CN115955264B - Unmanned aerial vehicle carried RIS auxiliary AF relay collaborative construction and optimization method - Google Patents

Unmanned aerial vehicle carried RIS auxiliary AF relay collaborative construction and optimization method Download PDF

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CN115955264B
CN115955264B CN202310232010.7A CN202310232010A CN115955264B CN 115955264 B CN115955264 B CN 115955264B CN 202310232010 A CN202310232010 A CN 202310232010A CN 115955264 B CN115955264 B CN 115955264B
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unmanned aerial
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CN115955264A (en
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张顺外
黄星博
陈博涛
钟积彬
王金
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Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a cooperative construction and optimization method of an AF relay assisted by an unmanned aerial vehicle carrying RIS (radio remote system), belonging to the technical field of wireless communication; the unmanned aerial vehicle carries RIS to provide extra communication links in the air, in order to strengthen the average reachable rate of the AF relay cooperative communication system of the ground; establishing a joint optimization problem model of unmanned aerial vehicle track optimization and RIS phase shift matrix optimization, and decomposing the original problem into two sub-problems of RIS phase shift matrix optimization and unmanned aerial vehicle track optimization; obtaining an optimal phase shift matrix by aligning phases of the received signals; converting the unmanned aerial vehicle track optimization sub-problem into a convex problem by adopting a successive convex approximation SCA method, and further solving by an iterative algorithm; the invention combines the flexible and portable air unmanned aerial vehicle carrying RIS with the ground AF relay cooperative communication system, and provides an efficient and reliable communication scheme under a complex scene by jointly optimizing the unmanned aerial vehicle track and the RIS phase shift matrix.

Description

Unmanned aerial vehicle carried RIS auxiliary AF relay collaborative construction and optimization method
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a relay cooperative construction and optimization method for an unmanned aerial vehicle carrying RIS auxiliary AF.
Background
Relay collaboration techniques may enable single antenna devices to form virtual Multiple-Input Multiple-Output (MIMO) to enjoy spatial diversity gains. Common collaboration modes include an Amplify-and-Forward (AF) mode, a Detect/decode-and-Forward (DF) mode, a code collaboration (Coded Cooperation, CC) mode, and the like. In the AF mode, the relay node first amplifies the received signals and then forwards them directly to the destination node. Although the AF mode performance is not prominent compared with other schemes, it has advantages of easy implementation and low time delay, and is often applied to actual wireless communication scenarios.
In complex or difficult to reach areas, such as earthquake-stricken centers, it is difficult to deploy conventional fixed relays to support reliable communications. Unmanned aerial vehicles (Unmanned Aerial Vehicle, UAVs) are considered as one of the promising solutions due to the advantages of flexible deployment, large coverage, etc. The drone may operate as a mobile high-altitude base station, deployed on demand to support communications quickly, or may operate as a mobile relay and work in concert with ground users to enhance throughput and extend coverage. The reconfigurable intelligent surface (Reconfigurable Intelligent Surface, RIS) is used as an emerging technology, works in a full duplex mode, has no self-interference, and has the advantages of low hardware cost, low power consumption, flexible deployment, intelligent reconfiguration and the like. Combining the advantages of both the drone and the RIS, we consider a drone carrying the RIS, the RIS being mounted on the drone, the drone no longer needing to be equipped with heavy transceiver equipment. The drone enhances the performance of the communication system as a mobile RIS in complex or difficult to reach areas. The unmanned aerial vehicle carrying RIS has the advantages of flexible deployment, large coverage area and the like of the unmanned aerial vehicle, and has the characteristics of low hardware cost, low power consumption, light weight and the like of the RIS. Therefore, the unmanned aerial vehicle carrying the RIS auxiliary AF relay cooperative communication system is very suitable for emergency reliable communication scenes, and further, it is important to optimize the unmanned aerial vehicle track and the RIS phase shift matrix in a combined mode to maximize the average reachable rate of the system.
Disclosure of Invention
The invention provides a method for collaborative construction and optimization of an unmanned aerial vehicle carrying RIS auxiliary AF relay, which aims to solve the technical problems in the prior art, and the average reachable rate of the system is maximized by jointly optimizing the track of the unmanned aerial vehicle and an RIS phase shift matrix on the basis of establishing a model of an unmanned aerial vehicle carrying RIS auxiliary AF relay collaborative communication system.
In order to achieve the above object, the technical scheme of the present invention is as follows:
an unmanned aerial vehicle carrying RIS auxiliary AF relay collaborative construction and optimization method comprises the following steps:
step 1.1, modeling of an AF relay cooperative communication system assisted by an unmanned aerial vehicle carrying RIS; the AF relay coordination system realizes information transmission on the ground, and the unmanned aerial vehicle carries RIS to provide an additional communication link in the air; obtaining an unmanned aerial vehicle track and RIS phase shift matrix joint optimization problem model corresponding to the average achievable rate of the implementation maximization system;
step 1.2, solving an RIS phase shift matrix optimization sub-problem according to unmanned aerial vehicle track information obtained in the previous iteration, and obtaining a closed solution of RIS phase shift;
step 1.3, converting the unmanned aerial vehicle track optimization sub-problem into a convex optimization problem according to the RIS phase shift obtained in the step 1.2, and further solving an optimal solution of the unmanned aerial vehicle track through a convex optimization tool CVX;
step 1.4, iteratively executing the step 1.2 and the step 1.3, and solving a joint optimization problem by alternately optimizing two sub-problems; when a predetermined convergence accuracy or maximum number of iterations is reached, the iteration is stopped.
Preferably, in step 1.1, the modeling of the unmanned aerial vehicle carrying the RIS-assisted AF relay cooperative communication system includes the following specific steps:
step 1.1.1, because the unmanned aerial vehicle works in the air with RIS, the channel between the source node S and RIS is based on the characteristics of uniform rectangular array URA formed by large-scale path loss close to free space and RIS elements
Figure SMS_1
Channel +.>
Figure SMS_2
Channel between destination node D and RIS +.>
Figure SMS_3
All seen as line-of-sight LoS channel with array response; because the relay works on the ground, the channel between the source node S and the relay node R is +.>
Figure SMS_4
Channel +.>
Figure SMS_5
Modeling as a LoS channel close to free space;
step 1.1.2, AF relays work in half duplex mode, each time frame is divided into two time slots, in the first time slot, the source node S sends out the signalInformation is respectively passed through channels
Figure SMS_6
And cascade channel->
Figure SMS_7
Figure SMS_8
Transmitting to a relay node R and a destination node D; in the second time slot, the relay node R amplifies and forwards the received signals, respectively through the channels +.>
Figure SMS_9
And cascade channel->
Figure SMS_10
Figure SMS_11
Transmitting to a destination node D; the achievable rate of the nth frame is:
Figure SMS_12
；
wherein:
Figure SMS_15
and->
Figure SMS_17
The power is respectively transmitted at the source node S and the relay node R; />
Figure SMS_21
Is the amplification gain of the relay node R, +.>
Figure SMS_14
；/>
Figure SMS_18
Is the power of additive white gaussian noise; />
Figure SMS_20
And (3) with
Figure SMS_23
A phase shift matrix corresponding to the first time slot and the second time slot of the nth frame respectively for RIS, wherein +.>
Figure SMS_13
For RIS element number, < > for>
Figure SMS_16
And->
Figure SMS_19
The number of elements in each row and each column is respectively represented; />
Figure SMS_22
Representing a transpose operation;
step 1.1.3, further obtaining an average reachable rate of the system as follows:
Figure SMS_24
；
wherein N is the total time frame number of the system information transmission;
step 1.1.4, unmanned aerial vehicle trajectory Q and RIS phase shift matrix
Figure SMS_25
And->
Figure SMS_26
The joint optimization problem model of (a) is as follows:
Figure SMS_27
Figure SMS_28
Figure SMS_29
Figure SMS_30
Figure SMS_31
，
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_32
and->
Figure SMS_33
Respectively representing the start and end positions of the unmanned aerial vehicle, < >>
Figure SMS_34
，/>
Figure SMS_35
Representing the position of the drone at the nth time frame,/->
Figure SMS_36
The maximum flight distance of the unmanned aerial vehicle is per time frame.
Preferably, in step 1.2, the solving the problem of optimizing the RIS phase shift matrix to obtain a closed solution of RIS phase shift specifically includes the following steps:
step 1.2.1, optimizing the RIS phase shift matrix of the first time slot according to the unmanned plane track obtained in the previous iteration in order to maximize the average reachable rate
Figure SMS_37
The phases from the different path signals are made equal at the destination node D, i.e. by aligning the phases of the received signals to obtain an optimal phase shift matrix, the RIS phase shift matrix for the first time slot is obtained as follows:
Figure SMS_38
；
wherein the method comprises the steps of
Figure SMS_39
，/>
Figure SMS_40
An optimal phase shift for the ith element of the RIS first time slot;
step 1.2.2, the same applies, in order to maximize the average achievable rate, to optimize the RIS phase shift matrix of the second time slot according to the unmanned trajectory obtained from the previous iteration
Figure SMS_41
The phases from the different path signals are made equal at the destination node D, i.e. by aligning the phases of the received signals to obtain an optimal phase shift matrix, the RIS phase shift matrix for the second time slot is obtained as follows:
Figure SMS_42
；
wherein the method comprises the steps of
Figure SMS_43
，/>
Figure SMS_44
The optimal phase shift is obtained for the ith element of the RIS second slot.
Preferably, in step 1.3, the problem of optimizing the unmanned aerial vehicle track is converted into a convex optimization problem, and an optimal solution of the unmanned aerial vehicle track is further obtained through a convex optimization tool CVX, which specifically comprises the following steps:
step 1.3.1, substituting the RIS phase shift obtained by optimization in step 1.2 into an objective function of a problem P1, and converting the joint optimization problem into an unmanned aerial vehicle track optimization sub-problem;
step 1.3.2, introducing a relaxation variable in order to solve the unmanned aerial vehicle track optimization sub-problem
Figure SMS_45
And->
Figure SMS_46
Iterative optimization is carried out on the sub-problem by applying a successive approximation SCA method, first-order Taylor expansion is introduced for ensuring the convergence of the solution, and an objective function in P1 is approximated to be a lower bound thereof; similarly, inOn the basis, similar processing is carried out on the constraint function, and the objective function and the constraint condition of the unmanned plane track optimization sub-problem after processing are convex, namely the convex problem;
step 1.3.3, solving the convex problem by using a convex optimization tool CVX to obtain an optimized unmanned aerial vehicle track
Figure SMS_47
。
Preferably, in step 1.4, step 1.2 and step 1.3 are iteratively performed, and the joint optimization problem is solved by alternately optimizing two sub-problems, which specifically includes the following steps:
step 1.4.1, setting an iteration stop condition, i.e. a predetermined convergence accuracy
Figure SMS_48
Or maximum number of iterations->
Figure SMS_49
；
Step 1.4.2, iteratively executing the step 1.2 and the step 1.3, and alternately optimizing the RIS phase shift matrix and the unmanned aerial vehicle track;
step 1.4.3, reaching a preset iteration stop condition to obtain an optimal RIS phase shift matrix
Figure SMS_50
、/>
Figure SMS_51
Unmanned plane track->
Figure SMS_52
And the average reachable rate of the unmanned aerial vehicle carrying the RIS auxiliary AF relay cooperative communication system is maximized.
The invention has the following beneficial effects: (1) The invention provides the unmanned aerial vehicle for carrying the RIS by combining the advantages of the unmanned aerial vehicle and the RIS, namely the RIS is arranged on the unmanned aerial vehicle, and the unmanned aerial vehicle does not need to be equipped with heavy receiving and transmitting equipment any more. The unmanned aerial vehicle carrying RIS has the advantages of flexible deployment, large coverage area and the like of the unmanned aerial vehicle, and has the characteristics of low hardware cost, low power consumption, intelligent reconfiguration and the like of the RIS. Often applied in an actual wireless communication scenario.
(2) The invention combines flexible and portable air unmanned aerial vehicle with RIS and ground AF relay cooperative communication, is very suitable for emergency reliable communication scenes such as earthquake disaster areas, and can realize enhanced communication, blind supplement communication and the like when ground equipment can not meet the emergency communication requirement. Meanwhile, in view of the importance of jointly optimizing the unmanned aerial vehicle track and the RIS phase shift matrix to maximize the average reachable rate of the system, a joint optimization problem model for unmanned aerial vehicle track optimization and RIS phase shift matrix optimization is established. First, the original problem is decomposed into two sub-problems of RIS phase shift matrix optimization and unmanned aerial vehicle trajectory optimization. Then, an optimal phase shift matrix is obtained by aligning the phases of the received signals. Secondly, as the unmanned aerial vehicle trajectory optimization sub-problem is still a complex non-convex problem, the unmanned aerial vehicle trajectory optimization sub-problem is converted into a convex problem by adopting a Successive Convex Approximation (SCA) method and is further solved by an iterative algorithm. The invention combines flexible and portable air unmanned aerial vehicle carrying RIS with ground AF relay cooperative communication, and provides a solution for realizing efficient communication in complex scenes by jointly optimizing unmanned aerial vehicle track and RIS phase shift matrix.
Drawings
FIG. 1 is a model diagram of an AF relay cooperative communication system carried by an unmanned aerial vehicle with RIS assistance in the invention;
FIG. 2 is a graph comparing the performance of the proposed communication scheme with other reference schemes in the present invention;
FIG. 3 is a graph showing the performance of the joint optimization algorithm of the unmanned aerial vehicle track and the RIS phase shift matrix and other reference algorithms.
Detailed Description
FIG. 1 is a model diagram of an AF relay cooperative communication system carried by an unmanned aerial vehicle with RIS assistance in the invention; as shown in fig. 1, the method for collaborative construction and optimization of an unmanned aerial vehicle carrying an RIS auxiliary AF relay includes the following steps:
step 1.1, modeling of an AF relay cooperative communication system assisted by an unmanned aerial vehicle carrying RIS; the AF relay cooperative communication system realizes information transmission on the ground, and the unmanned aerial vehicle carries RIS to provide an additional communication link in the air; obtaining an unmanned aerial vehicle track and RIS phase shift matrix joint optimization problem model corresponding to the average achievable rate of the implementation maximization system;
step 1.2, solving an RIS phase shift matrix optimization sub-problem according to the track information of the unmanned aerial vehicle obtained in the previous iteration, and obtaining a closed solution of RIS phase shift;
step 1.3, converting the unmanned aerial vehicle track optimization sub-problem into a convex optimization problem according to the RIS phase shift obtained in the step 1.2, and further solving an optimal solution of the unmanned aerial vehicle track through a convex optimization tool CVX;
step 1.4, iteratively executing the step 1.2 and the step 1.3, and solving a joint optimization problem by alternately optimizing two sub-problems; when a predetermined convergence accuracy or maximum number of iterations is reached, the iteration is stopped.
Further, in step 1.1, the modeling of the unmanned aerial vehicle carrying the RIS auxiliary AF relay cooperative communication system specifically includes the following steps:
firstly, a model of an unmanned aerial vehicle carrying RIS auxiliary AF relay cooperative communication system is established, and an information source node (S) transmits information to a destination node (D) through a Half Duplex (HD) AF relay (R) on the ground. It is assumed that there is no direct communication link between S and D, which is blocked by an obstacle. Considering that emergency communication requires a higher Average Achievable Rate (AAR) than usual scenario, an unmanned aerial vehicle carrying RIS is deployed to enhance the AF relay cooperative communication system, and an additional link can be provided between S and D without consuming transmission power. S, R, D are single antennas, the positions of which are respectively coordinated
Figure SMS_59
, />
Figure SMS_54
And
Figure SMS_64
and (3) representing. Unmanned plane flies at fixed height H with flight period of T p Divided into N time frames of equal duration
Figure SMS_58
I.e. +.>
Figure SMS_65
The maximum flight distance of the unmanned aerial vehicle per time frame is +.>
Figure SMS_60
. Since the unmanned aerial vehicle works in the air with RIS, the channel between the source node S and RIS is>
Figure SMS_69
Channel +.>
Figure SMS_68
Channel between destination node D and RIS +.>
Figure SMS_71
All considered as line of sight (LoS) channels with array responses; since the relay node works on the ground, the channel between the source node S and the relay node R is ∈>
Figure SMS_53
Channel +.>
Figure SMS_62
Modeling as a LoS channel close to free space; meanwhile, since R is operated in HD mode, each time frame is divided into two equal time slots, and the drone is considered stationary within each time frame. In the first time slot, the source node S transmits information via the channels +.>
Figure SMS_56
And cascade channel->
Figure SMS_70
Figure SMS_57
Transmitting to the relay node R and the destination node D, and amplifying and forwarding the received signals by the relay node R in the second time slot through channels +.>
Figure SMS_61
And cascade channel->
Figure SMS_63
Figure SMS_67
To the destination node D. Marking the position of the n-th frame of the unmanned aerial vehicle in the coordinate system as +.>
Figure SMS_66
The start position and the end position are defined as +.>
Figure SMS_72
And->
Figure SMS_55
。
Since the drone works in air with its carrying RIS, it follows a large scale path loss close to free space and a Uniform Rectangular Array (URA) of RIS elements, the channel
Figure SMS_73
、/>
Figure SMS_74
、/>
Figure SMS_75
Are all considered as line-of-sight LoS channels with array response, as follows:
Figure SMS_76
(1)
Figure SMS_77
(2)
Figure SMS_78
(3)
wherein the method comprises the steps of
Figure SMS_80
Channel gain per unit distance,/>
Figure SMS_83
Is the path loss coefficient, +.>
Figure SMS_85
、
Figure SMS_79
、/>
Figure SMS_84
Representing the distances between S and RIS, R and RIS, and D and RIS, respectively, at the nth time frame. />
Figure SMS_86
,/>
Figure SMS_87
And->
Figure SMS_81
Is the array response of a rectangular array of M elements. d is the antenna spacing, ">
Figure SMS_82
Is the carrier wavelength.
Since the AF relay works on the ground, the channel between S and R and D is modeled as the following channel:
Figure SMS_88
(4)
Figure SMS_89
(5)
wherein the method comprises the steps of
Figure SMS_90
Is the path loss coefficient, +.>
Figure SMS_91
And->
Figure SMS_92
Respectively, the distances between S and R and D.
According to the channel information of S and RIS, D and RIS and S and R, the received signals at the first time slots R and D of the nth time frame are as follows:
Figure SMS_93
(6)
Figure SMS_94
(7)
wherein the method comprises the steps of
Figure SMS_95
And->
Figure SMS_96
Is the power at S and D +.>
Figure SMS_97
Is a gaussian white noise of (c). />
Figure SMS_98
And the phase shift matrix corresponding to the first time slot of the nth frame is used as the RIS.
According to the channel information of R and RIS, R and D, D and RIS, the signal at the second time slot D of the nth frame is as follows:
Figure SMS_99
(8)
wherein the method comprises the steps of
Figure SMS_100
Is the amplification gain of the AF relay, +.>
Figure SMS_101
，/>
Figure SMS_102
Is the power of the additive white gaussian noise.
Figure SMS_103
And the phase shift matrix corresponding to the first time slot of the nth frame is used as the RIS.
Combining the signals received in two time slots, the achievable rate of the nth time frame is
Figure SMS_104
(9)
Wherein:
Figure SMS_106
and->
Figure SMS_109
The power is respectively transmitted at the source node S and the relay node R; />
Figure SMS_111
Is the amplification gain of the AF relay,
Figure SMS_107
；/>
Figure SMS_110
is the power of additive white gaussian noise; />
Figure SMS_113
And (3) with
Figure SMS_114
A phase shift matrix corresponding to the first time slot and the second time slot of the nth frame respectively for RIS, wherein +.>
Figure SMS_105
For RIS element number, < > for>
Figure SMS_108
And->
Figure SMS_112
The number of elements in each row and each column is respectively represented;
further deriving AAR of the system:
Figure SMS_115
； (10)
thus, the problem model of the system AAR with respect to RIS phase shift and unmanned trajectory is as follows:
Figure SMS_116
(11a)
Figure SMS_117
(11b)
Figure SMS_118
(11c)
Figure SMS_119
(11d)
Figure SMS_120
。 (11e)
further, in step 1.2, the method solves the problem of optimizing the RIS phase shift matrix according to the trajectory information of the unmanned aerial vehicle obtained in the previous iteration, and obtains a closed solution of the RIS phase shift, which specifically comprises the following steps:
firstly, according to the unmanned plane track obtained in the previous iteration, in the first time slot
Figure SMS_121
Is->
Figure SMS_122
Can be written in the following form:
Figure SMS_123
(12)
wherein the method comprises the steps of
Figure SMS_124
(13)
Figure SMS_125
(14)
Figure SMS_126
And->
Figure SMS_127
Azimuth and altitude angles of RIS relative to S, respectively, when representing the nth time frame.
To maximize AAR, the phases of the signals from the different paths need to be equal at D, from which it is available
Figure SMS_128
(15)
The phase shift design of the second slot RIS is similar to the first slot, we directly give a closed-loop solution:
Figure SMS_129
(16)
wherein the method comprises the steps of
Figure SMS_130
、/>
Figure SMS_131
Definition and calculation method of (2) and->
Figure SMS_132
、/>
Figure SMS_133
Similarly.
Thus, the RIS phase shift matrix for two slots is as follows
Figure SMS_134
(17)
Figure SMS_135
(18)
Further, in step 1.3, the problem of optimizing the unmanned aerial vehicle track is converted into a convex optimization problem, and an optimal solution of the unmanned aerial vehicle track is further obtained through a convex optimization tool CVX, which specifically comprises the following steps:
optimizing the unmanned aerial vehicle track by applying a successive approximation (Successive Convex Approximation, SCA) algorithm according to the RIS phase shift solved in step 1.2, firstly substituting the RIS phase shift optimized in step 1.2 into the objective function of P1, and introducing a relaxation variable
Figure SMS_136
and/>
Figure SMS_137
：
Figure SMS_138
(19a)
Figure SMS_139
(19b)
Figure SMS_140
(19c)
Figure SMS_141
(19d)
Figure SMS_142
(19e)
Wherein the method comprises the steps of
Figure SMS_143
，/>
Figure SMS_144
。
Notably, the solution for P2 is only available when the equal sign of constraints (19 b) and (19 c) is established. To solve for P2, the quotients are given as follows:
lemma 1 given by
Figure SMS_145
,/>
Figure SMS_146
,/>
Figure SMS_147
,/>
Figure SMS_148
，/>
Figure SMS_149
,/>
Figure SMS_150
About->
Figure SMS_151
，/>
Figure SMS_152
Is a convex function.
According to the quotation mark 1,
Figure SMS_153
about->
Figure SMS_154
，/>
Figure SMS_155
Is a convex function. However, one objective function is not concave and the maximization problem is not convex, and P2 is still difficult to solve. Therefore, we apply the SCA method to iteratively optimize the P2 problem. To ensure convergence of the solution, a first-order taylor expansion is introduced, approximating the objective function in P2 to its lower bound. We solve the solution of the previous iteration +.>
Figure SMS_156
，/>
Figure SMS_157
As a given point of this iteration
Figure SMS_158
，/>
Figure SMS_159
Thereby giving the lower bound function at a given point:
Figure SMS_160
(20)
wherein the method comprises the steps of
Figure SMS_161
(21)
Figure SMS_162
(22)
Figure SMS_163
(23)
Since the first-order taylor expansion of the convex function can be implemented in the complex domain and the distance is always real, to ensure that the first-order taylor expansion is in the real domain, we give:
Figure SMS_164
(24)
Figure SMS_165
(25)
while substituting (20) into the objective function of P2, P2 is related to
Figure SMS_166
，/>
Figure SMS_167
Still non-convex. Therefore, we apply the SCA method as well, giving ++on the basis of the lemma 2>
Figure SMS_168
，/>
Figure SMS_169
Is a lower bound of (c).
And (4) lemma 2: assume that
Figure SMS_171
Is the product of two functions, i.e. +.>
Figure SMS_174
. Wherein->
Figure SMS_177
And->
Figure SMS_172
Are convex functions and are not negative. For arbitrary->
Figure SMS_173
,/>
Figure SMS_176
Can be written as +.>
Figure SMS_178
. Thus->
Figure SMS_170
The convex upper bound of (2) may be obtained by linear expansion. Arbitrary given->
Figure SMS_175
Can be obtained
Figure SMS_179
(26)
Based on the quotation mark 2,
Figure SMS_180
，/>
Figure SMS_181
the upper bound of (2) is as follows:
Figure SMS_182
(27)
Figure SMS_183
(28)
in combination with (24), (25), (27), (28), the following constraints can be obtained:
Figure SMS_184
(29)
Figure SMS_185
(30)
thus, P2 can be converted into the following form:
Figure SMS_186
(31a)
Figure SMS_187
(31b)
Figure SMS_188
(31c)
Figure SMS_189
(31d)
Figure SMS_190
(31e)
since the objective function and constraint conditions in P3 are both convex, this is a convex problem that is solved by the convex optimization tool CVX, resulting in an optimized unmanned trajectory
Figure SMS_191
。/>
Further, in step 1.4, step 1.2 and step 1.3 are iteratively performed, and the joint optimization problem is solved by alternately optimizing the two sub-problems. When a predetermined convergence accuracy or maximum number of iterations is reached, the iteration is stopped. The method comprises the following specific steps: first setting an iteration stop condition, i.e. a predetermined convergence accuracy
Figure SMS_192
Or maximum number of iterations->
Figure SMS_193
The method comprises the steps of carrying out a first treatment on the surface of the Then, iteratively executing the steps 1.2 and 1.3, and alternately optimizing the RIS phase shift matrix and the unmanned aerial vehicle track; stopping iteration when a preset iteration stopping condition is reached to obtain an optimal RIS phase shift matrix +.>
Figure SMS_194
、/>
Figure SMS_195
Unmanned plane track->
Figure SMS_196
Enabling the unmanned aerial vehicle to carry RIS auxiliary AF relay cooperative communication systemThe average achievable rate is maximum. Since the average achievable rate obtained by each iterative solution is non-decreasing and the average achievable rate is bounded, this ensures the convergence of the iterative method.
See fig. 2, fig. 3 simulation experiments and effect analysis:
the invention carries out simulation analysis on the average reachable rate of the AF relay cooperative communication system carried by the unmanned aerial vehicle, and specific simulation parameters are shown in table 1.
Table 1: simulation parameter setting
Figure SMS_197
FIG. 2 compares the unmanned aerial vehicle carrying RIS-assisted AF relay coordination scheme (RIS-enabled UAV) proposed by the present invention&AF Relay) and AF-Only Relay solutions (AF Relay Only), and UAV Only with RIS solutions (RIS-enabled UAV Only). As shown in FIG. 2, the AAR of the proposed solution is significantly better than either the AF Relay Only solution or the RIS-enabled UAV Only solution. For example when
Figure SMS_198
When the AAR of the AF Relay Only scheme and the RIS-enabled UAV Only scheme are 1.79 bps/Hz and 1.32 bps/Hz, respectively, the AAR of the proposed scheme is as high as 2.02 bps/Hz. This is because the proposed solution combines the advantages of RIS, drone and AF relay, with the three. The results also show that the AAR of the proposed scheme and the RIS-enabled UAV Only scheme increases with increasing time frame N, while the AAR of the AF Relay Only Relay scheme remains unchanged.
FIG. 3 compares a joint optimization algorithm (Trajectory) of the Trajectory of the proposed unmanned aerial vehicle and the RIS phase shift matrix in an unmanned aerial vehicle-carried RIS-assisted AF relay cooperative communication system&Shift opt.) and AAR performance with only the optimized RIS Phase Shift matrix algorithm (RIS Phase opt.), without the optimization algorithm (No opt.). As shown in fig. 3, the AAR of the proposed joint optimization algorithm is significantly better than the RIS Phase opt. Algorithm and the No opt. Algorithm, and this advantage becomes more pronounced as M increases. For example when
Figure SMS_199
When the RIS Phase Opt algorithm and the No Opt algorithm have AAR of about 1.51 bps/Hz and 1.35 bps/Hz, respectively, the AAR of the proposed algorithm can reach up to 1.90 bps/Hz. The method and the system illustrate that the provided algorithm utilizes the optimal phase shift of the RIS and the optimal track of the unmanned aerial vehicle, and the advantage of carrying the RIS by the unmanned aerial vehicle is exerted to the maximum extent.
The present invention is not limited to the above embodiments, but can be modified in any way to achieve the same effects as those of the present invention.

Claims (5)

1. The method for collaborative construction and optimization of the AF relay assisted by the RIS carried by the unmanned aerial vehicle is characterized by comprising the following steps of:
step 1.1, modeling of an AF relay cooperative communication system assisted by an unmanned aerial vehicle carrying RIS; the AF relay cooperative communication system realizes information transmission on the ground, and the unmanned aerial vehicle carries RIS to provide an additional communication link in the air; obtaining an unmanned aerial vehicle track and RIS phase shift matrix joint optimization problem model corresponding to the average reachable rate of the maximized system;
step 1.2, solving an RIS phase shift matrix optimization sub-problem according to unmanned aerial vehicle track information obtained in the previous iteration, and obtaining a closed solution of RIS phase shift;
step 1.3, converting the unmanned aerial vehicle track optimization sub-problem into a convex optimization problem according to the RIS phase shift obtained in the step 1.2, and further solving an optimal solution of the unmanned aerial vehicle track through a convex optimization tool CVX;
step 1.4, iteratively executing the step 1.2 and the step 1.3, and solving a joint optimization problem by alternately optimizing two sub-problems; when a predetermined convergence accuracy or maximum number of iterations is reached, the iteration is stopped.
2. The method for collaborative construction and optimization of an unmanned aerial vehicle-carried RIS-assisted AF relay according to claim 1, wherein in step 1.1, the unmanned aerial vehicle-carried RIS-assisted AF relay collaborative communication system is modeled as follows:
step 1.1.1, channel h between source node S and RIS based on uniform rectangular array URA characteristics formed by large-scale path loss close to free space and RIS element SI [n]Channel h between relay node R and RIS RI [n]Channel h between destination node D and RIS ID [n]All seen as line-of-sight LoS channel with array response; channel h between source node S and relay node R SR Channel h between relay node R and destination node D RD Modeling as a LoS channel close to free space;
step 1.1.2, AF relays work in half duplex mode, each time frame is divided into two time slots, in the first time slot, the source node S sends information through the channel h respectively SR And concatenated channel h SI [n]h ID [n]Transmitting to a relay node R and a destination node D; in the second time slot, the relay node R amplifies and forwards the received signals, and the signals are transmitted through the channel h respectively RD And concatenated channel h RI [n]h ID [n]Transmitting to a destination node D; according to the received signals of two time slots, the achievable rate of the nth frame is
Figure FDA0004199709300000011
Wherein: p (P) S And P R The power is respectively transmitted at the source node S and the relay node R; beta is the amplification gain of the relay node R,
Figure FDA0004199709300000012
σ 2 is the power of additive white gaussian noise; />
Figure FDA0004199709300000013
And (3) with
Figure FDA0004199709300000021
Phase shift matrices corresponding to the first and second slots of the nth frame, respectively, of RIS, where m=m x ×M y For the number of RIS elements, M x And M is as follows y The number of elements in each row and each column is respectively represented; () T Representing a transpose operation;
step 1.1.3, further obtaining the average reachable rate of the system as
Figure FDA0004199709300000022
Wherein N is the total time frame number of the system information transmission;
step 1.1.4, unmanned aerial vehicle trajectory Q and the joint optimization problem model of RIS phase shift matrix Θ and Ω are as follows:
Figure FDA0004199709300000023
/>
Figure FDA0004199709300000024
Figure FDA0004199709300000025
q[1]＝q 0 ，q[N]＝q F ,
Figure FDA0004199709300000026
wherein q 0 And q F Respectively representing the starting position and the ending position of the unmanned aerial vehicle; q [ n ]]N epsilon {1,2, …, N-1} represents the position of the unmanned aerial vehicle at the nth time frame; d (D) max The maximum flight distance of the unmanned aerial vehicle is per time frame.
3. The collaborative construction and optimization method for the relay with the RIS auxiliary AF according to claim 2 is characterized in that in step 1.2, the closed solution of the RIS phase shift is obtained by solving the RIS phase shift matrix optimization sub-problem, and the specific steps are as follows:
step 1.2.1, in order to maximize the average achievable rate, according to the unmanned plane trajectory obtained in the previous iteration, optimizing the RIS phase shift matrix Θ [ n ] of the first time slot, so that the phases of signals from different paths are equal at the destination node D, that is, the optimal phase shift matrix is obtained by aligning the phases of the received signals, and the RIS phase shift matrix of the first time slot is obtained as follows:
Figure FDA0004199709300000027
wherein the method comprises the steps of
Figure FDA0004199709300000028
An optimal phase shift for the ith element of the RIS first time slot;
step 1.2.2, in order to maximize the average achievable rate, optimizing the RIS phase shift matrix Ω [ n ] of the second time slot according to the unmanned trajectory obtained in the previous iteration, so that the phases of signals from different paths are equal at the destination node D, that is, the optimal phase shift matrix is obtained by aligning the phases of the received signals, and the RIS phase shift matrix of the second time slot is obtained as follows:
Figure FDA0004199709300000031
wherein the method comprises the steps of
Figure FDA0004199709300000032
The optimal phase shift is obtained for the ith element of the RIS second slot.
4. The collaborative construction and optimization method for the unmanned aerial vehicle-carried RIS auxiliary AF relay according to claim 2 is characterized in that in step 1.3, the unmanned aerial vehicle track optimization sub-problem is converted into a convex optimization problem, and the optimal solution of the unmanned aerial vehicle track is further obtained through a convex optimization tool CVX, specifically comprising the following steps:
step 1.3.1, substituting the RIS phase shift obtained by optimization in step 1.2 into an objective function of a problem P1, and converting the joint optimization problem into an unmanned aerial vehicle track optimization sub-problem;
step 1.3.2, in order to solve the unmanned aerial vehicle trajectory optimization sub-problem, introducing relaxation variables u= [ u 1], u 2], …, u [ N ] and v= [ v 1, v 2, …, v [ N ] ], performing iterative optimization on the sub-problem by using a successive approximation SCA method, and in order to ensure the convergence of the solution, introducing a first-order Taylor expansion, and approximating an objective function in P1 as a lower bound thereof; on the basis, similar processing is carried out on the constraint function, and the objective function and the constraint condition of the unmanned aerial vehicle track optimization sub-problem after processing are convex, namely the convex problem;
step 1.3.3, solving the convex problem by using a convex optimization tool CVX to obtain an optimized unmanned aerial vehicle track Q * 。
5. The collaborative construction and optimization method for the unmanned aerial vehicle-carried RIS-assisted AF relay according to claim 3, wherein in step 1.4, the steps 1.2 and 1.3 are iteratively executed, and the joint optimization problem is solved by alternately optimizing two sub-problems, specifically comprising the following steps:
step 1.4.1, setting an iteration stop condition, namely a predetermined convergence accuracy epsilon or a maximum number of iterations k max ；
Step 1.4.2, iteratively executing the step 1.2 and the step 1.3, and alternately optimizing the RIS phase shift matrix and the unmanned aerial vehicle track;
step 1.4.3, reaching a preset iteration stop condition to obtain an optimal RIS phase shift matrix Θ * [n]、Ω * [n]Trajectory Q with unmanned aerial vehicle * And the average reachable rate of the unmanned aerial vehicle carrying the RIS auxiliary AF relay cooperative communication system is maximized.
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