups with fewer use of prior knowledge. To address this issue, a lightweight Cramer-Rao lower bound (CRLB)-ratio-weight fusion (WF) method is proposed, which approximates inverse CRLB of each subarray using antenna number reciprocals to eliminate real-time CRLB computation. This reduces complexity and prior knowledge dependence while preserving fusion performance. Moreover, a multi-branch deep neural network (MBDNN) is constructed to further enhance direction-of-arrival (DOA) sensing by leveraging candidate angles from multiple subarrays. The subarray-specific branch networks are integrated with a shared regression module to effectively eliminate pseudo-solutions and fuse true angles. Simulation results show that the proposed CRLB-ratio-WF method achieves DOA sensing performance comparable to CRLB-based methods, while significantly reducing the reliance on prior knowledge. More notably, the proposed MBDNN has superior performance in low-SNR ranges. At SNR $= -15$ dB, it achieves an order-of-magnitude improvement in estimation accuracy compared to CRLB-ratio-WF method.
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