CN113645634B - Method for setting network deployment parameters of multi-antenna unmanned aerial vehicle under 6G - Google Patents
Method for setting network deployment parameters of multi-antenna unmanned aerial vehicle under 6G Download PDFInfo
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Abstract
Because the unmanned aerial vehicle probably looks at the distance link and is improving communication link quality simultaneously, also brings strong interference for the user of adjacent cell. Therefore, the invention provides a method for setting network deployment parameters of a multi-antenna unmanned aerial vehicle under 6G. The method comprises the following specific steps: firstly, determining a deployment center and a deployment height of an unmanned aerial vehicle base station group according to environmental parameters, and determining the range of an unmanned aerial vehicle base station serving ground users; secondly, introducing a multi-antenna model, and calculating the power intensity of the interference signal and the useful signal by gamma approximation; then, based on the useful and interference signal strength of the multi-antenna unmanned aerial vehicle network, acquiring the average traversal rate of ground users in the network coverage range, and exploring the change condition of the average traversal rate of the ground users along with the group radius of the unmanned aerial vehicles and the load factor of the unmanned aerial vehicle base station; and finally, setting traversal precision, traversing the unmanned aerial vehicle group radius and the load factor, and selecting the parameter with the highest average traversal rate of the ground users to obtain an optimal unmanned aerial vehicle base station deployment scheme.
Description
Technical Field
The present invention relates to The field of wireless Communication technologies, and in particular, to a deployment design and parameter setting method for a multi-antenna drone network in The 6th Generation Mobile Communication Technology (referred to as 6G).
Background
The use of a flight platform such as an Unmanned Aerial Vehicle (UAV) is rapidly increasing, and by means of a line-of-sight (LOS) link with high probability and a high dynamic mobility capability, network blind area coverage or hotspot supplementation can be realized, which becomes an important solution for network coverage and capacity enhancement in the future. Unmanned aerial vehicles have different roles in the field of wireless communication. Firstly, the unmanned aerial vehicle can be used as an aerial base station for real-time communication; secondly, the unmanned aerial vehicle can be used as a hotspot supplement to assist the communication of a ground macro base station or as a special air user and a ground user to coexist. These potential features of drones are used in various cases, for example, in order to improve communication quality, a base station of a drone is deployed in a natural disaster area, an area with extreme user density, or a remote area such as a rural mountain area. In these areas, it is more feasible to invest in an infrastructure that can provide communication services in a shorter time, and therefore, the advantage of the unmanned aerial vehicle as a space base station capable of being deployed rapidly is highlighted.
In addition, the unmanned aerial vehicle deployed on the low-altitude platform can establish a line-of-sight link with a ground user, and shelters such as buildings and trees are effectively avoided, so that the source end and the destination end can be in direct communication. For an unmanned aerial vehicle deployed on an aerial platform, in current research, relay equipment is usually added to improve the probability of a non-line-of-sight (NLOS) link, so as to further improve the performance of the line-of-sight link. The unmanned aerial vehicle base station can also provide key effects on the spot when improving energy-conserving thing networking communication, and particularly, the unmanned aerial vehicle node can be followed thing networking device and collected user data to forward it to other equipment, play the effect of relay node promptly. Although drones have great advantages in communication networks, the deployment location of drones is a key challenge when it is used as an auxiliary base station to assist a macro base station.
With the advent of the 5G period and the vision and demand of 6G, drones are increasingly valued as air communication base stations. Because unmanned aerial vehicle has the height-adjustable and advantage of avoiding the barrier voluntarily when flight in the air, compare with the fixed communication base station in ground, it has the probability of bigger and carries out the line of sight link transmission with the ground user, and this communication service quality that can effectively improve the ground user and the demand that satisfies user connection rate. In wireless communication network model design, we will typically assume that users are placed in fixed locations or randomly distributed, while base stations are fixed within a cellular grid. Although the distribution of the base stations and the users is beneficial to the derivation of the network performance index, the model is too ideal and cannot be directly applied to actual deployment. Consequently, it is more reasonable to adopt the binomial point process of unmanned aerial vehicle spatial distribution, specifically, ground user communicates with the unmanned aerial vehicle node rather than nearest. Meanwhile, the concept of the unmanned aerial vehicle system network taking the user as the center, namely the concept of defining the unmanned aerial vehicle system network taking the user as the center by using a 'honeycomb removing' method breaks through the architecture of controlling the user by the traditional network. In the unmanned aerial vehicle system network architecture taking users as centers, the ground users can independently select service groups according to the distribution of nearby unmanned aerial vehicles, so that the received signal power is effectively improved.
Disclosure of Invention
According to the invention, a parameter deployment method based on the group radius of the unmanned aerial vehicle network, the height of the unmanned aerial vehicle and the number of the antennas carried by the unmanned aerial vehicle and the number of the scheduling users under a multi-antenna scene is considered, and a ground user can ensure the best signal quality by selecting a base station with a larger reference signal. Specifically, the invention is selected based on the received signal power received by the user, but the invention improves the method not only selects a unique reference which is nearest to the user equipment or has the maximum power for the user equipment to serve, but also selects a plurality of unmanned aerial vehicle base stations which are nearest to the user equipment to serve for transmission. In the present invention, fig. 1 specifically describes a typical method and standard for a user to select a service drone base station, that is, a potential service site set is selected for each user, a dynamic drone group is constructed, and the drone group serving each user changes with the movement of the user or the drone base station, so as to achieve better communication quality.
For a typical user i, we choose the nearest B i And the unmanned aerial vehicles carry out communication service for the unmanned aerial vehicles, so that a unmanned aerial vehicle cluster is formed. That is, if the position O of the typical user i is projected to the plane where the group of unmanned aerial vehicles is located to obtain O ', the unmanned aerial vehicles serving the typical user i are randomly distributed in a circular area with the circle center O' and the radius R as the center, and all the B in the circular area i The set formed by the unmanned aerial vehicle base stations is called an unmanned aerial vehicle group theta i 。
The deployment method of the multi-antenna unmanned aerial vehicle network comprises the following steps:
Drone group Θ serving typical user i i In the method, each unmanned aerial vehicle can schedule K users, so that the unmanned aerial vehicle cluster theta is formed i The total scheduled user number of the unmanned aerial vehicle base station is KB i And call this user set omega i . As can be readily derived, | Θ i |＝B i ，|Ω i |＝KB i . In the present invention, KB is said to be exclusive of typical user i i 1 scheduled user is i intra-cluster user, other scheduled users in the network model are called i clusterTo the user. When the distance d between a typical user i and its surrounding users j ij <During R, the unmanned aerial vehicle group theta respectively serving the two users i And Θ j There will be some overlap. The base station of the drone, which is in this overlap area, will serve both the typical user i and the other surrounding users j, and the drone will allocate the transmission power by means of the antenna beam.
And step 210, introducing a multi-antenna model, and calculating the power strength of the interference signal and the power strength of the useful signal through gamma approximation based on first moment estimation and second moment estimation.
In the zero-forcing beamforming design of a downlink transmitting end, the beamforming matrix designed for a typical user can eliminate the interference of the users in a cluster on downlink transmission. For zero-forcing beam matrix V i Since it is necessary to eliminate the intra-cluster interference, the zero-forcing receive beamformer designed for user i needs to be associated with other KB's in the drone group i -1 scheduled users are mutually orthogonal. The normalized zero-forcing receive beamforming matrix can be designed as
Wherein, in the formula (1),
is a MB i ×MB i Identity matrix of order, H -i Refers to a group of drones B serving a typical user i i To other KB within the drone group i -1 channel matrix between users in a cluster, H -i + Is H -i The left inverse matrix of (d).
First, we select a particular representative user as user 1, and express the strength of the channel transmission of the useful signal sent to the representative user
Wherein, g b1 Is a model of the channel between drone base station b and typical user 1, as previously mentioned
β b1 Is the total average excess path loss, f, between drone base station b and typical user 1 b1 Is the rayleigh fading component between drone base station b and typical user 1.
Secondly, will | h 11 || 2 Is approximated as a random variable of gamma distribution, i.e. | | h 11 || 2 ～Γ(k 1 ,θ 1 ) Or is shown as
Further can obtain
In the zero-forcing beamforming design of the transmitting end, the invention allocates equal transmitting power for each isotropic beam. Specifically, to project the channel vectors onto the zero-forcing beamforming vector, the beams received by typical user 1 are all located in other KB's within the drone group 1 -1 interfering channel vector in the null vector space of the subspace formed. Therefore, the power of the transmitted signal after beamforming needs to be scaled proportionally on the shape parameter of the random variable of gamma distribution, and the scaling parameter is
Due to B 1 The number of unmanned aerial vehicles in the unmanned aerial vehicle cooperation cluster for communication service of a typical user 1 is used, and the invention uses the average number of unmanned aerial vehicles in the unmanned aerial vehicle cooperation cluster
To indicate that is
In summary, the signal power distribution model after projection can be obtained as
Wherein the content of the first and second substances,
it follows that the power of the useful signal is related to the number of antennas carried by the drone and the number of scheduled users.
Similar to the useful signal, the interference channel strength h between the interfering user j and the cluster of drones serving the typical user 1 can be obtained 11 || 2 Is composed of
Wherein the content of the first and second substances,
in the zero-forcing beamforming design of the transmitting end, equal transmitting power is configured for all the same-directional beams. Specifically, in order to project the channel vector onto the zero-forcing beamforming vector, the beam received by the interfering user j is a one-dimensional subspace, and therefore the effect of reducing inter-cluster interference by zero-forcing beam analysis is achieved. Therefore, the power of the transmitted signal after beamforming needs to be scaled proportionally on the shape parameter of the random variable of gamma distribution, and the scaling parameter is
Then all interference signals in the plane of the unmanned aerial vehicle are added to obtain the total interference intensity distribution, namely
The average traversal rate for a typical user can be written in the form:
in the average traversal rate expression, σ 2 Normalized background noise variance, i.e., normalized background noise power, in the downlink;
as the interference signal strength v 1 (ii) a laplace transform of;
for useful signal strength ζ 1 Is performed by the laplace transform.
Laplace transform of useful signal strength
Laplace transform of interference signal strength
And step 230, setting traversal precision, traversing the unmanned aerial vehicle group radius and the load factor, and selecting a parameter with the highest average traversal rate of the ground users to obtain an optimal unmanned aerial vehicle base station deployment scheme.
Advantageous effects
The invention provides an unmanned aerial vehicle network model under multiple input and multiple output aiming at unmanned aerial vehicle base station communication service, and an unmanned aerial vehicle can realize rapid network blind area coverage and increase wireless network capacity and coverage area by virtue of the characteristics of high dynamic mobility and a large probability line of sight link. However, the large-probability line-of-sight link not only improves the service quality of the communication link, but also brings strong interference to users in neighboring cells, and becomes an important factor for limiting the network performance. Therefore, the invention solves the problem of adjacent cell interference through a multi-input multi-output technology, introduces a special line-of-sight and non-line-of-sight link of the unmanned aerial vehicle, selects a service site by using a station selection reference taking a user as a center, and analyzes the network performance by using a random geometric tool.
The method comprises the steps of determining the setting of deployment parameters when the performance of the network is optimal through the variation relation of the average traversal rate of a user along with the radius of an unmanned aerial vehicle base station group, the load factor of the unmanned aerial vehicle base stations in the group and the density of the unmanned aerial vehicles, and realizing optimal deployment of the multi-antenna unmanned aerial vehicle network after determining the setting of the deployment parameters.
Drawings
Fig. 1 is a model schematic diagram of a multi-antenna drone network deployment of the present invention;
FIG. 2 is a flow chart of an algorithm implementation of the present invention;
FIG. 3 is a graph of the average traversal rate of a user as a function of the unmanned aerial vehicle load factor;
fig. 4 is a graph of average traversal rate of users as a function of drone group radius;
FIG. 5 is a graph of average traversal rate of users versus deployment density of drones;
Detailed Description
The invention provides an unmanned aerial vehicle network model under multiple input and multiple output aiming at improving the service quality of a communication link at the approximate apparent distance of an unmanned aerial vehicle network, bringing strong interference to users in adjacent cells, and being an important factor for limiting the network performance. The method adopts a battle selection reference with a user as a center, utilizes a random geometric tool to analyze network performance, effectively simplifies signal power and interference power by using a gamma approximation method, and solves the problem of adjacent cell interference by adopting a zero-forcing beamforming mode. The network model is shown in figure 1. In FIG. 1, for a typical user i, we chooseGet the nearest B i Each unmanned aerial vehicle carries out communication service for the unmanned aerial vehicle, thereby forming an unmanned aerial vehicle cluster. That is, if the position O of the typical user i is projected to the plane where the group of unmanned aerial vehicles is located to obtain O ', the unmanned aerial vehicles serving the typical user i are randomly distributed in a circular area with the circle center at O' and the radius at R. The cumulative interference of a user comes from two parts: serving interference within the drone swarm, interference of other drone groups.
When the unmanned aerial vehicle base station serves, in a scene considered by the invention, the average traversal rate of the user is determined by parameters such as the unmanned aerial vehicle group radius, the number of the unmanned aerial vehicle carrying antennas, the number of the unmanned aerial vehicle dispatching users, the unmanned aerial vehicle deployment height, the unmanned aerial vehicle deployment density and the user density. And determining the optimal parameter setting according to the variation relation of the average traversal rate of the user along with the deployment parameters.
The algorithm flow of this case is shown in fig. 2, and the specific implementation steps are as follows:
step 300, determining a deployment center and a deployment height of the unmanned aerial vehicle base station group according to environmental parameters between the unmanned aerial vehicle base station and the ground user, and determining a range of the unmanned aerial vehicle base station serving the ground user.
In step 310, a multi-antenna model is introduced, and the power strength of the interference signal and the power strength of the useful signal are calculated through gamma approximation based on first-order moment estimation and second-order moment estimation.
And 320, acquiring the average traversal rate of the ground users in the network coverage range based on the useful signal and interference signal strength of the multi-antenna unmanned aerial vehicle network, and exploring the change condition of the average traversal rate of the ground users along with the group radius of the unmanned aerial vehicle base station and the load factor of the unmanned aerial vehicle base station in the group.
And 330, setting traversal precision, traversing the unmanned aerial vehicle group radius and the load factor, and selecting a parameter with the highest average traversal rate of ground users to obtain an optimal unmanned aerial vehicle base station deployment scheme.
The simulation results are shown in fig. 3, 4 and 5.
Figure 3 shows the trend of the average traversal rate of the ground users along with the variation between the load factors of the unmanned aerial vehicles, i.e. the variation trend between the ratio of the number of antennas carried by each unmanned aerial vehicle to the number of users scheduled, when different unmanned aerial vehicles carry antennas, and different unmanned aerial vehicle group radiuses. Finally, with the increase of the load factor of the unmanned aerial vehicle, when the number of the carried antennas is the same, smaller antenna freedom degree can be brought in the zero forcing beam forming, so that the power intensity of useful signals can be reduced, and because the number of users is increased, the inter-cluster interference of the unmanned aerial vehicle can be increased, the signal-to-interference-and-noise ratio of user receiving signals in a downlink is further reduced, and the average traversal rate is reduced. In addition, when the number of users scheduled by the unmanned aerial vehicle is small, the load factor is small, the reduction of the average traversal rate of the users is slow, and the absolute value of the slope of the curve is small; when the number of users scheduled by the unmanned aerial vehicle is large, the load factor is large, the average traversal rate of the users is reduced rapidly, and the absolute value of the slope of the curve is large, so that in actual deployment of the unmanned aerial vehicle antenna, fewer users need to be scheduled and more antennas need to be scheduled, the average traversal rate of the users is improved, and the communication service quality is improved.
Figure 4 shows the trend of the variation between the average traversal rate of the user and the radius of the drone group for different numbers of antennas carried by the drone and for different drone heights. The method can be finally obtained, firstly, along with the increase of the radius of the unmanned aerial vehicle group, the number of the unmanned aerial vehicles in the group serving typical users is increased, so that the useful signal intensity is increased, the signal-to-interference-and-noise ratio of the signals received by the users in the downlink is improved, and finally, the average traversal rate is increased; however, as the radius of the drone group continues to increase, the inter-cluster interference brought to the user also increases dramatically, resulting in a decrease in the signal-to-interference-and-noise ratio and a decrease in the average traversal rate of the user. As can be seen from fig. 4, as the radius of the group of drones increases, the traversal rate increases first and then decreases, there is a maximum value, and the maximum value decreases as the height of the drones increases and increases as the number of antennas carried by the drones increases. However, in general, when the number of the antennas carried by the drone is increased from 4 to 16, the gain of the average traversal rate for the ground users is not obvious to reduce the gain caused by the height of the drone, and when the radius of the drone group is about 0.025km, the average traversal rate for the users reaches an optimal value, and at this time, the optimal deployment of the drone group radius can be obtained.
FIG. 5 shows the average traversal rate of the user and the distribution density λ of the drones at different drone deployment heights h and different drone antenna numbers M b The relation between the unmanned aerial vehicles shows the common influence of the deployment height h of the unmanned aerial vehicles and the average traversal rate of the users caused by the number M of the antennas carried by each unmanned aerial vehicle. As the density of drones increases and approaches infinity, the average traversal rate of users also tends to approach asymptotic values. This is different from the phenomenon of a single-antenna base station, and when a single-antenna unmanned aerial vehicle provides communication service for a single-antenna user, the number of unmanned aerial vehicles in a group serving typical users increases with the increase of the density of the unmanned aerial vehicles, so that the useful signal strength is increased, the signal-to-interference-and-noise ratio of signals received by users in a downlink is improved, and finally the average traversal rate is increased; however, as the density of drones continues to increase, the inter-cluster interference brought to users also increases dramatically due to the fact that drones are distributed too densely, resulting in a reduction in the signal to interference and noise ratio and a reduction in the average traversal rate of users. In a word, in the communication transmission of the single-antenna system, as the density of the unmanned aerial vehicles is continuously increased, the average traversal rate of the user shows a trend of increasing first and then decreasing. However, a multi-antenna system is introduced, that is, each unmanned aerial vehicle carries a plurality of antennas and schedules a plurality of users, and effective projection is performed on the transmission power by adopting a zero-forcing beamforming mode, so that a good effect of reducing inter-cluster interference can be achieved finally. Therefore, with the increase of the density of the unmanned aerial vehicle, the inter-cluster interference received by the user does not increase sharply, that is, the rate of increase of the interference signal strength is far less than the rate of increase of the useful signal strength, so that the signal to interference plus noise ratio is still in an increased state, the average traversal rate of the user is always increased and finally tends to be saturated. Meanwhile, the unmanned aerial vehicle can be carried by the unmanned aerial vehicleThe number of the antennas makes up for the loss caused by the increase of the height of the unmanned aerial vehicle, so that the same traversal rate is achieved.
Claims (5)
1. A method for setting network deployment parameters of a multi-antenna unmanned aerial vehicle under 6G is characterized by comprising the following steps: according to the environmental parameters between the unmanned aerial vehicle base station and the ground users, the deployment center and the deployment height of the unmanned aerial vehicle base station group are determined, the range of the unmanned aerial vehicle base station serving the ground users is determined, namely for a typical user i, the scheme selects the nearest B i Each unmanned aerial vehicle carries out communication service for the unmanned aerial vehicles, so that an unmanned aerial vehicle cluster is formed, and the average number of unmanned aerial vehicles in the cluster
Wherein R is the service group radius;
introducing a multi-antenna model, calculating the power strength of interference signals and useful signals through gamma approximation based on first moment and second moment estimation, firstly, a normalized zero-forcing receiving beam forming matrix can be designed to be
Wherein the content of the first and second substances,
is a MB i ×MB i Identity matrix of order H -i Refers to a group of drones B serving a typical user i i To other KB within the drone group i -1 channel matrix between users in a cluster, H -i + Is H -i Left inverse matrix of, h ii The channel transmission strength of the useful signal for user i;
the channel transmission strength of the useful signal of the typical subscriber 1 is
Wherein, g b1 Is a model of the channel between drone base station b and typical user 1, i.e.
Θ 1 Is a set of drone base stations serving user 1, beta b1 Is the total average excess path loss, f, between drone base station b and typical user 1 b1 Is the rayleigh fading component between drone base station b and typical user 1;
through gamma approximation, the scheme converts | | | h 11 || 2 Is approximated as a random variable of gamma distribution, i.e. | | h 11 || 2 ～Γ(k 1 ,θ 1 ) Wherein, in the step (A),
in the zero-forcing beamforming design of the transmitting end, the scheme configures equal transmitting power for each isotropic beam, and in order to project a channel vector onto the zero-forcing beamforming vector, beams received by a typical user 1 are all located in other KB (KB) in the unmanned aerial vehicle group 1 In the zero vector space of the subspace formed by the vectors of the 1 interference channels, therefore, the transmitted signal power after the beam forming needs to be scaled in a certain proportion on the shape parameter of the gamma distribution random variable, and finally the projected signal power is obtained as
Wherein
The strength of the interference channel for a typical user is
Wherein, the first and the second end of the pipe are connected with each other,
β bj is the total average excess path loss between drone base station b and user j;
will be within the unmanned planeWith addition of interfering signals to obtain a total interference intensity distribution, i.e.
Wherein
Ω 1 Is a cluster of unmanned planes 1 A set formed by users scheduled by the medium unmanned aerial vehicle base station in total;
based on the strength of useful signals and interference signals of the multi-antenna unmanned aerial vehicle network, acquiring the average traversal rate of ground users in the network coverage range, and exploring the change condition of the average traversal rate of the ground users along with the group radius of the unmanned aerial vehicle base stations and the load factors of the unmanned aerial vehicle base stations in the group;
and setting traversal precision, traversing the unmanned aerial vehicle group radius and the load factor, and selecting the parameter with the highest average traversal rate of ground users to obtain an optimal unmanned aerial vehicle base station deployment scheme.
2. The method of claim 1, wherein selecting a serving base station for the ground user using a user-centric station selection reference further comprises: and obtaining path parameters of the line-of-sight link and the non-line-of-sight link, and determining the radius of the unmanned aerial vehicle group and the range of the deployment height.
3. The method of claim 1, characterized by introducing a multi-antenna model for the drone base station; the process sets the number of antennas carried by each unmanned aerial vehicle base station and the number of scheduling users, and reduces the influence of inter-cluster interference on the quality of a communication link through a multi-input multi-output method.
4. A method as claimed in claim 1 or 3, characterized in that, depending on the current channel state, the wanted-signal strength and the interfering-signal strength are gamma-approximated in accordance with first-order and second-order moment estimates; and designing the unmanned aerial vehicle antenna according to a zero-forcing beam forming method, so that the interference strength comes from inter-cluster users, but not intra-cluster users, and performing second gamma approximation on the useful signal strength and the interference signal strength according to the zero-forcing beam forming design to obtain an expression form of power.
5. The method according to claim 1, wherein traversal accuracy is set, the unmanned aerial vehicle group radius and the load factor are traversed, and the parameter with the highest average traversal rate of ground users is selected to obtain an optimal unmanned aerial vehicle base station deployment scheme.
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