Beam forming optimization method oriented to access of space-based Internet of Things

Abstract

The invention provides a space-based Internet of Things access-oriented beam forming optimization method, which comprises the following steps of: calculating directions of arrival of all signals in received collision signals according to signals received by a receiving end satellite in an lth time slot; calculating the optimal direction of the auxiliary beam generated in the mth time slot, the optimal side lobe level of the Bayliss window function and the optimal auxiliary beam gain peak limit variable; and generating an auxiliary beam directional diagram according to the beam forming parameters, the optimal direction of the auxiliary beam, the optimal sidelobe level of the Bayliss window function and the optimal auxiliary beam gain peak limit variable. By increasing the number of main lobe roll-off bands and optimizing the antenna pattern receiving direction, the space-based communication system capacity is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite communications, and in particular relates to a beamforming optimization method for space-based Internet of Things access. Background Art

[0002] Due to geographical conditions and economic cost constraints, ground-based IoT networks cannot provide communication services to remote areas such as deserts, oceans and forests. Low-orbit satellite IoT, with its global seamless coverage, strong anti-damage capability and low transmission delay, has become an important part of the future 6G integrated space, land, and sea intelligent connection of all things. However, due to the wide coverage of satellite IoT systems and the short burst characteristics of data collection services, terminals need to meet the characteristics of low power consumption and light control. This leads to a contradiction between terminal access constraints and the demand for large-capacity non-orthogonal multiple access, which makes it easy for a large number of data packets to collide, resulting in a rapid drop in system throughput.

[0003] There are many types of IoT services, but the ones suitable for satellite IoT services mainly include data collection and unmanned control services. At a transmission distance of hundreds of kilometers in low-orbit satellites, both power consumption and latency are significantly greater than those of ground networks, and a large number of data collection services have short burst characteristics. Conventional access methods based on resource reservation have extremely low resource utilization, while access methods based on dynamic applications have large signaling overhead, and latency and power consumption do not meet the requirements of satellite IoT. Random access technology seizes resources in a competitive manner, and users do not need to schedule, which improves system transmission efficiency and resource utilization, and can better adapt to satellite IoT short data packet burst services. However, due to the random transmission characteristics of random access terminals, when the load gradually increases, multiple terminals will access the system at the same time, and data packets at satellite access points will collide. However, the near-far effect of different terminals is not obvious, power control is not applicable, and collision signal separation conditions cannot be achieved, resulting in a sharp drop in actual throughput at medium and high loads. Therefore, how to create collision signal separation conditions, increase the probability of successful reception of collision data packets, and thus increase system throughput is a practical need to promote the application of satellite IoT. Summary of the invention

[0004] The main purpose of the present invention is to provide a beamforming optimization method for space-based Internet of Things access, which increases the number of main lobe roll-off bands, increases the success rate of collision signal separation as much as possible, improves system access performance, and effectively supports the large-capacity random access requirements of satellite Internet of Things terminals.

[0005] To achieve the above objectives, the present invention provides a beamforming optimization method for space-based Internet of Things access, comprising the following steps: Step 1: The receiving end satellite The signal received in the time slot is ,in, , is the total number of time slots, and then collision signal detection is performed on each time slot. The collision signal received by the time slot with a collision signal is ,in, ; Estimation of collision signals received by satellite The direction of arrival of all signals in ,in, , To detect the The number of collision signals in a time slot; Step 2: The direction of arrival of the collision signal estimated in step 1 , calculate the Optimal pointing direction of auxiliary beams generated by time slots , Bayliss window function optimal sidelobe level and the optimal auxiliary beam gain peak limit variable ; Step 3: Receive the optimal pointing direction of the auxiliary beam according to the system main channel receiving beamforming parameters and step 2 , Bayliss window function optimal sidelobe level and the optimal auxiliary beam gain peak limit variable Generate auxiliary beam patterns ; A further improvement of the present invention is that step 1 further comprises the following steps: Step 11: According to the signal received by the main channel receiving beam array of the system Snapshot data, sampling the received signal can be obtained: , in, for array data vector, , for array noise vector, ; is the signal complex envelope vector, , For the The complex envelope of a source, is the array manifold matrix, ,in, For the The steering vector of each source; Step 12, according to The estimated value of the covariance matrix of the received signal vector is: ; in, for array data vector, represents the conjugate transpose of a matrix; Step 13: Calculate the beamforming weights based on the covariance matrix calculated in step 12 As shown below: in, is the estimate of the covariance matrix, is the orientation vector, is the direction of arrival estimation variable, represents the inverse of the matrix, represents the conjugate transpose of a matrix; Step 14, the array space spectrum function is:

[0006] in, is the estimate of the covariance matrix, is the orientation vector, is the direction of arrival estimation variable, represents the inverse of the matrix, represents the conjugate transpose of the matrix, Perform a traversal search to find the peaks ; Step 15: align the peaks according to step 14 Search to get the collision signal The estimated direction of arrival of all signals in .

[0007] A further improvement of the present invention is that in step 14, Perform a traversal search to find the peaks The specific steps are as follows: Step a, with step length right Traverse and calculate the indivual The corresponding array space spectrum value ; Step b, set the decision threshold of the spatial spectrum to ,like , then execute step c, otherwise, is a non-spectral peak; Step c: sequentially calculate the spatial spectrum values ​​exceeding the decision threshold in step b. Three adjacent values , and For comparison, if and ,but is the turning point, is the spectrum peak value, output the current ,otherwise, is a non-inflection point, Non-spectral peak.

[0008] A further improvement of the present invention is that step 2 further comprises the following steps: Step 21: Based on the arrival direction of the collision data packet The carrier-to-noise ratio of the collision signal in the auxiliary channel is calculated using the intermediate value of the power and power gain ratio. , expressed as: Among them, the auxiliary beam receiving power gain is , is the optimal auxiliary beam gain peak limiting variable, is the auxiliary beam steering vector, is the auxiliary beam array weight vector, is the conventional beam steering vector, is the beam pointing of the auxiliary beam, represents the conjugate transpose of a matrix; The auxiliary beam The receiving gain of a strong collision signal, The auxiliary beam The receiving gain of a weaker collision signal; is the auxiliary channel noise power estimate; the main beam receiving gain is , The main channel receives the expected signal direction of the beam. The first The receiving gain of the collision signal is The first The receiving gain of the collision signal; In the main channel, the corresponding The signal-to-noise ratio of the collision signal estimate, In the main channel, the corresponding The signal-to-noise ratio of the collision signal estimate, is the estimated value of the noise power of the collision signal in the main channel; is the weight expression of the Bayliss window function, as shown below: in, The expression is as follows: in, The expression is as follows:

[0009] is the intermediate variable used to calculate the weight, where is the number of equal sidelobe levels, is the number of array elements, ,in For the sidelobe level, Bayliss gave a fourth-order polynomial coefficient, are the polynomial coefficients; Step 22: using the collision signal carrier-to-noise ratio in step 21 Maximization is the goal, and a mathematical model is established to solve the Optimal pointing of auxiliary beams in time slots , Bayliss window function optimal sidelobe level and the optimal auxiliary beam gain peak limit variable , expressed as:

[0010]

[0011]

[0012]

[0013]

[0014] in, For the The number of packets colliding in each time slot, is the set of collision signal pair numbers, is the demodulation threshold, is the main lobe width of the conventional beam; is the auxiliary beam sidelobe level R The lower limit of the constraint, is the auxiliary beam sidelobe level The upper limit of the constraint, is the auxiliary beam gain peak limit variable The lower limit of the constraint, is the auxiliary beam gain peak limit variable The optimization problem takes the maximum value of the strong signal carrier-to-interference-noise ratio as the optimization target. Constraint 1 is the constraint condition of the strong signal carrier-to-interference-noise ratio, that is, the strong signal carrier-to-interference-noise ratio must be greater than the demodulation threshold. Constraint 2 is the constraint condition of the auxiliary beam pointing, that is, the auxiliary beam pointing The change of must be within the main lobe width, constraint 3 is the constraint condition of the auxiliary beam sidelobe level, constraint 4 is the constraint condition of the auxiliary beam power gain peak, constraining its gain peak to be less than the main beam power gain peak; Step 23, for the mathematical model established in step 22, a particle swarm optimization algorithm is introduced to solve it.

[0015] A further improvement of the present invention is that step 23 is further implemented by the following steps: Step S1, the default problem in the particle swarm optimization algorithm is to minimize the collision signal carrier-to-noise ratio in step 21. The negative value of is mapped to the objective function ; Step S2, initialize the particle swarm and set the particle swarm size to , the maximum number of iterations is , each particle in the particle swarm There are three variables, namely auxiliary beam pointing , auxiliary beam sidelobe level and the auxiliary beam gain peak limit variable ,in, Indicates the number of the current particle. represents the number of iterations of the particle, , , create the initial particle swarm ; Step S3, assisting the beam pointing in step S2 The constraint is ,in, is the main lobe width of the conventional beam, and the side lobe level of the auxiliary beam The constraint is , auxiliary beam gain peak limit variable The constraint is , is the auxiliary beam sidelobe level R The lower limit of the constraint, is the auxiliary beam sidelobe level The upper limit of the constraint, is the auxiliary beam gain peak limit variable The lower limit of the constraint, is the auxiliary beam gain peak limit variable The upper limit of the constraint, the collision signal carrier-to-noise ratio in step S1 The negative value of is the objective function; Step S4, initializing the initial particle swarm created in step S2 The position of each particle in and speed , calculate the fitness function value corresponding to each particle ; Step S5, according to step S4, the fitness value of the particle is obtained as , initialize the local optimal position of the particle and the global optimal position ; Step S6, calculate the velocity of each particle in the iterative process of step S5 and location , the speed update formula is as follows:

[0016] in, is the inertia weight, which controls the search range of particles; is the local learning factor, which controls the step size of the particle approaching its local optimal position; is the global learning factor, which controls the step size of the particle approaching the global optimal position; are two random numbers to enhance the diversity of the search; the formula for position update is as follows: Step S7, calculate the fitness value of each particle under the conditions of step S5 and step S6 under the latest position ; Step S8: If the current number of iterations is less than the maximum number of iterations , set the termination threshold , based on the fitness function value calculated by the initial particle swarm in step S4 and the fitness function value calculated by the current new generation particle swarm in step S7 , determine whether the iteration converges: if , the iteration has not converged, and step S9 is executed; otherwise, the iteration has converged, and step S10 is executed; if the number of iterations reaches the maximum value , the iteration is terminated and step S10 is executed, where the termination threshold is Select of magnitude, Indicates taking the absolute value; Step S9, let the fitness function value , and return to step S5 to execute again; Step S10: Output the optimal fitness function value in the current particle swarm The negative value of The corresponding optimal individual .

[0017] A further improvement of the present invention is that step 3 further comprises the following steps: Step 31: receive beamforming parameters of the main channel of the system and the beam pointing of the auxiliary beam obtained in step 2 , Bayliss window function sidelobe level , get the array weight vector of the auxiliary beam ; Step 32, based on the auxiliary beam gain peak limit variable obtained in step 22 And the auxiliary beam optimal array weight vector obtained in step 31 obtains the optimal auxiliary beam pattern , expressed as: , in, is the optimal auxiliary beam gain peak limiting variable, is the auxiliary beam steering vector, , is the auxiliary beam array weight vector, is the conventional beam steering vector, is the beam pointing of the auxiliary beam, Indicates taking the absolute value, Represents the conjugate transpose of a matrix.

[0018] The beneficial effects of the present invention are as follows: on the one hand, the present invention utilizes the advantages of the auxiliary beam to create a difference in the signal-to-noise ratio of the received signal without the need for power control, thereby enabling multi-user detection in the satellite Internet of Things scenario; on the other hand, the present invention utilizes the auxiliary beam main lobe edge roll-off band to achieve a difference in the signal-to-noise ratio of the received signal, thereby satisfying the power domain separation conditions, increasing the probability of successful separation of the collision signal, improving the system access throughput, and reducing the system access packet loss rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of the auxiliary receiving beam pointing optimization process of the method of the present invention.

[0020] Figure 2 It is the auxiliary beam pattern optimized based on the Bayliss window function in the present invention.

[0021] Figure 3 It is a performance diagram of system throughput under different angular measurement errors in the present invention.

[0022] Figure 4 It is a performance diagram of system throughput under different amplitude and phase errors in the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] It should be noted here that in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the scheme of the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0025] In addition, it should be noted that the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or apparatus.

[0026] The beamforming optimization method for space-based Internet of Things access of the present invention mainly includes the following steps: Step 1: The receiving end satellite The signal received in the time slot is ,in, , is the total number of time slots, and then collision signal detection is performed on each time slot. The collision signal received by the time slot with a collision signal is ,in, ; Estimation of collision signals received by satellite The direction of arrival of all signals in ,in, , To detect the The number of collision signals in a time slot; Step 2: The direction of arrival of the collision signal estimated in step 1 , calculate the Optimal pointing direction of auxiliary beams generated by time slots , Bayliss window function optimal sidelobe level and the optimal auxiliary beam gain peak limit variable ; Step 3: Receive the optimal pointing direction of the auxiliary beam according to the system main channel receiving beamforming parameters and step 2 , Bayliss window function optimal sidelobe level and the optimal auxiliary beam gain peak limit variable Generate auxiliary beam patterns ; Combination Figures 1 to 4 The present invention is described in detail.

[0027] Step 1: The receiving end satellite The signal received in the time slot is ,in, , is the total number of time slots, and then collision signal detection is performed on each time slot. The collision signal received by the time slot with a collision signal is ,in, ; Estimation of collision signals received by satellite The direction of arrival of all signals in ,in, , To detect the The number of collision signals in a time slot

[0028] There are many DOA estimation methods in practical applications, such as the Capon algorithm and the MUSIC algorithm. Step 1 uses the Capon algorithm as an example to illustrate the specific process of DOA estimation. If conditions permit, other methods can also be used to estimate the DOA.

[0029] The specific steps in step 1 are as follows: Step 11: The main channel receiving beam of the system is , according to the received beam array Snapshot data, sampling the received signal can be obtained: , in, for array data vector, , for array noise vector, ; is the signal complex envelope vector, , For the The complex envelope of a source, is the array manifold matrix, ,in, For the The steering vector of each source; Step 12, according to The estimated value of the covariance matrix of the received signal vector is: ; in, for array data vector, represents the conjugate transpose of a matrix; Step 13: Calculate the beamforming weights based on the covariance matrix calculated in step 12 As shown below:

[0030] in, is the estimate of the covariance matrix, is the orientation vector, is the direction of arrival estimation variable, represents the inverse of the matrix, represents the conjugate transpose of a matrix; Step 14, the array space spectrum function is:

[0031] in, is the estimate of the covariance matrix, is the orientation vector, is the direction of arrival estimation variable, represents the inverse of the matrix, represents the conjugate transpose of the matrix, Perform a traversal search to find the peaks ; Step 15: align the peaks according to step 14 Search to get the collision signal The estimated direction of arrival of all signals in In step 14, Perform a traversal search to find the peaks The specific steps are as follows: Step a, with step length right Traverse and calculate the indivual The corresponding array space spectrum value ; Step b, set the decision threshold of the spatial spectrum to ,like , then execute step c, otherwise, is a non-spectral peak; Step c: sequentially calculate the spatial spectrum values ​​exceeding the decision threshold in step b. Three adjacent values , and For comparison, if and ,but is the turning point, is the spectrum peak value, output the current ,otherwise, is a non-inflection point, Non-spectral peak.

[0032] Step 2: The direction of arrival of the collision signal estimated in step 1 , calculate the Optimal pointing direction of auxiliary beams generated by time slots , Bayliss window function optimal sidelobe level and the optimal auxiliary beam gain peak limit variable .

[0033] There are many methods for solving optimization problems in practical applications, such as genetic algorithm, particle swarm optimization algorithm, ant colony algorithm, etc. This step uses the particle swarm optimization algorithm as an example to illustrate the specific process of solving the optimal pointing of the auxiliary beam and the maximum collision signal carrier-to-interference-noise ratio. If conditions permit, other methods can also be used for solving.

[0034] The specific implementation of this step is as follows: Step 21: Based on the arrival direction of the collision data packet The carrier-to-noise ratio of the collision signal in the auxiliary channel is calculated using the intermediate value of the power and power gain ratio. , which can be expressed as: Among them, the auxiliary beam receiving power gain is , is the optimal auxiliary beam gain peak limiting variable, is the auxiliary beam steering vector, is the auxiliary beam array weight vector, is the conventional beam steering vector, is the beam pointing of the auxiliary beam, represents the conjugate transpose of a matrix; The auxiliary beam The receiving gain of a strong collision signal, The auxiliary beam The receiving gain of a weaker collision signal. is the auxiliary channel noise power estimate. The main beam receiving gain is , The main channel receives the expected signal direction of the beam. The first The receiving gain of the collision signal is The first The receiving gain of the collision signal. In the main channel, the corresponding The signal-to-noise ratio of the collision signal estimate, In the main channel, the corresponding The signal-to-noise ratio of the collision signal estimate, is the estimated value of the noise power of the collision signal in the main channel. is the weight expression of the Bayliss window function, as shown below: in, The expression is as follows: in, The expression is as follows: is the intermediate variable used to calculate the weight, where is the number of equal sidelobe levels, is the number of array elements, ,in For the sidelobe level, Bayliss gave a fourth-order polynomial coefficient, as shown in Table 1: Table 1 Bayliss directivity diagram Coefficients and sidelobe levels

[0035] in, are polynomial coefficients, and the corresponding coefficients are shown in Table 2: Table 2 Polynomial coefficients

[0036] Step 22: using the collision signal carrier-to-noise ratio in step 21 Maximization is the goal, and a mathematical model is established to solve the Optimal pointing of auxiliary beams in time slots , Bayliss window function optimal sidelobe level and the optimal auxiliary beam gain peak limit variable , which can be expressed as:

[0037]

[0038]

[0039]

[0040]

[0041] in, For the The number of packets colliding in each time slot, is the set of collision signal pair numbers, is the demodulation threshold, is the main lobe width of the conventional beam; is the auxiliary beam sidelobe levelR The lower limit of the constraint, is the auxiliary beam sidelobe level The upper limit of the constraint, is the auxiliary beam gain peak limit variable The lower limit of the constraint, is the auxiliary beam gain peak limit variable The optimization problem takes the maximum value of the strong signal carrier-to-interference-noise ratio as the optimization target. Constraint 1 is the constraint condition of the strong signal carrier-to-interference-noise ratio, that is, the strong signal carrier-to-interference-noise ratio must be greater than the demodulation threshold. Constraint 2 is the constraint condition of the auxiliary beam pointing, that is, the auxiliary beam pointing The change of must be within the main lobe width, constraint 3 is the constraint condition of the auxiliary beam sidelobe level, constraint 4 is the constraint condition of the auxiliary beam power gain peak, constraining its gain peak to be less than the main beam power gain peak; Step 23, for the mathematical model established in step 22, a particle swarm optimization algorithm is introduced to solve it, including the following steps: Step S1, the default problem in the particle swarm optimization algorithm is to minimize the collision signal carrier-to-noise ratio in step 21. The negative value of is mapped to the objective function ; Step S2, initialize the particle swarm and set the particle swarm size to , the maximum number of iterations is , each particle in the particle swarm There are three variables, namely auxiliary beam pointing , auxiliary beam sidelobe level and the auxiliary beam gain peak limit variable ,in, Indicates the number of the current particle. represents the number of iterations of the particle, , , create the initial particle swarm ; Step S3, assisting the beam pointing in step S2 The constraint is ,in, is the main lobe width of the conventional beam, and the side lobe level of the auxiliary beam The constraint is , auxiliary beam gain peak limit variable The constraint is , is the auxiliary beam sidelobe level R The lower limit of the constraint, is the auxiliary beam sidelobe level The upper limit of the constraint, is the auxiliary beam gain peak limit variable The lower limit of the constraint, is the auxiliary beam gain peak limit variable The upper limit of the constraint, the collision signal carrier-to-noise ratio in step S1 The negative value of is the objective function; Step S4, initializing the initial particle swarm created in step 2 The position of each particle in and speed , calculate the fitness function value corresponding to each particle ; Step S5, according to step S4, the fitness value of the particle is obtained as , initialize the local optimal position of the particle and the global optimal position ; Step S6, calculate the velocity of each particle in the iterative process of step S5 and location , the speed update formula is as follows:

[0042] in, is the inertia weight, which controls the search range of particles; is the local learning factor, which controls the step size of the particle approaching its local optimal position; is the global learning factor, which controls the step size of the particle approaching the global optimal position; are two random numbers to enhance the diversity of the search; the formula for position update is as follows: Step S7, calculate the fitness value of each particle under the conditions of step S5 and step S6 under the latest position ; Step S8: If the current number of iterations is less than the maximum number of iterations , set the termination threshold , based on the fitness function value calculated by the initial particle swarm in step S4 and the fitness function value calculated by the current new generation particle swarm in step S7 , determine whether the iteration converges: if , the iteration has not converged, and step S9 is executed; otherwise, the iteration has converged, and step S10 is executed; if the number of iterations reaches the maximum value , the iteration is terminated and step S10 is executed, where the termination threshold is Select of magnitude, Indicates taking the absolute value; Step S9, let the fitness function value , and return to step S5 to execute again; Step S10: Output the optimal fitness function value in the current particle swarm The negative value of The corresponding optimal individual .

[0043] Step 3: Receive the optimal pointing direction of the auxiliary beam according to the system main channel receiving beamforming parameters and step 2 , Bayliss window function optimal sidelobe level and the optimal auxiliary beam gain peak limit variable Generate auxiliary beam patterns .

[0044] Step 3 further comprises the following steps: Step 31: receive beamforming parameters of the main channel of the system and the beam pointing of the auxiliary beam obtained in step 22 , Bayliss window function sidelobe level , get the array weight vector of the auxiliary beam ; Step 32, based on the auxiliary beam gain peak limit variable obtained in step 22 And the auxiliary beam optimal array weight vector obtained in step 31 obtains the optimal auxiliary beam pattern , which can be expressed as: , in, is the optimal auxiliary beam gain peak limiting variable, is the auxiliary beam steering vector, , is the auxiliary beam array weight vector, is the conventional beam steering vector, is the beam pointing of the auxiliary beam, Indicates taking the absolute value, Represents the conjugate transpose of a matrix.

[0045] The effect of the present invention can be further verified by the following simulation.

[0046] Experimental scenario: The number of array elements of the Balissy window function is set to 32, the number of zero points in the sidelobe area is set to 4, and the sidelobe level is set to 30; the scenario sets the carrier frequency to 2 GHz, the bandwidth to 20 kHz, the number of conventional beam array elements to 32, the array element spacing to half the wavelength, the satellite-to-ground link distance to 1000 km, the equivalent noise temperature to 290 K, the terminal transmit power to -10 dBW, the terminal transmit gain to 0 dBi, and the carrier-to-interference-to-noise ratio thresholds for separation of collision signals to 8 dB, 9 dB, and 10 dB, respectively.

[0047] Experimental content and results: Experiment 1, generating auxiliary beam patterns based on weight optimization of Bayliss window function.

[0048] Figure 2 It is the auxiliary beam pattern generated after optimizing the weights of the Bayliss window function. It can be seen from the simulation results that compared with the conventional beam pattern, the antenna pattern optimized by the Bayliss window function forms a depression in the specified direction, which can increase the power difference between the useful signal and the collision signal, so that the collision signal can be successfully separated and received with a greater probability.

[0049] Experiment 2 verifies the system throughput performance under different angle measurement errors.

[0050] Figure 3 It is the performance of system throughput. From the simulation results, it can be seen that compared with the traditional time-slot ALOHA random access technology, the introduction of the auxiliary beam optimization method based on Bayliss window function weight optimization to separate collision signals can effectively improve the system throughput, and in the presence of angle measurement errors, there is still a high probability that the collision signal can be successfully separated and received.

[0051] Experiment 3 verifies the system throughput performance under different amplitude and phase errors.

[0052] Figure 4 It is the performance of system throughput. From the simulation results, it can be seen that compared with the traditional time-slot ALOHA random access technology, the introduction of the auxiliary beam optimization method based on Bayliss window function weight optimization to separate collision signals can effectively improve the system throughput, and in the presence of angle measurement errors, there is still a high probability that the collision signal can be successfully separated and received.

[0053] The above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A beamforming optimization method for space-based Internet of Things access, characterized in that: The following steps are involved: Step 1: The receiving end satellite The signal received in the time slot is ,in, , is the total number of time slots, and then collision signal detection is performed on each time slot. The collision signal received by the time slot with a collision signal is ,in, ; Estimation of collision signals received by satellite The direction of arrival of all signals in ,in, , To detect the The number of collision signals in a time slot; Step 2: The direction of arrival of the collision signal estimated in step 1 , calculate the Optimal pointing direction of auxiliary beams generated by time slots , Bayliss window function optimal sidelobe level and the optimal auxiliary beam gain peak limit variable ; Step 3: Receive the optimal pointing direction of the auxiliary beam according to the system main channel receiving beamforming parameters and step 2 , Bayliss window function optimal sidelobe level and the optimal auxiliary beam gain peak limit variable Generate auxiliary beam patterns .

2. The beamforming optimization method for space-based Internet of Things access according to claim 1 is characterized in that: Step 1 further comprises the following steps: Step 11: The main channel receiving beam of the system is , according to the uplink receiving beam array received Snapshot data, sampling the received signal can be obtained: ， in, for array data vector, , for array noise vector, ; is the signal complex envelope vector, , For the The complex envelope of a source, is the array manifold matrix, ,in, For the The steering vector of each source; Step 12, according to The estimated value of the covariance matrix of the received signal vector is: ； in, for array data vector, represents the conjugate transpose of a matrix; Step 13: Calculate the beamforming weights based on the covariance matrix calculated in step 12 As shown below: in, is the estimate of the covariance matrix, is the orientation vector, is the direction of arrival estimation variable, represents the inverse of the matrix, represents the conjugate transpose of a matrix; Step 14, the array space spectrum function is: in, is the estimate of the covariance matrix, is the orientation vector, is the direction of arrival estimation variable, represents the inverse of the matrix, represents the conjugate transpose of the matrix, Perform a traversal search to find the peaks ; Step 15: align the peaks according to step 14 Search to get the collision signal The estimated direction of arrival of all signals in .

3. The beamforming optimization method for space-based Internet of Things access according to claim 2 is characterized in that: Step 14 further includes the following steps: Step a, with step length right Traverse and calculate the indivual The corresponding array space spectrum value ; Step b, set the decision threshold of the spatial spectrum to ,like , then execute step c, otherwise, is a non-spectral peak; Step c: sequentially calculate the spatial spectrum values ​​exceeding the decision threshold in step b. Three adjacent values , and For comparison, if and ,but is the turning point, is the spectrum peak value, output the current ,otherwise, is a non-inflection point, Non-spectral peak.

4. The beamforming optimization method for space-based Internet of Things access according to claim 1, characterized in that: Step 2 further comprises the following steps: Step 21: Based on the arrival direction of the collision data packet The carrier-to-noise ratio of the collision signal in the auxiliary channel is calculated using the intermediate value of the power and power gain ratio. , expressed as: Among them, the auxiliary beam receiving power gain is , is the optimal auxiliary beam gain peak limiting variable, is the auxiliary beam steering vector, is the auxiliary beam array weight vector, is the conventional beam steering vector, is the beam pointing of the auxiliary beam, represents the conjugate transpose of a matrix; The auxiliary beam The receiving gain of a strong collision signal, The auxiliary beam The receiving gain of a weaker collision signal; is the auxiliary channel noise power estimate; the main beam receiving gain is , The main channel receives the expected signal direction of the beam. The first The receiving gain of the collision signal is The first The receiving gain of the collision signal; In the main channel, the corresponding The signal-to-noise ratio of the collision signal estimate, In the main channel, the corresponding The signal-to-noise ratio of the collision signal estimate, is the estimated value of the noise power of the collision signal in the main channel; is the weight expression of the Bayliss window function, as shown below: in, The expression is as follows: in, The expression is as follows: is the intermediate variable used to calculate the weight, where is the number of equal sidelobe levels, is the number of array elements, ,in For the sidelobe level, Bayliss gave a fourth-order polynomial coefficient, are the polynomial coefficients; Step 22: using the collision signal carrier-to-noise ratio in step 21 Maximization is the goal, and a mathematical model is established to solve the Optimal pointing of auxiliary beams in time slots , Bayliss window function optimal sidelobe level and the optimal auxiliary beam gain peak limit variable , expressed as: in, For the The number of packets colliding in each time slot, is the set of collision signal pair numbers, is the demodulation threshold, is the main lobe width of the conventional beam; is the auxiliary beam sidelobe level R The lower limit of the constraint, is the auxiliary beam sidelobe level The upper limit of the constraint, is the auxiliary beam gain peak limit variable The lower limit of the constraint, is the auxiliary beam gain peak limit variable The optimization problem takes the maximum value of the strong signal carrier-to-interference-noise ratio as the optimization target. Constraint 1 is the constraint condition of the strong signal carrier-to-interference-noise ratio, that is, the strong signal carrier-to-interference-noise ratio must be greater than the demodulation threshold. Constraint 2 is the constraint condition of the auxiliary beam pointing, that is, the auxiliary beam pointing The change of must be within the main lobe width, constraint 3 is the constraint condition of the auxiliary beam sidelobe level, constraint 4 is the constraint condition of the auxiliary beam power gain peak, constraining its gain peak to be less than the main beam power gain peak; Step 23, for the mathematical model established in step 22, a particle swarm optimization algorithm is introduced to solve it.

5. The beamforming optimization method for space-based Internet of Things access according to claim 4 is characterized in that: Step 23 is further implemented by the following steps: Step S1, the default problem in the particle swarm optimization algorithm is to minimize the collision signal carrier-to-noise ratio in step 21. The negative value of is mapped to the objective function ; Step S2, initialize the particle swarm and set the particle swarm size to , the maximum number of iterations is , each particle in the particle swarm There are three variables, namely auxiliary beam pointing , auxiliary beam sidelobe level and the auxiliary beam gain peak limit variable ,in, Indicates the number of the current particle. represents the number of iterations of the particle, , , create the initial particle swarm ; Step S3, assisting the beam pointing in step S2 The constraint is ,in, is the main lobe width of the conventional beam, and the side lobe level of the auxiliary beam The constraint is , auxiliary beam gain peak limit variable The constraint is , is the auxiliary beam sidelobe level R The lower limit of the constraint, is the auxiliary beam sidelobe level The upper limit of the constraint, is the auxiliary beam gain peak limit variable The lower limit of the constraint, is the auxiliary beam gain peak limit variable The upper limit of the constraint, the collision signal carrier-to-noise ratio in step S1 The negative value of is the objective function; Step S4, initializing the initial particle swarm created in step 2 The position of each particle in and speed , calculate the fitness function value corresponding to each particle ; Step S5, according to step S4, the fitness value of the particle is obtained as , initialize the local optimal position of the particle and the global optimal position ; Step S6, calculate the velocity of each particle in the iterative process of step S5 and location , the speed update formula is as follows: in, is the inertia weight, which controls the search range of particles; is the local learning factor, which controls the step size of the particle approaching its local optimal position; is the global learning factor, which controls the step size of the particle approaching the global optimal position; are two random numbers to enhance the diversity of the search; the formula for position update is as follows: Step S7, calculate the fitness value of each particle under the conditions of step S5 and step S6 under the latest position ; Step S8: If the current number of iterations is less than the maximum number of iterations , set the termination threshold , based on the fitness function value calculated by the initial particle swarm in step S4 and the fitness function value calculated by the current new generation particle swarm in step S7 , determine whether the iteration converges: if , the iteration has not converged, and step S9 is executed; otherwise, the iteration has converged, and step S10 is executed; if the number of iterations reaches the maximum value , the iteration is terminated and step S10 is executed, where the termination threshold is Select of magnitude, Indicates taking the absolute value; Step S9, let the fitness function value , and return to step S5 to execute again; Step S10: Output the optimal fitness function value in the current particle swarm The negative value of The corresponding optimal individual .

6. The beamforming optimization method for space-based Internet of Things access according to claim 1, characterized in that: Step 3 further comprises the following steps: Step 31: receive beamforming parameters of the main channel of the system and the beam pointing of the auxiliary beam obtained in step 22 , Bayliss window function sidelobe level , get the array weight vector of the auxiliary beam ; Step 32, based on the auxiliary beam gain peak limit variable obtained in step 22 And the auxiliary beam optimal array weight vector obtained in step 31 obtains the optimal auxiliary beam pattern , expressed as: ， in, is the optimal auxiliary beam gain peak limiting variable, is the auxiliary beam steering vector, , is the auxiliary beam array weight vector, is the conventional beam steering vector, is the beam pointing of the auxiliary beam, Indicates taking the absolute value, Represents the conjugate transpose of a matrix.

Images