WO2020125173A1 - 一种动态调整收敛因子的波束成形方法 - Google Patents

一种动态调整收敛因子的波束成形方法 Download PDF

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WO2020125173A1
WO2020125173A1 PCT/CN2019/111110 CN2019111110W WO2020125173A1 WO 2020125173 A1 WO2020125173 A1 WO 2020125173A1 CN 2019111110 W CN2019111110 W CN 2019111110W WO 2020125173 A1 WO2020125173 A1 WO 2020125173A1
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convergence factor
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赵博
张云珊
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Zhejiang University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/24—Cell structures
    • H04W16/28—Cell structures using beam steering

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  • the invention relates to the field of array signal processing, in particular to a beamforming method for dynamically adjusting the convergence factor.
  • Beamforming technology is used for directional transmission or reception of signals/energy, which can effectively improve the transmission performance of wireless communication and the transmission efficiency of wireless power.
  • the purpose of the present invention is to optimize the shortcomings in the prior art, provide a beamforming method for dynamically adjusting the convergence factor, and improve the implementation efficiency of blind adaptive beamforming.
  • a beamforming method for dynamically adjusting the convergence factor including the following steps:
  • Step 1 The transmitting end transmits signals to the receiving end through the antenna array
  • Step 2 Detect the received signal strength in real time at the receiving end
  • Step 3 Dynamically adjust the convergence factor of the beamforming at the transmitting end in real time according to the received signal strength of the target to be measured, and substitute the adjusted convergence factor into the iterative algorithm of blind adaptive beamforming to change the weight value of the next beamforming.
  • the signal sent by the transmitter is a data signal or an energy signal, which is respectively used in wireless communication and power supply.
  • the framework of the convergence factor is determined: the value range of the signal strength is divided into two sub-segments; in each sub-segment, the value range of the convergence factor is traversed to determine the optimal value of each sub-segment Convergence factor; compare the number of iterations of the blind adaptive beamforming algorithm after the segmentation with the number of iterations before the segmentation, if it is less than the number of iterations before the segmentation, then retain the optimal segmentation, and continue in each sub-segment This process is repeated until the best convergence factor of all the best segments and each sub-segment is found.
  • the best convergence factors are searched from the lowest sub-segment until the best convergence factors of all sub-segments are determined.
  • the beneficial effects of the present invention are: the traditional blind adaptive beamforming uses a constant convergence factor to obtain the next weight vector, which takes a long time and is not suitable for tracking fast-moving equipment; the present invention dynamically and in real time according to the signal strength received by the target under test Adjust the convergence factor of the beamforming at the transmitting end, and substitute the adjusted convergence factor into the iterative algorithm of blind adaptive beamforming to change the weight value of the next beamforming.
  • the convergence factor of the beamforming at the transmitting end Adjust the convergence factor of the beamforming at the transmitting end, and substitute the adjusted convergence factor into the iterative algorithm of blind adaptive beamforming to change the weight value of the next beamforming.
  • FIG. 1 is a system block diagram of an embodiment of the present invention
  • FIG. 2 is a diagram of a dynamic adjustment convergence factor scheme according to an embodiment of the present invention.
  • FIG. 3 is a comparison diagram of the relationship between the number of iterations and the RSSI using a constant convergence factor and a dynamic convergence factor in a single experiment of an embodiment of the present invention
  • FIG. 4 is a statistical diagram of the ratio of the reduction in the number of iterations using the dynamic convergence factor over the constant convergence factor in multiple experiments in an embodiment of the present invention.
  • Each antenna in the transmit array initially outputs the same power vector Po .
  • w i By providing a plurality of weights of the transmit array weight vector w i change the amplitude and phase of all antenna beam to the target signal shaping.
  • RSSI received signal strength indicator
  • the current weight vector w i that maximizes RSSI is updated to the weight vector w i+1 of the next iteration, as in (1).
  • ⁇ represents the degree of influence of complex random disturbance on w i in each iteration.
  • a constant convergence factor ⁇ is used to update the weight vector of the transmit array to control the amplitude and phase of the transmit array antenna.
  • the value of the convergence factor ⁇ in each iteration will depend on the received strength indicator (RSSI) of the feedback.
  • RSSI received strength indicator
  • the convergence factor ⁇ (0 ⁇ ⁇ 1 )'S dynamic adjustment is shown in Figure 2.
  • the corresponding optimal convergence factor ⁇ is found according to the obtained frame of the determined convergence factor, and it is substituted into the blind adaptive beamforming iteration Formula, calculate the weight vector of the next beamforming.
  • the convergence factor is dynamically adjusted according to the received signal strength indicator (RSSI) value, the weight vector is changed, and the new weight vector is applied to the transmit array antenna, the amplitude and phase of the transmit array antenna are controlled, and the beam direction is changed.
  • FIG. 3 is a comparison diagram of the number of iterations and the RSSI relationship using a constant convergence factor and a dynamic convergence factor in a single experiment of an embodiment of the present invention.
  • FIG. 4 is a statistical diagram of the ratio of the reduction in the number of iterations using constant convergence factors and dynamic convergence factors in multiple experiments in an embodiment of the present invention.
  • the embodiment of the present invention improves the implementation efficiency of blind adaptive beamforming by dynamically adjusting the convergence factor in real time, and has the advantage of tracking fast-moving devices.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Radio Transmission System (AREA)
  • Variable-Direction Aerials And Aerial Arrays (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

本发明公开了一种动态调整收敛因子的波束成形方法，用于无线通信或无线供电系统的发射阵列，来实现波束的快速聚焦。传统的盲自适应波束成形使用恒定的收敛因子得到下一次的权重向量，耗时长，不适合跟踪快速移动的设备；本发明方法根据待测目标接收的信号强度实时动态的调整发射端波束成形的收敛因子，将调整后的收敛因子代入盲自适应波束成形的迭代算法中，改变下次波束成形的权重值。本发明通过实时动态调整收敛因子，提高盲自适应波束成形的实施效率，具有可跟踪快速移动的设备的优点。

Description

一种动态调整收敛因子的波束成形方法 技术领域
本发明涉及阵列信号处理领域，特别涉及一种动态调整收敛因子的波束成形方法。
背景技术
波束成形技术用于信号/能量的定向传输或接收，能有效的提高无线通信的传输性能和无线功率的传输效率。
但是传统波束成形技术很难快速调整波束去跟踪快速移动的设备。例如，在2017年2月8日发表在IEEE Access期刊的第五卷第1743页～1752页中“Far-Field RF Wireless Power Transfer with Blind Adaptive Beamforming for Internet of Things Devices”一文采用盲自适应波束成形算法对物联网设备进行远场无线功率传输。再例如，在2016年3月15日发表在IEEE Internet of Things Journal期刊第三卷的第986页～996页中“A Multiantenna RFID Reader With Blind Adaptive Beamforming”一文采用盲自适应波束成形算法来改善数据传输的范围和性能。但采用传统的盲自适应波束成形算法需要通过大量的迭代后才能将波束调到最佳，耗时长，不适合跟踪快速移动的设备。
发明内容
本发明的目的是为了优化现有技术中的不足，提供一种动态调整收敛因子的波束成形方法，提高盲自适应波束成形的实施效率。
本发明的目的是通过以下技术方案来实现的：一种动态调整收敛因子的波束成形方法，包括以下步骤：
步骤一、发射端通过天线阵列向接收端传输信号；
步骤二、在接收端实时检测接收到的信号强度；
步骤三、根据待测目标接收的信号强度实时动态的调整发射端波束成形的收敛因子，将调整后的收敛因子代入盲自适应波束成形的迭代算法中，改变下次波束成形的权重值。
进一步地，发射端发送的信号为数据信号或能量信号，分别应用于无线通信、供电中。
进一步地，所述步骤三中，确定收敛因子框架：将信号强度的取值范围分为两个子段；在各子段中，对收敛因子的取值范围进行遍历，确定各子段的最佳收敛因子；将分段后的盲自适应波束成形算法的迭代次数和分段前的迭代次数作对比，如果小于分段前的迭代次数，则保留该最佳分段，并继续在每个子段中重复该过程，直到找到所有最佳分段及各子段的最佳收敛因子。
进一步地，确定收敛因子框架过程中，从最低子段开始寻找最佳收敛因子，直到将所有子段的最佳收敛因子确定。
本发明的有益效果是：传统的盲自适应波束成形使用恒定的收敛因子得到下一次的权重向量，耗时长，不适合跟踪快速移动的设备；本发明根据待测目标接收的信号强度实时动态的调整发射端波束成形的收敛因子，将调整后的收敛因子代入盲自适应波束成形的迭代算法中，改变下次波束成形的权重值。通过实时动态调整收敛因子提高盲自适应波束成形的实施效率，具有可跟踪快速移动的设备的优点。
附图说明
图1是本发明的实施例的系统框图；
图2是本发明的实施例的动态调整收敛因子方案图；
图3是本发明的实施例在单次实验中，使用常值收敛因子和动态收敛因子的迭代次数和RSSI关系的对比图；
图4是本发明的实施例在多次实验中，使用动态收敛因子比使用常值收敛因子的迭代次数减少量占比统计图。
具体实施方式
下面结合附图和实施例，对本发明的具体实施方式作进一步详细描述。以下实施例用于说明本发明，但不用来限制本发明的范围。
本发明实施例的系统框图如图1所示，发射阵列中每个天线最初输出相同的功率矢量P o。通过设置发射阵列的复数权重向量w i改变所有天线的幅度和相位，以对目标进行信号的波束成形。再根据接收端的接收信号强度指示(RSSI) 对波束成形的效果进行评估。
在盲自适应波束成形算法的迭代中，通过将收敛因子β乘以复数随机扰动向量p K，将当前使RSSI最大的权重向量w i更新为下一次迭代的权重向量w i+1，如式(1)。这意味在每次迭代过程中，通过结合当前使RSSI最大的权重向量w i，以完全盲的方式去搜索最佳权重向量。可以看出，β表示在每次迭代中，复数随机扰动对w i的影响程度。
Figure PCTCN2019111110-appb-000001
在传统的盲自适应波束成形算法中，使用恒定的收敛因子β更新发射阵列的权重向量，控制发射阵列天线的幅值和相位。
我们考虑在迭代前期，采用较大的收敛因子β进行大范围盲搜；而在迭代后期，w i已较接近最佳权重向量，我们采用较小的β在w i周围进行小范围盲搜，最终找到最佳权重向量。通过动态调整收敛因子β缩短波束成形时间。
根据我们提出的方法，每次迭代中收敛因子β的取值将取决于反馈的接收强度指示(RSSI)，通过对归一化后的RSSI分段，实现对收敛因子β(0＜β＜1)的动态调整。动态调整收敛因子的方案如图2所示。
为使归一化后的RSSI从0达到某一值T，我们可以将该过程进行分段。首先，我们在RSSI＝0和RSSI＝T之间选择一点，如Ti。然后，该过程将被分为两段，即RSSI＝0到RSSI＝Ti和RSSI＝Ti到RSSI＝T。分别地，我们可以根据统计结果得到分段后的各段最佳收敛因子，如β0，β1。
若采用分段Ti后，在统计结果中显示算法的迭代次数少于未使用分段Ti的迭代次数，则选用RSSI＝Ti作为分段。并继续在RSSI＝0到RSSI＝Ti和RSSI＝Ti到RSSI＝T中寻找最佳分段，直到找到所有的最佳分段。
若采用分段Ti后，在统计结果中显示算法的迭代次数不少于未使用分段Ti的迭代次数，则不选用RSSI＝Ti作为分段，并继续寻找其他分段。直到找到所有的最佳分段。
在确定最佳分段后，我们通过对收敛因子β的取值范围进行遍历，即可依次确定各分段的最佳收敛因子β0，β1，...，βn。
在实验中，对接收端每次接收的信号强度指示(RSSI)进行归一化后，再根据已得到的确定收敛因子框架找到对应的最佳收敛因子β，并代入盲自适应波束成形的迭代算式，计算下一次波束成形的权重向量。在每次迭代中根据接收信号强度指示(RSSI)的值动态调整收敛因子，改变权重向量，并将新的权重向量作用于发射阵列天线，控制发射阵列天线的幅值和相位，改变波束方向。
我们对在盲自适应算法中使用常值收敛因子和动态收敛因子的迭代次数进行了对比分析。其中，图3是本发明的实施例在单次实验中，使用常值收敛因子和动态收敛因子的迭代次数和RSSI关系对比图。图4是本发明的实施例在多次实验中，使用常值收敛因子和动态收敛因子的迭代次数减少量占比统计图。
由以上实施例可以看出，本发明实施例通过实时动态调整收敛因子的方法，提高盲自适应波束成形的实施效率，具有可跟踪快速移动的设备的优点。
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明技术原理的前提下，还可以做出若干改进和变型，这些改进和变型也应视为本发明的保护范围。

Claims (4)

  1. 一种动态调整收敛因子的波束成形方法，其特征在于，包括以下步骤：
    步骤一、发射端通过天线阵列向接收端传输信号；
    步骤二、在接收端实时检测接收到的信号强度；
    步骤三、根据待测目标接收的信号强度实时动态的调整发射端波束成形的收敛因子，将调整后的收敛因子代入盲自适应波束成形的迭代算法中，改变下次波束成形的权重值。
  2. 根据权利要求1所述的一种动态调整收敛因子的波束成形方法，其特征在于，发射端发送的信号为数据信号或能量信号，分别应用于无线通信、供电中。
  3. 根据权利要求1所述的一种动态调整收敛因子的波束成形方法，其特征在于，所述步骤三中，确定收敛因子框架：将信号强度的取值范围分为两个子段；在各子段中，对收敛因子的取值范围进行遍历，确定各子段的最佳收敛因子；将分段后的盲自适应波束成形算法的迭代次数和分段前的迭代次数作对比，如果小于分段前的迭代次数，则保留该最佳分段，并继续在每个子段中重复该过程，直到找到所有最佳分段及各子段的最佳收敛因子。
  4. 根据权利要求3所述的一种动态调整收敛因子的波束成形方法，其特征在于，确定收敛因子框架过程中，从最低子段开始寻找最佳收敛因子，直到将所有子段的最佳收敛因子确定。
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