CN109639331B - Beam forming method for dynamically adjusting convergence factor - Google Patents
Beam forming method for dynamically adjusting convergence factor Download PDFInfo
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- CN109639331B CN109639331B CN201811567037.7A CN201811567037A CN109639331B CN 109639331 B CN109639331 B CN 109639331B CN 201811567037 A CN201811567037 A CN 201811567037A CN 109639331 B CN109639331 B CN 109639331B
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- 238000000034 method Methods 0.000 title claims abstract description 19
- 230000003044 adaptive effect Effects 0.000 claims abstract description 22
- 238000004891 communication Methods 0.000 claims abstract description 4
- 230000011218 segmentation Effects 0.000 claims description 8
- 230000005540 biological transmission Effects 0.000 description 5
- 238000002474 experimental method Methods 0.000 description 5
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- 238000012986 modification Methods 0.000 description 2
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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
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- 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
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- 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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Abstract
The invention discloses a beam forming method for dynamically adjusting convergence factors, which is used for a transmitting array of a wireless communication or wireless power supply system to realize the rapid focusing of beams. The traditional blind adaptive beamforming uses a constant convergence factor to obtain a next weight vector, is long in time consumption and is not suitable for tracking fast moving equipment; the method dynamically adjusts the convergence factor of the beam forming of the transmitting end in real time according to the signal intensity received by the target to be measured, substitutes the adjusted convergence factor into the iterative algorithm of the blind adaptive beam forming, and changes the weight value of the beam forming of the next time. The invention improves the implementation efficiency of blind adaptive beamforming by dynamically adjusting the convergence factor in real time, and has the advantage of being capable of tracking fast moving equipment.
Description
Technical Field
The present invention relates to the field of array signal processing, and in particular, to a beamforming method for dynamically adjusting a convergence factor.
Background
The beam forming technology is used for directional transmission or reception of signals/energy, and can effectively improve the transmission performance of wireless communication and the transmission efficiency of wireless power.
Conventional beamforming techniques have difficulty rapidly adjusting the beam to track fast moving devices. For example, in page 1743-1752 of the fifth volume of the IEEE Access journal published in 2, 8.2017, "Far-Field RF wireless power Transfer with blade Adaptive Beamforming for Internet of ThingsDevices" the text of which adopts Blind Adaptive Beamforming algorithm to perform Far-Field wireless power transmission on the Internet-of-things devices. As another example, the text "A Multiantenna RFID Reader With Blanket Adaptive Beamforming" published in pages 986-996 of volume III of the IEEE Internet of Things Journal by 3, 15 of 2016 uses a Blind Adaptive Beamforming algorithm to improve the range and performance of data transmission. However, the traditional blind adaptive beamforming algorithm needs a large number of iterations to adjust the beam to the best, is time-consuming, and is not suitable for tracking fast moving equipment.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a beam forming method for dynamically adjusting a convergence factor, so as to improve the implementation efficiency of blind adaptive beam forming.
The purpose of the invention is realized by the following technical scheme: a beamforming method for dynamically adjusting a convergence factor, comprising the steps of:
firstly, a transmitting end transmits signals to a receiving end through an antenna array;
step two, detecting the received signal strength in real time at a receiving end;
and step three, dynamically adjusting the convergence factor of beam forming at the transmitting end in real time according to the signal intensity received by the target to be detected, substituting the adjusted convergence factor into the iterative algorithm of blind adaptive beam forming, and changing the weight value of beam forming at the next time.
Furthermore, the signal sent by the transmitting terminal is a data signal or an energy signal, and is applied to wireless communication and power supply respectively.
Further, in the third step, a convergence factor frame is determined: dividing the value range of the signal intensity into two subsections; traversing the value range of the convergence factor in each sub-segment, and determining the optimal convergence factor of each sub-segment; and comparing the iteration times of the segmented blind adaptive beamforming algorithm with the iteration times before segmentation, if the iteration times of the segmented blind adaptive beamforming algorithm is less than the iteration times before segmentation, keeping the optimal segmentation, and continuously repeating the process in each sub-segment until all the optimal segments and the optimal convergence factor of each sub-segment are found.
Further, in the process of determining the convergence factor framework, the optimal convergence factor is found from the lowest sub-section until the optimal convergence factors of all sub-sections are determined.
The invention has the beneficial effects that: the traditional blind adaptive beamforming uses a constant convergence factor to obtain a next weight vector, is long in time consumption and is not suitable for tracking fast moving equipment; the invention dynamically adjusts the convergence factor of the beam forming of the transmitting end in real time according to the signal intensity received by the target to be measured, substitutes the adjusted convergence factor into the iterative algorithm of the blind adaptive beam forming, and changes the weight value of the beam forming of the next time. The implementation efficiency of blind adaptive beamforming is improved by dynamically adjusting the convergence factor in real time, and the method has the advantage of being capable of tracking fast moving equipment.
Drawings
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 for an embodiment of the present invention;
FIG. 3 is a graph comparing the number of iterations and RSSI relationships using a constant convergence factor and a dynamic convergence factor in a single experiment according to an embodiment of the present invention;
FIG. 4 is a statistical plot of the reduction of iterations using a dynamic convergence factor versus a constant convergence factor for multiple experiments according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The system block diagram of the embodiment of the invention is shown in figure 1, and each antenna in the transmitting array outputs the same power vector P initiallyo. By setting the complex weight vector w of the transmit arrayiThe amplitude and phase of all antennas are varied to beamform the signal to the target. And evaluating the effect of beam forming according to the Received Signal Strength Indicator (RSSI) of the receiving end.
In an iteration of the blind adaptive beamforming algorithm, the convergence factor β is multiplied by a complex random perturbation vector pkThe weight vector w that currently maximizes RSSIiUpdated as the weight vector w of the next iterationi+1As shown in formula (1). This means that during each iteration, the RSSI is currently maximized by incorporating the weight vector wiIt can be seen that β represents the complex random perturbation pair w in each iterationiDegree of influence of (c).
In the traditional blind adaptive beamforming algorithm, a constant convergence factor β is used to update the weight vector of the transmit array, and control the amplitude and phase of the transmit array antenna.
We consider a large blind search with a large convergence factor β in the early stages of the iteration, and w in the late stages of the iterationiHaving been closer to the optimal weight vector, we use a smaller β at wiAnd performing blind search in a small range around the target to finally find the optimal weight vector, and dynamically adjusting the convergence factor β to shorten the beamforming time.
According to the method, the value of the convergence factor beta in each iteration depends on the received strength indication (RSSI) of feedback, and the convergence factor beta (beta is more than 0 and less than 1) is dynamically adjusted by segmenting the normalized RSSI. The scheme for dynamically adjusting the convergence factor is shown in fig. 2.
To get the normalized RSSI from 0 to some value T, we can segment the process. First, we select a point, such as Ti, between RSSI-0 and RSSI-T. Then, the process will be divided into two segments, i.e. RSSI-0 to RSSI-Ti and RSSI-Ti to RSSI-T. Respectively, we can get the best convergence factor of each segmented segment, such as β 0, β 1, according to the statistical result.
And if the iteration times of the algorithm are less than that of the unused segment Ti as shown in the statistical result after the segment Ti is adopted, selecting the RSSI (received signal strength indicator) Ti as the segment. And continue to find the best segment among RSSI 0 to RSSI Ti and RSSI Ti to RSSI T until all best segments are found.
And if the iteration times of the algorithm are not less than the iteration times of the unused section Ti, the RSSI (received signal strength indicator) ═ Ti is not selected as the section, and other sections are continuously searched. Until all the best segments are found.
After the optimal segmentation is determined, the optimal convergence factors β 0, β 1, β n of each segmentation can be sequentially determined by traversing the value range of the convergence factor β.
In the experiment, after normalizing the signal strength indicator (RSSI) received by the receiving end each time, the corresponding optimal convergence factor β is found according to the obtained determined convergence factor frame, and substituted into the iterative formula of blind adaptive beamforming to calculate the weight vector of next beamforming. And dynamically adjusting a convergence factor according to the value of a Received Signal Strength Indicator (RSSI) in each iteration, changing the weight vector, applying the new weight vector to the transmitting array antenna, controlling the amplitude and the phase of the transmitting array antenna, and changing the beam direction.
We performed a comparative analysis of the number of iterations using a constant convergence factor and a dynamic convergence factor in a blind adaptive algorithm. Fig. 3 is a graph comparing the RSSI relationship and the iteration number of the embodiment of the present invention using the constant convergence factor and the dynamic convergence factor in a single experiment. FIG. 4 is a statistical plot of the reduction ratio of iterations using a constant convergence factor and a dynamic convergence factor over multiple experiments according to an embodiment of the present invention.
It can be seen from the above embodiments that, the embodiments of the present invention improve the implementation efficiency of blind adaptive beamforming by a method of dynamically adjusting the convergence factor in real time, and have the advantage of being able to track fast moving devices.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (3)
1. A beamforming method for dynamically adjusting a convergence factor, comprising the steps of:
firstly, a transmitting end transmits signals to a receiving end through an antenna array;
step two, detecting the received signal strength in real time at a receiving end;
step three, dynamically adjusting the convergence factor of the transmitting end beam forming in real time according to the signal intensity received by the target to be detected, and determining a convergence factor frame: dividing the value range of the signal intensity into two subsections; traversing the value range of the convergence factor in each sub-segment, and determining the optimal convergence factor of each sub-segment; comparing the iteration times of the segmented blind adaptive beamforming algorithm with the iteration times before segmentation, if the iteration times is less than the iteration times before segmentation, retaining the optimal segmentation, and continuously repeating the process in each sub-segment until all the optimal segments and the optimal convergence factor of each sub-segment are found; and substituting the adjusted convergence factor into an iterative algorithm of blind adaptive beamforming to change the weight value of next beamforming.
2. The beamforming method for dynamically adjusting a convergence factor according to claim 1, wherein the signal transmitted by the transmitting end is a data signal or an energy signal, and is applied to wireless communication and power supply, respectively.
3. The method of claim 1, wherein in the step of determining the convergence factor frame, the best convergence factor is found from the lowest sub-segment until the best convergence factors of all sub-segments are determined.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201811567037.7A CN109639331B (en) | 2018-12-20 | 2018-12-20 | Beam forming method for dynamically adjusting convergence factor |
| PCT/CN2019/111110 WO2020125173A1 (en) | 2018-12-20 | 2019-10-14 | Beamforming method for dynamically adjusting convergence factor |
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| CN201811567037.7A CN109639331B (en) | 2018-12-20 | 2018-12-20 | Beam forming method for dynamically adjusting convergence factor |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20000052485A (en) * | 1999-01-30 | 2000-08-25 | 최승원 | Signal processing method of array antenna system for cdma mobile telecommunication network |
| CN107241130A (en) * | 2017-06-13 | 2017-10-10 | 电子科技大学 | An a kind of bit feedback cooperative beam manufacturing process based on two time slots |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US20030228857A1 (en) * | 2002-06-06 | 2003-12-11 | Hitachi, Ltd. | Optimum scan for fixed-wireless smart antennas |
| KR100474291B1 (en) * | 2002-06-17 | 2005-03-10 | 엘지전자 주식회사 | Update method for beamforming weight vector of rake receiver |
| US7248897B2 (en) * | 2002-11-12 | 2007-07-24 | Chao-Hsing Hsu | Method of optimizing radiation pattern of smart antenna |
| US9318805B2 (en) * | 2012-08-21 | 2016-04-19 | Qualcomm Incorporated | Updating a beam pattern table |
| CN105959042B (en) * | 2016-04-27 | 2019-04-05 | 电子科技大学 | An a kind of bit feedback cooperative beam manufacturing process based on variable step size |
| CN109639331B (en) * | 2018-12-20 | 2020-10-16 | 浙江大学 | Beam forming method for dynamically adjusting convergence factor |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20000052485A (en) * | 1999-01-30 | 2000-08-25 | 최승원 | Signal processing method of array antenna system for cdma mobile telecommunication network |
| CN107241130A (en) * | 2017-06-13 | 2017-10-10 | 电子科技大学 | An a kind of bit feedback cooperative beam manufacturing process based on two time slots |
Non-Patent Citations (2)
| Title |
|---|
| "Far-Field RF Wireless Power Transfer with Blind Adaptive Beamforming for Internet of Things Devices";PAVAN S. YEDAVALLI等;《IEEE Access》;20170208;第5卷;第1743-1752页，图1-7 * |
| PAVAN S. YEDAVALLI等."Far-Field RF Wireless Power Transfer with Blind Adaptive Beamforming for Internet of Things Devices".《IEEE Access》.2017,第5卷 * |
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| WO2020125173A1 (en) | 2020-06-25 |
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