# Multi-Branch DNN and CRLB-Ratio-Weight Fusion for Enhanced DOA Sensing via a Massive H2AD MIMO Receiver

arXiv: https://arxiv.org/abs/2506.23203 · HTML: https://mlsanity.com/paper/2506.23203/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 29 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Networks for Direction-of-Arrival Estimation in Low SNR](https://mlsanity.com/paper/2011.08848/) (2020)
- [End-to-End Deep Learning for TDD MIMO Systems in the 6G Upper Midbands](https://mlsanity.com/paper/2402.01033/) (2024)
- [Fast Ambiguous DOA Elimination Method of DOA Measurement for Hybrid Massive MIMO Receiver](https://mlsanity.com/paper/2105.06634/) (2021)
- [Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array](https://mlsanity.com/paper/2204.12991/) (2022)
- [A New Heterogeneous Hybrid Massive MIMO Receiver with An Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning](https://mlsanity.com/paper/2308.08100/) (2023)
- [Machine-learning-aided Massive Hybrid Analog and Digital MIMO DOA Estimation for Future Wireless Networks](https://mlsanity.com/paper/2201.04452/) (2022)
- [Deep Learning Based DOA Estimation for Hybrid Massive MIMO Receive Array with Overlapped Subarrays](https://mlsanity.com/paper/2209.04806/) (2022)
- [Co-learning-aided Multi-modal-deep-learning Framework of Passive DOA Estimators for a Heterogeneous Hybrid Massive MIMO Receiver](https://mlsanity.com/paper/2405.09556/) (2024)
- [Multi-modal Iterative and Deep Fusion Frameworks for Enhanced Passive DOA Sensing via a Green Massive H2AD MIMO Receiver](https://mlsanity.com/paper/2411.06927/) (2024)
- [DOA Estimation for Hybrid Massive MIMO Systems using Mixed-ADCs: Performance Loss and Energy Efficiency](https://mlsanity.com/paper/2107.09934/) (2021)
- [Enhanced channel estimation for near-field IRS-aided multi-user MIMO system via a large deep residual network](https://mlsanity.com/paper/2410.20992/) (2024)

## Abstract (arXiv)

As a green MIMO structure, massive H2AD 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.