Energy efficiency optimization method for heterogeneous network based on relay cooperation energy collection

Abstract

The application discloses a heterogeneous network energy efficiency optimization method based on relay cooperation energy collection, comprising the following steps: S1, constructing a hybrid satellite-macro base station heterogeneous network model; S2, fine modeling of the signal transmission process and derivation of the ground user energy efficiency expression; S3, merging the power allocation coefficient and the beam forming vector in the original optimization variable set, reconstructing the original optimization variable fractional programming model, and obtaining the simplified equivalent optimization variable fractional programming model; S4, introducing an auxiliary variable, and using a continuous convex approximation algorithm to convexify the non-convex constraint in the equivalent optimization variable fractional programming model; S5, for the limitation of the initial value of the constraint condition, introducing a non-negative penalty variable to relax the constraint condition; S6, an iterative loop process, checking whether the change amount of the optimization target value is less than the preset convergence threshold. The method improves the energy efficiency of multiple ground users under the premise of ensuring the quality of communication services.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication and resource optimization technology, and particularly relates to a method for optimizing the energy efficiency of heterogeneous networks based on relay cooperative energy harvesting. Background Technology

[0002] With the rapid development of IoT technology and space information networks, traditional terrestrial communication systems are increasingly unable to meet the demands of large-scale connectivity in terms of coverage and spectrum resources. Integrating satellite communication systems with terrestrial cellular networks to build a space-ground integrated communication system has become an important development direction for improving network service capabilities. In particular, low-Earth orbit (LEO) satellites, due to their low latency and wide coverage, can achieve continuous cross-regional coverage when networked in conjunction with terrestrial macro base stations, effectively supporting the access and data transmission of massive numbers of IoT terminals.

[0003] In the aforementioned converged network architecture, introducing unmanned aerial vehicles (UAVs) as relay nodes can further enhance the system's flexibility and coverage. However, since UAVs rely on onboard batteries for power, their energy reserves are limited, and their flight time is restricted. This can easily lead to energy shortages when performing communication relay tasks, thus affecting the stability of the communication link and the overall system performance. Therefore, how to improve the energy utilization efficiency of UAVs while ensuring communication quality has become a critical technical problem that urgently needs to be solved.

[0004] On the other hand, in satellite-ground integrated networks, satellite links and ground macro base stations typically communicate using spectrum reuse. While this improves spectrum utilization, it also introduces complex cross-domain interference problems. This type of interference not only affects the service quality for ground users but also increases the difficulty of system resource scheduling and beamforming design, making the system optimization problem highly coupled and nonlinear.

[0005] To address the aforementioned issues, existing technologies have been researched from multiple directions. For example, in space-ground converged communication, existing solutions mainly focus on link access capabilities and coverage enhancement; in UAV-assisted communication, most research emphasizes UAV location optimization or single-link performance improvement; in Simultaneous Wireless Information and Power Transfer (SWIPT), existing methods are used for energy allocation optimization in single communication scenarios. However, most of these technologies are independent of each other, lacking a unified system modeling and joint optimization mechanism. Furthermore, in complex communication systems involving multiple nodes and multiple constraints, energy efficiency optimization problems are usually manifested as fractional non-convex optimization problems. Existing solution methods, such as fractional programming, alternating optimization, and traditional convex optimization methods, generally suffer from the following shortcomings: First, these methods are highly dependent on initial values, making it difficult to obtain suitable initial value points in complex systems; second, these solution methods have limited ability to handle non-convex constraints and are prone to getting trapped in local optima; and third, these methods lack a unified mechanism for handling multi-variable coupling relationships. Therefore, a new technical solution is urgently needed in the space-ground converged network to uniformly model the hybrid satellite-macro base station IoT network in the energy-constrained environment of UAVs, and to provide a feasible, convergent, stable and complexity-controllable energy efficiency optimization method to make up for the shortcomings of existing technologies. Summary of the Invention

[0006] The purpose of this invention is to address the following problems existing in the prior art: insufficient system stability due to limited energy of UAV relay nodes, severe interference in space-ground fusion networks, and the non-convex and difficult-to-solve energy efficiency optimization problem caused by multivariate coupling. This invention provides a heterogeneous network energy efficiency optimization method based on the Penalty Successive Convex Approximation (PSCA) algorithm.

[0007] This invention improves the energy efficiency of multiple ground users while ensuring the quality of communication services by introducing the SWIPT mechanism and the Decode-and-Forward (DF) cooperative relay strategy. It also jointly optimizes beamforming, power allocation and power division coefficients to improve the energy efficiency of hybrid satellite-macro base station networks.

[0008] The method of this invention provides the following technical solution:

[0009] A method for optimizing the energy efficiency of heterogeneous networks based on relay cooperative energy harvesting, the optimization method comprising the following steps:

[0010] S1. Construct a hybrid satellite-macro base station heterogeneous network model, establish the topological relationship between the ground network composed of macro base stations, UAVs, and ground users, and the satellite network composed of satellites and satellite service terminals, and determine the communication mechanism parameters;

[0011] S2. Based on the hybrid satellite-macro base station heterogeneous network model, the signal transmission process is modeled in detail. The energy efficiency of the main network of the system is maximized as the objective function. Under the constraints of UAV energy harvesting requirements and cross-domain interference, a fractional programming model is established with beamforming vector, power allocation coefficient and power split ratio as optimization variables.

[0012] S3. Merge the power allocation coefficient and beamforming vector in the original set of optimization variables and define them as a new beamforming vector. Based on the new variables, reconstruct the fractional programming model of the original optimization variables to obtain a simplified fractional programming model of the equivalent optimization variables.

[0013] S4. Introduce auxiliary variables and use the continuous convex approximation algorithm to make the non-convex constraints in the fractional programming model of the equivalent optimization variables convex.

[0014] S5. To address the constraints on the initial value, a non-negative penalty variable is introduced to relax the constraints, and the weighted sum of the penalty variable is subtracted from the energy efficiency maximization objective function.

[0015] S6. Iterative Loop Process: Initialize all optimization variables; in the nth iteration, based on the optimization values ​​of the previous round of continuous convex approximation, solve for the optimal solution of the current round of continuous convex approximation; then, use the optimal solution obtained in this round to repeatedly update the continuous convex approximation and calculate the optimal value of the target optimization problem; finally, check whether the change in the optimization target value is less than the preset convergence threshold. If the condition is met, terminate the iteration; otherwise, continue to the next round of iteration.

[0016] Furthermore, the hybrid satellite-macro base station heterogeneous network model includes a system equipped with... Ground model composed of macro base stations (MBS) with root antennas. A ground user equipped with a single antenna and Each UAV is equipped with a single antenna, and each UAV serves one ground user; the satellite network includes one satellite equipped with... A low-orbit satellite with a root antenna and a device equipped with Fixed satellite service terminal with a root antenna.

[0017] Furthermore, based on the aforementioned hybrid satellite-macro base station heterogeneous network model, the signal transmission process is modeled in a more refined manner as follows:

[0018] The transmitted signal of a macro base station is defined as a weighted sum of the data symbols of each ground user, their beamforming vector, and the transmitted power. Based on wireless co-energy communication technology, the received signal of the UAV is divided into a radio frequency energy part for energy harvesting and a baseband signal part for information decoding. The signal-to-interference-plus-noise ratio (SIR) of the UAV terminal, the SIR of the ground user terminal, and the SIR of the satellite service terminal are calculated respectively.

[0019] Furthermore, the specific implementation process of refining the signal transmission process based on the hybrid satellite-macro base station heterogeneous network model is as follows:

[0020] The transmit signal of the macro base station MBS is defined as follows:

[0021] (1)

[0022] in, , , indicating the first Beamforming vectors for each ground user Represents the set of ground users. Represents the set of complex numbers. and They represent the first The transmission power and transmitted signals of each ground user;

[0023] No. The signal received by the UAV is represented as follows:

[0024] (2)

[0025] in, It is a macro base station MBS and the first Channels between UAVs This represents the additive white Gaussian noise at the UAV (Unmanned Aerial Vehicle) location. and Let these represent the complex Gaussian distribution and the noise variance, respectively.

[0026] Wireless cooperative communication technology is used at the UAV (Unmanned Aerial Vehicle) level. It is divided into two parts, with the energy harvesting and baseband signal expressions respectively represented as follows:

[0027] (3)

[0028] (4)

[0029] in, As an energy conversion factor, Indicates the power split ratio. Indicates the number of ground users. This indicates the conjugate transpose. Indicates the first Transmission power of each ground user Indicates the first Beamforming vectors for each ground user Indicates the first Signal for a ground user; This represents additive white Gaussian noise at the signal segmentation point. Indicates the noise variance; It is a macro base station MBS and the first Channels between UAVs The additive white Gaussian noise at the UAV is represented; then the first... The signal-to-interference-plus-noise ratio (SINR) of a UAV is expressed as:

[0030] (5)

[0031] No. The energy collected by the UAV is used for downlink signal transmission in the second time slot. For the first The UAV and its corresponding first The channel between ground users uses a decoding and forwarding protocol, the first... The signal-to-noise ratio (SNR) at each ground user location is expressed as:

[0032] (6)

[0033] in, This represents the noise variance at the ground user location;

[0034] The signal received by the satellite service terminal is defined as follows:

[0035] (7)

[0036] in, Indicates the transmission power at the LEO satellite. This refers to the channel between the satellite and the satellite service terminal. This represents the beamforming vector at the satellite end. Indicates satellite transmission signals and noise. This represents additive white Gaussian noise at the satellite service terminal. Indicates the noise variance. Let represent the interference channel from the macro base station to the satellite service terminal; combining formulas (5), (6), and (7), the SINR of the ground user and the satellite service terminal are respectively expressed as:

[0037] (8)

[0038] (9)

[0039] Furthermore, an energy efficiency (EE) optimization model is established, with beamforming vector, power allocation coefficient, and power split ratio as optimization variables.

[0040] (10)

[0041] in, The power consumption coefficient, representing energy efficiency, Represents static power consumption, for ,have and ; and These represent the SINR thresholds for ground users and satellite service terminals, respectively. This represents the minimum energy harvesting requirement for a UAV. and These represent the maximum transmit power of the macro base station (MBS) and the satellite, respectively. arrive Under the constraints, Indicates the first The SINR of each ground user must be greater than a given SINR threshold. Indicates the first The energy harvesting of each UAV must exceed the minimum energy harvesting threshold to ensure smooth communication. The range of power split ratio is specified. This stipulates the provisions for The power allocation factor for each ground user must be lower than the maximum transmit power of the macro base station MBS. This indicates that the satellite service terminal must exceed a given minimum threshold limit. The maximum transmission power conditions for satellites are specified. and This indicates that the beamforming vector must satisfy the normalization condition.

[0042] Furthermore, the power allocation coefficients and beamforming vectors of macro base stations (MBS) and satellites are merged;

[0043] First, for macro base station MBS, note for For satellites, remember for Then the optimization variable is... Simplified to .make Then equation (10) is transformed into the following form:

[0044] (11)

[0045] in, These represent beamforming vectors respectively. The maximum transmit power constraints of macro base stations (MBS) and satellites must be met.

[0046] Furthermore, auxiliary variables are introduced, and the continuous convex approximation algorithm is used to transform equation (11); auxiliary variables are introduced. , and Transform equation (11) into:

[0047] (12)

[0048] in, , , and This represents the new constraints introduced into the optimization problem after the auxiliary variable is introduced. In For non-convex structures, using a continuous convex approximation algorithm combined with Taylor expansion, we obtain its lower bound as: , express In the The iteration point;

[0049] Similarly, Substitute constraints And by performing simple transformations, then Transformed into:

[0050] (13)

[0051] (14)

[0052] By analyzing the nonconvex fractional terms and The process involves using a continuous convex approximation algorithm combined with Taylor expansion to construct a lower bound, thereby transforming the non-convex constraint into a convex constraint, expressed as:

[0053] (15)

[0054] (16)

[0055] in, express In the The Taylor expansion iteration point, express In the The Taylor expansion iteration point;

[0056] Constraints Using the same method, It can be transformed into A continuous convex approximation algorithm combined with Taylor expansion is used to transform non-convex terms. Transforming it into a convex constraint, then the constraint... Transform into:

[0057] (17)

[0058] Finally, using the same method, It can be transformed into non-convex terms Transforming it into a convex constraint, then the constraint... Represented as:

[0059] (18)

[0060] in, express In the The iteration point.

[0061] Furthermore, a non-negative penalty variable is introduced to relax the constraints; the penalty variable is... , Let the set of real numbers be represented, and then the optimization problem of equation (12) is transformed into a fractional programming model with equivalent optimization variables:

[0062] (19)

[0063] Furthermore, update the optimization variables and perform iterative calculations until the convergence condition is met, updating the optimization variables according to the following rules: based on the first... The values ​​of each optimization variable in the next iteration By combining convex optimization tools to optimize equation (17), we obtain the first... The values ​​of each optimization variable in the next iteration .

[0064] When the convergence condition is met Stop the iteration if the condition is met; otherwise, proceed to the next iteration. It is a given iteration termination threshold;

[0065] Finally, when the convergence condition is met... At that time, the result of the last calculation will be... It outputs data and maximizes system energy efficiency.

[0066] The present invention has the following beneficial effects:

[0067] This invention addresses the energy efficiency optimization problem of a converged communication system integrating UAVs with low-Earth orbit satellites and macro base stations. By introducing SWIPT and DF mechanisms, it effectively alleviates the energy constraints of UAVs and the problem of multi-user interference. Through joint optimization of beamforming, power allocation, and power splitting parameters, it significantly improves system energy efficiency while meeting service quality and interference constraints. It has the advantages of fast convergence, good stability, and strong practicality, and is suitable for wide-area coverage and energy-efficient communication scenarios of space-ground converged IoT. Attached Figure Description

[0068] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the above advantages of the present invention will become clearer.

[0069] Figure 1 This is a flowchart of a heterogeneous network energy efficiency optimization method based on relay cooperative energy harvesting according to the present invention.

[0070] Figure 2 This is a system modeling diagram in an example of the present invention;

[0071] Figure 3 This is a graph showing the energy efficiency performance when the number of ground users changes in an example of the present invention. Detailed Implementation

[0072] The following is a detailed description of the invention in conjunction with the accompanying drawings.

[0073] like Figure 1 As shown, this embodiment of the invention provides a method for optimizing the energy efficiency of heterogeneous networks based on relay cooperative energy harvesting, specifically including:

[0074] First, a hybrid satellite-macro base station heterogeneous network model is constructed, in which the terrestrial network consists of a single satellite-macro base station equipped with... Macro base station with root antenna A ground user equipped with a single antenna and The system consists of several unmanned aerial vehicles (UAVs) equipped with a single antenna, with each UAV corresponding to only one ground user. The satellite network includes one satellite equipped with... A low-orbit satellite with a root antenna and a device equipped with A fixed satellite service terminal with a single antenna; since the Channel State Information (CSI) of the satellite communication link and the ground link can be obtained separately through the backhaul channel, it is assumed that the cognitive network can acquire perfect CSI. In the first stage, the transmitted signal of the macro base station is represented as:

[0075] (1)

[0076] in, , Represents the beamforming vector. Represents the set of ground users. Represents the set of complex numbers. and They represent the first The transmit power and transmitted signal of each ground user, therefore, the first The signal received by the UAV can be represented as:

[0077] (2)

[0078] in, It is MBS and the first Channels between UAVs This represents additive white Gaussian noise at the UAV. and Let represent the complex Gaussian distribution and the noise variance, respectively.

[0079] Wireless cooperative communication technology is used at the UAV. It is divided into two parts, with the energy harvesting and signal segmentation expressions respectively:

[0080] (3)

[0081] (4)

[0082] in, As an energy conversion factor, Indicates the power split ratio. Indicates the number of ground users. This indicates the conjugate transpose. Indicates the first Transmission power of each ground user Indicates the first Beamforming vectors for each ground user Indicates the first Signal for a ground user; This represents additive white Gaussian noise at the signal segmentation point. Indicates the noise variance; It is a macro base station MBS and the first Channels between UAVs This represents the additive white Gaussian noise at the UAV (Unmanned Aerial Vehicle) location; The signal-to-interference-plus-noise ratio (SINR) of a UAV can be expressed as:

[0083] (5)

[0084] In the second time slot, the energy collected by the UAV is used for downlink signal transmission, denoted as the... The UAV to the first The channel between ground users is If UAV uses a decoding and forwarding protocol, then the first... The signal-to-noise ratio (SNR) at each ground user can be expressed as:

[0085] (6)

[0086] in, This represents the noise variance at the ground user's location. Meanwhile, due to frequency reuse between the satellite network and the terrestrial macro base station network, the signal received by the satellite service terminal can be represented as:

[0087] (7)

[0088] in, Indicates the transmission power at the LEO satellite. This refers to the channel between the satellite and the satellite service terminal. This represents the beamforming vector at the satellite end. and These represent the transmitted signal and noise, respectively. Indicates the noise variance. This represents the interference channel from the macro base station to the fixed satellite service terminal. Combining formulas (5), (6), and (7), the SINR of the ground user and the satellite terminal can be expressed as follows:

[0089] (8)

[0090] (9).

[0091] This patent aims to optimize network energy efficiency. The optimization objective is to maximize the energy efficiency (EE) of the terrestrial network. The optimization constraints include maximum power constraints for both the terrestrial and satellite networks, beamforming vector constraints, signal-to-interference-plus-noise ratio (SINNR) threshold constraints, and energy harvesting threshold constraints. The optimization variables are represented as follows: ,in , express Power segmentation ratio vector for each user , express Beamforming matrix for each user The beamforming vector at the satellite end. and They represent the first The transmit power of each ground user and the transmit power at the LEO satellite. Specifically, the optimization problem can be formulated as:

[0092] (10)

[0093] in, The power consumption coefficient, representing energy efficiency, This represents static power consumption. and These represent the SINR thresholds for ground users and satellite service terminals, respectively. Indicates the minimum energy harvesting threshold. and These represent the maximum transmission power of the MBS and the satellite, respectively.

[0094] Observation reveals that the objective function of formula (10) is non-convex. The problem is non-convex and non-smooth, and there is coupling between the optimization variables, which makes it difficult to handle. Therefore, we use a convex approximation algorithm to relax the optimization problem by achieving successful convex approximation and penalizing the variables.

[0095] First of all, let as well as Then the optimization variables can be transformed into The optimization problem (10) can be transformed into the following form:

[0096] (11)

[0097] in, Optimization constraints Transformed into the above formula Similarly, optimization constraints Transformed into the above formula .

[0098] For equation (11), by introducing auxiliary variables , and By performing relaxation, the optimization problem, which was originally difficult to solve directly, is transformed into a more manageable problem. Let... , , Then the optimization objective can be expressed as maximizing the auxiliary variable. Optimization variables in the original Based on the transformation Then equation (11) can be transformed into:

[0099] (12)

[0100] Among them, equation (12) adds a constraint. ,constraint Transform into Observing equation (12), it can be found that the constraint middle Since it is non-convex, using the continuous convex approximation algorithm, the lower bound expression can be obtained as follows: ,in express In the The iteration point.

[0101] Similarly, Substitute constraints And by performing simple transformations, then Transformed into:

[0102] (13)

[0103] (14)

[0104] By analyzing the nonconvex fractional terms and The process involves using a continuous convex approximation algorithm combined with Taylor expansion to construct a lower bound, thereby transforming the non-convex constraint into a convex constraint. It can be transformed into:

[0105] (15)

[0106] (16)

[0107] in, express In the The iteration point, express In the The iteration point of the nth iteration. Furthermore, a continuous convex approximation algorithm combined with the Taylor expansion formula is used to constrain... This is transformed into a convex approximation at the current iteration point, thus yielding a solvable convex optimization subproblem:

[0108] (17)

[0109] Using the same method, It can be transformed into non-convex terms Transforming it into a convex constraint, then the constraint... Represented as:

[0110] (18)

[0111] in, express In the The iteration point.

[0112] Finally, a continuous convex approximation algorithm combined with the Taylor expansion formula is used to constrain... It can be represented as:

[0113]

[0114] in, express In the The iteration point;

[0115] To ensure that the optimization problem always has feasible initial value points during the iterative solution process, and to ensure that the initial values ​​of the optimization variables satisfy all constraints, this patent introduces a penalty variable. , ,in Let represent the set of real numbers. This penalty variable is used to measure the degree of violation of constraints. By imposing additional penalties on infeasible solutions, it guides the optimization process to gradually converge toward the feasible region.

[0116] Therefore, Substituting these values ​​into the optimization objective and constraints, and simplifying the expression, the optimization problem can be represented as:

[0117] (19)

[0118] Problem (17) is a convex problem, which can be solved using convex optimization tools such as CVX and MOSEK solvers. Finally, the optimal beamforming vector, power allocation scheme, and power split ratio are output to maximize the system's energy efficiency.

[0119] Update the optimization variables and perform iterative calculations until the convergence condition is met. Update the optimization variables according to the following rules: Based on the first... The values ​​of each optimization variable in the next iteration By combining convex optimization tools to optimize equation (17), we obtain the first... The values ​​of each optimization variable in the next iteration .

[0120] When the convergence condition is met Stop the iteration if the condition is met; otherwise, proceed to the next iteration. It is a given iteration termination threshold;

[0121] Finally, when the convergence condition is met... At that time, the result of the last calculation will be... It outputs data and maximizes system energy efficiency.

[0122] The effectiveness of the proposed energy efficiency optimization technique for UAV-cooperative hybrid satellite-macro base station networks based on a penalized continuous convex approximation algorithm is verified through simulations on the MATLAB platform. Specific parameter settings are as follows: noise variance is... ,in Energy efficiency power consumption coefficient Static power consumption Energy conversion efficiency Minimum energy harvesting requirements .

[0123] Figure 3 The energy efficiency (EE) performance is presented as the number of ground users changes. The graph shows that EE improves with increasing number of ground users. Furthermore, the energy efficiency curve exhibits a correlation between transmission power and EE performance. The increase in [value] shows an increasing trend. This verifies the rationality of the optimization algorithm proposed in this invention. In summary, the simulation results fully demonstrate the effectiveness of the UAV-cooperative hybrid satellite-macro base station network energy efficiency optimization technology proposed in this invention.

[0124] This invention provides an energy efficiency optimization algorithm for a low-Earth orbit satellite-macro base station hybrid network with UAV collaboration. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method for optimizing the energy efficiency of heterogeneous networks based on relay cooperative energy harvesting, characterized in that, The optimization method includes the following steps: S1. Construct a hybrid satellite-macro base station heterogeneous network model, establish the topological relationship between the ground network composed of macro base stations, UAVs, and ground users, and the satellite network composed of satellites and satellite service terminals, and determine the communication mechanism parameters; S2. Based on the hybrid satellite-macro base station heterogeneous network model, the signal transmission process is modeled in detail. The energy efficiency of multiple ground users is maximized as the objective function. Under the constraints of UAV energy harvesting requirements and cross-domain interference, a fractional programming model is established with beamforming vector, power allocation coefficient and power split ratio as optimization variables. S3. Merge the power allocation coefficient and beamforming vector in the original set of optimization variables and define them as a new beamforming vector. Based on the new beamforming vector, reconstruct the fractional programming model of the original optimization variables to obtain a simplified fractional programming model of the equivalent optimization variables. S4. Introduce auxiliary variables and use the continuous convex approximation algorithm to make the non-convex constraints in the fractional programming model of the equivalent optimization variables convex. S5. To address the constraints on the initial value, a non-negative penalty variable is introduced to relax the constraints, and the weighted sum of the penalty variable is subtracted from the energy efficiency maximization objective function. S6. Iterative Loop Process: Initialize all optimization variables; in the nth iteration, based on the optimization values ​​of the previous round of continuous convex approximation, solve for the optimal solution of the current round of continuous convex approximation; then, use the optimal solution obtained in this round to repeatedly update the continuous convex approximation and calculate the optimal value of the target optimization problem; finally, check whether the change in the optimization target value is less than the preset convergence threshold. If the condition is met, terminate the iteration; otherwise, continue to the next round of iteration.

2. The method according to claim 1, characterized in that, The hybrid satellite-macro base station heterogeneous network model includes a system equipped with Ground model consisting of macro base stations (MBS) with root antennas. A ground user equipped with a single antenna and Each UAV is equipped with a single antenna, and each UAV serves one ground user; the satellite network includes one satellite equipped with... A low-orbit satellite with a root antenna and a device equipped with Fixed satellite service terminal with a root antenna.

3. The method according to claim 1, characterized in that, Based on the aforementioned hybrid satellite-macro base station heterogeneous network model, the detailed modeling of the signal transmission process is as follows: The transmitted signal of a macro base station is defined as a weighted sum of the data symbols of ground users, their beamforming vectors, and the transmitted power. Based on wireless co-energy communication technology, the received signal of the UAV is divided into a radio frequency energy part for energy harvesting and a baseband signal part for information decoding. Calculate the signal-to-interference-plus-noise ratio (SIR) at the UAV terminal, the SIR at the ground user terminal, and the SIR at the satellite service terminal, respectively.

4. The method according to claim 3, characterized in that, The specific implementation process of refining the signal transmission process based on the hybrid satellite-macro base station heterogeneous network model is as follows: The transmit signal of the macro base station MBS is defined as follows: (1) in, , , indicating the first Beamforming vectors for each ground user Represents the set of ground users. Represents the set of complex numbers. and They represent the first The transmission power and transmitted signals of each ground user; No. The signal received by the UAV is represented as follows: (2) in, It is a macro base station MBS and the first Channels between UAVs This represents the additive white Gaussian noise at the UAV (Unmanned Aerial Vehicle) location. and Let these represent the complex Gaussian distribution and the noise variance, respectively. Wireless cooperative communication technology is used at the UAV (Unmanned Aerial Vehicle) level. It is divided into two parts, with the energy harvesting and baseband signal expressions respectively represented as follows: (3) (4) in, As an energy conversion factor, Indicates the power split ratio. Indicates the number of ground users. This indicates the conjugate transpose. Indicates the first Transmission power of each ground user Indicates the first Beamforming vectors for each ground user Indicates the first Signal for a ground user; This represents additive white Gaussian noise at the signal segmentation point. Indicates the noise variance; It is a macro base station MBS and the first Channels between UAVs The additive white Gaussian noise at the UAV is represented; then the first... The signal-to-interference-plus-noise ratio (SINR) of a UAV is expressed as: (5) No. The energy collected by the UAV is used for downlink signal transmission in the second time slot. For the first The UAV and its corresponding first The channel between ground users uses a decoding and forwarding protocol. The signal-to-noise ratio (SNR) at each ground user location is expressed as: (6) in, This represents the noise variance at the ground user location; The signal received by the satellite service terminal is defined as follows: (7) in, Indicates the transmission power at the LEO satellite. This refers to the channel between the satellite and the satellite service terminal. This represents the beamforming vector at the satellite end. Indicates satellite transmission signals and noise. This represents additive white Gaussian noise at the satellite service terminal. Indicates the noise variance. Let represent the interference channel from the macro base station to the satellite service terminal; combining formulas (5), (6), and (7), the SINR of the ground user and the satellite service terminal are respectively expressed as: (8) (9)。 5. The method according to claim 3, characterized in that, The energy efficiency (EE) optimization model is established with beamforming vector, power allocation coefficient, and power split ratio as optimization variables as follows: (10) in, The power consumption coefficient, representing energy efficiency, Represents static power consumption, for ,have and ; and These represent the SINR thresholds for ground users and satellite service terminals, respectively. This represents the minimum energy harvesting requirement for a UAV. and These represent the maximum transmit power of the macro base station (MBS) and the satellite, respectively. arrive Under the constraints, Indicates the first The SINR of each ground user must be greater than a given SINR threshold. Indicates the first The energy harvesting of each UAV must exceed the minimum energy harvesting threshold to ensure smooth communication. The range of power split ratio is specified. This stipulates the provisions for The power allocation factor for each ground user must be lower than the maximum transmit power of the macro base station MBS. This indicates that the satellite service terminal must exceed a given minimum threshold limit. The maximum transmission power conditions for satellites are specified. and This indicates that the beamforming vector must satisfy the normalization condition.

6. The method according to claim 5, characterized in that, The power allocation coefficients and beamforming vectors of macro base stations (MBS) and satellites are merged. First, for macro base station MBS, note for For satellites, remember for Then the optimization variable is Simplified to ,make Then equation (10) is transformed into the following form: (11) in, These represent beamforming vectors respectively. The maximum transmit power constraints of macro base stations (MBS) and satellites must be met.

7. The method according to claim 6, characterized in that, an introduction of Auxiliary variables are introduced to transform equation (11) using a continuous convex approximation algorithm. , and Transform equation (11) into: (12) in, , , and This represents the new constraints introduced into the optimization problem after the auxiliary variable is introduced. In For non-convex structures, using a continuous convex approximation algorithm combined with Taylor expansion, we obtain its lower bound as: , express In the The iteration point; Similarly, Substitute constraints And by performing simple transformations, then Transformed into: （13） （14） By analyzing the nonconvex fractional terms and The process involves using a continuous convex approximation algorithm combined with Taylor expansion to construct a lower bound, thereby transforming the non-convex constraint into a convex constraint, expressed as: (15) (16) in, express In the The Taylor expansion iteration point, express In the The Taylor expansion iteration point; Constraints Using the same method, Transformed into A continuous convex approximation algorithm combined with Taylor expansion is used to transform non-convex terms. Transforming it into a convex constraint, then the constraint... Transform into: (17) Finally, using the same method, Transformed into non-convex terms Transforming it into a convex constraint, then the constraint... Represented as: (18) in, express In the The iteration point.

8. The method according to claim 7, characterized in that, In the process of relaxing constraints by introducing a non-negative penalty variable, the penalty variable is: , Let the set of real numbers be represented, and then the optimization problem of equation (12) is transformed into a fractional programming model with equivalent optimization variables: (19)。 9. The method according to claim 3, characterized in that, Update the optimization variables and perform iterative calculations until the convergence condition is met. Update the optimization variables according to the following rules: Based on the first... The values ​​of each optimization variable in the next iteration By combining convex optimization tools to optimize equation (17), we obtain the first... The values ​​of each optimization variable in the next iteration ; When the convergence condition is met Stop the iteration if the condition is met; otherwise, proceed to the next iteration. It is a given iteration termination threshold; Finally, when the convergence condition is met... At that time, the result of the last calculation will be... It outputs data and maximizes system energy efficiency.

Images