A New Heterogeneous Hybrid Massive MIMO Receiver With an Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning
Massive multiple input multiple output (MIMO) antenna arrays eventuate a huge amount of circuit costs and computational complexity. To satisfy the needs of high precision and low cost in future green wireless communication, the conventional hybrid analog and digital MIMO receive structure emerges a...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10767772/ |
| _version_ | 1826529226311860224 |
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| author | Feng Shu Baihua Shi Yiwen Chen Jiatong Bai Yifan Li Tingting Liu Zhu Han Xiaohu You |
| author_facet | Feng Shu Baihua Shi Yiwen Chen Jiatong Bai Yifan Li Tingting Liu Zhu Han Xiaohu You |
| author_sort | Feng Shu |
| collection | DOAJ |
| description | Massive multiple input multiple output (MIMO) antenna arrays eventuate a huge amount of circuit costs and computational complexity. To satisfy the needs of high precision and low cost in future green wireless communication, the conventional hybrid analog and digital MIMO receive structure emerges a natural choice. But it exists an issue of the phase ambiguity in direction of arrival (DOA) estimation and requires at least two time-slots to complete one-time DOA measurement with the first time-slot generating the set of candidate solutions and the second one to find a true direction by received beamforming over this set, which will lead to a low time-efficiency. To address this problem,a new heterogeneous sub-connected hybrid analog and digital (<inline-formula> <tex-math notation="LaTeX">$\mathrm {H}^{2}$ </tex-math></inline-formula>AD) MIMO structure is proposed with an intrinsic ability of removing phase ambiguity, and then a corresponding new framework is developed to implement a rapid high-precision DOA estimation using only single time-slot. The proposed framework consists of two steps: 1) form a set of candidate solutions using existing methods like MUSIC; 2) find the class of the true solutions and compute the class mean. To infer the set of true solutions, we propose two new clustering methods: weight global minimum distance (WGMD) and weight local minimum distance (WLMD). Next, we also enhance two classic clustering methods: accelerating local weighted k-means (ALW-K-means) and improved density. Additionally, the corresponding closed-form expression of Cramer-Rao lower bound (CRLB) is derived. Simulation results show that the proposed frameworks using the above four clustering can approach the CRLB in almost all signal to noise ratio (SNR) regions except for extremely low SNR (SNR <inline-formula> <tex-math notation="LaTeX">$\lt -5$ </tex-math></inline-formula> dB). Four clustering methods have an accuracy decreasing order as follows: WGMD, improved DBSCAN, ALW-K-means and WLMD. |
| first_indexed | 2025-03-14T01:00:11Z |
| format | Article |
| id | doaj.art-334bd51e4cb54409b4e1f7cbcbbf7bd1 |
| institution | Directory Open Access Journal |
| issn | 2831-316X |
| language | English |
| last_indexed | 2025-03-14T01:00:11Z |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj.art-334bd51e4cb54409b4e1f7cbcbbf7bd12025-03-13T20:56:03ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2025-01-013172910.1109/TMLCN.2024.350687410767772A New Heterogeneous Hybrid Massive MIMO Receiver With an Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine LearningFeng Shu0https://orcid.org/0000-0003-0073-1965Baihua Shi1https://orcid.org/0000-0002-8556-9241Yiwen Chen2Jiatong Bai3Yifan Li4https://orcid.org/0000-0001-6550-3002Tingting Liu5Zhu Han6https://orcid.org/0000-0002-6606-5822Xiaohu You7https://orcid.org/0000-0002-0809-8511School of Information and Communication Engineering, Hainan University, Haikou, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, ChinaDepartment of Electrical and Computer Engineering, University of Houston, Houston, TX, USANational Mobile Communications Research Laboratory, Southeast University, Nanjing, ChinaMassive multiple input multiple output (MIMO) antenna arrays eventuate a huge amount of circuit costs and computational complexity. To satisfy the needs of high precision and low cost in future green wireless communication, the conventional hybrid analog and digital MIMO receive structure emerges a natural choice. But it exists an issue of the phase ambiguity in direction of arrival (DOA) estimation and requires at least two time-slots to complete one-time DOA measurement with the first time-slot generating the set of candidate solutions and the second one to find a true direction by received beamforming over this set, which will lead to a low time-efficiency. To address this problem,a new heterogeneous sub-connected hybrid analog and digital (<inline-formula> <tex-math notation="LaTeX">$\mathrm {H}^{2}$ </tex-math></inline-formula>AD) MIMO structure is proposed with an intrinsic ability of removing phase ambiguity, and then a corresponding new framework is developed to implement a rapid high-precision DOA estimation using only single time-slot. The proposed framework consists of two steps: 1) form a set of candidate solutions using existing methods like MUSIC; 2) find the class of the true solutions and compute the class mean. To infer the set of true solutions, we propose two new clustering methods: weight global minimum distance (WGMD) and weight local minimum distance (WLMD). Next, we also enhance two classic clustering methods: accelerating local weighted k-means (ALW-K-means) and improved density. Additionally, the corresponding closed-form expression of Cramer-Rao lower bound (CRLB) is derived. Simulation results show that the proposed frameworks using the above four clustering can approach the CRLB in almost all signal to noise ratio (SNR) regions except for extremely low SNR (SNR <inline-formula> <tex-math notation="LaTeX">$\lt -5$ </tex-math></inline-formula> dB). Four clustering methods have an accuracy decreasing order as follows: WGMD, improved DBSCAN, ALW-K-means and WLMD.https://ieeexplore.ieee.org/document/10767772/DOAmassive MIMOH²ADmachine learning |
| spellingShingle | Feng Shu Baihua Shi Yiwen Chen Jiatong Bai Yifan Li Tingting Liu Zhu Han Xiaohu You A New Heterogeneous Hybrid Massive MIMO Receiver With an Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning IEEE Transactions on Machine Learning in Communications and Networking DOA massive MIMO H²AD machine learning |
| title | A New Heterogeneous Hybrid Massive MIMO Receiver With an Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning |
| title_full | A New Heterogeneous Hybrid Massive MIMO Receiver With an Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning |
| title_fullStr | A New Heterogeneous Hybrid Massive MIMO Receiver With an Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning |
| title_full_unstemmed | A New Heterogeneous Hybrid Massive MIMO Receiver With an Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning |
| title_short | A New Heterogeneous Hybrid Massive MIMO Receiver With an Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning |
| title_sort | new heterogeneous hybrid massive mimo receiver with an intrinsic ability of removing phase ambiguity of doa estimation via machine learning |
| topic | DOA massive MIMO H²AD machine learning |
| url | https://ieeexplore.ieee.org/document/10767772/ |
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