WO2022021656A1 - 用于oam-mimo动态信道的功率分配方法 - Google Patents

用于oam-mimo动态信道的功率分配方法 Download PDF

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WO2022021656A1
WO2022021656A1 PCT/CN2020/127132 CN2020127132W WO2022021656A1 WO 2022021656 A1 WO2022021656 A1 WO 2022021656A1 CN 2020127132 W CN2020127132 W CN 2020127132W WO 2022021656 A1 WO2022021656 A1 WO 2022021656A1
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oam
mimo
channel
trajectory
receiver
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PCT/CN2020/127132
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French (fr)
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王洋
修艳磊
施盼盼
廖希
林峰
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重庆邮电大学
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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
    • H04B7/0413—MIMO systems
    • H04B7/0426—Power distribution
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • the invention belongs to the technical field of wireless communication, and relates to a power allocation method for OAM-MIMO dynamic channel.
  • electromagnetic radiation carries both energy and momentum, and momentum can be decomposed into Linear Momentum (LM) and Angular Momentum (AM), where angular momentum includes spin angular momentum (Spin Angular Momentum, SAM) and Orbital Angular Momentum (OAM).
  • LM Linear Momentum
  • AM Angular Momentum
  • SAM spin Angular Momentum
  • OFAM Orbital Angular Momentum
  • the spin angular momentum is related to the polarization mode of the electromagnetic wave, and its value is Planck's constant, which corresponds to the left and right circular polarizations respectively.
  • the orbital angular momentum is related to the vortex phase structure of the electromagnetic field, and its value is called topological charge and modal value.
  • the superposition of different eigenstates are mutually orthogonal.
  • the electromagnetic wave carrying orbital angular momentum is different from the plane electromagnetic wave in that there are more phase rotation factors.
  • the specific manifestation is that the phase wavefront has a spatial helical phase structure around the beam axis, so it is often called a vortex wave. Due to the physical orthogonality between vortex waves of different eigenstates, multiple signals can be modulated onto vortex waves of different eigenstates for independent transmission, so that it does not depend on traditional resources such as time and frequency. It greatly increases the channel capacity of the wireless communication system, and provides a new solution for studying the problem of the shortage of wireless communication spectrum resources.
  • the vortex electromagnetic waves of different modes are equivalent to independent channels.
  • the system capacity can be maximized, which is improved in the case of limited power resources.
  • the performance of OAM-MIMO communication system is very necessary.
  • the purpose of the present invention is to provide a power allocation method for OAM-MIMO dynamic channel.
  • the present invention provides the following technical solutions:
  • the transmitter is placed at the top of the room, the receiver moves on the ground, and both the transmit and receive antennas are uniform circular arrays composed of N antenna elements.
  • D is the distance from the transmitting antenna array to the ground
  • d n,m is the distance from the transmitting antenna m to the receiving antenna n
  • x is the projection of the distance from the center of the circle projected from the transmitting array to the ground to the center of the receiving array on the x-axis
  • y is the distance on the x-axis. Projection of the y-axis. Therefore, the distance d n,m from the transmitting antenna m to the receiving antenna n can be expressed as:
  • R t and R r are the radii of the transmitting array and the receiving array, respectively, and are the azimuth angles of the transmitting array and receiving array antenna elements, respectively, and ⁇ 0 are the initial phases of the two array antenna elements, respectively.
  • the complex channel gain between the mth transmit antenna and the nth receive antenna of the MIMO system is:
  • the MIMO channel matrix can be expressed as:
  • the receiver When the transmitter and receiver transmit multiple modes, the receiver will receive the vortex signals of the multiple modes, so that the demodulation vector of the multiple mode vortex signals is used for the received l-mode vortex signal, Its process can be expressed as:
  • H is the channel matrix that the receiver moves to (x, y)
  • n is the independent and identically distributed Gaussian white noise
  • ⁇ q, l represent The multiplication result
  • x l w t T
  • x t is the transmitted signal
  • the OAM modal spectra of the receiver at the trajectory points A and J are the same, as well as at the trajectory points F and U, and the OAM modal spectra at the trajectory points A and F are the same. There is a slight difference, and the OAM modal spectrum at the four trajectory points is the highest in mode 4.
  • the trajectory points A and J are also symmetrical with the transmitter projection as the midpoint, as are the trajectory points F and U, so the OAM modal spectrum is also consistent, and the trajectory AFJU
  • the projected distances from the four points to the transmitter are the same, so the OAM modal spectrum at A and F is not much different, and the slight difference comes from the interference caused by modal demodulation.
  • the channel capacity of the receiver at the four trajectory points of AFJU is the same, which shows that the channel state of the receiver at the same distance from the projection of the transmitter is the same. This is because the vortex wave radiation field is axisymmetric, so the channel state of the receiver at any position on the same circle is consistent.
  • the state of the modal channel is constantly changing during the movement of the receiver. If the average power allocation method is adopted or the previous power allocation method is continued, more power may be allocated to the sub-channels with small channel gain. , and a smaller power is allocated to a sub-channel with a larger channel gain, which causes a great waste of power and leads to poor system performance. From the previous analysis, it can be seen that the modal channel states at the symmetrical trajectory points are consistent, so through the prediction of the modal channel state, in the case of unknown channel state information, the power pre-allocation method based on the moving trajectory can be adopted. Realizing the power allocation to the channel of the dynamic scene can well overcome the defects of the traditional method and realize the maximization of the capacity of the OAM-MIMO communication system.
  • the invention predicts the channel state information of the next receiver locus point by using the channel state information of the known receiver locus point according to the difference and connection of the channel state information of the OAM-MIMO system at different receiver positions in the dynamic scene, and then predicts the channel state information of the next receiver locus point according to the prediction.
  • the obtained channel state information finds a matching power allocation vector to complete the power pre-allocation of the next receiver trajectory point, which maximizes the system capacity under the condition of limited power. And its performance is greatly improved compared to the traditional average power distribution method or the continuation of the previous power distribution method.
  • FIG. 1 is a schematic diagram of an OAM-MIMO multi-modal multiplexing communication system in a dynamic scene
  • Fig. 2 is a schematic diagram of a moving track point of a receiver according to a rectangle
  • Fig. 3 is the schematic diagram of the moving track point of the receiver according to the S shape
  • Figure 4 shows the OAM modal spectrum of the receiver at four symmetrical positions of the AFJU (modal values are 1, 2, 3, and 4);
  • Fig. 5 is the channel capacity of the receiver at four symmetrical positions of AFJU;
  • Fig. 6 is a graph showing the change of OAM modal proportion with the moving trajectory of the receiver, (a) is the graph of the change of OAM modal proportion with the rectangular trajectory, (b) is the graph of the change of OAM modal proportion with the S-shaped trajectory;
  • Figure 7 is a graph showing the variation of system channel capacity with trajectory points, (a) is a graph of the variation of system channel capacity with a rectangular trajectory, and (b) is a graph of the variation of system channel capacity with an S-shaped trajectory;
  • FIG. 8 is a flow chart of the power pre-allocation algorithm based on the movement trajectory proposed by the present invention.
  • Figure 9 shows the performance comparison of the known CSI water injection allocation algorithm and the CSI unknown three power allocation algorithms under the rectangular trajectory
  • Figure 10 shows the performance comparison of the water injection allocation algorithms with known CSI and the three power allocation algorithms with unknown CSI under the S-shaped trajectory.
  • the present invention considers an OAM-MIMO multi-modal multiplexing communication system in a dynamic scene, as shown in FIG. 1 .
  • the transmitter of the system is placed at the top of the room, the receiver moves on the ground, the transmitting and receiving antennas are uniform circular arrays composed of N antenna elements, where D is the distance from the transmitting antenna array to the ground, and d n,m is The distance from the transmitting antenna m to the receiving antenna n, x is the projection of the distance from the center of the transmitting array to the ground projection to the center of the receiving array on the x-axis, and y is the projection on the y-axis.
  • the distance d n,m from the transmitting antenna m to the receiving antenna n can be expressed as:
  • D is the distance from the transmitting antenna array to the ground
  • R t and R r are the radii of the transmitting and receiving arrays, respectively
  • ⁇ 0 are the initial phases of the two array antenna elements, respectively.
  • the receiver and the transmitter are in a state of misalignment in most cases.
  • the misalignment of the transmitting array and the receiving array will cause interference between the modal channels.
  • the feed vector of the l-modal vortex wave x t denotes the original signal to be modulated onto the l-mode
  • x l denotes the transmitted l-mode vortex signal
  • H is the channel matrix H[h n,m (d n,m (x,y))] that the receiver moves to (x,y), and n is the independent and identically distributed Gaussian white noise.
  • the demodulation matrix W r [w 1 ,w 2 ,...,w L ] T for demodulation of L OAM modes at the receiving end, the demodulation vector of a single OAM mode q W rn is still IID Gaussian white noise. It can be known that when the demodulation vector w q is consistent with the OAM modal value of the feeding vector w t , The result is the transmission gain of the l-modal channel, while the OAM modes of the two vectors are inconsistent, The result is the channel interference caused by the l-mode channel to the q-mode channel. Therefore, the above formula can be expressed as:
  • ⁇ q,l represents the result of the multiplication.
  • ⁇ q,l represents the result of the multiplication.
  • ⁇ q,l has a value only if the OAM mode is equal to l.
  • the receiver and the transmitter are in a state of misalignment in most cases, resulting in ⁇ q, l also have a value when the OAM mode q and l are not equal, indicating that the l mode channel is for q Channel interference caused by modal channels. Therefore, the Signal-to-Interference-and-Noise-Ratio (SINR) of the l-mode vortex signal can be expressed as:
  • the channel state of the receiver at a special position is the same.
  • Figures 2 and 3 show two kinds of receiver movement trajectory point diagrams, which are respectively a rectangular movement trajectory (as shown in Figure 2) and an S-shaped movement trajectory (as shown in Figure 3).
  • a 1 , a 2 , b 1 , and b 2 represent the length of the transmitter projection center from the AF track line, the length from the MR track line, the length from the RW track line, and the length from the FM track line, respectively.
  • the blue circle represents the receiver
  • the purple circle represents the projection of the transmitter on the ground
  • the receiver moves from the trajectory point A to the trajectory point W around the transmitter on the ground.
  • a 1 , a 2 , b 1 , and b 2 represent the length of the transmitter projection center from the OR trajectory, the length from the GI trajectory, the length from the AG trajectory, and the distance from the RX trajectory, respectively.
  • the blue circle represents the receiver
  • the purple circle represents the projection of the transmitter on the ground
  • the receiver moves from the trajectory point A to the trajectory point X.
  • the OAM modal spectrum of the receiver at the four points of the rectangular moving trajectory AFJU has inter-modal interference.
  • the OAM modal spectrum of the receiver at the trajectory points A and J are consistent, also at trajectory points F and U, while the OAM modal spectra at trajectory points A and F are slightly different.
  • Figure 6 shows the change of the OAM modal proportion with the moving trajectory of the receiver, in which (a) is the change of the OAM modal proportion with the rectangular trajectory.
  • the simulation parameters are shown in Table 1, and (b) is the OAM modal proportion.
  • the ratio changes with the S-shaped trajectory, and the simulation parameters are shown in Table 2.
  • the point connected by the red dotted line is the maximum modal value of the OAM modal proportion at the current trajectory point.
  • the modal value with the largest proportion of OAM mode is constantly changing. This is because the distance between the receiver and the projection of the transmitter changes continuously during the movement process, and the modal channel state also changes continuously, so the modal value with the largest OAM modal proportion also changes continuously. Therefore, at the trajectory point where the channel state information is unknown, continuing the power allocation value of the previous trajectory point will cause the system performance to degrade.
  • the total power that can be allocated is P t .
  • the power allocation vector and channel state vector at are the power values allocated to each modal channel and the gain of each modal channel, as shown below:
  • Figures 9 and 10 describe the performance comparison between the known CSI water injection allocation algorithm, the CSI unknown equal power allocation algorithm and the continuation of the previous power allocation algorithm and the power pre-allocation algorithm under the rectangular trajectory and the S-shaped trajectory, respectively.
  • (a)-(d) respectively represent the performance comparison of the three power allocation algorithms under the unknown CSI at the four points of the EJQW in the rectangular trajectory and the water injection allocation algorithm with known CSI. It can be seen from the two figures that in the case of unknown CSI, the performance of the power pre-allocation algorithm is significantly better than that of the equal power allocation algorithm and the continuation of the previous power allocation algorithm, which can improve the capacity performance of the system.

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Abstract

本发明涉及一种用于OAM-MIMO动态信道的功率分配方法，属于无线通信技术领域。在动态的OAM-MIMO场景下，信道的状态会随着接收机的移动而改变，由于***中存在反馈时延和估计误差，因此很难准确地得到信道状态信息，进而会使得***的容量性能变得不太理想。本发明与传统的延续前次功率算法和平均功率分配算法相比，能在***总功率资源有限的情况下，有效地提升OAM-MIMO***的容量性能。最后的仿真实验结果也表明，在工作频率为10GHz，波长为3cm，阵元数为8，发射和接收UCA半径为9cm以及信道状态信息未知的情况下，本发明所提出的功率预分配算法要明显优于传统的平均功率分配算法和延续前次功率算法。

Description

用于OAM-MIMO动态信道的功率分配方法 技术领域
本发明属于无线通信技术领域，涉及用于OAM-MIMO动态信道的功率分配方法。
背景技术
随着无线用户的不断增长，满足无线网络中持续的容量增长需求变得越来越有挑战性。为了能够在有限的资源下满足持续增长的需求，必须依靠新的技术来提升***容量及频谱利用率。
根据麦克斯韦理论，电磁辐射同时携带有能量和动量，而动量又可以分解为线性动量(Linear Momentum，LM)与角动量(Angular Momentum，AM)，其中角动量包含自旋角动量(Spin Angular Momentum，SAM)和轨道角动量(Orbital Angular Momentum，OAM)两部分。自旋角动量与电磁波的极化方式有关，取值为普朗克常量，分别对应左右圆极化。而轨道角动量与电磁场的涡旋相位结构有关，取值为，其中称作拓扑电荷和模态值，理论上可以取任何一个值，取整数表示本征态，取分数表示多个本征态的叠加，不同的本征态之间是相互正交的。携带轨道角动量的电磁波不同于平面电磁波的地方是多了相位旋转因子，具体表现是相位波前具有围绕波束轴的空间螺旋相位结构，因而也常被称作涡旋波。由于不同本征态的涡旋波之间具备物理正交特性，可以将多路信号调制到不同本征态涡旋波上独立传输，从而在不依赖于诸如时间和频率等传统资源的情况下极大地增加无线通信***的信道容量，为研究无线通信频谱资源紧缺的问题提供了一个新的解决方案。
在OAM-MIMO通信***中，不同模态的涡旋电磁波相当于独立的信道，通过对不同模态的涡旋电磁波分配功率值，可使得***容量最大化，这在功率资源有限的情况下提升OAM-MIMO通信***的性能是很有必要的。
在以往的OAM-MIMO通信***中，研究的功率分配方案都是基于静态的场景，接收机和发射机都是静止不动的。然而在实际的应用中，接收机往往会处于移动的状态，这使得***的信道状态会随着接收机的移动而改变。由于在动态场景中会存在反馈时延和估计误差，通常***很难准确地得到信道状态信息。在信道状态信息未知的情况下，利用传统的平均功率分配方法或者延续前次功率分配方法会造成功率资源的浪费。因此，如何在动态信道下有效地分配功率资源，在一定程度上决定了OAM-MIMO通信***的性能。
发明内容
有鉴于此，本发明的目的在于提供一种用于OAM-MIMO动态信道的功率分配方法。
为达到上述目的，本发明提供如下技术方案：
在一个动态场景的OAM-MIMO多模态复用通信***中，发射机放置于房间顶部，接收机在地面移动，发射和接收天线都是由N天线单元组成的均匀圆形阵列。其中D为发射天线阵列到地面的距离，d n,m是发射天线m到接收天线n的距离，x为发射阵列到地面投影的圆心到接收阵列圆心距离在x轴的投影，y则为在y轴的投影。因此发射天线m到接收天线n的距离d n,m可以表示为：
Figure PCTCN2020127132-appb-000001
其中，R t和R r分别为发射阵列和接收阵列的半径，
Figure PCTCN2020127132-appb-000002
和
Figure PCTCN2020127132-appb-000003
则分别为发射阵列和接收阵列天线单元的方位角，
Figure PCTCN2020127132-appb-000004
和θ 0分别是两阵列天线单元的初始相位。
LOS场景中MIMO***第m个发射天线到第n个接收天线之间的复信道增益为：
Figure PCTCN2020127132-appb-000005
其中，
Figure PCTCN2020127132-appb-000006
表示对应于天线衰减的常数项。因此，MIMO信道矩阵可以表达为：
Figure PCTCN2020127132-appb-000007
当发射与接收机传输多个模态时，接收机会对多个模态的涡旋信号进行接收，从而对接收到的l模态涡旋信号使用多个模态涡旋信号的解调矢量，其过程可以表示为：
Figure PCTCN2020127132-appb-000008
其中，H为接收机移动到(x,y)处的信道矩阵，n为独立同分布的高斯白噪声，其中λ q,l表示
Figure PCTCN2020127132-appb-000009
相乘的结果，x l＝w t Tx t为发射信号，W r＝[w 1,w 2,...,w L] T为接收端解调L个OAM 模态的解调矩阵，
Figure PCTCN2020127132-appb-000010
为单个OAM模态q的解调矢量。因此，l模态涡旋信号的信干噪比(Signal-to-Interference-and-Noise-Ratio，SINR)可以表示为：
Figure PCTCN2020127132-appb-000011
其中σ 2表示噪声信号的方差，p l表示OAM模态为l的涡旋波的发射功率，可以得出信干噪比下的l模态信道容量为：
C l＝log 2(1+SINR l)
由上式中发射单个l模态时的信道容量，容易写出发射与接收机传输的模态数为L时的***容量为：
Figure PCTCN2020127132-appb-000012
在动态场景的OAM-MIMO***中，接收机在轨迹点A和J处的OAM模态谱是一致的，在轨迹点F和U处也是一样，而轨迹点A与F处的OAM模态谱略微有些差别，并且四个轨迹点处的OAM模态谱都是模态4占比最高。由于涡旋波的辐射场是是围绕轴中心对称的，轨迹点A和J也是以发射机投影为中点对称，轨迹点F和U也一样，因而OAM模态谱也是一致的，而且轨迹AFJU四点距离发射机投影的距离是一致的，从而A与F处的OAM模态谱差别不大，略微的差别来自于模态解调时引起的干扰。
在动态场景的OAM-MIMO***中，接收机在AFJU四个轨迹点处的信道容量是一样的，说明了接收机在与发射机投影等距离处的信道状态是相同的。这是因为涡旋波辐射场是呈轴中心对称的，从而接收机在同一圆上任意位置处的信道状态都是一致的。
在动态场景的OAM-MIMO***中，当接收机沿着轨迹点移动时，OAM模态占比最大的模态值在不断地变化。这是因为接收机在移动过程中与发射机投影的距离不断改变，模态信道状态也随之不断变化，从而OAM模态占比最大的模态值也不断地改变。因此，在信道状态信息未知的轨迹点处，继续延续上一轨迹点的功率分配值会造成***性能的下降。
综上所述，接收机在移动过程中，模态信道状态在不断变化，如果采用平均功率分配方法或者延续前次功率分配方法，可能会给信道增益较小的子信道分配了较大的功率，而对信道增益较大的子信道分配了较小的功率，使得功率存在较大的浪费，并且导致***性能变差。 而通过前面的分析可知，在对称的轨迹点处的模态信道状态是一致的，因此能够通过对模态信道状态的预测，在未知信道状态信息情况下，采用基于移动轨迹的功率预分配方法实现对动态场景的信道进行功率分配，能够很好地克服传统方法所存在的缺陷，实现OAM-MIMO通信***容量的最大化。
本发明的有益效果在于：
本发明根据动态场景下不同接收机位置处OAM-MIMO***信道状态信息存在的差异和联系，利用已知接收机轨迹点的信道状态信息预测下一接收机轨迹点的信道状态信息，然后根据预测得到的信道状态信息找到与之相匹配的功率分配矢量完成下一接收机轨迹点的功率预分配，实现了在功率有限的情况下***容量的最大化。并且其性能相比于传统的平均功率分配方法或者延续前次功率分配方法有着较大的提升。
本发明的其他优点、目标和特征在某种程度上将在随后的说明书中进行阐述，并且在某种程度上，基于对下文的考察研究对本领域技术人员而言将是显而易见的，或者可以从本发明的实践中得到教导。本发明的目标和其他优点可以通过下面的说明书来实现和获得。
附图说明
为了使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作优选的详细描述，其中：
图1为动态场景的OAM-MIMO多模态复用通信***示意图；
图2为接收机按矩形的移动轨迹点示意图；
图3为接收机按S形的移动轨迹点示意图；
图4为接收机在AFJU四个对称位置处的OAM模态谱(模态值分别为1,2,3,4)；
图5为接收机在AFJU四个对称位置处的信道容量；
图6为OAM模态占比随接收机移动轨迹变化图，(a)是OAM模态占比随矩形轨迹变化图，(b)是OAM模态占比随S形轨迹变化图；
图7为***信道容量随轨迹点变化图，(a)是***信道容量随矩形轨迹变化图，(b)是***信道容量随S形轨迹变化图；
图8为本发明提出的基于移动轨迹的功率预分配算法流程图；
图9为矩形轨迹下CSI已知的注水分配算法，CSI未知的三种功率分配算法的性能对比；
图10为S形轨迹下CSI已知的注水分配算法，CSI未知的三种功率分配算法的性能对比。
具体实施方式
以下通过特定的具体实例说明本发明的实施方式，本领域技术人员可由本说明书所揭露的内容轻易地了解本发明的其他优点与功效。本发明还可以通过另外不同的具体实施方式加以实施或应用，本说明书中的各项细节也可以基于不同观点与应用，在没有背离本发明的精神下进行各种修饰或改变。需要说明的是，以下实施例中所提供的图示仅以示意方式说明本发明的基本构想，在不冲突的情况下，以下实施例及实施例中的特征可以相互组合。
其中，附图仅用于示例性说明，表示的仅是示意图，而非实物图，不能理解为对本发明的限制；为了更好地说明本发明的实施例，附图某些部件会有省略、放大或缩小，并不代表实际产品的尺寸；对本领域技术人员来说，附图中某些公知结构及其说明可能省略是可以理解的。
本发明实施例的附图中相同或相似的标号对应相同或相似的部件；在本发明的描述中，需要理解的是，若有术语“上”、“下”、“左”、“右”、“前”、“后”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此附图中描述位置关系的用语仅用于示例性说明，不能理解为对本发明的限制，对于本领域的普通技术人员而言，可以根据具体情况理解上述术语的具体含义。
本发明考虑一个动态场景的OAM-MIMO多模态复用通信***，如图1所示。该***的发射机放置于房间的顶部，接收机在地面移动，发射和接收天线都是由N天线单元组成的均匀圆形阵列，其中D为发射天线阵列到地面的距离，d n,m是发射天线m到接收天线n的距离，x为发射阵列到地面投影的圆心到接收阵列圆心距离在x轴的投影，y则为在y轴的投影。
假设接收天线阵列圆心与发射天线阵列投影圆心的距离为(x 2+y 2) 1/2，其中x和y分别为其在x轴与y轴的投影。发射天线m到接收天线n的距离d n,m可以表示为：
Figure PCTCN2020127132-appb-000013
其中，D为发射天线阵列到地面的距离，R t和R r分别为发射阵列和接收阵列的半径，
Figure PCTCN2020127132-appb-000014
和θ n＝2πn/N+θ 0则分别为发射阵列和接收阵列天线单元的方位角，
Figure PCTCN2020127132-appb-000015
和θ 0分别是两阵列天线单元的初始相位。
由于接收机处于移动的状态，接收机与发射机大多数情况是处于不对齐的状态。当接收机与发射机传输多个模态时，发射阵列与接收阵列的不对齐则会引起模态信道间的干扰。为 了研究发射与接收机传输多个模态时，引起模态信道间干扰的影响，因而首先分析发射机发射单个模态，接收机多个模态接收的情况。假设发射阵列与接收阵列天线单元数都为N，发射与接收机传输的模态数为L，当发射机发射OAM模态为l的涡旋信号时，发射信号可以表示为：
x l＝w t Tx t
其中l模态涡旋波的馈电矢量
Figure PCTCN2020127132-appb-000016
x t表示需要调制到l模态上的原始信号，x l表示传输的l模态涡旋信号。在接收端，N个接收天线的输出信号为：
Figure PCTCN2020127132-appb-000017
其中，H为接收机移动到(x,y)处的信道矩阵H[h n,m(d n,m(x,y))]，n为独立同分布的高斯白噪声。当发射与接收机传输多个模态时，接收机会对多个模态的涡旋信号进行接收，从而对接收到的l模态涡旋信号使用多个模态涡旋信号的解调矢量，其过程可以表示为：
Figure PCTCN2020127132-appb-000018
其中接收端解调L个OAM模态的解调矩阵W r＝[w 1,w 2,...,w L] T，单个OAM模态q的解调矢量
Figure PCTCN2020127132-appb-000019
W rn依然是独立同分布的高斯白噪声。可以知道，当解调矢量w q与馈电矢量w t的OAM模态值一致时，
Figure PCTCN2020127132-appb-000020
的结果为l模态信道的传输增益，而两个矢量的OAM模态不一致，
Figure PCTCN2020127132-appb-000021
的结果则为l模态信道对于q模态信道所产生的信道干扰。因此，上式可以表示为：
y l＝[λ 1,l,...,λ q,l,...,λ L,l] Tx l+n
其中λ q,l表示
Figure PCTCN2020127132-appb-000022
相乘的结果。当视距下收发机对齐时，λ q,l只有在OAM模态与l相等时才有值。而接收机处于移动的状态，接收机与发射机大多数情况是处于不对齐的状态，导致λ q,l在OAM模态q与l不相等时也会有值，表示l模态信道对于q模态信道所产生的信道干扰。因此，l模态涡旋信号的信干噪比(Signal-to-Interference-and-Noise-Ratio，SINR)可以表示为：
Figure PCTCN2020127132-appb-000023
其中σ 2表示噪声信号的方差，p l表示OAM模态为l的涡旋波的发射功率，可以得出信干噪比下的l模态信道容量为：
C l＝log 2(1+SINR l)
由上式中发射单个l模态时的信道容量，容易写出发射与接收机传输的模态数为L时的***容量为：
Figure PCTCN2020127132-appb-000024
在动态场景的OAM-MIMO***中，由于涡旋波辐射场的对称性，使得接收机在特殊位置处的信道状态是相同的。
图2和图3给出了两种接收机移动轨迹点图，分别为矩形移动轨迹(如图2所示)和S形移动轨迹(如图3所示)。矩形移动轨迹中，a 1,a 2,b 1,b 2分别表示发射机投影圆心距离AF轨迹线的长度，距离MR轨迹线的长度，距离RW轨迹线的长度和距离FM轨迹线的长度，蓝色圆表示接收机，紫色圆表示发射机在地面的投影，接收机从轨迹点A到轨迹点W围绕着发射机在地面移动。S形移动轨迹中，a 1,a 2,b 1,b 2分别表示发射机投影圆心距离OR轨迹线的长度，距离GI轨迹线的长度，距离AG轨迹线的长度和距离RX轨迹线的长度，蓝色圆表示接收机，紫色圆表示发射机在地面的投影，接收机从轨迹点A移动到轨迹点X。
根据涡旋辐射场的对称性，选定矩形轨迹中AFJU四个对称的轨迹点，分析接收机在其位置处的OAM模态谱和信道容量，分别如图4和图5所示。从图4中可以看到，接收机在矩形移动轨迹AFJU四个点处的OAM模态谱都存在模态间的干扰，同时可以发现，接收机在轨迹点A和J处的OAM模态谱是一致的，在轨迹点F和U处也是一样，而轨迹点A与F处的OAM模态谱略微有些差别。从图5中可以看到，随着信噪比的增加，接收机在AFJU四个轨迹点处的信道容量是一样的，说明了接收机在与发射机投影等距离处的信道状态是相同的。
表1 接收机矩形移动轨迹仿真参数
Figure PCTCN2020127132-appb-000025
图6给出了OAM模态占比随接收机移动轨迹变化图，其中(a)为OAM模态占比随矩形轨迹变化图，仿真参数如表1所示，(b)为OAM模态占比随S形轨迹变化图，仿真参数如表 2所示，红色虚线连接的点为当前轨迹点处的OAM模态占比最大模态值。从图中可以看到，当接收机沿着轨迹点移动时，OAM模态占比最大的模态值在不断地变化。这是因为接收机在移动过程中与发射机投影的距离不断改变，模态信道状态也随之不断变化，从而OAM模态占比最大的模态值也不断地改变。因此，在信道状态信息未知的轨迹点处，继续延续上一轨迹点的功率分配值会造成***性能的下降。
表2 接收机S形移动轨迹仿真参数
Figure PCTCN2020127132-appb-000026
图7给出了信道容量随轨迹点变化图，其中(a)为***信道容量随矩形轨迹变化图，仿真参数如表1所示；(b)为***信道容量随S形轨迹变化图，仿真参数如表2所示，信噪比SNR＝30dB，功率分配采用的是注水分配算法。可以清楚地看到，信道容量在对称的轨迹点处是一致的。可以得出，接收机在移动过程中，模态信道状态在不断变化，但是在对称的轨迹点处的模态信道状态是一致的，因此能够通过对模态信道状态的预测，在未知信道状态信息情况下，预分配各模态信道的功率。
参见图8，下面介绍基于移动轨迹的功率预分配算法具体步骤。
1.假设***内可传输模态个数为N，可分配总功率为P t。初始化前j个已知轨迹点处的功率分配集合{P m-1,...,P m-j}和信道状态集合{h m-1,...,h m-j}，其中第j个轨迹点处的功率分配矢量和信道状态矢量为各模态信道分配到的功率值和各模态信道的增益，如下所示：
P m-j＝[p 1,...,p N] T
h m-j＝[λ 11,...,λ NN] T
2.首先在已知轨迹点的信道状态集合{h m-2,...,h m-j}中搜索与前一轨迹点信道状态h m-1相同的信道状态矢量h m-k,k＝2,...,j，如果存在信道状态矢量h m-k与h m-1相等，则对信道状态矢量h m-2与h m-k+1和h m-k-1进行判断，当信道状态矢量h m-2与h m-k+1相等时，h m-k-1是当前轨迹点的预测矢量h m；当信道状态矢量h m-2与h m-k-1相等时，h m-k+1则是当前轨迹点的预测矢量h m；若信道状态矢量h m-2与两者都不相等，则继续搜索信道状态矢量h m-k。如果不存在信道状态矢 量h m-k与h m-1相等，则表示前一轨迹点的信道状态不与其他任何已知轨迹点的信道状态一致，难以预测当前轨迹点的信道状态。
3.根据信道预测得出的当前信道状态矢量h m，在已知轨迹点处的功率分配集合{P m-1,...,P m-j}中找到对应的P m，其次将功率分配矢量P m应用于当前信道状态信息未知的轨迹点处，完成功率值的预分配。
图9和图10分别描述了矩形轨迹下和S形轨迹下CSI已知的注水分配算法，CSI未知的等功率分配算法和延续前次功率分配算法与功率预分配算法的性能对比。其中(a)-(d)分别表示矩形轨迹中EJQW四点处CSI未知下的三种功率分配算法与CSI已知的注水分配算法的性能对比。从两幅图中可以看到，在CSI未知的情况下，功率预分配算法性能要明显优于等功率分配算法和延续前次功率分配算法，能够很好地提升***的容量性能。
最后说明的是，以上实施例仅用以说明本发明的技术方案而非限制，尽管参照较佳实施例对本发明进行了详细说明，本领域的普通技术人员应当理解，可以对本发明的技术方案进行修改或者等同替换，而不脱离本技术方案的宗旨和范围，其均应涵盖在本发明的权利要求范围当中。

Claims (6)

  1. 用于OAM-MIMO动态信道的功率分配方法，其特征在于：该方法包括以下步骤：
    第一步：基于动态信道的OAM-MIMO多模态复用通信***模型设计；
    基于动态信道的OAM-MIMO多模态复用通信***由发射机、接收机、波束成形器组成，波束成形器用于产生具有多个模态的信号，发射机用于发射涡旋电磁波信号，接收机用于接收和解调具有多个模态的涡旋信号；
    第二步：推导基于动态信道的OAM-MIMO多模态复用通信***的在单个轨迹点处的信干噪比和***容量；
    根据第一步设计的基于动态信道的OAM-MIMO多模态复用通信***，推导出在单个轨迹点处，***中每个模态的信干噪比和总的***容量；
    第三步：推导***容量在各个轨迹点下的联系；
    根据第二步求出的单个轨迹点处的***容量，推导出在矩形轨迹点和S形轨迹点下各个轨迹点之间***容量的联系；
    第四步：动态场景的多模态传输功率分配优化；
    根据第三部推导出的各个轨迹点之间***容量的联系，采用基于移动轨迹的功率预分配算法对***功率进行最优分配，求出最优功率分配解。
  2. 根据权利要求1所述的用于OAM-MIMO动态信道的功率分配方法，其特征在于：所述接收机处于移动状态，同时传输多个模态，传输的模态数和天线数相等。
  3. 根据权利要求1所述的用于OAM-MIMO动态信道的功率分配方法，其特征在于：所述发射机和接收机均为一个均匀圆形天线阵列，每个天线阵元均匀的分布在圆周上且所有天线单元的天线极化方向相同。
  4. 根据权利要求1所述的用于OAM-MIMO动态信道的功率分配方法，其特征在于：所述第二步中，单个轨迹点的信干噪比为：
    Figure PCTCN2020127132-appb-100001
    从而信道容量为：
    Figure PCTCN2020127132-appb-100002
  5. 根据权利要求1所述的用于OAM-MIMO动态信道的功率分配方法，其特征在于：所述第三步中，矩形轨迹点为一共23个点均匀的分布在矩形的4条边上，S形轨迹点为一共24 个点均匀的分布在S形的各条边上。
  6. 根据权利要求1所述的用于OAM-MIMO动态信道的功率分配方法，其特征在于：所述各个轨迹点之间的联系为：对于互相对称的各个轨迹点，它们的信道容量是相同的。
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