在非对称大规模MIMO系统中基于集成—迁移学习的信道参数预测

何遵文; 李悦; 张焱; 张万成; 张恺恩; 郭柳; 王海明

doi:10.1631/FITEE.2200169

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在非对称大规模MIMO系统中基于集成—迁移学习的信道参数预测

常规文章 | Updated：2023-02-27

- 在非对称大规模MIMO系统中基于集成—迁移学习的信道参数预测
  Enhanced Publication
- Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems
- 信息与电子工程前沿（英文） 2023年24卷第2期页码：275-288
- Affiliations：
  
  1.School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
  2.State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China
- Author bio：
  
  †E-mail: hezunwen@bit.edu.cn;
  ‡Corresponding author
- Funds：
  
  National Key Research and Development Program of China(2020YFB1804901);National Natural Science Foundation of China(62271051;61871035)
- DOI：10.1631/FITEE.2200169
  中图分类号： TN92
- 纸质出版日期：2023-02-0 ，
  
  收稿日期：2022-04-26，
  
  录用日期：2022-09-28
- Accepted：
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何遵文, 李悦, 张焱, 等. 在非对称大规模MIMO系统中基于集成—迁移学习的信道参数预测[J]. 信息与电子工程前沿（英文）, 2023,24(2):275-288.

ZUNWEN HE, YUE LI, YAN ZHANG, et al. Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems. [J]. Frontiers of information technology & electronic engineering, 2023, 24(2): 275-288.
何遵文, 李悦, 张焱, 等. 在非对称大规模MIMO系统中基于集成—迁移学习的信道参数预测[J]. 信息与电子工程前沿（英文）, 2023,24(2):275-288. DOI： 10.1631/FITEE.2200169.

ZUNWEN HE, YUE LI, YAN ZHANG, et al. Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems. [J]. Frontiers of information technology & electronic engineering, 2023, 24(2): 275-288. DOI： 10.1631/FITEE.2200169.

摘要

为降低第六代移动网络中的数据处理负担和硬件成本，非对称大规模多入多出（multiple-input multiple-output，MIMO）系统被提出。然而，在非对称大规模MIMO系统中，上行和下行无线信道之间的互易性是无效的。因此，需要基站和用户设备都发送导频来预测双向信道，这会消耗更多传输和计算资源。本文提出一种基于集成迁移学习的非对称大规模MIMO系统的信道参数预测方法，可以预测多个下行信道参数，包括路径损耗、多径数、时延扩展和角度扩展。选择上行信道参数和环境特征来预测下行参数。此外，提出一种基于SHAP（SHapley Additive exPlanations）值和最小描述长度标准的两步特征选择算法，以降低由弱相关或不相关特征引起的计算复杂度和对模型准确性的负面影响。引入实例迁移方法，以支持预测模型应对在新的传播条件下难以在短时间内收集足够训练数据的问题。仿真结果表明，该方法比反向传播神经网络和3GPP TR 38.901信道模型更准确。当波束宽度或通信扇区发生变化时，所提出的基于实例迁移的方法在预测下行参数方面优于没有迁移学习的方法。

Abstract

Asymmetric massive multiple-input multiple-output (MIMO) systems have been proposed to reduce the burden of data processing and hardware cost in sixth-generation mobile networks (6G). However

in the asymmetric massive MIMO system

reciprocity between the uplink (UL) and downlink (DL) wireless channels is not valid. As a result

pilots are required to be sent by both the base station (BS) and user equipment (UE) to predict double-directional channels

which consumes more transmission and computational resources. In this paper we propose an ensemble-transfer-learning-based channel parameter prediction method for asymmetric massive MIMO systems. It can predict multiple DL channel parameters including path loss (PL)

multipath number

delay spread (DS)

and angular spread. Both the UL channel parameters and environment features are chosen to predict the DL parameters. Also

we propose a two-step feature selection algorithm based on the SHapley Additive exPlanations (SHAP) value and the minimum description length (MDL) criterion to reduce the computation complexity and negative impact on model accuracy caused by weakly correlated or uncorrelated features. In addition

the instance transfer method is introduced to support the prediction model in new propagation conditions

where it is difficult to collect enough training data in a short time. Simulation results show that the proposed method is more accurate than the back propagation neural network (BPNN) and the 3GPP TR 38.901 channel model. Additionally

the proposed instance-transfer-based method outperforms the method without transfer learning in predicting DL parameters when the beamwidth or the communication sector changes.

关键词

非对称大规模MIMO系统信道模型集成学习实例迁移参数预测

Keywords

Asymmetric massive multiple-input multiple-output (MIMO) systemChannel modelEnsemble learningInstance transferParameter prediction

references

3GPP, 2020. Study on Channel Model for Frequencies from 0.5 to 100 GHz. TR 38.901 V16.1.0. 3rdGeneration Partnership Project (3GPP), France.

Albreem MA, Juntti M, Shahabuddin S, 2019. Massive MIMO detection techniques: a survey. IEEE Commun Surv Tut, 21(4):3109-3132. doi: 10.1109/COMST.2019.2935810http://doi.org/10.1109/COMST.2019.2935810

Albreem MA, Habbash AH, Abu-Hudrouss AM, et al., 2021. Overview of precoding techniques for massive MIMO. IEEE Access, 9:60764-60801. doi: 10.1109/ACCESS.2021.3073325http://doi.org/10.1109/ACCESS.2021.3073325

Bazzi A, Slock DTM, Meilhac L, 2016. Detection of the number of superimposed signals using modified MDL criterion: a random matrix approach. Proc IEEE Int Conf on Acoustics, Speech and Signal Processing, p.4593-4597. doi: 10.1109/ICASSP.2016.7472547http://doi.org/10.1109/ICASSP.2016.7472547

Chen TQ, Guestrin C, 2016. XGBoost: a scalable tree boosting system. Proc 22nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.785-794. doi: 10.1145/2939672.2939785http://doi.org/10.1145/2939672.2939785

Chen YS, Lai FP, You JW, 2019. Analysis of antenna radiation characteristics using a hybrid ray tracing algorithm for indoor WiFi energy-harvesting rectennas. IEEE Access, 7:38833-38846. doi: 10.1109/ACCESS.2019.2906646http://doi.org/10.1109/ACCESS.2019.2906646

Erden F, Ozdemir O, Guvenc I, 2020. 28 GHz mmWave channel measurements and modeling in a library environment. Proc IEEE Radio and Wireless Symp, p.52-55. doi: 10.1109/RWS45077.2020.9050106http://doi.org/10.1109/RWS45077.2020.9050106

Gallieri M, Maciejowski JM, 2012. <math id="M261"><mrow><mi mathvariant="script">l</mi><mtext>asso</mtext></mrow></math> MPC: smart regulation of over-actuated systems. Proc American Control Conf, p.1217-1222. doi: 10.1109/ACC.2012.6315171http://doi.org/10.1109/ACC.2012.6315171

Gao DY, Hu BQ, Qin FJ, et al., 2021. A real-time gravity compensation method for INS based on BPNN. IEEE Sens J, 21(12):13584-13593. doi: 10.1109/JSEN.2021.3069960http://doi.org/10.1109/JSEN.2021.3069960

Han Y, Li MY, Jin S, et al., 2020. Deep learning-based FDD non-stationary massive MIMO downlink channel reconstruction. IEEE J Sel Areas Commun, 38(9):1980-1993. doi: 10.1109/JSAC.2020.3000836http://doi.org/10.1109/JSAC.2020.3000836

Hong W, Zhou JY, Chen JX, et al., 2020. Asymmetric full-digital beamforming mmWave massive MIMO systems for B5G/6G wireless communications. Proc IEEE Asia-Pacific Microwave Conf, p.31-32. doi: 10.1109/APMC47863.2020.9331559http://doi.org/10.1109/APMC47863.2020.9331559

Hong W, Jiang ZH, Yu C, et al., 2021. The role of millimeter-wave technologies in 5G/6G wireless communications. IEEE J Microw, 1(1):101-13584. doi: 10.1109/JMW.2020.3035541http://doi.org/10.1109/JMW.2020.3035541

Hu WM, Hu W, Maybank S, 2008. AdaBoost-based algorithm for network intrusion detection. IEEE Trans Syst Man Cybern B Cybern, 38(2):577-583. doi: 10.1109/TSMCB.2007.914695http://doi.org/10.1109/TSMCB.2007.914695

Huang L, Long T, Mao EK, 2009. Robust estimation of the number of sources using an MMSE-based MDL method. Proc IET Int Radar Conf, p.1-4. doi: 10.1049/cp.2009.0262http://doi.org/10.1049/cp.2009.0262

Huang L, Fu QB, Li GF, et al., 2019. Improvement of maximum variance weight partitioning particle filter in urban computing and intelligence. IEEE Access, 7:106527-106535. doi: 10.1109/ACCESS.2019.2932144http://doi.org/10.1109/ACCESS.2019.2932144

Jiang T, Zhang JH, Tang P, et al., 2021. A study of uplink and downlink channel spatial characteristics in an urban micro scenario at 28 GHz. Front Inform Technol Electron Eng, 22(4):488-502. doi: 10.1631/FITEE.2000443http://doi.org/10.1631/FITEE.2000443

Joo J, Park MC, Han DS, et al., 2019. Deep learning-based channel prediction in realistic vehicular communications. IEEE Access, 7:27846-27858. doi: 10.1109/ACCESS.2019.2901710http://doi.org/10.1109/ACCESS.2019.2901710

Kim MD, Liang JY, Lee J, et al., 2016. Directional multipath propagation characteristics based on 28GHz outdoor channel measurements. Proc 10th European Conf on Antennas and Propagation, p.1-5. doi: 10.1109/EuCAP.2016.7481755http://doi.org/10.1109/EuCAP.2016.7481755

Li SD, Liu YJ, Chen ZP, et al., 2017. Measurements and modelling of millimeter-wave channel at 28 GHz in the indoor complex environment for 5G radio systems. Proc 9th Int Conf on Wireless Communications and Signal Processing, p.1-6. doi: 10.1109/WCSP.2017.8170925http://doi.org/10.1109/WCSP.2017.8170925

Lin B, Gao FF, Zhang S, et al., 2021. Deep learning-based antenna selection and CSI extrapolation in massive MIMO systems. IEEE Trans Wirel Commun, 20(11):7669-7681. doi: 10.1109/TWC.2021.3087318http://doi.org/10.1109/TWC.2021.3087318

Lin CH, Chi CY, Chen LL, et al., 2018. Detection of sources in non-negative blind source separation by minimum description length criterion. IEEE Trans Neur Netw Learn Syst, 29(9):4022-4037. doi: 10.1109/TNNLS.2017.2749279http://doi.org/10.1109/TNNLS.2017.2749279

Liu XM, Tang JS, 2014. Mass classification in mammograms using selected geometry and texture features, and a new SVM-based feature selection method. IEEE Syst J, 8(3):910-920. doi: 10.1109/JSYST.2013.2286539http://doi.org/10.1109/JSYST.2013.2286539

Lundberg SM, Lee SI, 2017. A unified approach to interpreting model predictions. Proc 31st Int Conf on Neural Information Processing Systems, p.4768-4777. doi: 10.5555/3295222.3295230http://doi.org/10.5555/3295222.3295230

Luo XR, Zhang Y, He ZW, et al., 2019. A two-step environment-learning-based method for optimal UAV deployment. IEEE Access, 7:149328-149340. doi: 10.1109/ACCESS.2019.2947546http://doi.org/10.1109/ACCESS.2019.2947546

Medeđović P, Veletić M, Blagojević Ž, 2012. Wireless insite software verification via analysis and comparison of simulation and measurement results. Proc 35th Int Convention MIPRO, p.776-781.

Pan X, Yan CX, Zhang JK, 2019. Nonlinearity-based single-channel monopulse tracking method for OFDM-aided UAV A2G communications. IEEE Access, 7:148485-148494. doi: 10.1109/ACCESS.2019.2946960http://doi.org/10.1109/ACCESS.2019.2946960

Pardoe D, Stone P, 2010. Boosting for regression transfer. Proc 27th Int Conf on Machine Learning, p.863-870. doi: 10.5555/3104322.3104432http://doi.org/10.5555/3104322.3104432

Qiu JH, Xu K, Xia XC, et al., 2022. Secure transmission scheme based on fingerprint positioning in cell-free massive MIMO systems. IEEE Trans Signal Inform Process Netw, 8:92-105. doi: 10.1109/TSIPN.2022.3149112http://doi.org/10.1109/TSIPN.2022.3149112

Radhakrishnan V, Taghizadeh O, Mathar R, 2021. Impairments-aware resource allocation for FD massive MIMO relay networks: sum rate and delivery-time optimization perspectives. IEEE Trans Signal Inform Process Netw, 7:177-191. doi: 10.1109/TSIPN.2021.3054991http://doi.org/10.1109/TSIPN.2021.3054991

Sterba J, Kocur D, 2009. Pilot symbol aided channel estimation for OFDM system in frequency selective Rayleigh fading channel. Proc 19th Int Conf on Radioelektronika, p.77-80. doi: 10.1109/RADIOELEK.2009.5158729http://doi.org/10.1109/RADIOELEK.2009.5158729

Tuan DM, Cheon Y, Aoki Y, et al., 2016. Performance comparison of millimeter-wave communications system with different antenna beam-width. Proc 10th European Conf on Antennas and Propagation, p.1-5. doi: 10.1109/EuCAP.2016.7482003http://doi.org/10.1109/EuCAP.2016.7482003

Wang D, Nie FP, Huang H, 2015. Feature selection via global redundancy minimization. IEEE Trans Knowl Data Eng, 27(10):2743-2755. doi: 10.1109/TKDE.2015.2426703http://doi.org/10.1109/TKDE.2015.2426703

Wang DH, Yang Z, Yi Z, 2017. LightGBM: an effective miRNA classification method in breast cancer patients. Proc Int Conf on Computational Biology and Bioinformatics, p.7-11. doi: 10.1145/3155077.3155079http://doi.org/10.1145/3155077.3155079

Wang F, Liang JL, Wang ZD, et al., 2018. A variance-constrained approach to recursive filtering for nonlinear 2-D systems with measurement degradations. IEEE Trans Cybern, 48(6):1877-1887. doi: 10.1109/TCYB.2017.2716400http://doi.org/10.1109/TCYB.2017.2716400

Wang ZG, Xiao X, Rajasekaran S, 2020. Novel and efficient randomized algorithms for feature selection. Big Data Min Anal, 3(3):208-224. doi: 10.26599/BDMA.2020.9020005http://doi.org/10.26599/BDMA.2020.9020005

Wax M, Ziskind I, 1989. Detection of the number of coherent signals by the MDL principle. IEEE Trans Acoust Speech Signal Process, 37(8):1190-1196. doi: 10.1109/29.31267http://doi.org/10.1109/29.31267

Wimalajeewa T, Varshney PK, Su W, 2017. Detection of single versus multiple antenna transmission systems using pilot data. IEEE Trans Signal Inform Process Netw, 3(1):159-171. doi: 10.1109/TSIPN.2016.2601023http://doi.org/10.1109/TSIPN.2016.2601023

Wu XY, Zhang Y, Wang CX, et al., 2015. 28 GHz indoor channel measurements and modelling in laboratory environment using directional antennas. Proc 9th European Conf on Antennas and Propagation, p.1-5.

Yang GS, Zhang Y, He ZW, et al., 2019. Machine-learning-based prediction methods for path loss and delay spread in air-to-ground millimetre-wave channels. IET Microw Antenn Propag, 13(8):1113-1121. doi: 10.1049/iet-map.2018.6187http://doi.org/10.1049/iet-map.2018.6187

Yang YW, Gao FF, Ma XL, et al., 2019. Deep learning-based channel estimation for doubly selective fading channels. IEEE Access, 7:36579-36589. doi: 10.1109/ACCESS.2019.2901066http://doi.org/10.1109/ACCESS.2019.2901066

Yao JG, Liu X, Zhu XY, et al., 2015. Control of large-scale systems through dimension reduction. IEEE Trans Serv Comput, 8(4):563-575. doi: 10.1109/TSC.2014.2312946http://doi.org/10.1109/TSC.2014.2312946

Ye H, Li GY, Juang BH, 2018. Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wirel Commun Lett, 7(1):114-117. doi: 10.1109/LWC.2017.2757490http://doi.org/10.1109/LWC.2017.2757490

Zhang S, Liu YS, Gao FF, et al., 2021. Deep learning based channel extrapolation for large-scale antenna systems: opportunities, challenges and solutions. IEEE Wirel Commun, 28(6):160-167. doi: 10.1109/MWC.001.2000534http://doi.org/10.1109/MWC.001.2000534

Zhang SB, Zhang S, Gao FF, et al., 2021. Machine-learning-based prediction methods for path loss and delay spread in air-to-ground millimeter wave channels. IEEE Trans Commun, 69(10):6691-6705. doi: 10.1109/TCOMM.2021.3097726http://doi.org/10.1109/TCOMM.2021.3097726

Zhao SY, Huang B, Liu F, 2018. Localization of indoor mobile robot using minimum variance unbiased FIR filter. IEEE Trans Autom Sci Eng, 15(2):410-419. doi: 10.1109/TASE.2016.2599864http://doi.org/10.1109/TASE.2016.2599864

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