Method and device for dynamic hopping beam and resource allocation of multi-satellite facing non-uniform demand

Abstract

The application provides a multi-satellite dynamic jump-beam and resource allocation method and device for non-uniform demand, which comprises the following steps: constructing a network benefit minimization model and a target constraint condition of a multi-beam NGSO communication system; the network benefit minimization model is used for minimizing the network benefit of the multi-beam NGSO communication system under the condition of non-uniform traffic demand of each cell in the multi-beam NGSO communication system; and the jump-beam mode and the resource allocation mode of the multi-beam NGSO communication system are determined according to the network benefit minimization model and the target constraint condition of the multi-beam NGSO communication system. The method of the application realizes the balance between the network benefit and the queue stability of the NGSO communication system, and effectively solves the current situation and challenge of non-uniform traffic demand distribution in the NGSO communication process.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and in particular to a multi-satellite dynamic beam hopping and resource allocation method and apparatus for non-uniform demand. Background Technology

[0002] Satellite communication has become a key technology for next-generation communication systems due to its wide coverage, high flexibility, and cost-effectiveness. In recent years, the rapid development of non-geostationary orbit (NGSO) satellites has also accelerated the process of achieving seamless global coverage.

[0003] In related technologies, due to the development of data services, ground traffic exhibits non-uniform characteristics in both the time and spatial domains. Therefore, how to jointly optimize the onboard resources of the NGSO communication system across multiple dimensions to effectively address the current situation and challenges of uneven traffic demand distribution is a pressing issue that needs to be addressed by those skilled in the art. Summary of the Invention

[0004] To address the problems in the prior art, embodiments of the present invention provide a method and apparatus for multi-satellite dynamic beam hopping and resource allocation for non-uniform requirements.

[0005] Specifically, the embodiments of the present invention provide the following technical solutions:

[0006] In a first aspect, embodiments of the present invention provide a multi-satellite dynamic beam-hopping and resource allocation method for non-uniform demand, comprising:

[0007] A network benefit minimization model and objective constraints for a multi-beam NGSO communication system are constructed. The network benefit minimization model is used to minimize the network benefit of the multi-beam NGSO communication system under the condition of non-uniform traffic demand in each cell. The objective constraints include satellite and cell illumination relationship constraints, satellite bandwidth resource constraints, satellite power resource constraints, and long-term stability constraints on the amount of data to be transmitted in the cell.

[0008] Based on the network benefit minimization model of the multi-beam NGSO communication system and the target constraints, the beam skipping mode and resource allocation mode of the multi-beam NGSO communication system are determined.

[0009] Furthermore, the network benefit minimization model of the multi-beam NGSO communication system includes:

[0010]

[0011]

[0012] Where U(t) represents the network benefits of the multi-beam NGSO communication system; X(t) represents the illumination relationship between the satellite and the cell; B(t) represents the satellite bandwidth allocation method; P(t) represents the satellite power allocation method; T represents the time length; α represents the weighting factor; and K represents the cell set of the multi-beam NGSO communication system. The set of satellites in a multi-beam NGSO communication system is represented by k; s represents a cell; h represents a satellite. k,s (t) indicates the switching penalty term; P k,s (t) represents the illumination power of satellite s onto cell k;

[0013] The target constraints include:

[0014] C1:

[0015] C2:

[0016] C3:

[0017] C4:

[0018] C5:

[0019] C6:

[0020] C7:

[0021] Where C1 represents that each satellite s can generate at most L at the same time. s C1 represents a single-beam service cell; C2 indicates that cell k is illuminated by at most one satellite at a time; C3 represents a Boolean constraint; x k,s (t) indicates that cell k is illuminated by satellite s at time t; in C4, b k (t) represents the cell bandwidth; B tot Indicates total bandwidth; B ch This indicates that the total bandwidth B tot Divided into M blocks, each block has a bandwidth; C5 represents the cell bandwidth b. k The allocation method of (t) is as follows Type; C6 indicates that the single-beam transmit power is less than the maximum transmit power P. max C7 indicates that the cell's data queue cannot grow indefinitely; Q k (t) represents the amount of data to be transmitted in cell k.

[0022] Further, determining the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system based on the network benefit minimization model of the multi-beam NGSO communication system and the objective constraints includes:

[0023] Based on the Lyapunov optimization framework, the network benefit minimization model of the multi-beam NGSO communication system is transformed to obtain a single-time-slot target model; the single-time-slot target model is used to minimize the sum of cell capacity demand gap and system utility in each time slot;

[0024] Based on the single-time-slot target model, the beam-hopping mode and resource allocation mode of the multi-beam NGSO communication system are determined.

[0025] Furthermore, the single-slot target model includes:

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] Among them, Q k (t) represents the amount of data to be transmitted in cell k; R k (t) represents the data transmission capacity provided by cell k in time slot t; V represents the penalty factor.

[0034] Further, determining the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system based on the single-time-slot target model includes:

[0035] The single-slot target model is decomposed into a first model, a second model, and a third model. The first model is used to minimize the sum of cell capacity demand gap and satellite handover times given satellite power resources and satellite bandwidth resources. The second model is used to minimize cell capacity demand gap and system power consumption given cell illumination relationships and satellite bandwidth resources. The third model is used to minimize cell capacity demand gap given satellite bandwidth resources and satellite power resources.

[0036] Based on the first model, the second model, and the third model, the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system are determined.

[0037] Furthermore, the first model includes:

[0038]

[0039]

[0040]

[0041]

[0042] The second model includes:

[0043]

[0044]

[0045] The third model includes:

[0046]

[0047]

[0048]

[0049] Secondly, embodiments of the present invention also provide a multi-satellite dynamic beam-hopping and resource allocation device for non-uniform demand, comprising:

[0050] The construction module is used to construct a network benefit minimization model and objective constraints for a multi-beam NGSO communication system. The network benefit minimization model is used to minimize the network benefits of the multi-beam NGSO communication system under the condition of non-uniform traffic demand in each cell. The objective constraints include satellite and cell illumination relationship constraints, satellite bandwidth resource constraints, satellite power resource constraints, and long-term stability constraints on the amount of data to be transmitted in the cell.

[0051] The allocation module is used to determine the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system based on the network benefit minimization model of the multi-beam NGSO communication system and the target constraints.

[0052] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-star dynamic beam-hopping and resource allocation method for non-uniform demand as described in the first aspect.

[0053] Fourthly, embodiments of the present invention also provide a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the multi-star dynamic beam-hopping and resource allocation method for non-uniform demand as described in the first aspect.

[0054] Fifthly, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the multi-star dynamic beam hopping and resource allocation method for non-uniform demand as described in the first aspect.

[0055] The present invention provides a method and apparatus for dynamic beam hopping and resource allocation for multi-satellite communication systems with non-uniform demand. This method constructs a network benefit minimization model and target constraints for a multi-beam NGSO communication system. The network benefit minimization model minimizes the network benefit of the multi-beam NGSO communication system under non-uniform traffic demand in each cell. Solving the network benefit minimization model based on the target constraints determines the beam hopping and resource allocation methods for minimizing network benefit and maintaining queue stability. NGSO communication is then performed according to these determined methods, achieving a balance between network benefit and queue stability. This joint optimization of onboard resources across multiple dimensions of the NGSO communication system effectively addresses the challenges and challenges of uneven traffic demand distribution during NGSO communication. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0057] Figure 1 This is a flowchart illustrating the multi-satellite dynamic beam-hopping and resource allocation method for non-uniform demand provided in an embodiment of the present invention.

[0058] Figure 2 This is a schematic diagram of the multi-beam NGSO communication system provided in an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of the structure of the multi-star dynamic beam skipping and resource allocation method device for non-uniform demand provided in the embodiments of the present invention;

[0060] Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0062] The method of this invention can be applied to satellite communication scenarios. By determining the beam skipping method and resource allocation method under the conditions of minimizing network benefits and queuing stability in the NGSO communication system, a balance between network benefits and queuing stability in the NGSO communication system is achieved. This realizes the joint optimization of onboard resources in multiple dimensions of the NGSO communication system and effectively solves the current situation and challenges of uneven distribution of traffic demand in the NGSO communication process.

[0063] In related technologies, due to the development of data services, ground traffic exhibits non-uniform characteristics in both the time and spatial domains. Therefore, how to jointly optimize the onboard resources of the NGSO communication system across multiple dimensions to effectively address the current situation and challenges of uneven traffic demand distribution is a pressing issue that needs to be addressed by those skilled in the art.

[0064] To facilitate a clearer understanding of the technical solutions of the various embodiments of this application, some technical content related to the various embodiments of this application will be introduced first.

[0065] With the development of satellite payloads, flexible allocation of onboard resources has gradually become a focus of the industry. At the same time, because multi-beam satellites can generate a large number of beams and flexibly adjust and allocate these beam resources, they are regarded as a key enabling technology to cope with the rapid growth of traffic demand and the uneven distribution of traffic demand, so as to improve resource utilization and demand satisfaction rate.

[0066] To address the challenges of uneven traffic demand distribution, onboard resources across multiple dimensions, such as bandwidth and power, are jointly optimized. However, existing research primarily focuses on optimizing fixed beam resources, where the radiation direction of each beam is fixed. In recent years, beam hopping technology has been extensively studied to enhance the flexibility of onboard resource management. By selectively activating or deactivating beams at different locations in the time domain, beam hopping technology can effectively reduce inter-beam interference and improve system capacity. To overcome the challenge of non-uniform ground traffic demand, beam hopping schemes for satellites have been widely studied; however, most research focuses on single satellites, especially geostationary orbit satellites. For non-geostationary orbit (NGSO) satellites, overlapping coverage can enhance coverage capabilities, but it also makes them more susceptible to co-channel interference from neighboring satellites. To address this, existing research proposes beam hopping schemes for NGSO based on load balancing and anti-interference to meet the service needs of different cells. However, existing NGSO beam hopping research systems are configured with full frequency reuse, which can lead to severe cross-channel interference. Meanwhile, the randomness of ground traffic demand arrivals makes the future state of the network difficult to predict. Furthermore, considering the temporal coupling of traffic data, long-term performance optimization is more important than short-term optimization.

[0067] The present invention discloses a multi-satellite dynamic beam hopping and resource allocation method for non-uniform demand. This method constructs a network benefit minimization model and objective constraints for a multi-beam NGSO communication system. The network benefit minimization model minimizes the network benefit of the multi-beam NGSO communication system under non-uniform traffic demand in each cell. Solving the network benefit minimization model based on the objective constraints determines the beam hopping and resource allocation methods for minimizing network benefit and maintaining queue stability. NGSO communication is then performed according to these determined methods, achieving a balance between network benefit and queue stability. This method enables joint optimization of onboard resources across multiple dimensions of the NGSO communication system, effectively addressing the challenges and challenges of uneven traffic demand distribution during NGSO communication.

[0068] The following is combined Figures 1-4 The technical solution of the present invention will be described in detail with reference to specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0069] Figure 1 This is a flowchart illustrating an embodiment of the multi-satellite dynamic beam-hopping and resource allocation method for non-uniform demand provided by this invention. Figure 1As shown, the method provided in this embodiment includes:

[0070] Step 101: Construct a network benefit minimization model and objective constraints for a multi-beam NGSO communication system. The network benefit minimization model is used to minimize the network benefits of the multi-beam NGSO communication system under the condition of non-uniform traffic demand in each cell. The objective constraints include satellite and cell illumination relationship constraints, satellite bandwidth resource constraints, satellite power resource constraints, and long-term stability constraints on the amount of data to be transmitted in the cell.

[0071] Specifically, in existing technologies, ground traffic exhibits non-uniform characteristics in both the time and spatial domains. Faced with the non-uniform traffic demands of cells, how multi-beam NGSO communication systems can perform beam hopping and resource allocation to achieve long-term stability of the cell data queue—that is, prevent the cell data queue from growing indefinitely—is a problem that urgently needs to be solved by those skilled in the art.

[0072] To address the aforementioned issues, this application first constructs a network benefit minimization model and target constraints for a multi-beam NGSO communication system. The network benefit minimization model minimizes the network benefits of the multi-beam NGSO communication system under the condition of non-uniform traffic demand in each cell, thereby achieving a balance between network benefits and queue stability. Optionally, the target constraints include constraints on satellite and cell illumination relationships, satellite bandwidth resources, satellite power resources, and the long-term stability of the amount of data to be transmitted in the cells; that is, achieving a balance between network benefits and queue stability of the NGSO communication system while satisfying resource constraints, power constraints, etc.

[0073] For example, such as Figure 2 As shown, the multi-beam NGSO communication system operates in a time-slot manner, with time-slot index t∈[0,1,2,...,T]. Assume the target area contains K cells, served by S satellites. Let... For the community to gather, This represents a satellite ensemble. For a multi-beam NGSO system, each satellite can generate a maximum of L [beams] at a time. s Each beam serves a cell. Furthermore, due to the motion characteristics of satellites, a cell may be covered by multiple satellites, or even zero satellites. The correlation between satellites and cells can be represented by the following formula:

[0074]

[0075] Among them, X t For K×S t A binary variable matrix; The beam pattern of satellite s in time slot t can be represented as follows: x k,s (t) = 1 indicates that cell k is illuminated by satellite s at time t.

[0076] Step 102: Based on the network benefit minimization model and objective constraints of the multi-beam NGSO communication system, determine the beam skipping method and resource allocation method of the multi-beam NGSO communication system.

[0077] Specifically, after constructing the network benefit minimization model and objective constraints of the multi-beam NGSO communication system, the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system can be determined based on the network benefit minimization model and objective constraints. Optionally, the network benefit minimization model of the multi-beam NGSO communication system can be solved based on the objective constraints to determine the beam hopping mode and resource allocation mode under the conditions of network benefit minimization and queue stability. Then, NGSO communication can be carried out according to the determined beam hopping mode and resource allocation mode, thus achieving a balance between network benefit and queue stability of the NGSO communication system.

[0078] The method described in the above embodiments constructs a network benefit minimization model and target constraints for a multi-beam NGSO communication system. The network benefit minimization model minimizes the network benefits of the multi-beam NGSO communication system under the condition of non-uniform traffic demand in each cell. Then, by solving the network benefit minimization model based on the target constraints, the beam hopping method and resource allocation method for minimizing network benefits and maintaining queue stability in the NGSO communication system can be determined. NGSO communication is then performed according to the determined beam hopping method and resource allocation method, thus achieving a balance between network benefits and queue stability in the NGSO communication system. This achieves joint optimization of onboard resources across multiple dimensions of the NGSO communication system, effectively solving the current situation and challenges of uneven traffic demand distribution during NGSO communication.

[0079] In one embodiment, the network benefit minimization model for a multi-beam NGSO communication system includes:

[0080]

[0081]

[0082] Where U(t) represents the network benefits of the multi-beam NGSO communication system; X(t) represents the illumination relationship between the satellite and the cell; B(t) represents the satellite bandwidth allocation method; P(t) represents the satellite power allocation method; T represents the time length; α represents the weighting factor; and K represents the cell set of the multi-beam NGSO communication system. This represents the set of satellites in a multi-beam NGSO communication system; k represents a cell; s represents a satellite; h k,s (t) indicates the switching penalty term; P k,s (t) represents the illumination power of satellite s onto cell k;

[0083] The objective constraints include:

[0084] C1:

[0085] C2:

[0086] C3:

[0087] C4:

[0088] C5:

[0089] C6:

[0090] C7:

[0091] Where C1 represents that each satellite s generates at most Ls beam serving cells simultaneously; C2 represents that cell k is illuminated by at most one satellite simultaneously; C3 represents a Boolean constraint; x k,s (t) indicates that cell k is illuminated by satellite s at time t; in C4, b k (t) represents the cell bandwidth; B tot Indicates total bandwidth; B ch This indicates that the total bandwidth B tot Divided into M blocks, each block has a bandwidth; C5 represents the cell bandwidth b. k The allocation method of (t) is as follows Type; C6 indicates that the single-beam transmit power is less than the maximum transmit power P. max C7 indicates that the cell's data queue cannot grow indefinitely; Q k (t) represents the amount of data to be transmitted in cell k.

[0092] Specifically, in this embodiment, the network benefit minimization model is used to minimize the network benefit of a multi-beam NGSO communication system under the condition of non-uniform traffic demand in each cell; optionally, the network benefit minimization model is as follows:

[0093]

[0094]

[0095] Where U(t) represents the network benefits of the multi-beam NGSO communication system; X(t) represents the illumination relationship between the satellite and the cell; B(t) represents the satellite bandwidth allocation method; P(t) represents the satellite power allocation method; T represents the time length; α represents the weighting factor; and K represents the cell set of the multi-beam NGSO communication system. This represents the set of satellites in a multi-beam NGSO communication system; k represents a cell; s represents a satellite; h k,s (t) indicates the switching penalty term; P k,s (t) represents the illumination power of satellite s onto cell k.

[0096] Optionally, considering factors such as satellite handover and power consumption, in this embodiment of the application, network benefits are expressed as a weighted average of satellite power and handover penalty, i.e.

[0097]

[0098] Among them, switching penalty function A handover penalty is incurred when a cell is connected to satellites in both preceding and following time slots and a handover occurs. The handover penalty function is used to represent the impact of frequent cell handovers to different satellites on other network layers, such as signaling spikes and reduced network efficiency caused by the handover process. This is achieved by introducing the handover penalty function h. k,s (t), quantifying the impact of switching from one satellite to another. Furthermore, a Heaviside step function H(x) is defined. k,s (t)), if x k,s If (t) = 1, then its value is 1; otherwise, it is 0. Based on these definitions, a handover penalty function for cell k is established in time slot t, which depends on the previous time slot X. t-1 The connection matrix of P. k,s (t) represents the transmission power of satellite beam k in time slot t, and the weighting factor α is used to balance the impact of the handover penalty on the system.

[0099] In the target constraints, C1-C3 represent constraints on the beam illumination variable, where C1 indicates that each satellite can generate at most L simultaneously. s Each beam serves a cell, where C2 indicates that each cell is illuminated by at most one satellite at a time, and C3 is a Boolean constraint.

[0100] C4 and C5 describe the limitations of bandwidth allocation. C4 states that the bandwidth allocated to each cell is always less than the total bandwidth resources, while C5 describes the maximum number of available bandwidth selection schemes. Optionally, in order to fully utilize the flexibility of bandwidth and achieve a match between resources and demand, the total bandwidth B in this embodiment of the application is... tot It is divided into M blocks, each block having a bandwidth of B. ch =B tot / M. Each beam can only occupy a contiguous bandwidth block, therefore a total of This scheme proposes several possible bandwidth allocation strategies. Using this strategy, each beam can flexibly adjust its bandwidth length to suit the needs of the cell. To describe the relationship between bandwidth and beam, a variable matrix B of size K×1 is introduced. t B k (t) = i indicates that the ith bandwidth allocation scheme is applied to cell k at time t. Clearly, there is frequency band overlap between the above bandwidth allocation schemes. When multiple beams occupy overlapping frequency bands, co-channel interference may occur. To describe the impact of co-channel interference, an overlap factor is defined. in This indicates the number of overlapping blocks between beams k1 and k2. This indicates the number of blocks occupied by beam k1.

[0101] Therefore, the signal-to-interference-plus-noise ratio (SINR) of cell k (also known as the k-th cell) served by satellite s at time slot t can be derived as follows:

[0102]

[0103] Where P k,s (t) represents the transmission power of satellite beam k in time slot t, K B denoted by Boltzmann constant, and T represents the noise temperature of the receiver. and These represent intra-satellite interference and inter-satellite interference, respectively, which are caused by the occupation of overlapping frequency bandwidths. and It can be represented as:

[0104]

[0105]

[0106] Where is in the formula Let represent the channel coefficients from satellite s' to cell k served by satellite s, when the target cell of s' is k'. Using the channel model widely used in multi-beam satellite systems, it can be expressed as:

[0107]

[0108] in This represents the transmit antenna gain from the satellite beam s illuminating cell k′ to cell k. This represents the receiving antenna gain of cell k. k≠k′ represents the channel coefficient of the interfering signal, and k=k′ represents the desired signal. d k,s′ λ is the distance between cell k and satellite s′, and λ is the wavelength.

[0109] Therefore, the traffic volume (channel capacity) provided by cell k in time slot t can be derived as follows:

[0110]

[0111] In the formula, x k,s (t) indicates whether cell k is illuminated by satellite s, |B k (t)|B ch This indicates the bandwidth occupied by cell k.

[0112] C6 requires that the single-beam transmit power should be less than the maximum transmit power P. max .

[0113] C7 is the long-term queue stability constraint, meaning that the data queue of each cell cannot grow indefinitely. Within each time slot, it is assumed that data arrival in each cell is an independent and identically distributed random process, a process that has been widely adopted and validated. Using D... k (t) represents the arrival of data in cell k in time slot t, where D k (t)∈[0,D k,max ], D k,max D represents k The maximum value of (t) is related to the geographical distribution and user density within the cell. Following the strategy described above, the remaining data from each cell is transmitted to that cell via satellite, which is the data initiation process. Therefore, the queue state of each cell can evolve as follows:

[0114] Q k (t+1)=max{Q k (t)-R k (t),0}+D k (t)

[0115] That is, the amount of data to be transmitted at time t+1 is equal to the amount of data to be transmitted at time t minus the channel capacity at time t, plus the amount of newly arrived data.

[0116] To ensure network stability, the following constraints are adopted.

[0117]

[0118] In other words, the queue length of each unit cannot grow indefinitely over time, thereby achieving long-term stability of the cell data queue.

[0119] The method described in the above embodiments, after constructing a network benefit minimization model and objective constraints for a multi-beam NGSO communication system, solves the network benefit minimization model based on the objective constraints. This allows the determination of the NGSO communication system's network benefit minimization and beam hopping and resource allocation methods under stable queue conditions. It achieves joint optimization of power variables, beam illumination variables, and bandwidth allocation variables, effectively reducing switching frequency and balancing the load among multiple satellites while minimizing the long-term performance of the multi-beam NGSO communication system, while ensuring system stability.

[0120] In one embodiment, based on the network benefit minimization model and objective constraints of the multi-beam NGSO communication system, the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system are determined, including:

[0121] Based on the Lyapunov optimization framework, the network benefit minimization model of the multi-beam NGSO communication system is transformed to obtain the single-slot target model; the single-slot target model is used to minimize the sum of cell capacity demand gap and system utility in each time slot;

[0122] Based on the single-time-slot target model, the beam-hopping mode and resource allocation mode of the multi-beam NGSO communication system are determined.

[0123] Specifically, in this embodiment, after constructing the network benefit minimization model and objective constraints of the multi-beam NGSO communication system, the network benefit minimization model of the multi-beam NGSO communication system can be solved based on the objective constraints. This determines the network benefit minimization of the NGSO communication system and the beam hopping method and resource allocation method under queue stability conditions, achieving joint optimization of onboard resources in multiple dimensions of the NGSO communication system. This effectively solves the current situation and challenges of uneven distribution of traffic demand during NGSO communication. Optionally, in the process of solving the network benefit minimization model of the multi-beam NGSO communication system based on objective constraints, there is a bottleneck that long time slot optimization problems are difficult to solve directly using offline algorithms. This is because as the number of time slots increases, the cross-time slot coupling caused by queue updates exponentially expands the scope of the optimization variables, making the problem more difficult to solve. At the same time, the unpredictability of demand arrival also makes offline algorithms difficult to apply.

[0124] To address the aforementioned issues, this embodiment of the application, based on the Lyapunov optimization framework, transforms the network benefit minimization model of the multi-beam NGSO communication system into a single-time-slot target model. This single-time-slot target model minimizes the sum of the cell capacity demand gap and system utility for each time slot. Furthermore, based on the single-time-slot target model, the beam-hopping method and resource allocation method of the multi-beam NGSO communication system can be determined. In other words, this embodiment transforms the challenging stochastic optimization problem for each time slot into a deterministic problem, making decisions solely based on the current network and queue states of each time slot, without requiring any stochastic statistical distribution parameters or relying on future information. This achieves joint optimization of power variables, beam illumination variables, and bandwidth allocation variables, minimizing system utility while ensuring the long-term asymptotic stability of all data queues.

[0125] Alternatively, a quadratic Lyapunov function can be constructed based on the Lyapunov optimization method as follows:

[0126]

[0127] The single-slot Lyapunov drift term from time slot t to time slot t+1 is defined as:

[0128]

[0129] Instead of controlling for Lyapunov drift, a balance between the objective function and queue stability is achieved by defining a penalty for Lyapunov drift, as shown below:

[0130]

[0131] The penalty factor V is a key parameter reflecting the relative importance of system utility. Adjusting the value of V allows for a balance between the stability and utility of the system queue. Therefore, minimizing Lyapunov drift plus penalty can stabilize the system queue and minimize its utility. Optionally, the Lyapunov drift plus penalty can be constrained as follows:

[0132]

[0133] The upper bound of the Lyapunov drift plus penalty term consists of three components, and the constant C is expressed as follows:

[0134]

[0135] The second component is the square of the difference between the data to be transmitted stored in the buffer and the transmission capacity, commonly referred to as the capacity demand gap. Minimizing this component improves the match between demand and resources, which is crucial for resource-constrained satellite communications. The third component, defined as system utility, intuitively illustrates that adjusting the penalty factor V to optimize frequency switching and load balancing objectives inevitably affects transmission performance.

[0136] Accordingly, this is transformed into a single-slot drift plus penalty minimization problem, which can be expressed as:

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144] Among them, Q k (t) represents the amount of data to be transmitted in cell k; R k (t) represents the data transmission capacity provided by cell k in time slot t; V represents the penalty factor.

[0145] The main idea of ​​the transformation is to minimize the Lyapunov upper bound for each time slot while satisfying all instantaneous constraints, thereby minimizing system utility while ensuring the long-term asymptotic stability of all data queues. This method aims to make decisions solely based on the network and queue states of each time slot, without requiring any stochastic statistical distribution parameters. In other words, this application proposes an online adaptive algorithm based on the Lyapunov optimization framework. By establishing a queue model with long-term constraints in the stochastic optimization problem, this algorithm can transform a challenging stochastic optimization problem into a deterministic problem in each time slot, independent of future information. A penalty factor is introduced to balance system utility and queue backlog, achieving an infinite approximation of the optimal network utility under the premise of system stability.

[0146] The method described in the above embodiments, based on the Lyapunov optimization framework, transforms the network benefit minimization model of the multi-beam NGSO communication system into a single-timeslot target model. Based on this single-timeslot target model, the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system can be determined. In other words, this embodiment transforms the challenging stochastic optimization problem of each timeslot into a deterministic problem, making decisions solely based on the current network and queue states of each timeslot. This solves the bottleneck problem that, with the increase of timeslots, the cross-timeslot coupling caused by queue updates exponentially expands the scope of optimization variables, making it difficult to directly solve long-timeslot optimization problems using offline algorithms. It also addresses the problem that the unpredictability of demand arrival makes offline algorithms unsuitable. Therefore, based on the single-timeslot target model, the joint optimization of power variables, beam illumination variables, and bandwidth allocation variables can be achieved quickly and accurately, minimizing system utility while ensuring the long-term asymptotic stability of all data queues.

[0147] In one embodiment, based on a single-time-slot target model, the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system are determined, including:

[0148] The single-slot target model is decomposed into a first model, a second model, and a third model. The first model is used to minimize the cell capacity demand gap and the sum of satellite handover times given satellite power resources and satellite bandwidth resources. The second model is used to minimize the cell capacity demand gap and system power consumption given cell illumination relationships and satellite bandwidth resources. The third model is used to minimize the cell capacity demand gap given satellite bandwidth resources and satellite power resources.

[0149] Based on the first, second, and third models, the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system are determined.

[0150] Specifically, in this embodiment of the application, the network benefit minimization model of the multi-beam NGSO communication system is transformed based on the Lyapunov optimization framework to obtain a single-time-slot target model. During the joint optimization of power variables, beam illumination variables, and bandwidth allocation variables based on the single-time-slot target model, the following challenges still exist: the single-time-slot target model simultaneously contains continuous variables (P(t)) and discrete variables (X(t), B(t)), and they are highly coupled. This is due to inter-channel interference between different cells caused by occupying overlapping frequency bands.

[0151] To address the aforementioned issues, this application embodiment decomposes the single-slot target model into a first model, a second model, and a third model. The first model minimizes the sum of cell capacity demand gap and satellite handover times given satellite power and bandwidth resources. The second model minimizes the cell capacity demand gap and system power consumption given cell illumination relationships and satellite bandwidth resources. The third model minimizes the cell capacity demand gap given satellite bandwidth and power resources.

[0152] Optionally, the first model is used to solve the beam illumination optimization problem. Given the configuration of satellite power control and bandwidth allocation, the first model can be expressed as:

[0153]

[0154]

[0155]

[0156]

[0157] The above problem is a nonlinear integer programming problem because the variable x is a binary variable and exists in the expression for the transmission rate. Multivariate coupling in the form of [formula missing].

[0158] To address this problem, an efficient beam illumination scheme is derived based on matching theory. The beam illumination problem can be abstracted and reformulated as involving two groups of participants: the satellite group S and the cell group. The matching problem. Furthermore, considering the many-to-one matching model, each cell... At most one satellite can be assigned, and each satellite s∈S can serve a maximum of L simultaneously. s There are several cells. Furthermore, if satellite s is assigned to cell k, then cell k is assigned to satellite s, and vice versa.

[0159] Definition 1: Define μ as a matching mapping between the cell set κ and the satellite set S, such that...

[0160]

[0161]

[0162] μ(k)=s,if and only if k∈μ(s),

[0163] Where |μ(*)| represents the cardinality of the matching result, and |μ(*)| = 0 indicates that the participant did not match. The beam illumination variable can be recovered as follows:

[0164]

[0165] To obtain a stable solution to the aforementioned matching problem, a preference list needs to be established for each participant, i.e., each cell and satellite, and a stable solution should be obtained based on the Gale-Shapley algorithm. Typically, the preference list is constructed based on an objective function. However, the existence of co-channel interference between cells introduces dynamic changes to the preference list, known as an "externality" in matching theory, leading to unstable matching results. To address this challenge, the concept of exchange matching is adopted, defined as follows:

[0166] Definition 2: Given a many-to-one matching μ and two (cell-satellite) pairs (k1, s1) and (k2, s2) where μ(k1) = (s1) and μ(k2) = (s2), and u1 ≠ u2, if they satisfy

[0167]

[0168] Where (k1, k2) are the swap blocking pairs, Defined as a commutative matching of μ, φ({μ}) is the objective function under the matching μ. Furthermore, in Definition 2, one of the elements k2 can be an empty point of satellite s2 in μ, where |μ(s2)| <K max Maximum. In this case, we have and

[0169] Definition 3: If there are no swapping blocking pairs, then the matching is bilaterally swap-stable.

[0170] Therefore, after decomposing the single-slot target model into the first model, the beam-hopping mode of the multi-beam NGSO communication system can be determined based on matching theory.

[0171] Optionally, the second model is used to solve the satellite transmit power optimization problem. Given the beam illumination configuration and bandwidth allocation, the second model can be expressed as:

[0172]

[0173]

[0174] The original problem is highly nonconvex due to the coupling between variables in the objective function. Therefore, a substitution function is used. Point P obtained in the (i-1)th iteration (i-1) (t) is used to approximate the original objective function. It satisfies the following assumptions: It is strictly convex in P(t); in It is a gradient operator; based on the above assumptions, the following convex function is adopted:

[0175]

[0176] Where α is a positive constant. Therefore, it can be approximated by the following quadratic convex optimization problem:

[0177]

[0178] Specifically, it can be decomposed into multiple independent quadratic convex optimization problems, whose closed-form solutions can be obtained in parallel, and can be derived as follows:

[0179]

[0180] It can be updated using the following equation

[0181] P (i) (t)=P (i-1) (t)+β (i) (P * (t)-P (i-1) (t))

[0182] Where β (i) (t) represents the step size of the i-th iteration update P, which can be obtained through an exact linear search and can be expressed as:

[0183]

[0184] In other words, by transforming the second model into a convex optimization problem, the power allocation method of the multi-beam NGSO communication system can be determined.

[0185] Optionally, the third model is used to solve the bandwidth allocation optimization problem. Given the beam illumination configuration and power control, the third model can be expressed as:

[0186]

[0187]

[0188]

[0189] Alternatively, a many-to-one matching game can be used, involving two factors: the neighborhood... and bandwidth strategy in Each bandwidth strategy It can be assigned to multiple cells And each community Only one bandwidth strategy can be used. In addition, due to the existence of interference, there are still externalities when constructing preference lists for each participant. Therefore, exchange matching is adopted to address this challenge, which is defined as follows:

[0190] Definition 4: Given a matching Π and two (cell - bandwidth policy) pairs (k1, B1) and (k2, B2), where Π(k1) = B1 and Π(k2) = B2, and k1 ≠ k2, if the following is satisfied

[0191]

[0192] then (k1, k2) is an exchange - blocking pair, where is defined as the exchange matching of Π, ω(Π) is the objective function under the matching Π. In addition, in Definition 4, cell k1 can be an open point of B2 in the bandwidth policy Π, and Π(B2) < K. In this case, there are and

[0193] Thus, after decomposing the single - slot objective model into the third model, the bandwidth allocation method for the multi - beam NGSO communication system can be determined based on matching theory.

[0194] The method of the above - mentioned embodiment proposes a long - term NGSO hopping beam and resource optimization scheme. This scheme fully explores the degrees of freedom of on - satellite resources in four dimensions: power, space, time, and bandwidth. To reduce the explosive growth of the action space caused by long - term optimization, the Lyapunov function is used to reduce the long - term optimization objective and constraints to an optimization problem of a single slot. In the single - slot problem, it not only includes the system utility term but also the demand - capacity square term, achieving a trade - off between system utility and demand matching.

[0195] Next, the multi - satellite dynamic hopping beam and resource allocation device for non - uniform demands provided by the present invention will be described. The multi - satellite dynamic hopping beam and resource allocation device for non - uniform demands described below can be correspondingly referred to the multi - satellite dynamic hopping beam and resource allocation method for non - uniform demands described above.

[0196] Figure 3 is a schematic structural diagram of the multi - satellite dynamic hopping beam and resource allocation device for non - uniform demands provided by the present invention. The multi - satellite dynamic hopping beam and resource allocation device for non - uniform demands provided in this embodiment includes:

[0197] Module 710 is used to construct a network benefit minimization model and objective constraints for a multi-beam NGSO communication system. The network benefit minimization model is used to minimize the network benefits of the multi-beam NGSO communication system under the condition of non-uniform traffic demand in each cell. The objective constraints include satellite and cell illumination relationship constraints, satellite bandwidth resource constraints, satellite power resource constraints, and long-term stability constraints on the amount of data to be transmitted in the cell.

[0198] The allocation module 720 is used to determine the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system based on the network benefit minimization model and objective constraints of the multi-beam NGSO communication system.

[0199] The apparatus of this invention is used to execute the method in any of the foregoing method embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0200] Figure 4 A schematic diagram of the physical structure of an electronic device is provided. This electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communication bus 840. The processor 810, communications interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a multi-satellite dynamic beam hopping and resource allocation method for non-uniform demand. This method includes: constructing a network benefit minimization model and objective constraints for a multi-beam NGSO communication system; the network benefit minimization model is used to minimize the network benefit of the multi-beam NGSO communication system under the condition of non-uniform traffic demand in each cell; the objective constraints include satellite and cell illumination relationship constraints, satellite bandwidth resource constraints, satellite power resource constraints, and long-term stability constraints on the amount of data to be transmitted in the cells; and determining the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system based on the network benefit minimization model and objective constraints.

[0201] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0202] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer is able to execute the multi-satellite dynamic beam hopping and resource allocation method for non-uniform demand provided by the above methods. The method includes: constructing a network benefit minimization model and target constraints for a multi-beam NGSO communication system; the network benefit minimization model is used to minimize the network benefit of the multi-beam NGSO communication system under the condition of non-uniform traffic demand in each cell of the multi-beam NGSO communication system; the target constraints include satellite and cell illumination relationship constraints, satellite bandwidth resource constraints, satellite power resource constraints, and long-term stability constraints on the amount of data to be transmitted in the cell; and determining the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system according to the network benefit minimization model and target constraints of the multi-beam NGSO communication system.

[0203] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the aforementioned multi-satellite dynamic beam hopping and resource allocation methods for non-uniform demand. The method includes: constructing a network benefit minimization model and target constraints for a multi-beam NGSO communication system; the network benefit minimization model is used to minimize the network benefit of the multi-beam NGSO communication system under the condition of non-uniform traffic demand in each cell; the target constraints include satellite and cell illumination relationship constraints, satellite bandwidth resource constraints, satellite power resource constraints, and long-term stability constraints on the amount of data to be transmitted in the cells; and determining the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system based on the network benefit minimization model and target constraints.

[0204] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0205] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0206] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-satellite dynamic beam hopping and resource allocation method for non-uniform demand, characterized in that, include: Construct a network benefit minimization model and objective constraints for a multibeam non-geostationary orbit (NGSO) communication system for Earth; The network benefit minimization model is used to minimize the network benefit of a multi-beam NGSO communication system under the condition of non-uniform traffic demand in each cell. The target constraints include satellite and cell illumination relationship constraints, satellite bandwidth resource constraints, satellite power resource constraints, and long-term stability constraints on the amount of data to be transmitted in the cell. Based on the network benefit minimization model of the multi-beam NGSO communication system and the target constraints, the beam skipping mode and resource allocation mode of the multi-beam NGSO communication system are determined. The network benefit minimization model for the multi-beam NGSO communication system includes: ； ； Where U(t) represents the network benefits of the multi-beam NGSO communication system; X(t) represents the illumination relationship between the satellite and the cell; B(t) represents the satellite bandwidth allocation method; P(t) represents the satellite power allocation method; T represents the time length; α represents the weighting factor; and K represents the cell set of the multi-beam NGSO communication system. This represents the set of satellites in a multi-beam NGSO communication system; k represents a cell; s represents a satellite. Indicates switching the penalty option; This represents the illumination power of satellite s onto cell k; The target constraints include: ； ； ； ； ； ； ； Where C1 indicates that each satellite s generates at most [number] simultaneous [events]. C1 represents a single-beam service cell; C2 indicates that cell k is illuminated by at most one satellite at a time; C3 represents a Boolean constraint. Indicates the community At any moment by satellite Irradiation; C4 Medium Indicates the cell bandwidth; Indicates the total bandwidth; This indicates the total bandwidth. Divided into Block, the bandwidth of each block; C5 represents the cell bandwidth. The allocation method is as follows Type; C6 indicates that the single-beam transmit power is less than the maximum transmit power. C7 indicates that the cell's data queue cannot grow indefinitely. This represents the amount of data to be transmitted in cell k.

2. The multi-satellite dynamic beam hopping and resource allocation method for non-uniform demand as described in claim 1, characterized in that, The step of determining the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system based on the network benefit minimization model of the multi-beam NGSO communication system and the objective constraints includes: Based on the Lyapunov optimization framework, the network benefit minimization model of the multi-beam NGSO communication system is transformed to obtain a single-time-slot target model; the single-time-slot target model is used to minimize the sum of cell capacity demand gap and system utility in each time slot; Based on the single-time-slot target model, the beam-hopping mode and resource allocation mode of the multi-beam NGSO communication system are determined.

3. The multi-satellite dynamic beam hopping and resource allocation method for non-uniform demand as described in claim 2, characterized in that, The single-slot target model includes: ； ； ； ； ； ； ； in, This represents the amount of data to be transmitted in cell k. V represents the data transmission capacity provided by cell k in time slot t; V represents the penalty factor.

4. The multi-satellite dynamic beam hopping and resource allocation method for non-uniform demand as described in claim 3, characterized in that, The step of determining the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system based on the single-time-slot target model includes: The single-slot target model is decomposed into a first model, a second model, and a third model. The first model is used to minimize the sum of cell capacity demand gap and satellite handover times given satellite power resources and satellite bandwidth resources. The second model is used to minimize cell capacity demand gap and system power consumption given cell illumination relationships and satellite bandwidth resources. The third model is used to minimize cell capacity demand gap given satellite bandwidth resources and satellite power resources. Based on the first model, the second model, and the third model, the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system are determined.

5. The multi-satellite dynamic beam hopping and resource allocation method for non-uniform demand as described in claim 4, characterized in that, The first model includes: ； ； ； ； The second model includes: ； ； The third model includes: ； ； 。 6. A multi-satellite dynamic beam-hopping and resource allocation device for non-uniform demand, used to implement the multi-satellite dynamic beam-hopping and resource allocation method for non-uniform demand as described in any one of claims 1 to 5, characterized in that, include: The building block is used to construct the network benefit minimization model and objective constraints for a multi-beam NGSO communication system; The network benefit minimization model is used to minimize the network benefit of a multi-beam NGSO communication system under the condition of non-uniform traffic demand in each cell. The target constraints include satellite and cell illumination relationship constraints, satellite bandwidth resource constraints, satellite power resource constraints, and long-term stability constraints on the amount of data to be transmitted in the cell. The allocation module is used to determine the beam hopping mode and resource allocation mode of the multi-beam NGSO communication system based on the network benefit minimization model of the multi-beam NGSO communication system and the target constraints.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the multi-star dynamic beam-hopping and resource allocation method for non-uniform demand as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the multi-star dynamic beam-hopping and resource allocation method for non-uniform demand as described in any one of claims 1 to 5.

9. A computer program product having executable instructions stored thereon, characterized in that, When executed by the processor, this instruction causes the processor to implement the multi-star dynamic beam-hopping and resource allocation method for non-uniform demand as described in any one of claims 1 to 5.

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