Hongli Zhou, Yang Liu, Cheng Lv, Yuting Li, Jia Yu
{"title":"基于波束空间的二维海量MIMO系统DOA估计算法","authors":"Hongli Zhou, Yang Liu, Cheng Lv, Yuting Li, Jia Yu","doi":"10.1109/ICCSN.2019.8905399","DOIUrl":null,"url":null,"abstract":"Massive multiple-input multiple-output (MIMO) is significantly promising in the fifth generation (5G) mobile communication systems. The direction-of-arrival (DOA) is particularly crucial for base stations (BSs) to perform beam-forming in massive MIMO systems. Due to being equipped with hundreds of antennas at the BSs, the traditional DOA estimation algorithms such as MUSIC and ESPRIT algorithm have extremely high computational complexity and are not suitable for realistic massive MIMO systems. In this paper, a novel two-dimensional beamspace-based Propagator Method (2D BPM) algorithm with a uniform rectangular array (URA) for DOA estimation is proposed. We firstly utilize beamspace transform matrix to convert the steering matrix of the array space into the beamspace. And then DOA estimation is performed based on the PM algorithm. Therefore, the proposed algorithm can significantly decrease the dimension of the received signal vector and avoid eigenvalue decomposition (EVD) on a high-dimensional covariance matrix compared with subspace algorithms. The 2D BPM algorithm achieves better performance and lower computational complexity. Numerical simulation results clearly demonstrate that the proposed approach has not only a high DOA estimation precision but also a lower computational complexity in massive MIMO systems.","PeriodicalId":330766,"journal":{"name":"2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Beamspace-Based DOA Estimation Algorithm for 2D Massive MIMO Systems\",\"authors\":\"Hongli Zhou, Yang Liu, Cheng Lv, Yuting Li, Jia Yu\",\"doi\":\"10.1109/ICCSN.2019.8905399\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Massive multiple-input multiple-output (MIMO) is significantly promising in the fifth generation (5G) mobile communication systems. The direction-of-arrival (DOA) is particularly crucial for base stations (BSs) to perform beam-forming in massive MIMO systems. Due to being equipped with hundreds of antennas at the BSs, the traditional DOA estimation algorithms such as MUSIC and ESPRIT algorithm have extremely high computational complexity and are not suitable for realistic massive MIMO systems. In this paper, a novel two-dimensional beamspace-based Propagator Method (2D BPM) algorithm with a uniform rectangular array (URA) for DOA estimation is proposed. We firstly utilize beamspace transform matrix to convert the steering matrix of the array space into the beamspace. And then DOA estimation is performed based on the PM algorithm. Therefore, the proposed algorithm can significantly decrease the dimension of the received signal vector and avoid eigenvalue decomposition (EVD) on a high-dimensional covariance matrix compared with subspace algorithms. The 2D BPM algorithm achieves better performance and lower computational complexity. Numerical simulation results clearly demonstrate that the proposed approach has not only a high DOA estimation precision but also a lower computational complexity in massive MIMO systems.\",\"PeriodicalId\":330766,\"journal\":{\"name\":\"2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSN.2019.8905399\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSN.2019.8905399","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Beamspace-Based DOA Estimation Algorithm for 2D Massive MIMO Systems
Massive multiple-input multiple-output (MIMO) is significantly promising in the fifth generation (5G) mobile communication systems. The direction-of-arrival (DOA) is particularly crucial for base stations (BSs) to perform beam-forming in massive MIMO systems. Due to being equipped with hundreds of antennas at the BSs, the traditional DOA estimation algorithms such as MUSIC and ESPRIT algorithm have extremely high computational complexity and are not suitable for realistic massive MIMO systems. In this paper, a novel two-dimensional beamspace-based Propagator Method (2D BPM) algorithm with a uniform rectangular array (URA) for DOA estimation is proposed. We firstly utilize beamspace transform matrix to convert the steering matrix of the array space into the beamspace. And then DOA estimation is performed based on the PM algorithm. Therefore, the proposed algorithm can significantly decrease the dimension of the received signal vector and avoid eigenvalue decomposition (EVD) on a high-dimensional covariance matrix compared with subspace algorithms. The 2D BPM algorithm achieves better performance and lower computational complexity. Numerical simulation results clearly demonstrate that the proposed approach has not only a high DOA estimation precision but also a lower computational complexity in massive MIMO systems.