CN115379393A - Full-duplex relay unmanned aerial vehicle energy efficiency optimization method facing interference coordination - Google Patents

Full-duplex relay unmanned aerial vehicle energy efficiency optimization method facing interference coordination Download PDF

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CN115379393A
CN115379393A CN202210875780.9A CN202210875780A CN115379393A CN 115379393 A CN115379393 A CN 115379393A CN 202210875780 A CN202210875780 A CN 202210875780A CN 115379393 A CN115379393 A CN 115379393A
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unmanned aerial
aerial vehicle
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王伟
苏悦悦
杨伟峰
李汉艺
黄叶婷
王可为
戚楠
刘一甲
黄赞奇
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Nanjing University of Aeronautics and Astronautics
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/14—Two-way operation using the same type of signal, i.e. duplex
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
    • 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

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Abstract

The invention discloses an energy efficiency optimization method of a full-duplex relay unmanned aerial vehicle facing interference coordination, which comprises the following steps: establishing a full-duplex relay unmanned aerial vehicle interference coordination model; definition of T 1 UAV and UAV-T 2 Data transmission, definition T g ‑T e Data transmission; constructing an energy efficiency formula and a constraint model; constructing an objective function; track optimization subproblem convex transformation and power optimization subproblem convex transformation; a joint power and path planning algorithm. The invention provides an interference coordination-oriented relay unmanned aerial vehicle energy efficiency optimization method, which aims at transmitting power and unmanned aerial vehicleThe method comprises the steps of performing combined optimization on the flight path of the unmanned aerial vehicle, constraining the speed, the path coordinate, the data transmission rate, the causality of information and the flight distance of each time slot, converting a non-convex problem into a convex problem by using a convex optimization algorithm, and decomposing the convex problem into two sub-problems by using a block coordinate descent method, thereby obtaining an efficient iterative algorithm; simulation results show that the energy efficiency of the system can be improved while mutual interference is reduced.

Description

Full-duplex relay unmanned aerial vehicle energy efficiency optimization method facing interference coordination
Technical Field
The invention relates to the technical field of unmanned aerial vehicle relay auxiliary interference coordination, in particular to an energy efficiency optimization method of a full-duplex relay unmanned aerial vehicle facing interference coordination.
Background
In recent years, as an emerging technology, unmanned aerial vehicles are widely applied to assistance of wireless networks due to low cost, flexible deployment and high mobility, especially in communication scenes (such as fire and earthquake affected areas) which are difficult to reach by human beings. The academia has made a great deal of effort in integrating drone-supported communications into long term evolution and upcoming 5G networks, such as cellular offloading of overloaded base stations, charging of device-to-device scenarios, and data collection.
A series of important works innovatively research the paradigm of UAVs as mobile data collectors, and the research direction can be mainly divided into the following three directions: (1) The transmission power and the track of the UAV are jointly optimized, and the network throughput is improved to the maximum extent; (2) Jointly scheduling the wake-up mode of the ground user and the track of the unmanned aerial vehicle; and (3) reducing the energy consumption of the UAV or the energy consumption of the ground user.
Notably, UAVs are limited in use time due to physical size, weight, and on-board battery capacity. Currently, unmanned planes on the market, such as DJI, yuneec, have a flight time of about 530 minutes. Therefore, in drone assisted networks, helping to transmit more data per unit of power (i.e., maximizing energy efficiency) is a particularly urgent and critical issue. However, in addition to severe path loss and physical blockage, transceivers may also be subject to strong interference from neighboring infrastructure, including ground user equipment, neighboring base stations, and other airborne drones. Worse, for an airborne drone relay, it may suffer from two interferers, a first hop and a second hop. On the other hand, compared with a ground communication system, the unmanned aerial vehicle auxiliary wireless system has the advantages of superior link quality of a ground node channel and strong interference to other users. Therefore, interference coordination between drone relays and other neighboring nodes is one of the key challenges faced by drone relay communications.
The existing work is mainly suitable for a classical communication scene without mutual interference and is not feasible under a multi-user scene. Resource allocation strategies suggest that these works have inadequate networks sharing the same spectral band with co-located ground users, since most UAV-related relay works consider only one source destination, and the UAV-assisted user interference coordination problem is ignored. How to eliminate the mutual interference among the ground users and improve the energy efficiency is an urgent problem to be solved.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an energy efficiency optimization method of a full-duplex relay unmanned aerial vehicle facing interference coordination, which can reduce mutual interference and improve the energy efficiency of a system.
In order to solve the technical problem, the invention provides an energy efficiency optimization method of a full-duplex relay unmanned aerial vehicle facing interference coordination, which comprises the following steps:
step 1, establishing a full-duplex relay unmanned aerial vehicle interference coordination model;
step 2, defining T based on full-duplex relay unmanned aerial vehicle interference coordination communication scene model 1 UAV and UAV-T 2 Data transfer, definition T g -T e Data transmission;
step 3,According to relay unmanned aerial vehicle data transmission and T g -T e Data transmission is carried out, and an energy efficiency formula and a constraint model are constructed by combining an unmanned aerial vehicle energy consumption formula;
step 4, determining an optimization target according to an energy efficiency formula and a constraint model, and constructing a target function;
step 5, for the objective function which is difficult to solve, performing convex transformation of the trajectory optimization subproblem and convex transformation of the power optimization subproblem by using a convex optimization method;
and 6, designing a combined power and path planning algorithm, and solving a trajectory optimization sub-problem and a power optimization sub-problem after convex transformation.
Preferably, in step 1, the establishing of the full-duplex relay unmanned aerial vehicle interference coordination model specifically includes: full-duplex relay unmanned aerial vehicle interference coordination model with two coexisting transmitting-receiving pairs, namely T 1 -T 2 And T g -T e The working mode of the intelligent jammer of our party is full duplex, and eavesdropping is carried out on communication users of the enemy while the interference is released; let T be 1 Intended to transmit its signal to T 2 At the same time, T g There is a signal to transmit to T e However, due to severe path loss or congestion between one transceiver, no available direct channel link exists; due to the limitation of resources such as time slots and channel frequencies, communication must be carried out at the same time and the same frequency generally occurs in a device-to-device network, so that mutual interference can be generated between communication pairs, and the gyroplane flies in a designated air area to assist relay T 1 -T 2 Transmitting; in order to fully utilize the relay capacity and improve the data transmission energy efficiency, an FDR technology is applied to an unmanned aerial vehicle, and the unmanned aerial vehicle is provided with two antennas, a receiving antenna and a transmitting antenna; the unmanned aerial vehicle is also provided with a global positioning system, automatically finishes designed airlines, and automatically hovers when needed; to optimize energy efficiency, consider the optimized variables as: 1) T is 1 And the transmit power of the drone; 2) Trajectory of the drone; let T 1 ，T 2 ，T g ，T e The coordinates are respectively (x) S ，y S )，(x Z ，y Z )，(x G ，y G )，(x E ，y E ) (ii) a Assuming that the unmanned aerial vehicle flies at a fixed height H within a limited time T, the time interval of the unmanned aerial vehicle is divided into M sections, and the duration and the track length of each time slot are respectively set to be T/M, DD m In the mth time slot, the flying speed of the unmanned aerial vehicle can be approximated to be
Figure BDA0003762435400000021
Meanwhile, the UAV relay is supposed to only load data in the first time slot and only unload data in the last time slot, and the UAV to ground node T is supposed to ensure that the ground-air channel obeys the fading of free space 1 And T 2 Is expressed as
Figure BDA0003762435400000031
Wherein d is i，j Are respectively a node T 1 ，T 2 ，T g ，T e Distance to the drone.
Preferably, in step 2, T is defined 1 The UAV data transmission is in particular: in the relay unmanned aerial vehicle communication system, the unmanned aerial vehicle receives signals of
Figure BDA0003762435400000032
Figure BDA0003762435400000033
Wherein S t，m 、S g，m ，S r，m Respectively represent nodes T 1 ，T 2 And a signal transmitted by the drone; p 1，m 、P g 、P r，m Respectively represent nodes T 1 ，T g And the transmit power of the drone; h is rr Is the relay self-interference channel gain; k is a radical of 0 Is a self-interference cancellation factor; n is 1 Is a mean value of
Figure BDA0003762435400000034
White gaussian noise of (1); due to the processing delay of the unmanned plane, the received signal cannot be forwarded immediately, and the unmanned plane can successfully decode under the condition that tau represents the time processing delayThe forwarded signal in the mth time interval is S t，m-τ At the mth time slot, the unmanned aerial vehicle arrives at the ground node T 1 Has a data signal to interference plus noise ratio of
Figure BDA0003762435400000035
By the above, at the mth time slot, the unmanned aerial vehicle arrives at the node T 1 Has an offload channel capacity of
Figure BDA0003762435400000036
Defining UAV-T 2 The data transmission specifically comprises the following steps: in relay unmanned plane T 2 In the information system, the unmanned aerial vehicle firstly receives the ground node T 1 Then the signal is decoded and forwarded to the node T 2 (ii) a Furthermore, T 2 Will also receive a message from node T g The interference signal of (a); thus, T 2 The received signal is represented as
Figure BDA0003762435400000037
Wherein d is g，2 To T 2 The distance of (d) is a constant value; n is 2 Is a mean value of
Figure BDA0003762435400000038
White gaussian noise; at the mth time slot, the unmanned plane arrives at the ground node T 2 Has a data signal to interference plus noise ratio of
Figure BDA0003762435400000039
From above, at the mth time slot, the unmanned aerial vehicle arrives at the ground node T 2 Has an offload channel capacity of C 2，m ＝B log 2 (1+SINR 2，m )。
Preferably, in step 2, T is defined g -T e The data transmission specifically comprises the following steps: direct connection of links T on the ground g -T e In a communication system, T e Will not only receive the message from T g The signal can receive the unmanned aerial vehicle and the node T 1 Interference of (2); thus, node T e The received signal may be represented as
Figure BDA00037624354000000310
Wherein d is 0 、dd 0 Are respectively T g -T e 、T 1 -T e The distance of (d); n is e Is a mean value of
Figure BDA0003762435400000041
White gaussian noise of (1); in the m-th time slot, T g -T e The offload SINR of the link is
Figure BDA0003762435400000042
From above, in the m-th slot, T g -T e Has an offload channel capacity of
Figure BDA0003762435400000043
Preferably, in step 3, the energy efficiency formula is specifically constructed as follows: at UAV-T 2 In the link, the unmanned aerial vehicle firstly receives the ground node T 2 Then the signal is decoded and forwarded to the node T 2 At the same time T 2 Receiving from node T g The interference signal of (a); direct connection of links T on the ground g -T e In, T e Will not only receive the message from T g The signal of (2) can receive the unmanned aerial vehicle and the node T 1 Interference of (2); thus, the unmanned aerial vehicle energy efficiency expression is
Figure BDA0003762435400000044
Wherein, P tot,m For the total power consumption of the drone at the mth time slot, therefore, the optimization problem turns into
Figure BDA0003762435400000045
Wherein the content of the first and second substances,
Figure BDA0003762435400000046
namely node T 1 、T g And an unmanned aerial vehicle power set;
Figure BDA0003762435400000047
respectively is an unmanned aerial vehicle horizontal coordinate set and an unmanned aerial vehicle vertical coordinate set under the optimal energy efficiency.
Preferably, in step 3, constructing the constraint model specifically includes: the data transmission rate, the causality of information and the flight of the unmanned aerial vehicle are restrained;
(a) A data transmission rate constraint; in order to ensure the transmission delay and ensure the normal communication of the communication system, define C 2,th And C g,th As a node T 1 And T g M-1, when M = τ, τ +1
Figure BDA0003762435400000048
(b) Information causality constraints; for T 1 For UAV communication systems, causality of information is that the total number of bits of data loaded by a drone at a certain stage is greater than the total number of bits of data unloaded at the next stage; UAV Slave node T 1 The total number of bits of the data loaded cumulatively cannot be less than the number unloaded to the node T 2 Expressed as the total number of bits of
Figure BDA0003762435400000051
Wherein, C 1,0 Data loaded for the m =1 slot drone;
(c) Unmanned aerial vehicle flight constraints; in practice, the starting point and the end point of the drone are preset, setting x 0 ＝x S ,y 0 ＝y S ,x M-1 ＝x Z ,y M-1 ＝y Z And, in addition, setting the flying speed of the unmanned aerial vehicle to be less than the maximum flying speed V max I.e. v.ltoreq.V max Is provided with
Figure BDA0003762435400000052
Wherein the content of the first and second substances,
Figure BDA0003762435400000053
preferably, in step 4, the constructing of the objective function specifically includes: since the formula P1 is in a fractional form, the analysis of the optimization problem becomes complicated, and therefore, the fractional objective function is converted into a subtraction form using the Dinkelbach method; energy efficiency can be maximized by optimizing the transmitting power and track of the unmanned aerial vehicle, and q is set * ＝max{η EE }，
Figure BDA0003762435400000054
Wherein q is * Is the optimal energy efficiency;
Figure BDA0003762435400000055
namely, a power solution set under the optimal energy efficiency;
Figure BDA0003762435400000056
the method comprises the following steps of (1) obtaining a track solution set under the optimal energy efficiency; the above-mentioned expression target function P1 is converted into solving formula
Figure BDA0003762435400000057
In the form of a maximum value, i.e.
Figure BDA0003762435400000058
Thus, constructing the objective function places an emphasis on the equivalent optimization problem as described below, if and only if
Figure BDA0003762435400000059
minV' = -V =0 has
Figure BDA00037624354000000510
Then P2 may be converted to
Figure BDA00037624354000000511
According to the formula
Figure BDA00037624354000000512
And minV' = -V =0; must satisfy
Figure BDA00037624354000000513
When | V' | is less than or equal to δ, obtaining the optimum
Figure BDA00037624354000000514
Wherein δ is a preset tolerance; q is iteratively updated.
Preferably, in step 5, the trajectory optimization subproblem convex transformation is specifically performedComprises the following steps: assuming that the transmitting power of the ground node and the unmanned aerial vehicle is a fixed value, the track optimization problem can be expressed as
Figure BDA00037624354000000515
Due to the presence of a non-convex term P in the objective function tot,m In which there is C 1,m ，C 2,m And C g,m Causing this to be a non-convex problem; converting the track optimization problem into a convex problem by utilizing a first-order Taylor expansion and relaxation theory;
(a) Converting an objective function; using a first order Taylor approximation, equation P3 is transformed to
Figure BDA0003762435400000061
Wherein the content of the first and second substances,
Figure BDA0003762435400000062
Figure BDA0003762435400000063
for unmanned aerial vehicle node T e Taylor expansion point of distance;
(b) Data transmission rate conversion; to C 1,m Performing a first order Taylor expansion of C 2,m Converted into convex function and substituted into formula C 1,m ≥η m To obtain
Figure BDA0003762435400000064
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00037624354000000613
Figure BDA0003762435400000065
Figure BDA0003762435400000066
for unmanned aerial vehicle and node T e Obtaining a Taylor expansion point by distance; will be formula C 2,m Substitution into C 2,m ≥η m Is transformed to obtain
Figure BDA0003762435400000067
Wherein
Figure BDA0003762435400000068
Thus formula C 1,m ≥η m ,m＝1,2…M-1、C 2,m ≥η m M =1,2 … M-1 is converted to a convex function; for the same reason, formula C 2,m ≥C 2,th M =1,2 … M-1 can be converted to η m ≥C 2,th And, substituting C after first-order Taylor expansion g,m ≥C g,th ,m＝1,2…M-1；
(c) Distance constraint conversion; due to the fact that
Figure BDA0003762435400000069
Is such that constraint C is present g,m ≥C g,th M =1,2 … M-1 and
Figure BDA00037624354000000610
the expression concavity and convexity are difficult to determine; therefore, four new variables S are introduced 1,m ,S 2,m ,S g,m ,S e,m The problem is converted into a mathematical convex form by using a relaxation theory and removing integer constraint; wherein
Figure BDA00037624354000000611
Since the above formula is not a convex function; the distance constraint is changed into an inequality, and the distance constraint can be obtained
Figure BDA00037624354000000612
Figure BDA0003762435400000071
And the convex conversion of the formula is completed by using first-order Taylor expansion;
(d) Information causality conversion; will be formula C 1,m ≥η m ,C 2,m ≥η m After convex transformation, substituting into formula
Figure BDA0003762435400000072
Completing convex conversion;
(e) Carrying out flight distance constraint conversion; by the formula
Figure BDA0003762435400000073
Namely, it is
Figure BDA0003762435400000074
Is non-convex, and is converted into M-1 by using relaxation theory and first-order Taylor expansion when M =1,2
Figure BDA0003762435400000075
And
Figure BDA0003762435400000076
wherein
Figure BDA0003762435400000077
Is DD m So that P4 finally transforms into
Figure BDA0003762435400000078
Wherein
Figure BDA0003762435400000079
Is the set of all variables.
Preferably, in step 5, the convex transformation of the power optimization sub-problem is specifically:
(a) Converting an objective function; assuming that the flight trajectory of the drone is fixed, i.e. to be assembled
Figure BDA00037624354000000710
The value of sum η is set to a constant value, and the power optimization problem can ultimately be modeled as
Figure BDA00037624354000000711
Obviously, this is a non-convex problem, obtained by a first order Taylor expansion
Figure BDA00037624354000000712
(b) Constraint conversion; under the condition that the flight track of the unmanned aerial vehicle is fixed, constraint C is adjusted 1,m ≥η m Middle node T 1 And unmanned aerial vehicle transmitting power P 1,m And P r,m Respectively performing first-order Taylor expansion, and finally converting into a convex form; similarly, when M =1,2 2,m ≥η m And C g,m ≥C g,th Also converted to convex form; in addition, to the constraint
Figure BDA00037624354000000713
Convex conversion is performed in the same way, and in conclusion, P6 is finally converted into
Figure BDA00037624354000000714
With the addition of q,
Figure BDA00037624354000000715
and η, to achieve P7; to obtain the results of the measurements of, q,
Figure BDA00037624354000000716
Figure BDA00037624354000000717
and
Figure BDA00037624354000000718
is a joint optimization problem, and an interior point method can effectively obtain an optimal solution.
Preferably, in step 6, the algorithm for combining power and path planning specifically includes: iterating the trajectory and power by using a block coordinate descent method
Figure BDA0003762435400000081
When the target function convergence or difference of each of P5 and P6 is lower than the threshold, the algorithm converges with the complexity of
Figure BDA0003762435400000082
Wherein k is tr 、k p 、k q Is the iteration number of the continuous convex approximation middle track, power and q; c tr 、C p 、C q Is a barrier parameter to problem translation; xi tr 、ξ p 、ξ q For accuracy, without loss of generality, powerA track and
Figure BDA0003762435400000083
is the same, denoted as C b The algorithm complexity is
Figure BDA0003762435400000084
After the iteration of power and track is finished, when
Figure BDA0003762435400000085
When Q is a threshold, convergence is satisfied.
The invention has the beneficial effects that: the invention provides an interference coordination-oriented energy efficiency optimization method of a relay unmanned aerial vehicle, which is used for jointly optimizing transmitting power and a flight track of the unmanned aerial vehicle, converting a non-convex problem into a convex problem by using a convex optimization algorithm through constraining the speed, track coordinates, data transmission rate, causality of information and the flight distance of each time slot, and decomposing the non-convex problem into two sub-problems by using a block coordinate descent method, thereby obtaining an efficient iterative algorithm; simulation results show that the energy efficiency of the system can be improved while mutual interference is reduced.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a diagram of a full-duplex drone interference coordination model according to the present invention.
Fig. 3 is a graph of the optimum power curve and trajectory for the present invention.
Fig. 4 is a diagram illustrating the procedure of transmitting bits according to the present invention.
FIG. 5 is a graphical representation of a comparison of the performance of several algorithms of the present invention.
Detailed Description
As shown in fig. 1, an energy efficiency optimization method for a full-duplex relay unmanned aerial vehicle facing interference coordination includes the following steps:
step 1, establishing a full-duplex relay unmanned aerial vehicle interference coordination model;
step 2, defining T based on full-duplex relay unmanned aerial vehicle interference coordination communication scene model 1 UAV and UAV-T 2 Data transfer, definition T g -T e Data transmission;
step 3, transmitting and T according to relay unmanned aerial vehicle data g -T e Data transmission is carried out, and an energy efficiency formula and a constraint model are constructed by combining an unmanned aerial vehicle energy consumption formula;
step 4, determining an optimization target according to an energy efficiency formula and a constraint model, and constructing a target function;
step 5, for the objective function which is difficult to solve, performing track optimization sub-problem convex transformation and power optimization sub-problem convex transformation by using a convex optimization method;
and 6, designing a combined power and path planning algorithm, and solving a trajectory optimization sub-problem and a power optimization sub-problem after convex transformation.
In step 1, establishing a full-duplex relay unmanned aerial vehicle interference coordination model specifically comprises the following steps: full-duplex relay unmanned aerial vehicle interference coordination model with two coexisting transmitting-receiving pairs, namely T 1 -T 2 And T g -T e The working mode of the intelligent jammer of our party is full duplex, and eavesdropping is carried out on communication users of the enemy while the interference is released; let T be 1 Intended to transmit its signal to T 2 At the same time, T g There is a signal to transmit to T e However, due to severe path loss or congestion between one transceiver, no available direct channel link exists; due to the limitation of resources such as time slots and channel frequencies, communication must be carried out at the same time and the same frequency generally occurs in a device-to-device network, so that mutual interference can be generated between communication pairs, and the gyroplane flies in a designated air area to assist relay T 1 -T 2 Transmitting; in order to fully utilize the relay capacity and improve the data transmission energy efficiency, an FDR technology is applied to an unmanned aerial vehicle, and the unmanned aerial vehicle is provided with two antennas, a receiving antenna and a transmitting antenna; the unmanned aerial vehicle is also provided with a global positioning system, automatically finishes designed airlines, and automatically hovers when needed; to optimize energy efficiency, consider the optimized variables as: 1) T is 1 And the transmit power of the drone; 2) Trajectory of the drone; let T 1 ，T 2 ，T g ，T e The coordinates are respectively (x) S ，y S )，(x Z ，y Z )，(x G ，y G )，(x E ，y E ) (ii) a Assuming that the unmanned aerial vehicle flies at a fixed height H within a limited time T, the time interval of the unmanned aerial vehicle is divided into M sections, and the duration and the track length of each time slot are respectively set to be T/M, DD m In the mth time slot, the flight speed of the unmanned aerial vehicle can be approximate to
Figure BDA0003762435400000091
Meanwhile, the UAV relay is assumed to only load data in the first time slot and only unload data in the last time slot, and the UAV to the ground node T is assumed that a ground-air channel obeys free-space fading 1 And T 2 Is expressed as
Figure BDA0003762435400000092
Wherein d is i，j Are respectively a node T 1 ，T 2 ，T g ，T e Distance to the drone.
In step 2, define T 1 The UAV data transmission is in particular: in the relay unmanned aerial vehicle communication system, the signal received by the unmanned aerial vehicle is
Figure BDA0003762435400000093
Wherein S t，m 、S g，m ，S r，m Respectively represent nodes T 1 ，T 2 And a signal transmitted by the drone; p 1，m 、P g 、P r，m Respectively represent nodes T 1 ，T g And the transmit power of the drone; h is rr Is the relay self-interference channel gain; k is a radical of 0 Is a self-interference cancellation factor; n is 1 Is a mean value of
Figure BDA00037624354000001010
White gaussian noise of (1); due to the processing delay of the unmanned aerial vehicle, the received signal cannot be forwarded immediately, and if tau represents the time processing delay, the forwarded signal of the unmanned aerial vehicle in the mth time interval is S under the condition of successful decoding t,m-τ At the mth time slot, the unmanned aerial vehicle arrives at the ground node T 1 Has a data signal to interference plus noise ratio of
Figure BDA0003762435400000101
By the above, at the mth time slot, the unmanned aerial vehicle arrives at the node T 1 Has an offload channel capacity of
Figure BDA0003762435400000102
In step 2, define UAV-T 2 The data transmission specifically comprises the following steps: in relay unmanned aerial vehicle T 2 In the information system, the unmanned aerial vehicle firstly receives the ground node T 1 Then the signal is decoded and forwarded to the node T 2 (ii) a Furthermore, T 2 Will also receive a message from node T g The interference signal of (a); thus, T 2 The received signal is represented as
Figure BDA0003762435400000103
Wherein d is g,2 To T 2 The distance of (d) is a constant value; n is 2 Is a mean value of
Figure BDA0003762435400000104
White gaussian noise of (1); at the mth time slot, the unmanned aerial vehicle arrives at the ground node T 2 Has a data signal to interference plus noise ratio of
Figure BDA0003762435400000105
From above, at the mth time slot, the unmanned aerial vehicle arrives at the ground node T 2 Has an offload channel capacity of C 2,m ＝B log 2 (1+SINR 2,m )。
In step 2, define T g -T e The data transmission specifically comprises the following steps: direct connection of links T on the ground g -T e In a communication system, T e Will not only receive the message from T g The signal can receive the unmanned aerial vehicle and the node T 1 Interference of (2); thus, node T e The received signal may be represented as
Figure BDA0003762435400000106
Wherein d is 0 、dd 0 Are respectively T g -T e 、T 1 -T e The distance of (d); n is e Is a mean value of
Figure BDA0003762435400000107
White gaussian noise of (1); in the m-th time slot, T g -T e The unloaded SINR of the link is
Figure BDA0003762435400000108
From above, in the m-th slot, T g -T e Of an offload channel capacity of
Figure BDA0003762435400000109
The energy efficiency formula is specifically constructed as follows: at UAV-T 2 In the link, the unmanned aerial vehicle receives the ground node T firstly 2 Then the signal is decoded and forwarded to the node T 2 At the same time T 2 Receiving from node T g The interference signal of (a); direct connection of links T on the ground g -T e In, T e Will not only receive the message from T g The signal can receive the unmanned aerial vehicle and the node T 1 Interference of (2); thus, the unmanned aerial vehicle energy efficiency expression is
Figure BDA0003762435400000111
Wherein, P tot,m For the total power consumption of the drone at the mth time slot, therefore, the optimization problem turns into
Figure BDA0003762435400000112
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003762435400000113
namely node T 1 、T g And an unmanned aerial vehicle power set;
Figure BDA0003762435400000114
in step 3, respectively optimizing energy efficiency, the construction of the constraint model specifically comprises: the data transmission rate, the causality of information and the flight of the unmanned aerial vehicle are restrained;
(a) A data transmission rate constraint; in order to ensure the transmission delay and ensure the normal communication of the communication system, define C 2,th And C g,th As a node T 1 And T g The threshold value of the data transmission rate is as follows when M = tau, tau +1, … M-1
Figure BDA0003762435400000115
(b) Information causality constraints; for T 1 For UAV communication systems, causality of information is that the total number of bits of data loaded by a drone at a certain stage is greater than the total number of bits of data unloaded at the next stage; UAV Slave node T 1 The total number of bits of the data loaded cumulatively cannot be less than the number unloaded to the node T 2 Expressed as the total number of bits of
Figure BDA0003762435400000116
Wherein, C 1,0 Data loaded for the m =1 slot drone;
(c) Unmanned aerial vehicle flight constraints; in practice, the starting point and the end point of the drone are preset, setting x 0 ＝x S ,y 0 ＝y S ,x M-1 ＝x Z ,y M-1 ＝y Z And, in addition, setting the flying speed of the unmanned aerial vehicle to be less than the maximum flying speed V max I.e. v.ltoreq.V max Is provided with
Figure BDA0003762435400000117
Wherein the content of the first and second substances,
Figure BDA0003762435400000118
in step 4, the construction of the objective function specifically comprises: since the formula P1 is in a fractional form, the analysis of the optimization problem becomes complicated, and therefore, the fractional objective function is converted into a subtraction form using the Dinkelbach method; energy efficiency can be maximized by optimizing the transmitting power and track of the unmanned aerial vehicle, and q is set * ＝max{η EE }，
Figure BDA0003762435400000121
Wherein, the first and the second end of the pipe are connected with each other,q * is the optimal energy efficiency;
Figure BDA0003762435400000122
namely, a power solution set under the optimal energy efficiency;
Figure BDA0003762435400000123
the method comprises the following steps of (1) obtaining a track solution set under the optimal energy efficiency; the above-mentioned expression target function P1 is converted into solving formula
Figure BDA0003762435400000124
In the form of a maximum value of (i.e.
Figure BDA0003762435400000125
Thus, constructing the objective function places an emphasis on the equivalent optimization problem as described below, if and only if
Figure BDA0003762435400000126
In which min V' = -V =0 has
Figure BDA0003762435400000127
Then P2 may be converted to
Figure BDA0003762435400000128
According to the formula
Figure BDA0003762435400000129
And min V' = -V =0; must satisfy
Figure BDA00037624354000001210
When the | V' | is less than or equal to the δ, the optimum is obtained
Figure BDA00037624354000001211
Wherein δ is a preset tolerance; q is iteratively updated.
In step 5, the trajectory optimization subproblem convex transformation specifically comprises: assuming that the transmitting power of the ground node and the unmanned aerial vehicle is a fixed value, the track optimization problem can be expressed as
Figure BDA00037624354000001212
Due to the presence of non-convex terms P in the objective function tot,m In which there is C 1,m ，C 2,m And C g,m Causing this to be a non-convex problem; converting the track optimization problem into a convex problem by utilizing a first-order Taylor expansion and relaxation theory;
(a) And (5) converting the objective function. Using a first order Taylor approximation, equation P3 is transformed to
Figure BDA00037624354000001213
Wherein the content of the first and second substances,
Figure BDA00037624354000001214
Figure BDA00037624354000001215
for unmanned aerial vehicle node T e Taylor expansion point of distance;
(b) Data transmission rate conversion; to C 1,m Performing a first order Taylor expansion of C 2,m Converting into convex function, substituting into formula C 1,m ≥η m To obtain
Figure BDA0003762435400000131
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003762435400000132
Figure BDA0003762435400000133
for unmanned aerial vehicle and node T e Obtaining a Taylor expansion point by distance; will be formula C 2,m Substitution into C 2,m ≥η m Is converted to obtain
Figure BDA0003762435400000134
Wherein
Figure BDA0003762435400000135
Thus formula C 1,m ≥η m ,m＝1,2....M-1、C 2,m ≥η m M =1,2.. M-1 is converted to a convex function; for the same reason, formula C 2,m ≥C 2,th ,m＝1,2. M-1 can be converted to η m ≥C 2,th And, substituting C after first-order Taylor expansion g,m ≥C g,th ,m＝1,2...M-1；
(c) Distance constraint conversion; due to the fact that
Figure BDA0003762435400000136
Is such that constraint C is present g,m ≥C g,th M =1,2
Figure BDA0003762435400000137
The expression unevenness is difficult to determine; therefore, four new variables S are introduced 1,m ,S 2,m ,S g,m ,S e,m The problem is converted into a mathematical convex form by using a relaxation theory and removing integer constraint; wherein
Figure BDA0003762435400000138
Since the above formula is not a convex function; the distance constraint is changed into an inequality, and the distance constraint can be obtained
Figure BDA0003762435400000139
Figure BDA00037624354000001310
And the convex conversion of the formula is completed by using first-order Taylor expansion;
(d) Information causality conversion; will be formula C 1,m ≥η m ,C 2,m ≥η m After convex transformation, substituting into formula
Figure BDA00037624354000001311
Completing convex conversion;
(e) Carrying out flight distance constraint conversion; by the formula
Figure BDA00037624354000001312
Namely, it is
Figure BDA00037624354000001313
Is non-convex, when M =1,2.. M-1, using relaxation theory and first order Taylor expansionIs converted into
Figure BDA0003762435400000141
And
Figure BDA0003762435400000142
wherein
Figure BDA0003762435400000143
Is DD m The taylor expansion point of (1). Thus, P4 eventually converts to
Figure BDA0003762435400000144
Wherein
Figure BDA0003762435400000145
Is the set of all variables.
In step 5, the convex transformation of the power optimization subproblem specifically comprises the following steps:
(a) Converting an objective function; assuming that the flight trajectory of the drone is fixed, i.e. to be assembled
Figure BDA0003762435400000146
The value of sum η is set to a constant value, and the power optimization problem can ultimately be modeled as
Figure BDA0003762435400000147
Obviously, this is a non-convex problem, obtained by a first order Taylor expansion
Figure BDA0003762435400000148
(b) Constraint conversion; under the condition that the flight track of the unmanned aerial vehicle is fixed, constraint C is adjusted 1,m ≥η m Middle node T 1 And unmanned aerial vehicle transmitting power P 1,m And P r,m Respectively performing first-order Taylor expansion, and finally converting into a convex form; similarly, when M =1,2 2,m ≥η m And C g,m ≥C g,th Also converted into convex form; in addition, to the constraint
Figure BDA0003762435400000149
Convex conversion is performed in the same way, and in conclusion, P6 is finally converted into
Figure BDA00037624354000001410
With the addition of q,
Figure BDA00037624354000001411
and eta, realizing P7; so as to obtain the q of the compound,
Figure BDA00037624354000001412
and
Figure BDA00037624354000001413
is a joint optimization problem, and an interior point method can effectively obtain an optimal solution.
In step 6, the combined power and path planning algorithm specifically comprises: iterating the trajectory and power by using a block coordinate descent method
Figure BDA00037624354000001414
When the respective objective functions of P5 and P6 converge or the difference is below a threshold, the algorithm converges. With a complexity of
Figure BDA00037624354000001415
Wherein k is tr 、k p 、k q Is the iteration times of the continuous convex approximation mid-trajectory, power, q; c tr 、C p 、C q Is a barrier parameter to problem translation; xi tr 、ξ p 、ξ q For accuracy, without loss of generality, power, trajectory, and
Figure BDA00037624354000001416
is the same, denoted as C b The algorithm complexity is
Figure BDA00037624354000001417
After the iteration of power and track is finished, when
Figure BDA0003762435400000151
When Q is a threshold, convergence is satisfied.
Fig. 2 shows a working scenario diagram of the drone relay. The unmanned aerial vehicle is used as a relay and needs to be connected with a slave ground node T 1 Receiving information and forwarding to ground node T 2 . Ground node T while performing relay work g And T e Information is also being transmitted. Thus, ground node T g Can interfere with the information transmission relayed by the drone. How to optimize the information transmission of the relay of the unmanned aerial vehicle and realize the maximization of the energy efficiency of the unmanned aerial vehicle is a research problem of the invention.
Fig. 3 shows the optimal power curve and trajectory of the drone as a function of x-axis position, respectively. Without loss of generality, T g ＝(500，500)，T e = (2000, 500). The following five conclusions can be drawn.
(1) As shown in fig. 3 (a) and 3 (b), the ground node T is present in the 2 nd to 6 th time periods 1 Is taken as P s，min Optimal power of UAV, P r，max . This is because UAVs and Ts 2 Is far away, whereas UAV and T 1 Is much lower than the number of bits received by the UAV. And the UAV only receives and does not forward the number of bits of the 1 st time slot, the data starts to accumulate. To reduce the number of accumulated bits, the number of bits can be reduced by reducing the ground node T 1 Increasing the power of the UAV to achieve
(2) After the sixth time slot, the ground node T 1 Always takes the maximum value P s，max . This is because the UAV approaches T g At times, the UAV's expressive power is higher than receptive power. In this case, the ground node T is intended to improve energy efficiency 1 Increasing its transmission power.
(3) Following UAV with T 2 The decrease in distance between, the transmission rate of the UAV increases. But due to the limited reception capabilities of the UAV, the data accumulated by the drone is cleared during each timeslot. In this case, the UAV does not require high transmit power to guarantee output capability. The power of the UAV is reduced as shown in fig. 3 (b).
(4) In addition, as shown in FIG. 3 (c), when the UAV is connectedNear T g And T e Time, T is greater due to the greater proportion of UAV transmissions in EE g And T e Is far away. To ensure maximum EE, T is reduced g The power of the UAV antenna reduces interference and improves UAV transmission.
(5) Also, as shown in FIG. 3 (d), when the UAV approaches T g And T e In order to reduce interference with the UAV, the UAV avoids T g Thereby improving energy efficiency.
Fig. 4 shows the number of transmission bits for different time slots. Note that the "UAV buffered" curve represents the number of bits accumulated at the UAV, and the "UAV forwarded" curve represents the number of bits the UAV forwarded on each time slot. As shown in fig. 5, the difference between the "UAV cached and UAV forwarded" curve and the "UAV received" curve is zero, indicating that the information causality is satisfied. In addition, consistent with fig. 4, in the 10 th time slot, the accumulated number of bits is zero, which indicates that the number of bits for UAV data forwarding is the same as the number of bits received.
FIG. 5 shows a comparison of the performance of several algorithms, due to T g And T e In different locations, performance comparisons with algorithms such as "full duplex-brute force search", "full duplex-actual utility", "full duplex-genetic algorithm", "full duplex-random selection", and "half duplex = brute force search" are shown. The full duplex-brute force search algorithm in the simulation finds the optimal solution under the full duplex scheme by traversing all feasible parameters; the full duplex-genetic algorithm is a heuristic algorithm for searching an optimal solution under a full duplex scheme; the full duplex-random selection algorithm is realized by multiple random selections and calculation of an average value under a full duplex scheme; the "half-duplex-brute-force search" algorithm finds the optimal solution under the half-duplex scheme by traversing all the feasible parameters. The following conclusions can be drawn.
(1) Specifically, at T g And T e The EE obtained by the algorithm of the invention is well matched with the full duplex-brute force search algorithm, which shows that the solution of convex approximation is optimal.
(2) The difference between the two algorithms is less than 2% compared to "full duplex-real utility" without approximation. Therefore, the approximation of the proposed algorithm is very close to the original optimization problem.
(3) Due to the randomness of the "full duplex-genetic algorithm" and the "full duplex-random selection" algorithms, EEs of these two algorithms is significantly lower than the algorithm of the present invention.
(4) Note that the EE for "full duplex-violence search" is almost twice that for "half duplex-violence search". This is because the FD simultaneous transmission and reception can increase the transmission rate.
(5) Furthermore, with T g And T e Increase in distance, channel capacity C of these algorithms g，m And decreases. The results show that the efficiency of all algorithms is reduced.

Claims (10)

1. An energy efficiency optimization method for a full-duplex relay unmanned aerial vehicle facing interference coordination is characterized by comprising the following steps:
step 1, establishing a full-duplex relay unmanned aerial vehicle interference coordination model;
step 2, defining T based on full-duplex relay unmanned aerial vehicle interference coordination communication scene model 1 UAV and UAV-T 2 Data transmission, definition T g -T e Data transmission;
step 3, transmitting and T according to relay unmanned aerial vehicle data g -T e Data transmission is carried out, and an energy efficiency formula and a constraint model are constructed by combining an unmanned aerial vehicle energy consumption formula;
step 4, determining an optimization target according to an energy efficiency formula and a constraint model, and constructing a target function;
step 5, for the objective function which is difficult to solve, performing track optimization sub-problem convex transformation and power optimization sub-problem convex transformation by using a convex optimization method;
and 6, designing a combined power and path planning algorithm, and solving a trajectory optimization sub-problem and a power optimization sub-problem after convex transformation.
2. The method for energy efficiency optimization of full-duplex relay unmanned aerial vehicle facing interference coordination according to claim 1, wherein in step 1, full-duplex relay unmanned aerial vehicle is establishedThe machine interference coordination model specifically comprises the following steps: full-duplex relay unmanned aerial vehicle interference coordination model with two coexisting transmitting-receiving pairs, namely T 1 -T 2 And T g -T e The working mode of the intelligent jammer of our party is full duplex, and eavesdropping is carried out on communication users of the enemy while the interference is released; let T be 1 Intended to transmit its signal to T 2 At the same time, T g There is a signal to transmit to T e In the designated air area, the gyroplane flies to assist the relay T 1 -T 2 Transmitting, namely applying an FDR technology and a global positioning system on the unmanned aerial vehicle; to optimize energy efficiency, consider the optimized variables as: 1) T is 1 And the transmit power of the drone; 2) Trajectory of the drone; let T 1 ，T 2 ，T g ，T e The coordinates are respectively (x) S ，y S )，(x Z ，y Z )，(x G ，y G )，(x E ，y E ) (ii) a Assuming that the unmanned aerial vehicle flies at a fixed height H within a limited time T, the time interval of the unmanned aerial vehicle is divided into M sections, and the duration and the track length of each time slot are respectively set to be T/M, DD m In the mth time slot, the flying speed of the unmanned aerial vehicle is approximately equal to
Figure FDA0003762435390000011
Meanwhile, the UAV relay is assumed to only load data in the first time slot and only unload data in the last time slot, and the UAV to the ground node T is assumed that a ground-air channel obeys free-space fading 1 And T 2 Is expressed as
Figure FDA0003762435390000012
Wherein d is i,j Are respectively a node T 1 ，T 2 ，T g ，T e Distance to the drone.
3. The method for energy efficiency optimization of full-duplex relay unmanned aerial vehicle facing interference coordination according to claim 1, wherein in step 2, T is defined 1 The UAV data transmission is in particular: nobody is in relayIn the machine communication system, the signal received by the unmanned aerial vehicle is
Figure FDA0003762435390000021
Figure FDA0003762435390000022
Wherein S t,m 、S g,m ，S r,m Respectively represent nodes T 1 ，T 2 And a signal transmitted by the drone; p is 1,m 、P g 、P r,m Respectively represent nodes T 1 ，T g And the transmit power of the drone; h is rr Is the relay self-interference channel gain; k is a radical of 0 Is a self-interference cancellation factor; n is 1 Is a mean value of
Figure FDA0003762435390000023
White gaussian noise; due to the processing delay of the unmanned aerial vehicle, the received signal cannot be forwarded immediately, and if tau represents the time processing delay, the forwarded signal of the unmanned aerial vehicle in the mth time interval is S under the condition of successful decoding t,m-τ At the mth time slot, the unmanned aerial vehicle arrives at the ground node T 1 Has a data signal to interference plus noise ratio of
Figure FDA0003762435390000024
By the above, at the mth time slot, the unmanned aerial vehicle arrives at the node T 1 Of an offload channel capacity of
Figure FDA0003762435390000025
Defining UAV-T 2 The data transmission specifically comprises the following steps: in relay unmanned aerial vehicle T 2 In the information system, the unmanned aerial vehicle firstly receives the ground node T 1 Then the signal is decoded and forwarded to the node T 2 (ii) a Furthermore, T 2 Will also receive a message from node T g The interference signal of (a); thus, T 2 The received signal is represented as
Figure FDA0003762435390000026
Wherein d is g,2 To T 2 The distance of (d) is a constant value; n is a radical of an alkyl radical 2 Is a mean value of
Figure FDA0003762435390000027
White gaussian noise of (1); at the mth time slot, the unmanned aerial vehicle arrives at the ground node T 2 Has a data signal to interference plus noise ratio of
Figure FDA0003762435390000028
From above, at the mth time slot, the unmanned aerial vehicle arrives at the ground node T 2 Has an offload channel capacity of C 2,m ＝B log 2 (1+SINR 2,m )。
4. The method for optimizing energy efficiency of full-duplex relay unmanned aerial vehicle facing interference coordination according to claim 1, wherein in step 2, T is defined g -T e The data transmission specifically comprises the following steps: directly connecting links T on the ground g -T e In a communication system, T e Will not only receive the message from T g The signal of (2) can receive the unmanned aerial vehicle and the node T 1 Interference of (2); thus, node T e The received signal is represented as
Figure FDA0003762435390000029
Wherein d is 0 、dd 0 Are respectively T g -T e 、T 1 -T e The distance of (d); n is e Is a mean value of
Figure FDA00037624353900000210
White gaussian noise of (1); in the m-th time slot, T g -T e The offload SINR of the link is
Figure FDA0003762435390000031
From above, in the m-th slot, T g -T e Has an offload channel capacity of
Figure FDA0003762435390000032
5. The energy efficiency optimization method for the full-duplex relay unmanned aerial vehicle facing the interference coordination as claimed in claim 1, wherein in step 3, the energy efficiency formula is specifically constructed as follows: at UAV-T 2 In the link, the unmanned aerial vehicle firstly receives the ground node T 2 Then the signal is decoded and forwarded to the node T 2 At the same time T 2 Receiving from node T g The interference signal of (a); direct connection of links T on the ground g -T e In, T e Will not only receive the message from T g The signal can receive the unmanned aerial vehicle and the node T 1 Interference of (2); thus, the unmanned aerial vehicle energy efficiency expression is
Figure FDA0003762435390000033
Wherein, P tot,m For the total power consumption of the drone at the mth time slot, therefore, the optimization problem turns into
Figure FDA0003762435390000034
Wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003762435390000035
namely node T 1 、T g And an unmanned aerial vehicle power set;
Figure FDA0003762435390000036
respectively is an unmanned aerial vehicle horizontal coordinate set and an unmanned aerial vehicle vertical coordinate set under the optimal energy efficiency.
6. The energy efficiency optimization method for the full-duplex relay unmanned aerial vehicle facing the interference coordination as claimed in claim 1, wherein in the step 3, the construction of the constraint model specifically comprises: the data transmission rate, the causality of information and the flight of the unmanned aerial vehicle are restrained;
(a) A data transmission rate constraint; in order to ensure the transmission delay and ensure the normal communication of the communication system, define C 2,th And C g,th As a node T 1 And T g M-1, when M = τ, τ +1
Figure FDA0003762435390000037
(b) Information causality constraints; for T 1 For UAV communication systems, causality of information is that the total number of bits of data loaded by a drone at a certain stage is greater than the total number of bits of data unloaded at the next stage; UAV Slave node T 1 The total number of bits of the data loaded cumulatively cannot be less than the number unloaded to the node T 2 Expressed as the total number of bits of
Figure FDA0003762435390000041
Wherein, C 1,0 Data loaded for the m =1 slot drone;
(c) Unmanned aerial vehicle flight constraints; in practice, the starting point and the end point of the drone are preset, setting x 0 ＝x S ,y 0 ＝y S ,x M-1 ＝x Z ,y M-1 ＝y Z Furthermore, the flying speed of the unmanned aerial vehicle is set to be lower than the maximum flying speed V max I.e. v.ltoreq.V max Is provided with
Figure FDA0003762435390000042
Wherein the content of the first and second substances,
Figure FDA0003762435390000043
7. the method for optimizing energy efficiency of the full-duplex relay unmanned aerial vehicle for interference coordination according to claim 1, wherein in the step 4, the establishment of the objective function specifically comprises: since the formula P1 is in a fractional form, the analysis of the optimization problem becomes complicated, and therefore, the fractional objective function is converted into a subtraction form using the Dinkelbach method; maximizing energy efficiency by optimizing the transmitting power and the track of the unmanned aerial vehicle and enabling q to be obtained * ＝max{η EE }，
Figure FDA0003762435390000044
Wherein q is * Is the optimal energy efficiency;
Figure FDA0003762435390000045
namely, a power solution set under the optimal energy efficiency;
Figure FDA0003762435390000046
the method comprises the following steps of (1) obtaining a track solution set under the optimal energy efficiency; the above-mentioned expression target function P1 is converted into solving formula
Figure FDA0003762435390000047
In the form of a maximum value of (i.e.
Figure FDA0003762435390000048
Thus, constructing the objective function places an emphasis on the equivalent optimization problem as described below, if and only if
Figure FDA0003762435390000049
minV' = -V =0 has
Figure FDA00037624353900000410
Then P2 is converted into
Figure FDA00037624353900000411
According to the formula
Figure FDA00037624353900000412
And minV' = -V =0; must satisfy
Figure FDA00037624353900000413
When | V' | is less than or equal to δ, obtaining the optimal q * ,
Figure FDA00037624353900000414
η * (ii) a Wherein δ is a preset tolerance; q is iteratively updated.
8. The full-duplex relay drone energy efficiency for interference coordination according to claim 1The optimization method is characterized in that in the step 5, the trajectory optimization subproblem convex transformation specifically comprises the following steps: assuming that the transmitting power of the ground node and the unmanned aerial vehicle is constant, the track optimization problem is expressed as
Figure FDA0003762435390000051
Due to the presence of non-convex terms P in the objective function tot,m In which there is C 1,m ，C 2,m And C g,m Causing this to be a non-convex problem; converting the trajectory optimization problem into a convex problem by using a first-order Taylor expansion and relaxation theory;
(a) Converting an objective function; using a first order Taylor approximation, equation P3 is transformed to
Figure FDA0003762435390000052
Wherein the content of the first and second substances,
Figure FDA0003762435390000053
Figure FDA0003762435390000054
for unmanned aerial vehicle node T e Taylor's expansion point of distance;
(b) Data transmission rate conversion; to C 1,m Performing a first order Taylor expansion of C 2,m Converting into convex function, substituting into formula C 1,m ≥η m To obtain
Figure FDA0003762435390000055
Wherein the content of the first and second substances,
Figure FDA0003762435390000056
Figure FDA0003762435390000057
for unmanned aerial vehicle and node T e Obtaining a Taylor expansion point by distance; will be formula C 2,m Substitution into C 2,m ≥η m Is converted into
Figure FDA0003762435390000058
Wherein
Figure FDA0003762435390000059
Thus formula C 1,m ≥η m ,m＝1,2....M-1、C 2,m ≥η m M =1,2.. M-1 is converted to a convex function; for the same reason, formula C 2,m ≥C 2,th M =1,2 m ≥C 2,th In addition, substitution of C after first-order Taylor expansion g,m ≥C g,th ,m＝1,2...M-1；
(c) Distance constraint conversion; due to the fact that
Figure FDA00037624353900000510
Is such that constraint C is present g,m ≥C g,th M =1,2
Figure FDA00037624353900000511
The expression unevenness is difficult to determine; thus, four new variables S are introduced 1,m ,S 2,m ,S g,m ,S e,m The problem is converted into a mathematical convex form by using a relaxation theory and removing integer constraint; wherein
Figure FDA00037624353900000512
Since the above formula is not a convex function; the distance constraint is changed into an inequality to obtain
Figure FDA00037624353900000513
Figure FDA0003762435390000061
And the convex conversion of the formula is completed by using first-order Taylor expansion;
(d) Information causality conversion; will be formula C 1,m ≥η m ,C 2,m ≥η m After convex transformation, substituting into formula
Figure FDA0003762435390000062
Completing convex conversion;
(e) Carrying out flight distance constraint conversion; by the formula
Figure FDA0003762435390000063
Namely, it is
Figure FDA0003762435390000064
Is non-convex, and is converted into M-1 by using relaxation theory and first-order Taylor expansion when M =1,2
Figure FDA0003762435390000065
And
Figure FDA0003762435390000066
wherein
Figure FDA0003762435390000067
Is DD m So that P4 finally transforms into
Figure FDA0003762435390000068
Wherein
Figure FDA0003762435390000069
Is the set of all variables.
9. The energy efficiency optimization method for the full-duplex relay unmanned aerial vehicle facing the interference coordination as claimed in claim 1, wherein in step 5, the convex transformation of the power optimization sub-problem specifically is as follows:
(a) Converting an objective function; assuming that the flight trajectory of the drone is fixed, i.e. to be assembled
Figure FDA00037624353900000610
The value of sum η is set to a constant value and the power optimization problem is finally modeled as
Figure FDA00037624353900000611
Obviously, this is oneA non-convex problem, developed by a first order Taylor expansion
Figure FDA00037624353900000612
(b) Constraint conversion; under the condition that the flight track of the unmanned aerial vehicle is fixed, constraint C is adjusted 1,m ≥η m Middle node T 1 And unmanned aerial vehicle transmitting power P 1,m And P r,m Respectively performing first-order Taylor expansion, and finally converting into a convex form; similarly, when M =1,2 2,m ≥η m And C g,m ≥C g,th Also converted to convex form; in addition, to the constraint
Figure FDA00037624353900000613
Convex conversion is performed in the same way, and in conclusion, P6 is finally converted into
Figure FDA00037624353900000614
With the flow of q in mind,
Figure FDA00037624353900000615
and η, to achieve P7; to obtain the results of the measurements of, q,
Figure FDA00037624353900000616
Figure FDA00037624353900000617
and
Figure FDA00037624353900000618
is a joint optimization problem, and an interior point method effectively obtains an optimal solution.
10. The energy efficiency optimization method for the full-duplex relay unmanned aerial vehicle facing the interference coordination as claimed in claim 1, wherein in step 6, the joint power and path planning algorithm specifically comprises: iterating the trajectory and power by using a block coordinate descent method
Figure FDA0003762435390000071
When the target function convergence or difference of each of P5 and P6 is lower than the threshold, the algorithm converges with the complexity of
Figure FDA0003762435390000072
Wherein k is tr 、k p 、k q Is the iteration times of the continuous convex approximation mid-trajectory, power, q; c tr 、C p 、C q Is a barrier parameter to problem translation; xi tr 、ξ p 、ξ q For accuracy, without loss of generality, power, trajectory, and
Figure FDA0003762435390000073
is the same, denoted as C b The algorithm complexity is
Figure FDA0003762435390000074
After the iteration of power and track is finished, when
Figure FDA0003762435390000075
When Q is a threshold, convergence is satisfied.
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