A 3D spectrum sharing method and device for cognitive drone network

Abstract

The present invention provides a 3D spectrum sharing method and device for a cognitive drone network, and relates to the field of wireless communication technology. A three-dimensional composite spatiotemporal spectrum perception model is constructed based on a line-of-sight probability model and the flexibility of drone positions, and the probability of interference-free transmission between drones is derived to obtain the achievable throughput of the cognitive drone network downlink. A cognitive drone network throughput optimization model is then formulated to maximize the downlink throughput by jointly optimizing the drone perception time, transmission power, and flight altitude. The present invention maximizes the downlink throughput of the cognitive drone network by jointly optimizing the drone perception time, drone transmission power, and drone flight altitude, effectively utilizing the idle spectrum resources of ground base stations, and further improving spectrum utilization and throughput.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and device for 3D spectrum sharing of a cognitive drone network. Background Art

[0002] Due to the high mobility and flexibility of drones and the rapid development of the Internet of Things, drones can support a variety of applications and independently complete various complex tasks. At present, drones have been used in aerial surveys, photography, search and rescue, traffic control, cargo delivery, precision agriculture, and telecommunications. These bandwidth-dependent drone applications and densely deployed drone networks require the transmission of large amounts of data, and people have put forward more stringent requirements on the communication performance of drones. However, the authorized spectrum for drone communications is limited, and it is not economically feasible to establish new dedicated networks. Drones usually compete with a large number of other devices for spectrum resources in unlicensed frequency bands. Therefore, drones are about to face the problem of spectrum resource shortage, and the expansion of drone applications is facing severe challenges. Cognitive radio is a technology that can overcome the overcrowding and scarcity of spectrum. Drones based on cognitive radio technology can continuously sense the wireless environment and dynamically access ground base stations, making full use of their idle spectrum resources without interfering with the operation of ground base stations. It not only alleviates the pressure of shortage of drone spectrum resources, but also significantly improves the reliability and security of drone network transmission.

[0003] As a secondary user, cognitive drones perform spectrum sensing to detect the spectrum occupancy of ground base stations. When the ground base station is detected to be idle, it opportunistically accesses and transmits data. The cognitive drone network dynamically shares spectrum resources with the ground base station. At the same time, drones have controllable maneuverability in three-dimensional space, creating more opportunities for sharing spectrum resources with ground base stations by utilizing spatial spectrum heterogeneity. In particular, thanks to the spatial isolation of air communication links and ground communication links, drone communications can safely reuse spectrum resources traditionally authorized to ground base stations.

[0004] However, most existing studies on cognitive UAV spectrum sharing focus on time-domain or spatial-domain spectrum sensing in a two-dimensional spectrum space, and assume that all UAVs share the same opportunity to access the idle spectrum of the primary user. Neither the three-dimensional spatial characteristics of each UAV are fully considered, nor is the spectrum resource utilization of ground base stations high. Summary of the invention

[0005] In view of the problem that the prior art does not fully consider the three-dimensional spatial characteristics of each UAV and the low spectrum resource utilization of ground base stations, the present invention proposes a 3D spectrum sharing method and device for a cognitive UAV network.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] On the one hand, a 3D spectrum sharing method for a cognitive drone network is provided, comprising:

[0008] S1: Get the coordinates of the ground base station, drone and drone receiver;

[0009] S2: Calculate the parameters of the drone air-to-ground channel model and create a 3D spatiotemporal spectrum perception model;

[0010] S3: Calculate the false detection probability and detection probability of the UAV under 3D spatiotemporal spectrum perception;

[0011] S4: Calculate the probability of interference-free transmission between UAVs based on the distribution of UAVs and the pre-set minimum distance constraint;

[0012] S5: Calculate the downlink throughput obtained by the UAV when it is within the transmission area of ​​the ground base station and outside the transmission area;

[0013] S6: Construct an optimization model for the downlink throughput of the cognitive UAV network to maximize the downlink throughput and complete the 3D spectrum sharing of the cognitive UAV network.

[0014] Optionally, in step S1, obtaining the coordinates of the ground base station, the UAV, and the UAV receiver includes:

[0015] S11: Establish a three-dimensional Cartesian coordinate system with the ground base station as the origin; set the transmission area radius of the ground base station to R p , the perception area radius is R s ; The ground base station transmission power is P D ;

[0016] S12: Randomly and evenly distribute N drones in the s The three-dimensional coordinates of the UAV are expressed as (x i ,y i ,z i ), the transmission power of the UAV is The UAV receivers on the ground are randomly and evenly distributed in the s In the ground area with a radius of , the three-dimensional coordinates of the drone receiver are expressed as

[0017] Optionally, in step S2, calculating the parameters of the drone air-to-ground channel model and creating a 3D spatiotemporal spectrum sensing model includes:

[0018] S21: According to the coordinates of the UAV and the ground base station, the line-of-sight propagation probability from the ground base station to the UAV is determined according to the following formula (1):

[0019]

[0020] The non-line-of-sight propagation probability is determined according to the following formula (2):

[0021]

[0022] in, is the line-of-sight propagation probability from the ground base station to the UAV; is the non-line-of-sight propagation probability from the ground base station to the UAV; B and C are constants that depend on the propagation environment, is the elevation angle from the ground base station to the drone, Indicates the horizontal distance from the drone to the ground base station;

[0023] S22: According to the coordinates of the UAV and the UAV receiver, the line-of-sight propagation probability from the UAV to its corresponding UAV receiver is determined according to the following formula (3):

[0024]

[0025] The non-line-of-sight propagation probability is determined according to the following formula (4):

[0026]

[0027] in, is the line-of-sight propagation probability from a UAV to its corresponding UAV receiver; is the non-line-of-sight propagation probability from a UAV to its corresponding UAV receiver; is the elevation angle from the drone receiver to the corresponding drone; Indicates the horizontal distance from the drone to the corresponding drone receiver;

[0028] S23: According to formulas (1)-(4), a 3D spatiotemporal spectrum sensing model is created. The 3D spatiotemporal spectrum sensing model is expressed by the following formula (5):

[0029]

[0030] Among them, H0 means that the ground base station is idle or the UAV is outside the transmission area of ​​the ground base station, and H1 means that the UAV is within the transmission area of ​​the ground base station and the ground base station is in working state; i (n) means the i-th UAV receives the n-th sample signal, s i (n) is the ground base station transmission signal, n i (n) is zero-mean, is the Gaussian white noise with variance; represents the distance from the i-th UAV to the ground base station; g is the channel gain from the UAV to the ground base station, the parameter η<1 is the excessive attenuation factor of non-line-of-sight propagation, αGA It is the path loss index from the ground node to the aerial node.

[0031] Optionally, in step S3, calculating the false detection probability and the detection probability under the 3D spatiotemporal spectrum perception of the drone includes:

[0032] S31: Based on the created 3D spatiotemporal spectrum perception model, 3D spatiotemporal spectrum perception analysis is performed on each UAV through energy detection; the detection statistic of the signal received by the i-th UAV is

[0033]

[0034] Where M represents the number of samples. represents the signal-to-noise ratio received by the i-th UAV; when M is large, due to the central limit theorem, the distribution is close to the conditional Gaussian distribution of the following formula (7):

[0035]

[0036] in,

[0037]

[0038] S32: Based on the analysis of the 3D spatiotemporal spectrum perception of the UAV, the false detection probability P of the i-th UAV is obtained f,i , expressed by the following formula (8):

[0039]

[0040] And the detection probability P d,i , expressed by the following formula (9):

[0041]

[0042] Where Q(·) is the Gaussian Q function, λ represents the decision threshold of energy detection, P0 represents the probability that the ground base station is idle, P1 represents the probability that the ground base station is busy and P0+P1=1; σ0 represents the noise when the ground base station is idle, σ1 represents the noise when the ground base station is busy; and They represent the false detection probability and detection probability under pure time domain spectrum sensing, respectively. The superscripts T0 and T1 represent the actual idle and working states of the ground base station in the time domain, respectively.

[0043] Optionally, in step S4, the probability of non-interference transmission between drones is calculated based on the distribution of drones and the minimum distance constraint, including:

[0044] S41: According to formula (10), the probability density function of the distance between the drone and its nearest neighbor drone is calculated:

[0045]

[0046] Among them, l represents the distance between a drone and other (N-1) drones among N drones, then l min represents the distance between a UAV and its nearest neighbor UAV; f lmin (l) The probability density function representing the distance between a UAV and its nearest neighbor UAV;

[0047] S42: Based on the probability density function, the probability P of non-interference transmission between drones is calculated according to formula (11) Trans :

[0048]

[0049] Among them, D min is the minimum distance constraint between UAVs.

[0050] Optionally, in step S5, calculating the downlink throughput obtained when the drone is located within the transmission area and outside the transmission area of ​​the ground base station includes:

[0051] S51: According to formula (12), the downlink throughput R1 obtained by the UAV located in the transmission area of ​​the ground base station is calculated:

[0052]

[0053] Among them, the ground base station transmission area is 0 <d i ≤R p ; N p represents the number of UAVs in the transmission area of ​​the ground base station, T is the duration of one frame, and τ is the perception time;

[0054] It is expressed as:

[0055] It is expressed as:

[0056] Where h represents the channel power gain between ground nodes that follows Rayleigh distribution, α GG is the path loss exponent between two ground nodes;

[0057] S52: According to formula (13), calculate the interference I of the UAV in the ground base station transmission area to the main user in operation UP :

[0058]

[0059] S53: According to formula (14), the downlink throughput R2 obtained by the UAV outside the transmission area of ​​the ground base station is calculated:

[0060]

[0061] Among them, the area outside the ground base station transmission area is R p <d i ≤R s ; N s represents the number of UAVs outside the transmission area of ​​the ground base station, and β represents the signal-to-noise ratio threshold of the UAV receiver.

[0062] Optionally, in step S6, an optimization model of the downlink throughput of the cognitive drone network is constructed to maximize the downlink throughput and complete 3D spectrum sharing of the cognitive drone network, including:

[0063] S61: Obtain the independent variables and constraints of the maximization function in the cognitive drone network downlink throughput, and create an optimization model for the cognitive drone network downlink throughput; the optimization model is formula (15):

[0064]

[0065] Where OP represents the optimization problem; st represents the constraint condition; R NET represents the downlink throughput of cognitive UAV network; I up Indicates the interference size of the ground base station, I th represents the maximum interference threshold that the ground base station can tolerate, represents the minimum detection probability threshold, P d,i represents the detection probability of the i-th drone, P U,min and P U,max represents the minimum transmission power and maximum transmission power of the UAV, z i represents the flying height of the i-th UAV, H min and H max Indicates the minimum and maximum altitudes of the drone's flight;

[0066] S62: Based on the optimization model, the optimization results of the perception time, transmission power and flight altitude of the drone group are obtained to complete the 3D spectrum sharing of the cognitive drone network.

[0067] Optionally, in step S62, based on the cognitive drone network downlink throughput optimization model, the optimization results of the perception time, transmission power and flight altitude of the drone group are obtained, including:

[0068] S621: Based on the cognitive UAV network downlink throughput optimization model, decomposition and maximization of the objective function and constraints:

[0069] I: The first model with the perception time of the drone network as the independent variable is obtained, which is expressed by the following formula (16):

[0070]

[0071] Among them, OP1 represents optimization problem 1;

[0072] II: The second model with the transmission power of the UAV network as the independent variable is obtained, which is expressed by the following formula (17):

[0073]

[0074] Among them, OP2 represents optimization problem 2;

[0075] III: The third model with the flight height of the drone network as the independent variable is obtained, which is expressed by the following formula (18):

[0076]

[0077] Among them, OP3 represents optimization problem 3;

[0078] S622: Iteratively solve the first model, the second model, and the third model to obtain optimization results of the UAV network parameters.

[0079] On the one hand, a 3D spectrum sharing device for a cognitive drone network is provided, and the device is applied to any of the above methods; comprising:

[0080] A coordinate acquisition module is used to obtain the coordinates of the ground base station, the UAV and the UAV receiver;

[0081] 3D space-time spectrum perception model module, used to calculate the parameters of the drone air-to-ground channel model and create a 3D space-time spectrum perception model;

[0082] Probabilistic detection module, used to calculate the false detection probability and detection probability of the drone under 3D spatiotemporal spectrum perception using energy detection;

[0083] A transmission probability calculation module is used to calculate the probability of interference-free transmission between UAVs based on the distribution of UAVs and a pre-set minimum distance constraint;

[0084] The downlink throughput module is used to calculate the downlink throughput obtained when the UAV is located in the transmission area of ​​the ground base station and outside the transmission area;

[0085] The optimization sharing module is used to build an optimization model for the downlink throughput of the cognitive drone network, maximize the downlink throughput, and complete the 3D spectrum sharing of the cognitive drone network.

[0086] Optionally, the coordinate acquisition module includes:

[0087] The coordinate system establishment submodule is used to establish a three-dimensional Cartesian coordinate system with the ground base station as the origin; the radius of its transmission area is R p , the perception area radius is R s ; The ground base station transmission power is P D ;

[0088] The basic parameter acquisition submodule is used to randomly and evenly distribute N drones in the s The three-dimensional coordinates of the UAV are expressed as (x i ,y i ,z i ), the transmission power of the UAV is The UAV receivers on the ground are randomly and evenly distributed in the s In the ground area with a radius of

[0089] The above technical solution of the embodiment of the present invention has at least the following beneficial effects:

[0090] In the above scheme, the working area of ​​the ground base station is divided into a transmission area and a sensing area, and the drones are randomly and evenly distributed in the sphere with the ground base station as the center. s In a hemisphere with a radius of 100m, the flexibility of the aerial position of the flying drone is utilized, and the line-of-sight propagation link and non-line-of-sight propagation link of the ground base station signal are jointly considered to define a 3D spatiotemporal spectrum perception hypothesis test model. Drones located in different areas use the idle spectrum resources of the ground base station in different dimensions. Based on the distribution of drones and the minimum distance constraint, the probability of interference-free transmission between drones is calculated, and then the expression of the downlink throughput of the cognitive drone network is obtained based on the perception results and the position status of the drone. The drone perception time, drone transmission power and drone flight altitude are jointly optimized to maximize the downlink throughput of the cognitive drone network, effectively utilize the idle spectrum resources of the ground base station, and further improve the spectrum utilization and throughput. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0092] Figure 1 is a flow chart of a 3D spectrum sharing method for a cognitive drone network provided by an embodiment of the present invention;

[0093] Figure 2 is a flow chart of a 3D spectrum sharing method for a cognitive drone network provided by an embodiment of the present invention;

[0094] Figure 3 It is a schematic diagram of a 3D spectrum sharing system model between a cognitive drone network and a ground base station provided by an embodiment of the present invention;

[0095] Figure 4 It is a schematic diagram of cognitive drone network downlink throughput performance that can be achieved by optimizing different drone operating parameters provided by an embodiment of the present invention;

[0096] Figure 5 This is a diagram of a 3D spectrum sharing device for a cognitive drone network provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0097] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0098] The embodiment of the present invention provides a 3D spectrum sharing method for a cognitive drone network, such as Figure 1 The flowchart of the 3D spectrum sharing method of the cognitive drone network shown includes:

[0099] S101: Obtain the coordinates of the ground base station, the UAV and the UAV receiver;

[0100] S102: Calculate the parameters of the drone air-to-ground channel model and create a 3D space-time spectrum perception model;

[0101] S103: Calculate the false detection probability and detection probability of the UAV under 3D spatiotemporal spectrum perception;

[0102] S104: Calculating the probability of interference-free transmission between drones based on the distribution of drones and a pre-set minimum distance constraint;

[0103] S105: Calculate the downlink throughput obtained by the UAV when it is within the transmission area of ​​the ground base station and outside the transmission area;

[0104] S106: Construct an optimization model for the downlink throughput of the cognitive drone network to maximize the downlink throughput and complete the 3D spectrum sharing of the cognitive drone network.

[0105] Optionally, the coordinates of the ground base station, the UAV, and the UAV receiver are obtained, including:

[0106] Establish a three-dimensional Cartesian coordinate system with the ground base station as the origin; set the transmission area radius of the ground base station to R p , the perception area radius is R s; The ground base station transmission power is P D ;

[0107] Randomly and evenly distribute N drones in the s The three-dimensional coordinates of the UAV are expressed as (x i ,y i ,z i ), the transmission power of the UAV is The UAV receivers on the ground are randomly and evenly distributed in the s In the ground area with a radius of , the three-dimensional coordinates of the drone receiver are expressed as

[0108] Optionally, calculate the parameters of the drone air-to-ground channel model and create a 3D spatiotemporal spectrum sensing model, including:

[0109] According to the coordinates of the UAV and the ground base station, the line-of-sight propagation probability from the ground base station to the UAV is determined according to the following formula (1):

[0110]

[0111] The non-line-of-sight propagation probability is determined according to the following formula (2):

[0112]

[0113] in, is the line-of-sight propagation probability from the ground base station to the UAV; is the non-line-of-sight propagation probability from the ground base station to the UAV; B and C are constants that depend on the propagation environment, is the elevation angle from the ground base station to the drone, Indicates the horizontal distance from the UAV to the ground base station;

[0114] According to the coordinates of the UAV and the UAV receiver, the line-of-sight propagation probability from the UAV to its corresponding UAV receiver is determined according to the following formula (3):

[0115]

[0116] The non-line-of-sight propagation probability is determined according to the following formula (4):

[0117]

[0118] in, is the line-of-sight propagation probability from a UAV to its corresponding UAV receiver; is the non-line-of-sight propagation probability from a UAV to its corresponding UAV receiver; is the elevation angle from the drone receiver to the corresponding drone; Indicates the horizontal distance from the drone to the corresponding drone receiver;

[0119] According to formulas (1)-(4), a 3D spatiotemporal spectrum sensing model is created. The 3D spatiotemporal spectrum sensing model is expressed by the following formula (5):

[0120]

[0121] Among them, H0 means that the ground base station is idle or the UAV is outside the transmission area of ​​the ground base station, and H1 means that the UAV is within the transmission area of ​​the ground base station and the ground base station is in working state; i (n) means the i-th UAV receives the n-th sample signal, s i (n) is the ground base station transmission signal, n i (n) is zero-mean, is the Gaussian white noise with variance; represents the distance from the i-th UAV to the ground base station; g is the channel gain from the UAV to the ground base station, the parameter η<1 is the excessive attenuation factor of non-line-of-sight propagation, α GA It is the path loss index from the ground node to the aerial node.

[0122] Optionally, the false detection probability and the detection probability under the 3D spatiotemporal spectrum perception of the UAV are calculated, including:

[0123] Based on the created 3D spatiotemporal spectrum perception model, 3D spatiotemporal spectrum perception analysis is performed on each UAV through energy detection; the detection statistic of the signal received by the i-th UAV is

[0124]

[0125] Where M represents the number of samples. represents the signal-to-noise ratio received by the i-th UAV; when M is large, due to the central limit theorem, the distribution is close to the conditional Gaussian distribution of the following formula (7):

[0126]

[0127] in,

[0128]

[0129] Based on the analysis of the 3D spatiotemporal spectrum perception of the UAV, the false detection probability P of the i-th UAV is obtained. f,i , expressed by the following formula (8):

[0130]

[0131] And the detection probability P d,i, expressed by the following formula (9):

[0132]

[0133] Where Q(·) is the Gaussian Q function, λ represents the decision threshold of energy detection, P0 represents the probability that the ground base station is idle, P1 represents the probability that the ground base station is busy and P0+P1=1; σ0 represents the noise when the ground base station is idle, σ1 represents the noise when the ground base station is busy; and They represent the false detection probability and detection probability under pure time domain spectrum sensing, respectively. The superscripts T0 and T1 represent the actual idle and working states of the ground base station in the time domain, respectively.

[0134] Optionally, the probability of non-interference transmission between UAVs is calculated based on the distribution of UAVs and the minimum distance constraint, including:

[0135] According to formula (10), the probability density function of the distance between a UAV and its nearest neighbor UAV is calculated:

[0136]

[0137] Among them, l represents the distance between a drone and other (N-1) drones among N drones, then l min represents the distance between a UAV and its nearest neighbor UAV; f lmin (l) The probability density function representing the distance between a UAV and its nearest neighbor UAV;

[0138] Based on the probability density function, the probability P of non-interference transmission between UAVs is calculated according to formula (11): Trans :

[0139]

[0140] Among them, D min is the minimum distance constraint between UAVs.

[0141] Optionally, calculating the downlink throughput obtained when the UAV is located within the transmission area and outside the transmission area of ​​the ground base station includes:

[0142] According to formula (12), the downlink throughput R1 achieved by the UAV located in the transmission area of ​​the ground base station is calculated:

[0143]

[0144] Among them, the ground base station transmission area is 0 <d i ≤R p ; N prepresents the number of UAVs in the transmission area of ​​the ground base station, T is the duration of one frame, and τ is the perception time;

[0145] It is expressed as:

[0146] It is expressed as:

[0147] Where h represents the channel power gain between ground nodes that follows Rayleigh distribution, α GG is the path loss exponent between two ground nodes;

[0148] According to formula (13), the interference I of the UAV to the main users in the ground base station transmission area is calculated. UP :

[0149]

[0150] According to formula (14), the downlink throughput R2 achieved by the UAV outside the transmission area of ​​the ground base station is calculated:

[0151]

[0152] Among them, the area outside the ground base station transmission area is R p <d i ≤R s ; N s represents the number of UAVs outside the transmission area of ​​the ground base station, and β represents the signal-to-noise ratio threshold of the UAV receiver.

[0153] Optionally, an optimization model for the downlink throughput of the cognitive drone network is constructed to maximize the downlink throughput and complete 3D spectrum sharing of the cognitive drone network, including:

[0154] Obtain the independent variables and constraints of the maximization function in the cognitive UAV network downlink throughput, and create an optimization model for the cognitive UAV network downlink throughput; the optimization model is formula (15):

[0155]

[0156] Where OP represents the optimization problem; st represents the constraint condition; R NET represents the downlink throughput of cognitive UAV network; I up Indicates the interference size of the ground base station, I th represents the maximum interference threshold that the ground base station can tolerate, represents the minimum detection probability threshold, P d,i represents the detection probability of the i-th drone, P U,minand P U,max represents the minimum transmission power and maximum transmission power of the UAV, z i represents the flying height of the i-th UAV, H min and H max Indicates the minimum and maximum altitudes of the drone's flight;

[0157] Based on the optimization model, the optimization results of perception time, transmission power and flight altitude of the drone swarm are obtained to complete the 3D spectrum sharing of the cognitive drone network.

[0158] Optionally, based on the cognitive drone network downlink throughput optimization model, the perception time, transmission power and flight altitude optimization results of the drone swarm are obtained, including:

[0159] Based on the cognitive UAV network downlink throughput optimization model, the maximization objective function and constraints are decomposed:

[0160] I: The first model with the perception time of the drone network as the independent variable is obtained, which is expressed by the following formula (16):

[0161]

[0162] Among them, OP1 represents optimization problem 1;

[0163] II: The second model with the transmission power of the UAV network as the independent variable is obtained, which is expressed by the following formula (17):

[0164]

[0165] Among them, OP2 represents optimization problem 2;

[0166] III: The third model with the flight height of the drone network as the independent variable is obtained, which is expressed by the following formula (18):

[0167]

[0168] Among them, OP3 represents optimization problem 3;

[0169] The first model, the second model and the third model are iteratively solved to obtain the optimization results of the UAV network parameters.

[0170] The embodiment of the present invention provides a 3D spectrum sharing method for a cognitive drone network, such as Figure 2 The flowchart of the 3D spectrum sharing method of the cognitive drone network shown includes:

[0171] S201: Establish a three-dimensional Cartesian coordinate system with the ground base station as the origin; set the transmission area radius of the ground base station to R p, the perception area radius is R s ; The ground base station transmission power is P D ;

[0172] In a feasible implementation, the cognitive drone network spectrum sharing model includes a ground base station, N drones, and a drone receiver corresponding to each drone on the ground; the ground base station serves as a primary user and the N drones serve as secondary users.

[0173] S202: Randomly and evenly distribute N drones in the s The three-dimensional coordinates of the UAV are expressed as (x i ,y i ,z i ), the transmission power of the UAV is The UAV receivers on the ground are randomly and evenly distributed in the s In the ground area with a radius of

[0174] In a feasible implementation, the working area of ​​the ground base station is divided into a transmission area and a sensing area, and the drones are randomly and evenly distributed in a hemisphere with the ground base station as the center and the sensing area as the radius. A three-dimensional Cartesian coordinate system is established with the ground base station as the origin, and the radius of the transmission area is R p , the perception area radius is R s ; The ground base station transmission power is P D .like Figure 3 As shown in FIG. 1 , a schematic diagram of a 3D spectrum sharing system model between a cognitive UAV network and a ground base station provided in an embodiment of the present invention is shown; it is assumed that the system model of spectrum sharing between the cognitive UAV network and the ground base station includes a ground base station, N UAVs participating in spectrum sensing and sharing, and their corresponding ground UAV receivers. The N UAVs are randomly and evenly distributed in the R s In a hemisphere with a radius of i ,y i ,z i ), the transmission power of the UAV is The corresponding UAV receivers are randomly and evenly distributed in the R s In the ground area with radius , the three-dimensional coordinates are expressed as The length of a frame is T, the number of samples is M, and M = τ·f s After each UAV samples the received ground base station signal once, each UAV can collect M sample data and build a 3D spatiotemporal spectrum perception model of the UAV based on the position of the UAV.

[0175] In a feasible implementation, it can be assumed that there are 20 drones, 20 drone receivers and 1 ground base station in the cognitive drone network spectrum sharing system. The transmission area radius of the ground base station is R p = 250m, the sensing area radius is R s =500m, 20 drones are randomly and evenly distributed in a hemisphere with a radius of 500, and drone receivers are randomly and evenly distributed in a ground area with a radius of 500. The three-dimensional coordinates of the ground base station are (0,0,0), and the coordinates of the 20 drones are (x1,y1,z1),(x2,y2,z2),…,(x 20 ,y 20 ,z 20 ), the corresponding UAV receiver coordinates are

[0176] S203: According to the coordinates of the UAV and the ground base station, the line-of-sight propagation probability from the ground base station to the UAV is determined according to the following formula (1):

[0177]

[0178] The non-line-of-sight propagation probability is determined according to the following formula (2):

[0179]

[0180] in, is the line-of-sight propagation probability from the ground base station to the UAV; is the non-line-of-sight propagation probability from the ground base station to the UAV; B and C are constants that depend on the propagation environment, is the elevation angle from the ground base station to the drone, Indicates the horizontal distance from the drone to the ground base station;

[0181] S204: According to the coordinates of the UAV and the UAV receiver, the line-of-sight propagation probability from the UAV to its corresponding UAV receiver is determined according to the following formula (3):

[0182]

[0183] The non-line-of-sight propagation probability is determined according to the following formula (4):

[0184]

[0185] in, is the line-of-sight propagation probability from a UAV to its corresponding UAV receiver; is the non-line-of-sight propagation probability from a UAV to its corresponding UAV receiver; is the elevation angle from the drone receiver to the corresponding drone; Indicates the horizontal distance from the drone to the corresponding drone receiver;

[0186] S205: According to formulas (1)-(4), a 3D spatiotemporal spectrum sensing model is created. The 3D spatiotemporal spectrum sensing model is represented by the following formula (5):

[0187]

[0188] Among them, H0 means that the ground base station is idle or the UAV is outside the transmission area of ​​the ground base station, and H1 means that the UAV is within the transmission area of ​​the ground base station and the ground base station is in working state; i (n) means the i-th UAV receives the n-th sample signal, s i (n) is the ground base station transmission signal, n i (n) is zero-mean, is the Gaussian white noise with variance; represents the distance from the i-th UAV to the ground base station; g is the channel gain from the UAV to the ground base station, the parameter η<1 is the excessive attenuation factor of non-line-of-sight propagation, α GA It is the path loss index from the ground node to the aerial node.

[0189] In a feasible implementation, the flexibility of the flying drone's aerial position is utilized, and the line-of-sight propagation link and non-line-of-sight propagation link of the ground base station signal are jointly considered to define a 3D spatiotemporal spectrum perception hypothesis testing model. Drones located in different areas use the idle spectrum resources in different dimensions of the ground base station.

[0190] S206: Based on the created 3D spatiotemporal spectrum perception model, 3D spatiotemporal spectrum perception analysis is performed on each UAV through energy detection; the detection statistic of the signal received by the i-th UAV is

[0191]

[0192] Where M represents the number of samples. represents the signal-to-noise ratio received by the i-th UAV;

[0193] In a feasible implementation, for each drone, 3D spatiotemporal spectrum perception analysis is performed on each drone through energy detection. When the number of samples M is large, due to the central limit theorem, the energy statistic E i The distribution is close to the conditional Gaussian distribution, that is, its expression is:

[0194]

[0195] in, Represents the signal-to-noise ratio received by the i-th UAV.

[0196] S207: Based on the analysis of the 3D spatiotemporal spectrum perception of the UAV, the false detection probability P of the i-th UAV is obtained. f,i , expressed by the following formula (8):

[0197]

[0198]

[0199] And the detection probability P d,i , expressed by the following formula (9):

[0200]

[0201] Where Q(·) is the Gaussian Q function, λ represents the decision threshold of energy detection, P0 represents the probability that the ground base station is idle, P1 represents the probability that the ground base station is busy and P0+P1=1; σ0 represents the noise when the ground base station is idle, σ1 represents the noise when the ground base station is busy; and They represent the false detection probability and detection probability under pure time domain spectrum sensing, respectively. The superscripts T0 and T1 represent the actual idle and working states of the ground base station in the time domain, respectively.

[0202] In a feasible implementation, the ground base station transmission power P is determined D =0.03W, noise variance σ 2 =10 -5 , each drone performs 3D spatiotemporal spectrum sensing:

[0203]

[0204] Based on the idle state of the ground base station and the position of the UAV from the ground base station, a 3D spatiotemporal spectrum perception model of the UAV is constructed. Based on the constructed 3D spatiotemporal spectrum perception model of the cognitive UAV, the energy detection method is executed to perform hypothesis testing on the UAV, and the false detection probability and detection probability of the UAV in space and time are derived.

[0205] S208: According to formula (10), the probability density function of the distance between the drone and its nearest neighbor drone is calculated:

[0206]

[0207] Among them, l represents the distance between a drone and other (N-1) drones among N drones, then l min represents the distance between a UAV and its nearest neighbor UAV; f lmin (l) The probability density function representing the distance between a UAV and its nearest neighbor UAV;

[0208] S209: Based on the probability density function, calculate the probability P of non-interference transmission between drones according to formula (11) Trans :

[0209]

[0210] Among them, D min is the minimum distance constraint between UAVs.

[0211] In a feasible implementation, based on the uniform random distribution characteristics of UAVs, the probability of interference-free transmission between UAVs is derived.

[0212] Among them, the minimum distance constraint of the drone can be set to D min =50m, and then calculate the probability of non-interference transmission between drones P Trans .

[0213] S210: According to formula (12), the downlink throughput R1 obtained by the UAV located in the transmission area of ​​the ground base station is calculated:

[0214]

[0215] Among them, the ground base station transmission area is 0 <d i ≤R p ; N p represents the number of UAVs in the transmission area of ​​the ground base station, T is the duration of one frame, and τ is the perception time;

[0216] It is expressed as:

[0217] It is expressed as:

[0218] Where h represents the channel power gain between ground nodes that follows Rayleigh distribution, α GG is the path loss exponent between two ground nodes;

[0219] S211: According to formula (13), calculate the interference I of the UAV in the ground base station transmission area to the main user in operation UP :

[0220]

[0221] S212: According to formula (14), the downlink throughput R2 obtained by the UAV outside the transmission area of ​​the ground base station is calculated:

[0222]

[0223] Among them, the area outside the ground base station transmission area is Rp <d i ≤R s ; N s represents the number of UAVs outside the transmission area of ​​the ground base station, and β represents the signal-to-noise ratio threshold of the UAV receiver.

[0224] In a feasible implementation, the probability of interference-free transmission between drones is calculated based on the distribution of drones and the minimum distance constraint, and then the expression of the downlink throughput of the cognitive drone network is obtained based on the perception results and the position status of the drones.

[0225] In a feasible implementation, the number of drones located in the ground base station transmission area (0<d i ≤R p ) and the downlink throughput R1 achieved by the UAV outside the transmission area of ​​the ground base station (R p <d i ≤R s The downlink throughput achieved by the UAV of the cognitive UAV network is R2, and the downlink throughput achieved by the cognitive UAV network is R NET =R1+R2.

[0226] In a feasible implementation, the maximum interference threshold I that the ground base station can tolerate is determined. th =-43dBm, signal-to-noise ratio threshold β = 0dB, minimum detection probability threshold

[0227] S213: Obtain the independent variables and constraints of the maximization function in the cognitive drone network downlink throughput, and create an optimization model for the cognitive drone network downlink throughput; the optimization model is formula (15):

[0228]

[0229] Where OP represents the optimization problem; st represents the constraint condition; R NET represents the downlink throughput of cognitive UAV network; I th represents the maximum interference threshold that the ground base station can tolerate, represents the minimum detection probability threshold, P d,i represents the detection probability of the i-th drone, P U,min and P U,max represents the minimum transmission power and maximum transmission power of the UAV, z i represents the flying height of the i-th UAV, H min and H max Indicates the minimum and maximum altitudes for the drone to fly.

[0230] In a feasible implementation, the downlink throughput of the cognitive drone network is maximized by jointly optimizing the drone perception time, drone transmission power, and drone flight altitude, effectively utilizing the idle spectrum resources of the ground base station, and further improving spectrum utilization and throughput.

[0231] In a feasible implementation, the downlink throughput optimization model of the cognitive drone network includes: a cognitive drone network downlink throughput maximization function and constraints; the independent variables of the cognitive drone network downlink throughput maximization function include: drone perception time τ, transmission power P Ui and flight altitude z i The constraints include: interference constraints of cognitive UAV networks on working ground base stations, minimum detection probability constraints, perception time length constraints, single UAV transmission power constraints and single UAV flight altitude constraints.

[0232] S214: Based on the optimization model, the optimization results of the perception time, transmission power and flight altitude of the drone group are obtained to complete the 3D spectrum sharing of the cognitive drone network.

[0233] In a feasible implementation, based on the cognitive UAV network downlink throughput optimization model, the maximization objective function and constraints are decomposed.

[0234] By decomposing the maximization objective function and constraints, the downlink throughput optimization model of cognitive drone networks is decomposed into: the first model with the perception time of the drone swarm as the independent variable, the second model with the transmission power of the drone swarm as the independent variable, and the third model with the flight altitude of the drone swarm as the independent variable:

[0235] I: The first model with the perception time of the drone network as the independent variable is obtained, which is expressed by the following formula (16):

[0236]

[0237] Among them, OP1 represents optimization problem 1;

[0238] II: The second model with the transmission power of the UAV network as the independent variable is obtained, which is expressed by the following formula (17):

[0239]

[0240] Among them, OP2 represents optimization problem 2;

[0241] III: The third model with the flight height of the drone network as the independent variable is obtained, which is expressed by the following formula (18):

[0242]

[0243] Among them, OP3 represents optimization problem 3;

[0244] The first model, the second model and the third model are iteratively solved to obtain the optimization results of the UAV network parameters.

[0245] In a feasible implementation, Figure 4 As shown in FIG. 1 , a schematic diagram of the downlink throughput performance in the cognitive UAV network that can be obtained by optimizing different UAV working parameters provided by this embodiment varies with the ratio of the idle probability to the working probability of the ground base station. The horizontal axis in the figure is the ratio of the idle probability to the working probability of the ground base station P0 / P1, and the vertical axis is the downlink throughput of the cognitive UAV network. The result is that under the interference threshold I th =-43dBm, signal-to-noise ratio threshold β = 0dB, target detection probability From Figure 4 It can be seen that with the increase of the ratio P0 / P1, the downlink throughput of the cognitive drone network continues to increase. Under the same ratio P0 / P1, the downlink throughput of the cognitive drone network in the present invention continues to increase with the optimization of drone perception time, transmission probability and flight altitude. This shows that the method provided by the present invention can effectively improve the downlink throughput achieved by the cognitive drone network, and the spectrum utilization rate is improved.

[0246] On the one hand, if Figure 5 As shown, a 3D spectrum sharing device 100 for a cognitive drone network is provided, and the device is applied to any of the above methods; comprising:

[0247] A coordinate acquisition module 110 is used to acquire the coordinates of the ground base station, the UAV and the UAV receiver;

[0248] 3D space-time spectrum perception model module 120, used to calculate the parameters of the UAV air-to-ground channel model and create a 3D space-time spectrum perception model;

[0249] The probability detection module 130 is used to calculate the false detection probability and the detection probability under the 3D spatiotemporal spectrum perception of the UAV by using energy detection;

[0250] The transmission probability calculation module 140 is used to calculate the probability of interference-free transmission between UAVs based on the distribution of UAVs and a preset minimum distance constraint;

[0251] A downlink throughput module 150 is used to calculate the downlink throughput obtained when the UAV is located in the transmission area of ​​the ground base station and outside the transmission area;

[0252] The optimization sharing module 160 is used to construct an optimization model for the downlink throughput of the cognitive drone network, maximize the downlink throughput, and complete the 3D spectrum sharing of the cognitive drone network.

[0253] Optionally, the coordinate acquisition module includes:

[0254] The coordinate system establishment submodule is used to establish a three-dimensional Cartesian coordinate system with the ground base station as the origin; the radius of its transmission area is R p , the perception area radius is R s ; The ground base station transmission power is P D ;

[0255] The basic parameter acquisition submodule is used to randomly and evenly distribute N drones in the s The three-dimensional coordinates of the UAV are expressed as (x i ,y i ,z i ), the transmission power of the UAV is The UAV receivers on the ground are randomly and evenly distributed in the s In the ground area with a radius of , the three-dimensional coordinates of the drone receiver are expressed as

[0256] In a feasible implementation, the working area of ​​the ground base station is divided into a transmission area and a sensing area, and the drones are randomly and evenly distributed in a sphere with the ground base station as the center. s In a hemisphere with a radius of 100m, the flexibility of the aerial position of the flying drone is utilized, and the line-of-sight propagation link and non-line-of-sight propagation link of the ground base station signal are jointly considered to define a 3D spatiotemporal spectrum perception hypothesis test model. Drones located in different areas use the idle spectrum resources of the ground base station in different dimensions. Based on the distribution of drones and the minimum distance constraint, the probability of interference-free transmission between drones is calculated, and then the expression of the downlink throughput of the cognitive drone network is obtained based on the perception results and the position status of the drone. The drone perception time, drone transmission power and drone flight altitude are jointly optimized to maximize the downlink throughput of the cognitive drone network, effectively utilize the idle spectrum resources of the ground base station, and further improve the spectrum utilization and throughput.

[0257] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0258] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A 3D spectrum sharing method for a cognitive drone network, characterized in that: include: S1: Get the coordinates of the ground base station, drone and drone receiver; In step S1, obtaining the coordinates of the ground base station, the UAV and the UAV receiver includes: S11: Establish a three-dimensional Cartesian coordinate system with the ground base station as the origin; set the transmission area radius of the ground base station to R p , the perception area radius is R s ; The ground base station transmission power is P D ; S12: Randomly and evenly distribute N drones in the s The three-dimensional coordinates of the UAV are expressed as (x i ,y i ,z i ); The transmission power of the UAV is The UAV receivers on the ground are randomly and evenly distributed in the s In the ground area with a radius of S2: Calculate the parameters of the UAV air-to-ground channel model and create a 3D space-time spectrum perception model; In step S2, the parameters of the drone air-to-ground channel model are calculated to create a 3D spatiotemporal spectrum sensing model, including: S21: According to the coordinates of the UAV and the ground base station, the line-of-sight propagation probability from the ground base station to the UAV is determined according to the following formula (1): The non-line-of-sight propagation probability is determined according to the following formula (2): in, is the line-of-sight propagation probability from the ground base station to the UAV; is the non-line-of-sight propagation probability from the ground base station to the UAV; B and C are constants that depend on the propagation environment, is the elevation angle from the ground base station to the UAV, Indicates the horizontal distance from the UAV to the ground base station; S22: According to the coordinates of the UAV and the UAV receiver, the line-of-sight propagation probability from the UAV to its corresponding UAV receiver is determined according to the following formula (3): The non-line-of-sight propagation probability is determined according to the following formula (4): in, is the line-of-sight propagation probability from a UAV to its corresponding UAV receiver; is the non-line-of-sight propagation probability from a UAV to its corresponding UAV receiver; is the elevation angle from the drone receiver to the corresponding drone; Indicates the horizontal distance from the UAV to the corresponding UAV receiver; S23: According to formulas (1)-(4), a 3D spatiotemporal spectrum sensing model is created. The 3D spatiotemporal spectrum sensing model is represented by the following formula (5): Among them, H0 means that the ground base station is idle or the UAV is outside the transmission area of ​​the ground base station, and H1 means that the UAV is within the transmission area of ​​the ground base station and the ground base station is in working state; i (n) means the i-th UAV receives the n-th sample signal, s i (n) is the ground base station transmission signal, n i (n) is zero-mean, is the Gaussian white noise with variance; represents the distance from the i-th UAV to the ground base station; g is the channel gain from the UAV to the ground base station, the parameter η<1 is the excessive attenuation factor of non-line-of-sight propagation, α GA is the path loss index between the ground node and the aerial node; S3: Calculate the false detection probability and detection probability of the UAV under 3D spatiotemporal spectrum perception; S4: Calculate the probability of interference-free transmission between UAVs based on the distribution of UAVs and the pre-set minimum distance constraint; S5: Calculate the downlink throughput obtained by the UAV when it is within the transmission area of ​​the ground base station and outside the transmission area; S6: construct an optimization model for the downlink throughput of the cognitive drone network to maximize the downlink throughput and complete 3D spectrum sharing of the cognitive drone network; In step S6, an optimization model of the downlink throughput of the cognitive drone network is constructed to maximize the downlink throughput and complete 3D spectrum sharing of the cognitive drone network, including: S61: Obtain the independent variables and constraints of the maximization function in the cognitive drone network downlink throughput, and create an optimization model for the cognitive drone network downlink throughput; the optimization model is formula (15): Where OP represents the optimization problem; st represents the constraint condition; R NET represents the downlink throughput of cognitive UAV network; I up Indicates the interference size of the ground base station, I th represents the maximum interference threshold that the ground base station can tolerate, represents the minimum detection probability threshold, P d,i represents the detection probability of the i-th drone, P U,min and P U,max represents the minimum transmission power and maximum transmission power of the UAV, z i represents the flying height of the i-th UAV, H min and H max Indicates the minimum and maximum altitudes of the drone's flight; S62: Based on the optimization model, the optimization results of the perception time, transmission power and flight altitude of the drone group are obtained to complete the 3D spectrum sharing of the cognitive drone network.

2. The 3D spectrum sharing method of cognitive drone network according to claim 1, characterized in that: In step S3, calculating the false detection probability and detection probability under the 3D spatiotemporal spectrum perception of the drone includes: S31: Based on the created 3D spatiotemporal spectrum perception model, 3D spatiotemporal spectrum perception analysis is performed on each UAV through energy detection; the detection statistic of the signal received by the i-th UAV is Where M represents the number of samples. represents the signal-to-noise ratio received by the i-th UAV; when M is large, due to the central limit theorem, the distribution is close to the conditional Gaussian distribution of the following formula (7): in, S32: Based on the analysis of the 3D spatiotemporal spectrum perception of the UAV, the false detection probability P of the i-th UAV is obtained f,i , expressed by the following formula (8): And the detection probability P d,i , expressed by the following formula (9): Where Q(·) is the Gaussian Q function, λ represents the decision threshold of energy detection, P0 represents the probability that the ground base station is idle, P1 represents the probability that the ground base station is busy and P0+P1=1; σ0 represents the noise when the ground base station is idle, σ1 represents the noise when the ground base station is busy; and They represent the false detection probability and detection probability under pure time domain spectrum sensing, respectively. The superscripts T0 and T1 represent the actual idle and working states of the ground base station in the time domain, respectively.

3. The 3D spectrum sharing method of cognitive drone network according to claim 2, characterized in that: In step S4, the probability of non-interference transmission between drones is calculated based on the distribution of drones and the minimum distance constraint, including: S41: According to formula (10), the probability density function of the distance between the drone and its nearest neighbor drone is calculated: Where l represents the distance between N drones, then l min represents the distance between a UAV and its nearest neighbor UAV; f lmin (l) The probability density function representing the distance between a UAV and its nearest neighbor UAV; S42: Based on the probability density function, the probability P of non-interference transmission between drones is calculated according to formula (11) Trans : Among them, D min is the minimum distance constraint between UAVs.

4. The method for 3D spectrum sharing in a cognitive drone network according to claim 3, characterized in that: In step S5, the downlink throughput obtained when the drone is located within the transmission area and outside the transmission area of ​​the ground base station is calculated, including: S51: According to formula (12), the downlink throughput R1 obtained by the UAV located in the transmission area of ​​the ground base station is calculated: The ground base station transmission area is 0 <d i ≤R p ; N p represents the number of UAVs in the transmission area of ​​the ground base station, T is the duration of one frame, and τ is the perception time; It is expressed as: It is expressed as: Where h represents the channel power gain between ground nodes that follows Rayleigh distribution, α GG is the path loss exponent between two ground nodes; S52: According to formula (13), calculate the interference I of the UAV in the ground base station transmission area to the main user in operation UP : S53: According to formula (14), the downlink throughput R2 obtained by the UAV outside the transmission area of ​​the ground base station is calculated: Among them, the area outside the ground base station transmission area is R p <d i ≤R s ; N s represents the number of UAVs outside the transmission area of ​​the ground base station, and β represents the signal-to-noise ratio threshold of the UAV receiver.

5. The method for 3D spectrum sharing in a cognitive drone network according to claim 4, characterized in that: In step S62, based on the cognitive drone network downlink throughput optimization model, the optimization results of the perception time, transmission power and flight altitude of the drone group are obtained, including: S621: Based on the cognitive drone network downlink throughput optimization model, decompose the maximization objective function and the constraint conditions: I: The first model with the perception time of the drone network as the independent variable is obtained, which is expressed by the following formula (16): Among them, OP1 represents optimization problem 1; II: A second model with the transmission power of the drone network as the independent variable is obtained, which is expressed by the following formula (17): Among them, OP2 represents optimization problem 2; III: A third model with the flight height of the drone network as the independent variable is obtained, which is expressed by the following formula (18): Among them, OP3 represents optimization problem 3; S622: Iteratively solve the first model, the second model and the third model to obtain optimization results of the drone network parameters.

6. A 3D spectrum sharing device for a cognitive drone network, characterized in that: The device is applied to any one of the methods described in claims 1-5; comprising: A coordinate acquisition module is used to obtain the coordinates of the ground base station, the UAV and the UAV receiver; A 3D space-time spectrum perception model module, used to calculate the parameters of the UAV air-to-ground channel model and create a 3D space-time spectrum perception model; A probability detection module, used to calculate the false detection probability and the detection probability of the UAV under 3D spatiotemporal spectrum perception by using energy detection; A transmission probability calculation module is used to calculate the probability of interference-free transmission between UAVs based on the distribution of UAVs and a pre-set minimum distance constraint; The downlink throughput module is used to calculate the downlink throughput obtained when the UAV is located in the transmission area of ​​the ground base station and outside the transmission area; The optimization sharing module is used to construct an optimization model for the downlink throughput of the cognitive drone network, maximize the downlink throughput, and complete the 3D spectrum sharing of the cognitive drone network.

7. The 3D spectrum sharing device of the cognitive drone network according to claim 6, characterized in that: The coordinate acquisition module comprises: The coordinate system establishment submodule is used to establish a three-dimensional Cartesian coordinate system with the ground base station as the origin; the radius of its transmission area is R p , the perception area radius is R s ; The ground base station transmission power is P D ; The basic parameter acquisition submodule is used to randomly and evenly distribute N drones in the s The three-dimensional coordinates of the UAV are expressed as (x i ,y i ,z i ); the transmission power of the UAV is P Ui ; The UAV receivers on the ground are randomly and evenly distributed in the R s In the ground area with a radius of , the three-dimensional coordinates of the UAV receiver are expressed as (R xi ,R yi ,0).

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