Spectrum resource allocation method and device, equipment, storage medium and program product

Abstract

The invention relates to a spectrum resource allocation method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring multi-dimensional state parameters of a coexistence system of a geostationary orbit and a non-geostationary orbit sharing the same frequency band; processing the multi-dimensional state parameters based on a preset spectrum demand calculation model, and predicting the spectrum demand of the coexistence system in the next time period; monitoring an aggregated interference value generated by the non-geostationary orbit system to the geostationary orbit system, and if the aggregated interference value exceeds a preset safety threshold value, determining a frequency spectrum division proportion of the geostationary orbit system and the non-geostationary orbit system based on the aggregated interference value and the safety threshold value; and according to the frequency spectrum division proportion and the frequency spectrum demand, performing frequency spectrum resource allocation for the geostationary orbit system and the non-geostationary orbit system in the next time period. By adopting the method, the reasonable allocation of the spectrum resources of the GSO and NGSO coexistence system can be realized, and the satellite communication service quality is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, and in particular to a spectrum resource allocation method and device, a computer device, a computer readable storage medium, and a computer program product. BACKGROUND

[0002] Currently, there are two important systems in the field of satellite communication, namely, geostationary orbit (GSO) and non-geostationary orbit (NGSO). When the GSO system and the NGSO system share the same frequency band for communication, the transmission of radio signals will cause mutual interference between the systems, and therefore it is necessary to ensure the optimal quality of service and the lowest risk of interference for the coexistence of the GSO and NGSO systems.

[0003] In related technologies, a static interference avoidance strategy is mainly used, that is, fixed frequency division and power limitation are used to reduce the interference between the GSO and NGSO systems. However, the states of the GSO and NGSO systems are dynamically changing, and this static interference avoidance strategy cannot adapt to this dynamic change characteristic, which easily leads to unreasonable allocation of spectrum resources and affects the quality of satellite communication services. SUMMARY

[0004] Therefore, it is necessary to provide a spectrum resource allocation method, device, computer device, computer readable storage medium, and computer program product capable of realizing reasonable allocation of spectrum resources for the coexistence of the GSO and NGSO systems and ensuring the quality of satellite communication services.

[0005] In a first aspect, the present application provides a spectrum resource allocation method, comprising:

[0006] obtaining a multi-dimensional state parameter of a coexistence system of a geostationary orbit and a non-geostationary orbit sharing the same frequency band; the multi-dimensional state parameter comprises a current average spectrum efficiency of the coexistence system, a proportion of busy time in daily traffic, a proportion of global traffic occurring in a hot spot cluster, a busy time offset coefficient, a monthly to daily conversion rate, and a predicted monthly global traffic of a next time period;

[0007] processing the multi-dimensional state parameter based on a preset spectrum demand calculation model to predict the spectrum demand of the coexistence system in the next time period;

[0008] monitoring an aggregated interference value generated by the non-geostationary orbit system to the geostationary orbit system, and if the aggregated interference value exceeds a preset safety threshold, determining a spectrum division ratio for the geostationary orbit system and the non-geostationary orbit system based on the aggregated interference value and the safety threshold;

[0009] allocating spectrum resources for the next time period for the geostationary orbit system and the non-geostationary orbit system according to the spectrum division proportion and the spectrum demand.

[0010] In one embodiment, the current average spectrum efficiency in the multi-dimensional state parameter is obtained by:

[0011] link quality of the coexisting system is monitored to obtain an average effective data rate during communication of the coexisting system;

[0012] a carrier bandwidth of the coexisting system is obtained, and the average spectrum efficiency is determined based on the average effective data rate and the carrier bandwidth.

[0013] In one embodiment, the proportion of the daily traffic during busy hours, the proportion of global traffic occurring in the hotspot cluster, the busy hour offset coefficient, and the monthly-to-daily conversion rate in the multi-dimensional state parameter are obtained by:

[0014] The proportion of the daily traffic during busy hours, the proportion of global traffic occurring in the hotspot cluster, the busy hour offset coefficient, and the monthly-to-daily conversion rate are obtained from a database or static configuration information of the satellite communication system.

[0015] In one embodiment, the monthly global traffic in the multi-dimensional state parameter is obtained by:

[0016] Traffic data of each beam of the coexisting system is recorded to form a traffic time series; each element in the traffic time series contains the traffic data of each beam;

[0017] The traffic time series is processed by a time series prediction model to output busy hour traffic prediction values of all beams in a time interval of the next time period; the next time period is divided into multiple time intervals;

[0018] The busy hour traffic prediction values of each time interval of the next time period are summed to obtain a prediction value of the monthly global traffic of the next time period.

[0019] In one embodiment, the spectrum demand calculation model is represented as:

[0020]

[0021] wherein represents the spectrum demand, represents the monthly global traffic, represents the proportion of the daily traffic during busy hours, H represents a busy hour offset coefficient, MD represents a month-day conversion rate, and eff represents an efficiency factor. SE represents an average spectral efficiency.

[0022] In one embodiment, the determining the spectrum division ratio of the geostationary orbit system and the non-geostationary orbit system based on the aggregated interference value and the safety threshold comprises:

[0023] obtaining a maximum interference limit value allowed by the coexistence system and a predetermined adjustment step size;

[0024] determining an actual adjustment step size based on the aggregated interference value, the safety threshold and the maximum interference limit value with the adjustment step size as a reference;

[0025] adjusting the current spectrum division ratio of the coexistence system by the actual adjustment step size to obtain the spectrum division ratio of the geostationary orbit system and the non-geostationary orbit system.

[0026] In a second aspect, the present application further provides a spectrum resource allocation device, comprising:

[0027] a parameter obtaining module configured to obtain a multi-dimensional state parameter of a coexistence system of a geostationary orbit and a non-geostationary orbit sharing a same frequency band; the multi-dimensional state parameter comprises a current average spectral efficiency of the coexistence system, a proportion of busy hours in a daily traffic, a global traffic proportion occurring in a hotspot cluster, a busy hour offset coefficient, a month-day conversion rate, and a predicted month global traffic of a next time period;

[0028] a demand prediction module configured to process the multi-dimensional state parameter based on a preset spectrum demand calculation model to predict a spectrum demand of the coexistence system in the next time period;

[0029] a spectrum division module configured to monitor an aggregated interference value generated by the non-geostationary orbit system to the geostationary orbit system, and if the aggregated interference value exceeds a preset safety threshold, to determine a spectrum division ratio of the geostationary orbit system and the non-geostationary orbit system based on the aggregated interference value and the safety threshold;

[0030] a spectrum allocation module configured to perform spectrum resource allocation of the geostationary orbit system and the non-geostationary orbit system in the next time period according to the spectrum division ratio and the spectrum demand.

[0031] In a third aspect, the present application further provides a computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0032] acquire multi-dimensional state parameters of a coexistence system of a geostationary orbit and a non-geostationary orbit sharing a same frequency band; the multi-dimensional state parameters include current average spectrum efficiency of the coexistence system, proportion of daily traffic in busy hours, proportion of global traffic occurring in a hot spot cluster, busy hour offset coefficient, monthly daily conversion rate, and monthly global traffic of a next time period predicted;

[0033] process the multi-dimensional state parameters based on a preset spectrum demand calculation model, and predict spectrum demand of the coexistence system in the next time period;

[0034] monitor an aggregated interference value generated by the non-geostationary orbit system to the geostationary orbit system, and if the aggregated interference value exceeds a preset safety threshold, determine a spectrum division ratio of the geostationary orbit system and the non-geostationary orbit system based on the aggregated interference value and the safety threshold;

[0035] perform spectrum resource allocation for the geostationary orbit system and the non-geostationary orbit system in the next time period according to the spectrum division ratio and the spectrum demand.

[0036] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the following steps:

[0037] acquire multi-dimensional state parameters of a coexistence system of a geostationary orbit and a non-geostationary orbit sharing a same frequency band; the multi-dimensional state parameters include current average spectrum efficiency of the coexistence system, proportion of daily traffic in busy hours, proportion of global traffic occurring in a hot spot cluster, busy hour offset coefficient, monthly daily conversion rate, and monthly global traffic of a next time period predicted;

[0038] process the multi-dimensional state parameters based on a preset spectrum demand calculation model, and predict spectrum demand of the coexistence system in the next time period;

[0039] monitor an aggregated interference value generated by the non-geostationary orbit system to the geostationary orbit system, and if the aggregated interference value exceeds a preset safety threshold, determine a spectrum division ratio of the geostationary orbit system and the non-geostationary orbit system based on the aggregated interference value and the safety threshold;

[0040] perform spectrum resource allocation for the geostationary orbit system and the non-geostationary orbit system in the next time period according to the spectrum division ratio and the spectrum demand.

[0041] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, the computer program being executed by a processor to implement the following steps:

[0042] acquire multi-dimensional state parameters of a coexistence system of a geostationary orbit and a non-geostationary orbit sharing a same frequency band; the multi-dimensional state parameters include current average spectrum efficiency, proportion of daily traffic in busy hours, proportion of global traffic occurring in a hotspot cluster, busy hour offset coefficient, monthly daily conversion rate, and monthly global traffic of the coexistence system;

[0043] perform processing on the multi-dimensional state parameters based on a preset spectrum demand calculation model to predict spectrum demand of the coexistence system in a next period;

[0044] monitor an aggregated interference value generated by the non-geostationary orbit system to the geostationary orbit system, and if the aggregated interference value exceeds a preset safety threshold, determine a spectrum division ratio of the geostationary orbit system and the non-geostationary orbit system based on the aggregated interference value and the safety threshold;

[0045] perform spectrum resource allocation for the geostationary orbit system and the non-geostationary orbit system in the next period according to the spectrum division ratio and the spectrum demand.

[0046] The spectrum resource allocation method, device, computer device, computer readable storage medium, and computer program product fully consider the dynamic change characteristics of the NGSO constellation, establish a multi-dimensional mathematical model according to real-time average spectrum efficiency, busy hour traffic demand, and other parameters, allocate the most reasonable spectrum resources for the GSO and NGSO systems through a dynamic spectrum demand calculation model, realize interference suppression, improve spectrum utilization, and can adjust the spectrum resource allocation according to real-time parameter monitoring results to ensure optimal satellite communication service quality while minimizing the risk of interference between systems. The method can significantly improve the spectrum demand evaluation accuracy, provide a scientific and effective solution for spectrum planning and management of a satellite communication system, solve the demand quantification problem of the GSO and NGSO coexistence system spectrum sharing, and realize high-precision and low-complexity spectrum demand prediction. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the present application or the related art. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other related drawings can also be obtained from these drawings without creative labor.

[0048] Figure 1 Flowchart of a spectrum resource allocation method in an embodiment;

[0049] Figure 2 Flowchart of a global traffic prediction process in an embodiment;

[0050] Figure 3 Flowchart of a spectrum resource allocation method in another embodiment;

[0051] Figure 4 Block diagram of a spectrum resource allocation apparatus in an embodiment;

[0052] Figure 5 Internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0053] In order to make the purposes, technical solutions and advantages of the present application clearer, the following will further describe the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application. It should be noted that the terms "include" and "have" and any variations thereof used in the present application are intended to cover non-exclusive inclusion. The term "multiple" used in the present application refers to two and more than two.

[0054] It can be understood that the GSO system has played an irreplaceable role in the fields of broadcast television, weather service and the like for a long time due to its fixed orbit height and stable coverage. However, with the rapid development of low-orbit satellite Internet, the NGSO system is reshaping the satellite communication pattern due to its low latency and wide coverage. However, the rapid expansion of the NGSO constellation also brings the problem of interference with the spectrum sharing of the GSO satellite system. Reasonable allocation of spectrum resources can improve spectrum utilization, reduce interference between GSO and NGSO coexistence systems, and improve service quality. However, due to the essential differences between GSO and NGSO systems in terms of orbit characteristics, coverage mode and operation mechanism, how to design a reasonable spectrum resource allocation strategy is a challenge and a key problem to be solved.

[0055] Based on this, the present application provides a spectrum resource allocation method for solving the demand quantification problem of GSO and NGSO coexistence system spectrum sharing, realizing high-precision and low-complexity spectrum demand prediction, as shown in Figure 1 It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is realized through the interaction of the terminal and the server. In the embodiment, the following steps are included:

[0056] In step S110, multi-dimensional state parameters of coexistence systems of geostationary orbits and non-geostationary orbits sharing the same frequency band are obtained; the multi-dimensional state parameters include the current average spectrum efficiency of the coexistence system, the proportion of busy hours in daily traffic, the global traffic proportion occurring in the hotspot cluster, the busy hour offset coefficient, the monthly day conversion rate, and the predicted monthly global traffic of the next time period.

[0057] The geostationary orbit (GSO) is a special geosynchronous orbit, and the satellite runs on this orbit, and the running period is the same as the earth rotation period, and the orbit is located on the earth equatorial plane.

[0058] The non-geostationary orbit (NGSO) refers to a satellite orbit that does not maintain a fixed position relative to the earth, and is opposite to the geostationary orbit.

[0059] The average spectrum efficiency is used to measure the ability of a unit of spectrum resource to transmit data in a unit of time. The higher the spectrum efficiency, the smaller the bandwidth required to complete the same business volume, and the lower the spectrum demand.

[0060] The proportion of busy hours in daily traffic refers to the ratio of the traffic in the busiest period of a day (for example, 1 hour, referred to as “busy hours”) to the total traffic of the whole day. This proportion determines the relationship between busy hour traffic and daily average traffic.

[0061] It can be understood that the business traffic is not uniformly distributed in geography, and is usually concentrated in urban areas, airports, ports and other areas, which are referred to as “hotspot clusters”. The global traffic proportion occurring in the hotspot cluster represents the proportion of global total traffic generated in these hotspot areas. This proportion is the basis for frequency reuse design. The core advantage of the NGSO system (especially the low-orbit constellation) is to improve the capacity through multi-point beam and frequency reuse technology. Hotspot areas need to be allocated more beam and frequency resources. The higher this proportion, the higher the geographical concentration of business, and the higher the requirements for the frequency reuse capability and flexibility of the system.

[0062] It can be understood that, due to the earth rotation and time zone difference, the global "busy hour" will not occur at the same time. The busy hour offset coefficient is a factor used to describe the degree of time overlap of the global busy hour. Since the satellite is constantly moving, it can serve different time zones in turn. The larger the busy hour offset is, the more "busy hours" of different continents can be served by the same satellite or the same spectrum resource in turn, thereby improving the space-time multiplexing efficiency of the spectrum resource and reducing the spectrum demand of the system. Therefore, the coefficient is crucial for global resource scheduling and spectrum demand calculation of the NGSO constellation.

[0063] wherein the monthly-to-daily conversion rate is a coefficient for converting the total monthly traffic into the average daily traffic. For example, the monthly-to-daily conversion rate is generally 1 / 30 or 1 / 31.

[0064] wherein the monthly global traffic is the total amount of data successfully transmitted by all users (or all beams) of the coexistence system in a month. The monthly global traffic is a macroscopic index for measuring the total traffic load of the network and is the starting point for calculating the spectrum demand, because the spectrum is ultimately used to carry these traffics.

[0065] It should be noted that, in this step, the current average spectrum efficiency is obtained in real time according to the current network situation. The proportion of daily traffic in the busy hour, the proportion of global traffic occurring in the hotspot cluster, the busy hour offset coefficient and the monthly-to-daily conversion rate are relatively stable and can be called from the database or static configuration information. The monthly global traffic is a parameter of the next period, and therefore, needs to be obtained by combining a prediction model (for example, a time series prediction model).

[0066] In step S120, based on the preset spectrum demand calculation model, the multi-dimensional state parameters are processed to predict the spectrum demand of the coexistence system in the next period.

[0067] wherein the spectrum demand calculation model is a calculation model representing the relationship between the state parameters and the spectrum demand, and specifically takes the state parameters as the input and the spectrum demand as the output.

[0068] Specifically, after obtaining the multi-dimensional state parameters of the GSO and NGSO coexistence system sharing the same frequency band, the spectrum demand calculation model can be input with the state parameters as the input variables, and a spectrum demand can be calculated as the spectrum demand of the GSO and NGSO coexistence system in the next period.

[0069] In step S130, the aggregated interference value generated by the non-geostationary orbit system to the geostationary orbit system is monitored, and if the aggregated interference value exceeds the preset safety threshold, the spectrum division ratio of the geostationary orbit system and the non-geostationary orbit system is determined based on the aggregated interference value and the safety threshold.

[0070] The spectrum allocation ratio refers to a proportion of spectrum resources allocated to the geostationary orbit system or the non-geostationary orbit system in total spectrum resources of the two systems.

[0071] In specific implementation, the aggregated interference value generated by the NGSO system to the GSO system can be monitored in real time, and whether the calculation and allocation of spectrum demand are needed is determined according to the monitored aggregated interference value. If the aggregated interference value is greater than the safety threshold, the calculation and allocation of spectrum demand are needed, and the spectrum division ratio of the geostationary orbit system to the non-geostationary orbit system is determined based on the monitored aggregated interference value and the safety threshold. Otherwise, the calculation and allocation of spectrum demand are not needed.

[0072] In this step, the calculation and allocation of spectrum demand are performed again when the aggregated interference value generated by the NGSO system to the GSO system is greater than the safety threshold, which can avoid the waste of computing resources caused by the calculation and allocation of spectrum demand when the interference between the NGSO system and the GSO system is weak or does not exist.

[0073] In step S140, spectrum resources of the next time period are allocated to the geostationary orbit system and the non-geostationary orbit system according to the spectrum division ratio and the spectrum demand.

[0074] Specifically, after the spectrum division ratio is determined, the spectrum demand can be divided into two parts according to the determined spectrum division ratio, and allocated to the geostationary orbit system and the non-geostationary orbit system as the basis for allocating spectrum resources of the next time period of the geostationary orbit system and the non-geostationary orbit system.

[0075] For example, assuming that the spectrum allocation ratio α is a proportion of spectrum resources allocated to the geostationary orbit system (GSO) in total spectrum resources of the two systems, and the spectrum demand is S, the final allocation scheme can be represented as: W GSO = α×S, W NGSO =(1-α)×S. Wherein, W GSO is the spectrum resources allocated to the GSO system, and W NGSO is the spectrum resources allocated to the NGSO system.

[0076] Further, after the allocation of spectrum resources is completed, the frequency band division of the GSO and the NGSO system can be dynamically adjusted through a spectrum management interface (such as a software-defined networking controller (SDN)), and a new frequency band division scheme is issued to the two systems for execution.

[0077] In the above spectrum resource allocation method, for the coexistence system of geostationary orbit and non-geostationary orbit sharing the same frequency band, by acquiring the multi-dimensional state parameters including average spectrum efficiency, proportion of daily traffic in busy time, global traffic proportion occurring in hot spot cluster, busy time offset coefficient, monthly day conversion rate, and monthly global traffic, the spectrum demand calculation model is used to calculate the spectrum demand of the next period, and when the aggregate interference value of the NGSO system to the GSO system exceeds the safety threshold, the spectrum division ratio is determined according to the aggregate interference value and the safety threshold, and the frequency band division of the GSO and NGSO system is realized. This method fully considers the dynamic change characteristics of the NGSO constellation, establishes a multi-dimensional mathematical model according to real-time average spectrum efficiency, busy time traffic demand and other parameters, allocates the most reasonable spectrum resources for the GSO and NGSO system through the dynamic spectrum demand calculation model, realizes interference suppression, improves spectrum utilization, and can adjust the spectrum resource allocation according to the real-time parameter monitoring result, which is used to ensure the optimal quality of satellite communication service, and at the same time, the interference risk between systems is minimized. This method can significantly improve the spectrum demand evaluation accuracy, provide a scientific and effective solution for spectrum planning and management of satellite communication system, solve the demand quantization problem of GSO and NGSO coexistence system spectrum sharing, and realize high-precision and low-complexity spectrum demand prediction.

[0078] In an exemplary embodiment, the determination process of the spectrum demand calculation model is as follows:

[0079] First, consider the spectrum demand of multi-dimensional parameters The calculation formula of the spectrum demand of the multi-dimensional parameters is as follows:

[0080] (1)

[0081] Wherein, B represents the number of beams in the frequency reuse cluster, B represents the carrier bandwidth (MHz), R represents the average effective data rate of the carrier, and eff represents the efficiency factor (used to consider the average load of each carrier), which is a coefficient between (0, 1] and directly reflects the average load level of the carrier. The higher the average load, the closer eff is to 1; the lower the average load, the closer eff is to 0.

[0082] The single-beam busy time traffic in formula (1) is calculated in units of megabytes (MBytes) per month, and the specific calculation formula is as follows:

[0083] (2)

[0084] Wherein, represents the monthly global traffic prediction value (MBytes / month), represents the proportion of daily traffic in busy hours (peak hours of service), represents the proportion of global traffic occurring within the hotspot cluster, H represents the busy hour shift coefficient (value between 0 and 1), and MD represents the monthly-day conversion rate, represents the number of beams within the frequency reuse cluster.

[0085] Substituting formula (2) into formula (1), the spectrum demand (MHz) can be further represented by formula (3) as:

[0086] (3)

[0087] The relationship between the average effective data rate R and the carrier bandwidth B in formula (3) can be represented as: where SE represents the average spectrum efficiency, and therefore, formula (3) can be further converted to formula (4):

[0088] (4)

[0089] Formula (4) can represent the spectrum demand calculation model used by the present application to predict the spectrum demand of the coexistence system in the next period.

[0090] Further, after obtaining the multi-dimensional state parameters of the coexistence system of the GSO and NGSO in step S110, each state parameter: the current average spectrum efficiency , the proportion of daily traffic in busy hours , the proportion of global traffic occurring within the hotspot cluster , the busy hour shift coefficient H, the monthly-day conversion rate MD, and the predicted monthly global traffic in the next period are substituted into the above formula (4) to calculate the spectrum demand information S of the coexistence system of the GSO and NGSO in the next period t+1.

[0091] In this embodiment, by establishing a mathematical model containing key parameters such as average spectrum efficiency, proportion of daily traffic in busy hours, proportion of global traffic occurring within the hotspot cluster, busy hour shift coefficient, monthly-day conversion rate, and monthly global traffic, quantitative evaluation of the spectrum demand of the coexistence system of the GSO and NGSO is realized, so that a spectrum demand calculation method based on multi-dimensional state parameter modeling can be designed based on the spectrum demand calculation model.

[0092] In an exemplary embodiment, the current average spectrum efficiency in the multi-dimensional state parameters is obtained by: performing link quality monitoring on the coexistence system to obtain the average effective data rate during communication of the coexistence system; obtaining the carrier bandwidth of the coexistence system, and determining the average spectrum efficiency based on the average effective data rate and the carrier bandwidth.

[0093] Specifically, the coexistence system of the GSO and the NGSO can be monitored by the link quality monitoring system, the average effective data rate during communication of the coexistence system is obtained, and the carrier bandwidth is obtained, and based on the average effective data rate and the carrier bandwidth, the average spectrum efficiency is determined. Specifically, the ratio of the average effective data rate to the carrier bandwidth can be obtained, and the average spectrum efficiency is obtained based on the ratio.

[0094] In some embodiments, the average effective data rate and the carrier bandwidth can be substituted into the average spectrum efficiency calculation formula shown in the following formula (5) to calculate the average spectrum efficiency.

[0095] (5)

[0096] wherein, represents the average spectrum efficiency of the current period, represents the average effective data rate of the current period, represents the carrier bandwidth of the current period.

[0097] In the embodiment, based on the relationship between the spectrum efficiency and the spectrum demand, the current average spectrum efficiency is introduced as the basis for spectrum demand calculation to realize dynamic evaluation of the spectrum demand.

[0098] In an exemplary embodiment, the proportion of the daily traffic in the busy hours, the proportion of the global traffic occurring in the hotspot cluster, the busy hour offset coefficient, and the monthly-to-daily conversion rate in the multi-dimensional state parameters are obtained in the following manner: the proportion of the daily traffic in the busy hours, the proportion of the global traffic occurring in the hotspot cluster, the busy hour offset coefficient, and the monthly-to-daily conversion rate are obtained from the database or the static configuration information of the satellite communication system.

[0099] In a specific implementation, the proportion of the daily traffic in the busy hours, the proportion of the global traffic occurring in the hotspot cluster, the busy hour offset coefficient, and the monthly-to-daily conversion rate and other parameters are relatively stable and can be pre-configured, and therefore, when calculating the spectrum demand, these parameters can be directly called from the operator database or the static configuration information based on the ITU-R standard.

[0100] In the embodiment, the proportion of the daily traffic in the busy hours, the proportion of the global traffic occurring in the hotspot cluster, the busy hour offset coefficient, and the monthly-to-daily conversion rate are introduced as the basis for evaluation of the spectrum demand, which can realize accurate evaluation of the spectrum demand; through the distribution of the traffic in time (busy hour proportion), space (hotspot proportion), and space-time (busy hour offset coefficient), the space-time multiplexing of the spectrum can be determined, and then the spectrum resource demand can be evaluated on the basis of improving the utilization efficiency of the spectrum resources.

[0101] In an exemplary embodiment, asFigure 2 As shown, the monthly global traffic in the multi-dimensional state parameter is obtained in the following way:

[0102] Step S111, record the traffic data of each beam of the coexistence system to form a traffic time series; each element in the traffic time series contains the traffic data of each beam;

[0103] Step S112, process the traffic time series by a time series prediction model to output the busy hour traffic prediction value of all beams in a time interval of the next time period; the next time period is divided into multiple time intervals;

[0104] Step S113, sum the busy hour traffic prediction values of each time interval of the next time period to obtain the prediction value of the monthly global traffic of the next time period.

[0105] The time series prediction model can be a Long Short-Term Memory (LSTM).

[0106] Specifically, the traffic data of each beam can be recorded according to time slices (such as every hour / day) based on the traffic statistical module of the satellite gateway in the GSO and NGSO coexistence system to form a traffic time series. The historical traffic rules are analyzed by a machine learning algorithm (such as a time series prediction model) to dynamically predict the monthly global traffic.

[0107] For example, based on the traffic time series (ω is the size of the sliding window), the busy hour traffic of the t+1 time interval is predicted by the LSTM model . The process is represented by the formula:

[0108] (6)

[0109] The prediction values of the busy hour traffic of each time interval of the next time period are summed to obtain the prediction value of the monthly global traffic of the next time period , which is represented by the formula:

[0110] (7)

[0111] In this embodiment, considering that the business traffic fluctuates dramatically every day, even every hour, but the monthly global traffic averages all these short-term fluctuations and is more stable and reliable, therefore, introducing the monthly global traffic for dynamic evaluation of spectrum demand can ensure the reliability of the evaluation result, thereby improving the rationality and accuracy of spectrum resource allocation based on predicted spectrum demand.

[0112] In an exemplary embodiment, the step of determining the spectrum division ratio of the geostationary orbit system and the non-geostationary orbit system based on the aggregated interference value and the safety threshold in step S130 comprises: obtaining a maximum interference limit value allowed by the coexisting system and a predetermined ratio adjustment step; determining an actual adjustment step based on the aggregated interference value, the safety threshold and the maximum interference limit value with the ratio adjustment step as a reference; and adjusting the current spectrum division ratio of the coexisting system by the actual adjustment step to obtain the spectrum division ratio of the geostationary orbit system and the non-geostationary orbit system.

[0113] Specifically, if the aggregated interference value exceeds the preset safety threshold, the spectrum division ratio adjustment mechanism is triggered. In addition to the aggregated interference value and the safety threshold, the maximum interference limit value allowed by the coexisting system and the predetermined ratio adjustment step are also needed for adjusting the spectrum division ratio. The actual adjustment step is determined based on the aggregated interference value, the safety threshold and the maximum interference limit value with the ratio adjustment step as a reference. Then the current spectrum division ratio of the coexisting system is adjusted by the actual adjustment step. Specifically, the current spectrum division ratio can be added to the actual adjustment step to obtain the spectrum division ratio of the geostationary orbit system and the non-geostationary orbit system.

[0114] Let the current spectrum division ratio be α (the ratio allocated to the GSO system), then 1-α is the ratio allocated to the NGSO system. The adjusted spectrum division ratio α' can be calculated according to the following formula (8):

[0115] (8)

[0116] wherein, represents the actual adjustment step, represents the current monitored aggregated interference value; represents the safety threshold, represents the maximum interference limit value allowed by the coexisting system; represents the ratio adjustment step (a configurable parameter, such as 0.05).

[0117] In this embodiment, the spectrum division ratio of the geostationary orbit system and the non-geostationary orbit system is dynamically adjusted in combination with the real-time monitored aggregated interference value, which can optimize the satellite communication performance and minimize the interference risk between systems, and achieve optimal spectrum resource allocation to ensure efficient spectrum sharing of the GSO and NGSO systems.

[0118] Reference Figure 3 The flowchart of the spectrum resource allocation method provided by another embodiment is shown in FIG. 3. The method comprises the following steps:

[0119] In step S310, the current average spectrum efficiency is obtained in real time based on the current network situation.

[0120] Specifically, the average effective data rate during the communication of the coexisting system can be obtained by performing link quality monitoring on the coexisting system; the carrier bandwidth of the coexisting system is obtained, and the average spectral efficiency is determined based on the average effective data rate and the carrier bandwidth.

[0121] Step S320, the proportion of the relatively stable daily traffic in the busy time, the proportion of the global traffic occurring in the hotspot cluster, the busy time offset coefficient and the monthly-day conversion rate in the daily traffic are called from the database or static configuration information.

[0122] Step S330, the time series prediction model processes the historical traffic time series to predict the monthly global traffic of the next period.

[0123] Step S340, input each state parameter obtained in steps S310 to S330 into the spectrum demand calculation model to calculate a spectrum demand S that can adapt to the network state of the future short period (such as the next busy time).

[0124] Step S350, the aggregate interference value generated by the non-geostationary orbit system to the geostationary orbit system is monitored, and if the aggregate interference value exceeds the preset safety threshold, the spectrum division ratio of the geostationary orbit system and the non-geostationary orbit system is determined based on the aggregate interference value and the safety threshold.

[0125] Step S360, according to the spectrum division ratio and the spectrum demand, the spectrum resource allocation of the next period for the geostationary orbit system and the non-geostationary orbit system is performed.

[0126] The currently commonly used spectrum allocation method is mainly based on a static interference avoidance strategy, which reduces the interference between systems through fixed frequency division and power limitation. The present application proposes a multi-dimensional parameter spectrum demand analysis method for GSO and NGSO coexisting systems, which can analyze the optimal spectrum resource allocation scheme through a joint optimization algorithm according to the real-time beam topology, dynamic traffic characteristics and link spectral efficiency parameters of the NGSO constellation, in order to ensure the optimal satellite communication performance and the lowest system interference risk, and realize efficient spectrum sharing of GSO and NGSO systems.

[0127] In an application embodiment, based on the above spectrum demand calculation model, actual and typical parameter values are selected to calculate and analyze the uplink and downlink spectrum demand of the GSO and NGSO coexisting system, and the results are shown in Table 1.

[0128] Table 1 Spectrum demand analysis table

[0129]

[0130] The uplink state parameters are substituted into the spectrum demand calculation model shown in formula (4) above to obtain:

[0131]

[0132] The downlink state parameters are substituted into the spectrum demand calculation model shown in formula (4) above to obtain:

[0133]

[0134] Due to the increasing popularity of cloud services and the rise of large-volume message transmission including photos and videos, communication traffic is becoming more symmetrical, so it can be assumed that the traffic levels of the uplink and downlink are similar. However, due to the inherent technical characteristics and lower power of the uplink, its spectrum efficiency is usually slightly lower than that of the downlink, resulting in slightly more spectrum resources required for the uplink than for the downlink.

[0135] It should be understood that, although each step in the flowchart involved in each embodiment as described above is shown in sequence according to the direction of the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, there is no strict order limitation for the execution of these steps, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps.

[0136] Based on the same inventive concept, the embodiments of the present application also provide a spectrum resource allocation apparatus for implementing the spectrum resource allocation method described above. The implementation scheme for solving the problem provided by the apparatus is similar to the implementation scheme described in the above method, so the specific limitations in one or more spectrum resource allocation apparatus embodiments provided below can refer to the limitations of the spectrum resource allocation method described above, which will not be repeated here.

[0137] In one exemplary embodiment, as shown in Figure 4 a spectrum resource allocation apparatus is provided, comprising:

[0138] The parameter acquisition module 410 is configured to acquire multi-dimensional state parameters of a coexistence system of geostationary orbit and non-geostationary orbit sharing the same frequency band, wherein the multi-dimensional state parameters include current average spectrum efficiency of the coexistence system, proportion of daily traffic in busy hours, global traffic proportion occurring in a hot spot cluster, busy hour offset coefficient, monthly to daily conversion rate, and predicted monthly global traffic of the next time period.

[0139] a demand prediction module 420, configured to process the multi-dimensional state parameters based on a preset spectrum demand calculation model, and predict spectrum demand of the coexistence system in a next time period;

[0140] a spectrum division module 430, configured to monitor an aggregated interference value generated by the non-geostationary orbit system to the geostationary orbit system, and determine a spectrum division ratio for the geostationary orbit system and the non-geostationary orbit system based on the aggregated interference value and a safety threshold if the aggregated interference value exceeds the safety threshold;

[0141] a spectrum allocation module 440, configured to perform spectrum resource allocation for the geostationary orbit system and the non-geostationary orbit system in the next time period according to the spectrum division ratio and the spectrum demand.

[0142] In one of the embodiments, the parameter acquisition module 410 is further configured to perform link quality monitoring on the coexistence system, acquire an average effective data rate in communication of the coexistence system, acquire a carrier bandwidth of the coexistence system, and determine an average spectrum efficiency based on the average effective data rate and the carrier bandwidth.

[0143] In one of the embodiments, the parameter acquisition module 410 is further configured to acquire, from a database or static configuration information of the satellite communication system, a proportion of busy hour traffic in daily traffic, a global traffic proportion occurring in a hotspot cluster, a busy hour offset coefficient, and a monthly-to-daily conversion rate.

[0144] In one of the embodiments, the parameter acquisition module 410 is further configured to record traffic data of each beam of the coexistence system to form a traffic time sequence, each element in the traffic time sequence containing traffic data of each beam, process the traffic time sequence through a time sequence prediction model, and output a busy hour traffic prediction value of all beams in a time interval of a next time period; the next time period is divided into multiple time intervals; and the busy hour traffic prediction values of the time intervals of the next time period are summed to obtain a prediction value of monthly global traffic of the next time period.

[0145] In one of the embodiments, the spectrum demand calculation model is represented as:

[0146]

[0147] wherein, represents the spectrum demand, represents the monthly global traffic, represents the proportion of busy hour traffic in daily traffic, represents the global traffic proportion occurring in the hotspot cluster, H represents the busy hour offset coefficient, MD represents the monthly-to-daily conversion rate, eff represents the efficiency factor, and SE represents the average spectrum efficiency.

[0148] In one embodiment, the spectrum partitioning module 430 is further configured to obtain the maximum allowable interference limit of the coexisting system and a predetermined proportional adjustment step size; based on the proportional adjustment step size, determine the actual adjustment step size based on the aggregated interference value, the safety threshold and the maximum interference limit; and adjust the current spectrum partitioning ratio of the coexisting system through the actual adjustment step size to obtain the spectrum partitioning ratio of the geostationary orbit system and the non-geostationary orbit system.

[0149] Each module in the aforementioned spectrum resource allocation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0150] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a spectrum resource allocation method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0151] Those skilled in the art will understand that Figure 5The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0152] In an embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.

[0153] In an embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0154] In an embodiment, a computer program product is provided, including a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0155] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0156] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0157] The above-described embodiments are merely illustrative of several embodiments of the present application, which are described in more detail and in a specific manner, but should not be construed as limiting the scope of the patent of the present application. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for allocating spectrum resources, characterized in that, The method includes: Obtain multidimensional state parameters of a coexistence system of geostationary orbit and non-geostationary orbit sharing the same frequency band; the multidimensional state parameters include the current average spectral efficiency of the coexistence system, the proportion of daily traffic during busy hours, the proportion of global traffic occurring in hotspot clusters, the busy hour offset coefficient, the monthly-daily conversion rate, and the predicted monthly global traffic for the next period. Based on a preset spectrum demand calculation model, the multidimensional state parameters are processed to predict the spectrum demand of the coexisting system in the next time period. Monitor the aggregated interference value generated by the non-geostationary orbit system on the geostationary orbit system. If the aggregated interference value exceeds a preset safety threshold, determine the spectrum division ratio between the geostationary orbit system and the non-geostationary orbit system based on the aggregated interference value and the safety threshold. According to the spectrum allocation ratio and the spectrum requirements, spectrum resources are allocated for the geostationary orbit system and the non-geostationary orbit system for the next time period.

2. The method according to claim 1, characterized in that, The current average spectral efficiency in the multidimensional state parameters is obtained in the following way: Link quality monitoring is performed on the coexisting system to obtain the average effective data rate during communication in the coexisting system; Obtain the carrier bandwidth of the coexistence system, and determine the average spectral efficiency based on the average effective data rate and the carrier bandwidth.

3. The method according to claim 1, characterized in that, The proportion of daily traffic occurring during busy hours, the proportion of global traffic occurring within hotspot clusters, the busy hour offset coefficient, and the monthly-daily conversion rate in the multidimensional state parameters are obtained through the following methods: Obtain the proportion of daily traffic during busy hours, the proportion of global traffic occurring within hotspot clusters, the busy hour offset coefficient, and the monthly-daily conversion rate from the static configuration information of the database or satellite communication system.

4. The method according to claim 1, characterized in that, The monthly global flow in the multidimensional state parameters is obtained through the following method: The flow data of each beam in the coexistence system is recorded to form a flow time series; each element in the flow time series contains the flow data of each beam. The traffic time series is processed by a time series prediction model to output the peak hour traffic prediction values ​​for all beams in a time interval of the next period; the next period is divided into multiple time intervals. The predicted peak hour traffic values ​​for each time interval of the next period are summed to obtain the predicted monthly global traffic value for the next period.

5. The method according to any one of claims 1 to 4, characterized in that, The spectrum demand calculation model is expressed as follows: in, Indicates spectrum demand. This represents the total monthly traffic. This indicates the proportion of daily traffic during peak hours. This represents the proportion of global traffic occurring within hotspot clusters, H represents the busy hour offset coefficient, MD represents the monthly / daily conversion rate, eff represents the efficiency factor, and SE represents the average spectral efficiency.

6. The method according to claim 1, characterized in that, The determination of the spectrum allocation ratio between the geostationary orbit system and the non-geostationary orbit system based on the aggregated interference value and the safety threshold includes: Obtain the maximum allowable interference limit and the predetermined proportional adjustment step size of the coexistence system; Based on the aforementioned proportional adjustment step size, and taking into account the aggregated interference value, the safety threshold, and the maximum interference limit, the actual adjustment step size is determined. By adjusting the actual adjustment step size, the current spectrum division ratio of the coexisting system is adjusted to obtain the spectrum division ratio between the geostationary orbit system and the non-geostationary orbit system.

7. A spectrum resource allocation device, characterized in that, The device includes: The parameter acquisition module is used to acquire multi-dimensional state parameters of the coexistence system of geostationary orbit and non-geostationary orbit sharing the same frequency band. The multi-dimensional state parameters include the current average spectral efficiency of the coexistence system, the proportion of daily traffic during busy hours, the proportion of global traffic occurring in hotspot clusters, the busy hour offset coefficient, the monthly-daily conversion rate, and the predicted monthly global traffic for the next period. The demand forecasting module is used to process the multidimensional state parameters based on a preset spectrum demand calculation model to predict the spectrum demand of the coexisting system in the next time period. The spectrum partitioning module is used to monitor the aggregate interference value generated by the non-geostationary orbit system on the geostationary orbit system. If the aggregate interference value exceeds a preset safety threshold, the spectrum partitioning ratio between the geostationary orbit system and the non-geostationary orbit system is determined based on the aggregate interference value and the safety threshold. The spectrum allocation module is used to allocate spectrum resources for the geostationary orbit system and the non-geostationary orbit system for the next time period according to the spectrum allocation ratio and the spectrum requirements.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, 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.

10. A computer program product, comprising a computer program, 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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