An Information Transmission Method for Aerial-Ground Cognitive Radio Networks Based on User Location Information

Abstract

The present invention relates to the field of wireless communication technologies, and particularly to an information transmission method for an air-ground cognitive wireless network based on user location information. First, the unmanned aerial vehicle (UAV) relies on a positioning system to obtain the accurate location information of users, and screens and groups the users within its service range according to certain criteria to serve different user groups in different time slots. Secondly, in each specific time slot, with the maximization of the total energy efficiency of the UAV network as the criterion, the obtained location information is used to jointly optimize and design the beamforming weight vector and transmission power of the UAV, and the zero-forcing method is used to obtain the beamforming weight vector. Finally, according to the obtained beamforming weight vector, the optimal power allocation scheme is obtained through the Dinkelbach method and the Lagrange multiplier method, so as to ensure that all users within the group communicate simultaneously. The information transmission method provided by the present invention improves the service quality of the UAV for each secondary user and realizes the efficient data transmission of the UAV to the secondary users.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to an information transmission method for an air-ground cognitive wireless network based on user location information. Background Art

[0002] The terrestrial cellular network can provide instant and high-speed data transmission services for users in areas with a high population density. However, due to geographical conditions and business model limitations, it cannot meet the requirements of users for full-area coverage and ubiquitous access. Drone communication, on the other hand, has the advantages of small size, flexibility, and little restriction by traffic conditions. It can move flexibly in three-dimensional space as an aerial base station to establish a line-of-sight communication link, thus effectively avoiding signal blockage and occlusion and reducing communication costs. Therefore, drone communication has been widely studied and applied. With the official commercial use of the 5th generation mobile communication, various emerging technologies and services have developed rapidly, and the demand for mobile access and data traffic services has been increasing day by day. In the current situation of shortage of spectrum resources, it is difficult for a single communication network to meet the communication needs of users. In this case, constructing a wireless cognitive network by comprehensively utilizing the advantages of the terrestrial cellular network and the drone communication network can provide users with a larger communication capacity, thus meeting the service requirements of different users and the diverse communication needs in vertical fields.

[0003] However, in actual situations, there are still various problems in drone communication: First, due to hardware device limitations, the processing ability of drones is limited, and they cannot serve a large number of users simultaneously. Therefore, a practical user grouping scheme is needed to solve the problem of overloading in drone communication. Second, due to limitations in factors such as the flight performance, endurance time, and mission payload of drones, it is more practical to consider the energy efficiency issue to achieve an effective compromise between the effectiveness of information transmission and power consumption in the drone communication system. Third, existing optimized design schemes for drone communication usually require accurate and known channel state information, which not only increases the implementation complexity of the system but also requires additional consumption of spectrum and power resources. To overcome the above disadvantages, the present invention provides an information transmission method for an air-ground cognitive wireless network based on user location information. Summary of the Invention

[0004] To solve the above-mentioned technical problems in the prior art, the present invention proposes an information transmission method for an air-ground cognitive wireless network based on user location information. Due to the occlusion of ground buildings, the service ability of cellular base stations is restricted. This method uses the accurate location information of users obtained by a positioning method for user grouping. In each specific time slot, with the goal of maximizing the total energy efficiency of the drone network, considering the total transmit power constraint of the drone and the interference power constraint to primary users, the beamforming weight vector and transmit power of the drone are jointly optimized to ensure that all users can communicate simultaneously.

[0005] Technical solutions adopted by the present invention:

[0006] An information transmission method for an air-ground cognitive radio network based on user location information, wherein the terrestrial cellular network serves as the primary network to provide communication services for primary users, and the unmanned aerial vehicle (UAV) network serves as the secondary network to provide data connections for secondary users. Cognitive radio technology is adopted between the two networks to share frequency resources and improve spectrum efficiency. The method specifically includes the following steps:

[0007] Step a: The UAV obtains the accurate location information of the user through a positioning system;

[0008] Step b: According to the obtained accurate location information of the user, screen and group the secondary users within its service range, ensure that one group of secondary users is served in each time slot, and at the same time serve all users in combination with the time division multiple access (TDMA) method;

[0009] Step c: In a given time slot, taking the maximization of the total energy efficiency of the UAV secondary network as the criterion, considering the interference power constraint on the primary user and the total transmission power constraint of the UAV, jointly optimize and design the UAV beamforming weight vector and transmission power;

[0010] Step d: Obtain the beamforming weight vector by using a low-complexity zero-forcing method;

[0011] Step e: Based on the obtained beamforming weight vector, use a method combining the Dinkelbach method and the Lagrange multiplier method to obtain an optimal power allocation scheme, and realize the efficient transmission of data of users within the group.

[0012] Further, step a specifically includes the following steps:

[0013] Calculate the elevation angle θ and azimuth angle of the user relative to the UAV The channel vector between the UAV and the user can be specifically expressed as:

[0014]

[0015] where ρ is the small-scale fading, and respectively represent the array steering vectors of the planar array in the X-axis and Y-axis directions, and can be respectively expressed as:

[0016]

[0017] where θ and respectively represent the elevation angle and azimuth angle of the user relative to the uniform planar array; β = 2π / λ, where λ is the carrier wavelength; d1 and d2 respectively represent the adjacent element spacings of the antenna array in the x-axis direction and y-axis direction; N1 and N2 respectively represent the number of array elements of the antenna array in the x-axis direction and y-axis direction;T Denotes transpose.

[0018] Further, step b specifically includes the following steps:

[0019] Step b1: Define the correlation between the secondary user and the primary user channels as

[0020] where θ i represents the pitch angle of the i-th secondary user relative to the UAV, represents the azimuth angle of the i-th secondary user relative to the UAV, represents the channel vector of the i-th secondary user, θ p represents the pitch angle of the primary user relative to the UAV, represents the azimuth angle of the primary user relative to the UAV, represents the channel vector of the primary user, |·| represents the absolute value of the element, ||·|| represents the vector 2-norm, and H represents the conjugate transpose;

[0021] Initialize the screening threshold δ p , the loop count i = 1 and the screening set T p ;

[0022] Step b2: Calculate the correlation between the channel vector of the i-th secondary user and the channel vector of the primary user. If then T p = T p ∪{i};

[0023] Step b3: Update the loop count i = i + 1;

[0024] Step b4: If i > M, the loop ends and proceeds to step b5; otherwise, execute step b2;

[0025] Step b5: Based on the Euclidean distance between the UAV and the secondary users, arrange the selected K users in ascending order of distance, and use the first S users as the central users of S user groups respectively;

[0026] Step b6: Calculate the channel correlation between the remaining K - S secondary users and each group central user, and sequentially assign the remaining secondary users to the user group with the smallest correlation value with the central user.

[0027] Further, step c specifically includes the following steps:

[0028] For multiple users in each group, jointly optimize the beamforming weight vector and the UAV transmission power, and establish an optimization problem with the maximization of the total energy efficiency of the UAV secondary network within a given time slot as the criterion. The specific optimization problem can be expressed as:

[0029]

[0030] Among them, Sub i is the set of users in the i-th user group, where the number of users is S i ; P c is the circuit power; B k is the bandwidth allocated by the UAV to the k-th secondary user; w k is the beamforming weight vector corresponding to the signal of the k-th secondary user; p k is the transmission power of the UAV to the k-th secondary user; 1 ≤ k ≤ S i ; P max is the maximum transmission power of the UAV; E[·] represents the mathematical expectation; H represents the conjugate transpose;

[0031] γ k is the output signal-to-interference-plus-noise ratio of the k-th user, which can be specifically expressed as:

[0032]

[0033] Among them, h k and σ k 2 are the channel vector and noise power of the k-th secondary user respectively, p j represents the transmission power of the UAV to the j-th secondary user different from the current k in the current user group, w j represents the beamforming weight vector corresponding to the signal of the j-th secondary user different from the current k in the current user group;

[0034] I P is the interference power threshold tolerated by the primary user; I is the interference power to the primary user, which can be specifically expressed as:

[0035]

[0036] Among them, h p is the channel vector of the primary user;

[0037] Fix the UAV transmission power According to Jensen's inequality, the original optimization problem (3) can be simplified as:

[0038]

[0039] According to Mullen's inequality, the optimization problem is further approximated, that is, the overall expectation of the user signal-to-interference-plus-noise ratio is transformed into the expectations of the numerator and denominator of the user signal-to-interference-plus-noise ratio respectively, specifically:

[0040]

[0041] Among them, is h k The channel correlation matrix of, which can be directly obtained by using accurate angle information, can be specifically expressed as:

[0042]

[0043] Among them, θ k represents the pitch angle of the k-th secondary user relative to the UAV, represents the azimuth angle of the k-th secondary user relative to the UAV, represents the channel vector of the k-th secondary user.

[0044] Furthermore, the specific steps of step d include the following steps:

[0045] Adopt the zero-forcing method based on the array steering vector to make the signal sent to the target user orthogonal to the channels of other users, so as to eliminate the interference between users, that is, the intra-group interference satisfies the following formula:

[0046]

[0047] The interference to the primary user satisfies the following formula:

[0048]

[0049] Furthermore, the optimization problem is transformed into:

[0050]

[0051] Thus, the beamforming weight vector corresponding to each user in this group is solved Mathematically, it can be expressed in a closed form:

[0052]

[0053] Among them, represents the null space projection matrix of G k ; I N represents the identity matrix; G k can be expressed as:

[0054]

[0055] Among them, θ1, θ k -1, θ k+1 , respectively represent the pitch angles of the 1st, k - 1, k + 1, S i -th secondary users relative to the UAV, respectively represent the azimuth angles of the 1st, k - 1, k + 1, S i -th secondary users relative to the UAV, represent the channel vectors of the 1st, k - 1, k + 1, and S i secondary users respectively.

[0056] Furthermore, step e specifically includes the following steps:

[0057] Substitute the result obtained from formula (12) into the original optimization problem (3), and let to obtain:

[0058]

[0059] According to the Dinkelbach method, this optimization objective function can be equivalently transformed into a non - fractional form by introducing an additional parameter η, that is:

[0060]

[0061] For a given p k , the objective function can be re - defined as:

[0062]

[0063] If η * represents the optimal energy efficiency, then when f(η * ) = 0, equations (14) and (15) are equivalent; the Lagrangian function of equation (15) is:

[0064]

[0065] where λ is the Lagrange multiplier; taking the derivative of the Lagrangian function and setting it to 0 gives the optimal power allocation:

[0066]

[0067] Update the Lagrange multiplier λ based on the gradient descent method:

[0068]

[0069] where t i is the step size;

[0070] The specific steps to achieve its optimal power allocation based on Dinkelbach and the Lagrangian function are as follows:

[0071] Step e1: Initialization: The iteration number i = 0, λ = 1, let

[0072] calculate the precision ε = 0.001; where is the initial transmission power, η0 To initialize the optimal energy efficiency;

[0073] Step e2: Let i = i + 1, and calculate the optimal power allocation according to Equation (18) Update the Lagrange multiplier according to Formula (19), and update the optimal energy efficiency

[0074] Step e3: If the convergence condition |η i -η i-1 | ≤ ε is satisfied, the iteration ends, and the optimal transmission power is output Otherwise, execute Step e2.

[0075] Advantages of the present invention: The method for information transmission in an air-ground cognitive radio network based on user location information proposed by the present invention first effectively reduces the number of users to be served by the UAV within a time slot and the interference to the primary users by using a grouping algorithm, thereby improving the quality of service of the UAV to each secondary user; secondly, considering the limited processing payload of the UAV, the low-complexity algorithm designed by the present invention can quickly solve a more practical and complex energy efficiency optimization problem, thereby realizing the efficient data transmission of the UAV to the secondary users. Description of the Drawings

[0076] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0077] Figure 1 It is a schematic diagram of the model of the air-ground cognitive radio network in the embodiment of the present invention;

[0078] Figure 2 It is a schematic diagram of the steps of the method for information transmission in an air-ground cognitive radio network based on user location information in the embodiment of the present invention. Detailed Embodiment

[0079] The following will describe the embodiments of the present invention in detail with reference to the drawings.

[0080] The embodiment of the present invention provides a method for information transmission in an air-ground cognitive radio network based on user location information.

[0081] As Figure 1As shown in the figure, it is a schematic diagram of the model of the air-ground cognitive radio network in this embodiment. The terrestrial cellular network serves as the primary network to provide communication services for primary users, and the UAV network serves as the secondary network to provide data connections for secondary users. Cognitive radio technology is used between the two networks, namely the primary network and the secondary network, to achieve shared frequency resources and improve spectrum efficiency. Among them, the UAV is equipped with a uniform planar array of N1×N2 antennas, and each terrestrial user is configured with a single antenna.

[0082] As Figure 2 shown, the method for information transmission in the air-ground cognitive radio network based on user location information in this embodiment includes the following steps: First, the UAV relies on a positioning system such as Beidou to obtain the accurate location information of users; and screens and groups the users within its service range according to certain criteria to achieve the purpose of serving different user groups in different time slots; Second, in each specific time slot, with the goal of maximizing the total energy efficiency of the UAV network, the location information of the obtained users is used to jointly optimize and design the beamforming weight vector and transmit power of the multiple antennas configured on the UAV; and the zero-forcing method is used to obtain the beamforming weight vector; Finally, according to the obtained beamforming weight vector, the optimal power allocation scheme is obtained by combining the Dinkelbach method and the Lagrange multiplier method, so as to ensure that all users within the group can communicate simultaneously.

[0083] The detailed steps of the method are as follows:

[0084] Step a: The UAV obtains the accurate location information of users through the Beidou positioning system;

[0085] Calculate the elevation angle θ and azimuth angle of the user relative to the UAV Thus, the channel vector between the UAV and the user can be specifically expressed as:

[0086]

[0087] where ρ is the small-scale fading, and respectively represent the array steering vectors of the planar array in the X-axis and Y-axis directions, and can be respectively expressed as:

[0088]

[0089] where θ and respectively represent the elevation angle and azimuth angle of the user relative to the uniform planar array; β = 2π / λ, λ is the carrier wavelength; d1 and d2 respectively represent the adjacent element spacing of the antenna array in the x-axis direction and y-axis direction; N1 and N2 respectively represent the number of array elements of the antenna array in the x-axis direction and y-axis direction; TDenotes transpose. Since ρ is instantaneously variable, it is difficult to obtain the accurate channel vector h. Due to the limited payload of the UAV, to avoid excessive energy consumption caused by channel information feedback, characterizes the channel vector between the UAV and the user.

[0090] Step b: According to the obtained accurate location information of the user, screen and group the secondary users within its service range, ensure that a group of secondary users are served in each time slot, and at the same time serve all users in combination with the time division multiple access method;

[0091] Based on the location information of the user, group the users. First, calculate the correlation between the channels of M secondary users and the primary user; then screen the secondary users according to the correlation value, and retain the secondary users with a correlation less than a certain threshold; then sort the K secondary users obtained by screening in ascending order according to their distances from the UAV, and select the first S users as the central users of each group; finally, allocate the remaining K - S secondary users to S user groups, calculate the channel correlation between the remaining K - S secondary users and the central user of each group, and allocate the remaining secondary users according to the correlation value, and allocate the remaining secondary users to the user group with the smallest correlation value with the central user in turn. The UAV serves a group of secondary users in each time slot, and at the same time serves all users in combination with the time division multiple access method;

[0092] The specific grouping algorithm steps are as follows:

[0093] Step b1: Define the correlation between the channels of the secondary user and the primary user as

[0094] where θ i represents the pitch angle of the i-th secondary user relative to the UAV, represents the azimuth angle of the i-th secondary user relative to the UAV, represents the channel vector of the i-th secondary user, θ p represents the pitch angle of the primary user relative to the UAV, represents the azimuth angle of the primary user relative to the UAV, represents the channel vector of the primary user, |·| represents the absolute value of the element, ||·|| represents the vector 2-norm, and H represents the conjugate transpose;

[0095] Initialize the screening threshold δ p , the loop count i = 1 and the screening set T p ;

[0096] Step b2: Calculate the channel vector of the i-th secondary user and the channel vector of the primary user. If then T p= T p ∪ {i};

[0097] Step b3: Update the loop count i = i + 1;

[0098] Step b4: If i > M, end the loop and go to Step b5; otherwise, execute Step b2;

[0099] Step b5: Based on the Euclidean distance between the UAV and the secondary users, arrange the selected K users in ascending order of distance, and use the first S users as the central users of the S user groups respectively;

[0100] Step b6: Calculate the channel correlation between the remaining K - S secondary users and the central user of each group, and sequentially assign the remaining secondary users to the user group with the smallest correlation value with the central user; among them, the calculation of the channel correlation of different secondary users refers to the calculation of the channel correlation between the secondary users and the primary users in Step b1 above, that is, replacing the channel vector of the primary user with the channel vector of the central user of each group here for calculation.

[0101] Step c: Within a given time slot, with the goal of maximizing the total energy efficiency of the UAV secondary network, considering the interference power constraint on the primary users and the total transmission power constraint of the UAV, jointly optimize the design of the UAV beamforming weight vector and the transmission power;

[0102] Under the conditions that the total transmission power of the UAV does not exceed its maximum transmission power and the interference power of the UAV network on the primary users does not exceed the threshold value, with the goal of maximizing the total energy efficiency of the UAV secondary network within a given time slot, jointly optimize the UAV beamforming weight vector and the transmission power. For multiple users in each group, jointly optimize the beamforming weight vector and the UAV transmission power, and establish an optimization problem with the goal of maximizing the total energy efficiency of the UAV secondary network within a given time slot. The specific optimization problem can be expressed as:

[0103]

[0104] Among them, Sub i is the set of users in the i-th user group, where the number of users is S i ; P c is the circuit power; B k is the bandwidth allocated by the UAV to the k-th secondary user; w k is the beamforming weight vector corresponding to the signal of the k-th secondary user; p k is the transmission power of the UAV to the k-th secondary user; 1 ≤ k ≤ S i ; P max is the maximum transmission power of the UAV; E[`] represents the mathematical expectation; H represents the conjugate transpose;

[0105] γ k is the output signal-to-interference-plus-noise ratio (SINR) of the k-th user, which can be specifically expressed as:

[0106]

[0107] where h k and σ k 2 are the channel vector and noise power of the k-th secondary user respectively, p j represents the transmission power of the UAV to the j-th secondary user different from the current k in the current user group, and w j represents the beamforming weight vector corresponding to the signal of the j-th secondary user different from the current k in the current user group;

[0108] I P is the interference power threshold tolerable by the primary user; I is the interference power to the primary user, which can be specifically expressed as:

[0109]

[0110] p is the channel vector of the primary user.

[0111] Fix the UAV transmission power According to Jensen's inequality, the original optimization problem (3) can be simplified as:

[0112]

[0113] According to Mullen's inequality, the optimization problem is further approximated, that is, converting the overall expectation of the user SINR into the expectations of the numerator and denominator of the user SINR respectively, specifically:

[0114]

[0115] where, is the channel correlation matrix of h k and can be directly obtained using accurate angle information, which can be specifically expressed as:

[0116]

[0117] where θ k represents the elevation angle of the k-th secondary user relative to the UAV, represents the azimuth angle of the k-th secondary user relative to the UAV, represents the channel vector of the k-th secondary user.

[0118] Step d: Obtain the beamforming weight vector using a low-complexity zero-forcing method;

[0119] The optimization problem (6) has a problem of variable coupling. Considering the limited processing capacity of the UAV, a zero-forcing method with low complexity is used to solve the beamforming weight vector.

[0120] The zero-forcing method based on the array steering vector is adopted to make the signal sent to the target user orthogonal to the channels of other users, so as to eliminate the interference between users.

[0121] That is, the intra-group interference satisfies the following formula:

[0122]

[0123] The interference to the primary user satisfies the following formula:

[0124]

[0125] Furthermore, the optimization problem is transformed into:

[0126]

[0127] Thus, the beamforming weight vector corresponding to each user in this group is solved. Mathematically, it can be expressed in a closed form:

[0128]

[0129] Among them, represents the null space projection matrix of G k ; I N represents the identity matrix with main diagonal elements of 1; G k can be expressed as:

[0130]

[0131] Among them, θ1, θ k -1, θ k+1 , respectively represent the elevation angles of the 1st, k-1, k+1, S i th secondary users relative to the UAV, respectively represent the azimuth angles of the 1st, k-1, k+1, S i th secondary users relative to the UAV, respectively represent the channel vectors of the 1st, k-1, k+1, S i th secondary users.

[0132] Step e: Based on the obtained beamforming weight vector, a method combining the Dinkelbach method and the Lagrange multiplier method is used to obtain the optimal power allocation scheme to achieve efficient transmission of data among users in the group;

[0133] The Substitute it into the original optimization problem (3), and let We can get:

[0134]

[0135] According to the Dinkelbach method, the optimization objective function can be equivalently transformed into a non-fractional form by introducing an additional parameter variable η, that is:

[0136]

[0137] For a given p k , the objective function can be redefined as:

[0138]

[0139] If η * represents the optimal energy efficiency, then when f(η * ) = 0, equations (14) and (15) are equivalent. The Lagrangian function of equation (15) is:

[0140]

[0141] where λ is the Lagrange multiplier. Taking the derivative of the Lagrangian function and setting it to 0 gives the optimal power allocation:

[0142]

[0143] Update the Lagrange multiplier λ based on the gradient descent method, λ = λ(i + 1):

[0144]

[0145] where t i is the step size.

[0146] The specific steps to achieve its optimal power allocation based on Dinkelbach and the Lagrangian function are as follows:

[0147] Step e1: Initialization: The number of iterations i = 0, λ = 1, Let

[0148] Calculate the precision ε = 0.001; where is the initial transmission power, and η 0 is the initial optimal energy efficiency;

[0149] Step e2: Let i = i + 1, calculate the optimal power allocation according to equation (18) Update the Lagrange multiplier according to formula (19), and update the optimal energy efficiency

[0150] Step e3: If the convergence condition |η i - η i-1 | ≤ ε is satisfied, the iteration ends and the optimal transmit power is output Otherwise, execute step e2.

[0151] In summary, for the air - ground cognitive radio network information transmission method based on user location information provided in this embodiment, first, the grouping algorithm is used to effectively reduce the number of users that the UAV needs to serve within a time slot and the interference to the primary users, thereby improving the service quality of the UAV for each secondary user. Secondly, considering the limited processing payload of the UAV, the low - complexity algorithm designed by the present invention can quickly solve a more practical and complex energy - efficiency optimization problem, thereby realizing the efficient data transmission of the UAV to the secondary users. The information transmission method provided by the present invention, under the condition of only knowing the user location information, through the joint optimization design of beamforming and power allocation, under the condition of spectrum sharing between the ground cellular network and the UAV network, provides wireless communication services for multiple secondary users simultaneously. It not only avoids many problems brought by channel information feedback, but also improves the spectrum efficiency of the system.

[0152] Any process or method description shown in the flowchart or described in other ways herein can be understood to represent code including one or more executable instructions for implementing specific logical functions or processes in a circuit, segment, or portion, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in an opposite order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the technical field to which the embodiments of the present invention belong.

[0153] Those of ordinary skill in the technical field can understand that all or part of the steps carried by the method of the above - mentioned embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer - readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0154] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above - mentioned terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0155] The above embodiments are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. An information transmission method for an air-ground cognitive wireless network based on user location information, characterized in that, The terrestrial cellular network serves as the primary network to provide communication services for primary users, and the UAV network serves as the secondary network to provide data connections for secondary users. Cognitive radio technology is adopted between the two networks to share frequency resources and improve spectrum efficiency. The method specifically includes the following steps: Step a: The UAV obtains the accurate location information of users through the positioning system; Step b: According to the obtained accurate location information of users, screen and group the secondary users within its service range, ensure that one group of secondary users is served in each time slot, and serve all users in combination with the time division multiple access method; Step c: In a given time slot, with the goal of maximizing the total energy efficiency of the UAV secondary network, considering the interference power constraint on primary users and the total transmission power constraint of the UAV, jointly optimize the beamforming weight vector and transmission power of the UAV; Step d: Obtain the beamforming weight vector by using a low-complexity zero-forcing method; Step e: Based on the obtained beamforming weight vector, use a method combining the Dinkelbach method and the Lagrange multiplier method to obtain the optimal power allocation scheme and achieve efficient transmission of data of users within the group.

2. The method for transmitting information of an air-ground cognitive radio network based on user location information according to claim 1, wherein The specific steps of step a include the following steps: Calculate the pitch angle θ and azimuth angle of the user relative to the UAV The channel vector between the UAV and the user can be specifically expressed as: where ρ is the small-scale fading, and respectively represent the array steering vectors of the planar array along the X-axis and Y-axis, and can be respectively expressed as: where, θ and respectively represent the elevation angle and azimuth angle of the user relative to the uniform planar array; β = 2π / λ, where λ is the carrier wavelength; d1 and d2 respectively represent the adjacent element spacing of the antenna array in the x-axis direction and y-axis direction; N1 and N2 respectively represent the number of elements of the antenna array in the x-axis direction and y-axis direction; T represents the transpose.

3. The method for transmitting air-ground cognitive radio network information based on user location information according to claim 2, wherein The specific steps of step b include the following steps: Step b1: Define the correlation between the channels of secondary users and primary users as where, θ i represents the pitch angle of the i-th secondary user relative to the UAV, represents the azimuth angle of the i-th secondary user relative to the UAV, represents the channel vector of the i-th secondary user, θ p represents the pitch angle of the primary user relative to the UAV, represents the azimuth angle of the primary user relative to the UAV, represents the channel vector of the primary user, |·| represents the absolute value of an element, ||·|| represents the vector 2-norm, and H represents the conjugate transpose; Initialize the screening threshold value δ p , the loop count i = 1 and the screening set T p ; Step b2: Calculate the channel vector of the $i$-th secondary user and the channel vector of the primary user for correlation. If then $T p = T p \cup\{i\}$; Step b3: Update the loop count i = i + 1; Step b4: If i > M, the loop ends and proceed to step b5; otherwise, execute step b2; M is the number of secondary users; Step b5: Based on the Euclidean distance between the UAV and secondary users, arrange the selected K users in ascending order of distance, and use the first S users as the central users of S user groups respectively; Step b6: Calculate the channel correlation between the remaining K - S secondary users and each group central user, and sequentially assign the remaining secondary users to the user group with the smallest correlation value with the central user.

4. The method for transmitting air-ground cognitive radio network information based on user location information according to claim 3, wherein The specific steps of step c include the following steps: For multiple users within each group, jointly optimize the beamforming weight vector and the UAV transmission power, and establish an optimization problem with the goal of maximizing the total energy efficiency of the UAV secondary network in a given time slot. The specific optimization problem can be expressed as: Among them, Sub i is the set of users in the \(i\)-th user group, where the number of users is \(S\) i ; \(P\) c is the circuit power; \(B\) k is the bandwidth allocated by the UAV to the \(k\)-th secondary user; \(w\) k is the beamforming weight vector corresponding to the signal of the \(k\)-th secondary user; \(p\) k is the transmit power of the UAV to the \(k\)-th secondary user; \(1\leq k\leq S\) i ; \(P\) max is the maximum transmit power of the UAV; \(E[\cdot]\) represents the mathematical expectation; \(H\) represents the conjugate transpose; γ k is the output signal-to-interference-plus-noise ratio for the k-th user, which can be specifically expressed as: where h k and σ k 2 are the channel vector and noise power of the k-th secondary user respectively, p j represents the transmission power of the UAV to the j-th secondary user different from the current k in the current user group, w j represents the beamforming weight vector corresponding to the signal of the j-th secondary user different from the current k in the current user group; I P The interference power threshold tolerable by the primary user; I is the interference power to the primary user, which can be specifically expressed as: where h p is the channel vector of the primary user; Fixed UAV transmission power According to Jensen's inequality, the original optimization problem (3) can be simplified as follows: According to the Mullen inequality, further approximate the optimization problem, that is, convert the overall expectation of the user signal-to-interference-plus-noise ratio into the expectation of the numerator and denominator of the user signal-to-interference-plus-noise ratio respectively. Specifically: Among them, is the channel correlation matrix of h k which can be directly obtained using accurate angle information and can be specifically expressed as: where, θ k represents the pitch angle of the k-th secondary user relative to the UAV, represents the azimuth angle of the k-th secondary user relative to the UAV, represents the channel vector of the k-th secondary user.

5. The method for transmitting air-ground cognitive radio network information based on user location information according to claim 4, wherein The specific steps of step d include the following steps: Adopt a zero-forcing method based on the array steering vector to make the signal sent to the target user orthogonal to the channels of other users to eliminate interference between users, that is, the in-group interference satisfies the following formula: The interference to primary users satisfies the following formula: Furthermore, convert the optimization problem into: Thus, the beamforming weight vector corresponding to each user in this group is solved Mathematically, it can be expressed in a closed form Among them, represents the null space projection matrix of G k ; I N represents the identity matrix; G k can be expressed as: Among them, θ1, θ k-1 , θ k+1 , respectively represent the pitch angles of the 1st, k - 1, k + 1, and S i th secondary users relative to the UAV, respectively represent the azimuth angles of the 1st, k - 1, k + 1, and S i th secondary users relative to the UAV, respectively represent the channel vectors of the 1st, k - 1, k + 1, and S i th secondary users.

6. The method for transmitting information in an air-ground cognitive wireless network based on user location information according to claim 5, wherein The specific steps of step e include the following steps: Substitute the result of formula (12) into the original optimization problem (3), and let we can obtain: we can get: According to the Dinkelbach method, the optimization objective function can be equivalently transformed into a non-fractional form by introducing an additional parameter η, that is: For a given p k , the objective function can be redefined as: If η * represents the optimal energy efficiency, then when f(η * ) = 0, equations (14) and (15) are equivalent; the Lagrangian function of equation (15) is: Where λ is the Lagrange multiplier; take the derivative of the Lagrangian function and set it to 0 to obtain the optimal power allocation: Update the Lagrange multiplier λ based on the gradient descent method: where t i is the step size; The specific steps to achieve its optimal power allocation based on Dinkelbach and the Lagrangian function are as follows: Step e1: Initialization: The iteration number i = 0, λ = 1, Let The calculation accuracy ε = 0.001; where, is the initial transmission power, η 0 is the initial optimal energy efficiency; Step e2: Let \(i = i + 1\) and calculate the optimal power allocation according to Equation (18). Update the Lagrange multiplier according to Equation (19) and update the optimal energy efficiency. Step e3: If the convergence condition |η i - η i-1 | ≤ ε is satisfied, the iteration ends and the optimal transmission power is output Otherwise, step e2 is executed.

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