CN112672376A - Unmanned aerial vehicle deployment method in unmanned aerial vehicle-assisted cellular network - Google Patents
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Abstract
An unmanned aerial vehicle deployment method in an unmanned aerial vehicle-assisted cellular network belongs to the technical field of wireless communication. Firstly, the base station collects information according to different information sources, and establishes a probability line-of-sight transmission model according to the collected information. Secondly, unmanned aerial vehicle deployment scenario parameters and user classifications are selected. Thirdly, selecting a key performance index according to specific scenes and requirements based on the model and the proposed unmanned aerial vehicle deployment and user access method; the method comprises the following steps of (1) evaluating selected key performance indexes for three types of users by adopting an analysis method of a random geometric theory in a given group of parameter settings; after the multiple sets of parameter settings are evaluated, the size of the optimal repelling area and the optimal unmanned aerial vehicle height are found. Finally, the unmanned aerial vehicle deployment scenario is implemented. The interference suppression and evaluation method has the advantages of simplicity, quickness and universality, can obviously reduce signaling overhead and system complexity, is transparent to users, does not need the users to carry out additional measurement and report, and reduces user burden.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and relates to an unmanned aerial vehicle deployment method of an unmanned aerial vehicle-assisted cellular network.
Background
Unmanned Aerial Vehicles (UAVs) as air access points can effectively improve coverage, regional spectral efficiency, and user experience quality, so introducing unmanned aerial vehicle communication into a cellular network is a promising technology, and can significantly improve the communication performance of unmanned aerial vehicles and existing ground users. Compared with ground access points, the flexible, fast and on-demand deployment of drones enables them to provide comprehensive coverage and data transfer services for emergency scenarios covering bugs, hot spot areas, remote areas or communication failures without the need to build new ground communication related infrastructure. Furthermore, another significant advantage of drones compared to ground base stations is the higher probability of serving ground users over line-of-sight (LOS) links, which enables users to obtain better useful signal quality than non-line-of-sight (NLOS) links. However, due to the scarcity of spectrum resources, the sharing of spectrum between the drones and the terrestrial cellular base station will introduce mutual interference, and the mutual interference is more serious due to the line-of-sight propagation condition of the drones. Therefore, in order to suppress interference caused by introduction of the unmanned aerial vehicle into cellular communication and fully utilize the advantage of line-of-sight transmission of the unmanned aerial vehicle to expand network coverage and enhance network capacity, an effective unmanned aerial vehicle three-dimensional deployment scheme needs to be designed.
The advantages and disadvantages of the unmanned aerial vehicle deployment scheme have strong correlation with a plurality of factors, such as deployment geographic environment, ground base station position, ground user position, air-to-ground channel characteristics, self height and the like, so that the optimal unmanned aerial vehicle three-dimensional deployment is very challenging. In addition, interference between drones and ground stations further complicates drone deployment issues. Aiming at the problems, a general method takes a certain performance index as an optimization target and an unmanned aerial vehicle deployment position as a class of optimization variables, and then combines different communication requirements to attach corresponding limiting conditions, so as to form an optimization problem, and carries out algorithm design of position deployment based on an optimization theory. The scenes in which studies using the above methods are usually focused are mostly a limited number of ground base stations in fixed locations or only one ground base station is considered (see the documents T.Zhang, Y.Wang, Y.Liu, W.xu and A.Nallanathan, Cache-engineering UAV Communications: Network Deployment and Resource Allocation, IEEE Transactions on Wireless Communications,2020,19(11): 7470-. However, the complexity of the optimization problem is further enhanced by the current cellular network intensive deployment, and the multiple ground base station model with fixed positions cannot reflect the non-regularity and the variability of real network nodes in spatial distribution, so that the unmanned aerial vehicle serving as a supplementary node also has the non-regularity and the variability. In order to better embody the spatial distribution characteristics of the existing network nodes, another type of research based on random geometry theory has attracted extensive attention in academia and industry. For simplicity of Analysis, many documents assume that ground base stations (or ground users) and drones are subject to the homogeneous Poisson Point Process (PPP) independent of each other (see documents J. Liu, M. Sheng, R. Lyu and J. Li, Performance Analysis and Optimization of UAV Integrated Terrestrial Cellular Network, IEEE Internet of Things Journal,2019,6(2): 1841-. However, since the drones are auxiliary access points of the cellular network, it is not realistic to assume that the ground base station or the positions of the drones are independent of each other, and it is necessary to consider that the drones are deployed in the edge area of the ground base station, otherwise strong interference is introduced to degrade the user experience. Meanwhile, the height of the unmanned aerial vehicle is arranged in a competitive relationship between the coverage area and the signal quality, for example, the higher the deployment height of the unmanned aerial vehicle is, the larger the coverage area is, so that fewer unmanned aerial vehicles can be arranged in the edge area, but the path loss can cause the reduction of the signal quality of a user. Therefore, unmanned aerial vehicle deployment needs to compromise horizontal deployment relative to ground base station position correlation and high deployment itself.
Based on the above, the invention provides an unmanned aerial vehicle deployment scheme based on spatial correlation, which can protect a ground base station communication link and unmanned aerial vehicle communication from being influenced by strong interference between the ground base station communication link and the unmanned aerial vehicle communication, and provides a matching analysis method based on a random geometric theory for quickly evaluating the optimal level and height deployment scheme under a specific application scene and performance requirements. Specifically, each ground base station is provided with a exclusion area, and the unmanned aerial vehicle is only deployed outside the exclusion area, so that under the deployment scheme, the ground base station position and the unmanned aerial vehicle horizontal projection can be modeled as a poisson point process and a poisson hole process respectively. Considering that the spatial position of the drone also has a component in the vertical dimension, i.e. height, the three-dimensional spatial position of the drone is subject to a height-tagged poisson-hole process, i.e. a tagged poisson-hole process. Since the exclusion region destroys the independence of the poisson point process, it is difficult to give an accurate expression of the interference characteristic, and thus it is difficult to search and optimize the optimal exclusion region and height size under the strategy. The present invention therefore proposes to solve the above problems by using a method of deriving an approximation of the interference characteristics and a method of approximating the interference characteristics using other point processes whose spatial characteristics are similar and easy to analyze.
The invention is funded by the national science foundation project (No. 61701071).
Disclosure of Invention
In the prior art, a cellular network assisted by an unmanned aerial vehicle deploys the unmanned aerial vehicle randomly above the cellular network, but the same frequency interference problem is also caused after the unmanned aerial vehicle communication is introduced into the cellular network, and the deployment height of the unmanned aerial vehicle also has great influence on the system performance and the user experience. Aiming at the problems, the invention provides an unmanned aerial vehicle deployment scheme based on space exclusion unmanned aerial vehicle auxiliary cellular network communication, and provides a performance evaluation and optimization method matched with the scheme. The unmanned aerial vehicle deployment method not only suppresses the interference problem caused by introducing unmanned aerial vehicle communication into cellular communication, but also considers the optimization scheme of unmanned aerial vehicle high deployment.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for drone space deployment in a drone-assisted cellular communication network, comprising the steps of:
the method comprises the following steps: information gathering and modeling
Step 1.1, the base station collects information: according to different information sources, the process of collecting information by the base station mainly comprises three processes:
(1) the user feeds back information to the base station, including user location information and a Signal-to-interference ratio (SIR) threshold required in the user communication process. The base station can estimate the user density lambda according to the informationc。
(2) The unmanned aerial vehicle feeds back information including the transmitting power mu to the base stationuMain lobe gain G of antenna arraymSide lobe gain GsVertically downward coverage of beam width
Regulatory range of flight altitude of unmanned aerial vehicle, wherein the lowest altitude is hmAnd a maximum height hM. The unmanned aerial vehicle feeds back the position to the base station, and the base station can estimate the density lambda of the unmanned aerial vehicle according to the position informationu。
(3) And the base station acquires other system information through the network side. Including ground base station density lambdagBase station transmit power mugA path loss model for a ground link, a path loss model for an air-to-ground link, etc. The path loss model of the ground link is
αgIs the ground path loss exponent and alphag>2, x is the location of the ground base station. The path loss model of the air-to-ground link
αxIs the path loss exponent alpha of the drone and the user communicationx> 2, where the LOS link has a path LOSs exponent of αLAnd the path loss exponent of the NLOS link is alphaNX is the projected position of the drone on the ground, and h is the flight height of the drone. Probabilistic line-of-sight transmission model parameters A and B for air-to-ground links, where A and B are two model parameters of the Sigmoid function (S-curve), can be obtained from the network side by fitting an actual but complex line-of-sight transmission probabilistic model (see documents: A. Al-Hour, S. Kandepan and S. Lardner, Optimal LAP Altitude for Maximum C)overage,IEEE Wireless Communications Letters,2014,3(6):569-572.)。
The Sigmoid function expression is
Wherein,
is the elevation angle, and a and B are the two model parameters of the function (S-curve).
Step 1.2, establishing a model: according to the collected information, namely the density of the ground base station and the density of the users, the spatial position distribution of the ground base station and the spatial position distribution of the users are respectively modeled into two mutually independent poisson point process models phigAnd phicEach density is lambdagAnd λc. The air-to-ground link state is modeled as a probabilistic line-of-sight transmission model in which the channel condition is represented by a probability pLWith probability 1-p for LOS propagation linksLPropagating links for NLOS, where pLIs the LOS probability.
Establishing a flat-top antenna array directional pattern model of the unmanned aerial vehicle according to the main lobe gain, the side lobe gain and the half-power beam width in the collected information:
wherein phi is larger than the range of (-pi/2, pi/2)]Is the angle of arrival of the transmit beam relative to the vertically downward direction. Modeling small-scale channel fading as Rayleigh fading and fading factor gxObey an exponential distribution.
Step two: selecting unmanned aerial vehicle deployment scenario parameters and user classifications
Step 2.1 unmanned aerial vehicle deployment mainly comprises two parts, namely unmanned aerial vehicle position deployment and unmanned aerial vehicle beam coverage, and aims to reduce mutual interference with a ground base station.
A first part: set rejectionThe interference from unmanned aerial vehicle communication that the inside user of reduction exclusion area received is regional, improves the inside user communication performance of exclusion area. Specifically, the area of the ground base station is set, the spatial position of the unmanned aerial vehicle is deployed outside the exclusion area of the base station (or the unmanned aerial vehicle in the exclusion area is controlled to fly to a random position outside the exclusion area), and all the unmanned aerial vehicles are located at the same height h, then the spatial distribution of the unmanned aerial vehicles is modeled as a density λuLabeled Poisson Hole Process (PHP) of (1), noted as ΦuWherein the label for each point is the flight height h of the drone. The exclusion zone is: the area of the base station is not more than 1/lambdagMay be a square, circular or other symmetrically shaped area; the area is preferably circular, and when the area is circular, the radius of the circular area is called as exclusion radius, which is expressed as D, and the value range is within the range
In the meantime. The flight height of the unmanned aerial vehicle needs to meet the regulation range of the flight height of the unmanned aerial vehicle, and the value range is (h)m，hM)。
A second part: the unmanned aerial vehicle covers with the vertical beam, further reduces unmanned aerial vehicle to the interference of repelling regional inside user. Each drone covers a beam vertically downwards, with the main lobe of the beam having a width of
The radius of the main lobe coverage area of the drone on the ground is then
According to the flat-top antenna array directional diagram model of the unmanned aerial vehicle, when a user is located in the coverage area of the main lobe, the beam gain of the unmanned aerial vehicle is GmAnd when the user is not located in the coverage area of the main lobe, the beam gain of the unmanned aerial vehicle is set to be Gs。
Step 2.2, a user access mechanism, wherein users are divided into three parts based on the exclusion area of the base station and the main lobe coverage area of the unmanned aerial vehicle: 1) the users in the exclusion area are connected to the nearest ground base station for communication and are marked as ground center users; 2) users located outside the exclusion area and within the main lobe coverage area of the unmanned aerial vehicle are connected to the nearest unmanned aerial vehicle for communication and are marked as unmanned aerial vehicle users; 3) users outside the exclusion zone but outside the main lobe coverage area of the drone connect to the nearest ground base station for communication, denoted as ground edge users.
The steps 2.1 and 2.2 are combined to form the specific method for unmanned aerial vehicle deployment and user service provided by the invention.
Step three: performance evaluation and optimization
The overall process of performance evaluation and optimization is based on the model established in the step 1 and by adopting the unmanned aerial vehicle deployment and user access method provided in the step 2, a key performance index is selected according to specific scenes and requirements; subsequently, evaluating the selected key performance indexes for three types of users (namely, a ground center user, a ground edge user and an unmanned aerial vehicle user) by adopting an analysis method of a random geometric theory in a given set of parameter settings (including the size of a base station exclusion area and the flight height of the unmanned aerial vehicle); finally, after the multiple sets of parameter settings are evaluated, the size of the optimal rejection area (or the optimal rejection radius value) and the optimal unmanned aerial vehicle height are searched by comparing the sizes of the key performance indexes. The specific process comprises the following steps:
step 3.1 evaluation procedure: based on the model established in step 1 and the unmanned aerial vehicle deployment and user access method provided in step 2, a key performance index is selected according to specific scenes and requirements, and then the selected key performance index is evaluated for three types of users (namely, a ground center user, a ground edge user and an unmanned aerial vehicle user) by adopting an analysis method of a random geometric theory in a given set of parameter settings (including the size of a base station exclusion area and the flight height of the unmanned aerial vehicle).
The analysis method is two methods for analyzing the statistical distribution of Signal-to-Interference ratios (SIRs) of ground center users, ground edge users and unmanned aerial vehicle users by using a random geometric theory: 1) theoretical lower bound of signal-to-interference ratio distribution of different types of users given by Poisson point process based on virtual unmanned aerial vehicleWherein the Poisson Point Process for the virtual drone is a Density of
The poisson point process of (a); 2) and (3) approximating the Poisson hole process in the step (2.1) to a Poisson point process with the same density, and further obtaining an approximate result of the signal-to-interference ratio distribution of different types of users.
The exclusion area is set by selecting at least two values within the value range of the exclusion radius D. And the setting of the flying height is to select at least two values in the value range of the height h of the unmanned aerial vehicle. And then, the performances of the three types of users which can be obtained by adopting the unmanned aerial vehicle deployment and user access method are analyzed, namely, the income effects of the unmanned aerial vehicle deployment and user service method under the conditions of different exclusion areas and unmanned aerial vehicle heights are analyzed.
The key performance indicators include link level indicators and network level indicators, and are determined according to specific scenes and requirements:
1) link level indicator: probability of user success, expressed as
Wherein A isGC,AUUAnd AGEThe proportion of the ground center users, the unmanned plane users and the ground edge users to the total users respectively,
and
the signal-to-interference ratio threshold theta of the ground center user, the unmanned aerial vehicle user and the ground edge user respectivelyc,θuAnd thetaeThe success probability of. The specific expression has two types of theoretical upper bound result and approximate result. The theoretical upper bound is:
wherein, Ig1Interference from a co-frequency base station to a ground center user;
the interference from the same-frequency unmanned aerial vehicle to the ground user is the upper bound; i isg2Interference from a co-frequency base station to an unmanned aerial vehicle user;
interference from a same-frequency unmanned aerial vehicle to an unmanned aerial vehicle user is in an upper bound; i isg3Interference from co-frequency base stations to ground edge users;
the interference from the same-frequency unmanned aerial vehicle to the ground edge user is the upper bound;
and
are respectively interference Ig1,Ig2，Ig3，
And
performing Laplace transformation; f. of1(r),f2(r) and f3(r) distance distributions from ground center users, drone users, and ground edge users to the respective service sites, respectively. The approximate result is:
wherein,
the interference from the same-frequency unmanned aerial vehicle to the ground user is approximate;
the interference from the same-frequency unmanned aerial vehicle to the unmanned aerial vehicle user is approximate;
the interference from the same-frequency unmanned aerial vehicle to the ground edge user is approximate.
And
are respectively interference
And
performing Laplace transformation;
2) network level metrics include regional spectral efficiency, network energy efficiency, and the like. The regional spectral efficiency is expressed as
Wherein λgAnd λuThe density of ground base stations and drones, respectively. The network energy efficiency is expressed as
Wherein ASE is the regional spectral efficiency of the network; lambda [ alpha ]gAnd λuDensity of ground base station and unmanned aerial vehicle respectively; xigAnd xiuThe total power consumed by the ground base station and the drone, respectively.
Step 3.2, optimizing process: and 3.1, setting a plurality of groups of different rejection areas and flight heights to evaluate key performance indexes, selecting the rejection radius and the flight height corresponding to the maximum performance (such as the maximum user success probability, the maximum area spectrum efficiency or the maximum network energy efficiency) as the optimal activation area and the optimal flight height based on the obtained analysis result, and setting the rejection radius and the flight height as the final rejection area and the final unmanned aerial vehicle height.
Step four: implementing unmanned aerial vehicle deployment scenarios
And 4.1, based on the estimated optimal repulsion area and optimal height, feeding back to the unmanned aerial vehicle by the ground base station, and controlling the unmanned aerial vehicle to be deployed outside the optimal repulsion area and fly at the optimal height.
And 4.2, the ground base station feeds back the classification information to the user, and the user accesses the network according to the received instruction, wherein the ground center user communicates with the ground base station, the unmanned aerial vehicle user communicates with the unmanned aerial vehicle, and the ground edge user communicates with the ground base station.
The invention has the beneficial effects that: the unmanned aerial vehicle deployment method in the unmanned aerial vehicle-assisted cellular network communication considers the problem of interference on the original ground cellular network after the unmanned aerial vehicle communication is introduced. The matching model and the evaluation method can suppress the unmanned aerial vehicle height selection of the unmanned aerial vehicle-assisted cellular network and the mutual interference between the unmanned aerial vehicle and the cellular communication to a certain degree, ensure that the communication performance of a central user is not deteriorated due to the introduction of the unmanned aerial vehicle communication, and simultaneously enhance the communication performance of users located at the edge of a cell. In particular, the proposed evaluation method is a statistically optimized network-wide approach. The method is significantly different from the existing majority of joint optimization methods based only on single-cell optimization or a limited number of multi-cells or random deployment of drones over a cellular network. The information collected by this method is mostly relatively static (or slowly changing) and does not need to be measured and reported frequently. Therefore, decisions made based on this evaluation method (e.g., optimal exclusion zone size and optimal fly height) can be valid over a longer time frame, avoiding the extensive computational and resource consumption introduced by frequent decisions. In conclusion, the interference suppression and evaluation method has the advantages of simplicity, rapidness and universality, can obviously reduce signaling overhead and system complexity, is transparent to users, does not need the users to carry out additional measurement and report, and reduces user burden.
Drawings
FIG. 1 is a schematic diagram of the system architecture of the present invention.
Fig. 2(a) is a ground base station side work flow diagram of the present invention.
Fig. 2(b) is an unmanned-side workflow diagram of the present invention.
FIG. 2(c) is a user-side workflow diagram of the present invention.
Fig. 3 is a curve of the relationship between the user success probability, the repulsion radius D and the unmanned aerial vehicle height h in step 3.2.
Detailed Description
The following detailed description of the invention refers to the accompanying drawings.
The schematic diagram is shown in fig. 1. The analysis is performed by taking a circular exclusion area as an example, but the invention is not limited to a circle, and can be a square, a sector, a triangle or other irregular shapes.
The specific implementation mode of the base station end comprises the following steps:
the method comprises the following steps: information gathering and modeling
Step 1.1: information collection
The ground base station obtains the user position information through user report so as to estimate the density lambda of the userc. Signaling interaction is carried out between the base station and the unmanned aerial vehicle, and the base station acquires information such as the position of the unmanned aerial vehicle so as to estimate the density lambda of the unmanned aerial vehicleuTransmit power configuration mu of droneuMain lobe gain G of the drone antenna arraymSide lobe gain GsVertically downward coverage of beam width
Regulatory range of flight altitude of unmanned aerial vehicle, wherein the lowest altitude is hmAnd a maximum height hM. The base station obtains other information through the network side, such as the path loss model of the ground link as
αgIs the ground path loss exponent and alphag>2, x is the location of the ground base station. The path loss model of the air-to-ground link
αxIs the path loss exponent alpha of the drone and the user communicationx> 2, where the LOS link has a path LOSs exponent of αLAnd the path loss exponent of the NLOS link is alphaNX is the projected position of the drone on the ground, and h is the flight height of the drone. The model parameters A and B of the probability line-of-sight transmission of the air-to-ground link, where A and B are two model parameters of the Sigmoid function (S-curve), can be obtained by fitting an actual but complex line-of-sight transmission probability model from the network side (see documents: A. Al-Hournani, S.Kandepan and S.Lardner, optical LAP Altitude for Maximum Coverage, IEEE Wireless Communications Letters,2014,3(6): 569-572.). The Sigmoid function expression is
Wherein,
is the elevation angle, and a and B are the two model parameters of the function (S-curve). Small scale channel fading model, exemplified by Rayleigh fading, i.e. fading factor gxObey an exponential distribution. Obtaining an estimate of base station densityg. Configuration of transmission power of base station by mugAnd (4) showing.
Step 1.2: modeling
According to the information collected in step 1.1, the spatial position distribution of the two network nodes, namely the ground base station density and the user density, is respectively modeled into two mutually independent poisson point process models phigAnd phicEach density is lambdagAnd λc. The air-to-ground link state is modeled as a probabilistic line-of-sight transmission model in which the channel condition is represented by a probability pLWith probability 1-p for LOS propagation linksLThe link is propagated for NLOS. LOS probability pLExpressed as:
wherein r and h represent the horizontal and vertical distances between the drone and the ground user, respectively. Probability of NLOS propagation link is pN＝PN(r,h)＝1-PL(r, h). Establishing a flat-top antenna array directional pattern model of the unmanned aerial vehicle according to the main lobe gain, the side lobe gain and the half-power beam width in the collected information:
wherein φ epsilon (- π/2, π/2) is the angle of arrival corresponding to the transmission beam with respect to the vertical downward direction.
Step two: selecting drone deployment parameters and user classifications
Step 2.1 unmanned aerial vehicle deployment mainly comprises two parts, namely unmanned aerial vehicle position deployment and unmanned aerial vehicle beam coverage.
A first part: set for the exclusion area, reduce the interference that comes from unmanned aerial vehicle communication that the inside user of exclusion area received, improve the inside user communication performance of exclusion area. Specifically, the area of the ground base station is set, the spatial position of the unmanned aerial vehicle is deployed outside the exclusion area of the base station, and all the unmanned aerial vehicles are located at the same height h, so that the spatial distribution of the unmanned aerial vehicles is modeled as the density lambda at the momentuLabeled Poisson Hole Process (PHP) of (1), noted as ΦuWherein the label for each point is the flight height h of the drone. The exclusion zone is: the area of the base station is not more than 1/lambdagThe radius of the circular area is called the exclusion radius, denoted D, in the interval
At least two values are taken. The flight altitude of the unmanned aerial vehicle needs to meet the regulation range of the flight altitude of the unmanned aerial vehicle within the interval (h)m，hM) At least two values are taken.
A second part: the unmanned aerial vehicle covers with the vertical beam, further reduces unmanned aerial vehicle to the interference of repelling regional inside user. Each drone covers a beam vertically downwards, with the main lobe of the beam having a width of
The radius of the main lobe coverage area of the drone on the ground is then
According to the flat-top antenna array directional diagram model of the unmanned aerial vehicle, when a user is located in the coverage area of the main lobe, the beam gain of the unmanned aerial vehicle is GmAnd when the user is not located in the coverage area of the main lobe, the beam gain of the unmanned aerial vehicle is set to be Gs。
Step 2.2, a user access mechanism, wherein users are divided into three parts based on the exclusion area of the base station and the main lobe coverage area of the unmanned aerial vehicle: 1) the users in the exclusion area are connected to the nearest ground base station for communication and are marked as ground center users; 2) users located outside the exclusion area and within the main lobe coverage area of the unmanned aerial vehicle are connected to the nearest unmanned aerial vehicle for communication and are marked as unmanned aerial vehicle users; 3) users outside the exclusion zone but outside the main lobe coverage area of the drone connect to the nearest ground base station for communication, denoted as ground edge users.
Step three: performance evaluation and optimization
Step 3.1 Performance evaluation
Since future networks are densely deployed, interference limited networks are considered, i.e. the impact of thermal noise on performance is neglected. Based on the stationarity of the poisson point process, only the center user at the origin can be considered to get the average performance of the center. Likewise, considering only edge users located at the origin may result in an average performance of the edge users. Specifically, the invention takes three indexes of success probability (link level performance index), area spectrum efficiency (network level performance index) and network energy efficiency (network level performance index) of the user as key performance indexes, and according to the specific performance and requirement concerned at present, one of the indexes can be used as a basis for searching the optimal exclusion radius, for example, the success probability of the user is selected as the key performance index for optimizing.
The three indexes of the success probability, the regional spectrum efficiency and the network energy efficiency of the user are specifically defined as follows:
1. probability of success for a user
The success probability of a user can be generally expressed as the probability that the received SIR of the user is greater than a Signal-to-Interference Ratio (SIR) threshold given the threshold. By adopting the total probability formula, the success probability p of the whole user can be obtainedsIs shown as
Wherein
And
respectively the proportion of the ground center user, the unmanned aerial vehicle user and the ground edge user, D is the radius of the exclusion area of the base station, RuIs the radius, lambda, of the main lobe coverage area of the drone on the groundgAnd λuThe density of ground base stations and drones respectively,
density of a virtual unmanned aerial vehicle poisson point process; thetac，θuAnd thetaeSignal-to-interference ratio thresholds of a ground center user, an unmanned aerial vehicle user and a ground edge user respectively;
and
respectively, the complementary cumulative distribution functions of the signal-to-interference ratios of the ground center users, the unmanned aerial vehicle users and the ground edge users.
2. Regional spectral Efficiency (ASE)
The regional spectral efficiency is the spectral efficiency that can be achieved per unit area. Based on a given SIR threshold, one can obtain
Wherein
And
the ratio of the users at the center of the ground to the users at the edge of the ground, D is the radius of the exclusion area of the base station, RuIs the radius, lambda, of the main lobe coverage area of the drone on the groundgAnd λuThe density of ground base stations and drones respectively,
density of a virtual unmanned aerial vehicle poisson point process; thetac，θuAnd thetaeSignal-to-interference ratio thresholds of a ground center user, an unmanned aerial vehicle user and a ground edge user respectively;
and
respectively, the complementary cumulative distribution functions of the signal-to-interference ratios of the ground center users, the unmanned aerial vehicle users and the ground edge users.
3. Network Energy Efficiency (NEE)
Since the energy consumption of the cellular network mainly comes from the energy consumption of the base station and the drone, a linear power consumption model is used for calculating the power consumed by a certain base station, as follows:
ξg＝agμg+wg，ξu＝auμu+wu
wherein a isgAnd auEfficiency, w, of ground base station and unmanned aerial vehicle power amplifier, respectivelygAnd wuThe static power consumption (such as circuit power consumption, power consumption for signal processing and flight control) of the base station and drone, respectively, independent of the transmit power, and these parameters may be derived from the network side at step one. Energy efficiency can be defined as the ratio of the spectral efficiency achieved per unit area to the total power consumed and can therefore be expressed as
Wherein ASE is the regional spectral efficiency of the network; NEE is network energy efficiency; lambda [ alpha ]gAnd λuDensity of ground base station and unmanned aerial vehicle respectively; xigAnd xiuThe total power consumed by the ground base station and the drone, respectively.
According to the definition of three indexes of success probability, regional spectrum efficiency and network energy efficiency of users, the signal-to-interference ratio is a basic physical quantity reflecting the accessibility of the users and the network, and the analysis of the SIR statistical characteristics of central users and edge users is a necessary way for realizing the evaluation of the three indexes. Therefore, the following description will be made in detail to analyze the SIR statistical characteristics of the central user and the edge users based on the established point process model and the proposed interference suppression method. Specifically, the present invention proposes the following two analysis methods: 1) giving a theoretical lower bound of statistical distribution of signal-to-interference ratios of different types of users based on a Poisson point process of a virtual unmanned aerial vehicle; 2) and (3) approximating the Poisson hole process in the step (2.1) to a Poisson point process with the same density, and further obtaining an approximate result of the signal-to-interference ratio distribution of different types of users. The specific process is as follows:
3.1.1 theoretical lower bound of SIR statistical distribution for different types of users based on virtual UAV PPP
Because the actually deployed drones are a subset of virtual drones, if all the virtual drones are activated, the interference from the co-frequency drones is greater than the interference from the actually deployed drones. Therefore, the SIR statistics distribution for different types of users will get a lower theoretical bound. For facilitating the writing of theoretical formulas, r is recorded1＝min(D-r,Ru)，
And F (α, y) ═ F2F1(1, 1-2/alpha; 2-2/alpha; y) is a Gaussian hypergeometric function.
1. Theoretical lower bound of central base station user SIR statistical distribution
Based on the network model established in step 1 and the unmanned aerial vehicle deployment and user access strategy proposed in step 2, the theoretical lower bound of the user SIR statistical distribution (specifically, complementary cumulative distribution function of SIR) in the exclusion area is
Wherein
Is the distance distribution of the ground center users to their serving ground base stations, Ig1Interference from a co-frequency base station to a ground center user;
the interference from the same-frequency unmanned aerial vehicle to the ground user is the upper bound.
And
is interference Ig1And
the Laplace transformation of (1) is expressed as
And
wherein r is an integral variable and t is an integral variable; i is the summation index, when i-L indicates that all variables concerned correspond to the variables of the LOS link, e.g. αi＝αLIs the path LOSs index, P, of the LOS linki(t,h)＝PL(t, h) is the probability of being a LOS link, and αi＝αNIs the path loss index, P, of the NLOS linki(t,h)＝PN(t, h) is the probability of being an NLOS link;
2. statistical distribution of UAV user SIR
Based on the network model established in step 1 and the deployment and user access policies of the unmanned aerial vehicle proposed in step 2, the theoretical lower bound of the user SIR statistical distribution (specifically, the complementary cumulative distribution function of SIRs) that is located outside the exclusion area and within the coverage area of the main lobe of the unmanned aerial vehicle is represented as follows:
wherein
Is the distance distribution of the unmanned aerial vehicle user to its serving unmanned aerial vehicle, Ig2Interference from a co-frequency base station to an unmanned aerial vehicle user;
the interference from the same-frequency unmanned aerial vehicle to the unmanned aerial vehicle user is the upper bound.
And
is interference Ig2And
the Laplace transformation of (1) has the expression:
3. statistical distribution of edge base station user SIR
Based on the network model established in step 1 and the deployment and user access policies of the unmanned aerial vehicle proposed in step 2, the theoretical lower bound of the user SIR statistical distribution (specifically, the complementary cumulative distribution function of SIR) outside the exclusion area and within the coverage area of the main lobe of the unmanned aerial vehicle is respectively expressed as
Wherein
Is the distance distribution of the ground edge users to their serving ground base stations, and
Ig3interference from co-frequency base stations to ground edge users;
the interference from the same-frequency unmanned aerial vehicle to the ground edge user is the upper bound.
And
is interference Ig3And
the Laplace transformation of (1) is expressed as
3.1.2 approximation of SIR statistical distribution for different types of users based on an approximate PPP model
By approximating drones deployed outside the exclusion zone to dilute PPP of the same density, the interference from drones is an approximation of the actual interference. Therefore, the SIR statistical distribution of different types of users will get an approximate theoretical result.
1. Approximate statistical distribution of central base station user SIR
Based on the network model established in step 1 and the unmanned plane deployment and user access strategy proposed in step 2, the theoretical result of the user SIR statistical distribution (specifically, complementary cumulative distribution function of SIR) inside the exclusion area is approximated as:
wherein
The interference from the same-frequency unmanned aerial vehicle to the ground user is approximate, and the Laplace is transformed into:
2. statistical distribution of UAV user SIR
Based on the network model established in step 1 and the deployment and user access policies of the drone proposed in step 2, the statistical distribution of user SIRs (specifically, complementary cumulative distribution function of SIRs) outside the exclusion area and within the coverage area of the main lobe of the drone is approximated as:
wherein
Interference from a same-frequency unmanned aerial vehicle to an unmanned aerial vehicle user is approximate, and the Laplace is transformed into:
3. statistical distribution of edge base station user SIR
Based on the network model established in step 1 and the deployment and user access policies of the drone proposed in step 2, the user SIR statistical distribution (specifically, complementary cumulative distribution function of SIRs) outside the exclusion area and within the coverage area of the main lobe of the drone is approximated by:
wherein
The interference from the same-frequency unmanned aerial vehicle to the ground edge user is approximate, and the Laplace is transformed into:
step 3.2: optimization process
In that
Set M different values D of exclusion zone radius1，D2，D3，…DMM is not less than 2 and is in (h)m，hM) Set N different flight heights to take value h1，h2，h3，…hNN is more than or equal to 2. And selecting one key performance index and an analysis method in the step 3.1 for each group of values of the rejection radius and the flight altitude, evaluating the selected performance index, researching the relation between the key performance and the rejection radius and the flight altitude, and selecting the value of the rejection radius and the flight altitude corresponding to the maximum performance as the optimal rejection area radius and the optimal flight altitude of the unmanned aerial vehicle.
An example is given as reference, where in this example the base station may obtain some system parameters from the user, drone and network side as follows: the density and the transmitting power of the ground base station are respectively lambdag＝10-5And mug40; virtual noneThe density and the transmitting power of the man-machine are respectively
And muu1 is ═ 1; the main lobe gain, the side lobe gain and the half-power beam width of the unmanned aerial vehicle are respectively Gm＝10，Gs1 and
the minimum and maximum heights of the unmanned aerial vehicle allowed to fly are respectively h m50 and h M300; the road LOSs indexes of NLOS and LOS are respectively alphaN4 and αL2.5; the parameters of the probabilistic line-of-sight transmission model of the air-to-ground link are respectively A equal to 11.95 and B equal to 0.136; the signal-to-interference ratio thresholds of the three users are all 1.
At this time, the drone altitude is chosen every 20, from 50 to 160, for a total of 13 drone altitudes, and the base station rejection radius is chosen every 20, from 20 to 160, for a total of 8 drone altitudes. Finally, parameter configurations of 13 × 8 — 104 sets of repulsion radius and drone altitude can be obtained, and after performance evaluation of user success probability is performed based on an approximation result of SIR statistical distribution, a three-dimensional graph of user success probability as shown in fig. 3 is drawn, where it can be seen that when the drone altitude is 130 and the repulsion radius is 60, the maximum user success probability can be obtained. Thus, in this example, the optimal repulsion radius is 60 meters and the optimal drone height is 130 meters.
Step four: implementing unmanned aerial vehicle deployment scenarios
And 4.1, based on the estimated optimal repulsion area and optimal height, feeding back to the unmanned aerial vehicle by the ground base station, and controlling the unmanned aerial vehicle to be deployed outside the optimal repulsion area and fly at the optimal height.
And 4.2, the ground base station feeds back the classification information to the user, and the user accesses the network according to the received instruction, wherein the ground center user communicates with the ground base station, the unmanned aerial vehicle user communicates with the unmanned aerial vehicle, and the ground edge user communicates with the ground base station.
As can be seen from the above description, the deployment scheme of the unmanned aerial vehicle of the present invention is significantly different from the existing deployment scheme of the unmanned aerial vehicle. The proposed solution provides direct physical isolation of the interference sources based on distance, while providing flexibility in height selection. The scheme is simple to operate, is convenient for the realization of an actual system, can effectively inhibit the interference of a central user and an edge user, is transparent to the user and does not increase the burden of the user. In addition, the scheme also provides a limit and an approximate method of success probability, and provides a quick, effective and accurate evaluation method for calculating the optimal exclusion area size on the base station side. The regional spectral efficiency and energy efficiency can be solved based on the success probability.
The above-mentioned embodiments only express the embodiments of the present invention, but not should be understood as the limitation of the scope of the invention patent, it should be noted that, for those skilled in the art, many variations and modifications can be made without departing from the concept of the present invention, and these all fall into the protection scope of the present invention.
Claims (3)
1. An unmanned aerial vehicle deployment method in an unmanned aerial vehicle-assisted cellular network is characterized by comprising the following steps:
the method comprises the following steps: information gathering and modeling
Step 1.1, the base station collects information: according to different information sources, the process of collecting information by the base station mainly comprises three processes:
(1) the user feeds back information to the base station, wherein the information comprises user position information and a signal-to-interference ratio threshold value required in the user communication process; the base station can estimate the user density lambda according to the informationc；
(2) The unmanned aerial vehicle feeds back information including the transmitting power mu to the base stationuMain lobe gain G of antenna arraymSide lobe gain GsVertically downward coverage of beam width
Regulatory range of flight altitude of unmanned aerial vehicle, wherein the lowest altitude is hmAnd a maximum height hM(ii) a The unmanned aerial vehicle feeds back the position to the base station, and the base station sends a position signal according to the positionInformation estimable unmanned aerial vehicle density lambdau；
(3) The base station acquires other system information through a network side; including ground base station density lambdagBase station transmit power mugPath loss models for ground links, air-to-ground links, etc.; the path loss model of the ground link is
αgIs the ground path loss exponent and alphag>2, x is the location of the ground base station; the path loss model of the air-to-ground link
αxIs the path loss exponent alpha of the drone and the user communicationx> 2, where the LOS link has a path LOSs exponent of αLAnd the path loss exponent of the NLOS link is alphaNX is the projected position of the drone on the ground, and h is the flight height of the drone;
step 1.2, establishing a model: according to the collected information, namely the density of the ground base station and the density of the users, the spatial position distribution of the ground base station and the spatial position distribution of the users are respectively modeled into two mutually independent poisson point process models phigAnd phicEach density is lambdagAnd λc(ii) a The air-to-ground link state is modeled as a probabilistic line-of-sight transmission model in which the channel condition is represented by a probability pLWith probability 1-p for LOS propagation linksLPropagating links for NLOS, where pLIs the LOS probability;
establishing a flat-top antenna array directional pattern model of the unmanned aerial vehicle according to the main lobe gain, the side lobe gain and the half-power beam width in the collected information:
wherein phi is larger than the range of (-pi/2, pi/2)]Is the angle of arrival relative to the corresponding transmit beamAn angle in a vertically downward direction; modeling small-scale channel fading as Rayleigh fading and fading factor gxObey an exponential distribution;
step two: selecting unmanned aerial vehicle deployment scenario parameters and user classifications
Step 2.1 unmanned aerial vehicle deploys and mainly includes unmanned aerial vehicle's position deployment and unmanned aerial vehicle's beam covers two parts, specifically does:
a first part: setting a rejection area, reducing interference from unmanned aerial vehicle communication on users in the rejection area, and improving the user communication performance in the rejection area; specifically, the area of the ground base station is set, the space position of the unmanned aerial vehicle is deployed outside the exclusion area of the base station or the unmanned aerial vehicle in the exclusion area is controlled to fly to the random position outside the exclusion area, and all the unmanned aerial vehicles are located at the same height h, then the space distribution of the unmanned aerial vehicles is modeled into the density lambdauThe labeling of Poisson's cave process, recorded as ΦuWherein the label for each point is the flight altitude h of the drone;
a second part: the unmanned aerial vehicle covers the vertical wave beam, so that the interference of the unmanned aerial vehicle to users in a rejection area is further reduced; each drone covers a beam vertically downwards, with the main lobe of the beam having a width of
The radius of the main lobe coverage area of the drone on the ground is then
According to the flat-top antenna array directional diagram model of the unmanned aerial vehicle, when a user is located in the coverage area of the main lobe, the beam gain of the unmanned aerial vehicle is GmAnd when the user is not located in the coverage area of the main lobe, the beam gain of the unmanned aerial vehicle is set to be Gs；
Step 2.2, a user access mechanism, wherein users are divided into three types based on the exclusion area of the base station and the main lobe coverage area of the unmanned aerial vehicle: 1) the users in the exclusion area are connected to the nearest ground base station for communication and are marked as ground center users; 2) users located outside the exclusion area and within the main lobe coverage area of the unmanned aerial vehicle are connected to the nearest unmanned aerial vehicle for communication and are marked as unmanned aerial vehicle users; 3) the users located outside the exclusion area and outside the main lobe coverage area of the unmanned aerial vehicle are connected to the nearest ground base station for communication and are marked as ground edge users;
step three: performance evaluation and optimization
Step 3.1 evaluation procedure: based on the model established in the step 1 and the unmanned aerial vehicle deployment and user access method provided in the step 2, selecting a key performance index according to specific scenes and requirements, and then setting a set of given parameters to evaluate the selected key performance index for three types of users by adopting a random geometric theory analysis method, wherein the parameters comprise the size of a base station exclusion area and the flight height of the unmanned aerial vehicle;
the exclusion area is set by selecting at least two values within the value range of the exclusion radius D; the flying height is set by selecting at least two values within the value range of the height h of the unmanned aerial vehicle; then, the performance of the three types of users obtained by adopting the unmanned aerial vehicle deployment and user access method is analyzed, namely the income effect of the unmanned aerial vehicle deployment and user service method is analyzed under the conditions of different exclusion areas and unmanned aerial vehicle heights;
the key performance indicators include link level indicators and network level indicators, and are determined according to specific scenes and requirements:
1) link level indicator: probability of user success, expressed as
Wherein A isGC,AUUAnd AGEThe proportion of the ground center users, the unmanned plane users and the ground edge users to the total users respectively,
and
for ground-centric users, unmanned-aerial-vehicle users and ground-edge, respectivelyIndoor signal-to-interference ratio threshold thetac,θuAnd thetaeThe success probability of; the specific expression has two types of theoretical upper bound result and approximate result; the theoretical upper bound is:
wherein, Ig1Interference from a co-frequency base station to a ground center user;
the interference from the same-frequency unmanned aerial vehicle to the ground user is the upper bound; i isg2Interference from a co-frequency base station to an unmanned aerial vehicle user;
interference from a same-frequency unmanned aerial vehicle to an unmanned aerial vehicle user is in an upper bound; i isg3Interference from co-frequency base stations to ground edge users;
the interference from the same-frequency unmanned aerial vehicle to the ground edge user is the upper bound;
and
are respectively interference Ig1,Ig2，Ig3，
And
performing Laplace transformation; f. of1(r),f2(r) and f3(r) distance distributions from ground center users, unmanned aerial vehicle users and ground edge users to respective service sites; the approximate result is:
wherein,
the interference from the same-frequency unmanned aerial vehicle to the ground user is approximate;
the interference from the same-frequency unmanned aerial vehicle to the unmanned aerial vehicle user is approximate;
the interference from the same-frequency unmanned aerial vehicle to the ground edge user is approximate;
and
are respectively interference
And
performing Laplace transformation;
2) the network level indexes comprise regional spectrum efficiency, network energy efficiency and the like; the regional spectral efficiency is expressed as:
wherein λgAnd λuDensity of ground base station and unmanned aerial vehicle respectively; the network energy efficiency is expressed as
Wherein ASE is the regional spectral efficiency of the network; lambda [ alpha ]gAnd λuDensity of ground base station and unmanned aerial vehicle respectively; xigAnd xiuThe total power consumed by the ground base station and the unmanned aerial vehicle respectively;
step 3.2, optimizing process: adopting the evaluation process of the step 3.1, setting a plurality of groups of different repulsion areas and flight heights to evaluate key performance indexes, selecting the repulsion radius and the flight height corresponding to the maximum performance value as an optimal activation area and an optimal flight height based on the obtained analysis result, and taking the values of the repulsion radius and the flight height at the moment as final repulsion areas and unmanned aerial vehicle height settings;
step four: implementing unmanned aerial vehicle deployment scenarios
Step 4.1, based on the estimated optimal repulsion area and optimal height, feeding back to the unmanned aerial vehicle by the ground base station, and controlling the unmanned aerial vehicle to be deployed outside the optimal repulsion area and fly at the optimal height;
and 4.2, the ground base station feeds back the classification information to the user, and the user accesses the network according to the received instruction, wherein the ground center user communicates with the ground base station, the unmanned aerial vehicle user communicates with the unmanned aerial vehicle, and the ground edge user communicates with the ground base station.
2. A method for unmanned aerial vehicle deployment in unmanned aerial vehicle assisted cellular network according to claim 1, wherein the exclusion area in step 2.1 is: the area of the base station is not more than 1/lambdagMay be a square, circular or other symmetrically shaped area; the area is preferably circular, and when the area is circular, the radius of the circular area is called as exclusion radius, which is expressed as D, and the value range is within the range
To (c) to (d); the flight height of the unmanned aerial vehicle needs to meet the regulation range of the flight height of the unmanned aerial vehicle, and the value range is (h)m，hM)。
3. The unmanned aerial vehicle deployment method in unmanned aerial vehicle-assisted cellular network as claimed in claim 1, wherein step 3.1 is an analysis method of random geometry theory, and comprises two methods for analyzing the statistical distribution of signal-to-interference ratios of ground center users, ground edge users and unmanned aerial vehicle users: 1) giving theoretical lower bound of signal-to-interference ratio distribution of different types of users based on Poisson point process of virtual unmanned aerial vehicle, wherein the Poisson point process of the virtual unmanned aerial vehicle is density
The poisson point process of (a); 2) and (3) approximating the Poisson hole process in the step (2.1) to a Poisson point process with the same density, and further obtaining an approximate result of the signal-to-interference ratio distribution of different types of users.
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