Accurate channel state information (CSI) is extremely crucial to realize high-accurate hybrid precoding, in millimeter wave communication systems. In order to improve the CSI estimation performance, several traditional channel estimators have been developed by exploiting the sparse or low-rank property, whilst they bring huge channel training overhead and computational complexity. In this work, based on jointly sparse and low-rank property of massive multiple input multiple output (MIMO) channel, one non-convex mmWave channel estimation problem is formulated and a novel scheme to acquire one accurate CSI estimation result with greatly reduced training overhead is proposed. Specifically, the non-convex problem is reformulated as two simple sub-problems, by exploiting the alternating direction method of multipliers (ADMM) technique. Based on the low-rank characteristic, one fast gradient descent matrix completion algorithm is developed to accurately solve the first sub-problem. On this basis, the compressed sensing (CS) technique to acquire the accurate CSI estimation matrix is further utilized. Numerical simulation validates that the method could achieve the much higher channel estimation accuracy, yet only incurs the lower overhead compared with the traditional scheme.
机构:
Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R ChinaTsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
Hu, Chen
Wang, Xiaodong
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Columbia Univ, Dept Elect Engn, New York, NY 10027 USATsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
Wang, Xiaodong
Dai, Linglong
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Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R ChinaTsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
Dai, Linglong
Ma, Junjie
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Columbia Univ, Dept Elect Engn, New York, NY 10027 USA
Harvard Univ, Dept Elect Engn, Cambridge, MA 02138 USATsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
机构:
Chinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China
Univ Chinese Acad Sci, Sch Elect Elect & Commun, Beijing 100049, Peoples R ChinaChinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China
Srivastav, Prateek Saurabh
Chen, Lan
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Chinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R ChinaChinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China
Chen, Lan
Wahla, Arfan Haider
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Chinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China
Univ Chinese Acad Sci, Sch Elect Elect & Commun, Beijing 100049, Peoples R ChinaChinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China
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Univ Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R ChinaUniv Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R China
Wu, Xianda
Yang, Guanghua
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机构:
Jinan Univ, Inst Phys Internet, Zhuhai 519070, Peoples R ChinaUniv Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R China
Yang, Guanghua
Hou, Fen
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Univ Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R ChinaUniv Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R China
Hou, Fen
Ma, Shaodan
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Univ Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R ChinaUniv Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R China
机构:
South China Normal Univ, Sch Elect & Informat Engn, Foshan 528000, Peoples R China
Univ Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R ChinaSouth China Normal Univ, Sch Elect & Informat Engn, Foshan 528000, Peoples R China
Wu, Xianda
Ma, Shaodan
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Univ Macau, Dept Elect & Comp Engn, State Key Lab Internet Things Smart City, Zhuhai 999078, Peoples R ChinaSouth China Normal Univ, Sch Elect & Informat Engn, Foshan 528000, Peoples R China
Ma, Shaodan
Yang, Xi
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East China Normal Univ, Sch Commun & Elect Engn, Shanghai 200000, Peoples R China
Univ Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R ChinaSouth China Normal Univ, Sch Elect & Informat Engn, Foshan 528000, Peoples R China
Yang, Xi
Yang, Guanghua
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机构:
Jinan Univ, Inst Phys Internet, Sch Intelligent Syst Sci & Engn, Zhuhai 519070, Peoples R ChinaSouth China Normal Univ, Sch Elect & Informat Engn, Foshan 528000, Peoples R China