WO2020114303A1 - 应用盲自适应波束成形算法的射频能量收集系统及方法 - Google Patents
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
- H02J50/20—Circuit arrangements or systems for wireless supply or distribution of electric power using microwaves or radio frequency waves
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
- H02J50/40—Circuit arrangements or systems for wireless supply or distribution of electric power using two or more transmitting or receiving devices
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- the invention relates to the technical field of wireless energy transmission, in particular to a radio frequency energy collection system and method applying a blind adaptive beamforming algorithm.
- Non-radiative near-field coupling transmits energy at a relatively short distance, which is not suitable for applications in the Internet of Things scenario. Therefore, we focused our attention on far-field RF charging.
- Far-field RF charging is mainly divided into two types: non-directional RF and directional beamforming RF.
- directional beamforming controls the amplitude and phase of the transmit signal of each transmit antenna, so that when the transmit signal reaches the receiving end, it can be effectively superimposed on the receiving end. In this way, the signal received by the receiving antenna can be enhanced.
- Beamforming technology can be applied to overcome the loss of free space, because it can guide the wave signal directly to the target receiver and reduce adjacent interference.
- the main algorithms for implementing beamforming are EGC weights and random weights, which all require accurate channel estimation and complex arithmetic calculations.
- the received signal can be expressed as:
- x is the transmitted signal
- h is the channel parameter vector, which is a complex number
- H is the conjugate transpose matrix
- w is the beamforming weight vector, and the vector is also a complex number
- G is the channel considering that the antenna is transmitting The gain of the end and the receiver and the total gain after the spatial transmission loss determined by the frequency and the transmission distance.
- the received signal strength indication value in the receiver can be expressed as:
- the optimal weight value that can maximize the received signal is equivalent to a typical matched filter in communication.
- the matched filter can satisfy
- 2 N
- maximize the signal-to-noise ratio expressed as:
- RSSI can be re-expressed as:
- RSSI mf NG
- the present invention proposes a radio frequency energy collection system using a blind adaptive beamforming algorithm, which uses a lightweight blind adaptive beamforming algorithm, does not require exact channel estimation, and greatly reduces The complexity of channel estimation.
- the technical solution for solving the above problems of the present invention is: a radio frequency energy collection system applying a blind adaptive beamforming algorithm, and its special features are:
- It includes a transmitter and an energy receiver; the transmitter emits a radio frequency signal, and the energy receiver receives the radio frequency signal from the transmitter;
- the transmitter includes N transmitter modules, where N is a positive integer
- Each transmitter module includes an MCU and an RF front end, and each RF front end is connected to a transmission antenna.
- the above MCU is a field programmable gate array FPGA.
- the radio frequency front end is a radio frequency antenna.
- the present invention also proposes a working method of the above-mentioned radio frequency energy collection system system using blind adaptive beamforming algorithm, which is special in that it includes the following steps:
- Each MCU generates a random weight value w init as a starting value
- Each transmitting antenna will add the corresponding random weight value winit to its own transmission signal, and send out a continuous wave with fixed amplitude and frequency after weight adjustment;
- the energy receiver measures the power of the received continuous wave and feeds back the received energy information to the MCU in the transmitter in the form of RSSI;
- the MCU compares multiple RSSIs and selects the best weights, and then uses the best weights as a benchmark to generate multiple new weights. These weight values are refined after a limited number of repeated cycles to obtain the best
- the weight value is the weight value that can realize beamforming.
- step 4 specifically
- ⁇ is the disturbance factor
- the invention discloses a radio frequency energy collection system using a blind adaptive beamforming algorithm. By repeatedly optimizing the weight w in beamforming to obtain channel characteristics, complex calculations in channel estimation are avoided.
- the invention discloses a working method of a radio frequency energy collection system applying a blind adaptive beamforming algorithm, which generates an optimal beamforming parameter through iteration, and after obtaining this parameter, it is applied to a radio frequency wireless energy transmission system to obtain Increase the energy received by the RF receiver; the algorithm innovatively proposes an iterative concept, which reduces the necessity and complexity of channel estimation compared to the original method, the operation process is simple, and the system operation is greatly reduced. Cost while increasing efficiency.
- FIG. 1 is a structural diagram of a radio frequency energy collection system applying blind adaptive beamforming algorithm of the present invention
- FIG. 2 is an algorithm flowchart of a radio frequency energy collection system applying blind adaptive beamforming algorithm of the present invention.
- a radio frequency energy collection system using a blind adaptive beamforming algorithm includes a transmitter and an energy receiver; the transmitter sends out a radio frequency signal, and the energy receiver receives the radio frequency signal from the transmitter;
- the transmitter includes N transmitter modules, where N is a positive integer
- Each transmitter module includes an MCU and an RF front end, and each RF front end is connected to a transmitting antenna.
- the above MCU is a field programmable gate array FPGA.
- the radio frequency front end is a radio frequency antenna.
- each transmission module is at its maximum power K (dB), the maximum transmission power is determined by a special transmission module, different transmission modules have different maximum transmission power. Some are 30dB, some are 20dB, and some are other powers.
- K maximum power
- the implementation of the blind adaptive beamforming algorithm is done in the software environment of the MCU.
- the present invention also proposes a working method of the above-mentioned radio frequency energy collection system system using blind adaptive beamforming algorithm, which is special in that it includes the following steps:
- Each MCU generates a random weight value w init as a starting value
- Each transmitting antenna will add the corresponding random weight value winit to its own transmission signal, and send out a continuous wave with fixed amplitude and frequency after weight adjustment;
- the energy receiver measures the power of the received continuous wave and feeds back the received energy information to the MCU in the transmitter in the form of RSSI;
- the blind adaptive beamforming algorithm is used to optimize the weight w in beamforming to obtain channel characteristics:
- the MCU compares multiple RSSIs and selects the best weights, and then uses the best weights as a benchmark to generate multiple new weights. These weight values are refined after a limited number of repeated cycles to obtain the best weight value. , That is, the weight value that can achieve beamforming.
- step 4 specifically
- N N dimension
- ⁇ is the disturbance factor
- the blind adaptive beamforming algorithm obtains the channel characteristics by repeatedly optimizing the weight w in the beamforming, so the complicated calculation in the channel estimation is avoided. Its performance will be better than random beamforming algorithm or equal gain combining algorithm. After a series of repeated operations, it is close to the optimal beamforming. In the blind adaptive beamforming algorithm, after a limited number of repeated operations, the beamforming weights will gather to a specific value, which is also the optimal beamforming weight value.
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Abstract
提供一种应用盲自适应波束成形算法的射频能量收集系统及方法，涉及无线能量传输技术领域。该系统包括发射端和能量接收器；所述发射端发出射频信号，能量接收器接收发射端发出的射频信号；发射端包括N个发射端模块，其中，N为正整数；每个发射端模块包括一个MCU和一个射频前端，每个射频前端连接一个发射天线。该方法通过循环迭代产生一个最佳的波束成形参数。该系统及方法避免了信道估计中的复杂运算。
Description
本发明涉及无线能量传输技术领域，特别涉及一种应用盲自适应波束成形算法的射频能量收集系统及方法。
现有的无线能量传输技术主要有两大类型：非辐射近场耦合和远场射频辐射传输。非辐射近场耦合技术传输能量的距离比较短，不适用于物联网场景的应用。因此，我们把注意力集中到远场射频充电上。远场射频充电主要分为两种类型：非定向射频和定向波束成形射频。
现有的技术中，有人尝试探究利用非定向射频来实现能量传输，同时也有学者尝试利用环境电磁波去对设备充电，例如蜂窝信号和Wifi。但是这些方案都无法解决一个重要的问题：远场的传输效率损耗1/d
2。
在远场射频应用中，一个克服自由空间损耗(free space path loss)的方法是定向波束成形(Directive beamforming)。从概念上看，定向波束成形控制了每个发射天线的发射信号的幅度和相位，使得发射信号到达接收端的时候，能够在接收端上有效地叠加。这样一来，接收天线接收到的信号便能增强。波束成形技术能够被应用到克服自由空间损耗上，因为它能够引导波信号直接传输到目标接收器上，并减少相邻的干扰。实现波束成形的主要算法有EGC权重，随机权重，这些都需要准确的信道估计，需要复杂的算术计算。
射频天线系统的类型有很多种，对于应用了波束成形算法的天线系统，接收到的信号可以表示成：
其中x是发射信号；h是信道参数矢量，该矢量是一个复数；H表示的是共轭转置矩阵；w是波束成形权重矢量，该矢量也是一个复数；G是信道中考虑 到天线在发射端和接收端的增益以及由频率和传输距离决定的空间传输损耗后的总增益。在无线能量传输系统中，在接收器中的接收信号强度指示值可以表示为：
RSSI＝G|h
Hw|
2P
x。
我们的目的是要使RSSI最大化，理论上，能够使接收信号最大化的最佳权重值等同于通信中的典型的匹配滤波器，该匹配滤波器能够在满足||w||
2＝N的情况下，使信噪比最大化，表示为：
因此，RSSI可以重新表示为：
RSSI
mf＝NG||h||
2P
x。
要实现波束成形算法，需要准确知道各个信道系数的值h
n(n＝1,2,...,N),否则无法合理计算波束成型的参数。但是在实际中，发射端是不能知道信道的情况，亦即不能知道信道系数。接收端也无法知道信道的情况，因为接收端只能返回RSSI值。
发明内容
为解决上述背景技术中存在的问题，本发明提出一种应用盲自适应波束成形算法的射频能量收集系统，该系统采用轻量的盲自适应波束成形算法，不要求确切的信道估计，大大减少了信道估计的复杂性。
本发明解决上述问题的技术方案是：一种应用盲自适应波束成形算法的射频能量收集系统，其特殊之处在于：
包括发射端和能量接收器；所述发射端发出射频信号，能量接收器接收发射端发出的射频信号；
发射端包括N个发射端模块，其中，N为正整数；
每个发射端模块包括一个MCU和一个射频前端，每个射频前端连接一个发 射天线。
进一步地，上述MCU为现场可编程门阵列FPGA。
进一步地，上述射频前端为射频天线。
另外，本发明还提出一种上述应用盲自适应波束成形算法的射频能量收集系统系统的工作方法，其特殊之处在于，包括以下步骤：
1)每个MCU产生一个随机权重值w
init作为起始值；
2)每个发射天线会把相应的随机权重值w
init加到自己的发射信号上，并对外发出一个经过权重调整后的、具有固定幅度和频率的连续波；
3)能量接收器测量接收连续波的功率，并以RSSI的形式把接收到的能量的信息反馈至发射端中的MCU；
4)MCU对多个RSSI进行比较后选取最佳权重，再用所述最佳权重作为基准，产生多个新的权重，这些权重值在经过有限次重复循环后得到提炼，从而得到最佳的权重值，即能实现波束成型的权重值。
进一步地，上述步骤4)中，具体为
4.1)初始化i＝0和
4.2)判断条件
是否成立，若成立，则执行第3步，若不成立，则结束该算法；
4.3)i＝i+1,随机产生k个N维的扰动向量p
k～lN(0,I),k＝1,...,K；
4.4)在产生K个N维扰动向量之后，产生K个权重向量：
其中，β是扰动因子；
4.5)对于k＝1～K,根据下列公式计算在接收端上的RSSI值：
并把K个RSSI值全部传到发射端上
4.6)通过MCU、单片机或者任何有计算能力的处理器较并找出第五步得出的K个RSSI值中的最大RSSI值，并记录下来作为下一次循环的数据：
4.7)执行第二步。
本发明的优点：
本发明一种应用盲自适应波束成形算法的射频能量收集系统，通过重复对波束成形中的权重w进行优化来获取信道特征，所以避免了信道估计中的复杂运算。本发明一种应用盲自适应波束成形算法的射频能量收集系统的工作方法，通过循环迭代(Iteration)产生一个最佳的波束成形参数，得到此参数后，应用到射频无线能量传输系统上从而得到增大射频接收端所接收到的能量；该算法创新地提出一个迭代的理念，从而较原来的方法上，减去了信道估计的必要性以及复杂性，操作过程简单，大大减少了系统运行的成本，同时提高了效率。
图1是本发明应用盲自适应波束成形算法的射频能量收集系统的结构图；
图2是本发明应用盲自适应波束成形算法的射频能量收集系统的算法流程图。
为使本发明实施方式的目的、技术方案和优点更加清楚，下面将结合本发明实施方式中的附图，对本发明实施方式中的技术方案进行清楚、完整地描述，显然，所描述的实施方式是本发明一部分实施方式，而不是全部的实施方式。基于本发明中的实施方式，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施方式，都属于本发明保护的范围。因此，以下对在附图中提供的本发明的实施方式的详细描述并非旨在限制要求保护的本发明的范围，而是仅仅表示本发明的选定实施方式。基于本发明中的实施方式，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施方式，都属于本发明保护的范围。
一种应用盲自适应波束成形算法的射频能量收集系统，包括发射端和能量接收器；所述发射端发出射频信号，能量接收器接收发射端发出的射频信号；
发射端包括N个发射端模块，其中，N为正整数；
每个发射端模块包括一个MCU和一个射频前端，每个射频前端连接一个发射天线。
进一步地，上述MCU为现场可编程门阵列FPGA。
进一步地，上述射频前端为射频天线。
这些发射天线，都是按照某一特定频率M向空间发送电磁波，因此，发射端的天线相互之间会有一个
的间距。此外，每个发射模块的发射功率都在其最大功率K(dB)，最大发射功率是由特殊的发射模块决定的，不同的发射模块有不同的最大发射功率。有些是30dB，有些是20dB，有些也是其它的功率。盲自适应波束成形算法的实现是在MCU的软件环境下完成的。
另外，本发明还提出一种上述应用盲自适应波束成形算法的射频能量收集系统系统的工作方法，其特殊之处在于，包括以下步骤：
1)每个MCU产生一个随机权重值w
init作为起始值；
2)每个发射天线会把相应的随机权重值w
init加到自己的发射信号上，并对外发出一个经过权重调整后的、具有固定幅度和频率的连续波；
3)能量接收器测量接收连续波的功率，并以RSSI的形式把接收到的能量的信息反馈至发射端中的MCU；
4)利用盲自适应波束成形算法对波束成形中的权重w进行优化来获取信道特征：
MCU对多个RSSI进行比较后选取最佳权重，再用所述最佳权重作为基准，产生多个新的权重，这些权重值在经过有限次重复循环后得到提炼，从而得到最佳的权重值，即能实现波束成型的权重值。
进一步地，上述步骤4)中，具体为
4.1)初始化i＝0和
其中，N表示N维；
4.2)判断条件
是否成立，若成立，则执行第3步，若不成立，则结束该算法；
4.3)i＝i+1,随机产生k个N维的扰动向量p
k～lN(0,I),k＝1,...,K；
4.4)在产生K个N维扰动向量之后，产生K个权重向量：
其中，β是扰动因子；
4.5)对于k＝1～K,根据下列公式计算在接收端上的RSSI值：
并把K个RSSI值全部传到发射端上
4.6)通过MCU比较并找出第五步得出的K个RSSI值中的最大RSSI值，并记录下来作为下一次循环的数据：
4.7)执行第二步。
上述算法的思路可以概括为：在第i次循环上，我们能够从第(i-1)次循环中产生最佳权重
然后，这个最佳权重又被K个新的扰动向量调整，这K个新的扰动向量满足正太分布和复杂分布，p
k～lN(0,I),k＝1,...,K，从而再产生K个新权重:
公式中，β是扰动因子，该扰动因子会通过调整每个天线的幅度和相位来影响权重。在K个产生的新权重中再选出M值：
有限次的重复循环这个步骤，最终会得到一个最佳的权重值，这个值就是能实现波束成型的权重值。
盲自适应波束成形算法通过重复对波束成形中的权重w进行优化来获取信道特征，所以避免了信道估计中的复杂运算。它的性能会比随机波束成形算法或等增益结合算法要更好，通过一系列重复运算后，接近最优波束成形。在盲自适应波束成形算法里面，通过有限次的重复运算之后，波束成型权重会聚拢至一个特定的值，这个值也是最佳的波束成形权重值。
以上所述仅为本发明的实施例，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的系统领域，均同理包括在本发明的专利保护范围内。
Claims (5)
- 一种应用盲自适应波束成形算法的射频能量收集系统，其特征在于：包括发射端和能量接收器；所述发射端发出射频信号，能量接收器接收发射端发出的射频信号；发射端包括N个发射端模块，其中，N为正整数；每个发射端模块包括一个MCU和一个射频前端，每个射频前端连接一个发射天线。
- 根据权利要求1所述的一种应用盲自适应波束成形算法的射频能量收集系统，其特征在于：所述MCU为现场可编程门阵列FPGA。
- 根据权利要求2所述的一种应用盲自适应波束成形算法的射频能量收集系统，其特征在于：所述射频前端为射频天线。
- 一种应用盲自适应波束成形算法的射频能量收集系统的工作方法，其特征在于，包括以下步骤：1)每个MCU产生一个随机权重值w init作为起始值；2)每个发射天线会把相应的随机权重值w init加到自己的发射信号上，并对外发出一个经过权重调整后的、具有固定幅度和频率的连续波；3)能量接收器测量接收连续波的功率，并以RSSI的形式把接收到的能量的信息反馈至发射端中的MCU；4)利用盲自适应波束成形算法对波束成形中的权重w进行优化来获取信道特征：MCU对多个RSSI进行比较后选取最佳权重，再用所述最佳权重作为基准，产生多个新的权重，这些权重值在经过有限次重复循环后得到提炼，从而得到最佳的权重值，即能实现波束成型的权重值。
- 根据权利要求4所述的一种应用盲自适应波束成形算法的射频能量收集系统的工作方法，其特征在于：步骤4)中，具体为4.1)初始化i＝0和
其中，N表示N维；
4.2)判断条件是否成立，若成立，则执行第3步，若不成立，则结束该算法；
4.3)i＝i+1,随机产生k个N维的扰动向量p k～lN(0,I),k＝1,...,K；4.4)在产生K个N维扰动向量之后，产生K个权重向量：
其中，β是扰动因子；4.5)对于k＝1～K,根据下列公式计算在接收端上的RSSI值：
并把K个RSSI值全部传到发射端上4.6)比较并找出第五步得出的K个RSSI值中的最大RSSI值，并记录下来作为下一次循环的数据：
4.7)执行第二步。
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