CN116545489A - Vibration robustness improving method and system of unmanned aerial vehicle, storage medium and computing equipment - Google Patents
Vibration robustness improving method and system of unmanned aerial vehicle, storage medium and computing equipment Download PDFInfo
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Abstract
The invention discloses a vibration robustness improving method, a system, a storage medium and a computing device of an unmanned aerial vehicle, wherein the method comprises the steps of defining an unmanned aerial vehicle vibration variable, establishing a user signal-to-noise ratio taking the unmanned aerial vehicle vibration variable into consideration, and calculating the expectation of the user signal-to-noise ratio as an auxiliary signal-to-noise ratio variable of a user based on base station beam forming, a reconfigurable intelligent reflecting surface reflection coefficient and unmanned aerial vehicle and user coordinates; based on the signal-to-noise ratio auxiliary variable of the user, calculating the base station beam forming of the user; based on the signal-to-noise ratio auxiliary variable of the user and the base station beam forming, calculating the reflection coefficient of the reconfigurable intelligent reflecting surface, performing iterative calculation according to the mode until reaching the termination condition, and outputting the weighted sum rate of multiple users. According to the invention, the influence of uncertain oscillation of the unmanned aerial vehicle in the air is overcome by optimizing the reflection coefficient of the reconfigurable intelligent reflecting surface and the beam forming of the multi-antenna base station, the weighted sum rate of multiple users is improved, and the oscillation robustness of the unmanned aerial vehicle of the system is improved.
Description
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a vibration robustness improving method, system, storage medium and computing equipment of an unmanned aerial vehicle.
Background
Unmanned aerial vehicles have recently attracted considerable attention as a significant application in wireless networks due to their agile mobility and cost effectiveness. Unmanned aerial vehicles have proven suitable for establishing line-of-sight links with ground users, thereby avoiding obstacles that compromise the overall communication quality. Dense urban scenarios further exacerbate the obstacle problem, making unmanned aerial vehicles a viable solution for establishing reliable networks on demand. Due to the rapid deployment characteristics of drones, drones have been identified as a critical technique for handling emergency situations to expedite rescue actions, such as assisting a first reaction team in areas where connectivity may not be available or where network infrastructure is temporarily damaged or unavailable. Unmanned aerial vehicles may provide backup connections in these areas or utilize advanced sensing and positioning techniques to find missing persons. However, since they are conceived as air base stations carrying one or more active antennas, their total power consumption will increase significantly due to the weight of the active elements, the power being radiated to reach ground targets, and the additional power burden required to establish the backhaul link and process the incoming packets.
To overcome the above problems, unmanned aerial vehicles require lightweight and low-power-consumption equipment instead of active antennas. In this case, reconfigurable smart reflective surfaces have received great attention because they are able to control the propagation environment by changing the reflection, absorption and amplitude of the material at which the signal is reflected. This new powerful tool can effectively redirect propagating signals with very limited power consumption: the varactors on the reconfigurable smart reflective surface can adjust the signal phase shift and absorb the input signal in a real-time reconfigurable manner. The passive, flexible and configurable elements of the reconfigurable intelligent reflective surface may be combined with a variety of applications including communication with improved electromagnetic field exposure efficiency, extremely accurate positioning mechanisms, and user-centric quality of service enhanced connections.
Conventional unmanned aerial vehicle systems may be combined with suitably sized reconfigurable intelligent reflective surfaces. A theoretical framework is proposed in paper "Optimization of wireless relaying with flexible UAV-borne reflecting surfaces" (IEEE Transactions on Communications, vol. 69, no. 1, pp. -325, 2021.) to analyze unmanned aerial vehicle system performance assisted by reconfigurable intelligent reflective surfaces, providing a closed approximation expression of end-to-end outage probability, traversal capacity, and energy efficiency to reduce unmanned aerial vehicle energy consumption. But is not practical to use without a scenario that is not suitable for multiple users. A unmanned aerial vehicle group communication system assisted by reconfigurable intelligent reflectors is proposed in the paper "UAV swarm-enabled aerial reconfigurable intelligent surface: modeling, analysis, and optimization" (IEEE Transactions on Communications, doi: 10.1109/TCOMM.2022.3173369.). The weighting and the speed of ground users are improved to the maximum extent by optimizing the deployment position of the unmanned aerial vehicle, the beamforming of the base station and the reflection coefficient of the reconfigurable intelligent reflecting surface, but the influence caused by the vibration of the unmanned aerial vehicle is not considered. While advanced sensor systems and GPS antennas can keep the drone stable and stationary when hovering in a selected area, i.e., when adverse weather conditions come, the drone controller will take drone movement countermeasures automatically in a reactive manner. However, such adjustments do not prevent the occurrence of drone oscillations, which may still lead to errors in the real-time configuration of the reconfigurable intelligent reflective surface, such that overall communication performance may be negatively impacted. Therefore, how to make the unmanned aerial vehicle system assisted by the reconfigurable intelligent reflecting surface overcome the influence of uncertain oscillation of the unmanned aerial vehicle becomes a problem to be solved.
Disclosure of Invention
Aiming at the defect that the existing unmanned aerial vehicle system assisted by the reconfigurable intelligent reflecting surface cannot overcome the influence of unmanned aerial vehicle oscillation, the invention provides a method, a system, a storage medium and a computing device for improving the oscillation robustness of the unmanned aerial vehicle.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the first aspect of the invention provides a method for improving vibration robustness of an unmanned aerial vehicle, which comprises the following steps:
defining an unmanned aerial vehicle oscillation variable, and establishing a user signal-to-noise ratio considering the unmanned aerial vehicle oscillation variable;
and carrying out iterative computation until a termination condition is reached, and outputting the weighted sum rate of multiple users, wherein the iterative computation is as follows:
calculating the expectation of the signal to noise ratio of the user based on the base station beam forming, the reflection coefficient of the reconfigurable intelligent reflecting surface, the unmanned aerial vehicle and the user coordinates, and taking the expectation as an auxiliary variable of the signal to noise ratio of the user;
calculating the base station beam forming of the user based on the signal-to-noise ratio auxiliary variable of the user;
and calculating the reflection coefficient of the reconfigurable intelligent reflecting surface based on the signal-to-noise ratio auxiliary variable of the user and the base station beam forming of the user.
Further, the vibration variables of the unmanned aerial vehicle are defined as follows:
；
wherein, unmanned plane oscillation variable representing kth user, < ->Receive array response vector representing reconfigurable smart reflective surface,/->Reflection array response vector representing reconfigurable intelligent reflection surface for kth user, +.>Representing unmanned plane coordinates>Representing kth user coordinates,/->Indicating that the unmanned aerial vehicle is oscillating,
expressed as:
representing the oscillation of the unmanned aerial vehicle in the directions of the x axis, the y axis and the z axis, +.>，/>，/>Independent of each other, and obeys the mean value of 0 and the variance of +.>，/>，/>Is a standard normal distribution of (c).
Further, the establishing the user signal-to-noise ratio considering the unmanned aerial vehicle oscillation variable comprises:
；
wherein, representing the kth user considering the unmanned aerial vehicle concussion variableSignal to noise ratio>Representing the reflection coefficient of the reconfigurable intelligent reflecting surface, < ->Representing the channel matrix between the reconfigurable intelligent reflective surface and the kth user, +.>Representing the channel matrix between the base station and the reconfigurable intelligent reflecting surface +.>The base station is beamformed with,，/>base station beamforming, denoted kth user,>，/>indicates the number of users->The superscript H denotes the conjugate transpose, which is the variance of the additive white gaussian noise.
Further, calculating the user signal-to-noise ratio expectation includes:
；
wherein, representing the desire of the kth user signal-to-noise ratio, < + >>，Representation->The superscript T denotes the transpose,
expressed as: />，
For the channel power gain at the reference distance +.>In the case of an antenna array,
expressed as: />，
By solving for mathematical expectations, the random variables are eliminated, i.eEliminates unmanned aerial vehicle oscillation->。
Further, based on the signal-to-noise ratio auxiliary variable of the user, calculating the base station beamforming of the user comprises:
based on the signal-to-noise ratio auxiliary variable of the user, the base station beam forming calculation auxiliary variable of the user is calculated as follows:
；
wherein, base station beamforming calculation auxiliary variable representing kth user,/>，，/>Is the weight of the kth user;
calculating auxiliary variables according to base station beamforming of kth userThe base station beam forming of the kth user is calculated as follows:
；
wherein, represents an M-order identity matrix>For the intermediate calculated variables of the current iteration,
by one-dimensional search update, the update is as follows:
；
wherein, indicate->Intermediate calculation variable for a number of iterations,/->Indicate->Intermediate calculation variable for a number of iterations,/->In steps.
Further, calculating a reflection coefficient of the reconfigurable intelligent reflection surface based on the signal-to-noise ratio auxiliary variable of the user and the base station beam forming of the user comprises:
the reflection coefficient calculation auxiliary variable for the kth user is calculated based on the signal-to-noise ratio auxiliary variable of the user as follows:
；
wherein, a reflection coefficient calculation auxiliary variable representing the kth user,/->；
And calculating auxiliary variables and base station beam forming of the user based on the reflection coefficient of the kth user, and reconstructing the reflection coefficient of the intelligent reflection surface reflection unit as follows:
；
wherein, reflection unit representing a reconfigurable intelligent reflection surface>Reflection coefficient of>Representing the number of reflecting units of the reconfigurable intelligent reflecting surface, < >>Representation->Is used for the conjugation of (a),
，，
indicating (I)>Middle->Line->Column element->Representation->Middle->Element(s)>Representation->Is a conjugate of (c).
Further, the termination condition is:
reaching the maximum iteration number or converging the objective function;
the objective function is:
；
；
；
；
wherein, representing the weighted sum rate of multiple users +.>Representation->Is>Line->Column element->Representation->Is>Line->Column element->Representing the base station power.
The second aspect of the present invention provides an oscillation robustness improving system of an unmanned aerial vehicle, configured to implement the foregoing oscillation robustness improving method of an unmanned aerial vehicle, where the system includes:
the initialization module is used for defining the unmanned aerial vehicle oscillation variable and establishing a user signal-to-noise ratio considering the unmanned aerial vehicle oscillation variable;
the iterative calculation module is used for carrying out iterative calculation until reaching a termination condition, and outputting the weighted sum rate of multiple users, wherein the iterative calculation is as follows:
calculating the expectation of the signal to noise ratio of the user based on the base station beam forming, the reflection coefficient of the reconfigurable intelligent reflecting surface, the unmanned aerial vehicle and the user coordinates, and taking the expectation as an auxiliary variable of the signal to noise ratio of the user;
calculating the base station beam forming of the user based on the signal-to-noise ratio auxiliary variable of the user;
and calculating the reflection coefficient of the reconfigurable intelligent reflecting surface based on the signal-to-noise ratio auxiliary variable of the user and the base station beam forming of the user.
A third aspect of the invention provides a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods described herein.
A fourth aspect of the invention provides a computing device comprising,
one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods according to the foregoing.
Compared with the prior art, the invention has the following advantages:
(1) According to the invention, the influence of unmanned aerial vehicle oscillation is considered, and the negative influence of unmanned aerial vehicle uncertain oscillation on the whole communication is overcome by optimizing the beam forming of the base station and the reflection coefficient of the reconfigurable intelligent reflecting surface.
(2) The optimization algorithm is a closed solution, has low complexity, is suitable for multi-user scenes, and optimizes the weighted sum rate of multiple users.
(3) According to the method, the channel model applicable to the uncertain oscillation scene of the unmanned aerial vehicle is generated by processing the probability density function of the unmanned aerial vehicle oscillation.
Drawings
Fig. 1 is a flowchart of a method for improving vibration robustness of an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an unmanned aerial vehicle system assisted by a reconfigurable intelligent reflecting surface in an uncertain oscillation scene of the unmanned aerial vehicle designed by the invention;
FIG. 3 is a graph comparing weighted sum rates for multiple users without consideration of unmanned uncertainty oscillations in an embodiment of the present invention;
FIG. 4 is a graph showing the comparison of the number of reflection units of different reconstructed intelligent reflection surfaces with the weighted sum rate of multiple users at the altitude of the unmanned aerial vehicle in accordance with the embodiment of the present invention;
fig. 5 is a graph comparing the weighted sum rates of multiple users at different user weights and base station powers in an embodiment of the present invention.
Detailed Description
The invention is further described below. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
The invention provides a vibration robustness improving method of an unmanned aerial vehicle, which comprises the following steps:
defining an unmanned aerial vehicle oscillation variable, and establishing a user signal-to-noise ratio considering the unmanned aerial vehicle oscillation variable;
and (3) performing iterative computation until a termination condition is reached, outputting the weighted sum rate of multiple users, wherein the iterative computation is as follows:
calculating the expectation of the signal to noise ratio of the user based on the base station beam forming, the reflection coefficient of the reconfigurable intelligent reflecting surface and the coordinates of the unmanned aerial vehicle and the user, and taking the expectation as the signal to noise ratio auxiliary variable of the user;
based on the signal-to-noise ratio auxiliary variable of the user, calculating the base station beam forming of the user;
and calculating the reflection coefficient of the reconfigurable intelligent reflection surface based on the signal-to-noise ratio auxiliary variable of the user and the base station beam forming of the user.
The termination condition is that the maximum number of iterations is reached, or the objective function converges.
Based on the above inventive concept, the method for improving the vibration robustness of the unmanned aerial vehicle provided by an embodiment of the present invention, referring to fig. 1, specifically comprises the following implementation processes:
step 1, setting unmanned aerial vehicle system parameters in combination with the unmanned aerial vehicle system of fig. 2, specifically as follows:
(1a) Setting the signal frequency of the base stationAnd calculates the signal wavelength +.>Wherein->Is the speed of light;
(1b) Setting base station coordinates and unmanned aerial vehicle coordinatesThe reflecting unit interval of the reconfigurable intelligent reflecting surface is half of the signal wavelength, namely +.>；
(1c) Is generated according to uniform distributionA user coordinate, wherein the kth user coordinate is +.>；
(1d) Setting the maximum iteration numberBase station antenna number->Number of reflective units of reconfigurable intelligent reflective surface；
(1e) Setting base stationPower of。
Step 2, defining an unmanned aerial vehicle oscillation variable, establishing a user signal-to-noise ratio considering the unmanned aerial vehicle oscillation variable, initializing a base station beam forming and a reconfigurable intelligent reflecting surface reflection coefficient, and specifically comprising the following steps:
the number of initialization iterations is 0.
And then generating a channel matrix between the base station and the reconfigurable intelligent reflecting surface and a channel matrix between the reconfigurable intelligent reflecting surface and a kth user according to the base station, the unmanned aerial vehicle and the user position, wherein the channel matrix is specifically as follows:
（1）
（2）
wherein, for the channel matrix between the base station and the reconfigurable intelligent reflecting surface +.>For the channel matrix between the reconfigurable intelligent reflecting surface and the kth user, +.>For the channel power gain at the reference distance,representing the oscillations of the unmanned aerial vehicle in the x-axis, y-axis and z-axis directions, +.>，/>，/>Independent of each other, and obeys the mean value of 0 and the variance of +.>，/>，/>Is normal distribution of->For an antenna array, the superscript H denotes the conjugate transpose of the matrix,/->Representing the received array response vector of the reconfigurable intelligent reflective surface,representation vector->Is>The values of the individual elements, namely the reflection units of the reconfigurable intelligent reflection surface +.>Is a receive array response vector,/->Representing the reflection array response vector for the kth user's reconfigurable intelligent reflecting surface,representation vector->Is>The values of the individual elements. Wherein,
（3）
（4）
in the method, in the process of the invention,and->Reflection units respectively representing reconfigurable intelligent reflection surfaces>Relative to the coordinates of the drone->Represents the horizontal angle between the base station and the unmanned aerial vehicle, < >>Representing the horizontal angle between the drone and the kth user.
The signal sent by the base station can be expressed as，
Wherein, base station beamforming for kth user, < >>Is the data symbol of the kth user, and。
the signal received by the kth user isWherein->Is represented by mean 0, variance +.>Additive white gaussian noise of +.>Is a reconfigurable intelligent reflection surface reflection coefficient matrix, and +.>Is 0.
The signal-to-noise ratio of the kth user is:
（5）
wherein the superscript H denotes the conjugate transpose, the superscript T denotes the transpose,，/>。
in order to handle the effects of the drone oscillations the following transformations will be performed:
（6）
wherein, ；/>representation->Is>Element(s)>Representation->Is>Element(s)>Representation->Is>Line->Column elements.
Defining the vibration variable of the unmanned aerial vehicle as follows:
，
then，/>Is->Is>Element, satisfy->，，/>Definitions->，
The above formula (6) can be converted into:
（7）
（8）
wherein, representation->Conjugation of->And solving the +.f. by probability density function of vibration of unmanned plane>Is>Line->The values of the column elements are as follows:
（9）
wherein, ，/>，/>，，/>，/>，/>，，/>，/>for the elevation angle between base station and unmanned aerial vehicle, +.>Is the elevation angle between the drone and the kth user,/, for>，/>. Wherein the method comprises the steps ofAnd->Reflection unit representing a reconfigurable intelligent reflection surface>And reflection unit->Relative to the abscissa of the unmanned aerial vehicle->And->Reflection unit representing a reconfigurable intelligent reflection surface>And reflection unit->Relative to the ordinate of the drone.
It should be noted that the number of the substrates,is a random variable, and is eliminated by solving for mathematical expectations, i.eEliminating the random variable->The mathematical expectation of the kth user signal-to-noise ratio becomes:
（10）
the multi-user weighted sum rate maximization problem can be expressed as,
（11）
；
；
；
wherein, is the weight of the kth user, +.>Representation->Is>Line->Column elements.
The constraint condition shows that the sum of the second norms of the modes of the beam forming of the base station is ensured to be smaller than the power of the base station, the reflection coefficient mode length of the reflection unit of each reconfigurable intelligent reflection surface is smaller than 1,
finally, initializing base station beam forming in feasible domainReflection coefficient of reconfigurable intelligent reflecting surface>And initializing the intermediate calculation variable +.>。
Step 3, taking mathematical expectations of the signal to noise ratio of the user as signal to noise ratio auxiliary variables of the user, and calculating the signal to noise ratio auxiliary variables of the user based on initialized base station beam forming, reconfigurable intelligent reflecting surface reflection coefficients and unmanned aerial vehicle and user coordinates, wherein the signal to noise ratio auxiliary variables are as follows:
the signal-to-noise ratio auxiliary variables for the kth user are:。
step 4, based on the auxiliary variable and the intermediate calculation variable of the signal-to-noise ratio of the userCalculating base station beam forming +.>The following are provided:
first, a base station beamforming calculation auxiliary variable for a kth user is calculated based on a signal-to-noise ratio auxiliary variable of the kth user:
（12）
wherein, ，
then updating the intermediate calculated variables by one-dimensional search，
，
Wherein, for step length, superscript->Indicate->And iterating for a plurality of times.
Finally according to the obtainedAnd->And calculating the base station beam forming of the kth user.
The specific implementation process is as follows:
when the reflection coefficient of the reconfigurable intelligent reflecting surfaceThe base station beam forming optimization problem is as follows:
；
；
at this time, an auxiliary variable is introduced，/>Thereby solving the partial formula as the sum formula:
；/>
；
solving forThe method can obtain:
；
and then will beCarry in->Obtaining:
；
；
at this time, the Lagrangian dual function is constructed for the above problems:
；
re-pairingThe base station beam forming of the kth user can be obtained by seeking:
（13）
in the method, in the process of the invention,for the intermediate calculation variable of this iteration, +.>Represents an identity matrix of order M,。
step 5, calculating the reflection coefficient of the reconfigurable intelligent reflection surface based on the signal-to-noise ratio auxiliary variable of the user and the base station beam forming of the userThe method is characterized by comprising the following steps:
first, a reflection coefficient calculation auxiliary variable for a kth user is calculated based on a signal-to-noise ratio auxiliary variable for the kth user:
（14）
，/>
（15）
wherein, is->Is>Element(s)>，/>，，/>，/>，/>。
Finally, according to the obtainedAnd the base station beam forming of the user, and reconstructing the reflection coefficient of the intelligent reflection surface reflection unit.
The specific implementation process is as follows:
beamforming when a base stationThe reflection coefficient optimization problem of the reconfigurable intelligent reflecting surface is as follows:
；
；
wherein, reflection unit being a reconfigurable intelligent reflection surface>Is a reflection coefficient of (c).
At this time, an auxiliary variable is introduced，/>Thereby solving the partial formula as the sum formula:
；
；
solving forCan get->
；
And then will beCarry in->Obtaining:
；
；
wherein, ，，/>；
then the reflection coefficient of the rest reflection elements is kept unchanged, the first one is optimizedReflection coefficient of the reflection unit:
（16）
wherein, representation->Middle->Line->Column element->Representation->Middle->The elements.
Step 6, judging whether the maximum iteration number is reachedOr the objective function converges, if yes, executing the step 7, otherwise, adding 1 to the iteration times, and jumping to the step 3;
step 7, based on obtaining the optimal base station beam formingReflection coefficient of reconfigurable intelligent reflecting surface>The weighted sum rate of the multiple users is output.
The effect of the method of the invention will be further described in connection with simulation experiments,
1. simulation conditions and parameter settings:
the simulation experiments of this example were performed under MATLAB R2020a software. A base station equipped with 4 antennas is placed at coordinates (0, 10) m and a drone is located at coordinates (25, 25, 50) m and equipped with a reflective element with 16) Is provided. 3 single antenna users (+)>) Uniformly and randomly distributed in a circle with a radius of 5m centered on (50, 20, 0) m. The maximum power of the base station is set to +.>dBm, noise power is set to +.>dBm. Furthermore, the base station frequency is set to +.>GHz. Finally, assume a channel power gain at a reference distance of 1mdB, and unmanned aerial vehicle oscillation variance +.>，/>，/>All are->All simulation results were based on monte carlo experiments, simulating and averaging 1000 sets of random user positions.
2. Simulation conclusion:
fig. 3 is a graph comparing the present invention with the weighted sum rate of multiple users without taking into account the influence of unmanned aerial vehicle uncertain oscillations, wherein the scheme without taking into account unmanned aerial vehicle oscillation influence can be obtained by setting the oscillation variance on each axis to 0 and evaluating the weighted sum rate of worst multiple users in the unmanned aerial vehicle oscillation scene. As can be seen from the figure, the weighted sum rate of the multiple users of the present invention can converge to an optimal value and be higher than that of multiple users not considering the drone oscillation influence scheme. The unmanned aerial vehicle oscillation causes unavoidable phase distortion of signals, so that the optimization effect on the reflection coefficient of the base station beam forming and reconfigurable intelligent reflection surface is affected. The method can effectively overcome the influence of unmanned aerial vehicle vibration, and improves the unmanned aerial vehicle vibration robustness of the system.
FIG. 4 shows the number of reflection units of the present invention at different reconstructed intelligent reflection surfacesA comparison of the weighted sum rate of multiple users at the altitude of the unmanned aerial vehicle, wherein +.>. Notably, the weighted sum rate of the multiple users of the present invention follows +.>Which may indicate that the robustness of the invention increases with +.>Is increased to raise. This result is due to the focusing power of the reflected beam following +.>Is increased to make the focusing direction of the reflected light beam more accurateAnd (5) determining. In addition, as the height of the unmanned aerial vehicle is reduced, the channel distance is shortened, on one hand, the attenuation of signal power is reduced, and on the other hand, as the channel distance is shortened, the phase offset of the signal is also smaller under the same oscillation angle of the unmanned aerial vehicle, and the influence caused by the oscillation of the unmanned aerial vehicle is also smaller, so that the weighted sum rate of multiple users is improved.
Fig. 5 is a graph comparing the weighted sum rates of multiple users under different user weights and base station powers according to the present invention, and it can be seen from the graph that the weighted sum rates of multiple users increase with the increase of the base station power, because the increase of the base station power can increase the energy of the base station beamforming, thereby increasing the signal to noise ratio. The weighted sum rate of multiple users also increases as the weight difference of each user becomes larger, since the reflected beam cannot be focused on only a single user when the weight of each user is close, and the influence of multiple users must be considered, resulting in a decrease in the signal-to-noise ratio of each user due to the influence. However, when one user has a greater weight than the other users, the effect of the other users is reduced and the reflected beam may be focused on the user with the greatest weight for which the reconfigurable intelligent reflective surface reflection coefficient is adjusted, thereby increasing the signal-to-noise ratio of that user. While the signal-to-noise ratio of other users is reduced, the weighting and rate impact on multiple users is less because of the smaller weight.
By integrating the simulation results and analysis, the invention can overcome the influence of uncertain oscillation of the unmanned aerial vehicle, promote the weighted sum rate of multiple users and promote the oscillation robustness of the unmanned aerial vehicle of the system by optimizing the reflection coefficient of the reconfigurable intelligent reflecting surface and the beam forming of the multi-antenna base station. And performance may be improved by increasing the number of reflective elements of the reconfigurable intelligent reflective surface.
Based on the above inventive concept, the invention also provides an oscillation robustness improving system of the unmanned aerial vehicle, which is used for realizing the oscillation robustness improving method of the unmanned aerial vehicle, and the system comprises the following steps:
the initialization module is used for defining the unmanned aerial vehicle oscillation variable and establishing a user signal-to-noise ratio considering the unmanned aerial vehicle oscillation variable;
the iterative calculation module is used for carrying out iterative calculation until reaching a termination condition, and outputting the weighted sum rate of multiple users, wherein the iterative calculation is as follows:
calculating the expectation of the signal to noise ratio of the user based on the base station beam forming, the reflection coefficient of the reconfigurable intelligent reflecting surface, the unmanned aerial vehicle and the user coordinates, and taking the expectation as an auxiliary variable of the signal to noise ratio of the user;
calculating the base station beam forming of the user based on the signal-to-noise ratio auxiliary variable of the user;
and calculating the reflection coefficient of the reconfigurable intelligent reflecting surface based on the signal-to-noise ratio auxiliary variable of the user and the base station beam forming of the user.
It should be noted that, the system embodiment corresponds to the above method embodiment, and the implementation manner of the above method embodiment is applicable to the system embodiment and can achieve the same or similar technical effects, so that the description thereof is omitted herein.
The present invention also provides a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform the concussion robustness improving method of the unmanned aerial vehicle.
The present invention also provides a computing device comprising,
one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing the concussion robustness hoisting method of the drone described above.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Claims (10)
1. The vibration robustness improving method of the unmanned aerial vehicle is characterized by comprising the following steps of:
defining an unmanned aerial vehicle oscillation variable, and establishing a user signal-to-noise ratio considering the unmanned aerial vehicle oscillation variable;
and carrying out iterative computation until a termination condition is reached, and outputting the weighted sum rate of multiple users, wherein the iterative computation is as follows:
calculating the expectation of the signal to noise ratio of the user based on the base station beam forming, the reflection coefficient of the reconfigurable intelligent reflecting surface, the unmanned aerial vehicle and the user coordinates, and taking the expectation as an auxiliary variable of the signal to noise ratio of the user;
calculating the base station beam forming of the user based on the signal-to-noise ratio auxiliary variable of the user;
and calculating the reflection coefficient of the reconfigurable intelligent reflecting surface based on the signal-to-noise ratio auxiliary variable of the user and the base station beam forming of the user.
2. The method for improving the vibration robustness of the unmanned aerial vehicle according to claim 1, wherein the vibration variables of the unmanned aerial vehicle are defined as follows:
；
wherein, unmanned plane oscillation variable representing kth user, < ->Receive array response vector representing reconfigurable smart reflective surface,/->Reflection array response vector representing reconfigurable intelligent reflection surface for kth user, +.>Representing unmanned plane coordinates>Representing kth user coordinates,/->Indicating that the unmanned aerial vehicle is oscillating,
expressed as:
representing the oscillation of the unmanned aerial vehicle in the directions of the x axis, the y axis and the z axis, +.>，/>，/>Independent of each other, and obeys the mean value of 0 and the variance of +.>，/>，/>Is a standard normal distribution of (c).
3. The method for improving the oscillation robustness of the unmanned aerial vehicle according to claim 2, wherein the establishing a user signal-to-noise ratio considering the oscillation variable of the unmanned aerial vehicle comprises:
；
wherein, representing the user signal-to-noise ratio of the kth user considering the unmanned aerial vehicle concussion variable, < + >>Representing the reflection coefficient of the reconfigurable intelligent reflecting surface, < ->Representing the channel matrix between the reconfigurable intelligent reflective surface and the kth user, +.>Representing the channel matrix between the base station and the reconfigurable intelligent reflecting surface +.>The base station is beamformed with,，/>base station beamforming, denoted kth user,>，/>indicates the number of users->The superscript H denotes the conjugate transpose, which is the variance of the additive white gaussian noise.
4. A method of improving the robustness against oscillations of an unmanned aerial vehicle according to claim 3, wherein calculating the expectation of the user's signal-to-noise ratio comprises:
；
wherein, representing the desire of the kth user signal-to-noise ratio, < + >>，/>Representation ofThe superscript T denotes the transpose,
expressed as: />，
For the channel power gain at the reference distance +.>In the case of an antenna array,
expressed as: />，
By finding mathematical expectationsEliminating random variables, i.eEliminates unmanned aerial vehicle oscillation->。
5. The method for improving vibration robustness of unmanned aerial vehicle according to claim 4, wherein calculating base station beamforming of the user based on the signal-to-noise ratio auxiliary variable of the user comprises:
based on the signal-to-noise ratio auxiliary variable of the user, the base station beam forming calculation auxiliary variable of the user is calculated as follows:
；
wherein, base station beamforming calculation auxiliary variable representing kth user,/>，，/>Is the weight of the kth user;
calculating auxiliary variables according to base station beamforming of kth userThe base station beam forming of the kth user is calculated as follows:
；
wherein, represents an M-order identity matrix>For the intermediate calculated variables of the current iteration,
by one-dimensional search update, the update is as follows:
；
wherein, indicate->Intermediate calculation variable for a number of iterations,/->Indicate->Intermediate calculation variable for a number of iterations,/->In steps.
6. The method for improving oscillation robustness of an unmanned aerial vehicle according to claim 5, wherein calculating the reflection coefficient of the reconfigurable intelligent reflection surface based on the signal-to-noise ratio auxiliary variable of the user and the base station beamforming of the user comprises:
the reflection coefficient calculation auxiliary variable for the kth user is calculated based on the signal-to-noise ratio auxiliary variable of the user as follows:
；
wherein, a reflection coefficient calculation auxiliary variable representing the kth user,/->；
And calculating auxiliary variables and base station beam forming of the user based on the reflection coefficient of the kth user, and reconstructing the reflection coefficient of the intelligent reflection surface reflection unit as follows:
；
wherein, reflection unit representing a reconfigurable intelligent reflection surface>Reflection coefficient of>Representing the number of reflecting units of the reconfigurable intelligent reflecting surface, < >>Representation->Is used for the conjugation of (a),
，，
indicating (I)>Middle->Line->Column element->Representation->Middle->Element(s)>Representation->Is a conjugate of (c).
7. The method for improving the oscillation robustness of the unmanned aerial vehicle according to claim 6, wherein the termination condition is:
reaching the maximum iteration number or converging the objective function;
the objective function is:
；
；
；
；
wherein, representing the weighted sum rate of multiple users +.>Representation->Is>Line->Column element->Representation->Is>Line->Column element->Representing the base station power.
8. An oscillating robustness improving system of an unmanned aerial vehicle, for implementing the oscillating robustness improving method of an unmanned aerial vehicle according to any one of claims 1 to 7, the system comprising:
the initialization module is used for defining the unmanned aerial vehicle oscillation variable and establishing a user signal-to-noise ratio considering the unmanned aerial vehicle oscillation variable;
the iterative calculation module is used for carrying out iterative calculation until reaching a termination condition, and outputting the weighted sum rate of multiple users, wherein the iterative calculation is as follows:
calculating the expectation of the signal to noise ratio of the user based on the base station beam forming, the reflection coefficient of the reconfigurable intelligent reflecting surface, the unmanned aerial vehicle and the user coordinates, and taking the expectation as an auxiliary variable of the signal to noise ratio of the user;
calculating the base station beam forming of the user based on the signal-to-noise ratio auxiliary variable of the user;
and calculating the reflection coefficient of the reconfigurable intelligent reflecting surface based on the signal-to-noise ratio auxiliary variable of the user and the base station beam forming of the user.
9. A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
10. A computing device, comprising,
one or more processors, memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-7.
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