Multi-Branch DNN and CRLB-Ratio-Weight Fusion for Enhanced DOA Sensing via a Massive H$^2$AD MIMO Receiver

• URL: http://arxiv.org/abs/2506.23203v1
• Date: Sun, 29 Jun 2025 12:14:59 GMT
• Title: Multi-Branch DNN and CRLB-Ratio-Weight Fusion for Enhanced DOA Sensing via a Massive H$^2$AD MIMO Receiver
• Authors: Feng Shu, Jiatong Bai, Di Wu, Wei Zhu, Bin Deng, Fuhui Zhou, Jiangzhou Wang,
• Abstract summary: H$2$AD is viewed as a potential technology for the future 6G wireless network.<n>A lightweight Cramer-Rao lower bound (CRLB)-magnitude-weight fusion (WF) method is proposed.<n>A multi-branch deep neural network (MBDNN) is constructed to further enhance direction-of-arrival sensing.
• Score: 37.9135579979676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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