基于天线分组的高铁大规模多输入多输出自适应波束赋形方案
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摘要
针对高铁大规模多输入多输出(MIMO)系统的吞吐量未被充分提升的问题,提出一种基于天线分组的自适应波束传输方案。首先利用基站(BS)预知的列车位置信息,并将波束赋形技术引入高速场景,建立高铁大规模MIMO的三维模型;其次验证BS天线分组情况下,子波束的吞吐量与其对应的发射天线数满足非线性关系,且子波束天线数变化并未对其他波束的吞吐量产生影响。基于此,以天线分组的自适应波束赋形方案对列车运行至不同位置的波束数和子波束所需的发射天线数进行调整,保证不同位置的最优系统吞吐量。计算机仿真表明,该方案与传统的单波束、双波束、八波束相比,在列车距基站125 m范围内分别实现了系统吞吐量87.9%、62.3%、50.6%的提升,在125 m之外与单波束赋形的系统吞吐量相近。实验结果表明,所提方案无论列车距BS较近或较远时,系统吞吐量均处于最佳水平,更好地适应高速铁路环境。
Aiming at the throughput of high-speed railway Multiple Input Multiple Output(MIMO) system has not been fully improved, an adaptive beam transmission scheme based on antenna grouping was proposed. Firstly, the train position information was predicted by the Base Station(BS), and the beamforming technology was introduced into high-speed railway environment to establish a high-speed Massive MIMO three-dimensional model. Secondly, it was verified that in BS antenna grouping situation, the throughput of a sub-beam and its corresponding number of transmit antennas satisfied nonlinear relationship and the number change of sub-beam antennas did not affect the throughput of other beams. Finally, based on the above, an adaptive beamforming scheme based on antenna grouping was used to adjust the number of beams required and the number of transmit antennas required by the sub-beams when the train runed at different locations to ensure optimal system throughput at all the locations. The computer simulation results show that compared with the traditional single-beam, dual-beam and eight-beam schemes, the proposed scheme achieves 87.9%, 62.3%, and 50.6% improvement respectively in system throughput when the distance between the train and the BS is less than 125 m, achieves a similar system throughput of single beamforming when the distance is more than 125 m. The experimental results show that the proposed scheme has best system throughput whether the train is far away from or close to the BS, and is better adapted to high-speed railway environment.
Aiming at the throughput of high-speed railway Multiple Input Multiple Output(MIMO) system has not been fully improved, an adaptive beam transmission scheme based on antenna grouping was proposed. Firstly, the train position information was predicted by the Base Station(BS), and the beamforming technology was introduced into high-speed railway environment to establish a high-speed Massive MIMO three-dimensional model. Secondly, it was verified that in BS antenna grouping situation, the throughput of a sub-beam and its corresponding number of transmit antennas satisfied nonlinear relationship and the number change of sub-beam antennas did not affect the throughput of other beams. Finally, based on the above, an adaptive beamforming scheme based on antenna grouping was used to adjust the number of beams required and the number of transmit antennas required by the sub-beams when the train runed at different locations to ensure optimal system throughput at all the locations. The computer simulation results show that compared with the traditional single-beam, dual-beam and eight-beam schemes, the proposed scheme achieves 87.9%, 62.3%, and 50.6% improvement respectively in system throughput when the distance between the train and the BS is less than 125 m, achieves a similar system throughput of single beamforming when the distance is more than 125 m. The experimental results show that the proposed scheme has best system throughput whether the train is far away from or close to the BS, and is better adapted to high-speed railway environment.
引文
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[15]张小飞,陈华伟,仇小锋,等.阵列信号处理及Matlab实现[M].北京:电子工业出版社,2015:40-42.(ZHANG X F,CHENG H W,QIU X F,et al.Array Signal Processing and Matlab Implementation[M].Beijing:Publishing House of Electronics Industry,2015:40-42.)
[16]LIU L,TAO C,QIU J,et al.Position-based modeling for wireless channel on high-speed railway under a viaduct at 2.35 GHz[J].IEEE Journal on Selected Areas in Communications,2012,30(4):834-845.
[2]高倩,张福金.基于高速铁路通信的多波束机会波束赋形技术[J].计算机工程与应用,2013,49(18):56-60.(GAO Q,ZHANG F J.Multi-beam opportunistic beamforming for high-speed railway communication[J].Computer Engineering and Applications,2013,49(18):56-60.)
[3]CHENG M,FANG X,YAN L.Beamforming and alamouti STBCcombined downlink transmission schemes in communication systems for high-speed railway[C]//WCSP 2013:Proceedings of the 2013International Conference on Wireless Communications and Signal Processing.Piscataway,NJ:IEEE,2013:1-6.
[4]程梦.高铁下一代无线通信系统的波束赋形与大规模多天线传输技术研究[D].成都:西南交通大学,2015:40-52.(CHENGM.Beamforming and large-scale multi-antenna transmission technologies for next-generation wireless communications of high speed railway[D].Chengdu:Southwest Jiaotong University,2015:40-52.)
[5]何励励.高铁毫米波通信与雷达探测波束赋形技术[D].成都:西南交通大学,2017:17-34.(HE L L.High-speed rail millimeter wave communication and radar detection beamforming technology[D].Chengdu:Southwest Jiaotong University,2017:17-34.)
[6]王晨.高速铁路环境下波束赋形技术探究[D].北京:北京交通大学,2013:24-53.(WANG C.Research on beamforming for high speed railway[D].Beijing:Beijing Jiaotong University,2013:24-53.)
[7]闫莉,方旭明.高铁基于毫米波的自适应波束分合传输方案[J].电子与信息学报,2016,38(1):146-152.(YAN L,FANG X M.Adaptive beam splitting or integrating scheme for railway millimeter wave wireless communications[J].Journal of Electronics and Information Technology,2016,38(1):146-152.)
[8]姜莉莉.大规模MIMO系统中基于天线分组技术的能效分析[D].郑州:郑州大学,2017:21-34.(JIANG L L.Energy efficiency analysis based on antenna grouping in massive MIMO systems[D].Zhenzhou:Zhengzhou University,2017:21-34.)
[9]陆莹.能效优化的大规模MIMO无线传输技术[D].南京:东南大学,2016:9-19.(LU Y.Research on energy efficient transmission technology for massive MIMO wireless communication systems[D].Nanjing:Southeast University,2016:9-19.)
[10]孙德春,刘祖军,易克初.使用变换域预测的TDD系统信道互易性补偿方法[J].西安电子科技大学学报,2012,39(6):22-25.(SUN D C,LIU Z J,YI K C.Compensation for channel nonreciprocity by using transform-domain prediction in TDD-MIMO-OFDM systems[J].Journal of Xidian University,2012,39(6):22-25.)
[11]张宇.MIMO-OFDM系统信道估计与同步技术的研究[D].哈尔滨:哈尔滨工业大学,2009:68-84.(ZHANG Y.Study of channel estimation and synchronization for MIMO-OFDM systems[D].Haerbin:Harbin Institute of Technology,2009:68-84.)
[12]包超峰,王瑞峰,孔德龙.基于改进Fitz算法的多普勒频移估计方法的研究[J].计算机测量与控制,2014,22(2):611-613.(BAO C F,WANG R F,KONG D L.Study on Doppler frequency shift estimation method based on the improved Fitz algorithm[J].Computer Measurement and Control,2014,22(2):611-613.)
[13]PIIRANINEN O.Adaptive compensation of Doppler shift in a mobile communication system:USA,6473594[P].2002-10-29.
[14]KYSTI P,MEINILJ,HENTILL,et al.WINNER II channel models[J].Radio Technologies and Concepts for IMT-Advanced,2009,50(2):52-92.
[15]张小飞,陈华伟,仇小锋,等.阵列信号处理及Matlab实现[M].北京:电子工业出版社,2015:40-42.(ZHANG X F,CHENG H W,QIU X F,et al.Array Signal Processing and Matlab Implementation[M].Beijing:Publishing House of Electronics Industry,2015:40-42.)
[16]LIU L,TAO C,QIU J,et al.Position-based modeling for wireless channel on high-speed railway under a viaduct at 2.35 GHz[J].IEEE Journal on Selected Areas in Communications,2012,30(4):834-845.