# Co-learning-aided Multi-modal-deep-learning Framework of Passive DOA Estimators for a Heterogeneous Hybrid Massive MIMO Receiver

arXiv: https://arxiv.org/abs/2405.09556 · HTML: https://mlsanity.com/paper/2405.09556/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 27 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Deep Networks for Direction-of-Arrival Estimation in Low SNR](https://mlsanity.com/paper/2011.08848/) (2020)
- [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)
- [Enhanced Secrecy Rate Maximization for Directional Modulation Networks via IRS](https://mlsanity.com/paper/2008.05067/) (2020)
- [Machine-learning-aided Massive Hybrid Analog and Digital MIMO DOA Estimation for Future Wireless Networks](https://mlsanity.com/paper/2201.04452/) (2022)
- [Impact of Low-Resolution ADC on DOA Estimation Performance for Massive MIMO Receive Array](https://mlsanity.com/paper/2011.00451/) (2020)
- [Low-complexity Joint Phase Adjustment and Receive Beamforming for Directional Modulation Networks via IRS](https://mlsanity.com/paper/2207.04829/) (2022)
- [Two Low-complexity DOA Estimators for Massive/Ultra-massive MIMO Receive Array](https://mlsanity.com/paper/2204.09411/) (2022)

## Built on by (most cited first, 2 of 2)

- [Multi-Branch DNN and CRLB-Ratio-Weight Fusion for Enhanced DOA Sensing via a Massive H2AD MIMO Receiver](https://mlsanity.com/paper/2506.23203/) (2025)
- [DNN-based Methods of Jointly Sensing Number and Directions of Targets via a Green Massive H2AD MIMO Receiver](https://mlsanity.com/paper/2507.22906/) (2025)

## Abstract (arXiv)

Due to its excellent performance in rate and resolution, fully-digital (FD) massive multiple-input multiple-output (MIMO) antenna arrays has been widely applied in data transmission and direction of arrival (DOA) measurements, etc. But it confronts with two main challenges: high computational complexity and circuit cost. The two problems may be addressed well by hybrid analog-digital (HAD) structure. But there exists the problem of phase ambiguity for HAD, which leads to its low-efficiency or high-latency. Does exist there such a MIMO structure of owning low-cost, low-complexity and high time efficiency at the same time. To satisfy the three properties, a novel heterogeneous hybrid MIMO receiver structure of integrating FD and heterogeneous HAD (rmH2AD-FD) is proposed and corresponding multi-modal (MD)-learning framework is developed. The framework includes three major stages: 1) generate the candidate sets via root multiple signal classification (Root-MUSIC) or deep learning (DL); 2) infer the class of true solutions from candidate sets using machine learning (ML) methods; 3) fuse the two-part true solutions to achieve a better DOA estimation. The above process form two methods named MD-Root-MUSIC and MDDL. To improve DOA estimation accuracy and reduce the clustering complexity, a co-learning-aided MD framework is proposed to form two enhanced methods named CoMDDL and CoMD-RootMUSIC. Moreover, the Cramer-Rao lower bound (CRLB) for the proposed rmH2AD-FD structure is also derived. Experimental results demonstrate that our proposed four methods could approach the CRLB for signal-to-noise ratio (SNR) > 0 dB and the proposed CoMDDL and MDDL perform better than CoMD-RootMUSIC and MD-RootMUSIC, particularly in the extremely low SNR region.