CN115955264A - Unmanned aerial vehicle carried RIS assisted AF relay collaborative construction and optimization method - Google Patents

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

Info

Publication number
CN115955264A
CN115955264A CN202310232010.7A CN202310232010A CN115955264A CN 115955264 A CN115955264 A CN 115955264A CN 202310232010 A CN202310232010 A CN 202310232010A CN 115955264 A CN115955264 A CN 115955264A
Authority
CN
China
Prior art keywords
ris
unmanned aerial
aerial vehicle
phase shift
optimization
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202310232010.7A
Other languages
Chinese (zh)
Other versions
CN115955264B (en
Inventor
张顺外
黄星博
陈博涛
钟积彬
王金
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Posts and Telecommunications
Original Assignee
Nanjing University of Posts and Telecommunications
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Posts and Telecommunications filed Critical Nanjing University of Posts and Telecommunications
Priority to CN202310232010.7A priority Critical patent/CN115955264B/en
Publication of CN115955264A publication Critical patent/CN115955264A/en
Application granted granted Critical
Publication of CN115955264B publication Critical patent/CN115955264B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Radio Relay Systems (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a cooperative construction and optimization method for an unmanned aerial vehicle carrying RIS auxiliary AF relay, belonging to the technical field of wireless communication; the unmanned aerial vehicle carries the RIS to provide an additional communication link in the air so as to enhance the average reachable rate of the ground AF relay cooperative communication system; establishing a combined optimization problem model of unmanned aerial vehicle track optimization and RIS phase shift matrix optimization, and decomposing an 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 the phases of the received signals; converting the unmanned aerial vehicle trajectory optimization sub-problem into a convex problem by adopting a successive convex approximation SCA method, and further solving by using an iterative algorithm; the invention combines a flexible and light air unmanned aerial vehicle carrying RIS with a ground AF relay cooperative communication system, and provides a high-efficiency 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 assisted AF relay collaborative construction and optimization method
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a cooperative construction and optimization method for an unmanned aerial vehicle carrying RIS auxiliary AF relay.
Background
The relay cooperation technology enables a single antenna device to form a virtual Multiple-Input Multiple-Output (MIMO) to enjoy spatial diversity gain. Common Cooperation modes include an Amplify-and-Forward (AF) mode, a Detect/decode-and-Forward (DF) mode, a Coded Cooperation (CC) mode, and the like. In the AF mode, the relay node first amplifies the received signals and then directly forwards them to the destination node. Although the performance of the AF method is not outstanding compared with other schemes, the AF method has the advantages of easy implementation, low time delay and the like, and is often applied to practical wireless communication scenarios.
In complex or difficult to reach areas, such as earthquake disaster area centers, it is difficult to deploy traditional fixed relays to support reliable communications. Unmanned Aerial Vehicles (UAVs) are considered as one of promising solutions due to advantages of flexible deployment, large coverage, and the like. Drones may operate as mobile high-altitude base stations, deployed on-demand to support communications quickly, or may operate as mobile relays and cooperate with ground users to enhance throughput and extend coverage. As a new emerging technology, the Reconfigurable Intelligent Surface (RIS) 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 unmanned aerial vehicle and RIS, we have considered an unmanned aerial vehicle to carry the RIS, and the RIS is installed on unmanned aerial vehicle, and unmanned aerial vehicle no longer need be equipped with heavy transceiver. Drones serve as mobile RIS to enhance the performance of communication systems in complex or difficult to reach areas. The unmanned aerial vehicle carries the RIS, and the RIS has the advantages of flexible deployment, large coverage range 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-carried RIS-assisted AF relay cooperative communication system is very suitable for emergency reliable communication scenes, and further, the average reachable rate of the system is maximized through jointly optimizing the track of the unmanned aerial vehicle and the RIS phase shift matrix.
Disclosure of Invention
The invention aims to solve the technical problems in the prior art and provides a cooperative construction and optimization method for an unmanned aerial vehicle carried RIS auxiliary AF relay.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an Unmanned Aerial Vehicle (UAV) carried RIS assisted AF relay collaborative construction and optimization method comprises the following steps:
step 1.1, an unmanned aerial vehicle carries a RIS to assist the modeling of an AF relay cooperative communication system; the AF relay cooperation system realizes information transmission on the ground, and the unmanned aerial vehicle carries the 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 maximization system;
step 1.2, solving an RIS phase shift matrix optimization subproblem according to the unmanned aerial vehicle track information obtained by the previous iteration to obtain a closed solution of RIS phase shift;
step 1.3, converting the unmanned aerial vehicle track optimization subproblem into a convex optimization problem according to the RIS phase shift obtained in the step 1.2, and further solving the 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 cooperative communication system of the relay assisted by the unmanned aerial vehicle carrying the RIS AF relay specifically includes the following steps:
step 1.1.1, because the unmanned aerial vehicle carries the RIS to work in the air, based on the uniform rectangular array URA characteristic formed by the large-scale path loss close to the free space and the RIS element, the channel between the source node S and the RIS
Figure SMS_1
In the relayChannel between node R and RIS->
Figure SMS_2
Channel between destination node D and RIS->
Figure SMS_3
All viewed as line-of-sight LoS channels with array response; since the relay works on the ground, the channel between the source node S and the relay node R is combined>
Figure SMS_4
And the channel between the relay node R and the destination node D is/are->
Figure SMS_5
Modeling as a LoS channel near free space;
step 1.1.2, AF relay works in a half-duplex mode, each time frame is divided into two time slots, and information sent by an information source node S in the first time slot passes through a channel respectively
Figure SMS_6
And the 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 signal, and respectively judges whether the signal is received through the channel>
Figure SMS_9
And concatenated channel +>
Figure SMS_10
Figure SMS_11
Transmitting to a destination node D; according to the received signals of two time slots, the achievable rate of the nth frame is as follows:
Figure SMS_12
；
wherein:
Figure SMS_15
and &>
Figure SMS_17
Respectively transmitting power at an information source node S and a relay node R; />
Figure SMS_21
Is the amplification gain of the relay node R, is greater than>
Figure SMS_14
；/>
Figure SMS_18
Is the power of additive white gaussian noise; />
Figure SMS_20
And
Figure SMS_23
phase shift matrices corresponding to the first and second time slots of the n-th frame, respectively, of the RIS, wherein->
Figure SMS_13
Is the number of RIS elements and is based on the number of the RIS elements>
Figure SMS_16
And/or>
Figure SMS_19
Respectively representing the number of elements in each row and each column; />
Figure SMS_22
Representing a transpose operation;
step 1.1.3, further obtaining that the average reachable rate of the system is as follows:
Figure SMS_24
；
wherein N is the total time frame number of system information transmission;
step (ii) of1.1.4 unmanned aerial vehicle trajectory Q and RIS phase shift matrix
Figure SMS_25
And &>
Figure SMS_26
The joint optimization problem model of (1) is as follows:
Figure SMS_27
Figure SMS_28
Figure SMS_29
Figure SMS_30
Figure SMS_31
，
wherein the content of the first and second substances,
Figure SMS_32
and/or>
Figure SMS_33
Respectively representing the starting and the end position of the drone, and>
Figure SMS_34
，/>
Figure SMS_35
represents the position of the drone at the nth time frame, <' > or>
Figure SMS_36
For maximum flight distance of the drone per time frame.
Preferably, in step 1.2, the solving of the RIS phase shift matrix optimization sub-problem to obtain the closed solution of the RIS phase shift includes the following specific steps:
step 1.2.1, in order to maximize the average reachable rate, the RIS phase shift matrix of the first time slot is optimized according to the unmanned aerial vehicle track obtained in the previous iteration
Figure SMS_37
The phases of the signals from different paths are made equal at the destination node D, i.e. 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 SMS_38
；
wherein
Figure SMS_39
，/>
Figure SMS_40
The optimal phase shift obtained for the ith element of the RIS first slot;
step 1.2.2, in the same way, in order to maximize the average reachable rate, the RIS phase shift matrix of the second time slot is optimized according to the unmanned aerial vehicle track obtained in the previous iteration
Figure SMS_41
The phases of the signals from different paths are made equal at the destination node D, i.e. 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 SMS_42
；
wherein
Figure SMS_43
，/>
Figure SMS_44
The optimal phase shift obtained for the ith element of the second slot of the RIS.
Preferably, in step 1.3, the sub-problem of unmanned aerial vehicle trajectory optimization is converted into a convex optimization problem, and further an optimal solution of the unmanned aerial vehicle trajectory is solved by a convex optimization tool CVX, specifically including the following steps:
step 1.3.1, substituting the RIS phase shift obtained by optimizing in step 1.2 into the objective function of the 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 track optimization sub-problem, a relaxation variable is introduced
Figure SMS_45
And &>
Figure SMS_46
Iterative optimization is carried out on the subproblem by applying a successive convex approximation SCA method, in order to ensure the convergence of the solution, first-order Taylor expansion is introduced, and the target function in the P1 is approximated to the lower bound of the target function; similarly, similar processing is carried out on the constraint function on the basis, and the target function and the constraint condition of the unmanned aerial vehicle track optimization subproblem are convex after the processing, namely the problem is convex;
step 1.3.3, solving the convex problem by using a convex optimization tool CVX to obtain the optimized unmanned aerial vehicle track
Figure SMS_47
。
Preferably, in step 1.4, step 1.2 and step 1.3 are iteratively executed, and the solution of the joint optimization problem is realized by alternately optimizing two sub-problems, which specifically includes the following steps:
step 1.4.1, setting iteration stop conditions, i.e. predetermined convergence accuracy
Figure SMS_48
Or maximum number of iterations>
Figure SMS_49
；
Step 1.4.2, iteratively executing step 1.2 and step 1.3, and alternately optimizing an RIS phase shift matrix and an unmanned aerial vehicle track;
step 1.4.3, reaching a predetermined iteration stopStopping the condition to obtain the optimal RIS phase shift matrix
Figure SMS_50
、/>
Figure SMS_51
And unmanned aerial vehicle locus->
Figure SMS_52
And the average reachable rate of the unmanned aerial vehicle carrying the RIS auxiliary AF relay cooperative communication system is the maximum.
The invention has the following beneficial effects: (1) By means of the advantages of flexible deployment and large coverage of the unmanned aerial vehicle, the RIS works in a full-duplex mode, the unmanned aerial vehicle has no self-interference, has the advantages of low hardware cost, low power consumption, intelligent reconstruction and the like, and by combining the advantages of the unmanned aerial vehicle and the RIS, the RIS is carried by the unmanned aerial vehicle, namely the RIS is installed on the unmanned aerial vehicle, and the unmanned aerial vehicle does not need to be provided with heavy transceiver. The unmanned aerial vehicle carries the RIS, and the advantages of flexible unmanned aerial vehicle deployment, large coverage area and the like are achieved, and the unmanned aerial vehicle has the characteristics of low hardware cost, low power consumption, intelligent reconstruction and the like. Are often applied in practical wireless communication scenarios.
(2) The invention combines the flexible and light RIS carried by the aerial unmanned aerial vehicle with the ground AF relay cooperative communication, is very suitable for emergency reliable communication scenes such as earthquake disaster areas, and the like, and when ground equipment can not meet the requirements of emergency communication, the RIS carried by the aerial unmanned aerial vehicle can realize enhanced communication, blind-repairing communication, and the like. 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 of unmanned aerial vehicle track optimization and RIS phase shift matrix optimization is established. Firstly, 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, because the unmanned aerial vehicle trajectory optimization subproblem is still a complex non-convex problem, the unmanned aerial vehicle trajectory optimization subproblem is converted into a convex problem by a Successive Convex Approximation (SCA) method and is further solved by an iterative algorithm. The invention combines flexible and light air unmanned aerial vehicle carrying RIS with ground AF relay cooperative communication, and provides a solution for realizing high-efficiency communication in a complex scene by jointly optimizing unmanned aerial vehicle track and RIS phase shift matrix.
Drawings
FIG. 1 is a model diagram of an unmanned aerial vehicle carrying RIS auxiliary AF relay cooperative communication system in the invention;
FIG. 2 is a graph comparing the performance of the communication scheme proposed in the present invention with other reference schemes;
fig. 3 is a performance comparison diagram of the joint optimization algorithm of the unmanned aerial vehicle trajectory and the RIS phase shift matrix in the invention and other reference algorithms.
Detailed Description
FIG. 1 is a model diagram of an unmanned aerial vehicle carrying RIS auxiliary AF relay cooperative communication system in the invention; as shown in fig. 1, an unmanned aerial vehicle-carried RIS assisted AF relay collaborative construction and optimization method includes the following steps:
step 1.1, modeling of an unmanned aerial vehicle carrying a RIS auxiliary AF relay cooperative communication system; the AF relay cooperative communication system realizes information transmission on the ground, and the unmanned aerial vehicle carries the 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 maximization system;
step 1.2, solving an RIS phase shift matrix optimization subproblem according to the track information of the unmanned aerial vehicle obtained by the previous iteration to obtain a closed solution of the 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 assisted AF relay cooperative communication system specifically includes the following steps:
firstly, an unmanned aerial vehicle carrying RIS auxiliary AF relay cooperative communication system model, an information source node, is establishedThe point (S) transmits information to the destination node (D) through Half Duplex (HD) AF relay (R) on the ground. Assume that there is no direct communication link between S and D, blocked by an obstacle. Considering the scenario that emergency communication requires a higher average reachable rate (AAR) than usual, an RIS-carrying drone 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 and D are single antennas, and the positions of the antennas are respectively coordinates
Figure SMS_59
, />
Figure SMS_54
And
Figure SMS_64
and (4) showing. The unmanned aerial vehicle flies at a fixed height H, and the flying period is T p Divided into N time frames of equal duration
Figure SMS_58
I.e. is->
Figure SMS_65
The maximum flight distance of the unmanned aerial vehicle per time frame is->
Figure SMS_60
. Because the unmanned aerial vehicle carries the RIS to work in the air, based on the large-scale path loss close to free space and the Uniform Rectangular Array (URA) characteristic formed by RIS elements, the channel between the source node S and the RIS is/are>
Figure SMS_69
Channel between relay node R and RIS->
Figure SMS_68
Channel between destination node D and RIS->
Figure SMS_71
All viewed as line-of-sight (LoS) channels with array response; since the relay node works on the ground, the information source node S and the relay node R are communicatedIs based on>
Figure SMS_53
And the channel between the relay node R and the destination node D is/are->
Figure SMS_62
Modeling as a LoS channel near free space; meanwhile, since R operates in HD mode, each time frame is divided into two equal time slots, the drone is considered stationary within each time frame. In the first time slot, the source node S sends information on the channel ≥ respectively>
Figure SMS_56
And the cascade channel->
Figure SMS_70
Figure SMS_57
Transmitting to a relay node R and a destination node D, in a second time slot, amplifying and forwarding the received signal by the relay node R, and respectively judging whether the signal is received or not through a channel>
Figure SMS_61
And concatenated channel +>
Figure SMS_63
Figure SMS_67
To the destination node D. Recording the position abscissa of the unmanned aerial vehicle in the nth frame in the coordinate system as ^ er>
Figure SMS_66
The starting position and the end position are each defined as->
Figure SMS_72
And &>
Figure SMS_55
。
Since the drone works airborne with the RIS, it follows a large scale path loss close to free space and the RIS element shapeInto a Uniform Rectangular Array (URA), channels
Figure SMS_73
、/>
Figure SMS_74
、/>
Figure SMS_75
Are all considered line-of-sight LoS channels with array response, as follows:
Figure SMS_76
(1)
Figure SMS_77
(2)
Figure SMS_78
(3)
wherein
Figure SMS_80
Is the channel gain per unit distance->
Figure SMS_83
Is the coefficient of the path loss, and,
Figure SMS_85
、/>
Figure SMS_79
、/>
Figure SMS_84
respectively represent the distances between S and RIS, R and RIS and D and RIS 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, and>
Figure SMS_82
is the carrier wavelength.
Since the AF relay operates on the ground, the channels between S and R and D are modeled as follows:
Figure SMS_88
(4)
Figure SMS_89
(5)
wherein
Figure SMS_90
Is a path loss factor, is asserted>
Figure SMS_91
And &>
Figure SMS_92
Respectively representing the distances between S and R and between 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
Figure SMS_95
And &>
Figure SMS_96
Is power at S and D->
Figure SMS_97
White gaussian noise. />
Figure SMS_98
And the phase shift matrix corresponding to the first time slot of the nth frame is 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
Figure SMS_100
Is the amplification gain of the AF relay, <' > is>
Figure SMS_101
，/>
Figure SMS_102
Is the power of additive white gaussian noise.
Figure SMS_103
And the phase shift matrix corresponding to the first time slot of the nth frame is 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/or>
Figure SMS_109
Transmitting power respectively for an information source node S and a relay node R; />
Figure SMS_111
Is the amplification gain of the AF relay and,
Figure SMS_107
；/>
Figure SMS_110
is the power of additive white gaussian noise; />
Figure SMS_113
And with
Figure SMS_114
Phase shift matrices corresponding to the first and second time slots of the n-th frame, respectively, of the RIS, wherein->
Figure SMS_105
Is the number of RIS elements and is based on the number of the RIS elements>
Figure SMS_108
And &>
Figure SMS_112
Respectively representing the number of elements in each row and each column;
further derive the AAR:
Figure SMS_115
； (10)
the problem model of the system AAR with respect to RIS phase shift and drone trajectory is therefore 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 RIS phase shift matrix optimization subproblem is solved according to the trajectory information of the unmanned aerial vehicle obtained by the previous iteration, and a closed solution of the RIS phase shift is obtained, specifically including the following steps:
firstly, according to the unmanned aerial vehicle track obtained by the previous iteration, in the first time slot
Figure SMS_121
In>
Figure SMS_122
The following can be written:
Figure SMS_123
(12)/>
wherein
Figure SMS_124
(13)
Figure SMS_125
(14)
Figure SMS_126
And &>
Figure SMS_127
The azimuth and elevation angles of the RIS with respect 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 can be derived
Figure SMS_128
(15)
The phase shift design of the second time slot RIS is similar to the first time slot, and we directly give a closed solution:
Figure SMS_129
(16)
wherein
Figure SMS_130
、/>
Figure SMS_131
And/or>
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 sub-problem of unmanned aerial vehicle trajectory optimization is converted into a convex optimization problem, and the optimal solution of the unmanned aerial vehicle trajectory is further solved through a convex optimization tool CVX, which specifically comprises the following steps:
optimizing the unmanned aerial vehicle track by applying a Successive Convex Approximation (SCA) algorithm according to the RIS phase shift solved in the step 1.2, firstly substituting the RIS phase shift obtained by optimization in the step 1.2 into an 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
Figure SMS_143
，/>
Figure SMS_144
。
It is noted that the solution of P2 can only be obtained when the equal signs of constraints (19 b) and (19 c) are satisfied. To solve for P2, the arguments are given as follows:
introduction 1: given
Figure SMS_145
,/>
Figure SMS_146
,/>
Figure SMS_147
,/>
Figure SMS_148
，/>
Figure SMS_149
,
Figure SMS_150
In connection with>
Figure SMS_151
，/>
Figure SMS_152
Is a convex function.
According to the theory of introduction 1, the method comprises the following steps of,
Figure SMS_153
about>
Figure SMS_154
，/>
Figure SMS_155
Is a convex function. However, one non-concave maximization problem of the objective function is non-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 solved by the previous iteration +>
Figure SMS_156
，/>
Figure SMS_157
As a given point of the current iteration
Figure SMS_158
，/>
Figure SMS_159
Thus giving the lower bound function at a given point:
Figure SMS_160
(20)
wherein
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)
although substituting (20) into the objective function of P2, P2 is about
Figure SMS_166
，/>
Figure SMS_167
Still non-convex. Therefore, we apply the SCA method as well to give ^ greater or greater on the basis of lem 2>
Figure SMS_168
，/>
Figure SMS_169
The upper bound of (c).
2, leading: suppose that
Figure SMS_171
Is the product of two functions, i.e.>
Figure SMS_174
. Wherein->
Figure SMS_177
And &>
Figure SMS_172
Are both convex functions and are non-negative. For arbitrary->
Figure SMS_173
,/>
Figure SMS_176
Can be written to>
Figure SMS_178
. Thus->
Figure SMS_170
The convex upper bound of (a) can be obtained by linear expansion. Is arbitrarily given>
Figure SMS_175
Is obtained by
Figure SMS_179
(26)
Based on the theory of 2, the method has the advantages that,
Figure SMS_180
，/>
Figure SMS_181
the upper bound of (c) 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 the constraint condition in the P3 are convex, the convex problem is solved by a convex optimization tool CVX, and the optimized unmanned aerial vehicle track is obtained
Figure SMS_191
。
Further, in step 1.4, step 1.2 and step 1.3 are iteratively executed, and the solution of the joint optimization problem is realized 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, an iteration stop condition, i.e., a predetermined convergence accuracy, is set
Figure SMS_192
Or maximum number of iterations>
Figure SMS_193
(ii) a Then, iteratively executing steps 1.2 and 1.3, and alternately optimizing the RIS phase shift matrix and the unmanned aerial vehicle track; when a predetermined iteration stop condition is reached, the iteration is stopped to obtain an optimal RIS phase shift matrix->
Figure SMS_194
、/>
Figure SMS_195
And unmanned aerial vehicle locus->
Figure SMS_196
The average reachable rate of the unmanned aerial vehicle carrying the RIS auxiliary AF relay cooperative communication system is the maximum. Since the average reachable rate obtained by each iteration solution is non-decreasing and the average reachable rate is bounded, the convergence of the iterative method is ensured.
Referring to fig. 2 and fig. 3, simulation experiment and effect analysis:
the invention carries out simulation analysis on the average reachable rate of the relay cooperative communication system with the RIS auxiliary AF carried by the unmanned aerial vehicle, and the specific simulation parameters are shown in the table 1.
Table 1: simulation parameter setting
Figure SMS_197
FIG. 2 compares the unmanned aerial vehicle carrying RIS auxiliary AF relay coordination scheme (RIS-enabled UAV) proposed by the present invention&AF Relay) and AF Relay Only (AF Relay Only), RIS-Only unmanned aerial vehicle (RIS-enabled UAV Only) are used. As shown in FIG. 2, the AAR of the proposed scheme is significantly better than either the AF Relay Only scheme or the RIS-enabled UAV Only scheme. For example when
Figure SMS_198
In time, 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, while the AAR of the proposed scheme is as high as 2.02 bps/Hz. This is because the proposed solution combines advantages of RIS, drone and AF relay, all 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 is a diagram comparing the joint optimization algorithm (Trajectory) of the proposed unmanned aerial vehicle Trajectory and RIS phase shift matrix in the unmanned aerial vehicle carried RIS assisted AF relay cooperative communication system&Shift opt.) versus optimizing the RIS Phase Shift matrix algorithm only (RIS Phase opt), without optimizing the AAR performance of the 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 No Opt algorithm, and this advantage increases with MIt is becoming more obvious. For example when
Figure SMS_199
While the AAR of the RIS Phase Opt algorithm and the No Opt algorithm are 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. This shows that the proposed algorithm utilizes the optimal phase shift of the RIS and the optimal trajectory of the drone, bringing the advantage of the drone carrying the RIS to the maximum.
The above is only a preferred embodiment of the present invention, however, the present invention is not limited to the above embodiment, and any equivalent changes and modifications made according to the present invention will still fall within the protection scope of the present invention.

Claims (5)

1. An Unmanned Aerial Vehicle (UAV) carried RIS assisted AF relay collaborative construction and optimization method is characterized by comprising the following steps:
step 1.1, an unmanned aerial vehicle carries a RIS to assist the modeling of an AF relay cooperative communication system; the AF relay cooperative communication system realizes information transmission on the ground, and the unmanned aerial vehicle carries the 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 maximization system;
step 1.2, solving an RIS phase shift matrix optimization subproblem according to the unmanned aerial vehicle track information obtained by the previous iteration to obtain 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 unmanned aerial vehicle carried RIS assisted AF relay cooperative construction and optimization method according to claim 1, wherein in step 1.1, the modeling of the unmanned aerial vehicle carried RIS assisted AF relay cooperative communication system specifically comprises the following steps:
step 1.1.1, based on the uniform rectangular array URA characteristics formed by large-scale path loss and RIS elements close to free space, channel between source node S and RIS
Figure QLYQS_1
Channel between relay node R and RIS->
Figure QLYQS_2
Channel between destination node D and RIS->
Figure QLYQS_3
All viewed as line-of-sight LoS channels with array response; channel between source node S and relay node R
Figure QLYQS_4
Channel between relay node R and destination node D>
Figure QLYQS_5
Modeling as a LoS channel near free space;
step 1.1.2, AF relay works in a half-duplex mode, each time frame is divided into two time slots, and information sent by an information source node S in the first time slot passes through a channel respectively
Figure QLYQS_6
And concatenated channel +>
Figure QLYQS_7
Figure QLYQS_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 signal, and respectively judges whether the signal is received through the channel>
Figure QLYQS_9
And concatenated channel +>
Figure QLYQS_10
Figure QLYQS_11
Transmitting to a destination node D; according to the received signals of two time slots, the achievable rate of the nth frame is as follows:
Figure QLYQS_12
；
wherein:
Figure QLYQS_14
and &>
Figure QLYQS_17
Respectively transmitting power at an information source node S and a relay node R; />
Figure QLYQS_19
Is the amplification gain of the relay node R,
Figure QLYQS_13
；/>
Figure QLYQS_16
is the power of additive white gaussian noise; />
Figure QLYQS_21
And
Figure QLYQS_23
phase shift matrices corresponding to the first and second time slots of the n-th frame, respectively, of the RIS, wherein->
Figure QLYQS_15
Is the number of RIS elements and is based on the number of the RIS elements>
Figure QLYQS_18
And/or>
Figure QLYQS_20
Respectively representing the number of elements in each row and each column; />
Figure QLYQS_22
Representing a transpose operation;
step 1.1.3, further obtaining that the average reachable rate of the system is as follows:
Figure QLYQS_24
；
wherein N is the total time frame number of system information transmission;
step 1.1.4, unmanned aerial vehicle trajectory Q and RIS phase shift matrix
Figure QLYQS_25
And &>
Figure QLYQS_26
The joint optimization problem model of (1) is as follows: />
Figure QLYQS_27
；
Figure QLYQS_28
；
Figure QLYQS_29
；
Figure QLYQS_30
；
Figure QLYQS_31
，
Wherein the content of the first and second substances,
Figure QLYQS_32
and/or>
Figure QLYQS_33
Respectively representing the starting and ending positions of the drone; />
Figure QLYQS_34
，/>
Figure QLYQS_35
Representing the position of the drone at the nth time frame; />
Figure QLYQS_36
For maximum flight distance of the drone per time frame.
3. The unmanned aerial vehicle carried RIS assisted AF relay cooperative construction and optimization method of claim 2, wherein in step 1.2, said solving RIS phase shift matrix optimization sub-problem, obtaining RIS phase shift closed solution, the concrete steps are as follows:
step 1.2.1, in order to maximize the average reachable rate, the RIS phase shift matrix of the first time slot is optimized according to the unmanned aerial vehicle track obtained in the previous iteration
Figure QLYQS_37
The phases of the signals from different paths are made equal at the destination node D, i.e. 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 QLYQS_38
；
wherein
Figure QLYQS_39
，/>
Figure QLYQS_40
The optimal phase shift obtained for the ith element of the RIS first slot;
step 1.2.2, in order to maximize the average reachable rate, the RIS phase shift matrix of the second time slot is optimized according to the unmanned aerial vehicle track obtained in the previous iteration
Figure QLYQS_41
The phases of the signals from different paths are made 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 slot is obtained as follows:
Figure QLYQS_42
；
wherein
Figure QLYQS_43
，/>
Figure QLYQS_44
The optimal phase shift obtained for the ith element of the RIS second slot.
4. The unmanned aerial vehicle carried RIS assisted AF relay cooperative construction and optimization method according to claim 2, wherein in step 1.3, the sub-problem of unmanned aerial vehicle trajectory optimization is converted into a convex optimization problem, and further an optimal solution of unmanned aerial vehicle trajectory is found by a convex optimization tool CVX, and the specific steps are as follows:
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 trajectory optimization sub-problem;
step 1.3.2, in order to solve the unmanned aerial vehicle track optimization sub-problem, a relaxation variable is introduced
Figure QLYQS_45
And
Figure QLYQS_46
applying successive convex approximation SCA methodPerforming iterative optimization on the subproblem, introducing first-order Taylor expansion to ensure the convergence of the solution, and approximating the objective function in P1 as the lower bound of the objective function; on the basis, similar processing is carried out on the constraint function, and the target function and the constraint condition of the unmanned aerial vehicle trajectory optimization subproblem are convex after processing, namely the problem is convex;
step 1.3.3, solving the convex problem by using a convex optimization tool CVX to obtain the optimized unmanned aerial vehicle track
Figure QLYQS_47
。
5. The unmanned aerial vehicle carried RIS assisted AF relay cooperative construction and optimization method according to claim 1, wherein in step 1.4, the iteration executes steps 1.2 and 1.3, and the solution of the joint optimization problem is realized by alternately optimizing two sub-problems, specifically including the following steps:
step 1.4.1, set iteration stop conditions, i.e. predetermined convergence accuracy
Figure QLYQS_48
Or maximum number of iterations>
Figure QLYQS_49
；
Step 1.4.2, iteratively executing step 1.2 and step 1.3, and alternately optimizing an RIS phase shift matrix and an unmanned aerial vehicle track;
step 1.4.3, reaching the preset iteration stop condition to obtain the optimal RIS phase shift matrix
Figure QLYQS_50
、/>
Figure QLYQS_51
And unmanned aerial vehicle locus->
Figure QLYQS_52
And the average reachable rate of the unmanned aerial vehicle carrying the RIS auxiliary AF relay cooperative communication system is the maximum. />
CN202310232010.7A 2023-03-13 2023-03-13 Unmanned aerial vehicle carried RIS auxiliary AF relay collaborative construction and optimization method Active CN115955264B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310232010.7A CN115955264B (en) 2023-03-13 2023-03-13 Unmanned aerial vehicle carried RIS auxiliary AF relay collaborative construction and optimization method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310232010.7A CN115955264B (en) 2023-03-13 2023-03-13 Unmanned aerial vehicle carried RIS auxiliary AF relay collaborative construction and optimization method

Publications (2)

Publication Number Publication Date
CN115955264A true CN115955264A (en) 2023-04-11
CN115955264B CN115955264B (en) 2023-05-30

Family

ID=85896300

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310232010.7A Active CN115955264B (en) 2023-03-13 2023-03-13 Unmanned aerial vehicle carried RIS auxiliary AF relay collaborative construction and optimization method

Country Status (1)

Country Link
CN (1) CN115955264B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113573293A (en) * 2021-07-14 2021-10-29 南通大学 Intelligent emergency communication system based on RIS
CN113890586A (en) * 2021-09-13 2022-01-04 广州大学 Auxiliary unmanned aerial vehicle communication method of intelligent super-surface and relay cooperation system
CN114422363A (en) * 2022-01-11 2022-04-29 北京科技大学 Unmanned aerial vehicle loaded RIS auxiliary communication system capacity optimization method and device
CN115037337A (en) * 2022-06-09 2022-09-09 北京信息科技大学 Intelligent reflecting surface driven multi-user cooperative transmission method
CN115276747A (en) * 2022-07-20 2022-11-01 华北电力大学（保定） Position and beam vector joint optimization method in unmanned aerial vehicle-assisted wireless communication system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113573293A (en) * 2021-07-14 2021-10-29 南通大学 Intelligent emergency communication system based on RIS
CN113890586A (en) * 2021-09-13 2022-01-04 广州大学 Auxiliary unmanned aerial vehicle communication method of intelligent super-surface and relay cooperation system
CN114422363A (en) * 2022-01-11 2022-04-29 北京科技大学 Unmanned aerial vehicle loaded RIS auxiliary communication system capacity optimization method and device
CN115037337A (en) * 2022-06-09 2022-09-09 北京信息科技大学 Intelligent reflecting surface driven multi-user cooperative transmission method
CN115276747A (en) * 2022-07-20 2022-11-01 华北电力大学（保定） Position and beam vector joint optimization method in unmanned aerial vehicle-assisted wireless communication system

Also Published As

Publication number Publication date
CN115955264B (en) 2023-05-30

Similar Documents

Publication Publication Date Title
Huo et al. Distributed and multilayer UAV networks for next-generation wireless communication and power transfer: A feasibility study
Ono et al. A wireless relay network based on unmanned aircraft system with rate optimization
CN109640257B (en) IOT network secure transmission method based on unmanned aerial vehicle
Elnabty et al. A survey on UAV placement optimization for UAV-assisted communication in 5G and beyond networks
CN109660292B (en) Flight height design method of wireless communication system based on multi-unmanned aerial vehicle coverage
CN113507304A (en) Intelligent reflector-assisted unmanned aerial vehicle safety communication method
CN114286312A (en) Method for enhancing unmanned aerial vehicle communication based on reconfigurable intelligent surface
CN113422634B (en) High-energy-efficiency secret transmission method of millimeter wave full-duplex unmanned aerial vehicle relay communication system
CN116669035A (en) Airborne intelligent reflecting surface-assisted general sense integrated safe transmission design method
Farrag et al. Outage probability analysis of UAV assisted mobile communications in THz channel
Khan Mathematical framework for 5G‐UAV relay
Abbasi et al. Uxnb-enabled cell-free massive MIMO with haps-assisted sub-thz backhauling
Anandpushparaj et al. Performance analysis of UAV cellular communications
CN115955264A (en) Unmanned aerial vehicle carried RIS assisted AF relay collaborative construction and optimization method
Wang et al. Location-assisted precoding for three-dimension massive MIMO in air-to-ground transmission
Song et al. Joint power allocation and beamforming for UAV-enabled relaying systems with channel estimation errors
CN115694602A (en) Combined optimization method for millimeter wave full-duplex unmanned aerial vehicle communication system
CN115276747A (en) Position and beam vector joint optimization method in unmanned aerial vehicle-assisted wireless communication system
CN114665947A (en) Optimization design method for joint power control and position planning of relay communication system supported by unmanned aerial vehicle
Goel et al. Performance analysis of drone assisted cooperative communication in hybrid channel environment
Ma et al. Reflection and relay dual-functional RIS assisted MU-MISO systems
Ding et al. Intelligent Reflecting Surfaces vs. Full-Duplex Relays: A Comparison in the Air
Le Cuong et al. Outage performance of UAV aided V2V-NOMA communication systems over double Rayleigh channels
KR101079656B1 (en) Method for Wireless Communications between Airborne Communication Nodes and Ground Terminals
Xie et al. BER performance of unmanned aerial vehicle assisted spatial modulation system in Rician channels

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant