时间:2024-05-04
左世元 范戎飞
摘要:准确及时的信道估计对实现高铁应用场景中的高吞吐量毫米波通信具有重要作用。然而,由于列车高速移动,信道条件变化迅速,频繁测量将带来巨大开销。针对上述问题,利用列车与基站间信道到达角(AoA)与离开角(AoD)经常性连续变化、偶发性骤变的特征,设计AoA与AoD连续变化跟踪与骤变检测算法。在信道AoA与AoD变化符合预期时,基于角度先验信息测量部分信道参数;在AoA与AoD发生骤变时,第一时间报警并通知系统重新测量信道整体参数。设计的收发机波束成形算法可提升AoA与AoD变化跟踪与骤变检测的性能。提出的混合方案可有效降低高速移动条件下的毫米波信道估计信令开销。高速移动对无线信道带来的快衰落影响,并且系统误比特率性能也得到了明显改善。
关键词:高铁;毫米波通信;信道估计
Abstract: Accurate and timely channel estimation plays an important role in realizing highthroughput millimeter wave communication in high-speed railway application scenarios. However, due to the high-speed movement of trains, the channel conditions change rapidly, and frequent measurements will bring huge overhead. Aiming at the above problems, based on the characteristics of frequent continuous changes and occasional sudden changes in the channel of the angle of arrival (AoA) and the angle of departure (AoD) between the train and the base station, the AoA and AoD continuous changes tracking and sudden changes detection algorithms are designed. When the AoA and AoD changes are in line with expectations, some parameters of the channel are measured based on the angle prior information, and when the AoA and AoD change suddenly, they will be alerted and notified to remeasure the overall channel parameters. The transceiver beamforming algorithm is designed to improve the performance of the tracking of AoA and AoD continuous changes and the detection of sudden changes. Through the hybrid scheme, the overhead of millimeter wave channel estimation signaling can be effectively reduced under high-speed mobile conditions.
Keywords: high-speed railway; millimeter wave; channel estimation
高铁是目前中短距離出行的重要交通工具,全程时间短,运送能力大,受气候影响小。在高铁上架载毫米波通信收发机与地面基站建立连接,可发挥大吞吐量的技术优势,为高铁乘客提供高速率无线接入,满足乘客5G时代的通信需求[1-2]。毫米波频段具有高衰减特征,需要精确的信道状态信息(CSI),以生成指向性强波束并实现高吞吐量通信。
毫米波的CSI由收发机间角度信息和每条传播路径的信道系数构成,其中,角度信息包括收发端有限条传播路径的离开角(AoD)和到达角(AoA)。信道估计是指收端通过发端多次发射的导频信号解算CSI。传统方法单次测量角度信息和信道系数,包括多阶段扇区穷举搜索AoA和AoD[3],或利用路径数的稀疏性,使用相对较少信令和正交匹配跟踪(OMP)等稀疏信号处理的方法来恢复信道信息[4-5]。然而,上述方法仍然需要较多导频序列以完成单次测量,在列车高速移动时更需要频繁更新CSI。这将造成较大开销,降低通信效率。
列车与当前地面基站之间的AoD和AoA呈现连续性变化。当这种变化持续到下一个地面基站出现时,信道角度信息将发生骤变。基于波束跟踪的算法[6]虽然可以对AoA和AoD的连续变化进行跟踪,但是当信道角度信息发生骤变时,该算法将失效。对此,本文设计了AoA和AoD的跟踪预测算法,并实时判断是否会出现新基站。当判断结果显示未出现新基站连接时,可根据AoA和AoD的预测值缩减其搜索空间,简化信道估计;当出现新基站连接时,将报警通知系统采用传统方法[5]来重新测量角度信息和信道系数。为加强AoA和AoD的跟踪预测能力和角度信息骤变检测能力,本文还设计了收发端波束成型算法。整体而言,本文在信道估计过程中降低了测角开销,提升了通信效率。
1.2角度时变模型
毫米波信道中的传播路径变化(对应角度信息变化)主要有两种:(1)列车与当前基站间传播路径的变化;(2)列车驶离当前基站与下一基站建立连接所产生的路径突变。这里我们先考虑第1种变化因素,此时AoA和AoD连续变化,并假设已经完成对信道角度信息的预估计。
2问题构建与算法设计
本节基于系统模型,首先提出高速移动条件下的信道估计解决思路,然后针对每个环节,构建具体的数学问题并给出相应的算法设计。问题解决流程如图1所示。
2.1问题构建
2.1.1基站切换检测问题
当高铁运行至两个基站的交界处时,需进行基站切换检测。如果不需切换基站通信,且列车与当前基站间的AoA与AoD处于连续变化中,可根据最近的角度信息预估当前角度信息,简化信道估计。如果需要切换到下一基站,AoA和AoD的历史信息将不再具有参考价值,需要重新运行传统的信道估计方法,以完成角度信息和信道状态信息的估计。
4结束语
本文主要研究了高速移动情况下的毫米波通信信道估计问题,基于信道路径角度變化规律构建了可跟踪预测路径角度连续变化、检测路径角度突变的信道估计体系,以达到节约信道估计导频量的效果。本论文研究结果可为毫米波通信在高铁等高速平台上的应用提供技术支持。
参考文献
[1] NOH G, HUI B, KIM I. High speed train communications in 5G: design elements to mitigate the impact of very high mobility [J]. IEEE wireless communications, 2020, 27(6): 98-106. DOI:10.1109/MWC.001.2000034
[2] YUE G R, YU D Z, CHENG L, et al. Millimeterwave system for high-speed train communications between train and trackside: system design and channel measurements [J]. IEEE transactions on vehicular technology, 2019, 68(12): 11746-11761. DOI:10.1109/TVT.2019.2919625
[3] CHIU S E, RONQUILLO N, JAVIDI T. Active learning and CSI acquisition for mmWave initial alignment [J]. IEEE journal on selected areas in communications, 2019, 37(11): 2474-2489. DOI:10.1109/JSAC.2019.2933967
[4] LEE J, GIL G T, LEE Y H. Exploiting spatial sparsity for estimating channels of hybrid MIMO systems in millimeter wave communications [C]//2014 IEEE Global Communications Conference. Austin, TX, USA:IEEE, 2014: 3326-3331. DOI: 10.1109/ GLOCOM.2014.7037320
[5] KE M L, GAO Z, WU Y P, et al. Compressive sensing-based adaptive active user detection andchannelestimation:massiveaccess meets massive MIMO [J]. IEEE transactions on signal processing, 2020, 68: 764-779. DOI:10.1109/TSP.2020.2967175
[6] ALKHATEEB A, AYACH OEL, LEUS G, et al. Channel estimation and hybrid precoding for millimeter wave cellular systems [J]. IEEE journal of selected topics in signal processing, 2014,8(5):831-846.DOI: 10.1109/JSTSP.2014.2334278
[7] BAJWA W U, HAUPT J, SAYEED A M, et al. Compressed channel sensing: a new approach to estimating sparse multipath channels [J]. Proceedings of the IEEE, 2010, 98(6): 1058-1076. DOI:10.1109/JPROC.2010.2042415
[8] PAIVA A R L, FODOR G, FREITAS W C, et al. Kalman-filter-based tracking of millimeterwave channel parameters for V2X applications [C]//2019 IEEE Conference on Standards for Communications and Networking (CSCN). Granada,Spain:IEEE,2019:1-7.DOI: 10.1109/CSCN.2019.8931350
[9] JAIN A, SARVEPALLI P, BHASHYAM S, et al. Algorithms for change detection with sparse signals [J]. IEEE transactions on signal processing, 2020, 68: 1331-1345. DOI:10.1109/ TSP.2020.2973115
[10] GENG J, LAI L F. Quickest change-point detection over multiple data streams via sequential observations [C]//2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Calgary, AB, Canada:IEEE, 2018: 4404-4408. DOI: 10.1109/ICASSP.2018.8461647
[11] RISTIC B, ARULAMPALAM S, GORDON N. Beyond the Kalman filter-particle filters for tracking applications [M]. London: Artech house, 2003
[12] KAY S M. Fundamentals of statistical signal processing [M]. London: Prentice Hall PTR, 1993
[13] PALOMAR D P, ELDAR Y C. Eldar (Eds.). Convex optimization in signal processing and communications [M]. Cambridge: Cambridge University Press, 2010
作者簡介
左世元,北京理工大学在读硕士研究生;主要研究方向为毫米波数字通信、联邦学习等。
范戎飞(通信作者),北京理工大学网络空间安全学院副教授、博士生导师;主要从事毫米波数字通信、边缘计算、联邦学习等研究;发表论文40余篇。
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