# DNN-based Methods of Jointly Sensing Number and Directions of Targets via a Green Massive H2AD MIMO Receiver

arXiv: https://arxiv.org/abs/2507.22906 · HTML: https://mlsanity.com/paper/2507.22906/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 15 Jul 2025.

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

- [DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm](https://mlsanity.com/paper/2109.10581/) (2021)
- [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)
- [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)
- [On Performance Loss of DOA Measurement Using Massive MIMO Receiver with Mixed-ADCs](https://mlsanity.com/paper/2104.02447/) (2021)
- [Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array](https://mlsanity.com/paper/2203.00917/) (2022)
- [DOA Estimation-Oriented Joint Array Partitioning and Beamforming Designs for ISAC Systems](https://mlsanity.com/paper/2410.12923/) (2024)

## Abstract (arXiv)

As a green MIMO structure, the heterogeneous hybrid analog-digital H2AD MIMO architecture has been shown to own a great potential to replace the massive or extremely large-scale fully-digital MIMO in the future wireless networks to address the three challenging problems faced by the latter: high energy consumption, high circuit cost, and high complexity. However, how to intelligently sense the number and direction of multi-emitters via such a structure is still an open hard problem. To address this, we propose a two-stage sensing framework that jointly estimates the number and direction values of multiple targets. Specifically, three target number sensing methods are designed: an improved eigen-domain clustering (EDC) framework, an enhanced deep neural network (DNN) based on five key statistical features, and an improved one-dimensional convolutional neural network (1D-CNN) utilizing full eigenvalues. Subsequently, a low-complexity and high-accuracy DOA estimation is achieved via the introduced online micro-clustering (OMC-DOA) method. Furthermore, we derive the Cram'er-Rao lower bound (CRLB) for the H2AD under multiple-source conditions as a theoretical performance benchmark. Simulation results show that the developed three methods achieve 100% number of targets sensing at moderate-to-high SNRs, while the improved 1D-CNN exhibits superior under extremely-low SNR conditions. The introduced OMC-DOA outperforms existing clustering and fusion-based DOA methods in multi-source environments.