Interference suppression method based on channel sensing user clustering and adaptive beam forming

Abstract

The invention relates to an interference suppression method based on channel sensing user clustering and adaptive beam forming, which comprises the following steps of: collecting channel information of all accessed ground users, and grouping the ground users according to the channel information; the method comprises the following steps: constructing an adaptive beam forming decision model by taking the maximum spectrum efficiency of a low earth orbit satellite communication system as a target; and taking the grouping result as the input of the adaptive beam forming decision model, and solving the adaptive beam forming decision model to obtain an adaptive beam forming decision. According to the invention, the defect that the traditional interference suppression scheme causes extra power loss and even communication interruption of the LEO satellite communication system is overcome, the communication efficiency of the LEO satellite network is improved, and the communication reliability of the GEO satellite network is ensured.

Description

Technical Field

[0001] The present invention relates to the field of satellite communications, and in particular to an interference suppression method based on channel-aware user clustering and adaptive beamforming. Background Art

[0002] Low Earth Orbit (LEO) satellite communication systems have become an integral part of sixth-generation mobile communications (6G). However, increasingly scarce spectrum resources are a concern. LEO satellite constellations will share limited frequency bands with geostationary Earth orbit (GEO) satellites. Ensuring LEO satellite system communication performance while meeting interference management requirements for satellite ground stations (GS) is a pressing issue.

[0003] Current work on interference mitigation in LEO-GEO coexistence systems focuses on two main areas: location-aware interference avoidance and resource allocation-based interference mitigation. Location-aware interference avoidance can be broadly categorized into two types: progressive pitch strategies and exclusion zone (EZ)-based techniques. With the progressive pitch strategy, when a LEO satellite passes through the main beam area of ​​a GEO satellite, the LEO satellite adjusts its orbit to avoid causing harmful interference to the GEO satellite. However, this consumes additional onboard energy. With the EZ-based technique, the LEO satellite within the EZ is forced to shut down its beam to avoid interference; however, the EZ reduces LEO coverage and creates a non-communication zone. On the other hand, resource allocation-based interference mitigation schemes adaptively reduce transmission power based on the interference situation. However, this can lead to a rapid degradation in communication performance.

[0004] To address the communication stability issues of LEO satellite systems, beamforming technology has been introduced to maximize LEO satellite communication performance. Existing research has proposed a low-complexity algorithm that combines hybrid beamforming with heuristic user scheduling to improve the spectral efficiency (SE) of LEO satellite communication systems. However, beamforming schemes that simultaneously meet the communication performance of LEO satellite systems while mitigating interference with GEO satellite systems remain under investigation. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an interference suppression method based on channel-aware user clustering and adaptive beamforming, which can alleviate the interference problem caused by the shortage of spectrum resources, ensure the high-efficiency communication of LEO satellites and the reliability of GS.

[0006] The technical solution adopted by the present invention to solve the technical problem is to provide an interference suppression method based on channel-aware user clustering and adaptive beamforming, comprising the following steps:

[0007] collecting channel information of all accessed ground users, and grouping the ground users according to the channel information;

[0008] To maximize the spectrum efficiency of low earth orbit satellite communication systems, an adaptive beamforming decision model is constructed;

[0009] The grouping result is used as the input of the adaptive beamforming decision model, and the adaptive beamforming decision model is solved to obtain an adaptive beamforming decision.

[0010] The collecting channel information of connected ground users and grouping the ground users according to the channel information specifically includes:

[0011] Collect channel information of all connected ground users and determine the number of user groups based on the covariance matrix of the channel information of all ground users;

[0012] For each terrestrial user, the cosine similarity between its channel and that of other terrestrial users is calculated, and the terrestrial users whose cosine similarity exceeds a threshold are grouped together.

[0013] The objective function of the adaptive beamforming decision model is expressed as: Among them, SINR i,c represents the signal-to-interference-and-noise ratio between low-Earth orbit satellite i and user group c, I represents the number of low-Earth orbit satellites, and C i The group number representing the ground user groups served by low-Earth orbit satellite i.

[0014] The constraints of the adaptive beamforming decision model include:

[0015] The power constraint is expressed as:

[0016] Interference constraint, expressed as:

[0017] Among them, w i,c The beamforming vectors for the ground user group c serving the low-Earth orbit satellite i, C i represents the number of ground user groups served by low earth orbit satellite i, P max represents the maximum transmission power that each low-Earth orbit satellite can provide, g il is the low earth orbit satellite-satellite ground station interference channel, ξ is the maximum interference noise ratio threshold that the satellite ground station can tolerate, σ nIndicates ground station receiving noise.

[0018] The solving of the adaptive beamforming decision model is specifically as follows:

[0019] The objective function of the adaptive beamforming decision model is transformed into a problem of minimizing the system mean square error through an equivalent transformation;

[0020] Optimizing multiple decision variables in the problem of minimizing the system mean square error using a block coordinate descent method;

[0021] The closed-form solution of beamforming variables is obtained using the Lagrange multiplier method, and the Lagrange multipliers are determined by an alternating optimization algorithm.

[0022] The problem of minimizing the system mean square error is expressed as: Among them, α i,c The weight coefficient of user group c served by low earth orbit satellite i, E i,c is the mean square error of a single ground user group, and Tr() represents the trace of the matrix.

[0023] When the block coordinate descent method is used to optimize the multiple decision variables in the problem of minimizing the system mean square error, the receiving coefficient is updated as follows: The weight coefficient is updated as: in, is the updated receiving coefficient, is the updated weight coefficient, represents the optimal channel from low earth orbit satellite i to user group c, w i,c is the beamforming vector of low earth orbit satellite i to user group c, represents the optimal channel from low earth orbit satellite j to user group c′, w j,c′ is the beamforming vector of low earth orbit satellite j for ground user group c′, σ n Represents the received noise, E i,c The mean square error for a single ground user group.

[0024] The closed-form solution of the beamforming variable is: in, is the closed-form solution of the beamforming variables, represents the optimal channel from low earth orbit satellite j to user group c′, u j,c′ represents the reception coefficient of user group c′ served by low-Earth orbit satellite j, α j,c′ represents the weight coefficient of user group c′ served by low earth orbit satellite j, μ l represents the Lagrange multiplier, g il is the low earth orbit satellite-satellite ground station interference channel, λi represents the Lagrange multiplier, I is the identity matrix, α i,c is the weight coefficient, u i,c is the acceptance coefficient.

[0025] The technical solution adopted by the present invention to solve its technical problem is: providing an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the computer program, the steps of the interference suppression method based on channel-aware user clustering and adaptive beamforming are implemented.

[0026] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the interference suppression method based on channel-aware user clustering and adaptive beamforming are implemented.

[0027] Beneficial effects

[0028] Due to the adoption of the above-mentioned technical solution, the present invention has the following advantages and positive effects compared to the existing technology: The present invention introduces beamforming technology into the interference suppression scheme, utilizes the WMMSE algorithm to design precoding vectors for all users, and reduces the beam gain in the direction of the GS, so that the LEO satellite communication system can meet the quality of service for users within the system while mitigating interference to the GS. The present invention further adopts a user clustering strategy based on channel information, clustering and grouping users before performing beamforming, reducing algorithm complexity to meet the onboard computing resource constraints and the real-time requirements of satellite precoding. The present invention's interference suppression method based on channel-aware user clustering and adaptive beamforming overcomes the shortcomings of traditional interference suppression schemes that cause onboard energy waste and communication interruptions, improves the communication spectrum efficiency of the LEO satellite communication system, and ensures the communication reliability of the GEO satellite system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flowchart of an interference suppression method based on channel-aware user clustering and adaptive beamforming according to a first embodiment of the present invention;

[0030] Figure 2 It is a system architecture diagram of the first embodiment of the present invention. DETAILED DESCRIPTION

[0031] Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiments are only used to illustrate the present invention and are not used in limiting the scope of the present invention.In addition, should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall equally within the scope limited by the appended claims of the application.

[0032] The first embodiment of the present invention relates to an interference suppression method based on channel-aware user clustering and adaptive beamforming. This method proposes a user clustering strategy in which a satellite collects channel information from all connected GUs. Based on this information, the GUs are divided into several clusters. The channels from the satellite to all GUs in the same cluster are similar and share the same precoding vector. Based on this strategy, the satellite groups users and provides information for subsequent beamforming decisions. This method also proposes an interference suppression strategy based on the WMMSE algorithm. This strategy formulates the co-channel interference problem in a LEO and GEO satellite coexistence system as a nonlinear mixed integer programming optimization problem. This problem can meet the interference management requirements of GEO satellite ground stations while performing LEO satellite system communications. The WMMSE algorithm is used to solve this problem to maximize the spectrum efficiency of the LEO satellite communication system.

[0033] Figure 2 The figure shows the system architecture used in this embodiment. This system architecture consists of four components: a LEO satellite constellation, GEO satellites, LEO ground users (GU), and a fixed, known ground station (GS). The LEO satellites and GU ground users form the LEO communication system, while the GEO satellites and GS form the GEO communication system. The GEO satellite system is the "primary user," while the LEO satellite system is considered the "secondary user." The communications of secondary users must not compromise the quality of service (QoS) of the primary user.

[0034] Among them, LEO-GU communication channel h ig It can be expressed as:

[0035]

[0036] in, is the LEO transmit gain, is the ground user receiving gain, a() is the UPA array response vector, θ and represents the azimuth and elevation angles of the response vector, θ ig and represents the off-axis angle between the low-Earth orbit satellite i and the ground user g, λ represents the wavelength, and r ig is the distance from LEO to ground users;

[0037] LEO-GS interference channel g il It can be expressed as:

[0038]

[0039] in, is the GS receiving gain, d il is the distance between LEO satellite and GS, θ i ' l and φ i ' l represents the off-axis angle between the low-Earth orbit satellite i and GS, represents the UPA array response vector of the interfering channel.

[0040] When a LEO satellite communicates with a GU, the GS receives both the downlink communication signal from the GEO and the downlink interference signal from the LEO. The interference impact is measured by the Interference-to-Noise Ratio (INR), which is defined as:

[0041]

[0042] Where I represents the number of satellites, K i represents the number of users served by Earth-orbiting satellite i, represents the beamforming vector of earth-orbiting satellite i to ground user k.

[0043] like Figure 1 As shown, the interference suppression method based on channel-aware user clustering and adaptive beamforming of this embodiment includes the following steps:

[0044] Step 1: Collect channel information of all accessed ground users and group the ground users according to the channel information.

[0045] For simplicity and practicality, this implementation uses a random access strategy. Each satellite transmits a downlink synchronization signal. After receiving the synchronization signal, the GU evaluates the channel quality to each satellite and selects the satellite with the best channel quality to transmit a random preamble. Once the satellite confirms the signal, the entire LEO satellite communication network is initialized, thus establishing a connection between the GU and the LEO satellite.

[0046] After the GU accesses the system, the LEO satellite collects the channel information of all connected GUs and determines the number of user groups based on the covariance matrix of the channel information of all ground users. Then, for each ground user, the cosine similarity between its channel and that of other ground users is calculated, and the ground users whose cosine similarity exceeds a threshold are grouped together.

[0047] Among them, the channel covariance matrix R of GU connected to LEO satellite H Expressed as: , perform eigenvalue decomposition on it, that is, R H =Q∧Q T (λ1≥λ2≥…≥λ M ), then through Determine the number of principal components C i , and use this number as the number of groups for ground users.

[0048] Step 2: With the goal of maximizing the spectrum efficiency of the low-Earth orbit satellite communication system, an adaptive beamforming decision model is constructed.

[0049] The objective function of the adaptive beamforming decision model constructed in this step is expressed as:

[0050]

[0051] The constraints of the adaptive beamforming decision model include:

[0052] The power constraint is expressed as:

[0053] Interference constraint, expressed as:

[0054] Among them, w i,c is the beamforming vector of low earth orbit satellite i to ground user cluster c, C i Indicates the number of ground user groups, P max Indicates the maximum transmission power of the satellite, g il is the low earth orbit satellite-satellite ground station interference channel, ξ is the maximum interference noise ratio threshold that the satellite ground station can tolerate, σ n Indicates ground station receiving noise.

[0055] Step 3: Using the grouping result as the input of the adaptive beamforming decision model, solving the adaptive beamforming decision model to obtain an adaptive beamforming decision.

[0056] This step may solve the adaptive beamforming decision model in the following manner, specifically:

[0057] The objective function of the adaptive beamforming decision model is transformed into the problem of minimizing the system mean square error through equivalent transformation, wherein the problem of minimizing the system mean square error is expressed as: Among them, α i,c The weight coefficient of user group c served by low earth orbit satellite i, E i,c is the mean square error of a single ground user group, expressed as: represents the optimal channel from low earth orbit satellite j to user group c′, w j,c′ is the beamforming vector of low earth orbit satellite j to ground user group c′, represents the optimal channel from satellite i to user group c, w i,c is the beamforming vector of low earth orbit satellite i to ground user group c, σ n represents the receiving noise, u i,c is the receiving coefficient, Tr() represents the trace of the matrix;

[0058] Considering that the problem of minimizing the system mean square error is complex for all variables but convex for each individual optimization variable, a simpler update method is needed to improve the parallel computing capability. Therefore, the block coordinate descent (BCD) method is used to optimize the multiple decision variables in the problem of minimizing the system mean square error. That is, the BCD algorithm is applied to update the receive beamforming coefficient u and the weight coefficient α, where the receive coefficient is updated as follows: The weight coefficient is updated as follows: in, is the updated receiving coefficient, is the updated weight coefficient.

[0059] The closed-form solution of the beamforming variables is obtained using the Lagrange multiplier method, which can be expressed as: in, is the closed-form solution of the beamforming variables. Finally, based on the Lagrange multiplier method and the alternative optimization method, the problem of minimizing the system mean square error can be solved in the closed-form solution of the beamforming vector, that is, by searching the power constraint multiplier through bisection to satisfy: Then the interference constraint multiplier is optimized by gradient descent method to make the INR converge to the interference threshold.

[0060] It is not difficult to find that the present invention introduces beamforming technology into the interference suppression scheme. Utilizing the WMMSE algorithm, it designs precoding vectors for all users and reduces the beam gain in the direction of the GS. This allows the LEO satellite communication system to meet the quality of service for users within the system while mitigating interference to the GS. The present invention further adopts a user clustering strategy based on channel information, clustering and grouping users before performing beamforming, reducing algorithm complexity to meet onboard computing resource constraints and the real-time requirements of satellite precoding. The present invention's interference suppression method based on channel-aware user clustering and adaptive beamforming overcomes the shortcomings of traditional interference suppression schemes, which can waste onboard energy and cause communication interruptions. It improves the communication spectrum efficiency of the LEO satellite communication system and ensures the communication reliability of the GEO satellite system.

[0061] A second embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the interference suppression method based on channel-aware user clustering and adaptive beamforming of the above-mentioned first embodiment are implemented.

[0062] A third embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the steps of the interference suppression method based on channel-aware user clustering and adaptive beamforming of the first embodiment.

[0063] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) that contain computer-usable program code.

[0064] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction method, which is implemented in the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0067] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An interference suppression method based on channel-aware user clustering and adaptive beamforming, characterized in that: The following steps are involved: collecting channel information of all accessed ground users, and grouping the ground users according to the channel information; An adaptive beamforming decision model is constructed with the goal of maximizing the spectrum efficiency of a low-Earth orbit satellite communication system. The grouping result is used as an input of the adaptive beamforming decision model, and the adaptive beamforming decision model is solved to obtain an adaptive beamforming decision.

2. The interference suppression method based on channel-aware user clustering and adaptive beamforming according to claim 1, characterized in that The collecting channel information of connected ground users and grouping the ground users according to the channel information specifically includes: Collect channel information of all connected ground users and determine the number of user groups based on the covariance matrix of the channel information of all ground users; For each terrestrial user, the cosine similarity between its channel and that of other terrestrial users is calculated, and the terrestrial users whose cosine similarity exceeds a threshold are grouped together.

3. The interference suppression method based on channel-aware user clustering and adaptive beamforming according to claim 1, characterized in that: The objective function of the adaptive beamforming decision model is expressed as: Among them, SINR i,c represents the signal-to-interference-and-noise ratio between low-Earth orbit satellite i and user group c, I represents the number of low-Earth orbit satellites, and C i The number of ground user groups served by low-Earth orbit satellite i.

4. The interference suppression method based on channel-aware user clustering and adaptive beamforming according to claim 1, characterized in that The constraints of the adaptive beamforming decision model include: The power constraint is expressed as: Interference constraint, expressed as: Among them, w i,c is the beamforming vector of low earth orbit satellite i to ground user cluster c, C i represents the number of ground user groups served by low earth orbit satellite i, P max represents the maximum transmission power that each low-Earth orbit satellite can provide, g il is the low earth orbit satellite-satellite ground station interference channel, ξ is the maximum interference noise ratio threshold that the satellite ground station can tolerate, σ n Indicates ground station receiving noise.

5. The interference suppression method based on channel-aware user clustering and adaptive beamforming according to claim 1, characterized in that: The solving of the adaptive beamforming decision model is specifically as follows: The objective function of the adaptive beamforming decision model is transformed into a problem of minimizing the system mean square error through an equivalent transformation; Optimizing multiple decision variables in the problem of minimizing the system mean square error using a block coordinate descent method; The closed-form solution of beamforming variables is obtained using the Lagrange multiplier method, and the Lagrange multipliers are determined by an alternating optimization algorithm.

6. The interference suppression method based on channel-aware user clustering and adaptive beamforming according to claim 5, characterized in that: The problem of minimizing the system mean square error is expressed as: Among them, α i,c The weight coefficient of user group c served by low earth orbit satellite i, E i,c is the mean square error of a single ground user group, and Tr() represents the trace of the matrix.

7. The interference suppression method based on channel-aware user clustering and adaptive beamforming according to claim 5, characterized in that: When the block coordinate descent method is used to optimize the multiple decision variables in the problem of minimizing the system mean square error, the receiving coefficient is updated as follows: The weight coefficient is updated as: in, is the updated receiving coefficient, is the updated weight coefficient, represents the optimal channel from low earth orbit satellite i to user group c, w i,c is the beamforming vector of low earth orbit satellite i to ground user group c, represents the optimal channel from low earth orbit satellite j to user group c′, w j,c′ is the beamforming vector of low earth orbit satellite j for ground user group c′, σ n Represents the received noise, E i,c is the mean square error of the ground user group.

8. The interference suppression method based on channel-aware user clustering and adaptive beamforming according to claim 5, characterized in that: The closed-form solution of the beamforming variable is: in, is the closed-form solution of the beamforming variables, represents the optimal channel of low earth orbit satellite j for user group c′, u j,c′ represents the reception coefficient of user group c′ served by low-Earth orbit satellite j, α j,c′ represents the weight coefficient of user group c′ served by low earth orbit satellite j, μ l represents the Lagrange multiplier, g il is the low earth orbit satellite-satellite ground station interference channel, λ i represents the Lagrange multiplier, I is the identity matrix, α i,c is the weight coefficient, u i,c is the acceptance coefficient.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the processor implements the steps of the interference suppression method based on channel-aware user clustering and adaptive beamforming as claimed in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer program implements the steps of the interference suppression method based on channel-aware user clustering and adaptive beamforming as claimed in any one of claims 1 to 8.

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