CN113242566A - Unmanned aerial vehicle base station selection method under shielding effect - Google Patents
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Abstract
The invention researches and discusses the shielding problem of buildings in dense urban areas, and provides a method for selecting a base station of an unmanned aerial vehicle under a shielding effect. The user selects the nearest unmanned aerial vehicle base station which is not shielded by the building in the connectable range as the service unmanned aerial vehicle base station, and when the unmanned aerial vehicle base station which is not shielded by the building does not exist, the nearest unmanned aerial vehicle base station which is shielded by the building once is selected as the service base station. When the coverage area of a single unmanned aerial vehicle base station is small, namely the angle of an unmanned aerial vehicle antenna is small, the coverage rate of the unmanned aerial vehicle base station covered by a building to the service of the user is effectively improved based on the provided unmanned aerial vehicle base station selection method considering the shielding effect.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to unmanned aerial vehicle base station communication in future fifth Generation mobile communication (Beyond 5th Generation, B5G) and sixth Generation mobile communication (6th Generation, 6G).
Background
In the face of the explosive growth of the future mobile data traffic, unmanned aerial vehicle communication is widely concerned in academia and industry as one of the main research directions of B5G to expand communication space. The ground station is often long in deployment period and high in cost, so that the existing ground station deployment scheme is not suitable for high-dynamic service scenes (such as a heat compensation scene with a large capacity requirement and a blind compensation scene with a high coverage requirement), and the goal of accessing the station according to the requirement by the space-time distribution of ground mobile users cannot be realized. Because the unmanned aerial vehicle has the characteristics of low cost, rapid deployment, wide coverage and the like, the unmanned aerial vehicle can be used as an aerial base station to provide communication service for ground users, on one hand, the unmanned aerial vehicle can improve the service quality of a traditional cellular system and shunt the service of a hot cell, on the other hand, the unmanned aerial vehicle can meet the requirement of rapid communication recovery in a disaster area, and the unmanned aerial vehicle is a main research direction of future mobile communication as effective supplement of ground base station communication.
In a future mobile communication system, an unmanned aerial vehicle serving as a base station for wireless communication service is a technology that can meet higher complexity and dynamic data requirements. Compared with the existing ground cell and satellite communication, the unmanned aerial vehicle base station communication has the following advantages: the better mobility enables the station to be deployed rapidly as required, and the link-of-sight (LOS) path is the most link between the station and the ground user and the macro station, so that the capacity of data transmission is improved. The unmanned aerial vehicle base station communication is mainly used for blind-patching and heat-patching scenes, namely, the unmanned aerial vehicle base station communication provides service for areas without ground base stations and areas after traditional communication interruption, or provides good link performance and capacity improvement for areas with dense users. In addition, the unmanned aerial vehicle base station can effectively improve link quality and user capacity by using the wave beam to serve the user.
Because the unmanned aerial vehicle base station is often used in the concurrent heating scene and ground mobile users are densely distributed in urban scenes, based on the actual wide beam model, the beam pitch angle and the deployment height of the unmanned aerial vehicle base station directly influence the beam coverage of the unmanned aerial vehicle base station. In addition, when the change of the unmanned aerial vehicle channel condition is analyzed in an urban environment, the shielding effect of a building and the penetration loss caused by the shielding effect need to be considered.
Disclosure of Invention
The invention researches and discusses the shielding problem of buildings in dense urban areas, and provides an unmanned aerial vehicle base station selection method under the shielding effect.
The unmanned aerial vehicle base station selection method considering the shielding effect comprises the following steps:
and 200, determining the range of the unmanned aerial vehicle base station which can be connected by the user according to the unmanned aerial vehicle deployment parameters and the antenna configuration.
The wide-beam unmanned aerial vehicle base station is deployed at a position h away from the ground in a dense urban area, the antenna angle is set to be theta, theta belongs to (0, pi/2), the influence of the antenna angle of the unmanned aerial vehicle on the beam gain is considered, and the beam gain G and the beam azimuth angle of the unmanned aerial vehicle base station
Is as follows
The sidelobe gain of the beam is approximately 0, the base station of the unmanned aerial vehicle can only provide service for users within the coverage range of the beam, and the signal intensity outside the coverage range of the beam is almost zero. Therefore, the range of the wide-beam drone base station connectable to the user is a circular area with the user as the center and the radius htan θ.
And step 210, determining the shielding condition of a link between any user and an unmanned aerial vehicle base station in the connectable range of the user through three-dimensional shielding modeling according to the acquired environmental parameters of the dense urban area, and introducing a penetration loss factor to obtain the penetration loss of the link.
Determining the penetration loss S of the user and the unmanned aerial vehicle base station link according to the distribution of the number of the blocked buildings, wherein the calculation formula is as follows:
S＝γK'，
where γ is the power loss ratio of the signal after passing through the buildings in a certain area, i.e. the penetration loss factor. Both the user's useful signal and the interfering signal need to take into account the effects of building blockage. For large buildings with multiple walls and millimeter wave beam scenarios where the penetration loss through the building is more severe, γ is 0 assuming all buildings are impenetrable. According to the invention, a beam model of a wide beam is researched, a building blocks a beam of an unmanned aerial vehicle base station in an urban environment, the penetration loss of the building is small, and the user can still be affected, namely gamma is greater than 0.
The useful signal and the interference signal of the user both need to consider the shielding effect of the building.
In the above formula, K 'is the number of buildings blocked on the three-dimensional space connection line between the unmanned aerial vehicle base station and the user, and K' obeys poisson distribution, and the expression is as follows:
wherein R is the distance from the user to the horizontal projection of the unmanned aerial vehicle base station; p ═ λ el [ ] ew ] is the normalized urban density derived from the building density λ, el ] and ew are the average length and width of the building, respectively, p ═ 0 when the floor is free of buildings and p ═ 1 when the building completely covers the floor; β is related to the average length and width of the building and the building density and is expressed as:
eta is the building blocking probability of the three-dimensional space, and the calculation formula is as follows:
wherein, the upper limit of integration
For corresponding position on horizontal projection connecting line of user and unmanned aerial vehicle base stationThe building at the location just blocks the height of the user's channel with the drone base station. Height distribution function f for buildingsH(h′b) From the ground to
And integrating the height position to obtain the probability that the building in the two-dimensional space blocks the horizontal projection connecting line between the user and the unmanned aerial vehicle base station but the three-dimensional space cannot block the channel between the user and the unmanned aerial vehicle base station, and further obtaining the building blocking probability eta in the three-dimensional space.
Wherein the height distribution function f of the buildingH(h′b) The expression, considering its rayleigh distribution subject to the characteristic value of the average height of the building,
wherein the content of the first and second substances,
hbis the average height of the building within a certain range.
P{K'＝0}＝e-η(βx+p/4)The corresponding signal does not experience penetration loss. When the penetration loss factor is large, the probability that K' is larger than 1 is considered to be small, and the influence of the large penetration loss of the signal passing through the buildings twice or more on the user can be ignored.
And step 220, selecting the unmanned aerial vehicle base station according to the determined shielding condition of the link between the user and the unmanned aerial vehicle base station in the connectable range of the user and the penetration loss of the link, and determining a user service base station. The unmanned aerial vehicle base station selection method considering the occlusion effect has the following rule:
when the unmanned aerial vehicle base station with the LOS path exists in the connectable range htan theta of the user, the user is served by the unmanned aerial vehicle base station beam with the LOS path, namely, the useful signal has no penetration LOSs. The user selects the unmanned aerial vehicle base station with the largest RSRP as the service base station, the user is served by the nearest LOS unmanned aerial vehicle base station at the moment, the rest unmanned aerial vehicle base stations in the connectable range are interference base stations, and the interference base stations possibly comprise unmanned aerial vehicle base stations with LOS paths and NLOS paths or unmanned aerial vehicle base stations with NLOS paths.
In this case, there is no beam of the drone shielded by a building in the Htan θ distance, that is, the serving drone is located on the LOS path to the user, and in the horizontal projection x of the connection line between the user and the serving drone base station, there is a building shield in the paths from other drone base stations to the user, so that the probability density function expression is obtained as follows:
wherein mu is deployment density of the unmanned aerial vehicle base station, and the expression of U (x) is as follows:
when the unmanned aerial vehicle base station with the LOS path does not exist in the connectable range htan theta of the user, the user is served by the unmanned aerial vehicle base station wave beam with the non-line-of-sight (NLOS) path, namely, the useful signal has penetration LOSs. The signal can be ignored through two and above huge influences to the user of the penetration loss of building, and the user selects the nearest unmanned aerial vehicle basic station that is sheltered from once by the building as the service base station this moment, and but all the other unmanned aerial vehicle basic stations in the range of connection are interference base stations to interference base station is the unmanned aerial vehicle basic station of NLOS footpath.
In this case, paths from the beams of all drones within the Htan θ distance to the user are blocked by the building, and the drone closest to the Htan θ distance is selected as the serving drone for the user, i.e., the serving drone is located in the NLOS path to the user, and the probability density function is
Advantageous effects
The invention provides an unmanned aerial vehicle base station selection method under an occlusion effect, aiming at the situation that the unmanned aerial vehicle base station signal received by a user possibly has penetration loss due to occlusion of a user-unmanned aerial vehicle base station link by dense urban buildings. Based on existing wide beam model, the coverage of a single unmanned aerial vehicle base station will be influenced by the antenna angle and the deployment height of the unmanned aerial vehicle base station, the building will block the unmanned aerial vehicle base station beam in urban environment, the penetration LOSs of the building is smaller when the wide beam is applied to the unmanned aerial vehicle base station, but the influence can still be caused to the user, therefore, when the user does not have the unmanned aerial vehicle base station with the LOS path in the connectable range, the user selects the nearest unmanned aerial vehicle base station service which is shielded once by the building.
The unmanned aerial vehicle base station selection considering the shielding effect has two conditions that the unmanned aerial vehicle with the LOS path and the unmanned aerial vehicle base station blocked by the building are used as the user service unmanned aerial vehicle base station. The beam coverage performance of the unmanned aerial vehicle base station network with the user as the center in the urban environment is analyzed, and when the coverage range of a single unmanned aerial vehicle base station is small, namely the angle of an unmanned aerial vehicle antenna is small, the coverage rate of the unmanned aerial vehicle base station shielded by a building to the service of the user is considered to be effectively improved.
Drawings
FIG. 1 is a diagram of a network scene of an unmanned aerial vehicle base station considering an occlusion effect according to the present invention;
FIG. 2 is a flow chart of an algorithm implementation of the present invention;
FIG. 3 is a diagram showing the relationship between Signal-to-Interference Ratio (SIR) coverage rate and the variation of the antenna angle of the UAV under the selection of the base station of the UAV considering the blocking effect;
fig. 4 is a graph of SIR coverage as a function of drone/average building height ratio for different penetration loss scenarios of the present invention.
Detailed Description
The following describes embodiments of the present invention in detail with reference to the accompanying drawings.
The invention provides a method for selecting an unmanned aerial vehicle base station under a shielding effect aiming at a scene with building shielding in a dense urban area, and a network model figure is shown as an attached figure 1. Considering the actual coverage model of the wide beam, that is, the influence of the antenna angle of the drone on the beam gain, the drone base station can only provide service for users within the beam coverage range, and the signal strength outside the beam coverage range is almost zero, so the coverage range of a single drone is affected by the deployment height of the drone base station and the antenna angle, and accordingly, the range in which a user can connect the drone base station is limited by the deployment height of the drone base station and the antenna angle.
Obtaining environmental parameters of the dense urban area, including height distribution, average length, width and density of buildings, establishing a three-dimensional space building blocking model, and obtaining the distribution of the number K' of the blocking buildings on the three-dimensional space connection line of the unmanned aerial vehicle base station and the user. And introducing a penetration loss factor, and determining the penetration loss S of the user and the unmanned aerial vehicle base station link according to the distribution of the number of the blocked buildings. Both the user's useful signal and the interfering signal need to take into account the effects of building blockage.
Based on the different sheltering conditions of the user and the unmanned aerial vehicle base station link within the unmanned aerial vehicle base station range that the user can connect, two types of users are given in the attached figure 1: the user who can connect the unmanned aerial vehicle base station and have LOS footpath unmanned aerial vehicle and the user who can connect the unmanned aerial vehicle base station and do not have LOS footpath unmanned aerial vehicle. For two types of users as shown in fig. 1, corresponding base station selection strategies for the unmanned aerial vehicle are given according to different situations, and include:
for a user who can be connected with an unmanned aerial vehicle base station and has an unmanned aerial vehicle with a LOS path, the user can be served by beams of the unmanned aerial vehicle base station with the LOS path, namely, the useful signals do not have penetration LOSs. The user selects the nearest LOS unmanned aerial vehicle base station as a service base station.
For the user who can connect the unmanned aerial vehicle base station and does not have the LOS path unmanned aerial vehicle, the user can only be served by the unmanned aerial vehicle base station wave beam of the NLOS path, namely, the useful signal has penetration LOSs. The influence of the signal through two or more times of penetrating loss of the building on the user can be ignored, and at the moment, the user selects the nearest unmanned aerial vehicle base station which is shielded by the building once as a service base station.
The algorithm flow of the invention is shown as the attached figure 2, and the specific implementation steps are as follows:
and 300, determining the range of the unmanned aerial vehicle base station which can be connected by the user according to the unmanned aerial vehicle deployment parameters and the antenna configuration.
And 310, determining the shielding condition of a link between any user and an unmanned aerial vehicle base station in the connectable range of the user through three-dimensional space shielding modeling according to the acquired environmental parameters of the dense urban area, and introducing a penetration loss factor to obtain the penetration loss of the link.
And 320, selecting the unmanned aerial vehicle base station according to the determined shielding condition of the link between the user and the unmanned aerial vehicle base station in the connectable range of the user and the penetration loss of the link, and determining a user service base station.
The simulation results are shown in fig. 3 and fig. 4, and the SIR coverage performance index of the network under the unmanned aerial vehicle base station selection scheme considering the occlusion effect is researched. The SIR threshold value is set to be-5 dB, the antenna angle value range is theta (0, pi/2) and belongs to, the unmanned aerial vehicle is hung at a height of 150m, and the average height of a building is 100 m. In addition, the penetration loss factor is set to 0.1 in FIG. 3, and the drone base station density is set to 30/km in FIG. 42The city density p is 0.4. Based on the simulation parameters, the variation relationship between the network SIR coverage rate and the antenna angle, the unmanned aerial vehicle density, the penetration loss factor and the city density is researched in the figures 3 and 4.
Fig. 3 shows the variation relationship of the wide-beam drone base station network coverage rate with the drone antenna angle under different drone densities and city densities. As can be seen from the figure: 1) when relevant parameters of the unmanned aerial vehicle and relevant parameters of a building are determined, the beam coverage rate of the unmanned aerial vehicle base station is increased and then reduced along with the change of the angle of the unmanned aerial vehicle base station antenna. The reason is that when the deployment height of the drone is determined, the drone beam pitch angle becomes the only variable affecting the single drone beam coverage, as the beam pitch angle increases, more users are within the beam coverage of the drone base station, and when the beam pitch angle increases to a certain limit and continues to increase, the beam interference between the drones increases, so that the signal-to-interference ratio of the users decreases, resulting in a decrease in coverage. Therefore, in urban scenes, when the density and deployment height of the drone base stations are determined, in order to improve the drone group beam coverage, the antenna angle of the drone base stations needs to be adjustedTo a suitable angle. 2) The unmanned aerial vehicle density mu influences the optimal unmanned aerial vehicle base station antenna angle, and the smaller the unmanned aerial vehicle density is, the larger the unmanned aerial vehicle base station antenna angle reaching the highest coverage rate is. The reason is that a single drone requires a greater beam coverage to meet coverage for all users when the drone density is low. 3) An increase in city density will result in reduced drone network coverage, while the optimal drone antenna angle will vary less when the drone density is greater (e.g., mu 120/km)2) When the density of the unmanned aerial vehicle is lower, the density of the unmanned aerial vehicle is obviously reduced along with the increase of the city density (for example, mu is 30/km)2). The reason is that when the density of the unmanned aerial vehicle is high, the coverage rate is less influenced by the city density, and the reduction amplitude is small; and when the unmanned aerial vehicle density is less, the coverage rate is greatly influenced by the building, and the optimal unmanned aerial vehicle antenna angle is reflected. When unmanned aerial vehicle antenna angle increases to a definite value after, continue to increase antenna angle and will lead to user's signal to interference ratio to reduce, city density increase will make the unmanned aerial vehicle signal of service cover receive the influence to beam interference between the unmanned aerial vehicle basic station reduces, compares under the less circumstances of city density, and the unmanned aerial vehicle signal of service receives the influence more seriously.
Fig. 4 depicts the total coverage P of the drone base station of the present invention at different penetration loss factorscAnd only considering the coverage rate P when the user can be connected with the unmanned aerial vehicle base station with the LOS path in the range of the unmanned aerial vehicle base stationc1Along with the change of unmanned aerial vehicle antenna angle. As can be seen from the figure: 1) penetration loss factor impact analysis: when the coverage rate is higher than that when gamma is 0.5 when gamma is 0.1, after the density of the unmanned aerial vehicles is enough to cover a limited urban area, the increase of the penetration loss factor increases the coverage capability of a single unmanned aerial vehicle base station and also increases the beam interference of other unmanned aerial vehicle base stations, and at the moment, the influence of the latter on the coverage rate causes the SIR value of most users to be reduced, so that the coverage rate is reduced. 2) Total coverage rate PcAnd only considering the coverage rate P when the user can be connected with the unmanned aerial vehicle base station with the LOS path in the range of the unmanned aerial vehicle base stationc1Comparative analysis of (2): two conditions of an unmanned aerial vehicle with LOS (remote location) path and an unmanned aerial vehicle blocked by a building as a user service unmanned aerial vehicle are considered, and when the coverage range of a single unmanned aerial vehicle base station is smallWhen the antenna angle of the drone is small (theta < 1rad) as shown in fig. 4, considering the service of the drone base station blocked by the building to the user will greatly improve the coverage rate. 3) Comparative analysis of the change in coverage with γ equal to 0: when the coverage of a single drone base station is small, as shown in fig. 4, when the drone antenna angle is small (θ < 0.8rad), the coverage of the drone base station is far greater than that when γ is 0, but as the coverage of the single drone base station increases, the coverage of the drone base station is smaller than that when γ is 0, and the peak coverage of γ is possibly higher than that when γ is greater than 0. The reason is that when the coverage of a single unmanned aerial vehicle is limited, if the blocked unmanned aerial vehicle beam can serve the user, the coverage rate of the unmanned aerial vehicle base station network is greatly improved, but with the increase of the coverage of the single unmanned aerial vehicle base station, the unmanned aerial vehicle base station network can sufficiently cover the whole area, and other unmanned aerial vehicles cannot serve the user who is blocked by the building before being served by the unmanned aerial vehicle base station beam; when γ is 0, there is no interference from building-blocked drone base station beams, so that when the single drone base station coverage is large enough, the coverage is higher than that of this section and the coverage peak may be large.
It should be understood by those skilled in the art that the above embodiments are only used for illustrating the present invention and are not to be taken as limiting the present invention, and the changes and modifications of the above embodiments are within the scope of the present invention.
Claims (5)
1. A method for selecting a base station of an unmanned aerial vehicle under an occlusion effect is characterized by comprising the following steps: the wide-beam unmanned aerial vehicle base station is deployed in a dense urban area, the coverage range of a single unmanned aerial vehicle base station is limited, and the range in which a user can be connected with the unmanned aerial vehicle base station is determined according to the deployment height and the beam width of the unmanned aerial vehicle base station; acquiring environmental parameters of dense urban areas, introducing a penetration loss factor, establishing a three-dimensional space building blocking model according to urban building distribution parameters as a power loss proportion of signals passing through a building, and determining penetration loss according to the blocking model and the penetration loss factor; according to whether the unmanned aerial vehicle base station with the LOS path exists in the connectable range of the user, the user selects the nearest LOS unmanned aerial vehicle base station to serve or the nearest unmanned aerial vehicle base station which is shielded once by a building as a service base station.
2. The method according to claim 1, wherein the wide-beam drone base station is deployed in a dense urban area at a height h from the ground, the antenna angle is set to θ, the drone base station is considered to be only able to provide service to users within the beam coverage range in consideration of the influence of the drone antenna angle on the beam gain, and the farthest distance of the user connectable wide-beam drone base station is htan θ if the signal intensity outside the beam coverage range is almost zero.
3. The method according to claim 1, wherein the dense urban environment parameters including building position distribution, height distribution, average length, width and density are obtained, a three-dimensional space building blocking model is established, distribution of the number K' of blocking buildings on a three-dimensional space connection line of an unmanned aerial vehicle base station and a user is obtained, and the expression is as follows:
wherein, R is the distance from the user to the horizontal projection of the unmanned aerial vehicle base station, p is the normalized city density obtained by the building density lambda, beta is the parameter related to the average length and width of the building and the building density, eta is the building blocking probability of the three-dimensional space, and the calculation formula is as follows:
wherein f isH(h'b) As a function of the height distribution of the building.
4. A method according to claims 1 and 3, characterized in that a penetration loss factor γ is introduced, and the penetration loss S of the user and drone base station links is determined from the distribution of the number of blocked buildings, calculated as:
S＝γK’，
both the user's useful signal and the interfering signal need to take into account the effects of building blockage.
5. The method of claims 1, 2 and 4, wherein the link status of the user due to building occlusion is in two cases, and accordingly, the base station selection scheme of the drone considering the occlusion effect comprises:
when the unmanned aerial vehicle base station with the LOS path exists in the connectable range h tan theta of the user, the user is served by the unmanned aerial vehicle base station beam with the LOS path, namely, the useful signal has no penetration LOSs, the user selects the unmanned aerial vehicle base station with the maximum RSRP as the service base station, the user is served by the nearest LOS unmanned aerial vehicle base station at the moment, and the rest unmanned aerial vehicle base stations in the connectable range are interference base stations;
when the unmanned aerial vehicle base station with the LOS path does not exist in the connectable range h tan theta of the user, the user is served by the unmanned aerial vehicle base station beam with the NLOS path, namely, the useful signal has penetration LOSs, the influence of the penetration LOSs of the signal passing through the buildings twice or more on the user can be ignored, and the user selects the nearest unmanned aerial vehicle base station which is shielded once by the buildings as the service base station.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113844974A (en) * | 2021-10-13 | 2021-12-28 | 广州广日电梯工业有限公司 | Method and device for installing elevator remote monitor |
| CN113993105A (en) * | 2021-10-21 | 2022-01-28 | 北京邮电大学 | Unmanned aerial vehicle three-dimensional deployment parameter setting method for urban hot spot area |
| CN114430296A (en) * | 2022-04-03 | 2022-05-03 | 江西师范大学 | Multi-antenna communication method and system for unmanned aerial vehicle relay station |
| CN115209422A (en) * | 2022-06-17 | 2022-10-18 | 北京邮电大学 | Unmanned aerial vehicle base station collaborative networking parameter configuration method in dense urban area |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2938117A1 (en) * | 2014-04-24 | 2015-10-28 | Alcatel Lucent | Adjusting geographical position of a drone base station |
| CN111194038A (en) * | 2020-01-07 | 2020-05-22 | 北京航空航天大学 | Position deployment method for multiple unmanned aerial vehicles mobile base stations |
| CN111970713A (en) * | 2020-08-14 | 2020-11-20 | 北京邮电大学 | Unmanned aerial vehicle base station deployment method and three-dimensional parameter setting for dense urban area |
| CN112367668A (en) * | 2020-10-30 | 2021-02-12 | 北京邮电大学 | Unmanned aerial vehicle base station deployment method utilizing reflected wave beam to supplement coverage |
-
2021
- 2021-04-09 CN CN202110381913.2A patent/CN113242566B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2938117A1 (en) * | 2014-04-24 | 2015-10-28 | Alcatel Lucent | Adjusting geographical position of a drone base station |
| CN111194038A (en) * | 2020-01-07 | 2020-05-22 | 北京航空航天大学 | Position deployment method for multiple unmanned aerial vehicles mobile base stations |
| CN111970713A (en) * | 2020-08-14 | 2020-11-20 | 北京邮电大学 | Unmanned aerial vehicle base station deployment method and three-dimensional parameter setting for dense urban area |
| CN112367668A (en) * | 2020-10-30 | 2021-02-12 | 北京邮电大学 | Unmanned aerial vehicle base station deployment method utilizing reflected wave beam to supplement coverage |
Non-Patent Citations (2)
| Title |
|---|
| TANG WENFEI ET AL.: "Modeling and Coverage Analysis of Power Control in User-Centric UAV Networks", 《2019 IEEE 30TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC)》 * |
| TANG WENFEI ET AL.: "Tractable Modelling and Performance Analysis of UAV Networks With 3D Blockage Effects", 《 IEEE WIRELESS COMMUNICATIONS LETTERS》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113844974A (en) * | 2021-10-13 | 2021-12-28 | 广州广日电梯工业有限公司 | Method and device for installing elevator remote monitor |
| CN113993105A (en) * | 2021-10-21 | 2022-01-28 | 北京邮电大学 | Unmanned aerial vehicle three-dimensional deployment parameter setting method for urban hot spot area |
| CN114430296A (en) * | 2022-04-03 | 2022-05-03 | 江西师范大学 | Multi-antenna communication method and system for unmanned aerial vehicle relay station |
| CN114430296B (en) * | 2022-04-03 | 2022-07-01 | 江西师范大学 | Multi-antenna communication method and system for unmanned aerial vehicle relay station |
| CN115209422A (en) * | 2022-06-17 | 2022-10-18 | 北京邮电大学 | Unmanned aerial vehicle base station collaborative networking parameter configuration method in dense urban area |
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