Learning-Aided Beam Prediction in mmWave MU-MIMO Systems for High-Speed Railway
The problem of beam alignment and tracking in high mobility scenarios such as high-speed railway(HSR) becomes extremely challenging, since large overhead cost and significant time delay are introduced for fast time-varying channel estimation. To tackle this challenge, we propose a learning-aided bea...
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| Vydáno v: | IEEE transactions on communications Ročník 70; číslo 1; s. 693 - 706 |
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| Jazyk: | angličtina |
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IEEE
01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0090-6778, 1558-0857 |
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| Abstract | The problem of beam alignment and tracking in high mobility scenarios such as high-speed railway(HSR) becomes extremely challenging, since large overhead cost and significant time delay are introduced for fast time-varying channel estimation. To tackle this challenge, we propose a learning-aided beam prediction scheme for HSR networks, which predicts the beam directions and the channel amplitudes within a period of future time with fine time granularity, using a group of observations. Concretely, we transform the problem of high-dimensional beam prediction into a two-stage task, i.e., a low-dimensional parameter estimation and a cascaded hybrid beamforming operation. In the first stage, the location and speed of a certain terminal are estimated by maximum likelihood criterion, and a data-driven data fusion module is designed to improve the final estimation accuracy and robustness. Then, the probable future beam directions and channel amplitudes are predicted, based on the HSR scenario priors including deterministic trajectory, motion model, and channel model. Furthermore, we incorporate a learnable non-linear mapping module into the overall beam prediction to allow non-linear tracks. Both of the proposed learnable modules are model-based and have a good interpretability. Compared to the existing beam management scheme, the proposed beam prediction has (near) zero overhead cost and time delay. Simulation results verify the effectiveness of the proposed scheme. |
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| AbstractList | The problem of beam alignment and tracking in high mobility scenarios such as high-speed railway(HSR) becomes extremely challenging, since large overhead cost and significant time delay are introduced for fast time-varying channel estimation. To tackle this challenge, we propose a learning-aided beam prediction scheme for HSR networks, which predicts the beam directions and the channel amplitudes within a period of future time with fine time granularity, using a group of observations. Concretely, we transform the problem of high-dimensional beam prediction into a two-stage task, i.e., a low-dimensional parameter estimation and a cascaded hybrid beamforming operation. In the first stage, the location and speed of a certain terminal are estimated by maximum likelihood criterion, and a data-driven data fusion module is designed to improve the final estimation accuracy and robustness. Then, the probable future beam directions and channel amplitudes are predicted, based on the HSR scenario priors including deterministic trajectory, motion model, and channel model. Furthermore, we incorporate a learnable non-linear mapping module into the overall beam prediction to allow non-linear tracks. Both of the proposed learnable modules are model-based and have a good interpretability. Compared to the existing beam management scheme, the proposed beam prediction has (near) zero overhead cost and time delay. Simulation results verify the effectiveness of the proposed scheme. |
| Author | Liu, Shengheng Lu, Zhaohua Huang, Yongming Meng, Fan |
| Author_xml | – sequence: 1 givenname: Fan orcidid: 0000-0002-9769-0057 surname: Meng fullname: Meng, Fan email: mengfan@pmlabs.com.cn organization: Purple Mountain Laboratories, Nanjing, China – sequence: 2 givenname: Shengheng orcidid: 0000-0001-6579-9798 surname: Liu fullname: Liu, Shengheng email: s.liu@seu.edu.cn organization: Purple Mountain Laboratories, Nanjing, China – sequence: 3 givenname: Yongming orcidid: 0000-0003-3616-4616 surname: Huang fullname: Huang, Yongming email: huangym@seu.edu.cn organization: Purple Mountain Laboratories, Nanjing, China – sequence: 4 givenname: Zhaohua surname: Lu fullname: Lu, Zhaohua email: lu.zhaohua@zte.com.cn organization: ZTE Corporation, Shenzhen, China |
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| References | ref13 ref12 ref15 ref14 (ref28) 2020 ref11 ref10 Sutton (ref18) 2018 (ref31) 2020 ref2 ref1 ref17 ref16 ref19 Vaswani (ref30) (ref27) 2020 ref24 Goodfellow (ref4) 2016 ref23 ref26 ref25 ref20 ref22 ref21 ref29 ref8 ref7 ref9 ref3 ref6 ref5 Zaidi (ref32) 2017 |
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| SubjectTerms | alternating optimization Amplitudes Array signal processing Beam prediction Beamforming data fusion Data integration High speed rail high-speed railway hybrid precoder Learning Maximum likelihood estimates Maximum likelihood estimation Millimeter waves Modules Overhead costs Parameter estimation Predictive models Radio frequency Railway tracks Time lag Training |
| Title | Learning-Aided Beam Prediction in mmWave MU-MIMO Systems for High-Speed Railway |
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