Method, apparatus, electronic device, and storage medium for determining beamforming weights

Abstract

The present invention discloses a method, apparatus, electronic device and storage medium for determining beamforming weights. The method includes: determining a first model, a second model and a third model; determining a fourth model based on the first model, the second model and the third model; the fourth model characterizes the received signal-to-noise ratio of the first terminal receiving signals sent by N mobile relays; on the premise of maximizing the received signal-to-noise ratio, determining a fifth model based on that the total transmission power of the N mobile relays is lower than a first threshold, the interference signal power of the N mobile relays on K second terminals is lower than a second threshold, and the fourth model; using the fifth model, and combining with the zero-forcing algorithm and the maximum Rayleigh quotient method, determining the beamforming weights used by the N mobile relays. The solution provided by the present invention can improve the received signal-to-noise ratio of the first terminal while ensuring the communication quality of the second terminal.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular, to a method, apparatus, electronic device, and storage medium for determining beamforming weights. Background Art

[0002] With the development of wireless communication technologies, various wireless communication devices and services have been booming. Among them, wireless communication devices mainly conduct communication services through the traditional cellular mobile network provided by base stations. Although the traditional cellular mobile network can provide a reliable communication link, due to the immobility of base stations, in terms of scalability and flexibility for the rapidly developing wireless communication field, the traditional cellular mobile network has its own limitations and will show more and more obvious restrictive effects.

[0003] Drone relays (which can act as mobile relays), with the characteristics of small size, high flexibility, and strong controllability, are widely used in scenarios such as traffic monitoring, geographical surveying, military operations, wilderness search and rescue, logistics transportation, disaster relief, hazardous material recovery, and fire control. Although the wide application of drone relays in various fields has proven their superiority, single-drone relay systems have problems such as weak anti-destruction ability, limited coverage, and difficulty in providing a stable and reliable communication link.

[0004] To address the problem of poor reliability in single-drone relay networks, communication networks can adopt the method of multi-drone relay cooperative communication. And distributed beamforming technology is a commonly used means for multi-drone relays to conduct cooperative communication.

[0005] In multi-drone relay networks, the working frequency bands of drone relays are mainly the IEEE S-band and the IEEE L-band. However, on these spectrums, there are also other wireless networks coexisting, such as WiFi, Bluetooth, etc. With the development of technology, the number of new devices working on these spectrums has increased sharply, making drone relays face the problem of inter-spectrum interference. Summary of the Invention

[0006] To solve the related technical problems, embodiments of the present invention provide a method, apparatus, electronic device, and storage medium for determining beamforming weights.

[0007] The technical solution of the embodiments of the present invention is implemented as follows:

[0008] Embodiments of the present invention provide a method for determining beamforming weights, including:

[0009] Determine a first model, a second model, and a third model; the first model characterizes the first received signals sent by a base station and received by each of N mobile relays; N is an integer greater than or equal to 2; the second model characterizes the second received signals sent by the N mobile relays based on the first received signals and received by a first terminal; the third model characterizes the third received signals sent by the N mobile relays based on the first received signals and received by each of K second terminals; wherein, the base station provides services for the K second terminals; the N mobile relays all provide services for the first terminal; K is an integer greater than or equal to 1;

[0010] Based on the first model, the second model, and the third model, determine a fourth model; the fourth model characterizes the received signal-to-noise ratio of signals sent by the N mobile relays and received by the first terminal;

[0011] On the premise of maximizing the received signal-to-noise ratio, based on that the total transmission power of the N mobile relays is lower than a first threshold, the interference signal power of the N mobile relays to the K second terminals is lower than a second threshold, and the fourth model, determine a fifth model;

[0012] Utilize the fifth model, and in combination with the zero-forcing algorithm and the maximum Rayleigh quotient method, determine the beamforming weights used by the N mobile relays.

[0013] In the above solution, the determining the fourth model based on the first model, the second model, and the third model includes:

[0014] Based on the first model, the second model, and the third model, determine a first parameter, a second parameter, and a third parameter; the first parameter characterizes the channel fading coefficient from the base station to the N mobile relays, the second parameter characterizes the channel fading coefficient from the N mobile relays to the first terminal, and the third parameter characterizes the channel fading coefficient from the N mobile relays to the second terminal;

[0015] Based on the determined first parameter, second parameter, and third parameter, determine the fourth model.

[0016] In the above solution, the utilizing the fifth model, and in combination with the zero-forcing algorithm and the maximum Rayleigh quotient method, to determine the beamforming weights used by the N mobile relays includes:

[0017] Set the interference signal power of the N mobile relays to the K second terminals in the fifth model to zero, and according to the orthogonal projection theorem, obtain a sixth model;

[0018] Utilize the sixth model, and in combination with the maximum Rayleigh quotient method, determine the beamforming weights used by the N mobile relays.

[0019] In the above solution, the utilizing the sixth model, and in combination with the maximum Rayleigh quotient method, to determine the beamforming weights used by the N mobile relays includes:

[0020] Perform a formal transformation on the sixth model to obtain a seventh model;

[0021] Using the seventh model and combining with the maximum Rayleigh quotient method, determine the beamforming weights used by N mobile relays.

[0022] In the above solution, based on the multipath Rayleigh channel between the base station and each mobile relay, determine the first model.

[0023] In the above solution, based on the multipath Rayleigh channel between each mobile relay and the first terminal, determine the second model.

[0024] In the above solution, based on the multipath Rayleigh channel between each mobile relay and each second terminal, determine the third model.

[0025] An embodiment of the present invention further provides a device for determining beamforming weights, including:

[0026] A first determination module, configured to determine a first model, a second model, and a third model; the first model represents the first received signal sent by the base station received by each mobile relay among N mobile relays; N is an integer greater than or equal to 2; the second model represents the second received signal sent by the N mobile relays based on the first received signal received by the first terminal; the third model represents the third received signal sent by the N mobile relays based on the first received signal received by each second terminal among K second terminals; wherein, the base station provides services for the K second terminals; the N mobile relays all provide services for the first terminal; K is an integer greater than or equal to 1;

[0027] A second determination module, configured to determine a fourth model based on the first model, the second model, and the third model; the fourth model represents the received signal-to-noise ratio of the signals sent by the N mobile relays received by the first terminal;

[0028] A third determination module, configured to, on the premise of maximizing the signal-to-noise ratio, based on that the total transmission power of the N mobile relays is lower than a first threshold, the interference signal power of the N mobile relays on the K second terminals is lower than a second threshold, and the fourth model, determine a fifth model;

[0029] A fourth determination module, configured to use the fifth model and combine with the zero-forcing algorithm and the maximum Rayleigh quotient method to determine the beamforming weights used by the N mobile relays.

[0030] An embodiment of the present invention further provides an electronic device, including: a processor and a memory for storing a computer program that can run on the processor; wherein,

[0031] When the processor is used to run the computer program, it executes the steps of any of the above methods.

[0032] An embodiment of the present invention also provides a storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0033] The method, device, electronic device, and storage medium for determining beamforming weights provided by the embodiments of the present invention determine a first model, a second model, and a third model; the first model represents the first received signals sent by a base station received by each of N mobile relays; N is an integer greater than or equal to 2; the second model represents the second received signals sent by the N mobile relays based on the first received signals received by a first terminal; the third model represents the third received signals sent by the N mobile relays based on the first received signals received by each of K second terminals; where the base station provides services for the K second terminals; the N mobile relays all provide services for the first terminal; K is an integer greater than or equal to 1; based on the first model, the second model, and the third model, a fourth model is determined; the fourth model represents the received signal-to-noise ratio of the signals sent by the N mobile relays received by the first terminal; on the premise of maximizing the signal-to-noise ratio, based on that the total transmission power of the N mobile relays is lower than a first threshold, the interference signal power of the N mobile relays on the K second terminals is lower than a second threshold, and the fourth model, a fifth model is determined; using the fifth model, and combining the zero-forcing algorithm and the maximum Rayleigh quotient method, the beamforming weights used by the N mobile relays are determined. The solution of the embodiments of the present invention, under the condition of ensuring that the total transmission power of the mobile relays is lower than the first threshold and the interference signal power received by the second terminals is lower than the second threshold, uses the zero-forcing algorithm and the maximum Rayleigh quotient method to determine the beamforming weights of the mobile relays when the received signal-to-noise ratio of the first terminal is the largest, so as to be able to improve the received signal-to-noise ratio of the first terminal while ensuring the communication quality of the second terminals. Description of the Drawings

[0034] Figure 1 It is a schematic flowchart of the method for determining beamforming weights according to the embodiments of the present invention;

[0035] Figure 2 It is a schematic diagram of the network architecture according to the application embodiment of the present invention;

[0036] Figure 3 It is a schematic structural diagram of the device for determining beamforming weights according to the embodiments of the present invention;

[0037] Figure 4 It is a schematic diagram of the hardware structure of the electronic device according to the embodiments of the present invention. Detailed Embodiments

[0038] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0039] In view of the problem of poor reliability of single unmanned aerial vehicle (UAV) relay networks, the communication network can adopt the method of multi-UAV relay cooperative communication. The distributed beamforming technology is a commonly used means for multi-UAV relay cooperative communication. The distributed beamforming technology has received extensive attention because it can obtain full diversity gain and high energy efficiency. The process of distributed beamforming is to assign an appropriate weighting vector to each array (an array can be understood as a group of UAV arrays, and a group of UAV arrays contains multiple UAVs) to compensate for the propagation delay of each element (referring to a single UAV in a group of UAV arrays), so that the array outputs can be superimposed in the same direction in a desired direction, thereby enabling the array to generate a main lobe beam in this direction, making the signal strongest in this direction and weaker in other directions, suppressing the signal reception in other directions to a certain extent, and the devices in other directions are not interfered by the signals of this array. The multi-UAV relay network adopting the distributed beamforming technology can improve the spectrum utilization rate and the transmission capacity of the system.

[0040] However, in the multi-UAV relay network, other wireless networks such as WiFi and Bluetooth also coexist in the working frequency band of the UAV relay. Moreover, the number of new devices working in these spectrums has increased sharply, making the UAV relay face the problem of inter-spectrum interference.

[0041] Therefore, on this premise, when the multi-UAV relay communication system (which can be understood as a multi-UAV relay network) adopts a cognitive radio network, the multi-UAV relay communication system will face the problem of inter-spectrum interference in the cognitive radio network. Here, adopting a cognitive radio network can ensure that the multi-UAV relay network does not interfere with the communication of other network users, or shares spectrum resources with other network users and performs communication tasks on the premise that the interference is lower than a predetermined threshold.

[0042] Based on this, in various embodiments of the present invention, when the multi-UAV relay communication system adopts a cognitive radio network, in the design of distributed beamforming for the multi-UAV relay, considering that the interference power of the primary network users (which can be understood as the network users in the primary network of the cognitive radio network) is lower than the threshold and the total transmission power of the multi-UAV relay (which can be understood as the total power of all UAV relays in an array sending signals) is lower than the threshold, the zero-forcing algorithm is used to set the signal interference of the UAV relay to the primary network users to zero, and on the premise of maximizing the signal-to-noise ratio (SNR) of the multi-UAV relay network users (which can be understood as the network users in the secondary network of the cognitive radio network), the received SNR of the multi-UAV relay network users receiving the signals sent by the UAV relay is transformed into a form conforming to the Rayleigh quotient, and the maximum Rayleigh quotient method is used to determine the beamforming weight values used by multiple UAV relays.

[0043] In the solution of the embodiment of the present invention, by optimizing the weights of beamforming, while ensuring the communication quality of the main network users, the received signal-to-noise ratio of the multi-UAV relay network users is improved, that is, the communication quality of the multi-UAV relay network users is enhanced and the interference to the main network users is reduced, so as to improve the spectrum utilization rate and ensure the UAV relay communication quality.

[0044] The embodiment of the present invention provides a method for determining beamforming weights, which is applied to electronic devices such as control devices of multi-UAV relays, such as Figure 1 As shown, the method includes:

[0045] Step 101: Determine a first model, a second model, and a third model; the first model represents the first received signal sent by the base station received by each of the N mobile relays; N is an integer greater than or equal to 2; the second model represents the second received signal sent by the N mobile relays based on the first received signal received by the first terminal; the third model represents the third received signal sent by the N mobile relays based on the first received signal received by each of the K second terminals;

[0046] Wherein, the base station provides services for the K second terminals; the N mobile relays all provide services for the first terminal; K is an integer greater than or equal to 1;

[0047] Step 102: Based on the first model, the second model, and the third model, determine a fourth model; the fourth model represents the received signal-to-noise ratio of the first terminal receiving the signals sent by the N mobile relays;

[0048] Step 103: On the premise of maximizing the received signal-to-noise ratio, based on the total transmission power of the N mobile relays being lower than a first threshold, the interference signal power of the N mobile relays to the K second terminals being lower than a second threshold, and the fourth model, determine a fifth model;

[0049] Step 104: Use the fifth model, and in combination with the zero-forcing algorithm and the maximum Rayleigh quotient method, determine the beamforming weights used by the N mobile relays.

[0050] In practical applications, the method of the embodiment of the present invention can be applied to a cognitive radio network. The cognitive radio network includes a main network composed of a base station and K second terminals; a secondary network composed of a base station, N mobile relays, and at least one first terminal. Among them, the mobile relays can be UAV relays, and the N mobile relays can adopt distributed beamforming technology and can all provide services for the first terminal.

[0051] In practical applications, since the multi-path Rayleigh channel can describe the channel more accurately, therefore, in the embodiments of the present invention, the multi-path Rayleigh channel can be used to describe the channels between the base station and the mobile relay, between the mobile relay and the first terminal, and between the mobile relay and the second terminal, and based on the multi-path Rayleigh channels between the base station and each mobile relay, the first model is determined; based on the multi-path Rayleigh channels between each mobile relay and the first terminal, the second model is determined; based on the multi-path Rayleigh channels between each mobile relay and each second terminal, the third model is determined.

[0052] In practical applications, the channel coefficients of the multi-path Rayleigh channel can be expressed as:

[0053]

[0054] Wherein, represents the mean value of the channel coefficient h, and ε is uniformly distributed in the interval [0, 2π]. represents the variance of the channel coefficient, with a mean value of zero, represents an independent and identically distributed Gaussian random variable, and α represents the instability degree of the channel.

[0055] In practical applications, based on the above channel coefficients, the first model can specifically be:

[0056]

[0057] Wherein, x i represents the first received signal sent by the base station received by the i-th mobile relay, P t represents the transmission power of the base station, g t,i represents the channel fading coefficient from the base station to the i-th mobile relay, and its channel uncertainty parameter is expressed as α t , s represents the power-normalized signal sent by the base station, and n i represents the Gaussian noise of the i-th mobile relay.

[0058] In practical applications, the second model can specifically be:

[0059]

[0060] Wherein, y s represents the second received signal sent by N mobile relays based on the first received signal received by the first terminal, h i,s represents the channel fading coefficient from the i-th mobile relay to the first terminal, and its channel uncertainty parameter is expressed as α s , w i represents the beamforming weight assigned to the i-th mobile relay, x i represents the first received signal sent by the base station received by the i-th mobile relay, and n sDenote the Gaussian noise of the first terminal as P t Denote the transmit power of the base station as g t,i Denote the channel fading coefficient from the base station to the i-th mobile relay as \(h_{i}\), s represents the power-normalized signal sent by the base station, and n i Denote the Gaussian noise of the i-th mobile relay as \(n_{i}\).

[0061] In practical applications, the third model can specifically be:

[0062]

[0063] Wherein, y k Denote the third received signals sent by the N mobile relays received by each of the K second terminals based on the first received signal, \(h_{ik}\) i,k Denote the channel fading coefficient from the i-th mobile relay to the k-th second terminal, and its channel uncertainty parameter is denoted as \(\alpha_{ik}\) p , w i Denote the beamforming weight assigned to the i-th mobile relay, \(x_{i}\) i Denote the first received signal sent by the base station received by the i-th mobile relay, \(x_{i}\) k Denote the Gaussian noise of the k-th second terminal, P t Denote the transmit power of the base station, g t,i Denote the channel fading coefficient from the base station to the i-th mobile relay, s represents the power-normalized signal sent by the base station, and n i Denote the Gaussian noise of the i-th mobile relay.

[0064] In one embodiment, determining the fourth model based on the first model, the second model, and the third model includes:

[0065] Determine the first parameter, the second parameter, and the third parameter based on the first model, the second model, and the third model; the first parameter characterizes the channel fading coefficient from the base station to the N mobile relays, the second parameter characterizes the channel fading coefficient from the N mobile relays to the first terminal, and the third parameter characterizes the channel fading coefficient from the N mobile relays to the second terminal;

[0066] Determine the fourth model based on the determined first parameter, second parameter, and third parameter.

[0067] In practical applications, the first parameter characterizes the channel fading coefficient from the base station to the N mobile relays, including the channel fading coefficient from the base station to each of the N mobile relays, specifically including: the channel fading coefficient from the base station to the first mobile relay among the N mobile relays, the channel fading coefficient from the base station to the second mobile relay among the N mobile relays, …, the channel fading coefficient from the base station to the N-th mobile relay among the N mobile relays.

[0068] The second parameter characterizes the channel fading coefficients of N mobile relays to the first terminal, including the channel fading coefficients of each of the N mobile relays to the first terminal, specifically including: the channel fading coefficient of the first mobile relay among the N mobile relays to the first terminal, the channel fading coefficient of the second mobile relay among the N mobile relays to the first terminal, …, the channel fading coefficient of the Nth mobile relay among the N mobile relays to the first terminal.

[0069] The third parameter characterizes the channel fading coefficients of N mobile relays to the second terminal, including the channel fading coefficients of each of the N mobile relays to the second terminal, specifically including: the channel fading coefficient of the first mobile relay among the N mobile relays to the second terminal, the channel fading coefficient of the second mobile relay among the N mobile relays to the second terminal, …, the channel fading coefficient of the Nth mobile relay among the N mobile relays to the second terminal.

[0070] In practical applications, the fourth model can specifically be:

[0071]

[0072] Wherein, F t,s = P t (g t ⊙h s )(g t ⊙h s ) H ,

[0073] g t = [g t,1 g t,2 …g t,N T , h s = [h 1,s h 2,s …h N,s T

[0074] w = [w 1 w 2 …w N T

[0075] Wherein, SNR represents the received signal-to-noise ratio of the first terminal receiving the signals sent by N mobile relays, w 1 represents the beamforming weight assigned to the first mobile relay among the N mobile relays, w 2 represents the beamforming weight assigned to the second mobile relay among the N mobile relays, w N represents the beamforming weight assigned to the Nth mobile relay among the N mobile relays, P t ​​​Denotes the transmission power of the base station, g t,1 Denotes the channel fading coefficient from the base station to the first mobile relay, g t,2 Denotes the channel fading coefficient from the base station to the second mobile relay, g t,N Denotes the channel fading coefficient from the base station to the Nth mobile relay, h 1,s Denotes the channel fading coefficient from the first mobile relay to the first terminal, h 2,s Denotes the channel fading coefficient from the second mobile relay to the first terminal, h N,s Denotes the channel fading coefficient from the Nth mobile relay to the first terminal, Denotes the noise power of each drone, Denotes the noise power of the secondary user.

[0076] In practical applications, under the premise of maximizing the received signal-to-noise ratio, based on the total transmission power of N mobile relays being lower than the first threshold, the interference signal power of N mobile relays to K second terminals being lower than the second threshold, and the fourth model, the fifth model is determined.

[0077] Among them, the fifth model can specifically be:

[0078] s.t.w H Dw ≤ P tot , w H F t,k w ≤ I th (6)

[0079] Among them, F t,s = P t (g t ⊙h s )(g t ⊙h s ) H ,

[0080] F t,k = P t (g t ⊙h k )(g t ⊙h k ) H , g t = [g t,1 g t,2 …g t,N T

[0081] h s = [h 1,s h 2,s …h N,s T , h​​k = [h 1,k h 2,k …h N,k T

[0082] w = [w 1 w 2 …w N T

[0083]

[0084] Among them, w 1 represents the beamforming weight assigned to the first mobile relay among N mobile relays, w 2 represents the beamforming weight assigned to the second mobile relay among N mobile relays, w N represents the beamforming weight assigned to the Nth mobile relay among N mobile relays, P t represents the transmission power of the base station, g t,1 represents the channel fading coefficient from the base station to the first mobile relay, g t,2 represents the channel fading coefficient from the base station to the second mobile relay, g t,N represents the channel fading coefficient from the base station to the Nth mobile relay, h 1,s represents the channel fading coefficient from the first mobile relay to the first terminal, h 2,s represents the channel fading coefficient from the second mobile relay to the first terminal, h N,s represents the channel fading coefficient from the Nth mobile relay to the first terminal, represents the noise power of each UAV, represents the noise power of the secondary user, I represents the identity matrix, P tot represents the maximum total transmission power of N mobile relays, h 1,k represents the channel fading coefficient from the first mobile relay to the kth second terminal, h 2,k represents the channel fading coefficient from the second mobile relay to the kth second terminal, h N,k represents the channel fading coefficient from the Nth mobile relay to the kth second terminal, I th represents the maximum interference signal power of the kth second terminal, represents obtaining the maximum value of A by optimizing w, s.t. represents the constraint condition.

[0085] In one embodiment, the method for determining the beamforming weights used by N mobile relays by using the fifth model and combining the zero-forcing algorithm and the maximum Rayleigh quotient method includes:

[0086] ​​Set the interference signal power of the N mobile relays on the K second terminals in the fifth model to zero, and obtain a sixth model according to the orthogonal projection theorem;

[0087] Utilize the sixth model and combine it with the maximum Rayleigh quotient method to determine the beamforming weight values used by the N mobile relays.

[0088] In practical applications, when the interference signal power of the N mobile relays on the K second terminals in the fifth model is set to zero, the above formula (6) can be changed to:

[0089] s.t. w H Dw ≤ P tot , H k w = 0 (7)

[0090] where, F t,s = P t (g t ⊙h s )(g t ⊙h s ) H ,

[0091] F t,k = P t (g t ⊙h k )(g t ⊙h k ) H , g t = [g t,1 g t,2 … g t,N T

[0092] h s = [h 1,s h 2,s … h N,s T , h k = [h 1,k h 2,k … h N,k T

[0093] w = [w 1 w 2 … w N T

[0094] H k = [h 1 h 2 ... h K ​​​​​T

[0095] Among them, w 1 represents the beamforming weight assigned to the first mobile relay among N mobile relays, w 2 represents the beamforming weight assigned to the second mobile relay among N mobile relays, w N represents the beamforming weight assigned to the Nth mobile relay among N mobile relays, P t represents the transmit power of the base station, g t,1 represents the channel fading coefficient from the base station to the first mobile relay, g t,2 represents the channel fading coefficient from the base station to the second mobile relay, g t,N represents the channel fading coefficient from the base station to the Nth mobile relay, h 1,s represents the channel fading coefficient from the first mobile relay to the first terminal, h 2,s represents the channel fading coefficient from the second mobile relay to the first terminal, h N,s represents the channel fading coefficient from the Nth mobile relay to the first terminal, represents the noise power of each unmanned aerial vehicle, represents the noise power of the secondary user, I represents the identity matrix, P tot represents the maximum total transmit power of N mobile relays, h 1,k represents the channel fading coefficient from the first mobile relay to the kth second terminal, h 2,k represents the channel fading coefficient from the second mobile relay to the kth second terminal, h N,k represents the channel fading coefficient from the Nth mobile relay to the kth second terminal.

[0096] In practical applications, according to the above formula (7) and the orthogonal projection theorem, the optimal beamforming weights of N mobile relays can be obtained. The optimal beamforming weights of N mobile relays are as follows:

[0097] w * = Ru (8)

[0098] Among them, R = I - X,

[0099] H k = [h 1 h 2 ...h K T

[0100] h k = [h 1,k h 2,k …h N,k T ​​

[0101] where w * represents the optimal beamforming weights of N mobile relays, u represents the solution vector, and h 1,k represents the channel fading coefficient from the first mobile relay to the k-th second terminal, and h 2,k represents the channel fading coefficient from the second mobile relay to the k-th second terminal, and h N,k represents the channel fading coefficient from the N-th mobile relay to the k-th second terminal.

[0102] In practical applications, according to the above formula (7) and the above formula (8), the sixth model can be obtained. Specifically, the sixth model can be:

[0103] s.t. u H R H DRu ≤ P tot (9)

[0104] where F t,s = P t (g t ⊙ h s )(g t ⊙ h s ) H ,

[0105] F t,k = P t (g t ⊙ h k )(g t ⊙ h k ) H , g t = [g t,1 g t,2 … g t,N T

[0106] h s = [h 1,s h 2,s … h N,s T , h k = [h 1,k h 2,k … h N,k T

[0107] w = [w 1 w 2 … w N T

[0108] H​​​​k = [h 1 h 2 ...h K T

[0109] where, w 1 represents the beamforming weight assigned to the first mobile relay among N mobile relays, w 2 represents the beamforming weight assigned to the second mobile relay among N mobile relays, w N represents the beamforming weight assigned to the Nth mobile relay among N mobile relays, P t represents the transmit power of the base station, g t,1 represents the channel fading coefficient from the base station to the first mobile relay, g t,2 represents the channel fading coefficient from the base station to the second mobile relay, g t,N represents the channel fading coefficient from the base station to the Nth mobile relay, h 1,s represents the channel fading coefficient from the first mobile relay to the first terminal, h 2,s represents the channel fading coefficient from the second mobile relay to the first terminal, h N,s represents the channel fading coefficient from the Nth mobile relay to the first terminal, represents the noise power of each unmanned aerial vehicle, represents the noise power of the secondary user, I represents the identity matrix, P tot represents the maximum total transmit power of N mobile relays, h 1,k represents the channel fading coefficient from the first mobile relay to the kth second terminal, h 2,k represents the channel fading coefficient from the second mobile relay to the kth second terminal, h N,k represents the channel fading coefficient from the Nth mobile relay to the kth second terminal, u represents the solution vector.

[0110] In one embodiment, the method for determining the beamforming weights used by N mobile relays by using the sixth model and combining with the maximum Rayleigh quotient method includes:

[0111] Performing a formal transformation on the sixth model to obtain a seventh model;

[0112] Using the seventh model and combining with the maximum Rayleigh quotient method to determine the beamforming weights used by N mobile relays.

[0113] In practical applications, setting The above formula (9) can be changed to:

[0114] s.t.p ≤ P tot , ​

[0115] where F t,s = P t (g t ⊙ h s )(g t ⊙ h s ) H ,

[0116] F t,k = P t (g t ⊙ h k )(g t ⊙ h k ) H , g t = [g t,1 g t,2 … g t,N T

[0117] h s = [h 1,s h 2,s … h N,s T , h k = [h 1,k h 2,k … h N,k T

[0118] w = [w 1 w 2 … w N T

[0119] H k = [h 1 h 2 ... h K T

[0120]

[0121] where w 1 represents the beamforming weight assigned to the first mobile relay among N mobile relays, w 2 represents the beamforming weight assigned to the second mobile relay among N mobile relays, w N represents the beamforming weight assigned to the Nth mobile relay among N mobile relays, P t represents the transmit power of the base station, g t,1 represents the channel fading coefficient from the base station to the first mobile relay, g t,2 ​​​​​Denotes the channel fading coefficient from the base station to the second mobile relay, g t,N Denotes the channel fading coefficient from the base station to the Nth mobile relay, h 1,s Denotes the channel fading coefficient from the first mobile relay to the first terminal, h 2,s Denotes the channel fading coefficient from the second mobile relay to the first terminal, h N,s Denotes the channel fading coefficient from the Nth mobile relay to the first terminal, Denotes the noise power of each unmanned aerial vehicle, Denotes the noise power of the secondary user, I represents the identity matrix, P tot Denotes the maximum total transmit power of N mobile relays, h 1,k Denotes the channel fading coefficient from the first mobile relay to the kth second terminal, h 2,k Denotes the channel fading coefficient from the second mobile relay to the kth second terminal, h N,k Denotes the channel fading coefficient from the Nth mobile relay to the kth second terminal, u represents the solution vector.

[0122] Here, the function in formula (10) increases as p increases. When p = P tot it can achieve the maximum value of the function. When p = P tot the seventh model is obtained. The seventh model can specifically be:

[0123]

[0124] where, F t,s = P t (g t ⊙h s )(g t ⊙h s ) H ,

[0125] F t,k = P t (g t ⊙h k )(g t ⊙h k ) H , g t = [g t,1 g t,2 …g t,N T

[0126] h s = [h 1,s h 2,s …h N,s T , h k ​​= [h 1,k h 2,k …h N,k T

[0127] w = [w 1 w 2 …w N T

[0128] H k = [h 1 h 2 ...h K T

[0129]

[0130]

[0131] Among them, w 1 represents the beamforming weight assigned to the first mobile relay among N mobile relays, w 2 represents the beamforming weight assigned to the second mobile relay among N mobile relays, w N represents the beamforming weight assigned to the Nth mobile relay among N mobile relays, P t represents the transmission power of the base station, g t,1 represents the channel fading coefficient from the base station to the first mobile relay, g t,2 represents the channel fading coefficient from the base station to the second mobile relay, g t,N represents the channel fading coefficient from the base station to the Nth mobile relay, h 1,s represents the channel fading coefficient from the first mobile relay to the first terminal, h 2,s represents the channel fading coefficient from the second mobile relay to the first terminal, h N,s represents the channel fading coefficient from the Nth mobile relay to the first terminal, represents the noise power of each UAV, represents the noise power of the secondary user, I represents the identity matrix, P tot represents the maximum total transmission power of N mobile relays, h 1,k represents the channel fading coefficient from the first mobile relay to the kth second terminal, h 2,k represents the channel fading coefficient from the second mobile relay to the kth second terminal, h N,k represents the channel fading coefficient from the Nth mobile relay to the kth second terminal, u represents the solution vector.

[0132] ​​​In practical applications, using the seventh model and combining with the maximum Rayleigh quotient method, the optimal solution vector in the above formula (11) is determined. The optimal solution vector is:

[0133]

[0134] where ρ{·} represents the principal eigenvector of the matrix, represents the optimal solution vector, and P tot represents the maximum total transmission power of N mobile relays.

[0135] According to formula (12), the beamforming weights used by N mobile relays are determined. The beamforming weights used by N mobile relays are:

[0136]

[0137] The maximum received signal-to-noise ratio of the corresponding first terminal is:

[0138]

[0139] where λ max {·} represents the maximum eigenvalue of the matrix.

[0140] The method, apparatus, electronic device, and storage medium for determining beamforming weights provided by an embodiment of the present invention determine a first model, a second model, and a third model; the first model characterizes the first received signals sent by a base station and received by each of N mobile relays; N is an integer greater than or equal to 2; the second model characterizes the second received signals sent by the N mobile relays based on the first received signals and received by a first terminal; the third model characterizes the third received signals sent by the N mobile relays based on the first received signals and received by each of K second terminals; wherein the base station provides services for the K second terminals; the N mobile relays all provide services for the first terminal; K is an integer greater than or equal to 1; based on the first model, the second model, and the third model, a fourth model is determined; the fourth model characterizes the received signal-to-noise ratio of the signals sent by the N mobile relays and received by the first terminal; on the premise of maximizing the signal-to-noise ratio, based on the total transmission power of the N mobile relays being lower than a first threshold, the interference signal power of the N mobile relays on the K second terminals being lower than a second threshold, and the fourth model, a fifth model is determined; using the fifth model, and combining the zero-forcing algorithm and the maximum Rayleigh quotient method, the beamforming weights used by the N mobile relays are determined. The solution of the embodiment of the present invention determines the beamforming weights of the mobile relays when the received signal-to-noise ratio of the first terminal is maximized by using the zero-forcing algorithm and the maximum Rayleigh quotient method under the condition that the total transmission power of the mobile relays is lower than the first threshold and the interference signal power received by the second terminal is lower than the second threshold, so as to be able to improve the received signal-to-noise ratio of the first terminal while ensuring the communication quality of the second terminal.

[0141] The present invention will be further described in detail below in conjunction with application embodiments.

[0142] This application embodiment proposes a distributed beamforming design method for the problem of multi-UAV relay cooperative communication in a cognitive radio network. The following will be combined with Figure 2 The specific implementation steps in this application embodiment will be described in detail.

[0143] As Figure 2As shown in the figure, an embodiment of this application includes a main network and a UAV relay network that shares spectrum resources with the main network. Among them, the main network includes K main users (which can be understood as the second terminal in the above embodiment), and the UAV relay network, as a secondary network, includes a base station, a secondary user (which can be understood as the first terminal in the above embodiment), and N UAV relays. During the communication process, the UAV relay receives the signal sent by the base station and forwards it to the secondary user after weighting by the beamforming vector w. Due to the broadcast characteristic of wireless communication, the main users will also be interfered by the signal sent by the UAV relay. Therefore, to reduce the interference of the UAV relay on the main users and enhance the received signal-to-noise ratio of the secondary user for receiving the signal of the UAV relay, under the condition that the total transmission power of multiple UAV relays is lower than the first threshold and the interference signal power received by each main user is lower than the second threshold, optimize the beamforming weights of the UAV relays during the distributed beamforming process to maximize the received signal-to-noise ratio of the secondary user.

[0144] The determination process of the beamforming weights in the embodiment of this application is as follows:

[0145] Step 1: Assume that the communication between the main network and the UAV relay network is that partial channel information is known, and describe the channel coefficient with a multipath Rayleigh channel containing channel uncertainty parameters, which is the above formula (1).

[0146] Step 2: Assume that the maximum interference threshold I of each main user in the main network th (which can be understood that the highest interference signal power of the second terminal is I th ), the transmission power P of the base station in the UAV relay network t and the maximum total transmission power P of all UAV relays tot .

[0147] Step 3: Determine that the optimization problem is to maximize the received signal-to-noise ratio of the secondary user under the constraint conditions that the total transmission power of all UAV relays is lower than the threshold and the interference signal power received by the main users is lower than the threshold. The determination process of the optimization problem (since the received signal-to-noise ratio of the secondary user needs to be obtained according to the signal in the secondary network, therefore, the determination process of the optimization problem can also be understood as the signal flow) includes the following steps:

[0148] Step 1, determine that the ground base station sends the signal to the UAV relay. The signal received by the i-th UAV relay can be expressed by the above formula (2).

[0149] Step 2, assign a beamforming weight to the signal received by each UAV relay, and send the signal received by the UAV relay after assigning the beamforming weight to the secondary user. The signal received by the secondary user can be expressed by the above formula (3).

[0150] Meanwhile, due to the broadcast nature of wireless communication, the primary users will also be interfered by the signals sent by the UAV relay. The interference signal received by the k-th primary user can be expressed by the above formula (4).

[0151] According to the signal flow, the optimization problem can be specifically expressed by the above formula (6).

[0152] Step 4: According to the zero-forcing criterion, completely zero out the signal interference of the UAV relay to the primary users, and obtain the general solution expression of the optimal beamforming weight according to the orthogonal projection theorem.

[0153] First, use the zero-forcing algorithm to zero out the signals of the UAV relay to the primary users, then the optimization problem can be expressed by the above formula (7); second, use the orthogonal projection theorem to obtain the general solution expression of the optimal beamforming weight, and the general solution expression can be expressed by the above formula (8). Based on this, the solution of the beamforming weight vector w in the original optimization problem can be transformed into the solution of the vector u. At this time, the optimization problem can be expressed by the above formula (9).

[0154] Step 5: Reconstruct the optimization problem to make the optimization problem satisfy the Rayleigh quotient form, and use the maximum Rayleigh quotient method to solve the optimal beamforming weight.

[0155] Here, transform the optimization problem, and after transformation, the optimization problem can be expressed by the above formula (10).

[0156] The function in the above formula (10) increases with the increase of p. When p = P tot , the maximum value of the function can be obtained. When p = P tot , the optimization problem can be expressed by the above formula (11).

[0157] At this time, the above formula (11) satisfies the Rayleigh quotient form and can be solved using the maximum Rayleigh quotient method. Solve using the maximum Rayleigh quotient method to obtain the global optimal solution (which can be understood as the optimal solution vector in the above embodiment). The global optimal solution can be expressed by the above formula (12). The optimal beamforming weight obtained using the global optimal solution can be expressed by the above formula (13), and the maximum received signal-to-noise ratio of the corresponding secondary user can be expressed by the above formula (14).

[0158] In summary, the above process is the flow of designing the beamforming weights of multiple UAV relays in a cognitive radio network based on partial channel information. Different from traditional communication networks, in this embodiment, a multi-UAV relay cooperative communication method is adopted in the cognitive radio network, which can not only ensure the communication quality of primary and secondary network users and the endurance of UAV relays, but also improve the spectrum utilization rate. In dealing with the optimization problem, the zero-forcing algorithm is used to completely zero the interference of UAV relays to primary users, and the optimization problem is transformed into a more easily solvable Rayleigh quotient form, avoiding the use of the relay joint optimization method to suppress interference and reducing the complexity of system implementation.

[0159] This embodiment provides a UAV relay cooperative communication method in a cognitive radio network. The method is based on a primary network and a UAV relay network sharing the spectrum. Among them, the primary network includes multiple primary users, and the UAV relay network, as a secondary network, includes a ground base station, a secondary user, and multiple UAV relays. By optimizing the beamforming weights at the UAV relay end, under the condition that the total transmission power of the UAV relay is lower than the threshold and the interference power received by each primary user is lower than the threshold, the received signal-to-noise ratio of the secondary user is maximized. This embodiment solves the distributed beamforming problem of multiple UAV relays in a cognitive radio network. The zero-forcing algorithm is used to zero the interference of UAV relays to primary users, and then the maximum Rayleigh quotient method is used to solve the optimal beamforming weights. It can improve the received signal-to-noise ratio of secondary users on the premise of ensuring the communication quality of primary users and the endurance of the UAV relay network, and has strong practical significance. Moreover, different from the traditional scenario where a single UAV relay is used as a communication relay, the multi-UAV relay cooperative communication technology adopted in this embodiment can expand the coverage of the UAV network, improve the system capacity and spectrum utilization rate. In addition, compared with the traditional relay joint optimization interference suppression scheme, the algorithm proposed in this embodiment can effectively reduce the complexity and improve the operation efficiency.

[0160] To implement the method of the embodiment of the present invention, the embodiment of the present invention also provides a device for determining beamforming weights, which is arranged on an electronic device, such as Figure 3 As shown, the device 300 for determining beamforming weights includes: a first determination module 301, a second determination module 302, a third determination module 303, and a fourth determination module 304; wherein,

[0161] The first determination module 301 is configured to determine a first model, a second model, and a third model; the first model represents the first received signals sent by the base station and received by each of the N mobile relays; N is an integer greater than or equal to 2; the second model represents the second received signals sent by the N mobile relays based on the first received signals and received by the first terminal; the third model represents the third received signals sent by the N mobile relays based on the first received signals and received by each of the K second terminals; wherein, the base station provides services for the K second terminals; the N mobile relays all provide services for the first terminal; K is an integer greater than or equal to 1;

[0162] The second determination module 302 is configured to determine a fourth model based on the first model, the second model, and the third model; the fourth model represents the received signal-to-noise ratio of the signals sent by the N mobile relays and received by the first terminal;

[0163] The third determination module 303 is configured to determine a fifth model based on that the total transmission power of the N mobile relays is lower than a first threshold, the interference signal power of the N mobile relays on the K second terminals is lower than a second threshold, and the fourth model, on the premise of maximizing the signal-to-noise ratio;

[0164] The fourth determination module 304 is configured to use the fifth model and combine the zero-forcing algorithm and the maximum Rayleigh quotient method to determine the beamforming weights used by the N mobile relays.

[0165] In an embodiment, the first determination module 301 is further configured to:

[0166] Determine a first parameter, a second parameter, and a third parameter based on the first model, the second model, and the third model; the first parameter represents the channel fading coefficient from the base station to the N mobile relays, the second parameter represents the channel fading coefficient from the N mobile relays to the first terminal, and the third parameter represents the channel fading coefficient from the N mobile relays to the second terminal;

[0167] Determine the fourth model based on the determined first parameter, second parameter, and third parameter.

[0168] In an embodiment, the fourth determination module 304 is further configured to:

[0169] Set the interference signal power of the N mobile relays on the K second terminals in the fifth model to zero, and obtain a sixth model according to the orthogonal projection theorem;

[0170] Use the sixth model and combine the maximum Rayleigh quotient method to determine the beamforming weights used by the N mobile relays.

[0171] In an embodiment, the fourth determination module 304 is further configured to:

[0172] Perform a formal transformation on the sixth model to obtain a seventh model;

[0173] Utilize the seventh model and combine it with the maximum Rayleigh quotient method to determine the beamforming weight values used by N mobile relays.

[0174] In one embodiment, the first determination module 301 is further configured to:

[0175] Determine the first model based on the multipath Rayleigh channel between the base station and each mobile relay.

[0176] In one embodiment, the first determination module 301 is further configured to:

[0177] Determine the second model based on the multipath Rayleigh channel between each mobile relay and the first terminal.

[0178] In one embodiment, the first determination module 301 is further configured to:

[0179] Determine the third model based on the multipath Rayleigh channel between each mobile relay and each second terminal.

[0180] In actual application, the first determination module 301, the second determination module 302, the third determination module 303, and the fourth determination module 304 may be implemented by a processor in a device for determining beamforming weight values.

[0181] It should be noted that: when the device for determining beamforming weight values provided in the above embodiments determines the beamforming weight values, only the division of the above program modules is used for illustration. In actual application, the above processing may be allocated to different program modules according to needs, that is, the internal structure of the terminal is divided into different program modules to complete all or part of the above-described processing. In addition, the device for determining beamforming weight values provided in the above embodiments and the embodiments of the method for determining beamforming weight values belong to the same concept, and the specific implementation process can be seen in the method embodiments, which will not be elaborated here.

[0182] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiments of the present invention, the embodiments of the present invention further provide an electronic device, as Figure 4 shown, the electronic device 400 includes:

[0183] A communication interface 401 capable of information interaction with other devices (such as network devices, terminals, etc.);

[0184] A processor 402 connected to the communication interface 401 to achieve information interaction with other devices, and when running a computer program, execute the method provided by one or more of the above technical solutions;

[0185] A memory 403 for storing a computer program that can run on the processor 402.

[0186] Specifically, the processor 402 is configured to perform the following operations:

[0187] Determine a first model, a second model, and a third model; the first model represents the first received signals sent by the base station received by each of the N mobile relays; N is an integer greater than or equal to 2; the second model represents the second received signals sent by the N mobile relays based on the first received signals received by the first terminal; the third model represents the third received signals sent by the N mobile relays based on the first received signals received by each of the K second terminals; wherein, the base station provides services for the K second terminals; the N mobile relays all provide services for the first terminal; K is an integer greater than or equal to 1;

[0188] Based on the first model, the second model, and the third model, determine a fourth model; the fourth model represents the received signal-to-noise ratio of the signals received by the first terminal from the N mobile relays.

[0189] On the premise of maximizing the received signal-to-noise ratio, based on that the total transmission power of the N mobile relays is lower than a first threshold, the interference signal power of the N mobile relays to the K second terminals is lower than a second threshold, and the fourth model, determine a fifth model;

[0190] Utilize the fifth model, and in combination with the zero-forcing algorithm and the maximum Rayleigh quotient method, determine the beamforming weights used by the N mobile relays.

[0191] In one embodiment, the processor 402 is further configured to perform the following operations:

[0192] Based on the first model, the second model, and the third model, determine a first parameter, a second parameter, and a third parameter; the first parameter represents the channel fading coefficient from the base station to the N mobile relays, the second parameter represents the channel fading coefficient from the N mobile relays to the first terminal, and the third parameter represents the channel fading coefficient from the N mobile relays to the second terminal;

[0193] Based on the determined first parameter, second parameter, and third parameter, determine the fourth model.

[0194] In one embodiment, the processor 402 is further configured to perform the following operations:

[0195] Set the interference signal power of the N mobile relays to the K second terminals in the fifth model to zero, and according to the orthogonal projection theorem, obtain a sixth model;

[0196] Utilize the sixth model, and in combination with the maximum Rayleigh quotient method, determine the beamforming weights used by the N mobile relays.

[0197] In one embodiment, the processor 402 is further configured to perform the following operations:

[0198] Perform a formal transformation on the sixth model to obtain a seventh model;

[0199] Use the seventh model and combine it with the maximum Rayleigh quotient method to determine the beamforming weights used by N mobile relays.

[0200] In one embodiment, the processor 402 is further configured to perform the following operations:

[0201] Determine the first model based on the multipath Rayleigh channel between the base station and each mobile relay.

[0202] In one embodiment, the processor 402 is further configured to perform the following operations:

[0203] Determine the second model based on the multipath Rayleigh channel between each mobile relay and the first terminal.

[0204] In one embodiment, the processor 402 is further configured to perform the following operations:

[0205] Determine the third model based on the multipath Rayleigh channel between each mobile relay and each second terminal.

[0206] It should be noted that: The specific process of the processor 402 performing the above operations can be found in the method embodiment and will not be elaborated here.

[0207] Of course, in actual application, each component in the electronic device 400 is coupled together through the bus system 404. It can be understood that the bus system 404 is used to realize the connection and communication between these components. The bus system 404 includes not only a data bus, but also a power bus, a control bus, and a status signal bus. However, for the sake of clear illustration, in Figure 4 all kinds of buses are labeled as the bus system 404.

[0208] The memory 403 in the embodiment of the present invention is used to store various types of data to support the operation of the electronic device 400. Examples of these data include: any computer program for operating on the electronic device 400.

[0209] The method disclosed in the above embodiments of the present invention can be applied to or implemented by the processor 402. The processor 402 may be an integrated circuit chip with signal processing capabilities. During implementation, the steps of the above method can be completed by the integrated logic circuit in the hardware of the processor 402 or instructions in software form. The above-mentioned processor 402 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 402 can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor, etc. Combining the steps of the method disclosed in the embodiments of the present invention can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor. The software module may be located in the storage medium, and this storage medium is located in the memory 403. The processor 402 reads the information in the memory 403 and combines its hardware to complete the steps of the foregoing method.

[0210] In an exemplary embodiment, the electronic device 400 can be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontroller units (MCUs), microprocessors, or other electronic components, and is used to execute the foregoing method.

[0211] It can be understood that the memory 403 in the embodiments of the present invention can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM, Read Only Memory), a programmable read-only memory (PROM, Programmable Read-Only Memory), an erasable programmable read-only memory (EPROM, Erasable Programmable Read-Only Memory), an electrically erasable programmable read-only memory (EEPROM, Electrically Erasable Programmable Read-Only Memory), a ferromagnetic random access memory (FRAM, ferromagnetic random access memory), a flash memory (FlashMemory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM, Compact Disc Read-Only Memory); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM, Random Access Memory), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM, Static Random Access Memory), synchronous static random access memory (SSRAM, Synchronous Static Random Access Memory), dynamic random access memory (DRAM, Dynamic Random Access Memory), synchronous dynamic random access memory (SDRAM, Synchronous Dynamic Random Access Memory), double data rate synchronous dynamic random access memory (DDRSDRAM, Double Data Rate Synchronous Dynamic Random Access Memory), enhanced synchronous dynamic random access memory (ESDRAM, Enhanced Synchronous Dynamic Random AccessMemory), sync link dynamic random access memory (SLDRAM, SyncLink Dynamic Random AccessMemory), direct rambus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memories.

[0212] In an exemplary embodiment, the embodiments of the present invention further provide a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, such as a memory 403 that stores a computer program. The above computer program can be executed by a processor 402 of an electronic device 400 to complete the steps described in the foregoing method. The computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM.

[0213] It should be noted that: "first", "second", etc. are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence.

[0214] In addition, the technical solutions described in the embodiments of the present invention can be arbitrarily combined without conflict.

[0215] The above is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A method for determining beamforming weights, characterized in that, it includes: determining a first model, a second model, and a third model; the first model represents the first received signals sent by the base station and received by each of the N mobile relays; N is an integer greater than or equal to 2; the second model represents the second received signals sent by the N mobile relays based on the first received signals and received by the first terminal; the third model represents the third received signals sent by the N mobile relays based on the first received signals and received by each of the K second terminals; wherein, the base station provides services for the K second terminals; the N mobile relays all provide services for the first terminal; K is an integer greater than or equal to 1; based on the first model, the second model, and the third model, determining a fourth model; the fourth model represents the received signal-to-noise ratio of the signals sent by the N mobile relays and received by the first terminal; on the premise of maximizing the received signal-to-noise ratio, based on the total transmission power of the N mobile relays being lower than a first threshold, the interference signal power of the N mobile relays on the K second terminals being lower than a second threshold, and the fourth model, determining a fifth model; using the fifth model, and combining with the zero-forcing algorithm and the maximum Rayleigh quotient method, determining the beamforming weights used by the N mobile relays; wherein, the first model includes: where x i represents the first received signal sent by the base station received by the i-th mobile relay, where i is an integer greater than or equal to 1 and less than or equal to N, and P t represents the transmission power of the base station, and g t,i represents the channel fading coefficient from the base station to the i-th mobile relay, s represents the power-normalized signal sent by the base station, and n i represents the Gaussian noise of the i-th mobile relay; the second model includes: where y s represents the second received signal sent by the N mobile relays received by the first terminal based on the first received signal, and h i,s represents the channel fading coefficient from the i-th mobile relay to the first terminal, and w i represents the beamforming weight assigned to the i-th mobile relay, and n s represents the Gaussian noise of the first terminal; the third model includes: where y k represents the third received signal sent by the N mobile relays received by each of the K second terminals based on the first received signal, and h i,k represents the channel fading coefficient from the i-th mobile relay to the k-th second terminal, and n k represents the Gaussian noise of the k-th second terminal; the fourth model includes: Among them, F t,s = P t (g t ⊙h s )(g t ⊙h s ) H , g t = [g t,1 g t,2 …g t,N T ,h s = [h 1,s h 2,s …h N,s T ,w = [w 1 w 2 …w N T ,​​​ Among them, SNR represents the received signal-to-noise ratio of the first terminal receiving the signals sent by N mobile relays, w 1 represents the beamforming weight assigned to the first mobile relay among the N mobile relays, w 2 represents the beamforming weight assigned to the second mobile relay among the N mobile relays, w N represents the beamforming weight assigned to the Nth mobile relay among the N mobile relays, g t,1 represents the channel fading coefficient from the base station to the first mobile relay, g t,2 represents the channel fading coefficient from the base station to the second mobile relay, g t,N represents the channel fading coefficient from the base station to the Nth mobile relay, h 1,s represents the channel fading coefficient from the first mobile relay to the first terminal, h 2,s represents the channel fading coefficient from the second mobile relay to the first terminal, h N,s represents the channel fading coefficient from the Nth mobile relay to the first terminal, represents the noise power of each mobile relay, represents the noise power of the first terminal; the fifth model includes: s.t.w H Dw ≤ P tot ,w H F t,k w ≤ I th ,where, F t,k = P t (g t ⊙ h k )(g t ⊙ h k ) H ,D = P t diag(|g t,1 | 2 |g t,2 | 2 … |g t,N | 2 ) + σ r 2 I, h k = [h 1,k h 2,k …h N,k T ,​ Among them, g t,1 represents the channel fading coefficient from the base station to the first mobile relay, g t,2 represents the channel fading coefficient from the base station to the second mobile relay, g t,N represents the channel fading coefficient from the base station to the Nth mobile relay, P tot represents the maximum total transmission power of N mobile relays, h 1,k represents the channel fading coefficient from the first mobile relay to the kth second terminal, h 2,k represents the channel fading coefficient from the second mobile relay to the kth second terminal, h N,k represents the channel fading coefficient from the Nth mobile relay to the kth second terminal, where k is an integer greater than or equal to 1 and less than or equal to K, I represents the identity matrix, I th represents the maximum interference signal power of the kth second terminal, represents obtaining the maximum value of A by optimizing w, and s.t. represents the constraint condition.

2. The method according to claim 1, characterized in that, the determining the fourth model based on the first model, the second model, and the third model includes: determining a first parameter, a second parameter, and a third parameter based on the first model, the second model, and the third model; the first parameter represents the channel fading coefficient from the base station to the N mobile relays, the second parameter represents the channel fading coefficient from the N mobile relays to the first terminal, and the third parameter represents the channel fading coefficient from the N mobile relays to the second terminal; determining the fourth model based on the determined first parameter, second parameter, and third parameter.

3. The method according to claim 1, characterized in that, the using the fifth model, and combining with the zero-forcing algorithm and the maximum Rayleigh quotient method, determining the beamforming weights used by the N mobile relays includes: setting the interference signal power of the N mobile relays on the K second terminals in the fifth model to zero, and according to the orthogonal projection theorem, obtaining a sixth model; using the sixth model, and combining with the maximum Rayleigh quotient method, determining the beamforming weights used by the N mobile relays; wherein, the sixth model includes: s.t.u H R H DRu ≤ P tot , where R = I - X, H k = [h 1 h 2 ... h K T ,​ wherein, u represents the solution vector; the using the sixth model, and combining with the maximum Rayleigh quotient method, determining the beamforming weights used by the N mobile relays includes: performing a formal transformation on the sixth model to obtain a seventh model; using the seventh model, and combining with the maximum Rayleigh quotient method, determining the beamforming weights used by the N mobile relays; wherein, the seventh model includes: Among them, p = P tot , The beamforming weights used by N mobile relays include: wherein, ρ{·} represents the principal eigenvector of the matrix.

4. The method according to any one of claims 1 to 3, characterized in that, Determine the first model based on the multipath Rayleigh channel between the base station and each mobile relay; wherein, The channel coefficients of the multipath Rayleigh channel include: where h represents the channel coefficient, represents the mean value of the channel coefficient, represents the variance of the channel coefficient.

5. The method according to any one of claims 1 to 3, characterized in that, Determine a second model based on the multipath Rayleigh channel between each mobile relay and the first terminal; wherein, The channel coefficients of the multipath Rayleigh channel include: where h represents the channel coefficient, represents the mean of the channel coefficient, represents the variance of the channel coefficient.

6. The method according to any one of claims 1 to 3, characterized in that, Determine a third model based on the multipath Rayleigh channel between each mobile relay and each second terminal; wherein, The channel coefficients of the multipath Rayleigh channel include: where h represents the channel coefficient, represents the mean value of the channel coefficient, represents the variance of the channel coefficient.

7. An apparatus for determining beamforming weights, characterized in that, comprising: A first determination module for determining a first model, a second model and a third model; The first model characterizes the first received signal transmitted by the base station received by each of the N mobile relays; N is an integer greater than or equal to 2; the second model characterizes the second received signal transmitted by the N mobile relays based on the first received signal received by the first terminal; the third model characterizes the third received signal transmitted by the N mobile relays based on the first received signal received by each of the K second terminals; wherein, the base station provides services for the K second terminals; the N mobile relays all provide services for the first terminal; K is an integer greater than or equal to 1; A second determination module for determining a fourth model based on the first model, the second model and the third model; the fourth model characterizes the received signal-to-noise ratio of the signals transmitted by the N mobile relays received by the first terminal; A third determination module for determining a fifth model on the premise of maximizing the signal-to-noise ratio, based on that the total transmission power of the N mobile relays is lower than a first threshold, the interference signal power of the N mobile relays on the K second terminals is lower than a second threshold, and the fourth model; A fourth determination module for using the fifth model and combining the zero-forcing algorithm and the maximum Rayleigh quotient method to determine the beamforming weights used by the N mobile relays; wherein, The first model includes: where x i represents the first received signal sent by the base station received by the i-th mobile relay, where i is an integer greater than or equal to 1 and less than or equal to N, and P t represents the transmission power of the base station, and g t,i represents the channel fading coefficient from the base station to the i-th mobile relay, s represents the power-normalized signal sent by the base station, and n i represents the Gaussian noise of the i-th mobile relay; The second model includes: where y s represents the second received signal sent by the N mobile relays received by the first terminal based on the first received signal, h i,s represents the channel fading coefficient from the i-th mobile relay to the first terminal, w i represents the beamforming weight assigned to the i-th mobile relay, n s represents the Gaussian noise of the first terminal; The third model includes: where y k represents the third received signal sent by each of the N mobile relays received by each of the K second terminals based on the first received signal, h i,k represents the channel fading coefficient from the i-th mobile relay to the k-th second terminal, n k represents the Gaussian noise of the k-th second terminal; The fourth model includes: Among them, F t,s = P t (g t ⊙h s )(g t ⊙h s ) H , g t = [g t,1 g t,2 … g t,N T , h s = [h 1,s h 2,s … h N,s T , w = [w 1 w 2 … w N T ,​​​ Among them, SNR represents the received signal-to-noise ratio of the first terminal receiving the signals sent by N mobile relays, w 1 represents the beamforming weight assigned to the first mobile relay among the N mobile relays, w 2 represents the beamforming weight assigned to the second mobile relay among the N mobile relays, w N represents the beamforming weight assigned to the Nth mobile relay among the N mobile relays, g t,1 represents the channel fading coefficient from the base station to the first mobile relay, g t,2 represents the channel fading coefficient from the base station to the second mobile relay, g t,N represents the channel fading coefficient from the base station to the Nth mobile relay, h 1,s represents the channel fading coefficient from the first mobile relay to the first terminal, h 2,s represents the channel fading coefficient from the second mobile relay to the first terminal, h N,s represents the channel fading coefficient from the Nth mobile relay to the first terminal, represents the noise power of each mobile relay, represents the noise power of the first terminal; The fifth model includes: s.t.w H Dw ≤ P tot ,w H F t,k w ≤ I th wherein, F t,k = P t (g t ⊙h k )(g t ⊙h k ) H , h k = [h 1,k h 2,k …h N,k T ,​ where, g t,1 represents the channel fading coefficient from the base station to the first mobile relay, g t,2 represents the channel fading coefficient from the base station to the second mobile relay, g t,N represents the channel fading coefficient from the base station to the Nth mobile relay, P tot represents the maximum total transmission power of the N mobile relays, h 1,k represents the channel fading coefficient from the first mobile relay to the kth second terminal, h 2,k represents the channel fading coefficient from the second mobile relay to the kth second terminal, h N,k represents the channel fading coefficient from the Nth mobile relay to the kth second terminal, k is an integer greater than or equal to 1 and less than or equal to K, I represents the identity matrix, I th represents the maximum interference signal power of the kth second terminal, represents obtaining the maximum value of A by optimizing w, s.t. represents the constraint condition.

8. An electronic device, characterized in that, comprising: A processor and a memory for storing a computer program that can run on the processor; wherein, When the processor is used to run the computer program, it executes the steps of the method according to any one of claims 1 to 6.

9. A storage medium, in which a computer program is stored, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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