Multi-beam antenna power control method

Abstract

The invention discloses a multi-beam antenna power control method, and relates to the technical field of antenna power control, and the method comprises the steps: carrying out the time sequence prediction of a user demand through an ARIMA model, decomposing a user demand sequence into an autoregression part and a moving average part, and obtaining the demand trend of a plurality of time slots in the future through recursion prediction in combination with a long-term mean value, the sensitivity to demand fluctuation is improved, and the problem of insufficient response to instantaneous demand change in a traditional method is solved; secondly, on the basis of a prediction result, a joint model of power distribution and time delay optimization is constructed, service quality, power consumption limitation and user fairness are brought into a unified framework, meanwhile, balance of time delay and power consumption is achieved by dynamically adjusting weights, the rationality of resource distribution is improved, and limitation of single-beam optimization is avoided; in addition, the optimization model is iteratively solved through a gradient descent method, a resource allocation strategy is dynamically generated, and resources are preferentially allocated to a high-demand area.

Description

Technical Field

[0001] The present invention relates to the technical field of antenna power control, and particularly to a multi-beam antenna power control method. Background Art

[0002] In a low-earth orbit satellite communication system, the multi-beam antenna technology can provide high-quality coverage in multiple directions simultaneously, and has become an important means to meet global communication needs.

[0003] In practical applications, user demands mostly show dynamic changes. The communication load in some areas may increase suddenly, while the resources in other areas are idle. The unbalanced distribution problem is one of the influencing factors for beam resource scheduling and power allocation effects. Traditional power control methods rely on preset power allocation strategies and lack the ability to quickly respond to instantaneous demand changes, making it difficult to balance system efficiency and user experience in a high-dynamic environment. At the same time, due to the relatively low orbit altitude of low-earth orbit satellites, there is a short available time window for the channel. This means that when user traffic surges, if power resources cannot be allocated in time, it will not only lead to a decline in the service quality of users in hot spots, but also affect the energy efficiency of the entire system due to excessive power consumption. Traditional power control methods mainly focus on individual beams and lack global perspective power resource adjustment, and cannot effectively cope with resource bottlenecks in high-dynamic communication scenarios.

[0004] To alleviate these problems, some traditional solutions optimize power allocation through resource reallocation strategies, such as giving priority to ensuring the power of high-demand beams. However, this method lacks the control of the balance between delay and energy efficiency, and the performance improvement is limited. With the further growth and complexity of demands, exploring a power control method that can dynamically adapt to user demand changes while taking into account the balance between delay and energy consumption is the core issue in the optimization of current multi-beam antenna technology. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides a multi-beam antenna power control method to solve the problems in low-earth orbit satellite communication, where dynamic changes in user demands lead to uneven load distribution, traditional power allocation strategies lack the ability to quickly respond, and it is difficult to balance delay and energy efficiency. At the same time, the communication window is short and the resource bottleneck problem is significant, and the single-beam optimization method cannot meet the global dynamic adjustment requirements.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: An embodiment of the present invention provides a multi-beam antenna power control method, which includes: Step S1, the satellite collects user information, including location information, channel state information, and queue length data; Step S2: Based on the user information, perform time series prediction on the user demands in multiple future scheduling time slots through an autoregressive integrated moving average (ARIMA) model to obtain a prediction result, where the prediction result includes the total demand within the beam coverage area and the specific demands of different users for resources; Step S3: On the basis of the prediction result, construct a joint optimization model for power allocation and delay optimization. The joint optimization model combines quality of service requirements, power consumption limitations, and user fairness constraints, and solves the optimization model to generate a resource allocation strategy; Step S4: Adjust the beam direction and power allocation according to the resource allocation strategy; Step S5: After the adjustment in Step S4 is completed, monitor the network performance metrics, including delay, throughput, and power consumption, evaluate the effect of the resource allocation strategy in Step S3 in combination with the performance metrics, and use the evaluation result as feedback to input into the ARIMA model and the optimization model in Step S3 to correct the resource allocation strategy.

[0008] As a preferred solution of the multi-beam antenna power control method described in the present invention, wherein: the step of performing time series prediction on the user demands in multiple future scheduling time slots through the autoregressive integrated moving average (ARIMA) model is as follows: Define the user demand sequence as , where is the demand sequence at the current moment, is the demand quantity in the past time slots, is the current time slot number, is the number of historical time slots used; Perform autoregressive part modeling. The autoregressive part calculation formula is: , where is the autoregressive part prediction value, is the autoregressive order, is the autoregressive coefficient, is the actual demand value at time , is the current autoregressive order, from to , Perform moving average part modeling. The moving average part calculation formula is: , where is the moving average part prediction value, is the moving average order, is the moving average coefficient, is the residual at time , is the current moving average order, from to .

[0009] As a preferred embodiment of the multi-beam antenna power control method according to the present invention, wherein: the step of performing time series prediction further includes performing comprehensive prediction, and the comprehensive prediction formula is: , wherein is the predicted demand at time , is the long-term mean of the demand sequence, recursively predicts the future time slots, and the prediction formula is: , wherein is the predicted demand sequence for the future time slots, is the predicted value for the th time slot in the future, is the number of time slots for prediction, is the current predicted time slot number, from to ; calculate the total demand, and the formula for the total demand within the beam coverage area is: , wherein is the total demand, is the total number of users covered by the beam, is the th user's predicted demand.

[0010] As a preferred embodiment of the multi-beam antenna power control method according to the present invention, wherein: the step of constructing the joint optimization model of power allocation and delay optimization is construct the objective function, and the objective function is: , wherein is the objective function value, is the dynamic weight parameter, is the power allocation for the th user, is the demand of the th user, is the delay of the th user, is the total number of users covered by the beam, is the current user number, from to , Define the constraint conditions that the optimization model needs to satisfy: , wherein, is the power limit, is the maximum delay limit, is the power allocation fairness index, is the fairness threshold.

[0011] As a preferred solution of the multi-beam antenna power control method described in the present invention, wherein: the step of solving the optimization model to generate the resource allocation strategy is, Perform iterative optimization and use the gradient descent method to iteratively solve. The calculation formula is: , wherein, are the power allocations of the current and the next round of iteration, is the current iteration round, is the learning rate, is the gradient of the objective function with respect to the power allocation; Solve and output the resource allocation strategy. The optimal power allocation strategy is: , wherein, is the set of optimal power allocations, is the th user's optimal power allocation.

[0012] As a preferred solution of the multi-beam antenna power control method described in the present invention, wherein: in the joint optimization model, the weight is dynamically adjusted. The weight adjustment formula is: , wherein, is the current average delay, is the target delay, is the adjustment coefficient.

[0013] As a preferred solution of the multi-beam antenna power control method described in the present invention, wherein: in step S3, a weight parameter is introduced to dynamically adjust the balance between power efficiency and delay.

[0014] As a preferred solution of the multi-beam antenna power control method described in the present invention, wherein: the step of combining the performance index to evaluate the effect of the resource allocation strategy in step S3, taking the evaluation result as feedback, inputting it into the ARIMA model and the optimization model in step S3, and correcting the resource allocation strategy is, Record performance metrics. The set of network performance metrics is as follows: , Among them, is the observed delay, is the observed throughput, is the observed power consumption.

[0015] As a preferred solution of the multi-beam antenna power control method described in the present invention, where: the step of combining the performance metric evaluation step S3 to evaluate the effect of the resource allocation strategy, and using the evaluation result as feedback and inputting it into the ARIMA model and the optimization model of step S3, the step of correcting the resource allocation strategy further includes, Calculate the evaluation effect. The evaluation formula is: , Among them, is the evaluation value, is the dynamic weight parameter, is the network performance metric, Calculate the feedback error. The feedback error is: , Among them, is the feedback error, is the target evaluation value, is the current evaluation value.

[0016] As a preferred solution of the multi-beam antenna power control method described in the present invention, where: in step S5, finally, according to the error correction requirement prediction, the correction formula is: , Among them, is the corrected demand prediction, is the original predicted demand, is the correction coefficient, Update the optimization model. The corrected optimization update formula is: , Among them, is the learning rate of feedback update, is the feedback error.

[0017] The beneficial effects of the present invention are as follows: In the present invention, the ARIMA model is used to perform time series prediction on user requirements. The user requirement sequence is decomposed into autoregressive and moving average parts, and combined with the long-term mean. Through recursive prediction, the demand trends for multiple future time slots are obtained, enhancing the sensitivity to demand fluctuations and solving the problem of insufficient response to instantaneous demand changes in traditional methods. Secondly, based on the prediction results, a joint model for power allocation and delay optimization is constructed, incorporating quality of service, power consumption constraints, and user fairness into a unified framework. At the same time, the balance between delay and power consumption is achieved by dynamically adjusting weights, improving the rationality of resource allocation and avoiding the limitations of single-beam optimization. In addition, the optimization model is iteratively solved by the gradient descent method to dynamically generate resource allocation strategies, preferentially allocating resources to high-demand areas, and weighing different performance objectives through dynamic weight adjustment, taking into account the quality of service in hot spots and the overall energy efficiency of the system. At the same time, a real-time monitoring and feedback mechanism is introduced, using network performance indicators as feedback information to dynamically correct demand prediction and optimization model parameters, improving resource utilization, quality of service, and overall system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0019] Figure 1 It is a flowchart of the multi-beam antenna power control method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will give a detailed description of the specific embodiments of the present invention with reference to the drawings in the specification.

[0021] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0022] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that mutually excludes other embodiments.

[0023] Example 1, refer to Figure 1, this embodiment provides a multi-beam antenna power control method, including the following steps: Step S1, the satellite collects user information, including location information, channel state information, and queue length data; Step S2, based on the user information, use the autoregressive integrated moving average (ARIMA) model to perform time series prediction on the user demands in multiple future scheduling time slots, and obtain the prediction results. The prediction results include the total demand within the beam coverage area and the specific demands of different users for resources; The step of performing time series prediction on the user demands in multiple future scheduling time slots by using the autoregressive integrated moving average (ARIMA) model is as follows: Define the user demand sequence as , where is the demand sequence at the current moment, is the demand in the past time slots, is the current time slot number, is the number of historical time slots used; Perform autoregressive part modeling. The autoregressive part calculation formula is: , where is the autoregressive part prediction value, is the autoregressive order, is the autoregressive coefficient, is the actual demand value at time , is the current autoregressive order, from to , Perform moving average part modeling. The moving average part calculation formula is: , where is the moving average part prediction value, is the moving average order, is the moving average coefficient, is the residual at time , is the current moving average order, from to ; The step of performing time series prediction also includes Performing comprehensive prediction. The comprehensive prediction formula is: , where is the predicted demand at time , is the long-term average value of the demand sequence, Recursively predict the future time slots, and the prediction formula is: , where is the predicted demand sequence for the future time slots, is the predicted value for the th time slot in the future, is the number of time slots for prediction, is the current time slot number for prediction, starting from to ; Calculate the total demand. The formula for the total demand within the beam coverage area is: , where is the total demand, is the total number of users covered by the beam, is the predicted demand for the th user; Specifically, the ARIMA model combines the autoregressive and moving average parts with the long-term mean to construct a user demand prediction model, and obtains the demand trend within multiple future time slots through recursive prediction.

[0024] Step S3, based on the prediction results, construct a joint optimization model for power allocation and delay optimization. The joint optimization model combines the quality of service requirements, power consumption limitations, and user fairness constraints, and solves the optimization model to generate a resource allocation strategy; The steps to construct a joint optimization model for power allocation and delay optimization are as follows: Construct the objective function. The objective function is: , where is the objective function value, is the dynamic weight parameter, is the power allocation for the th user, is the demand for the th user, is the delay for the th user, is the total number of users covered by the beam, is the current user number, starting from to , Define the constraint conditions. The optimization model needs to satisfy: , where is the power limit, is the maximum delay limit, is the power allocation fairness index, is the fairness threshold; The steps to solve the optimization model to generate the resource allocation strategy are as follows. Perform iterative optimization and use the gradient descent method for iterative solution. The calculation formula is: , where are the power allocations for the current and the next round of iteration, is the current iteration round, is the learning rate, is the gradient of the objective function with respect to the power allocation; Solve and output the resource allocation strategy. The optimal power allocation strategy is: , where is the set of optimal power allocations, is the th user's optimal power allocation; In the joint optimization model, dynamically adjust the weights. The weight adjustment formula is: , where is the current average delay, is the target delay, is the adjustment coefficient, Specifically, through dynamic weight and gradient optimization, balance between power consumption and delay, and combine with the constraint conditions to generate a resource allocation strategy that meets the quality of service requirements.

[0025] In step S3, introduce weight parameters to dynamically adjust the balance between power efficiency and delay; In step S4, adjust the beam direction and power allocation according to the resource allocation strategy; In step S5, after the adjustment in step S4 is completed, monitor the network performance metrics, including delay, throughput, and power consumption, evaluate the effect of the resource allocation strategy in step S3 in combination with the performance metrics, and use the evaluation result as feedback to input into the ARIMA model and the optimization model in step S3 to correct the resource allocation strategy; The steps to evaluate the effect of the resource allocation strategy in step S3 in combination with the performance metrics, and use the evaluation result as feedback to input into the ARIMA model and the optimization model in step S3 to correct the resource allocation strategy are as follows. Record the performance metrics. The network performance metric set is: , where The observed time delay The observed throughput The observed power consumption Combining the performance metrics to evaluate the effect of the resource allocation strategy in step S3, and taking the evaluation result as feedback, inputting it into the ARIMA model and the optimization model in step S3. The steps to correct the resource allocation strategy also include Calculating the evaluation effect, and the evaluation formula is where is the evaluation value is the dynamic weight parameter is the network performance metric Calculating the feedback error, and the feedback error is where is the feedback error is the target evaluation value is the current evaluation value In step S5, finally, predict according to the error correction requirement, and the correction formula is where is the corrected demand prediction is the original predicted demand is the correction coefficient Updating the optimization model, and the corrected optimization update formula is where is the learning rate of feedback update is the feedback error Specifically, based on the real-time monitored performance metrics, combining the feedback mechanism to dynamically adjust the demand prediction and the resource allocation strategy, enhancing the adaptability and robustness of the resource allocation strategy; the learning rate differentiates to effectively ensure the independence of model optimization and feedback update, avoiding conflicts with each other.

[0026] ​​​​In summary, for the present invention, an ARIMA model is used to perform time series prediction on user requirements. The user requirement sequence is decomposed into autoregressive and moving average parts, and combined with the long-term mean. Through recursive prediction, the demand trends for multiple future time slots are obtained, enhancing the sensitivity to demand fluctuations and solving the problem of insufficient response to instantaneous demand changes in traditional methods. Secondly, based on the prediction results, a joint model for power allocation and delay optimization is constructed, incorporating quality of service, power consumption constraints, and user fairness into a unified framework. At the same time, the balance between delay and power consumption is achieved by dynamically adjusting the weights, improving the rationality of resource allocation and avoiding the limitations of single-beam optimization. In addition, the optimization model is iteratively solved by the gradient descent method to dynamically generate a resource allocation strategy, preferentially allocating resources to high-demand areas, and weighing different performance objectives through dynamic weight adjustment, taking into account the quality of service in hot spots and the overall energy efficiency of the system. At the same time, a real-time monitoring and feedback mechanism is introduced, using network performance indicators as feedback information to dynamically correct the demand prediction and optimization model parameters, improving resource utilization, quality of service, and overall system performance.

[0027] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A multi-beam antenna power control method, characterized in that: including, Step S1, the satellite collects user information, including location information, channel state information, and queue length data; Step S2, based on the user information, the autoregressive integrated moving average (ARIMA) model is used to perform time series prediction on the user demands within multiple future scheduling time slots, and a prediction result is obtained. The prediction result includes the total demand within the beam coverage area and the specific demands of different users for resources; Step S3, based on the prediction result, a joint optimization model for power allocation and delay optimization is constructed. The joint optimization model combines the quality of service requirements, power consumption limitations, and user fairness constraints, and solves the optimization model to generate a resource allocation strategy; Step S4, adjusts the beam direction and power allocation according to the resource allocation strategy; Step S5, after the adjustment in Step S4 is completed, monitor the network performance metrics, including delay, throughput, and power consumption, evaluate the effect of the resource allocation strategy in Step S3 in combination with the performance metrics, and use the evaluation result as feedback to input into the ARIMA model and the optimization model in Step S3 to correct the resource allocation strategy.

2. The multi-beam antenna power control method according to claim 1, wherein: The step of performing time series prediction on the user demands within multiple future scheduling time slots through the autoregressive integrated moving average (ARIMA) model is as follows: Define the user demand sequence as , where is the demand sequence at the current moment, is the demand volume in the past time slots, is the current time slot number, is the number of historical time slots used; Perform autoregressive part modeling. The autoregressive part calculation formula is: ， Among them, is the predicted value of the autoregressive part, is the autoregressive order, is the autoregressive coefficient, is the time of the actual demand value, is the current autoregressive order, from to , Perform moving average part modeling. The moving average part calculation formula is: ， Among them, is the predicted value of the moving average part, is the order of the moving average, is the coefficient of the moving average, is the time residual, is the current moving average order, from to .

3. The multi-beam antenna power control method according to claim 2, characterized in that: The step of performing time series prediction further includes: Perform comprehensive prediction. The comprehensive prediction formula is: ， wherein, is the predicted demand at time , and is the long-term mean of the demand sequence For the future Recursively predict the following ， Among them, is the predicted demand sequence for the future time slots, is the predicted value for the th time slot in the future, is the number of predicted time slots, is the current predicted time slot number, from to ; Calculate the total demand. The calculation formula for the total demand within the beam coverage area is: ， Among them, is the total demand, is the total number of users covered by the beam, is the predicted demand of the 4. The multi-beam antenna power control method according to claim 3, characterized in that: The step of constructing the joint optimization model for power allocation and delay optimization is as follows: Construct the objective function. The objective function is: ， Among them, is the objective function value, is the dynamic weight parameter, is the power allocation of the th user, is the demand of the th user, is the delay of the th user, is the total number of users covered by the beam, is the current user number, from to , Define the constraint conditions. The optimization model needs to satisfy: ， Among them, is the power limit, is the maximum delay limit, is the power allocation fairness index, is the fairness threshold.

5. The multi-beam antenna power control method according to claim 4, characterized in that: The step of solving the optimization model to generate a resource allocation strategy is as follows: Perform iterative optimization, and use the gradient descent method for iterative solution. The calculation formula is: ， Among them, is the power allocation for the current and next iteration, is the current iteration round, is the learning rate, is the gradient of the objective function with respect to the power allocation; Solve and output the resource allocation strategy. The optimal power allocation strategy is: ， Among them, is the optimal power allocation set, is the optimal power allocation for the -th user.

6. The multi-beam antenna power control method according to claim 5, characterized in that: In the joint optimization model, dynamically adjust the weight. The weight adjustment formula is: ， Wherein, is the current average latency, is the target latency, is the adjustment coefficient.

7. The multi-beam antenna power control method according to claim 6, wherein: In Step S3, introduce a weight parameter to dynamically adjust the balance between power efficiency and delay.

8. The multi-beam antenna power control method according to claim 7, characterized in that: The step of evaluating the effect of the resource allocation strategy in Step S3 in combination with the performance metrics, using the evaluation result as feedback to input into the ARIMA model and the optimization model in Step S3, and correcting the resource allocation strategy is as follows: Record the performance metrics. The network performance metric set is: ， Among them, is the observed time delay, is the observed throughput, is the observed power consumption.

9. The multi-beam antenna power control method according to claim 8, wherein: The step of evaluating the effect of the resource allocation strategy in Step S3 in combination with the performance metrics, using the evaluation result as feedback to input into the ARIMA model and the optimization model in Step S3, and correcting the resource allocation strategy further includes: Calculate the evaluation effect. The evaluation formula is: ， Among them, is the evaluation value, is the dynamic weight parameter, is the network performance index, Calculate the feedback error. The feedback error is: ， Wherein, is the feedback error, is the target evaluation value, is the current evaluation value.

10. A multi-beam antenna power control method according to claim 9, characterized in that: In Step S5, finally correct the demand prediction according to the error. The correction formula is: ， Among them, is the revised demand forecast, is the original forecast demand, is the correction coefficient, Update the optimization model. The corrected optimization update formula is: ， Among them, is the learning rate for feedback update, is the feedback error.

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