CN112532300A - Trajectory optimization and resource allocation method for single unmanned aerial vehicle backscatter communication network - Google Patents

Trajectory optimization and resource allocation method for single unmanned aerial vehicle backscatter communication network Download PDF

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CN112532300A
CN112532300A CN202011342707.2A CN202011342707A CN112532300A CN 112532300 A CN112532300 A CN 112532300A CN 202011342707 A CN202011342707 A CN 202011342707A CN 112532300 A CN112532300 A CN 112532300A
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李曦
唐国强
纪红
张鹤立
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Beijing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B7/14—Relay systems
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract

The invention provides a trajectory optimization and resource allocation method for a single unmanned aerial vehicle backscatter communication network, and belongs to the technical field of unmanned aerial vehicle backscatter communication networks. The method comprises the following steps: step 1, clustering a target area formed by all ground backscatter nodes according to node positions, dividing the target area into N cells, and acquiring the mass center position of each cell; step 2, setting an unmanned aerial vehicle to continuously fly among cells, and hovering and acquiring data above the centroid position of each cell; step 3, establishing an optimization target of single unmanned aerial vehicle backscatter communication network trajectory optimization and resource allocation; step 4, decoupling the optimized target, converting the decoupled target into the hovering time of each cell and the proportional division problem of the collected energy and the transmission time in each cell, iteratively solving, and outputting the optimal service time T of each celliAnd a proportional parameter beta in each celli. The method reduces the energy consumption of the unmanned aerial vehicle, and has the maximum rangeTime resources are utilized in the degree, and the system energy efficiency is obviously improved.

Description

Trajectory optimization and resource allocation method for single unmanned aerial vehicle backscatter communication network
Technical Field
The invention relates to the technical field of unmanned aerial vehicle backscatter communication networks, in particular to a single unmanned aerial vehicle backscatter communication network trajectory optimization and resource allocation method oriented to energy efficiency optimization.
Background
As a cost-effective communication technique, the proposal of the backscatter communication technique provides an effective solution for a large number of energy-limited low-cost devices. The backscatter transmitter transmits information by reflecting the modulated signal without the need for active radio frequency components, reducing circuit power consumption by several orders of magnitude. In an ambient Backscatter network, Backscattering Nodes (BNs) may obtain the energy required for their operation from an ambient radio source such as a Wi-Fi signal. The BN collects energy from Radio Frequency (RF) signals emitted by the signal source and then forwards the data information to the receiver by backscattering. However, the data rate of the BN is low due to severe channel fading, and the communication range is limited due to limited available environmental energy.
An Unmanned Aerial Vehicle (UAV) performs well in providing wireless connection, and can implement functions such as relaying, data acquisition, or radio frequency signal sources by being equipped with a message processing or communication module. Unmanned aerial vehicle assisted networks have attracted increasing research attention due to their high mobility, ease of deployment, and high probability of line-of-sight connectivity with ground users. And the unmanned aerial vehicle is adopted for carrying out backscattering communication, so that the communication range can be greatly expanded, and the communication quality of the node is improved. Under the region that the node distribution is intensive and the scope is wider, there are two main ways that unmanned aerial vehicle gathered data, one is continuous flight, and one is hovering. How to balance the communication link status and transmission quality between the drone and BNs is one of the key issues that needs attention.
In drone-assisted backscatter communications networks, reference [1] considers a drone-assisted backscatter communications network in which ground users transmit data information to a flying drone by backscatter and optimize energy efficiency in consideration of the drone trajectory. Reference [2] considers the problem of uplink throughput maximization in a drone-assisted wireless backscatter network, where ground devices transmit data by the NOMA (Non-orthogonal Multiple Access) method. Reference [3] adopts a Time Division Multiple Access (TDMA) technology, so that a plurality of ground users upload their own data information to an unmanned aerial vehicle, and then the unmanned aerial vehicle flies to a base station to upload acquired data, thereby optimizing the energy efficiency of the system in consideration of the outage probability. At present, most of research on unmanned aerial vehicle-assisted backscatter communication networks considers that unmanned aerial vehicles work in a single mode, and the modes for collecting data by the unmanned aerial vehicles mainly comprise two modes, namely continuous flight and hovering. Continuous flight of a drone brings it closer to the distributed equipment, but the communication link is unstable during flight. When the unmanned aerial vehicle hovers at a certain specific position, the communication link state is better, but the communication quality of the edge device is difficult to guarantee. Therefore, how to balance the communication link status and transmission quality between the drone and BNs is one of the key issues that needs attention. In addition, existing research is directed to the energy problem of ground users, and does not consider the energy consumption of the unmanned aerial vehicle.
Reference documents:
[1]G.Yang,R.Dai and Y.Liang,“Energy-Efficient UAV Backscatter Communication with Joint Trajectory and Resource Optimization,”ICC 2019-2019IEEE International Conference on Communications(ICC),Shanghai,China,2019,pp.1-6.
[2]A.Farajzadeh,O.Ercetin and H.Yanikomeroglu,“UAV Data Collection Over NOMA Backscatter Networks:UAV Altitude and Trajectory Optimization,”ICC 2019-2019IEEE International Conference on Communications(ICC),Shanghai,China,2019,pp.1-7.
[3]S.Yang,Y.Deng,X.Tang,Y.Ding and J.Zhou,“Energy Efficiency Optimization for UAV-Assisted Backscatter Communications,”in IEEE Communications Letters,vol.23,no.11,pp. 2041-2045,Nov.2019.
disclosure of Invention
Aiming at the weight balance problem of the link state and the communication quality between an unmanned aerial vehicle and nodes under the scene of an unmanned aerial vehicle-assisted backscatter communication network, the invention provides an energy efficiency optimization-oriented single-unmanned aerial vehicle backscatter communication network trajectory optimization and resource allocation method.
The invention provides an energy efficiency optimization-oriented single unmanned aerial vehicle backscatter communication network trajectory optimization and resource allocation method, which comprises the following steps:
step 1, clustering a target area formed by all ground backscatter nodes according to node positions, dividing the target area into N cells, and acquiring the mass center position of each cell; n is a positive integer;
step 2, setting an unmanned aerial vehicle to continuously fly among cells, hovering and acquiring data above the centroid position of each cell, and after the data of the current cell is collected, flying to the next sub-region; the flight trajectory of the drone may be represented as a permutation and combination of the centroid positions of the cells;
it hovers at district D to establish unmanned aerial vehicleiHas a service time of TiI is 1,2, … N; wherein at time betaiTiIn the method, the unmanned aerial vehicle and the ground backscatter node collect energy from a radio frequency signal emitted by a signal tower and the rest time (1-beta)i)TiInternal and ground backscatter node backscatter transmits data to the drone, proportional parameter betaiGreater than 0 and less than 1;
step 3, establishing an optimization target of single unmanned aerial vehicle backscatter communication network trajectory optimization and resource allocation, namely: obtaining the service time T of each cell when the energy consumption q of the single unmanned-machine backscatter communication network is maximumiAnd a proportional parameter betai，i＝1,2,…N；
The energy consumption q is the throughput of all ground backscatter nodes/the total energy consumption of the unmanned aerial vehicle;
the constraint conditions to be met in the solution process comprise:
c1: the energy collected by each ground backscatter node in the cell needs to meet the minimum collected energy Enij；
C2: at time (1-. beta.)i)TiThe data transmission quantity of all ground backscatter nodes in the cell is met;
c3: the total flight time of the unmanned aerial vehicle plus the residence time of each cell is the set working time of the unmanned aerial vehicle;
C4：βigreater than 0 and less than 1;
C5：Tigreater than 0;
step 4, decoupling the optimization target in the step 3 by using a Dinkelbach-based score planning method, converting the decoupling into the problem of proportional division of the hovering time of each cell and the collected energy and the transmission time in each cell, then using a CVX tool to carry out iterative solution, and outputting the optimal service time T of each celliAnd a proportional parameter beta in each celli，i＝1,2,…N。
Compared with the prior art, the invention has the advantages and positive effects that: (1) the method reduces the energy consumption of the unmanned aerial vehicle, and simulation results show that compared with an equal-time service scheme that the hovering time of the unmanned aerial vehicle in each sub-area is consistent, the method takes the influence of the number of BN nodes in each sub-area into consideration, so that the unmanned aerial vehicle can flexibly adjust the hovering time in each sub-area, and the time resource is utilized to the greatest extent. (2) The method can meet the requirement of a user for transmitting data volume, and compared with an unmanned aerial vehicle hovering acquisition scheme that an unmanned aerial vehicle hovers right above a PB to acquire data of all nodes, the unmanned aerial vehicle hovers in a cell and flies among the cells in a compromise flight mode, so that the communication requirement of the edge nodes is guaranteed. (3) The method realizes better balance in optimizing the link state between the unmanned aerial vehicle and the node and solving the problem of communication quality, and obviously improves the energy efficiency of the system compared with the prior art.
Drawings
FIG. 1 is a schematic diagram of a network scenario model under study in accordance with the present invention;
FIG. 2 is a schematic diagram of a time slot structure of a ground backscatter node communication;
FIG. 3 is a flow chart illustrating a solution of the time resource allocation problem according to the present invention;
FIG. 4 is a pseudo code diagram of the present invention for solving the time resource allocation problem;
fig. 5 is a schematic diagram of BN node locations and cell divisions provided by a simulation experiment of the present invention;
FIG. 6 is a schematic diagram of energy efficiency convergence of the method of the present invention at different PB transmission powers;
FIG. 7 is a schematic diagram of the hovering time and time scale of each cell under different PB transmitting powers;
fig. 8 is a graph comparing EE and PB power for different drone acquisition data scenarios.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The single unmanned aerial vehicle backscatter communication network track optimization and resource allocation method for energy efficiency optimization jointly considers unmanned aerial vehicle tracks and time resource allocation, and a researched scene is a single unmanned aerial vehicle-assisted backscatter communication network. In the scenario shown in fig. 1, a gyroplane UAV collects data from M (M ≧ 1) ground backscatter nodes BN, which transmit the data by reflecting RF radio frequency signals emitted by PB (Power Beacon, Beacon); the ground backscatter nodes are uniformly and independently distributed around the PB; the drone and the reflection node BN can take energy from the PB. The height of PB is H, a stable energy source is provided, the wireless energy source of the system is a transmission power P of PBBAnd (4) showing.
Establishing a three-dimensional rectangular coordinate system based on PB, establishing an xy plane by using a ground plane, wherein a z axis is vertical to the ground, and the coordinate of PB is IPB(0,0, H), the coordinates of the drone are Iuav(x, y, h), h is unmanned aerial vehicle's height of hovering. And setting the operation time of the unmanned aerial vehicle for one circle of flight as T (T is more than 0).
Firstly, clustering a target area consisting of all ground backscatter nodes according to a clustering algorithm of K-Means clustering, dividing the target area into N sub-areas, and using D for each sub-areaiI ═ {1,2, …, N }, sub-region DiU for internal reflection nodeij,i＝{1,2,…,N},j＝{1,2,…,NiDenotes that N isiIs a sub-region DiThe number of reflective nodes within. Obtaining the mass center of N sub-regions, and using C ═ ce1,ce2,…,ceN]And (4) showing. The flight trajectory of the drone can be regarded as a permutation and combination of N centroids, i.e., Tr ═ ce(1),ce(2),…,ce(N),ce(N+1)]In which ce(N+1)＝ce(1)I.e. the starting point and the end point of the drone are co-located, ce(i)And (4) projecting coordinates of the hovering position of the unmanned aerial vehicle in the ith sub-area.
The channel model of the drone assisted backscatter communications network scenario is analyzed below. The link of the PB-BN and the link of the PB-UAV are modeled by a free space channel model. Wherein channel gain of PB-BN link
Figure BDA0002798967730000041
Channel gain for PB-UAV link is expressed as
Figure BDA0002798967730000042
Where, β is the unit path loss,
Figure BDA0002798967730000049
is PB and node UijDistance between dFPIs the distance between the drone and the PB.
UAVs are prone to establish G2A (Ground-to-Air) or A2G (Air-to-Ground) channels with Ground nodes, and suffer from path loss of line-of-sight (LoS) or non-line-of-sight (NLoS) links with a certain probability. Wherein, the LoS link depends on the positions of the unmanned aerial vehicle and the nodes, the density of the buildings, the physical environment and the like, and the probability of the path loss of the LoS link can be expressed as P LoS1/1+ aexp (-b (θ -a)), where θ is the angle between the UAV and BN, a and b are environment-dependent constants, and the angle is
Figure BDA00027989677300000410
Wherein the content of the first and second substances,
Figure BDA00027989677300000411
is UAV and node UijThe distance between them. Accordingly, the probability of a path loss of NLoS may be expressed as PNLoS＝1-PLoS。
UAV and BN node UijLoS path loss therebetween can be expressed as LLoS,ijThe following are:
Figure BDA0002798967730000043
wherein f represents the carrier frequency, ηLoSRepresenting the LoS link attenuation factor and c representing the electromagnetic wave propagation rate. Similarly, the NLoS path loss is expressed as:
Figure BDA0002798967730000044
wherein eta isNLoSRepresenting the attenuation factor of the NLoS link. Thus, the average path fading of the UAV-BN link can be expressed as:
Figure BDA0002798967730000045
the link between PB and UAV is the G2A channel model. Fading with UAV-BN link
Figure BDA0002798967730000046
Similarly, the channel fading between PB and UAV may be represented as gFPWill be
Figure BDA0002798967730000047
In the expression
Figure BDA0002798967730000048
Substituted by the distance d between PB and UAVFPAnd (4) finishing.
Considering the requirement of the user to the transmission data volume, the invention divides the user into a plurality of sub-regions based on the user distributionThe information acquisition of all nodes is realized by a compromise flight mode that an unmanned aerial vehicle hovers above a centroid in a cell to acquire data and continuously flies in the cell. The scheme for collecting data by the single unmanned aerial vehicle is as follows: is provided with each cell DiService time T of (i ═ 1,2, … N)iThe unmanned aerial vehicle is divided into two parts, and aiming at the problem that the energy of the airborne power supply of the unmanned aerial vehicle is limited, the unmanned aerial vehicle flies to each sub-area for a part of time betaiTiThe energy collecting device is used for collecting energy from an RF (radio frequency) signal sent by a ground signal source PB to prolong service time, meanwhile, a ground node can also obtain energy from the RF signal in the time period, but the collected energy needs to meet the minimum energy requirement of the node for operation, and meanwhile, in the time period, in order to meet the requirement of the node on data transmission quantity, data of all nodes need to be transmitted completely. And another part of the time (1-beta)i)TiThe method is used for the ground node to transmit data to the unmanned aerial vehicle in a TDMA access mode through backscattering, and the acquisition of the information of the sub-area is completed. The sum of the hover time for each sub-region and the time of flight between sub-regions is required to meet the total operating time requirement. While in order to guarantee the communication quality of each sub-area, betaiIt is required to be greater than 0 and less than 1. Proportion beta for collecting energy and collecting data in each subarea by optimizing unmanned aerial vehicleiAnd a hover time T for each sub-regioniThe communication link status and transmission quality problems between the unmanned machine and BNs can be better balanced.
Location information of the ground backscatter nodes may be collected by the PB. According to the two-dimensional positions of the PB and the backscattering nodes, BNs is firstly divided into several sub-areas through a K-Means clustering method. The coordinates of the centroid of each sub-region are projections of the hover position of the drone. As shown in fig. 2, the operation of the drone in each sub-area includes two phases of energy collection and backscatter communication. At betaiTiIn the time period, the unmanned aerial vehicle and the ground backscatter node collect energy from the RF (radio frequency) signal emitted by the PB, and the rest (1-beta)i)TiTime is equally divided into NiEach time block, each ground backscatter node selects a time slot to backscatter transmit its own data toUnmanned plane in the remaining NiSleep mode is maintained for 1 slot, with drone hovering over the cell centroid (ce)iAnd h) collecting information of all nodes on the ground.
Furthermore, the invention obtains the maximum energy efficiency of the system by optimizing the hovering time of the unmanned aerial vehicle in each cell and the proportion of the collected energy to the data transmission time in each cell.
In the sub-region DiInner, betaiTiThe time of the data is used for the unmanned aerial vehicle and the node to obtain energy from the RF signal sent by the PB, the rest time is transmitted to the unmanned aerial vehicle by the node in a backscattering way according to the TDMA access mode, and the ground node UijThe rate at which data is transmitted may be expressed as:
Figure BDA0002798967730000051
wherein sigma2Additive White Gaussian Noise (AWGN) on behalf of UAV receiver end, bijRepresents UijThe throughput of all BN nodes can be expressed as:
Figure BDA0002798967730000052
the energy consumption of the unmanned aerial vehicle mainly comprises three aspects: flight energy consumption, hover energy consumption, and energy consumption in collecting data. The flight energy consumption may be denoted as Efly＝PfTf. Wherein, PfIs the power consumption, T, of the unmanned aerial vehicle in flightfIs the total time of flight of the drone.
Unmanned aerial vehicle hovering energy consumption Ehover＝Ph(T-Tf). Wherein, PhIs the power consumption when the unmanned aerial vehicle suspends.
Energy consumption when unmanned aerial vehicle collects data
Figure BDA0002798967730000053
Wherein, PcIs that the unmanned plane is atPower consumption when receiving data from the BN.
Thus, the total energy consumption of the drone may be denoted as Etotal＝Ehover+Efly+Ec。
Therefore, the energy efficiency q of the single-drone assisted backscatter communications network system of the present invention can be expressed as:
Figure BDA0002798967730000061
thus, the optimization problem of the present invention can be expressed as:
Figure BDA0002798967730000062
where C1 represents the energy collected by each node to meet the minimum energy collected
Figure BDA0002798967730000063
To ensure that the node draws enough energy to warrant data transmission. Q in C2sRepresenting the minimum data volume requirement, R, of a nodeuC2 is the data transmission rate of the node, and the data volume transmission of the node needs to be satisfied. C3 is the requirement of the whole working time, that is, the unmanned aerial vehicle can fly back to the origin after collecting all the node information.
The optimization problem is solved by using a Dinkelbach-based fractional planning method.
According to the division result, the time resource allocation can be divided into two parts: the time resource allocation of different sub-areas and the division of the ratio of the collected energy to the time of transmitting data in each sub-area. Aiming at a target function in a fractional form, the Dinkelbach method provides a solution for solving a time-sharing programming problem by iterating a series of parameter programming problems. When it is satisfied with
Figure BDA0002798967730000064
When is, P1The problem may be solved to an optimal solution, with the upper corner index representing the variable value at which the optimal solution was obtained.
Thus, P can be substituted1The problem turns into the following P2The problems are as follows:
Figure BDA0002798967730000065
wherein the content of the first and second substances,
Figure BDA0002798967730000071
is a constant determined according to system parameters.
Converted P2The problem remains non-convex, but the objective function is observed to follow βiReduction of and TiIs increased. Beta is aiThe value range is as follows:
Figure BDA0002798967730000072
and the time that the unmanned aerial vehicle hovers in each cell is satisfied
Figure BDA0002798967730000073
Will P2Problem decoupling is two sub-problems:
Figure BDA0002798967730000074
Figure BDA0002798967730000075
where β and T are both N-dimensional vectors.
It is clear that P3、P4Both subproblems are concave functions and have their optimalityAnd therefore, each subproblem can be solved by adopting a CVX tool kit, and then the respective results are used for iterative solution to obtain the optimal system energy efficiency, and when the algorithm is converged, the optimal solution can be obtained.
The invention carries out the above conversion on the formula (7), decouples the formula (7) into two subproblems such as the formula (13) and the formula (14), and then carries out the solution, as shown in figure 3, the invention firstly distributes a proportional parameter beta by fixing the time slot of each subareaiSolving the problem P3Calculating the service time of the unmanned aerial vehicle in each cell, namely hovering time; then, according to the service time of each cell obtained currently, the problem P is solved4Calculating a time slot allocation ratio parameter beta of each celli(ii) a Then, calculating the energy efficiency of the system based on the current service time and time slot allocation proportion, judging whether the obtained energy efficiency meets the set convergence condition, if so, outputting the currently obtained service time of each cell and the time slot allocation proportion parameter in the cell, and if not, continuing to iteratively calculate the allocation proportion beta as aboveiAnd TiUntil the system energy efficiency meets the convergence condition. One pseudo code flow for solving is shown in FIG. 4, and the following is a description of the flow shown in FIG. 4.
Figure BDA0002798967730000076
Figure BDA0002798967730000081
In the above code, Convergence is used to identify whether the algorithm converges, and when a value True indicates Convergence, and when a value False indicates non-Convergence. Function f1Function f, as shown in equation (6)2As shown in formula (9):
f1＝q；
Figure BDA0002798967730000082
the error tolerance threshold epsilon can be set experimentally or empirically and takes a positive value.
The method of the invention is subjected to simulation experiments, and comprises the following steps:
the system is assumed to comprise 150 user BN nodes, the height of the ground PB is 5m, and the network covers an area of 40 m × 40 m. The flying height of the unmanned aerial vehicle is 30m, and the flying speed is 15 m/s. Iteration threshold is 10-4The power of PB is 30 dBm. The system operating time was 250s, and the other parameters are tabulated.
TABLE 1 System simulation parameters
Figure BDA0002798967730000083
Figure BDA0002798967730000091
A comparative scheme of the invention is described below:
(1) the first contrast scheme adopts an unmanned aerial vehicle hover service algorithm: unmanned aerial vehicle hovers and gathers the data of all nodes directly over PB, and the position is fixed.
(2) And the second comparison scheme adopts an equal time division algorithm: wherein the hovering time of the drone in each sub-area is set to be uniform.
As shown in fig. 5, the positions of all BN nodes and the partitioning result of sub-regions using the method of the present invention are given. The 150 BN nodes are divided into four sub-regions, and the centroid in each region is the ground projection of the hovering position of the drone in this sub-region. The trajectory of the unmanned aerial vehicle is that the unmanned aerial vehicle flies to the next adjacent cell after hovering over the centroid of each cell and acquiring data. The number of BN in each cell is N1＝53,N2＝34,N3＝25,N4It is clear that the denser area is divided into a larger number of BN nodes than 38.
As shown in fig. 6, the convergence of the method of the present invention is shown in the case of PB transmission powers of 1W, 3W, and 5W, respectively. The abscissa of fig. 6 is the number of iterations and the ordinate is the system energy consumption value. It can be seen that the system energy efficiency can reach 0.46 when the optimal energy efficiency value can be converged after about two iterations and the PB transmission power is 6W. Meanwhile, with the increase of PB transmitting power and the increase of energy acquired by the nodes and the unmanned aerial vehicle, the optimal energy efficiency value which can be converged by the method is gradually increased.
Fig. 7 shows the hovering time of each sub-area and the ratio of the collected energy inside the sub-area to the time of data transmission with PB transmission power of 1W, 3W, 6W, respectively, using the method of the present invention. In fig. 7, the abscissa of the left and right graphs is PB transmission power, the ordinate of the left graph is hover time, and the ordinate of the right graph is time allocation ratio. It can be seen that due to the sub-region D1With the largest number of BN nodes, i.e. greater data throughput requirements, the drones are therefore in sub-area D1The suspension time is longest. Meanwhile, due to the fact that the energy requirements of the BN nodes inside each sub-area and the amount of data to be transmitted are different, the proportion of data transmission time to energy collection time inside each cell is different. The energy collected by the BN node from the RF signal emitted by PB must meet its minimum energy requirement Enij. On the other hand, the time for acquiring the data must be enough for the unmanned aerial vehicle to finish the data acquisition of all BN nodes in the current sub-area within the time period. Meanwhile, the hovering time of the unmanned aerial vehicle in each sub-area cannot be changed along with the change of the PB transmitting power, and the proportion of time occupied by energy collected by each sub-area is reduced along with the increase of the PB transmitting power.
Fig. 8 shows a comparison graph of Energy Efficiency (EE) versus PB power for the inventive method and two comparison schemes for different drone acquisition data schemes. Where the PB transmitter power step is 0.5W. The abscissa in fig. 8 is PB transmission power, and the ordinate is system energy efficiency. It can be seen that the process of the present invention significantly improves the energy efficiency of the system compared to the two comparative schemes. The energy efficiency of the system adopting the method of the invention tends to be stable after the system begins to increase, and the energy efficiency of the system adopting the two comparison schemes is kept stable at a lower value. Due to service energy consumption of the drone and service path loss between the BN and the drone, the energy efficiency obtained by the static hover scheme is worst. The equal time division scheme does not consider the influence of the number of BN nodes in each sub-area, so that the obtained system energy efficiency is poor.

Claims (6)

1. A single unmanned aerial vehicle backscatter communication network trajectory optimization and resource allocation method is characterized by comprising the following steps:
step 1, clustering a target area formed by all ground backscatter nodes according to node positions, dividing the target area into N cells, and acquiring the mass center position of each cell; n is a positive integer;
step 2, setting the unmanned aerial vehicle to continuously fly among the cells, hovering the unmanned aerial vehicle above the centroid position of each cell to acquire data, wherein the flight track of the unmanned aerial vehicle is represented as the permutation and combination of the centroid positions of the cells;
it hovers at district D to establish unmanned aerial vehicleiHas a service time of TiI is 1,2, … N; wherein at time betaiTiIn the method, the unmanned aerial vehicle and the ground backscatter node collect energy from a radio frequency signal emitted by a signal tower and the rest time (1-beta)i)TiInternal and ground backscatter node backscatter transmits data to the drone, proportional parameter betaiGreater than 0 and less than 1; let cell DiHaving N thereiniA ground backscatter node for receiving the remaining (1-. beta.) ofi)TiTime being equally divided into NiEach ground backscatter node in the cell selects a time slot to backscatter transmit data to the unmanned aerial vehicle, and keeps a sleep mode in other time slots;
step 3, establishing an optimization target of single unmanned aerial vehicle backscatter communication network trajectory optimization and resource allocation, namely: obtaining service time T of each cell when energy efficiency q of single unmanned aerial vehicle backscatter communication network is maximumiAnd a proportional parameter betai，i＝1,2,…N；
The energy efficiency q is the throughput of all ground backscatter nodes/the total energy consumption of the unmanned aerial vehicle;
the constraint conditions to be met in the solution process comprise:
c1: each ground backscatter node in a cellThe collected energy needs to meet the minimum collected energy Enij；
C2: at time (1-. beta.)i)TiThe data transmission quantity of all ground backscatter nodes in the cell is met;
c3: the total flight time of the unmanned aerial vehicle plus the residence time of each cell is the set working time of the unmanned aerial vehicle;
C4：βigreater than 0 and less than 1;
C5：Tigreater than 0;
step 4, decoupling the optimization target in the step 3 by utilizing a Dinkelbach-based score planning method, converting the decoupling into the problem of proportional division of the hovering time of each cell and the collected energy and the transmission time in each cell, performing iterative solution, and outputting the optimal service time T of each celliAnd a proportional parameter beta in each celli，i＝1,2,…N。
2. The method of claim 1, wherein in step 3, the throughput of all ground backscatter nodes is represented as ThtotalObtained by calculation as follows:
Figure FDA0002798967720000011
wherein, bijIs cell DiInternal ground backscatter node UijReflection coefficient of (a)2Additive white Gaussian noise, P, on behalf of the receiver end of the UAVBIs the transmission power of the signal tower,
Figure FDA0002798967720000012
is a signal tower and ground backscatter node UijThe channel gain of the link of (a),
Figure FDA0002798967720000013
for unmanned aerial vehicle and ground backscatter node UijThe average path of the link of (a) fades.
3. The method according to claim 1, wherein in step 3, the established optimization goal is represented as P1The following are:
Figure FDA0002798967720000021
Figure FDA0002798967720000022
C2:(1-βi)TiRu≥NiQs,
Figure FDA0002798967720000023
C4:0＜βi＜1,
C5:Ti＞0,
wherein, PBIs the transmission power of the signal tower,
Figure FDA0002798967720000024
is a signal tower and ground backscatter node UijChannel gain of the link of, bijIs cell DiInternal ground backscatter node UijReflection coefficient of (2), RuAnd QsRespectively the data transmission rate and the minimum data volume of the ground backscatter node; t isfIs the total time of flight of the drone.
4. The method of claim 3, wherein in step 4, the optimization objective P is optimized by using a Dinkelbach-based score planning method1The conversion is performed as follows:
first, conversion to P2The problem is as follows:
Figure FDA0002798967720000025
s.t.C1-C5
wherein the content of the first and second substances,
Figure FDA0002798967720000026
for unmanned aerial vehicle and ground backscatter node UijAverage path fading of the link of (1); pcThe power consumption of the unmanned aerial vehicle when receiving data transmitted by the ground backscatter node; phPower consumption when the unmanned aerial vehicle suspends; pfIs the power consumption, T, of the unmanned aerial vehicle in flightfIs the total time of flight of the drone;
ciis a constant determined according to the system parameters,
Figure FDA0002798967720000027
further, P is added2Problem decoupling is the following two sub-problems P3And P4：
Figure FDA0002798967720000028
Figure FDA0002798967720000029
Wherein each of beta and T is betaiAnd TiIs a vector of dimension N.
5. The method according to claim 1 or 4, wherein in the step 4, in the iterative solution, (1) the slot allocation ratio β between the collected energy and the transmission time of each sub-area is given firstiSolving the problem P3Calculating the service time of the unmanned aerial vehicle in each cell; (2) then, according to the service time of each cell obtained currently, the problem P is solved4Calculating the time slot allocation ratio beta of each celli(ii) a (3) And then based on the current service time and slot allocationAnd (3) calculating the system energy efficiency, judging whether the obtained energy efficiency meets the set convergence condition, if so, outputting the currently obtained service time of each cell and the time slot allocation proportion parameter in the cell, and if not, continuously performing iteration from (1) to (3) until the system energy efficiency meets the convergence condition.
6. The method of claim 1 or 4, wherein in step 4, T is solvediAnd betaiThe process of (2) is as follows:
(1) initializing, including: setting an initial value of the energy efficiency q to be 0, and setting the iteration number l to be 0; setting a maximum iteration number L and an error tolerance threshold epsilon; setting an initial value beta of a time ratio of collected energy to transmitted data in each cell{0}；
(2) Using current energy efficiency q and time scale beta{l}Solving for P3Solving the hover time T of each cell under the current iteration number l{l}；
(3) Utilizing the hover time T of each cell{l}Solving a sub-problem P4Updated to beta{l+1}；
(4) Utilizing the hover time T of each cell{l}And the ratio beta{l+1}Computing the problem P2Function value f of2(β{l+1},T{l})：
Figure FDA0002798967720000031
If f2(β{l+1},T{l}) Less than or equal to the error tolerance threshold epsilon, then the current iteration value beta{l+1},T{l}For optimal solution, from β{l+1},T{l}Calculating to obtain optimal energy efficiency; otherwise, according to the current iteration value beta{l+1},T{l}Updating an energy efficiency value q;
(5) and (4) increasing the current iteration frequency L by 1, judging whether L is equal to L, if so, stopping iteration, and otherwise, continuing to execute the step (2).
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