Resource allocation parameter determination method and device, equipment, medium and product

Abstract

The invention provides a resource allocation parameter determination method and device, equipment, a medium and a product, and the method comprises the steps: obtaining the reachable information rates of a plurality of pieces of user equipment, and determining the dissatisfaction degree of the plurality of pieces of user equipment based on the relationship among the reachable information rates of the plurality of pieces of user equipment, the information rate demands of the plurality of pieces of user equipment and the dissatisfaction degree of the plurality of pieces of user equipment; the method comprises the steps of establishing a target initial function of user comprehensive dissatisfaction and a constraint condition of a satellite resource allocation parameter, and finally determining the satellite resource allocation parameter based on the target initial function of user comprehensive dissatisfaction and the constraint condition of the satellite resource allocation parameter. And satellite resources are fully utilized.

Description

Technical Field

[0001] The present invention relates to the field of satellite communication technology, and in particular to a method, device, equipment, medium and product for determining resource allocation parameters. Background Art

[0002] In multi-beam satellite communications, the communication traffic demand of user equipment is unevenly distributed, and the beam resources of the multi-beam satellite need to be flexibly allocated to achieve a better match between the communication needs of user equipment and the traffic that can be supplied by the satellite.

[0003] In related technologies, when flexibly allocating wireless resources to multiple user equipment within a beam, the resource allocation model used has low frequency band utilization within the beam, does not optimize power, and ignores the impact of power on the system. Summary of the Invention

[0004] According to one aspect of the present disclosure, a method for determining satellite resource allocation parameters is provided, where the satellite has multiple beams and multiple subbands. The method includes:

[0005] Determine, based on the channel coefficients of the beams to different user equipments on the different sub-bands, the power to be optimized of the beams on the different sub-bands, the time slot resources allocated to the user equipments in the different beams, and the total available bandwidth of the satellite, the achievable information rates of the user equipments;

[0006] Establishing a target initial function of user comprehensive dissatisfaction and constraint conditions of satellite resource allocation parameters based on the relationship between the achievable information rates of the plurality of user devices, the information rate requirements of the plurality of user devices, and the dissatisfaction of the plurality of user devices;

[0007] Based on the target initial function of the user comprehensive dissatisfaction and the constraint conditions of the satellite resource allocation parameters, the satellite resource allocation parameters are determined, where the satellite resource allocation parameters at least include target powers of the respective beams in different sub-bands.

[0008] According to another aspect of the present disclosure, a device for determining satellite resource allocation parameters is provided, wherein the satellite has multiple beams and multiple sub-bands, the device comprising:

[0009] a determination module, configured to determine a reachable information rate for multiple user equipments based on a channel coefficient of each beam to different user equipments on different sub-bands, a power to be optimized for each beam in different sub-bands, time slot resources allocated to the multiple user equipments in different beams, and a total available bandwidth of the satellite;

[0010] a modeling module, configured to establish a target initial function of user comprehensive dissatisfaction and constraint conditions of satellite resource allocation parameters based on the relationship between the achievable information rates of the plurality of user devices, the information rate requirements of the plurality of user devices, and the dissatisfaction of the plurality of user devices;

[0011] A solution module is configured to determine satellite resource allocation parameters based on the target initial function of the user comprehensive dissatisfaction and the constraints of the satellite resource allocation parameters, where the satellite resource allocation parameters at least include target powers of the respective beams in different sub-bands.

[0012] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0013] processor; and,

[0014] Memory for storing programs;

[0015] The program includes instructions, which, when executed by the processor, cause the processor to perform the method according to the exemplary embodiment of the present disclosure.

[0016] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the method according to the exemplary embodiments of the present disclosure.

[0017] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the method described in the exemplary embodiments of the present disclosure is implemented.

[0018] One or more technical solutions provided in the exemplary embodiments of the present disclosure can determine the achievable information rate of multiple user devices based on the channel coefficients of each beam to different user devices in different sub-bands, the power to be optimized of each beam in different sub-bands, the time slot resources allocated to multiple user devices in different beams, and the total available bandwidth of the satellite. The achievable information rate of each user device can be regarded as the total channel rate of multiple beams to the user device in multiple sub-bands. Therefore, based on the relationship between the achievable information rates of the multiple user devices, the information rate requirements of the multiple user devices, and the dissatisfaction of multiple users, a target initial function of the user's comprehensive dissatisfaction and constraints on the satellite resource allocation parameters can be established. Then, based on the target initial function of the user's comprehensive dissatisfaction and the constraints on the satellite resource allocation parameters, the power to be optimized of each beam in different sub-bands can be optimized, so that the determined satellite resource allocation parameters include the target power of each beam in the different sub-bands.

[0019] It can be seen that the exemplary embodiment of the present disclosure can introduce the power to be optimized of each beam in different sub-bands into the target initial function of the user's comprehensive dissatisfaction through the achievable information rate of multiple user devices, and then solve the target initial function of the user's comprehensive dissatisfaction to achieve the optimization of the power to be optimized of each beam in different sub-bands, thereby obtaining the target power of each beam in different sub-bands.

[0020] Moreover, since the power to be optimized of each beam in different sub-bands is related to the achievable information rate of multiple user devices, when the power to be optimized of each beam in different sub-bands is optimal, the achievable information rate of multiple user devices also reaches an optimal state. Therefore, the exemplary embodiment of the present disclosure achieves full utilization of satellite power resources by optimizing the power to be optimized of each beam in different sub-bands. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1 A schematic diagram illustrating an example satellite communication system in which the various methods described herein may be implemented;

[0023] Figure 2 A schematic diagram showing a basic flow chart of a method for determining satellite resource allocation parameters according to an exemplary embodiment of the present disclosure is shown;

[0024] Figure 3 A schematic diagram of a process for obtaining channel coefficients according to an exemplary embodiment of the present disclosure is shown;

[0025] Figure 4 A schematic diagram showing a specific flow chart of a method for determining satellite resource allocation parameters according to an exemplary embodiment of the present disclosure is shown;

[0026] Figure 5 A schematic block diagram of functional modules of a device for determining satellite resource allocation parameters according to an exemplary embodiment of the present disclosure is shown;

[0027] Figure 6 shows a schematic block diagram of a chip according to an exemplary embodiment of the present disclosure;

[0028] Figure 7 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0029] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0030] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0031] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0032] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0033] In modern satellite networks, multi-beam satellites feature multiple beams, each illuminating and servicing an elliptical area on Earth. This multi-beam technology can effectively increase system capacity. However, due to the scarcity of satellite resources and the competition between each beam for power and bandwidth, current satellite resource scheduling schemes struggle to meet the explosive growth in user terminal demand. Furthermore, because user needs in ocean, desert, and mountainous areas differ significantly from those in urban areas, wireless resource allocation and user access selection become key challenges limiting the ability to meet these needs. Therefore, intelligent radio resource allocation within digital payloads is crucial to achieving desired throughput targets.

[0034] The inventors discovered that user information flow demands exhibit a non-uniform distribution. Therefore, satellites must be flexible in allocating resources, such as power, to achieve a better match between information demand and information supply. However, in related technologies, when multiple user devices are on the same beam, a two-color frequency model can be used to allocate wireless resources within the beam. However, this wireless resource allocation method sets the power on each frequency band to be constant, ignoring the impact of power on system channel capacity, i.e., the system channel rate.

[0035] What is more serious is that the two-color frequency model assumes that it is unfavorable for user demand to be greater than the amount of information to be transmitted, or it is unfavorable for user demand to be less than the amount of information to be transmitted. However, in fact, satellite power resources come from solar energy and can be regarded as renewable resources. This assumption of the two-color frequency model cannot fully utilize the surplus power resources, which is not conducive to improving the overall system rate.

[0036] In response to the above problems, an exemplary embodiment of the present disclosure provides a method for determining satellite resource allocation parameters, which can fully consider the differences in satellite resource requirements between different user devices, establish a connection between the power to be optimized of each beam in different sub-bands and the achievable information rate of multiple user devices, and use the information rate requirements of multiple user devices as a reference to establish a target initial function of user comprehensive dissatisfaction and constraints on satellite resource allocation parameters. Then, the target initial function of user comprehensive dissatisfaction is solved to obtain the target power of each beam in different sub-bands, thereby achieving the purpose of fully utilizing satellite wireless resources.

[0037] The method provided by the present disclosure can be applied to non-terrestrial networks (NTN) communication systems, such as satellite communication systems. Figure 1 Schematic diagram of an example satellite communication system in which the various methods described herein may be implemented. Figure 1 As shown, a satellite communication system 100 according to an exemplary embodiment of the present disclosure may include a satellite 101 and a plurality of user equipments.

[0038] like Figure 1As shown, the satellite 101 can be a multi-beam satellite, whose onboard antenna can generate multiple isolated beams within its coverage area. Each user device can flexibly access a wave number, and each beam can also use a carrier of any sub-band. For example, the satellite can include a geostationary earth orbit (GEO) satellite, a medium earth orbit (MEO) satellite or a low earth orbit (LEO) satellite of a non-geostationary earth orbit (NGEO), or a high altitude platform (HAPS). The present disclosure does not limit the specific type of satellite.

[0039] like Figure 1 As shown, the user equipment (UE) is a device with wireless transceiver function, such as a ground station 102, or various terminal devices 103. It is understandable that Figure 1 Only one satellite and one ground station are shown. In actual use, multiple ground architectures can be adopted as needed. Each ground station can correspond to one or more satellites, etc. This disclosure does not elaborate or limit this.

[0040] The above-mentioned terminal device can communicate with the access network device (or also referred to as access device) in the radio access network (RAN), and may also be referred to as an access terminal, terminal, subscriber unit, user station, mobile station, remote station, remote terminal, mobile device, user terminal, user agent or user device, etc.

[0041] In one possible implementation, the above-mentioned terminal device can be a device deployed on land, and its operating speed can be maintained within a certain range. For example, the terminal device can be deployed as a user device indoors or outdoors, handheld or vehicle-mounted; it can also be deployed on the water surface (such as a ship, etc.).

[0042] In one possible implementation, the terminal device may be a handheld device with wireless communication capabilities, a vehicle-mounted device, a wearable device, a sensor, a terminal in the Internet of Things, a terminal in the Internet of Vehicles, a drone, a fifth generation (5G) network, and any form of user equipment in future networks, etc., which is not limited in this disclosure.

[0043] It is understandable that the terminal devices shown in the present disclosure can also communicate with each other through device-to-device (D2D), machine-to-machine (M2M), etc. The terminal devices shown in the present application can also be devices in the Internet of Things (IoT). The IoT network may include, for example, the Internet of Vehicles. Among them, the communication methods in the Internet of Vehicles system are collectively referred to as vehicle to other devices (vehicle to X, V2X, X can represent anything), for example, the V2X may include: vehicle to vehicle (V2V) communication, vehicle to infrastructure (V2I) communication, vehicle to pedestrian communication (V2P) or vehicle to network (V2N) communication, etc.

[0044] The ground station of the exemplary embodiments of the present disclosure can be used to connect a satellite to a base station, or a satellite to a core network. The satellite can provide wireless access services to user devices, schedule wireless resources to accessed terminal devices, and provide reliable wireless transmission protocols and data encryption protocols. The satellite can be a base station that uses artificial earth satellites and high-altitude aircraft as wireless communication, such as an evolved NodeB (eNB) and a next-generation NodeB (gNB). Alternatively, the satellite can also serve as a relay for these base stations, transparently transmitting their signals to user devices.

[0045] In practical applications, the satellite communication system of the exemplary embodiments of the present disclosure may further include a satellite server, which may be deployed on the satellite or on the ground. However, given the limited data processing capabilities of satellites, the satellite server may be deployed on the ground, communicating with the satellite via a ground station. The satellite server may be configured to execute the method for determining satellite resource allocation parameters and transmit the determined satellite resource allocation parameters to the satellite via the ground station.

[0046] The following describes the method of the exemplary embodiment of the present disclosure using a satellite server or a chip used in the satellite server as the execution subject. For ease of understanding, the number of satellite beams (i.e., beam antennas) is represented as N, the number of user devices is represented as M, and the number of subbands is represented as K.

[0047] Figure 2 A basic flow chart of a method for determining satellite resource allocation parameters according to an exemplary embodiment of the present disclosure is shown.

[0048] like Figure 2As shown, the method for determining satellite resource allocation parameters of the exemplary embodiment of the present disclosure may include:

[0049] Step 201: Determine the achievable information rate of multiple user devices based on the channel coefficients of each beam to different user devices in different sub-bands, the power to be optimized of each beam in different sub-bands, the time slot resources allocated to multiple user devices in different beams, and the total available bandwidth of the satellite.

[0050] In practical applications, exemplary embodiments of the present disclosure can determine the subband signal-to-interference-plus-noise ratio of each user device on different beams based on the channel coefficients of each beam to different user devices on multiple subbands, the power to be optimized of each beam in different subbands, and the noise of the ground receiver to the satellite. Then, based on the subband signal-to-interference-plus-noise ratios of the multiple user devices on different beams, the time slot resources allocated to the multiple user devices in different beams, and the total available bandwidth of the satellite, the achievable information rate of each user device can be determined.

[0051] It can be seen that the exemplary embodiments of the present disclosure take into account the sub-band signal to interference and noise ratio of each user equipment on different beams when determining the achievable information rate of the user equipment. By introducing the sub-band signal to interference and noise ratio, it can be ensured that when resources are allocated according to the finally obtained satellite resource allocation parameters, not only the overall speed of the satellite communication system can be relatively high, but also the communication efficiency of each user equipment can be relatively good.

[0052] Exemplarily, the exemplary embodiments of the present disclosure can determine the theoretical channel information of the target beam to a certain user device on the target subband based on the channel coefficient of the target beam to a certain user device on the target subband and the power to be optimized of the target beam on the target subband, determine the channel interference from the target beam to a certain user device on the target subband based on the channel coefficient of the non-target beam to a certain user device on the target subband and the power to be optimized of the non-target beam on the target subband, and finally determine the target subband signal-to-interference-plus-noise ratio of the user device on the target beam based on the theoretical channel information of the target beam to a certain user device on the target subband, the channel interference from the target beam to a certain user device on the target subband and the noise of the ground receiver to the satellite.

[0053] For example, when the total available bandwidth of a satellite is represented by B and is evenly divided into K sub-bands of different frequencies, the total available bandwidth of each sub-band is B0 = B / K. The noise of a ground receiver to the satellite can be determined by the power of the ground receiver's receiving noise floor and the total available bandwidth of each sub-band. For example, if the power of the ground receiver's receiving noise floor is represented by N0, the noise of the ground receiver to the satellite is N0B0.

[0054] In the exemplary embodiment of the present disclosure, time division multiplexing (TDM) technology can be used to allocate resources within the beam. For example, for the mth user equipment on the nth beam, the kth sub-band signal to interference noise ratio γ mn For example, it can be expressed as formula 1:

[0055]

[0056] Among them, p nk represents the power to be optimized for the nth beam in the kth subband, represents the channel coefficient of the nth beam to the mth user equipment on the kth subband, represents the channel interference from the nth beam to the mth user equipment on the kth subband, p n'k represents the power to be optimized for the n'th beam in the kth subband, represents the channel coefficient of the n'th beam to the mth user equipment on the kth subband.

[0057] The power of multiple beams in different sub-bands of the exemplary embodiment of the present disclosure can be expressed in the form of a power allocation vector P. When the number of beams of the satellite is expressed as N and the number of sub-bands is K, since each beam only selects one sub-band at a time, the power allocation vector P=(q nk ) N×K The time slot resources allocated to multiple user devices in different beams can be expressed in the form of a time slot allocation vector A. When the number of satellite beams is expressed as N and the number of user devices is expressed as M, since each user device can only access one beam at a time, the time slot allocation vector A = (a mn ) M×N .

[0058] For example, the exemplary embodiments of the present disclosure may determine the achievable information rate r of the mth user equipment by using the Shannon formula. m , which can be expressed as formula 2:

[0059]

[0060] Step 202: Based on the relationship between the achievable information rates of the multiple user devices, the information rate requirements of the multiple user devices, and the dissatisfaction of the multiple user devices, an initial target function of user comprehensive dissatisfaction and constraints of satellite resource allocation parameters are established.

[0061] In practical applications, the target initialization function for comprehensive user dissatisfaction in the exemplary embodiments of the present disclosure may be the accumulation of target initialization functions for dissatisfaction of multiple user devices. For a particular user device, user device dissatisfaction may be measured by whether the user device's reachable information rate meets the user device's information rate requirement. When the reachable information rate of the user device is less than the user device's information rate requirement, it indicates that the information rate supply is insufficient and the user device's dissatisfaction is relatively high. When the reachable information rate of the user device is greater than or equal to the user device's information rate requirement, it indicates that the information rate supply is relatively sufficient and the user device's dissatisfaction is relatively low.

[0062] It can be seen that the dissatisfaction of the user device of the exemplary embodiment of the present disclosure is negatively correlated with the achievable information rate of the user device, and the dissatisfaction of the user device is positively correlated with the information rate requirement of the user device. For example, if the information rate requirement of the user device is greater than the achievable information rate of the user device, the dissatisfaction of the user device is a constant satisfaction; if the information rate requirement of the user device is less than or equal to the achievable information rate of the user device, the dissatisfaction of the user device is determined by the information rate requirement of the user device and the achievable information rate of the user device. Based on the above principles, the target initial function of the user comprehensive dissatisfaction of the exemplary embodiment of the present disclosure can be specifically expressed as Formula 3:

[0063]

[0064] in, Represents the information rate requirement of the mth user device. As shown in Formula 3, when the number of user devices is M, the target initial function of user comprehensive dissatisfaction is expressed as the accumulation of the target initial functions of M user dissatisfaction. When the information rate requirement of the mth user device is Greater than the achievable information rate r of the mth user equipment m When , it means that the information rate requirement of the mth user equipment is not met. Therefore, the dissatisfaction of the mth user device is equal to When the information rate requirement of the mth user equipment Less than or equal to the achievable information rate r of the mth user equipment m When , it means that the information rate requirement of the mth user equipment is met. Therefore, the dissatisfaction of the mth user device is equal to 0.

[0065] Step 203: Based on the target initial function of user comprehensive dissatisfaction and the constraints of the satellite resource allocation parameters, the satellite resource allocation parameters are determined. The satellite resource allocation parameters at least include the target power of each beam in different sub-bands.

[0066] In practical applications, when user overall dissatisfaction is lowest, overall user satisfaction is highest, indicating the highest overall system rate and the ability to meet the information rate requirements of all user devices. Based on this, various methods can be used to minimize the target initial function for overall user dissatisfaction, thereby obtaining the target power for each beam in each subband. Once the target power for each beam in each subband is obtained, the satellite can control the frequency resources used by each beam in the subband according to the target power.

[0067] As a possible implementation manner, the exemplary embodiment of the present disclosure may further include a process for obtaining channel coefficients. Figure 3 FIG. 1 shows a schematic diagram of a process for obtaining channel coefficients according to an exemplary embodiment of the present disclosure. Figure 3 As shown, the acquisition process of the channel coefficient of the exemplary embodiment of the present disclosure may include:

[0068] Step 301: Based on the satellite's location information, location parameters of multiple user devices, and antenna parameters of multiple beams, determine the beam gain of each beam to different user devices. The antenna parameters of each beam include the maximum antenna gain of the beam and the scanning parameters of the beam.

[0069] In practical applications, the scanning parameters of the beams of the exemplary embodiment of the present disclosure may include scanning parameters of multiple beams. When obtaining the beam gains of each beam to different user equipment, the off-axis angle of each user equipment in different beam main axes can be obtained based on the satellite position information, the position parameters of each user equipment, and the scanning parameters of the multiple beams. Then, based on the off-axis angle of each user equipment in different beam main axes and the maximum antenna gain, the beam gains of the multiple beams to the same user equipment can be determined. Take the beam gain G(q mn ) as an example, it can be expressed as formula 4:

[0070]

[0071] in, θ mn represents the off-axis angle of the mth user equipment in the nth beam main axis, which can be the angle between the mth line and the nth beam main axis, where the mth line can represent the connection between the mth user equipment and the satellite, and the nth beam main axis can be determined by the scanning parameters of the kth beam and the position of the satellite, θ 3dB represents the 3 dB power angle of the nth beam, which can be determined by the scanning parameters of the kth beam and the position of the satellite, J1(·) represents the first-order Bessel function, and J3(·) represents the third-order Bessel function.

[0072] Step 302: Based on the satellite's position information, position parameters of multiple user equipments and the wavelength of each sub-band, determine the path loss from the satellite to different user equipments in each sub-band.

[0073] In practical applications, the path loss from the satellite to different user devices in each sub-band of the exemplary embodiment of the present disclosure is negatively correlated with the wavelength of each sub-band, and the path loss from the satellite to different user devices in each sub-band is also positively correlated with the distance from the satellite to different user devices. Take the path loss l from the satellite to the mth user device in the kth sub-band as an example. mk For example, it can be expressed as formula 5:

[0074] l mk =(4πd m / λ k ) 2 Formula 5

[0075] Among them, d m represents the distance from the satellite to the mth user equipment, which can be determined by the position information of the satellite and the position information of the mth user equipment, λ k represents the wavelength of the kth sub-band.

[0076] Step 303: Determine channel coefficients of multiple beams to different user equipment on each sub-band based on the path loss from the satellite to different user equipment on each sub-band, the beam gain of each beam to different user equipment, and the preset receiving gain of each user equipment.

[0077] In practical applications, the channel coefficient of each beam on each sub-band to different user equipment in the exemplary embodiment of the present disclosure is negatively correlated with the path loss from the satellite to different user equipment on each sub-band, and the channel coefficient of each beam on each sub-band to different user equipment is positively correlated with the beam gain of each beam to different user equipment. For example, it can be expressed as formula 6:

[0078]

[0079] in, represents the preset receiving gain of the mth user equipment.

[0080] As a possible implementation manner, when the time slot resources allocated to each user equipment in different beams are to be optimized, the satellite resource allocation parameter further includes: target time slot resources allocated to each user equipment in different beams. Figure 4 FIG. 1 shows a specific flow chart of a method for determining satellite resource allocation parameters according to an exemplary embodiment of the present disclosure. Figure 4As shown, the exemplary embodiment of the present disclosure determines the satellite resource allocation parameters based on the target initial function of the user's comprehensive dissatisfaction and the constraints of the satellite resource allocation parameters, which may include:

[0081] Step 401: Input the initial power values of multiple beams in different subbands, the initial values of time slot resources allocated to multiple user equipment in different beams, and the initial mapping relationship between beams and user equipment into the target initialization function of the user comprehensive dissatisfaction to obtain an initial value of the user comprehensive dissatisfaction. It should be understood that the power of each beam in different subbands, the time slot resources allocated to each user equipment in different beams, and the mapping relationship between beams and user equipment can be initialized.

[0082] The exemplary embodiments of the present disclosure can determine the initial mapping relationship between wavenumbers and user devices based on the principle of closest distance. For example, the ground coverage area of each beam can be determined based on the scanning parameters of each beam and the position information of the satellite. Then, based on the ground coverage area of each beam and the position information of multiple user devices, the user device closest to each beam's ground coverage area can be determined, thereby obtaining the initial mapping relationship between the beam and the user device.

[0083] The exemplary embodiment of the present disclosure can convert the rated power P of the satellite into T The power is distributed equally to each beam, so that the rated power of each beam is P0=P T / N, and then distribute the rated power P0 allocated to each beam to each sub-band in equal proportion, so as to obtain the initial power P0 / K of each sub-band.

[0084] When the time slot resources allocated to user devices in different beams in the exemplary embodiment of the present disclosure are initialized, the initial values of the time slot resources allocated to each user device in different beams are the same, that is, each beam can allocate time slot resources to each user device in equal proportion. In this case, if the time slot resources allocated to each user device in different beams include the time slot ratios allocated to the user devices in different beams, the time slot ratios allocated to each user device in different beams are the same.

[0085] Step 402: Based on the target initial function of user comprehensive dissatisfaction and the constraints of satellite resource allocation parameters, obtain the optimized values of time slot resources allocated to multiple user equipment in corresponding beams and the optimized power values of multiple beams in different sub-bands.

[0086] In practical applications, considering that the exemplary embodiments of the present disclosure require solving the problem of minimizing the target initial function of user comprehensive dissatisfaction, which is essentially a discontinuous optimization problem, the exemplary embodiments of the present disclosure can alternately optimize the target time slot resources allocated to each user device in different beams and the power to be optimized in different subbands of each beam until the optimization endpoint is reached. Moreover, solving the problem of minimizing the target initial function of user comprehensive dissatisfaction can essentially be viewed as a convex optimization problem. Therefore, it is necessary to ensure that the constraints on the satellite resource allocation parameters are applicable to solving convex optimization problems. Therefore, the constraints on the satellite resource allocation parameters of the exemplary embodiments of the present disclosure may include: convex optimization constraints on time slot resources and convex optimization constraints on power.

[0087] Exemplarily, when obtaining the optimized values of time slot resources allocated to multiple user devices in corresponding beams and the optimized power values of multiple beams in different subbands, the target initial function of user comprehensive dissatisfaction can be solved under the constraints of the convex optimization constraints of time slot resources while keeping the initial power value of each beam in different subbands constant, to obtain the optimized values of time slot resources allocated to multiple user devices in different beams; while keeping the optimized value of time slot resources allocated to each user device in the corresponding beam constant, the target initial function of user comprehensive dissatisfaction can be solved under the constraints of the convex optimization constraints of power, to obtain the optimized power values of multiple beams in different subbands.

[0088] Step 403: Input the optimized values of time slot resources allocated to multiple user equipment in different beams and the optimized power values of multiple beams in different sub-bands into the target initial function of user comprehensive dissatisfaction to determine the optimized value of user comprehensive dissatisfaction.

[0089] Step 404: Whether the difference between the initial value of the user's comprehensive dissatisfaction and the optimized value of the user's comprehensive dissatisfaction is less than or equal to a preset difference.

[0090] If the difference between the initial value of user comprehensive dissatisfaction and the optimized value of user comprehensive dissatisfaction is less than or equal to the preset difference, it means that the optimization of satellite resource allocation parameters has reached the end point, and step 405 can be executed. Otherwise, it means that the satellite resource allocation parameters can be further optimized, and step 406 can be executed. (I+1) -U I |≤δ1 The difference between the initial value of user comprehensive dissatisfaction and the optimized value of user comprehensive dissatisfaction|U (I+1) -U I |Whether it is less than or equal to the preset difference δ1.

[0091] Step 405: Based on the time slot optimization value allocated to each user equipment in different beams, determine the target time slot resources allocated to each user equipment in different beams, and based on the power optimization value to be optimized of each beam in different subbands, determine the target power of each beam in different subbands.

[0092] Step 406: Update the initial value of the time slot resources allocated to each user equipment in different beams based on the optimized value of the time slot resources allocated to each user equipment in different beams, and update the initial power value of each beam in different subbands based on the power optimized value of each beam in different subbands.

[0093] Step 407: Update the initial mapping relationship between the beam and the user equipment based on the initial value of the time slot resource allocated to each user equipment in different beams.

[0094] The exemplary embodiments of the present disclosure can obtain convex optimization constraints for time slot resources by performing convex programming on the initial constraints for time slot resources. If the initial constraints for time slot resources involve both integerized discontinuous optimization problems and continuous optimization problems, a branch-and-bound approach can be used to process the target initial function for user comprehensive dissatisfaction, relax the integer conditions into continuous conditions, and then use the related art minimum value solving method to solve the optimized values of time slot resources allocated to multiple user devices in different beams.

[0095] If the initial objective function for user comprehensive dissatisfaction is convex, the minimum value of this function can be directly solved under the convex optimization constraints of time slot resources to obtain the optimized values of time slot resources allocated to multiple user devices in different beams. If the initial objective function for user comprehensive dissatisfaction is not convex, it needs to be transformed to make it convex.

[0096] In practical applications, the first dissatisfaction upper limit relaxation parameter of the user device can be used to deform the target initial function of the user's comprehensive dissatisfaction to obtain the first target deformation function of the user's comprehensive dissatisfaction. In this case, the first target deformation function of the user's comprehensive dissatisfaction is a convex function. Therefore, under the constraints of the convex optimization constraints of the time slot resources, based on the first target deformation function of the user's comprehensive dissatisfaction, the optimization values of the time slot resources allocated to multiple user devices in different beams are determined.

[0097] When convex programming is performed on the initial constraints of the time slot resources, convex programming can be performed on the constraints included in the initial constraints of the time slot resources that are not applicable to the convex optimization problem, rather than performing convex programming on all constraints included in the initial constraints of the time slot resources.

[0098] Exemplarily, when the initial constraints of the time slot resources include: constraints on the time slot resources allocated to each user device in different beams, constraints on the access state values of each user device to different beams, convex constraints between the signal to interference and noise ratio of each beam to different user devices on multiple subbands and the access state values of each user device to different beams, and convex constraints between the first dissatisfaction upper limit relaxation parameter of the user device and the time slot resources allocated to each user device in multiple beams, there is no related constraint on the access state value of the user device to different beams, which presents a convex constraint, and the access state value of each user device to different beams is discretized. If the access state value of each user device to different beams is not convexly planned, it will make it difficult for the related constraints to present a convex constraint. Therefore, it is necessary to perform convex planning on the access state value of each user device to different beams.

[0099] For example, the large-M method can be used to perform convex programming on the constraints on the access state values of each user device for different beams to obtain convex constraints on the access state values of each user device for different beams. In this case, the convex optimization constraints on time slot resources include: convex constraints on the time slot resources allocated to each user device in different beams, convex constraints on the access state values of each user device for different beams, convex constraints between the signal-to-interference-and-noise ratio of each beam to different user devices on multiple subbands and the access state values of each user device for different beams; and convex constraints between the first dissatisfaction upper limit relaxation parameter of each user device and the time slot resources allocated to each user device in the multiple beams.

[0100] The exemplary embodiment of the present disclosure can obtain the convex optimization constraint condition of power by performing convex programming on the initial constraint condition of power. If the initial constraint condition of power includes both integer optimization problem and continuous optimization problem, the target initial function of user comprehensive dissatisfaction can be processed by branch and bound method, the integer condition is relaxed to continuous condition, and then the power optimization value of each beam in different sub-bands is solved by the minimum value solving method of related technology.

[0101] In practical applications, exemplary embodiments of the present disclosure can first subject the initial frequency constraints to convex optimization constraints to obtain convex power optimization constraints, and then solve for the power optimization value of each beam in different subbands. For example, the target initial function of the user's overall dissatisfaction can be deformed using the user device's second dissatisfaction upper limit relaxation parameter to obtain a second target deformed function of the user's overall dissatisfaction. Under the constraints of the convex power optimization constraints, the second target deformed function of the user's overall dissatisfaction is solved to obtain the power optimization values of multiple beams in different subbands.

[0102] When convex programming is performed on the initial power constraints, convex programming may be performed on the constraints included in the initial power constraints that are not applicable to the convex optimization problem, rather than on all the constraints included in the initial power constraints.

[0103] For example, when the initial power constraints include: power constraints for multiple beams in different subbands, constraints between the signal-to-interference-plus-noise ratio (SINR) of each beam to different user devices on the multiple subbands and the power selection state value of each beam in the different subbands, and constraints between the upper limit of dissatisfaction for each user device and the SINR of each beam to each user device on the multiple subbands, the power constraints for multiple beams in different subbands are convex constraints and do not substantially affect the convex optimization problem. However, the constraints on the SINR of each beam to different user devices on the multiple subbands and the power selection state value of each beam in the different subbands are non-convex constraints. Therefore, convex programming is required for the constraints between the SINR of each beam to different user devices on the multiple subbands and the power selection state value of each beam in the different subbands, and the constraints between the upper limit of dissatisfaction for each user device and the SINR of each beam to each user device on the multiple subbands.

[0104] For example, the large-M method can be used to perform convex planning on the power selection state value of each beam in different subbands to obtain the convex constraints on the power selection state value of each beam in different subbands. At the same time, the signal-to-interference-and-noise ratio γ of the kth subband of the mth user equipment on the nth beam is shown in Formula 1. mn From the expression, we can see that the constraints related to the signal-to-interference-plus-noise ratio (SINR) of a beam to each user device on multiple subbands are not convex constraints. We can introduce a relaxation parameter for the lower limit of the SINR of each beam to different user devices on multiple subbands, and perform convex programming on the constraints between the SINR of each beam to different user devices on multiple subbands and the power selection state value of each beam in different subbands, as well as the constraints between the upper limit of dissatisfaction of each user device and the SINR of each beam to each user device on multiple subbands. In this case, the power convex optimization constraints include: a convex constraint on the powers of multiple beams in different subbands, a convex constraint on the power selection state value of each beam in different subbands, a convex constraint between a lower limit relaxation parameter of the signal to interference plus noise ratio of each beam to different user equipment on multiple subbands and the power selection state value of each beam in different subbands, a convex constraint between a lower limit relaxation parameter of the signal to interference plus noise ratio of each beam to different user equipment on multiple subbands and the signal to interference plus noise ratio of each beam to different user equipment on multiple subbands, and a convex constraint between a second upper dissatisfaction limit relaxation parameter of each user equipment and the lower limit relaxation parameter of the signal to interference plus noise ratio of each beam to each user equipment on multiple subbands.

[0105] As a possible implementation, the target initial function of the user comprehensive dissatisfaction of the exemplary embodiment of the present disclosure can refer to Formula 3. The process of determining the satellite resource allocation parameters can be regarded as minimizing Formula 3, which can be expressed as Formula 7:

[0106]

[0107] Where P represents the power allocation vector, A represents the time slot allocation vector, and Equation 7 is subject to the initial constraints of the time slot resources and the initial constraints of the frequency. The initial constraints of the time slot resources and the initial constraints of the frequency can be uniformly expressed as: Where γ0 represents the minimum signal to interference and noise ratio threshold.

[0108] When obtaining the optimal values of time slot resources allocated to multiple user equipments in different beams, Equation 3 can be minimized, which can be expressed as Equation 8:

[0109]

[0110] Formula 8 is subject to the initial constraints of time slot resources, which can be expressed as Formula 9:

[0111]

[0112] Assume that the number of optimization times of the power of multiple beams in different sub-bands and the number of optimization times of the time slot resources allocated to multiple user equipment in different beams are both I times, and the first power optimization values of multiple beams in different sub-bands can form the first optimization result P of the power allocation vector. (I) When optimizing the time slot resource optimization values allocated to the plurality of user equipments in different beams, the first optimization result P of the power allocation vector is maintained. (I) Under the condition of constant, solve equation 7 to obtain the I+1th time slot resource optimization value allocated to multiple user equipment in different beams, that is, the I+1th optimization result A of the time slot allocation vector (I+1) , represents the I-th power optimization value of the n'th beam in the k-th subband, It represents the I-th power optimization value of the n-th beam in the k-th subband.

[0113] The exemplary embodiment of the present disclosure can regard the process of solving Equation 8 as the process of solving Problem 1. Problem 1 is a mixed integer optimization problem, which can be processed using the branch and bound method. The integer conditions are relaxed into continuous conditions, and Equation 8 is solved to obtain the optimized values of the time slot resources allocated to multiple user equipment in different beams.

[0114] For example, the access of the user equipment and the beam in the exemplary embodiment of the present disclosure is flexibly selected, and the initial constraint condition of the time slot resource shown in formula eight, wherein the sgn(a mn ) is an integer variable and needs to be constrained by convex optimization, and then solve Equation 8.

[0115] For example, you can set B = (b mn ) M×N Set as the user equipment's beam selection access vector, b mn The variable representing the selection of the mth user equipment for the nth beam can be regarded as the access state variable of the user equipment to the beam. The relationship between the beam selection variable and the time slot allocation variable conforms to Equation 10:

[0116]

[0117] If b mn =1, the mth user equipment accesses the nth beam, if b mn = 0, the m-th user equipment does not access the n-th beam. It can be seen that the selection variable of the m-th user equipment for the n-th beam is a discontinuous integer variable. The big-M method can be used to transform Equation 10 to form a convex optimization constraint on the access state value of each user equipment for different beams, which is specifically expressed as Equation 11:

[0118]

[0119] Among them, M1 represents a large positive number, for example, it can be set to 10000000, and ε1 represents a small positive number, for example, it can be set to 0.0001.

[0120] In order to convert the function type of Equation 8 into a convex function, the first dissatisfaction upper limit relaxation parameter of the user device can be used to transform Equation 8 to obtain the first target deformation function of the user's comprehensive dissatisfaction. For example: the first dissatisfaction upper limit relaxation parameter v of the mth user device is m Needs to be satisfied And v m ≥0, which can be converted into formula twelve:

[0121]

[0122] Taking into account The I-th power optimization value of the n-th beam in the k-th subband needs to be used and the I-th power optimization value of the n'th beam in the k-th subband Calculation is performed, and in this update, both are constant values, so, Keep constant.

[0123] The first unsatisfactory upper limit relaxation parameters of the M user equipments can form a first upper limit relaxation vector V = (v1, ..., v m ) 1×M , v1 represents the first dissatisfaction upper limit relaxation parameter of the first user device. In this case, the first objective deformation function of the user's comprehensive dissatisfaction can be expressed as Equation 13:

[0124]

[0125] Among them, Equation 13 can be subject to the convex optimization constraint of the time slot resource, which can be determined by Equations 9, 11, and 12, and further expressed as Equation 14:

[0126]

[0127] Finally, based on Equation 13 and Equation 14, the optimal values of time slot resources allocated to multiple user equipments in different beams can be solved. The solution method can be gradient descent method, Newton method, etc.

[0128] When obtaining the power optimization values of multiple beams in different subbands, Equation 3 can be minimized, which can be expressed as Equation 15:

[0129]

[0130] Equation 15 is subject to the initial constraints of frequency, which can be expressed as Equation 16:

[0131]

[0132] Assuming that the number of optimization times of the power of multiple beams in different subbands and the number of optimization times of the time slot resources allocated to multiple user equipment in different beams are both I times, when the optimization values of the time slot resources allocated to the multiple user equipment in different beams are first optimized, the obtained optimization values of the time slot resources allocated to the multiple user equipment in different beams can form the (I+1)th optimization result A of the time slot allocation vector. (I+1) , while maintaining the I+1th optimization result A of the time slot allocation vector (I+1) Under the condition of constant, the formula 16 is combined to solve the formula 15 to obtain the I+1th power optimization value of multiple beams in different sub-bands, that is, the Ith optimization result P of the power allocation vector (I+1) , It represents the (I+1)th timeslot resource optimization value allocated to the mth user equipment in the nth beam.

[0133] The exemplary embodiment of the present disclosure can regard the process of solving Equation 15 as the process of solving Problem 2. Problem 2 is a mixed integer optimization problem, which can be processed using the branch and bound method. The integer conditions are relaxed to continuous conditions, and Equation 15 is solved to obtain the optimized values of the time slot resources allocated to multiple user devices in different beams.

[0134] For example, the beam of the exemplary embodiment of the present disclosure is flexibly selected for different sub-bands, with respect to the initial constraint condition of the frequency shown in Formula 16, where the sgn(p nk ) is an integer variable and needs to be constrained by convex optimization to obtain the convex constraints on the power selection state values of each beam in different sub-bands.

[0135] For example, the exemplary embodiment of the present disclosure can be represented by C=(c nk ) N×K Indicates the usage of multiple beams for different sub-bands. C can be set as the frequency usage state vector, c nk It represents the usage of the nth beam for the kth subband, which can be regarded as the usage state variable of the beam for the subband, that is, the selection variable of the beam for the subband. The relationship between the power of the beam in the subband and the selection variable of the beam for the subband conforms to Equation 17:

[0136]

[0137] c nk Is a binary variable 0-1, if c nk =1, the nth beam selects the kth subband, if c nk = 0, the nth beam does not select the kth subband. It can be seen that the selection variable of the nth beam for the kth subband is a discontinuous integer variable. The big-M method can be used to transform Equation 17 to form a convex optimization constraint for the power selection state value of each beam in different subbands, which is specifically expressed as Equation 18:

[0138]

[0139] Wherein, M2 represents a large positive number, for example, it can be set to 10000000, and ε2 represents a small positive number, for example, it can be set to 0.00001.

[0140] In order to convert the function type of Equation 15 into a convex function, the second upper limit relaxation parameter of dissatisfaction of the user device can be used to transform Equation 15 to obtain the second target deformation function of the user's comprehensive dissatisfaction. For example: the second upper limit relaxation parameter of dissatisfaction of the mth user device is t m Needs to be satisfied And t m ≥0, which can be converted into formula 19:

[0141]

[0142] The second upper limit relaxation parameters of the M user equipments can form a second upper limit relaxation vector T=(t1,…,t m ) 1×M In this case, the second objective deformation function of user comprehensive dissatisfaction can be expressed as Equation 20:

[0143]

[0144] Wherein, Equation 20 may be subject to an improved power constraint, which may be determined by Equations 16, 18, and 19, and further expressed as Equation 21:

[0145]

[0146] By referring to Equation 1, we can see that the constraints related to the signal-to-interference-plus-noise ratio (SINR) of the beam to the user equipment on the subband are all non-convex constraints. Therefore, we can introduce a relaxation parameter for the lower limit of the SINR of the beam to the user equipment on the subband, perform convex programming on the constraints related to the SINR of the beam to the user equipment on the subband, and then use the successive convex approximation method to solve Problem 2.

[0147] When using the successive convex approximation method to solve problem 2, the signal-to-interference-plus-noise ratio constraints involving the beam to the user equipment on the subband can be converted into convex constraints. Based on the convex constraints on the power selection state value of each beam in different subbands and the convex constraints on the signal-to-interference-plus-noise ratio of each beam to different user equipment on multiple subbands, the initial frequency constraints are updated, and ultimately the convex optimization constraints on power are obtained.

[0148] For example, there are two non-convex constraints in Equation 21: as well as Therefore, the k-th subband signal to interference and noise ratio of the m-th user equipment on the n-th beam is introduced as The lower limit relaxation parameter By transforming these two non-convex constraints, we can obtain Equation 22 as shown below:

[0149]

[0150] in, It can be rewritten as Equation 23:

[0151]

[0152] Next, let Q = (q nk ) N×K , let p n'k =(q n'k )2 , p nk =(q nk ) 2 , using the Taylor expansion method, Equation 23 is changed to Equation 24 as shown below, and then the successive convex approximation algorithm is used to solve the power optimization value of each beam in different subbands:

[0153]

[0154] Among them, in the successive convex approximation algorithm, represents the vth power optimization value of the nth beam in the kth subband, represents the signal-to-interference-and-noise ratio of the kth subband of the mth user equipment on the nth beam The vth lower bound relaxation parameter optimization value.

[0155] The exemplary embodiments of the present disclosure may define the lower limit relaxation parameters of different sub-band signal to interference noise ratios of multiple user equipments on multiple beams as a lower limit relaxation parameter vector Γ. In this case, Equation 20 is transformed into Equation 25 as shown below:

[0156]

[0157] Among them, Equation 25 is subject to the power convex optimization constraint, which can be determined by Equations 21, 22, and 24, and specifically expressed as Equation 26:

[0158]

[0159] Finally, based on Equations 25 and 26, the successive approximation method can be used to solve the optimized power values of multiple beams in different subbands. For example, the initial power values of multiple beams in different subbands can be obtained by equally dividing the power of each subband within the beam. For details, please refer to the previous article until the following is finally satisfied: and

[0160] Among them, when the successive approximation method is used to solve the I+1th power optimization value of the nth beam in the kth subband, the I+1th power optimization value also has a cyclic iterative process in the successive approximation process. In the successive approximation process, represents the v+1th power optimization value of the nth beam in the kth subband, represents the signal-to-interference-and-noise ratio of the kth subband of the mth user equipment on the nth beam The v+1th lower bound relaxation parameter optimization value of , δ2 represents the iterative difference threshold of the total power, and δ3 represents the iterative difference threshold of the total signal to interference and noise ratio.

[0161] One or more technical solutions provided in the exemplary embodiments of the present disclosure can determine the achievable information rate of multiple user devices based on the channel coefficients of each beam to different user devices in different sub-bands, the power to be optimized of each beam in different sub-bands, the time slot resources allocated to multiple user devices in different beams, and the total available bandwidth of the satellite. The achievable information rate of each user device can be regarded as the total channel rate of multiple beams to the user device in multiple sub-bands. Therefore, based on the relationship between the achievable information rates of the multiple user devices, the information rate requirements of the multiple user devices, and the dissatisfaction of multiple users, a target initial function of the user's comprehensive dissatisfaction and constraints on the satellite resource allocation parameters can be established. Then, based on the target initial function of the user's comprehensive dissatisfaction and the constraints on the satellite resource allocation parameters, the power to be optimized of each beam in different sub-bands can be optimized, so that the determined satellite resource allocation parameters include the target power of each beam in the different sub-bands.

[0162] It can be seen that the exemplary embodiment of the present disclosure can introduce the power to be optimized of each beam in different sub-bands into the target initial function of the user's comprehensive dissatisfaction through the achievable information rate of multiple user devices, and then solve the target initial function of the user's comprehensive dissatisfaction to achieve the optimization of the power to be optimized of each beam in different sub-bands, thereby obtaining the target power of each beam in different sub-bands.

[0163] Moreover, since the power to be optimized of each beam in different sub-bands is related to the achievable information rate of multiple user devices, when the power to be optimized of each beam in different sub-bands is optimal, the achievable information rate of multiple user devices also reaches an optimal state. Therefore, the exemplary embodiment of the present disclosure achieves full utilization of satellite power resources by optimizing the power to be optimized of each beam in different sub-bands.

[0164] Optionally, in the process of optimizing the time slot resources allocated to the user equipment in the beam and the power of the beam in the subband, the exemplary embodiment of the present disclosure studies the influence of four variables, namely the power of the beam in the subband, the time slot resources allocated to the user equipment in the beam, the access status of the user equipment to the beam, and the usage of the beam in the subband, on the resource allocation effect. It considers many variable attributes and can better obtain the optimal time slot resources allocated to the user equipment in the beam and the power of the beam in the subband.

[0165] The target initialization function for comprehensive user dissatisfaction in the exemplary embodiments of the present disclosure can be viewed as a full-bandwidth utilization model. By minimizing this target initialization function, utilization can be significantly improved. Furthermore, the reachable information rate of the user device involved in this target initialization function takes into account inter-beam interference, making the full-bandwidth utilization model more realistic.

[0166] Taking into account the fact that the frequency constraint will become non-convex after the interference between beams is introduced, the successive convex approximation method can be used to solve the minimum value of the target initial function of the user's comprehensive dissatisfaction to obtain the target power of each beam in different sub-bands.

[0167] By solving the minimum value of the target initial function for user comprehensive dissatisfaction, the method of the exemplary embodiment of the present disclosure can ensure that when excess broadband resources can meet the needs of other user devices with unmet needs, the excess broadband resources can be used to meet the needs of other user devices with unmet needs. When the excess broadband resources cannot help other user devices meet their needs, more broadband resources can be provided to meet the needs of the user devices. Therefore, when allocating resources, the exemplary embodiment of the present disclosure does not consider the problem of resource waste, but instead allocates resources based on the satisfaction of user devices.

[0168] As can be seen, the method of the exemplary embodiments of the present disclosure can fully account for the differences in requirements between user devices and perform multi-dimensional resource allocation parameter optimization. This allows the allocation of resources to different user devices using satellite resource allocation parameters to better meet the needs of different user devices, thereby more fully utilizing satellite wireless resources. For example, the exemplary embodiments of the present disclosure can solve the resource allocation problem between multiple user devices in GEO multi-beam satellite scenarios, with a wide range of applicable scenarios and potential applications in the field of satellite communications.

[0169] The above mainly introduces the solution provided by the embodiment of the present disclosure from the perspective of the server. It can be understood that in order to realize the above functions, the server includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0170] The embodiments of the present disclosure can divide the server into functional units according to the above method examples. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiments of the present disclosure is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0171] In the case of dividing the functional modules according to their functions, an exemplary embodiment of the present disclosure provides an apparatus for determining satellite resource allocation parameters. The apparatus for determining satellite resource allocation parameters may be a server or a chip applied to the server. Figure 5 FIG. 1 shows a schematic block diagram of functional modules of a device for determining satellite resource allocation parameters according to an exemplary embodiment of the present disclosure. Figure 5 As shown, the satellite involved in the satellite resource allocation parameter determination device 500 has multiple beams and multiple sub-bands, and the device includes:

[0172] A determination module 501 is configured to determine a reachable information rate for multiple user equipments based on a channel coefficient of each beam to different user equipments on different subbands, a power to be optimized for each beam in different subbands, time slot resources allocated to the multiple user equipments in different beams, and a total available bandwidth of the satellite;

[0173] A modeling module 502 is configured to establish a target initial function of user comprehensive dissatisfaction and constraint conditions of satellite resource allocation parameters based on the relationship between the achievable information rates of the plurality of user devices, the information rate requirements of the plurality of user devices, and the dissatisfaction of the plurality of user devices;

[0174] The solution module 503 is configured to determine satellite resource allocation parameters based on the target initial function of the user comprehensive dissatisfaction and the constraints of the satellite resource allocation parameters, where the satellite resource allocation parameters at least include target powers of the respective beams in different sub-bands.

[0175] In one possible implementation, the determination module 501 is further used to determine the beam gain of each beam to different user devices based on the position information of the satellite, the position parameters of the multiple user devices, and the antenna parameters of the multiple beams, where the antenna parameters of each beam include the maximum antenna gain of the beam and the scanning parameters of the beam; based on the position information of the satellite, the position parameters of the multiple user devices, and the wavelength of each sub-band, determine the path loss from the satellite to different user devices on each sub-band; based on the path loss from the satellite to different user devices on each sub-band, the beam gain of each beam to different user devices, and the preset receiving gain of each user device, determine the channel coefficients of the multiple beams to different user devices on each sub-band.

[0176] In one possible implementation, the determination module 501 is configured to determine a subband signal-to-interference-plus-noise ratio (SINR) of each user equipment on different beams based on a channel coefficient of each beam to different user equipment on multiple subbands, a power to be optimized for each beam in different subbands, and noise from a ground receiver to the satellite; and determine a achievable information rate for the multiple user equipment based on the subband SINRs of the multiple user equipment on different beams, time slot resources allocated to the multiple user equipment in the different beams, and a total available bandwidth of the satellite.

[0177] In a possible implementation, the dissatisfaction of the user equipment is negatively correlated with the achievable information rate of the user equipment, and the dissatisfaction of the user equipment is positively correlated with the information rate requirement of the user equipment;

[0178] If the information rate requirement of the user equipment is greater than the achievable information rate of the user equipment, the unsatisfactory degree of the user equipment is a constant satisfaction degree;

[0179] If the information rate requirement of the user equipment is less than or equal to the achievable information rate of the user equipment, the dissatisfaction of the user equipment is determined by the information rate requirement of the user equipment and the achievable information rate of the user equipment.

[0180] In a possible implementation manner, the bandwidth allocated to each user equipment in different beams is determined by the total bandwidth available of the satellite and the time slot resources allocated to the user equipment in different beams;

[0181] When the time slot resources allocated to each user equipment in different beams need to be optimized, the satellite resource allocation parameters further include: target time slot resources allocated to each user equipment in different beams.

[0182] In a possible implementation, the solving module 503 is configured to input the initial power values of the multiple beams in different subbands, the initial values of the time slot resources allocated to the multiple user equipments in different beams, and the initial mapping relationship between the beams and the user equipments into the target initial function of the user comprehensive dissatisfaction, to obtain an initial value of the user comprehensive dissatisfaction;

[0183] Based on the target initial function of the user comprehensive dissatisfaction and the constraint conditions of the satellite resource allocation parameters, obtaining the optimized values of the time slot resources allocated to the multiple user equipments in the corresponding beams and the optimized power values of the multiple beams in different sub-bands;

[0184] Inputting the optimized values of time slot resources allocated to the plurality of user equipments in different beams and the optimized power values of the plurality of beams in different sub-bands into the target initial function of the user comprehensive dissatisfaction, to determine the optimized value of the user comprehensive dissatisfaction;

[0185] If the difference between the initial value of the user's comprehensive dissatisfaction and the optimized value of the user's comprehensive dissatisfaction is less than or equal to the preset difference, based on the time slot optimization value allocated to each user device in the different beams, determine the target time slot resources allocated to each user device in the different beams, and based on the power optimization value to be optimized of each beam in the different subbands, determine the target power of each beam in the different subbands.

[0186] In one possible implementation, the solution module 503 is used to update the initial value of the time slot resources allocated to each user device in different beams based on the optimized value of the time slot resources allocated to each user device in different beams, update the initial power value of each beam in different subbands based on the power optimization value of each beam in different subbands, and update the initial mapping relationship between the beam and the user device based on the initial value of the time slot resources allocated to each user device in different beams if the difference between the initial value of the user's comprehensive dissatisfaction and the optimized value of the user's comprehensive dissatisfaction is greater than a preset difference.

[0187] In one possible implementation, the constraints of the satellite resource allocation parameters include: a convex optimization constraint of time slot resources and a convex optimization constraint of power. The solution module 503 is used to solve the target initial function of the user's comprehensive dissatisfaction under the constraint of the convex optimization constraint of the time slot resources while keeping the initial power value of each beam in different sub-bands constant, and obtain the optimized values of time slot resources allocated to multiple user devices in different beams; while keeping the optimized value of time slot resources allocated to each user device in the corresponding beam constant, solve the target initial function of the user's comprehensive dissatisfaction under the constraint of the convex optimization constraint of power, and obtain the optimized values of power of multiple beams in different sub-bands.

[0188] In one possible implementation, the solution module 503 is used to use the first dissatisfaction upper limit relaxation parameter of the user equipment to deform the target initial function of the user's comprehensive dissatisfaction to obtain the first target deformation function of the user's comprehensive dissatisfaction; under the constraint of the convex optimization constraint of the time slot resources, based on the first target deformation function of the user's comprehensive dissatisfaction, determine the optimal values of the time slot resources allocated to the multiple user equipment in different beams.

[0189] In a possible implementation, the convex optimization constraint of the time slot resource includes:

[0190] a convex constraint on the time slot resources allocated to each of the user equipments in the plurality of beams;

[0191] Convex constraints on access state values of each user equipment to different beams;

[0192] a convex constraint between a signal to interference plus noise ratio (SINR) of each beam to different user equipments on a plurality of subbands and an access state value of each user equipment to different beams; and

[0193] A convex constraint is defined between a first unsatisfactory upper limit relaxation parameter of each user equipment and a time slot resource allocated to each user equipment in the plurality of beams.

[0194] In one possible implementation, the solution module 503 is used to use the second dissatisfaction upper limit relaxation parameter of the user equipment to deform the target initial function of the user's comprehensive dissatisfaction to obtain the second target deformation function of the user's comprehensive dissatisfaction; under the constraint of the power convex optimization constraint condition, the second target deformation function of the user's comprehensive dissatisfaction is solved to obtain the power optimization values of multiple beams in different sub-bands.

[0195] In a possible implementation, the power convex optimization constraint includes:

[0196] Convex power constraints of the plurality of beams in different sub-bands;

[0197] Convex constraints on the power selection state values of each beam in different sub-bands;

[0198] a convex constraint between a lower limit relaxation parameter of a signal to interference plus noise ratio of each beam to different user equipments on the multiple subbands and a power selection state value of each beam in different subbands;

[0199] A convex constraint between a lower limit relaxation parameter of the signal to interference plus noise ratio of each beam to different user equipments on multiple subbands and the signal to interference plus noise ratio of each beam to different user equipments on multiple subbands; and

[0200] A convex constraint is defined between a second unsatisfactory upper limit relaxation parameter of each user equipment and a lower limit relaxation parameter of a signal to interference plus noise ratio of each beam to each user equipment on the plurality of subbands.

[0201] Figure 6 FIG. 1 shows a schematic block diagram of a chip according to an exemplary embodiment of the present disclosure. Figure 6As shown, the chip 600 includes one or more (including two) processors 601 and a communication interface 602. The communication interface 602 can support the server to execute the data sending and receiving steps in the above method, and the processor 601 can support the server to execute the data processing steps in the above method.

[0202] Optional, such as Figure 6 As shown, the chip 600 also includes a memory 603, which may include a read-only memory and a random access memory, and provides operation instructions and data to the processor. Part of the memory may also include a non-volatile random access memory (NVRAM).

[0203] In some embodiments, as Figure 6 As shown, the processor 601 performs corresponding operations by calling the operation instructions stored in the memory (the operation instructions may be stored in the operating system). The processor 601 controls the processing operations of any one of the terminal devices, and the processor may also be called a central processing unit (CPU). The memory 603 may include a read-only memory and a random access memory, and provides instructions and data to the processor 601. A portion of the memory 603 may also include NVRAM. For example, in an application, the memory, the communication interface, and the memory are coupled together through a bus system, wherein the bus system may include a power bus, a control bus, and a status signal bus in addition to a data bus. However, for the sake of clarity, in Figure 6 Various buses are labeled as bus system 604 .

[0204] The methods disclosed in the above embodiments of the present disclosure can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor or by software instructions. The above processor may be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The methods, steps, and logic block diagrams disclosed in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in conjunction with the embodiments of the present disclosure can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0205] The exemplary embodiments of the present disclosure further provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being configured to cause the electronic device to perform a method according to an exemplary embodiment of the present disclosure when executed by the at least one processor.

[0206] Exemplary embodiments of the present disclosure further provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform a method according to an embodiment of the present disclosure.

[0207] Exemplary embodiments of the present disclosure further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor of a computer, it is used to cause the computer to perform the method according to the embodiment of the present disclosure.

[0208] refer to Figure 7, a block diagram of an electronic device 700 that can serve as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0209] like Figure 7 As shown, electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of device 700 can also be stored in RAM 703. Computing unit 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to bus 704.

[0210] like Figure 7 As shown, multiple components within electronic device 700 are connected to I / O interface 705, including an input unit 706, an output unit 707, a storage unit 708, and a communication unit 709. Input unit 706 can be any type of device capable of inputting information into electronic device 700. Input unit 706 can receive input digital or character information and generate key input signals related to user settings and / or function control of the electronic device. Output unit 707 can be any type of device capable of presenting information and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. Storage unit 708 may include, but is not limited to, a magnetic disk or an optical disk. Communication unit 709 allows electronic device 700 to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0211] like Figure 7As shown, the computing unit 701 can be various general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above. For example, in some embodiments, the method of the exemplary embodiments of the present disclosure may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 700 via the ROM 702 and / or the communication unit 709. In some embodiments, the computing unit 701 can be configured to perform the exemplary embodiment method of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0212] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0213] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0214] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0215] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0216] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0217] Computer systems may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other.

[0218] In the above embodiments, they can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present disclosure are performed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program or instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, a hard disk, or a tape; it can also be an optical medium, such as a digital video disc (DVD); it can also be a semiconductor medium, such as a solid state drive (SSD).

[0219] Although the present disclosure has been described with reference to specific features and embodiments thereof, it will be apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present disclosure. Accordingly, this specification and the drawings are merely illustrative of the present disclosure as defined by the appended claims and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present disclosure. Obviously, those skilled in the art may make various modifications and variations to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, the present disclosure is intended to include such modifications and variations if they fall within the scope of the claims of the present disclosure and their equivalents.

Claims

1. A method for determining satellite resource allocation parameters, characterized in that: The satellite has multiple beams and multiple sub-bands, and the method includes: Determine, based on the channel coefficients of the beams to different user equipments on the different sub-bands, the power to be optimized of the beams on the different sub-bands, the time slot resources allocated to the user equipments in the different beams, and the total available bandwidth of the satellite, the achievable information rates of the user equipments; Establishing a target initial function of user comprehensive dissatisfaction and constraint conditions of satellite resource allocation parameters based on the relationship between the achievable information rates of the plurality of user devices, the information rate requirements of the plurality of user devices, and the dissatisfaction of the plurality of user devices; Based on the target initial function of the user comprehensive dissatisfaction and the constraint conditions of the satellite resource allocation parameters, the satellite resource allocation parameters are determined, where the satellite resource allocation parameters at least include target powers of the respective beams in different sub-bands.

2. The method according to claim 1, characterized in that The method further comprises: Determining, based on the satellite position information, position parameters of the plurality of user equipments, and antenna parameters of the plurality of beams, a beam gain of each of the beams to different user equipments, where the antenna parameters of each beam include a maximum antenna gain of the beam and a scanning parameter of the beam; determining, based on the position information of the satellite, position parameters of the plurality of user equipments, and the wavelength of each sub-band, a path loss from the satellite to different user equipments in each sub-band; Based on the path loss from the satellite to different user equipments in each sub-band, the beam gain of each beam to different user equipments, and the preset reception gain of each user equipment, the channel coefficients of the multiple beams to different user equipments in each sub-band are determined.

3. The method according to claim 1, characterized in that The determining, based on the channel coefficients of the beams to different user equipments in different sub-bands, the power to be optimized of the beams in different sub-bands, the time slot resources allocated to the user equipments in the different beams, and the total available bandwidth of the satellite, a reachable information rate for the user equipments, includes: Determining a subband signal-to-interference-plus-noise ratio (SINR) of each user equipment on different beams based on channel coefficients of each beam to different user equipment on multiple subbands, powers to be optimized of each beam on different subbands, and noise from a ground receiver to the satellite; The achievable information rates of the multiple user equipments are determined based on subband signal to interference and noise ratios of the multiple user equipments on different beams, time slot resources allocated to the multiple user equipments in the different beams, and the total available bandwidth of the satellite.

4. The method according to claim 1, wherein The user equipment's dissatisfaction is negatively correlated with the achievable information rate of the user equipment, and the user equipment's dissatisfaction is positively correlated with the information rate requirement of the user equipment; If the information rate requirement of the user equipment is greater than the achievable information rate of the user equipment, the unsatisfactory degree of the user equipment is a constant satisfaction degree; If the information rate requirement of the user equipment is less than or equal to the achievable information rate of the user equipment, the dissatisfaction of the user equipment is determined by the information rate requirement of the user equipment and the achievable information rate of the user equipment.

5. The method according to any one of claims 1 to 4, characterized in that When the time slot resources allocated to each user equipment in different beams need to be optimized, the satellite resource allocation parameters further include: target time slot resources allocated to each user equipment in different beams.

6. The method according to claim 5, characterized in that The determining of the satellite resource allocation parameters based on the target initial function of the user comprehensive dissatisfaction and the constraint conditions of the satellite resource allocation parameters includes: Inputting initial power values of the plurality of beams in different subbands, initial values of time slot resources allocated to the plurality of user equipments in different beams, and initial mapping relationships between the beams and the user equipments into the target initial function of the user comprehensive dissatisfaction, to obtain an initial value of the user comprehensive dissatisfaction; Based on the target initial function of the user comprehensive dissatisfaction and the constraint conditions of the satellite resource allocation parameters, obtaining the optimized values of the time slot resources allocated to the multiple user equipments in the corresponding beams and the optimized power values of the multiple beams in different sub-bands; Inputting the optimized values of time slot resources allocated to the plurality of user equipments in different beams and the optimized power values of the plurality of beams in different sub-bands into the target initial function of the user comprehensive dissatisfaction, to determine the optimized value of the user comprehensive dissatisfaction; If the difference between the initial value of the user's comprehensive dissatisfaction and the optimized value of the user's comprehensive dissatisfaction is less than or equal to the preset difference, based on the time slot optimization value allocated to each user device in the different beams, determine the target time slot resources allocated to each user device in the different beams, and based on the power optimization value to be optimized of each beam in the different subbands, determine the target power of each beam in the different subbands.

7. The method according to claim 6, characterized in that The determining of the satellite resource allocation parameters based on the target initial function of the user comprehensive dissatisfaction and the constraint conditions of the satellite resource allocation parameters further includes: If the difference between the initial value of the user's comprehensive dissatisfaction and the optimized value of the user's comprehensive dissatisfaction is greater than the preset difference, the initial value of the time slot resource allocated to each user device in the different beams is updated based on the optimized value of the time slot resource allocated to each user device in the different beams, the initial power value of each beam in the different subbands is updated based on the power optimization value of each beam in the different subbands, and the initial mapping relationship between the beam and the user device is updated based on the initial value of the time slot resource allocated to each user device in the different beams.

8. The method according to claim 6, characterized in that The satellite resource allocation parameter constraint conditions include: a convex optimization constraint condition for time slot resources and a convex optimization constraint condition for power. The objective initial function based on the user comprehensive dissatisfaction and the satellite resource allocation parameter constraint conditions is used to obtain optimized time slot resource values allocated to multiple user equipment in corresponding beams and optimized power values of multiple beams in different sub-bands, including: While keeping the initial power value of each beam in different sub-bands constant, and under the constraint of the convex optimization constraint of the time slot resources, solving the target initial function of the user comprehensive dissatisfaction, to obtain the optimized values of the time slot resources allocated to the multiple user equipments in different beams; While keeping the optimized value of the time slot resources allocated to each user equipment in the corresponding beam constant, the target initial function of the user's comprehensive dissatisfaction is solved under the constraints of the power convex optimization constraint condition to obtain the power optimization values of multiple beams in different sub-bands.

9. The method according to claim 8, characterized in that The step of solving the target initial function of the user comprehensive dissatisfaction under the convex optimization constraint of the time slot resource to obtain the optimized values of the time slot resources allocated to the plurality of user equipments in the different beams includes: Deforming the target initial function of the user comprehensive dissatisfaction by using the first dissatisfaction upper limit relaxation parameter of the user device to obtain a first target deformed function of the user comprehensive dissatisfaction; Under the constraint of the convex optimization constraint condition of the time slot resources, based on the first objective deformation function of the user comprehensive dissatisfaction, the optimal values of the time slot resources allocated to the multiple user equipments in different beams are determined.

10. The method according to claim 8, characterized in that The convex optimization constraints of the time slot resources include: Convex constraints on time slot resources allocated to each of the user equipments in different beams; Convex constraints on access state values of each user equipment to different beams; a convex constraint between a signal to interference plus noise ratio (SINR) of each beam to different user equipments on a plurality of subbands and an access state value of each user equipment to different beams; and A convex constraint is defined between a first unsatisfactory upper limit relaxation parameter of each user equipment and a time slot resource allocated to each user equipment in the plurality of beams.

11. The method according to claim 8, characterized in that Solving the target initial function of the user comprehensive dissatisfaction under the constraints of the power convex optimization constraint condition to obtain power optimization values of the plurality of beams in different sub-bands includes: Deforming the target initial function of the user comprehensive dissatisfaction by using the second dissatisfaction upper limit relaxation parameter of the user device to obtain a second target deformed function of the user comprehensive dissatisfaction; Under the constraints of the power convex optimization constraint condition, the second objective deformation function of the user comprehensive dissatisfaction is solved to obtain power optimization values of the multiple beams in different sub-bands.

12. The method according to claim 8, characterized in that The power convex optimization constraints include: Convex power constraints of the plurality of beams in different sub-bands; Convex constraints on the power selection state values of each beam in different sub-bands; a convex constraint between a lower limit relaxation parameter of a signal to interference plus noise ratio of each beam to different user equipments on the multiple subbands and a power selection state value of each beam in different subbands; A convex constraint between a lower limit relaxation parameter of the signal to interference plus noise ratio of each beam to different user equipments on multiple subbands and the signal to interference plus noise ratio of each beam to different user equipments on multiple subbands; and A convex constraint is defined between a second unsatisfactory upper limit relaxation parameter of each user equipment and a signal to interference plus noise ratio lower limit relaxation parameter of each beam to each user equipment on the plurality of subbands.

13. A device for determining satellite resource allocation parameters, characterized in that: The satellite has multiple beams and multiple sub-bands, and the apparatus includes: a determination module, configured to determine a reachable information rate for multiple user equipments based on a channel coefficient of each beam to different user equipments on different sub-bands, a power to be optimized for each beam in different sub-bands, time slot resources allocated to the multiple user equipments in different beams, and a total available bandwidth of the satellite; a modeling module, configured to establish a target initial function of user comprehensive dissatisfaction and constraint conditions of satellite resource allocation parameters based on the relationship between the achievable information rates of the plurality of user devices, the information rate requirements of the plurality of user devices, and the dissatisfaction of the plurality of user devices; A solution module is configured to determine satellite resource allocation parameters based on the target initial function of the user comprehensive dissatisfaction and the constraints of the satellite resource allocation parameters, where the satellite resource allocation parameters at least include target powers of the respective beams in different sub-bands.

14. An electronic device, characterized in that: include: processor; as well as, Memory for storing programs; The program includes instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 12.

15. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 12.

16. A computer program product, characterized in that The method comprises a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 12 is implemented.

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