A network deployment method with maximum communication service coverage

Abstract

The present application relates to a network deployment method with the maximum communication service coverage, including multi-UAV collaborative flight deployment planning, and the specific steps are as follows: establish a hover-flight trajectory model by analyzing the characteristics of line-of-sight and non-line-of-sight communication links, calculate the number of UAVs and the number of deployment time slots; search for the optimal hovering position for M UAVs to connect in a single time slot, and ensure that the user coverage of multiple UAVs in the time slot is maximized when they are connected; expand the multi-UAV collaborative flight trajectory planning algorithm to K time slots, find the K deployment points of each UAV, and obtain the mobile deployment trajectory of multiple UAVs; combine the mobile deployment trajectory of multiple UAVs and the flight rules of UAV base stations to obtain the collaborative flight trajectory of multiple UAVs for emergency communication services. While improving the user coverage of UAV base stations, the present application can significantly reduce the mobile delay of regional coverage deployment of multiple UAVs and reduce the number of UAV resource configurations.

Description

Technical Field

[0001] The present application relates to the field of communication services, and in particular to a network deployment method with maximum communication service coverage. Background Art

[0002] my country has a large land area, diverse geological and climatic conditions, and frequent major natural disasters such as earthquakes. The occurrence of such disasters often leads to large-scale damage to infrastructure such as ground public communication networks. The small number of undamaged base stations cannot meet the communication needs of all users in the disaster area, bringing difficulties to emergency search and rescue and information transmission. The rapid deployment and coverage of emergency communication networks are of great significance to emergency rescue and communication services. Due to the complex environment at the disaster site, traditional communication support vehicles and satellite communication equipment are difficult to reach the disaster-stricken areas quickly. Due to its high mobility, flexible deployment, and large coverage, drones can be equipped with micro base stations for network deployment in disaster relief areas to provide temporary network services to users in disaster areas.

[0003] In response to the communication needs of disaster relief, researchers have conducted relevant research on the deployment of drone base stations. The researchers statically deployed drone base stations by optimizing parameters such as bandwidth and network capacity to reduce network deployment costs. However, due to the limitations of load, energy and communication distance, it is difficult for a single drone to achieve network coverage of a large area. Using multiple drones for dynamic deployment and collaborative link coverage can significantly improve communication service capabilities. The researchers plan the trajectories of multiple drones based on information such as user mobility and ground collection point locations to optimize the coverage and bandwidth of drones and improve the deployment efficiency of drones. However, whether it is static deployment or dynamic flight, it is necessary to ensure the backhaul communication link connection of drone base stations during the coverage process. Zhang et al. achieved backhaul connectivity through multiple relay drones, and Wang achieved connectivity between drones by statically deploying a large number of drones for grid connection, exchanging link connectivity for a greater resource cost. How to achieve collaborative flight of multiple drones while improving the resource utilization of drone base stations to ensure communication link connectivity and network coverage is a key issue that needs to be solved. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a network deployment method with the maximum communication service coverage, which can significantly reduce the mobile delay of multi-drone regional coverage deployment while improving the user coverage of drone base stations and reducing the number of drone resource configurations.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] The present application embodiment provides a network deployment method with maximum communication service coverage, including multi-UAV collaborative flight deployment planning, the steps are as follows:

[0007] By analyzing the characteristics of line-of-sight and non-line-of-sight communication links, a hover-flight trajectory model is established to calculate the number of drones and the number of deployment time slots;

[0008] Search for the optimal hovering position for M drones to connect in a single time slot to ensure that the user coverage rate is maximized when multiple drones are connected in the time slot;

[0009] The multi-UAV collaborative flight trajectory planning algorithm is extended to K time slots, K deployment points of each UAV are calculated, and the mobile deployment trajectory of multiple UAVs is obtained;

[0010] The coordinated flight trajectory of multiple UAVs for emergency communication services is obtained by combining the mobile deployment trajectories of multiple UAVs and the flight rules of UAV base stations.

[0011] The analysis of line-of-sight and non-line-of-sight communication link characteristics is specifically as follows:

[0012] The UAV base station communication link includes a line-of-sight link and a non-line-of-sight link. The path loss during the transmission process is expressed as the average path loss:

[0013] PL=P(LoS)*PL LoS +P(NLoS)*PL NLoS ;

[0014] Where P(LoS) and P(NLoS) represent the probability of forming two transmission links respectively:

[0015]

[0016] P(NLoS)=1-P(LoS);

[0017] a and b are constant values, and their values ​​are related to the environment. θ is the elevation angle of the user to the base station antenna, which can be obtained from Get, h 0 represents the base station antenna height, r 0 Indicates the ground coverage radius of the base station;

[0018] Line-of-sight transmission path loss PL Los :

[0019]

[0020] Non-line-of-sight transmission path loss PL NLos :

[0021]

[0022] The additional loss η caused by environmental factors in the line-of-sight channel Los , is the additional loss η caused by environmental factors in the non-line-of-sight channelNLos Determined by the environment, c is the speed of light, f c is the carrier frequency, d is the Euclidean distance between the transmitting end and the receiving end,

[0023] Average path loss:

[0024]

[0025] Establish a hover-flight trajectory model, assuming that the deployment trajectory of UAV m is composed of point set P m express:

[0026]

[0027] Assume that all drones find p m0 Same, K m represents the total number of deployment points of UAV m, requiring multiple connected UAVs to hover synchronously in the same time period, and all K m If K is set to the same value, the deployment time of each drone will be divided into K time slots {t(1), t(2), ..., t(K)}. Each drone completes the movement from the (k-1)th deployment point to the kth deployment point and hovers at the kth deployment point in the kth time slot. The entire deployment process completes the network coverage of the affected users according to this time slot distribution.

[0028] Drones in the same time slot have the same total time overhead, while the time overhead of drones in different time slots may not be consistent. Therefore, the time overhead of each time slot can be determined, and the size of the kth time slot is expressed as:

[0029] t(k)=t f (k)+t h (k);

[0030]

[0031] t f (k) is the maximum moving time cost of all drones in this time slot, which is given by the maximum moving distance of drones in this time slot (d k-1,k ) max To decide, h (k) is the minimum hovering time of the UAV at the deployment point in the time slot, which is proportional to the number of covered users in the time slot.

[0032] The total user coverage of mobile deployment is

[0033]

[0034] Where n(m,k) is the number of users covered by drone m at the kth deployment point, δ mnUsed to indicate whether the user n that has not been covered is covered by the drone m:

[0035]

[0036] The optimization goal is:

[0037]

[0038]

[0039] The optimization goal is the ratio of the total coverage rate of the drone to the moving time cost of the deployment process. n (k) is proportional to the number of covered users. The optimization of this formula can improve the utilization rate of drone resources. The three conditions represent the minimum threshold C of the total coverage rate. 0 and constraints on return distance.

[0040] The specific process of extending the multi-UAV collaborative flight trajectory planning algorithm to K time slots is as follows:

[0041] Select the deployment area of ​​the drone base station based on the scene information, calculate the number of drone deployments M and the deployment time slot K,

[0042] Using the PSO algorithm, M deployment points in a single time slot are obtained through M searches.

[0043] This process is extended to K time slots, the K deployment points of each UAV are found, and the mobile deployment trajectory of multiple UAVs is obtained.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The multi-UAV deployment scenario is modeled, and the number of UAV deployments and time slots required for the scenario are calculated based on the communication link connectivity constraints, so that multiple UAV base stations can maintain connectivity with ground base stations while providing network coverage, ensuring the timeliness of data return. On the premise of ensuring the connectivity of UAV communication links, the flight trajectories of multiple UAVs are planned, which can reduce the mobile time overhead while optimizing the UAV coverage of users, effectively improving the utilization of UAV resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1is a flow chart of the method of the present invention;

[0048] Figure 2 This is the algorithm implementation process of the method of the present invention. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0050] The terms "comprises," "comprising," or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0051] The following is a diagram of the present application. Figure 1 , a network deployment method with maximum communication service coverage, comprising the following steps:

[0052] By analyzing the characteristics of line-of-sight and non-line-of-sight communication links, a hover-flight trajectory model is established to calculate the number of drones and the number of deployment time slots;

[0053] Search for the optimal hovering position for M drones to connect in a single time slot to ensure that the user coverage rate is maximized when multiple drones are connected in the time slot;

[0054] The multi-UAV collaborative flight trajectory planning algorithm is extended to K time slots, K deployment points of each UAV are calculated, and the mobile deployment trajectory of multiple UAVs is obtained;

[0055] The coordinated flight trajectory of multiple UAVs for emergency communication services is obtained by combining the mobile deployment trajectories of multiple UAVs and the flight rules of UAV base stations.

[0056] Compared with the prior art, the present invention models the multi-UAV deployment scenario and calculates the number of UAV deployments and time slots required for the scenario based on the communication link connectivity constraints, so that the multi-UAV base stations can maintain connectivity with the ground base stations while providing network coverage, thereby ensuring the timeliness of data return.

[0057] In an optional embodiment, the path loss during the transmission process is expressed as an average path loss:

[0058] PL=P(LoS)*PLLoS +P(NLoS)*PL NLoS ;

[0059] Where P(LoS) and P(NLoS) represent the probability of forming two transmission links respectively:

[0060]

[0061] P(NLoS)=1-P(LoS);

[0062] a and b are constant values, and their values ​​are related to the environment. θ is the elevation angle of the user to the base station antenna, which can be obtained from Get, h 0 represents the base station antenna height, r 0 Indicates the ground coverage radius of the base station;

[0063] Line-of-sight transmission path loss PL Los :

[0064]

[0065] Non-line-of-sight transmission path loss PL NLos :

[0066]

[0067] The additional loss η caused by environmental factors in the line-of-sight channel Los , is the additional loss η caused by environmental factors in the non-line-of-sight channel NLos Determined by the environment, c is the speed of light, f c is the carrier frequency, and d represents the Euclidean distance between the transmitting end and the receiving end.

[0068] Average path loss:

[0069]

[0070] Assume that the base station transmits with power P t The received power at the receiving end is:

[0071] P r =P t -PL;

[0072] In order to ensure the stability of the transmission link, it is assumed that the receiving power must be greater than the threshold P rmin , the transmission power of the ground base station and the UAV base station is P t1 , P t2 , then the ground base station coverage radius R:

[0073]

[0074] UAV ground coverage radius r:

[0075]

[0076] The effective communication radius D between the ground base station and the drone is:

[0077]

[0078] The effective communication radius d between drone base stations:

[0079]

[0080] As a preferred solution, a hover-flight trajectory model is established, assuming that the deployment trajectory of UAV m is composed of the point set P m express:

[0081]

[0082] Assume that all drones find p m0 Same, K m Represents the total number of deployment points of UAV m. Two flight states are set for the UAV base station: moving state and hovering state. In the hovering state, each UAV provides reliable network coverage for ground users and waits for other UAVs to arrive at the corresponding deployment point in the same time slot. It connects with the ground base station through multi-hop relay of neighboring hovering UAVs to complete data backhaul.

[0083] Therefore, it is required that multiple connected drones hover synchronously in the same time period, and all K m Assuming the same value K (K>1), the deployment time of each UAV will be divided into K time slots {t(1), t(2), ..., t(K)}. Each UAV completes the movement from the (k-1)th deployment point to the kth deployment point and hovers at the kth deployment point in the kth time slot. The entire deployment process completes network coverage of the affected users according to this time slot distribution.

[0084] Drones in the same time slot have the same total time overhead, while the time overhead of drones in different time slots may not be consistent. Therefore, the time overhead of each time slot can be determined, and the size of the kth time slot is expressed as:

[0085] t(k)=t f (k)+t h (k);

[0086]

[0087] t f(k) is the maximum moving time cost of all drones in this time slot, which is given by the maximum moving distance of drones in this time slot (d k-1,k ) max To decide, h (k) is the minimum hovering time of the UAV at the deployment point in the time slot, which is proportional to the number of covered users in the time slot.

[0088] The dynamic deployment of drone base stations is to serve more disaster-affected users within a short period of time after a disaster occurs. The total user coverage of mobile deployment is

[0089]

[0090] Where n(m,k) is the number of users covered by drone m at the kth deployment point, δ mn Used to indicate whether the user n that has not been covered is covered by the drone m:

[0091]

[0092] The optimization goal is:

[0093]

[0094]

[0095] The optimization goal is the ratio of the total coverage rate of the drone to the moving time cost of the deployment process. n (k) is proportional to the number of covered users. The utilization rate of drone resources can be improved by optimizing this formula. The three conditions represent the minimum threshold C of the total coverage rate. 0 And restrictions on the distance back to the city.

[0096] See also Figure 2 The implementation process of the multi-UAV collaborative flight trajectory planning algorithm in this embodiment is as follows:

[0097] In order to more efficiently determine the deployment locations of multiple drones, it is first necessary to select the deployment area of ​​the drone base station based on the scene information, such as Figure 2 As shown in (1) to (2).

[0098] In this example, the drone base stations are deployed in the area in (2). Assuming that the side length of the divided rectangular area is L, a two-dimensional coordinate system is established according to the area boundary. The coordinates of the two ground base stations can be expressed as Q 1 (R,R),Q 2 (LR,LR).

[0099] Let the coverage diameter of the drone be 2r. Then, the number of drone deployments can be calculated based on the flight projection distance in the diagonal direction of the line connecting the two base stations in the area:

[0100]

[0101] [] is the rounding symbol, where d>2r;

[0102] The line connecting the ground base stations is the direction axis z of the UAV deployment point in a certain time slot 1 Then, with the locations of the two base stations as the center of the circle, the deployment direction (z 1 ,z 2 ,...,z k )(The counterclockwise arrow indicates the return process of the collaborative flight). In order to ensure the overall user coverage, the deployable position of the drone in each time slot needs to be controlled within a certain angle range on both sides of the direction axis. The number of drone deployment time slots can be determined based on the flight projection distance in the vertical direction of the area and the drone communication radius:

[0103]

[0104] Since the deployment area is about z 1 Symmetric, therefore, the total deployment time slot can be expressed as:

[0105] K=2K 0 -1;

[0106] At the same time, we can get the deployment interval θ for each time slot 0 :

[0107]

[0108] Note θ 1 Q 1 Q 2 With Q 2 , the angle between the two lines (0,L), θ 1 ≤π.

[0109] The execution logic of the multi-UAV collaborative flight trajectory planning algorithm in this embodiment includes the following steps:

[0110] Step 1: Set the following parameters, including Q 1 (R,R),Q 2 (LR,LR),M,K,{(x n ,y n ),n=1,2,...,N},θ 0 , R, r, v, D, d, C 0 , {Zk ,k=1,2,...,K};

[0111] Step 2: After multiple iterations, the optimal deployable position of each deployment point is obtained;

[0112] Step 3: Calculate the distance d between each deployment point and the starting point of the drone in each time slot mk ;

[0113] Step 4: Arrange the deployment points in each time slot in order of d mk Sort by size to determine the deployment order of each UAV in each time slot;

[0114] Step 5: The vertical coordinate of the first deployment point in each time slot after adjustment is expressed as y k , each time slot {P 1 ,...,P K} is deployed in the order of y k The size is sorted to minimize the distance between adjacent time slots.

[0115] d k-1,k ) max , so that the total deployment movement time overhead is minimized;

[0116] Step 6: Finally, the deployment point matrix A of multiple drones is formed K×M ={p mk} K×M , where element p mk represents the mth deployment point in the kth time slot.

[0117] According to the deployment point matrix, the trajectory deployment point of each UAV can be obtained. The deployment points in the time slot with the smallest distance to the starting point of the UAV (the farthest deployment point in this time slot is closest to the starting point relative to other time slots) are used as the first deployment point of each UAV. The UAV base stations are deployed along their respective trajectory deployment points in a coordinated manner according to the pendulum flight movement rules. When returning to the first deployment point, a cycle of temporary network deployment is completed.

[0118] Compared with the prior art, the present invention proposes a multi-UAV collaborative flight trajectory planning algorithm, which plans the flight trajectories of multiple UAVs while ensuring the connectivity of the UAV communication links. While optimizing the UAV coverage of users, it can reduce the mobile time overhead and effectively improve the utilization rate of UAV resources.

[0119] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A network deployment method with maximum communication service coverage, It is characterized in that Including multi-UAV collaborative flight deployment planning, the specific steps are as follows: By analyzing the characteristics of line-of-sight and non-line-of-sight communication links, a hover-flight trajectory model is established to calculate the number of drones and the number of deployment time slots; Search for the optimal hovering position for M drones to connect in a single time slot to ensure that the user coverage rate is maximized when multiple drones are connected in the time slot; The multi-UAV collaborative flight trajectory planning algorithm is extended to k time slots, k deployment points of each UAV are calculated, and the mobile deployment trajectory of multiple UAVs is obtained; Combine the mobile deployment trajectories of multiple drones and the flight rules of drone base stations to obtain the collaborative flight trajectories of multiple drones for emergency communication services; The total user coverage of mobile deployment is Where n(m,k) is the number of users covered by drone m at the kth deployment point, δ mn Used to indicate whether the user n that has not been covered is covered by the drone m: The optimization goal is: The optimization goal is the ratio of the total coverage rate of the drone to the moving time cost of the deployment process. n (k) is proportional to the number of covered users. The optimization of this formula can improve the utilization rate of drone resources. The three conditions represent the minimum threshold C of the total coverage rate. 0 and constraints on return distance; The specific process of extending the multi-UAV collaborative flight trajectory planning algorithm to k time slots is as follows: Select the deployment area of ​​the drone base station based on the scene information, calculate the number of drone deployments M and the deployment time slot k, Using the PSO algorithm, M deployment points in a single time slot are obtained through M searches. This process is extended to k time slots, k deployment points of each UAV are found, and the mobile deployment trajectory of multiple UAVs is obtained; The analysis of line-of-sight and non-line-of-sight communication link characteristics is specifically as follows: The UAV base station communication link includes a line-of-sight link and a non-line-of-sight link. The path loss during the transmission process is expressed as the average path loss: PL=P(LoS)*PL LoS +P(NLoS)*PL NLoS ; Where P(LoS) and P(NLoS) represent the probability of forming two transmission links respectively: P(NLoS)=1-P(LoS); a and b are constant values, and their values ​​are related to the environment. θ is the elevation angle of the user to the base station antenna, which can be obtained from Get, h 0 represents the base station antenna height, r 0 Indicates the ground coverage radius of the base station; Line-of-sight transmission path loss PL Los : Non-line-of-sight transmission path loss PL NLos : The additional loss η caused by environmental factors in the line-of-sight channel Los , is the additional loss η caused by environmental factors in the non-line-of-sight channel NLos Determined by the environment, c is the speed of light, f c is the carrier frequency, d is the Euclidean distance between the transmitting end and the receiving end, Average path loss:

2. A network deployment method with maximum communication service coverage according to claim 1, It is characterized in that Establish a hover-flight trajectory model, assuming that the deployment trajectory of UAV m is composed of point set P m express: p m0 is the starting point of UAV m, K m represents the total number of deployment points of UAV m, requiring multiple connected UAVs to hover synchronously in the same time period, and all K m If K is set to the same value, the deployment time of each drone will be divided into k time slots {t(1), t(2), ..., t(K)}. Each drone completes the movement from the (k-1)th deployment point to the kth deployment point and hovers at the kth deployment point in the kth time slot. The entire deployment process completes the network coverage of the affected users according to this time slot distribution. Drones in the same time slot have the same total time overhead, while the time overhead of drones in different time slots may not be consistent. Therefore, the time overhead of each time slot can be determined, and the size of the kth time slot is expressed as: t(k)=t f (k)+t h (k); t f (k) is the maximum moving time cost of all drones in this time slot, which is determined by the maximum moving distance of the drone in this time slot (d k-1,k ) max To decide, h (k) is the minimum hovering time of the UAV at the deployment point in the time slot, which is proportional to the number of covered users in the time slot.

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