CN112242874B - Optimization variable decoupling-based unmanned aerial vehicle relay transmission efficiency optimization method - Google Patents

Optimization variable decoupling-based unmanned aerial vehicle relay transmission efficiency optimization method Download PDF

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CN112242874B
CN112242874B CN202010501875.5A CN202010501875A CN112242874B CN 112242874 B CN112242874 B CN 112242874B CN 202010501875 A CN202010501875 A CN 202010501875A CN 112242874 B CN112242874 B CN 112242874B
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丁文锐
罗祎喆
齐电海
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Beihang University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/40—Monitoring; Testing of relay systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention discloses an unmanned aerial vehicle relay transmission efficiency optimization method based on optimization variable decoupling, and relates to an unmanned aerial vehicle relay transmission technology. The method comprises the steps of firstly, constructing an air-ground bidirectional relay transmission scene comprising an air base station, an air relay and a ground base station, wherein the air base station and the ground base station are used as two source nodes, and simultaneously, signals are sent to the air relay which is used as a receiving node at the same frequency. And then establishing a relay transmission energy efficiency optimization problem model by using the transmitting power and the signal-to-noise ratio of each node, and decoupling the energy efficiency optimization problem model into a power sub-optimization problem and an air relay position sub-optimization problem. And finally, respectively iterating the power sub-optimization problem and the air relay position sub-optimization problem by applying an alternative minimization idea, and outputting a group of solutions which enable the objective function to be minimum as final solutions. The invention reduces the solving complexity of the original energy efficiency optimization problem and ensures the robustness of the solving of the energy efficiency optimization problem.

Description

Optimization variable decoupling-based unmanned aerial vehicle relay transmission efficiency optimization method
Technical Field
The invention relates to an unmanned aerial vehicle relay transmission technology, in particular to an unmanned aerial vehicle relay transmission efficiency optimization method based on optimization variable decoupling, which can be suitable for relay transmission efficiency optimization of unmanned aerial vehicles of different models.
Background
The unmanned aerial vehicle relay transmission system is a data communication system for completing information transmission between an unmanned aerial vehicle executing a task and a ground control center by means of a relay unmanned aerial vehicle. The unmanned aerial vehicle relay transmission system is an information bridge for linking resource sharing between an unmanned aerial vehicle and a carrier-based and ground command platform or equipment in the whole flight process of the unmanned aerial vehicle, and has the basic functions of establishing a relay link between the unmanned aerial vehicle for executing tasks and the relay unmanned aerial vehicle and a ground control center and sending information such as images acquired by an unmanned aerial vehicle-mounted task sensor to the ground control center.
In a typical relay transmission system for an unmanned aerial vehicle, as shown in fig. 1, an aerial base station performs data interaction with a ground command base station through an aerial relay unmanned aerial vehicle.
The relay transmission modes are mainly divided into one-way relay and two-way relay.
Among them, 1) in the unidirectional relay, there are two general ways when the communication relay node forwards information: amplify-and-forward and decode-and-forward. In the amplification forwarding mode, a source node sends a signal to a relay node in a first time slot, the relay node performs power amplification on the received signal and forwards the amplified signal to a destination node in a second time slot.
In the decoding and forwarding mode, a source node sends a signal to a relay node in a first time slot, and the relay node decodes a received signal and sends a recoded signal to a destination node in a second time slot.
The two ways relay nodes process information differently, resulting in different interrupt performance. The existing literature research shows that the decoding forwarding performance is better than the amplifying forwarding performance under the condition of low signal-to-noise ratio, but the performances of the two modes are similar under the condition of high signal-to-noise ratio.
2) In the bidirectional relay, the bidirectional relay mechanism can fully utilize frequency spectrum resources and has higher frequency spectrum efficiency. In the amplify-and-forward bidirectional relay, two source nodes simultaneously send signals to a relay node in a first time slot, and the relay node amplifies the received mixed signals and broadcasts the amplified mixed signals to the two source nodes in a second time slot.
For decoding and forwarding bidirectional relay, researchers put forward a three-stage decoding and forwarding mode and a two-stage decoding and forwarding mode, wherein in the two-stage decoding and forwarding strategy, it is assumed that a relay node can perfectly decode signals from two source nodes received at the same frequency by using a multi-user detection technology, the assumption is too ideal in practical application, and the three-stage decoding and forwarding mode is more practical in consideration of the actual communication capacity of a current receiver. In the three-stage decoding and forwarding mode, two source nodes respectively send signals to a relay node in the first two time slots, the relay node decodes the received signals and broadcasts the mixed decoded signals to the two source nodes in the third time slot.
Different from a general ground communication system, the unmanned aerial vehicle relay transmission system faces a more complex channel environment (including direct path probability, path loss and large-scale shadow fading), and has higher requirements on the aspects of unmanned aerial vehicle energy consumption, information transmission reliability and the like, and the design of the method capable of optimizing the relay transmission efficiency has important practical application value.
Disclosure of Invention
The invention provides an optimization method of relay transmission efficiency of an unmanned aerial vehicle based on optimization variable decoupling, which aims to reduce relay transmission energy consumption of the unmanned aerial vehicle and is deployed in an aerial relay/aerial base station.
The optimization variable decoupling-based unmanned aerial vehicle relay transmission efficiency optimization method comprises the following specific steps:
the method comprises the following steps that firstly, an air-ground bidirectional relay transmission scene comprising an air base station, an air relay and a ground base station is constructed;
the aerial relay and the aerial base station respectively select unmanned aerial vehicles;
step two, the air base station and the ground base station are used as two source nodes, and signals are sent to an air relay serving as a receiving node at the same time in the same frequency;
thirdly, establishing a relay transmission energy efficiency optimization problem model by using the transmitting power and the signal-to-noise ratio of each node;
the received signal-to-noise ratio of the ground base station G is expressed as:
Figure RE-GDA0002837133030000021
PUrepresents the transmit power of the airborne base station U; pRRepresents the transmit power of the over-the-air relay R; pGRepresents the transmit power of the ground base station G; a. the1，A2And A3Coefficients of shadow fading mean values under direct-view/non-direct-view conditions respectively; mu.sLIs the shadow fading average under the direct-view condition; mu.sNLFor shadow fading under non-direct-view conditionsMean value;
Figure RE-GDA0002837133030000022
G0for antenna gain, W is the bandwidth, N0Is the noise power, LGRThe path loss of a ground base station G and an air relay R; a isLAnd aNLIs the road loss index; l isRUPath loss of an air relay R and an air base station U;
pLin order to look at the path probability,
Figure RE-GDA0002837133030000023
theta is the air-ground channel pitch angle, and m and n are constant values depending on the environment; p is a radical ofNLIs the non-direct-view path probability.
The received signal-to-noise ratio of the airborne base station U is expressed as:
Figure RE-GDA0002837133030000024
the objective function of the optimization problem is:
min wUPU+wRPR
PU,PR,PG,SR
wUrepresenting the power consumption weight, w, of the airborne base station URRepresents the power consumption weight of the over-the-air relay R; sRThree-dimensional coordinates representing the aerial relay R;
the constraint conditions of the energy efficiency optimization problem model are as follows:
s.t.C1 ΓG≥Γth1
C2 ΓU≥Γth2
C3 0≤PG≤Pgmax
C4 0≤PR，PU≤Pumax
C5 SR∈CR.
constraint C1 denotes: signal-to-noise ratio gamma of ground base station G end-to-end receptionGTo have no more thanBelow the signal-to-noise ratio threshold Γth1；
Constraint C2 denotes: signal-to-noise ratio gamma of air base station U end-to-end receptionUIs not lower than the SNR thresholdth2；
Constraint C3 denotes: the transmitting power of the ground base station G is more than or equal to 0 and less than or equal to the maximum transmitting power Pgmax；
Constraint C4 denotes: the transmission power of the aerial base station U and the aerial relay R is more than or equal to 0 and less than or equal to the maximum transmission power Pumax；
Constraint C5 denotes: safe airspace C with three-dimensional coordinates of aerial relay R depending on real-time application environmentR；
Decoupling an energy efficiency optimization problem model into a power sub optimization problem and an air relay position sub optimization problem;
the method specifically comprises the following steps:
1) the position of an aerial relay R is given, the power sub-optimization problem is redefined, the newly defined power sub-optimization problem is converted into a geometric planning standard form by adopting an original-dual interior point method and then is solved, and a group of transmitting power P is obtainedU，PRAnd PGA value of (d);
the power sub-optimization problem is expressed as:
min wUPU+wRPR
PU,PR,PG
s.t.
Figure RE-GDA0002837133030000031
Figure RE-GDA0002837133030000032
0≤PG≤Pgmax
0≤PU，PR≤Pumax
the constant is defined as a ═ a1μL+A2μNL，β＝A3μL。
2) Given transmit power PU，PRAnd PGRedefining the position sub-optimization problem, gradually raising the original problem by adopting a continuous convex approximation method, and then solving to obtain a coordinate value S of the air relay RR；
The position sub-optimization problem is expressed as:
min wUPU+wRPR
SR
s.t.
Figure RE-GDA0002837133030000041
Figure RE-GDA0002837133030000042
SR∈CR
step five, respectively iterating the power sub-optimization problem and the air relay position sub-optimization problem by applying an alternative minimization idea;
the specific process is as follows:
first, an initial position S of the aerial relay R is givenR0Calculating a group of transmitting power P according to the redefined power sub-optimization modelU0，PR0And PG0；
Then, the transmission power P is adjustedU0，PR0And PG0Bringing in the redefined position sub-optimization problem to obtain the position S of the aerial relay RR1；
Thirdly, relaying the position S of R in the airR1Introducing a redefined power sub-optimization model, and calculating to obtain a group of transmitting powers PU1，PR1And PG1(ii) a So far, a first iteration is completed to obtain a corresponding first group of solutions: sR1；PU1，PR1And PG1；
And continuing iteration by analogy until the iteration times are reached or the result of each iteration is brought into the objective function, and ending the iteration when the difference value of the results of the two adjacent objective functions is within the threshold range.
The threshold range is set manually according to industry standards.
And step six, outputting a group of solutions which enable the objective function to be minimum as final solutions.
Compared with the prior art, the invention has the following advantages:
(1) compared with the original energy efficiency optimization problem without optimization variable decoupling, the optimization problem characteristics (such as classification as a known classical optimization problem type or convex/non-convex characteristic analysis) are difficult to analyze due to the coupling influence of multiple optimization variables, the power sub-optimization problem can be classified as a classical geometric programming problem after the optimization variables are decoupled, the position sub-optimization problem can be determined as a non-convex optimization problem, and finally, after two sub-optimization problems are solved respectively and solved iteratively, the solving complexity of the original energy efficiency optimization problem is reduced.
(2) The invention relates to an unmanned aerial vehicle relay transmission efficiency optimization method based on optimized variable decoupling.
Drawings
Fig. 1 shows a typical relay transmission system for an unmanned aerial vehicle in the prior art.
FIG. 2 is a flowchart of an optimization variable decoupling-based optimization method for relay transmission efficiency of an unmanned aerial vehicle according to the present invention;
FIG. 3 is a diagram illustrating the results of the feasible solutions and power sub-optimization algorithm for exhaustive traversal according to the present invention;
FIG. 4 is a schematic diagram of the change of node transmission power with the coordinate of the aerial relay X-axis after the power sub optimization problem of the present invention is solved;
FIG. 5 is a schematic diagram of the variation of node transmission power with the altitude of an aerial relay after solving the power sub-optimization problem of the present invention;
FIG. 6 is a schematic three-dimensional position diagram of an aerial relay R after joint optimization under different SNR thresholds according to the present invention;
FIG. 7 is a performance comparison diagram of the joint optimization and the fixed location power optimization of the present invention;
fig. 8 is a performance comparison diagram of the joint optimization and the joint optimization of the fixed ground base station power according to the present invention.
Detailed Description
The present invention will be described in further detail and with reference to the accompanying drawings so that those skilled in the art can understand and practice the invention.
The invention discloses a relay transmission energy efficiency optimization method based on optimization variable decoupling, which specifically comprises the following steps: mathematical description of the relay transmission process; establishing a relay transmission energy efficiency optimization problem model on the basis of relay transmission; decoupling an energy efficiency optimization problem model into a power sub optimization problem and an air relay position sub optimization problem; solving a power sub optimization problem; solving an optimization problem of the aerial relay position; and iterating the results of the two sub-optimization problems, and solving the final solution of the original energy efficiency optimization problem.
As shown in fig. 2, the specific steps are as follows:
the method comprises the following steps that firstly, an air-ground bidirectional relay transmission scene comprising an air base station, an air relay and a ground base station is constructed;
as shown in fig. 1, the relay transmission scenario includes an air base station, an air relay, and a ground base station. In a traditional one-way communication scene, information transmission can be completed by directly communicating two source nodes only by consuming one time slot, but the information transmission completed by using one-way relay communication needs to consume two time slots, so that the frequency spectrum efficiency is reduced. In contrast, the bidirectional relay studied in the bidirectional communication scenario can fully utilize spectrum resources, and has higher spectrum efficiency. Based on the above considerations, the present invention contemplates that an airborne relay provides an amplify-and-forward relay service for bidirectional communication between an airborne base station and a ground base station, and that an unmanned aerial vehicle is deployed as an airborne base station to serve ground users (e.g., in an emergency communication scenario). However, the return link between the aerial base station and the ground base station is easily blocked by a mountain or a building, the transmission distance is long, and the path loss is overlarge, so that another unmanned aerial vehicle is considered to be deployed as an aerial relay to establish a two-hop return link between the aerial base station and the ground base station; this transmission scenario is referred to as air-to-ground bi-directional relaying.
Step two, the air base station and the ground base station are used as two source nodes, and signals are sent to an air relay serving as a receiving node at the same time in the same frequency;
considering that the air-ground bidirectional relay transmission mechanism includes two stages of Multiple Access (MA) and broadcast (broadcast-cast, BC), the air relay receives signals simultaneously transmitted by two source nodes (an air base station and a ground base station) at the same frequency in the Multiple Access stage, and broadcasts the received mixed signal to the two source nodes (the air base station and the ground base station) after performing power amplification on the received mixed signal in the broadcast stage, and the two source nodes can respectively remove the signals transmitted by the two source nodes from the received mixed signal to obtain useful signals transmitted by the other source node.
The transmission mechanism is also called bidirectional relay, and the bidirectional relay processes and fully utilizes time-frequency resources by reasonably designing signal multiple access, hybrid transmission and receiving end signals, thereby improving the utilization rate of channels.
R for aerial relay, U for aerial base station, and G for ground base station, and the three-dimensional coordinates are respectively represented by SR＝(xR,yR,hR)，SU＝(xU,yU,hU) And SGWhen the transmission power of the three nodes is (0,0,0), the transmission power is PR，PUAnd PG。
Thirdly, establishing a relay transmission energy efficiency optimization problem model by using the transmitting power and the signal-to-noise ratio of each node;
considering that the air-ground channel between the ground base station G and the air relay R uses a probabilistic direct-view/non-direct-view channel model, wherein the probabilistic direct-view/non-direct-view mode means that the air-ground channel is a direct-view path or a non-direct-view path is represented in a probability form, and the probability depends on the environment and the air-ground channel pitch angle.
Direct-view path probability pLExpressed as:
Figure RE-GDA0002837133030000061
where θ is the air-ground channel pitch angle, and m and n are environment-dependent constants;
pNLis the non-direct-view path probability.
Assuming channel reciprocity, the instantaneous received signal-to-noise ratio of the signal sent from the ground base station G received by the air relay R is expressed as:
Figure RE-GDA0002837133030000062
PRrepresents the transmit power of the over-the-air relay R; g0For antenna gain, W is the bandwidth, N0Is the noise power, LRGPath loss of an air relay R and a ground base station G; a isLAnd aNLIs the road loss index;
Figure RE-GDA0002837133030000063
for non-direct-view path shadow fading profiles,
Figure RE-GDA0002837133030000064
shadowing a fading profile for a direct-view path; n represents a normal distribution, and the values in parentheses represent the mean and variance, respectively.
The instantaneous received signal-to-noise ratio of the signal sent from the air relay R received by the ground base station G is expressed as:
Figure RE-GDA0002837133030000065
PGrepresents the transmission power of the ground base station G; l isGRThe path loss of a ground base station G and an air relay R;
the instantaneous received signal-to-noise ratio of the signal sent from the over-the-air relay R as received by the over-the-air base station U is expressed as:
Figure RE-GDA0002837133030000071
PUrepresents the transmit power of the airborne base station U; l isURPath loss of an air base station U and an air relay R;
Figure RE-GDA0002837133030000072
the instantaneous received signal-to-noise ratio of the over-the-air relay R receiving the signal sent from the over-the-air base station U is expressed as:
Figure RE-GDA0002837133030000073
LRUpath loss of an air relay R and an air base station U;
path loss L between three nodesRG，LGR，LRUAnd LURRespectively expressed as:
Figure RE-GDA0002837133030000074
Figure RE-GDA0002837133030000075
wherein f iscIs the carrier frequency, c is the electromagnetic wave velocity;
all nodes work in a half-duplex mode, and the air relay uses an amplification forwarding strategy, namely, the air base station U and the ground base station G simultaneously send signals to the air relay R in a multiple access stage, and the air relay R broadcasts a mixed signal with amplified power to the air base station U and the ground base station G in a broadcasting stage.
The end-to-end received signal-to-noise ratio at ground base station G is then expressed as:
Figure RE-GDA0002837133030000076
A1，A2and A3Constants of shadow fading mean values under direct-view/non-direct-view conditions respectively;
Figure RE-GDA0002837133030000077
Figure RE-GDA0002837133030000078
the end-to-end received signal-to-noise ratio at the air base station U is expressed as:
Figure RE-GDA0002837133030000079
and establishing a relay transmission energy efficiency optimization problem model on the basis of relay transmission based on the end-to-end receiving signal-to-noise ratio at the ground base station G and the air base station U.
According to the invention, the position of the air relay R and the transmitting power of the three nodes are optimized under the constraint conditions of a receiving signal-to-noise ratio threshold, an air relay safety airspace, a transmitting power limit and the like to minimize the transmitting power of the unmanned aerial vehicle. In addition, in actual deployment, the optimization process can be realized by considering the ground base station G (having more computing resources), and then the optimized optimal parameters can be sent to the air base station U and the air relay R through control information.
The objective function of the energy efficiency optimization problem is expressed as:
wUPU+wRPR (10)
wherein, wURepresenting the power consumption weight, w, of the airborne base station URRepresents the power consumption weight of the over-the-air relay R; the weights represent the severity of the same power consumption for the airborne base station U and the airborne relay R. Without loss of generality, set wU＞0，wR> 0 and wU+w R1. That is, if there are a limited number of drones in the service area, but there are multiple drones in the relay area that can serve as potential relay nodes, then the air base station U should reduce the transmission power as much as possible and the air relay R can consume relatively more transmission power because the air relay can consume relatively more transmission powerRelatively easily switched to other unmanned aerial vehicles in relay areas, and w can be set at the momentU＜wR(ii) a On the contrary, set wU>wR. Long-term reliability indicators are used instead of transient indicators when data transmission reliability constraints are considered. This is because the instantaneous reliability indicator is calculated using instantaneous channel state information, is closely related to the location of the air relay R and varies with changes in the location of the air relay R.
The optimal parameters optimized with the instantaneous channel state information for a particular drone location are often not the optimal parameters at the optimal location calculated. Therefore, the long-term signal-to-noise ratio using the channel state statistics is more reasonable. The long-term received signal-to-noise ratios at the ground base station G and the air base station U are respectively expressed as:
Figure RE-GDA0002837133030000081
Figure RE-GDA0002837133030000082
μLis the shadow fading average under the direct-view condition; mu.sNLThe average value of shadow fading under the condition of non-direct vision;
through the analysis on the optimization target and the constraint condition, the energy efficiency optimization method for jointly optimizing the node transmitting power and the air relay height is mathematically described as the following optimization problem:
the objective function is:
min wUPU+wRPR
PU,PR,PG,SR
the constraint conditions of the energy efficiency optimization problem model are as follows:
Figure RE-GDA0002837133030000091
wherein, gamma isth1And Γth2Respectively representing the signal-to-noise ratio threshold P of the receiving ends of the ground base station and the air base station in the air-ground bidirectional relaygmaxAnd PumaxRespectively representing the maximum transmit power of the ground base station and the drone (aerial base station, aerial relay),
Figure RE-GDA0002837133030000092
the relay R is an air relay depending on the safe airspace of the real-time application environment.
Constraint C1 denotes: signal-to-noise ratio gamma of ground base station G end-to-end receptionGIs not lower than the SNR thresholdth1；
Constraint C2 denotes: signal-to-noise ratio gamma of air base station U end-to-end receptionUIs not lower than the SNR thresholdth2；
Constraint C3 denotes: the transmitting power of the ground base station G is more than or equal to 0 and less than or equal to the maximum transmitting power Pgmax；
Constraint C4 denotes: the transmission power of the aerial base station U and the aerial relay R is more than or equal to 0 and less than or equal to the maximum transmission power Pumax；
Constraint C5 denotes: safe airspace C with three-dimensional coordinates of aerial relay R depending on real-time application environmentR；
Decoupling an energy efficiency optimization problem model into a power sub optimization problem and an air relay position sub optimization problem;
the energy efficiency optimization problem of the modeling of the invention is a multivariable nonlinear optimization problem, and two different kinds of optimization variables are considered: transmit power and over-the-air relay location. The invention decomposes the original optimization problem into two sub-optimization problems: a power sub-optimization problem and an air relay position sub-optimization problem. And on the basis of solving the two sub-optimization problems, applying an alternative minimization thought to iteratively solve the original optimization problem.
According to the mathematical description of the optimization problem, the multiple optimization variables are divided into two types of six variables including the unmanned aerial vehicle position SR(including three variables of X, Y and Z in a Cartesian coordinate system) and the transmission power P of three nodesU,PRAnd PGThe multiple optimization variables present a nonlinear coupling relation in the system average capacity expression. Meanwhile, through second-order derivation verification of a long-term performance expression in an inequality constraint, the optimization problem of the system autonomous evolution is mathematically a multivariable nonlinear coupling non-convex optimization problem with inequality constraints, the solving difficulty is high, and the consumption of computing resources is high.
The invention proposes a low-complexity autonomous evolution algorithm based on optimization variable decoupling and convex approximation to solve the optimization problem. The proposed algorithm first decomposes the original optimization problem into two sub-optimization problems by decoupling the two types of optimization variables, power and position: a power optimization problem and a location optimization problem. The power optimization problem can be proved to be a geometric programming problem, and the optimal solution can be solved quickly and efficiently by using a convex optimization theory; the position optimization problem is still a non-convex optimization problem of inequality constraint, and an approximate solution is rapidly obtained by applying a convex approximation method. The original problem can be solved by respectively solving the two sub-optimization problems and by iteratively solving the two sub-optimization problems.
The autonomous evolution algorithm based on the optimization variable decoupling and the convex approximation can reduce the algorithm complexity and benefit from two points: firstly, the invention proves that the power optimization sub-problem is a geometric planning problem by decoupling the original optimization problem into the power optimization sub-problem and the unmanned aerial vehicle position optimization sub-problem (variable decoupling is a universal method), so that the convex optimization theory can be applied to quickly and efficiently solve. Secondly, the sub-problem of the unmanned plane position optimization is still a non-convex optimization problem containing inequality constraint conditions, and the non-convex function in the sub-problem is as follows: according to the long-term performance index, the invention provides an approximate convex function of the long-term performance index, so that the unmanned plane position optimization subproblem can be quickly and efficiently solved by applying the approximate convex function, and the complexity of the whole algorithm is further reduced.
Meanwhile, the situation that a certain type of optimization variable is separately considered to be solved and another type of optimization variable is set as a fixed value corresponds to a plurality of commonly used optimization problems in practical application, for example, when the node transmission power is inconvenient to perform adaptive adjustment due to system complexity, the optimization problem can be described as that the transmission power is fixed to optimize the position of the unmanned aerial vehicle; and vice versa. Therefore, the original optimization problem can be decomposed into two sub-optimization problems according to the types of the optimization variables: the sub-optimization problem of the unmanned aerial vehicle position during fixed power and the sub-optimization problem of the power during fixed unmanned aerial vehicle position. The solution of the original optimization problem can be obtained by iterative solution of two sub-optimization problems,
the method specifically comprises the following steps:
1) the position of an aerial relay R is given, the power sub-optimization problem is redefined, the newly defined power sub-optimization problem is converted into a geometric planning standard form by adopting an original-dual interior point method and then is solved, and a group of transmitting power P is obtainedU，PRAnd PGA value of (d);
the power sub-optimization problem is expressed as:
Figure RE-GDA0002837133030000101
for convenience of description, the partial constant is defined as α ═ a1μL+A2μNL，β＝A3μL。
The optimization problem is observed and is referred to as a geometric programming problem. The primal-dual interior point method has higher robustness when solving the geometric planning problem and can provide effective indication when the problem is not solved, so the primal-dual interior point method is used for solving the geometric planning problem; defining a function Qi＝log(Pi) I ∈ { U, R, G }, the power sub-optimization approximation problem can be rewritten as a geometric programming standard form as follows:
Figure RE-GDA0002837133030000111
wherein Q ═ { Q ═ QU,QR,QG}，b1＝log(Γth1α)，b2＝log(Γth1β)，b3＝log(Γth2β)，b4＝ log(Γth2α)，b5＝log(Pgmin)，
Figure RE-GDA0002837133030000112
b7＝log(Pumax) And is
Figure RE-GDA0002837133030000113
The standard form of the geometric programming problem can be solved by applying a prime-dual interior point method, and the solving process can be carried out by means of a CVX equal convex optimization tool.
2) Given transmit power PU，PRAnd PGRedefining the position sub-optimization problem, gradually raising the original problem by adopting a continuous convex approximation method, and then solving to obtain a coordinate value S of the air relay RR；
The position sub-optimization problem is expressed as:
Figure RE-GDA0002837133030000114
the optimization problem can be proved to have a non-convex characteristic through second-order derivation verification, namely the position sub-optimization problem is a non-convex optimization problem, and the non-convex optimization problem is solved by adopting a continuous convex approximation method. Feasible solution for giving three-dimensional coordinate of unmanned aerial vehicle
Figure RE-GDA0002837133030000115
Better feasible solution of three-dimensional coordinate of unmanned aerial vehicle
Figure RE-GDA0002837133030000116
Obtained by solving the following optimization problem:
Figure RE-GDA0002837133030000121
wherein i represents the number of optimization iteration rounds; p1,P2,P3,P4All after a first-order Taylor convex approximationNumbers, respectively expressed as:
Figure RE-GDA0002837133030000122
Figure RE-GDA0002837133030000123
Figure RE-GDA0002837133030000124
Figure RE-GDA0002837133030000125
wherein the content of the first and second substances,
Figure RE-GDA0002837133030000126
Figure RE-GDA0002837133030000127
Figure RE-GDA0002837133030000128
hRrepresenting the flight altitude of the unmanned aerial vehicle; the optimization problem (17) is a convex optimization problem and can be solved quickly and efficiently by using a common convex optimization tool.
As shown in FIG. 3, the performance of the power sub-optimization problem solving algorithm is shown, where wU＝wR＝0.5，P G30 dBm. As can be seen from the figure, the solution obtained by the proposed power sub-optimization algorithm is very close to the globally optimal solution searched exhaustively, i.e. the algorithm performance is excellent.
FIG. 4 shows the X-axis coordinate X of the optimized node transmission power along with the aerial relay in the power sub-optimization problemRWherein the ordinate represents the ratio of the optimized node transmission power to the maximum transmission power thereof. It can be seen from the figure that the target function P can be made by adjusting the position of the aerial relay in the horizontal directionU+PRLower value
FIG. 5 shows the variation of the optimized transmission power with the altitude of the airborne relay in the power sub-optimization problem, in which the X-axis and Y-axis coordinates of the UAV are fixed as X-axis coordinates respectivelyR250m and yR＝250m，wU＝wR0.5. Similar to the conclusion in FIG. 4, it can be seen from FIG. 5 that the objective function P can be made by adjusting the height of the aerial relayU+PRThe smaller the value.
Step five, respectively iterating the power sub-optimization problem and the air relay position sub-optimization problem by applying an alternative minimization idea;
optimization target (1-w) due to position subR)PU+wRPRDoes not contain an optimization variable SRThis illustrates that for a given power of the location sub-optimization problem, the optimization objective (1-w) is madeR)PU+wRPRMinimum SRIs typically a set. This is because in the position sub-optimization problem, the power is fixed, i.e., the objective function is fixed, and therefore only the position needs to be optimized to ensure that the constraint condition is satisfied. At this time, the feasible solution of the position of the unmanned aerial vehicle, which can satisfy the constraint condition, is usually a set. It should be noted that, a general convex optimization tool (such as a CVX tool box) is used in the position sub-optimization algorithm, and one solution in the feasible solution set is randomly output. Therefore, using a conventional iterative algorithm that gives only one initial solution tends to trap the optimization results of the problem into a locally optimal solution. Therefore, the iterative algorithm provided by the invention gives a set of initial solutions, and selects a feasible solution which minimizes the objective function from a set of problem feasible solutions corresponding to the set of initial solutions as a final solution of the problem. Based on the solution result of the optimization problem (17), the position sub-optimization problem can be solved iteratively by giving an initial point and respectively carrying out three variables of the three-dimensional position, and the specific process is as follows:
first, an initial position S of the aerial relay R is givenR0Calculating a group of transmitting power P according to the redefined power sub-optimization modelU0，PR0And PG0；
Then, the transmission power P is adjustedU0，PR0And PG0Bringing in the redefined position sub-optimization problem to obtain the position S of the aerial relay RR1；
Thirdly, relaying the position S of R in the airR1Introducing a redefined power sub-optimization model, and calculating to obtain a group of transmitting powers PU1，PR1And PG1(ii) a At this point, the first iteration is completed;
and continuing iteration by analogy until the iteration times are reached or the result of each iteration is brought into the objective function, and ending the iteration when the difference value of the results of the two adjacent objective functions is within the threshold range.
The threshold range is set manually according to industry standards.
And step six, outputting a group of solutions which enable the objective function to be minimum as final solutions.
As shown in FIG. 6, different received SNR thresholds Γ are shownth2Three-dimensional position of the lower joint optimized aerial relay R, where the received signal-to-noise ratio Γth2The values are marked next to their corresponding hexagonal labels. It can be seen from fig. 6 that with the received signal-to-noise ratio threshold Γth2The position of the airborne relay R after joint optimization tends to move towards the airborne base station U.
As shown in fig. 7, the variation of the node transmission power with the threshold of the received signal-to-noise ratio is also shown after the joint power/position optimization and the fixed position power optimization are used, wherein the joint optimization and the power optimization are respectively represented by a solid line and a dashed line, and the ordinate represents the ratio of the optimized node transmission power to the maximum transmission power.
FIG. 8 shows a combined optimization and fixing of the ground base station power PGAfter the joint optimization, the unmanned aerial vehicle transmitting power and the receiving signal-to-noise ratio threshold gammath2The variation of (2). As can be seen from the figure, the optimized emission power sum P of the unmanned aerial vehicleU+PRFixed transmission power P with ground base stationGIs increased.
Preferably, the mathematical description of the relay transmission process comprises:
a probabilistic direct-view/non-direct-view channel air-ground channel model between a ground station and a relay unmanned aerial vehicle, and a receiving end signal-to-noise ratio model at a ground base station and an aerial base station under a typical relay transmission scene.
Preferably, establishing the relay transmission energy efficiency optimization problem model on the basis of relay transmission specifically means: the relay transmission energy efficiency is reasonably modeled into an optimization problem, and the relay transmission energy efficiency optimization can be realized by solving the energy efficiency optimization problem. The method comprises the steps of taking the minimum unmanned aerial vehicle transmitting power weighted sum as an optimization target, taking a receiving end signal-to-noise ratio threshold, the node maximum transmitting power and an air relay safe airspace as constraint conditions, and carrying out combined optimization on the node transmitting power and the air relay position.
Preferably, the power sub-optimization problem is solved as a geometric programming problem, and a primal-dual interior point method is adopted for solving.
Preferably, the sub-optimization problem of the air relay position is solved, the sub-optimization problem can be verified to be a non-convex optimization problem through second-order derivation, and a continuous convex approximation method is adopted for solving.
Preferably, the iterative solution of the original energy efficiency optimization problem is that: in the aspect of solving the specific energy efficiency optimization problem, the two sub-problems are solved respectively, and then the alternative minimization idea is applied, so that the original joint optimization problem can be solved in an iterative manner, and the original energy efficiency optimization problem can be solved finally.

Claims (1)

1. An unmanned aerial vehicle relay transmission efficiency optimization method based on optimization variable decoupling is characterized by comprising the following specific steps:
the method comprises the following steps that firstly, an air-ground bidirectional relay transmission scene comprising an air base station, an air relay and a ground base station is constructed;
the aerial relay and the aerial base station respectively select unmanned aerial vehicles;
step two, the air base station and the ground base station are used as two source nodes, and signals are sent to an air relay serving as a receiving node at the same time in the same frequency;
thirdly, establishing a relay transmission energy efficiency optimization problem model by using the transmitting power and the signal-to-noise ratio of each node;
the received signal-to-noise ratio of the ground base station G is expressed as:
Figure FDA0003063045250000011
μLis the shadow fading average under the direct-view condition; mu.sNLThe average value of shadow fading under the condition of non-direct vision;
Figure FDA0003063045250000012
Figure FDA0003063045250000013
G0for antenna gain, W is the bandwidth, N0Is the noise power, LGRThe path loss of a ground base station G and an air relay R; a isLAnd aNLIs the road loss index; l isRUPath loss of an air relay R and an air base station U;
pLin order to look at the path probability,
Figure FDA0003063045250000014
theta is the air-ground channel pitch angle, and m and n are constant values depending on the environment; p is a radical ofNLIs the non-direct-view path probability;
the received signal-to-noise ratio of the airborne base station U is expressed as:
Figure FDA0003063045250000015
the objective function of the optimization problem is as follows:
min wUPU+wRPR
PU,PR,PG,SR
PUrepresents the transmit power of the airborne base station U; pRRepresents the transmit power of the over-the-air relay R; pGRepresents the transmission power of the ground base station G; w is aURepresenting the power consumption weight, w, of the airborne base station URRepresenting an aerial relay RA power consumption weight; sRThree-dimensional coordinates representing the aerial relay R;
the constraint conditions of the energy efficiency optimization problem model are as follows:
s.t.C1 ΓG≥Γth1
C2 ΓU≥Γth2
C3 0≤PG≤Pgmax
C4 0≤PR，PU≤Pumax
C5 SR∈CR.
constraint C1 denotes: signal-to-noise ratio gamma of ground base station G end-to-end receptionGIs not lower than the SNR thresholdth1；
Constraint C2 denotes: signal-to-noise ratio gamma of air base station U end-to-end receptionUIs not lower than the SNR thresholdth2；
Constraint C3 denotes: the transmitting power of the ground base station G is more than or equal to 0 and less than or equal to the maximum transmitting power Pgmax；
Constraint C4 denotes: the transmission power of the aerial base station U and the aerial relay R is more than or equal to 0 and less than or equal to the maximum transmission power Pumax；
Constraint C5 denotes: safe airspace C with three-dimensional coordinates of aerial relay R depending on real-time application environmentR；
Decoupling an energy efficiency optimization problem model into a power sub optimization problem and an air relay position sub optimization problem;
the method specifically comprises the following steps:
1) the position of an aerial relay R is given, the power sub-optimization problem is redefined, the newly defined power sub-optimization problem is converted into a geometric planning standard form by adopting an original-dual interior point method and then is solved, and a group of transmitting power P is obtainedU，PRAnd PGA value of (d);
the power sub-optimization problem is expressed as:
min wUPU+wRPR
PU,PR,PG
Figure FDA0003063045250000021
Figure FDA0003063045250000022
0≤PG≤Pgmax
0≤PU，PR≤Pumax
the constant is defined as a ═ a1μL+A2μNL，β＝A3μL；
2) Given transmit power PU，PRAnd PGRedefining the position sub-optimization problem, gradually raising the original problem by adopting a continuous convex approximation method, and then solving to obtain a coordinate value S of the air relay RR；
The position sub-optimization problem is expressed as:
min wUPU+wRPR
SR
Figure FDA0003063045250000023
Figure FDA0003063045250000024
SR∈CR
step five, respectively iterating the power sub-optimization problem and the air relay position sub-optimization problem by applying an alternative minimization idea;
first, an initial position S of the aerial relay R is givenR0Calculating a group of transmitting power P according to the redefined power sub-optimization modelU0，PR0And PG0；
Then, willTransmission power PU0，PR0And PG0Bringing in the redefined position sub-optimization problem to obtain the position S of the aerial relay RR1；
Thirdly, relaying the position S of R in the airR1Introducing a redefined power sub-optimization model, and calculating to obtain a group of transmitting powers PU1，PR1And PG1(ii) a So far, a first iteration is completed to obtain a corresponding first group of solutions: sR1；PU1，PR1And PG1；
Continuing iteration by analogy until the iteration times are reached or the result of each iteration is brought into the objective function, and ending the iteration when the difference value of the results of the two adjacent objective functions is within the threshold range;
and step six, outputting a group of solutions which enable the objective function to be minimum as final solutions.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113034981B (en) * 2021-04-14 2022-07-08 北京航空航天大学 Multi-relay unmanned aerial vehicle flight path planning method and system in uncertain channel environment and storage medium
CN113347593B (en) * 2021-04-16 2022-05-03 重庆邮电大学 Relay selection method of unmanned aerial vehicle
CN113179547B (en) * 2021-04-21 2023-06-06 鹏城实验室 High-altitude base station and data transmission method based on same
CN113423070A (en) * 2021-05-26 2021-09-21 同济大学 Bidirectional multi-hop relay transmission control method for unmanned aerial vehicle relay network
CN113872662B (en) * 2021-08-17 2023-08-04 同济大学 Energy efficiency maximization adaptation method based on fixed wing unmanned aerial vehicle relay network
CN114665947B (en) * 2022-02-24 2023-07-25 南京邮电大学 Optimal design method for joint power control and position planning of relay communication system supported by unmanned aerial vehicle
CN114710218B (en) * 2022-05-31 2022-12-16 深圳市佳贤通信设备有限公司 Distributed node and base station communication efficiency optimization method based on 5G
CN115379465B (en) * 2022-08-24 2023-08-01 西安电子科技大学 Access and return integrated energy efficiency optimization method and system for air-to-ground network

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106131918A (en) * 2016-08-12 2016-11-16 梁广俊 The associating Path selection of energy acquisition node and power distribution method in wireless sense network
CN107040982A (en) * 2017-03-31 2017-08-11 南京邮电大学 A kind of user's scheduling and power distribution combined optimization method towards unmanned plane junction network
CN108156613A (en) * 2017-11-26 2018-06-12 南京邮电大学 A kind of relay node distribution method in unmanned plane relay multi-hop communication system
CN108566670A (en) * 2018-04-19 2018-09-21 郑州航空工业管理学院 Unmanned plane assists wireless sense network and its node scheduling and path planning power distribution design method
CN111050286A (en) * 2019-12-18 2020-04-21 北京邮电大学 Trajectory and resource optimization method in unmanned aerial vehicle auxiliary sensor network
US10637544B1 (en) * 2018-04-24 2020-04-28 Genghiscomm Holdings, LLC Distributed radio system
WO2020091546A1 (en) * 2018-11-02 2020-05-07 Lg Electronics Inc. Configuration coordination for power efficient operation for nr

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9891631B1 (en) * 2015-06-09 2018-02-13 Amazon Technologies, Inc. Application program interface for weight and balance metrics
CN106059840B (en) * 2016-08-02 2019-04-09 北京邮电大学 A kind of cognitive radio system power distribution method and device
CN108541357A (en) * 2017-06-28 2018-09-14 深圳市大疆创新科技有限公司 A kind of information processing method, unmanned plane and computer readable storage medium

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106131918A (en) * 2016-08-12 2016-11-16 梁广俊 The associating Path selection of energy acquisition node and power distribution method in wireless sense network
CN107040982A (en) * 2017-03-31 2017-08-11 南京邮电大学 A kind of user's scheduling and power distribution combined optimization method towards unmanned plane junction network
CN108156613A (en) * 2017-11-26 2018-06-12 南京邮电大学 A kind of relay node distribution method in unmanned plane relay multi-hop communication system
CN108566670A (en) * 2018-04-19 2018-09-21 郑州航空工业管理学院 Unmanned plane assists wireless sense network and its node scheduling and path planning power distribution design method
US10637544B1 (en) * 2018-04-24 2020-04-28 Genghiscomm Holdings, LLC Distributed radio system
WO2020091546A1 (en) * 2018-11-02 2020-05-07 Lg Electronics Inc. Configuration coordination for power efficient operation for nr
CN111050286A (en) * 2019-12-18 2020-04-21 北京邮电大学 Trajectory and resource optimization method in unmanned aerial vehicle auxiliary sensor network

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Energy-Efficient Resource Allocation for Secure UAV Communication Systems;Yuanxin Cai等;《 2019 IEEE Wireless Communications and Networking Conference (WCNC)》;20191031;全文 *
能效最优准则下的无人机中继***的功率分配算法;严晓琴;《数据采集与处理》;20181115;全文 *

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