LEO satellite joint hopping beam pattern scheduling and power distribution method

Abstract

The invention discloses an LEO satellite combined beam-hopping pattern scheduling and power distribution method, and belongs to the technical field of satellite communication, and the method comprises the steps: constructing a system model of a downlink of a beam-hopping low-orbit satellite communication system and a decision variable of BHP scheduling; constructing a joint BHP scheduling and power distribution optimization problem taking maximization of the minimum service satisfaction as an optimization target under the time delay constraint; setting a distance threshold value for avoiding co-channel interference between beams, and reducing the solution space scale of BHP scheduling; solving a power distribution optimal solution in each BHTS by a BHP scheduling method based on weight search; and solving the corresponding power allocation sub-problem BHTS in the whole beam hopping period. According to the method, the business satisfaction degree of each region can be maintained at a relatively high level, a suboptimal solution is adopted during BHP scheduling, an optimal solution is adopted during power distribution, and on the premise that the decision time is shortened and computing resources are saved, the performance of a global optimal solution is approached.

Description

Technical Field

[0001] The invention belongs to the technical field of satellite communications, and in particular relates to a LEO satellite joint beam hopping pattern scheduling and power allocation method. Background Art

[0002] Satellite networks are an important supplement to expand the coverage of existing ground mobile networks. As a typical resource-constrained system, the problem of insufficient wireless resources is particularly prominent in satellite communication systems. How to reasonably allocate limited wireless resources is a major focus of satellite communication system research. In traditional multi-beam satellite networks, onboard resources are evenly distributed to each beam, and these beams will continue to illuminate the ground during satellite operation. However, traditional multi-beam allocation methods are often inefficient in dealing with unevenly distributed business needs, and resource allocation is not efficient enough. At present, in low-orbit satellite communication systems, beam hopping combined with full-band multiplexing technology is generally used to increase system capacity. This method gives satellite communication systems greater flexibility, allowing the system to allocate resources in both time and power dimensions, thereby effectively meeting the uneven distribution of business and user performance requirements for latency.

[0003] At present, the academic community has a relatively mature research on the resource scheduling and allocation problem in multi-beam low-orbit satellite communication systems. In the current related research work, the wireless resources used include power resources, frequency resources, time domain resources and space resources, and the indicators used to measure the performance of the algorithm include system throughput performance, user fairness and user delay performance. However, most of the existing studies discuss the coupling relationship between power resources and frequency resources, ignoring the relationship between power resources and time domain resources. At the same time, when analyzing the impact of system delay on beam hopping pattern scheduling, most of the studies considered the limiting factor as the average delay between users, and did not start from the QoS requirements in the actual wireless communication system, and did not discuss the relationship between the queuing delay caused by beam hopping and the maximum delay upper limit that users can tolerate as a limiting condition. There is still a certain gap in research in this area.

[0004] The core idea of ​​the existing research on beam hopping scheduling in low-orbit satellite communication systems is to first determine the optimization objectives and constraints and construct an optimization problem based on them, and finally solve the optimization problem to obtain the solution of beam scheduling and resource allocation. The Chinese patent with publication number CN116744448A discloses a dynamic resource management method based on a low-orbit satellite beam hopping system. The method first constructs an optimization problem with maximization and rate as the optimization objectives and power, number of beams, etc. as constraints. The method then solves the optimization problem to obtain the joint allocation of satellite power, bandwidth, and number of beam time slots. This method only considers the maximum throughput performance of the satellite, and does not consider the differences between the business demands in different regions, and cannot meet the business needs of users with uneven distribution. The Chinese patent with publication number CN117060969A discloses a high-throughput satellite beam hopping time slot resource allocation method. The method aims to minimize the second-order difference sum between the capacity that the system can provide within the beam hopping period and the actual business demand of each cell, and takes the maximum number of beams supported by the satellite at the same time and the co-frequency interference distance as constraints to construct a beam hopping time slot resource allocation model. This method transforms the beam-hopping time slot resource allocation model into a multi-armed bandit model through the setting of agents, actions and immediate rewards; the agent is trained by reinforcement learning algorithm to solve the multi-armed bandit model and obtain the adaptive allocation of time slot resources. However, this method does not consider the accumulation of data queuing delay caused by beam hopping, and cannot meet the user's requirements for delay performance. In practical applications, it may cause a large number of user-requested data packets to be discarded due to timeout. The paper "Research on Beam-hopping Resource Scheduling Strategy for Low-orbit Communication Constellation, Zhang Panpan, University of Chinese Academy of Sciences (Institute of Microsatellite Innovation, Chinese Academy of Sciences), Year: 2024 Issue 02 Online Publication Time: 2024-01-16-2024-02-15" aims at the beam scheduling problem within a resource allocation cycle of low-orbit satellites, and establishes a resource scheduling optimization model with queuing delay and co-frequency interference minimization as the optimization objectives. By decoupling the problem, a scheduling strategy of demand clustering, time slot allocation and wave position matching is proposed, and a suitable optimization algorithm is designed for each step to form a complete strategy for beam-hopping time slot and wave position allocation. However, this method does not involve the allocation of power resources. The power allocated by the satellite to the user often directly determines the throughput performance level that the user can achieve, which is a major factor that must be considered when allocating satellite resources. The Chinese patent with publication number CN113873658A discloses a beam-hopping resource allocation method with user service weight gain as the objective function. The method first calculates the traffic volume and delay tolerance of the current time slot of each cell. Then, with the goal of maximizing the total deviation of all attributes to the decision-making scheme, an optimization problem is established based on the decision problem of maximizing the deviation.Finally, the optimization problem is solved, the optimal attribute pair decision scheme is determined, and resources are allocated to each cell according to the optimal attribute decision scheme, completing the beam hopping resource allocation with user service weight gain as the objective function. However, although this method can meet the real-time business service requirements and improve the overall throughput of the system, it adopts a greedy strategy when allocating power, which cannot cope well with the unevenly distributed user business needs.

[0005] In summary, the main disadvantage of the beam scheduling and resource allocation method for low-orbit satellite communication systems is that it does not comprehensively consider the inherent coupling relationship between beam-hopping pattern (BHP) scheduling and power allocation as well as user queuing delay. BHP scheduling will affect the distribution of satellite power, thereby affecting the channel capacity of the satellite user downlink. At the same time, the historical power allocation situation will affect the future BHP scheduling decision, thereby affecting the delay experience of users within the satellite coverage area. Therefore, when facing unevenly distributed user service needs, it is necessary to consider the BHP scheduling and power allocation issues in a unified manner and perform joint optimization on the premise of meeting user service needs and delay performance requirements. Summary of the invention

[0006] The object of the present invention is to provide a LEO satellite joint beam hopping pattern scheduling and power allocation method.

[0007] The technical solution adopted by the present invention to solve the technical problem is as follows:

[0008] The LEO satellite joint beam hopping pattern scheduling and power allocation method of the present invention specifically includes the following steps:

[0009] Step 1: Construct the system model of the downlink of the beam-hopping low-orbit satellite communication system and the decision variables of BHP scheduling;

[0010] Step 2: Construct a joint BHP scheduling and power allocation optimization problem with the goal of maximizing the minimum business satisfaction under delay constraints;

[0011] Step 3: Set the distance threshold to avoid co-channel interference between beams and reduce the solution space size of BHP scheduling;

[0012] Step 4: The BHP scheduling method based on weighted search makes BHP scheduling decisions in each BHTS to obtain the suboptimal solution of BHP scheduling, and then converts the original optimization problem into the corresponding power allocation sub-problem in each BHTS to obtain the optimal solution of power allocation in each BHTS;

[0013] Step 5: Solve the corresponding power allocation sub-problem for each BHTS during the entire beam hopping period.

[0014] Furthermore, the specific operation process of step one is as follows:

[0015] In the downlink communication scenario of the beam-hopping low-orbit satellite communication system, the maximum number of beams generated by any satellite at the same time is M. There are N cells within the coverage of any satellite, and M<<N. The beams generated by the satellite are all staring beams. When the satellite passes overhead, the beams generated by the satellite will continue to track the N cells. U n Mobile users, U n ≥1, represents the set of all cells in this communication scenario; the total number of users in the system is Use and represents the set of users in the nth cell and all users in the entire system; assuming that for any user Its average traffic intensity is r u , in bps; due to user traffic demand d u Independent of the user's distribution, assuming that the longitude and latitude coordinates of user u are (λ u ,φ u ), then the service demand of cell n is equivalently modeled as:

[0016] r n ={(λ u ,φ u ),r u}

[0017] For a given N cells and M beams, there are N! / (M!(NM)!) BHPs. The set of all BHPs is expressed as Assuming that the number of BHTSs in a beam hopping period is S and the time span of each BHTS is T, the set of all scheduled enabled BHPs for each beam hopping period is defined as:

[0018]

[0019] Where i is the index of BHTS, 1≤i≤S; B i For satellites at BHTS i from the collection The BHP selected and dispatched;

[0020] In this communication scenario, the satellite transmitting antenna gain at the i-th BHTS The calculation formula is:

[0021]

[0022] in Indicates the maximum transmission gain of the satellite transmitting antenna, It represents the angle between the vector of the satellite transmitting antenna pointing to the user u direction and the vector of the main lobe direction of the beam at the i-th BHTS, represents the normalized antenna gain radiation pattern of the satellite;

[0023] The carrier-to-interference-to-noise ratio of the downlink between the satellite and user u The calculation formula is:

[0024]

[0025] in and They represent the carrier-to-interference ratio and carrier-to-noise ratio of the downlink between the satellite and user u, respectively. The calculation formula is:

[0026]

[0027] in represents the effective isotropic radiated power of user u in the i-th BHTS; represents the ratio of antenna gain to noise temperature T; k represents the Boltzmann constant; PL FS Represents free space loss; PL A Represents the atmospheric loss caused by cloud and rain fading; PL SM Indicates the additional gain margin reserved to compensate for the shadow fading caused by multipath and obstacle blocking in the satellite-to-ground link; PL SL Indicates flicker loss; PL AD Indicates various additional losses; Indicates the bandwidth that user u can access;

[0028] Satellites are allocated to all users The set of transmit powers is:

[0029]

[0030] According to the above formula, the power allocation of all BHTSs in the entire beam hopping period is derived as follows:

[0031]

[0032] Assuming that the satellite's system bandwidth is evenly distributed to each user, and the satellite uses full-band multiplexing technology between the generated beams, each beam can access the satellite's full system bandwidth, thus obtaining represents the system bandwidth obtained by user u, B Wrepresents the system bandwidth of the satellite; the channel capacity of the downlink established between the i-th BHTS satellite and user u is calculated by the Shannon formula:

[0033]

[0034] In the BHTSi satellite beam The throughput provided by illuminated cell n is:

[0035]

[0036] Furthermore, the B i is defined as:

[0037]

[0038] in It is used to indicate the nth cell in BHP B i A binary variable indicating whether the is illuminated; Indicates that the nth cell is not illuminated at BHTS i, This means that the nth cell is illuminated by a beam at BHTS i, providing services for users in the cell.

[0039] Furthermore, the normalized antenna gain radiation pattern of the satellite The calculation formula is as follows:

[0040]

[0041] where θ represents the angle between the vector of the satellite transmitting antenna pointing in the direction of the user and the vector of the main lobe direction of the beam, f represents the operating frequency of the satellite, α represents the radius of the circular aperture antenna, c represents the speed of light, and J1(·) represents the first-order Bessel function of the first kind with respect to the variables in the brackets.

[0042] Furthermore, in step 2, the service satisfaction is defined as the total amount of data provided by the satellite to cell n within a beam hopping cycle. Divide by the total amount of services required by the cell R n =r n ×T×S; the total amount of data provided by the satellite to cell n as of the i-th BHTS The calculation formula is:

[0043]

[0044] The specific optimization problem OP1 is modeled as follows:

[0045]

[0046] The constraint (a) indicates that the number of beams enabled by the satellite at the same time cannot exceed the maximum number M; the constraint (b) indicates the decision variable is a binary variable; constraint (c) represents a decision variable The element B i Only from the collection Constraint (d) indicates that the sum of the power values ​​allocated to each user cannot exceed the total power P of the satellite payload. total ; Constraint (e) constrains the timing relationship of BHP to ensure that the BHP scheduling result can meet the user's requirements for delay performance, where represents the cumulative waiting delay of cell n at the end time of BHTS i, which is defined as:

[0047]

[0048] Where i represents the index of the current BHTS, i l Indicates the BHTS index when the cell was last illuminated by the beam; represents the minimum delay that cell n can accept, D u represents the delay acceptable to user u; constraint (f) ensures the total amount of data provided by the satellite to each cell Do not exceed the total volume of services required by the community.

[0049] Furthermore, in step 3, assuming that the radius of the cell covered by the satellite is R, when the center distance between the two cells is 3R or more, the CCI between the two beams covering the two cells is negligible, and the number of candidate BHPs is reduced by setting the distance threshold: First, set the distance threshold d lim =3R, then the distance d between the centers of the cell n within the satellite coverage and the other cells n' except the cell is calculated. n,n′ , n≠n′; if d n,n′ ≤d lim This means that cell n and cell n' are geographically adjacent. Finally, a BHP that does not have adjacent cells and has a beam illuminating it is defined as a valid BHP. Assuming that after preprocessing, there are K valid BHPs waiting to be scheduled and activated, the set of valid BHPs is for:

[0050]

[0051] Among them k For this collection The kth valid BHP in:

[0052] Β k =[βk,1 ,β k,2 ,…,β k,n ,…,β k,N ],β k,n ∈{0,1}

[0053] where β k,n = 0 means that within the kth effective BHP, no beam illuminates cell n; when β k,n When =1, it means that within the kth effective BHP, there is a beam illuminating cell n.

[0054] Furthermore, in step 4, for the BHP scheduling problem, in order to satisfy the two constraints (e) and (f) in the optimization problem OP1, the kth effective BHPΒ k The scheduling weight W in BHTSi i k The calculation formula is:

[0055]

[0056] where β k,n ∈Β k , It represents the cumulative traffic provided by the satellite to cell n by the end of BHTS(i-1). When i=1, ψ(n) represents the penalty weight function when calculating the scheduling weight;

[0057] For a given BHTS i, when determining the BHP to be scheduled within the BHTS, first all cells Calculate the corresponding Then, all valid BHPs are traversed and the scheduling priority W is calculated for each valid BHP. i k , and continuously update the current maximum weight W in the iteration max At the same time, update the BHP B enabled by the schedule i is the scheduling weight and W max Equal effective BHP; after completing the traversal, the final B i That is, the BHP that is scheduled and enabled in the current BHTS i.

[0058] Furthermore, in step 5, the power allocation sub-problem decomposed from the original optimization problem is modeled as:

[0059]

[0060] B i =Β k ,1≤i≤S, (b)

[0061]

[0062] The power allocation subproblem is transformed using the secondary transformation, and the transformed optimization problem is solved; after the secondary transformation, the optimization problem OP2 is equivalent to:

[0063]

[0064] B i =Β k ,1≤i≤S, (b)

[0065]

[0066] Among them, constraint (a) indicates that the number of beams enabled at the same time cannot exceed the maximum number of beams; constraint (b) indicates that the BHP scheduled to be enabled at the current moment must be a valid BHP; constraint (c) indicates that the sum of the power allocated to each user cannot exceed the total power of the satellite; constraint (d) indicates that the total throughput sent to each cell by the current BHTS does not exceed its remaining demand; constraint (e) indicates that the value of the new objective function z is obtained through the auxiliary variable y n Constraint (f) indicates that the satellite's transmission power will only be allocated to users in the cells illuminated by the beam in the BHP currently scheduled and enabled by the BHTS, and users not within the coverage of any beam will not receive power resources; z and y n is an auxiliary variable introduced during the secondary transformation. For a given allocated power sequence P i ,y n The calculation formula is:

[0067]

[0068] For each BHTS obtained in step 4, the BHP B i , P can be obtained by solving OP3 i and a solution of z; the solved P i Substituting into the above formula for y n The value of is updated, and the above steps are repeated until the z value converges, thus obtaining P at this time i The optimal solution of .

[0069] The beneficial effects of the present invention are:

[0070] (1) The present invention can realize joint BHP scheduling and power allocation under the premise of meeting user service requirements and delay performance requirements.

[0071] Traditional beam-hopping low-orbit satellite communication system resource allocation methods often ignore the accumulation of data queuing delays caused by beam hopping when constructing optimization problems, and only take system throughput performance into consideration. The present invention constructs an optimization problem with the maximum minimum service satisfaction as the optimization goal under delay constraints. By making BHP scheduling decisions separately in each BHTS, the complex optimization problem is broken down into power allocation sub-problems with lower complexity for each BHTS. The present invention can ensure the fairness of user scheduling between different regions while ensuring the user's demand for delay performance, and maintain the service satisfaction of each region at a high level.

[0072] (2) The present invention can use a suboptimal solution to approximate the performance of the optimal solution, thereby improving the speed at which the satellite completes joint beam hopping scheduling and power allocation decisions.

[0073] In actual satellite communication systems, scheduling decisions need to be completed in a very short time to ensure the timeliness of the decision. Although scheduling through the global optimal solution can more effectively utilize limited on-board resources, the time required to obtain the global optimal solution is often long, and the required computing resources are also massive. The present invention adopts a suboptimal solution when performing BHP scheduling and an optimal solution when performing power allocation. By combining the two, while shortening the decision time and saving computing resources, the approximation of the performance of the global optimal solution is achieved, which is more suitable for actual beam-hopping low-orbit satellite communication systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is the downlink communication scenario of the beam-hopping low-orbit satellite communication system.

[0075] Figure 2 Schematic diagram of the mechanism for scheduling BHP for satellites. DETAILED DESCRIPTION

[0076] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0077] The problem to be solved by the present invention is the joint optimization problem of BHP scheduling and power allocation in a beam-hopping low-orbit satellite communication system. The discrete nature of beam hopping in time will lead to additional accumulation of data packet queuing delays in the system, and BHP needs to be reasonably scheduled to meet users' requirements for delay performance. Therefore, how to determine the order of BHP scheduling is a key issue to ensure the user communication experience in a low-orbit satellite communication system. At the same time, the distribution of user business needs is extremely uneven, and the levels vary greatly. Satellites are required to jointly optimize BHP scheduling and power allocation to meet user business needs. Therefore, how to achieve the joint optimization of BHP scheduling and power allocation is the key to the throughput performance of the low-orbit satellite communication system and the user business satisfaction index in the system. The main problems in carrying out the joint optimization of BHP scheduling and power allocation in a beam-hopping low-orbit satellite communication system are:

[0078] (1) Construction of the optimization problem of joint BHP scheduling and power allocation. When constructing the joint BHP scheduling and power allocation problem, it is necessary to comprehensively consider the user service needs and the user delay performance requirements. However, constructing a multi-objective optimization problem will increase the difficulty of solving the optimization problem. At the same time, BHP scheduling and power allocation affect each other, and the decision variables corresponding to the two are discrete integer variables and continuous variables, respectively. This feature will further increase the difficulty of solving the optimization problem. Therefore, how to construct a reasonable joint BHP scheduling and power allocation optimization problem and convert the non-convex problem into a solvable convex optimization problem is the key to making resource allocation decisions in beam-hopping low-orbit satellite communication systems.

[0079] (2) The optimization method used to solve the optimization problem. In the prior art, meta-heuristic algorithms or deep reinforcement learning are usually used to obtain the global optimal solution of the optimization problem. However, in the beam-hopping low-orbit satellite communication system, as the user coverage wave position and the number of beams increase, the solution space size of the BHP scheduling power allocation will increase exponentially. This will cause the convergence speed of the meta-heuristic algorithm to obtain the global optimal solution to also slow down exponentially. In the actual system, the too slow algorithm convergence speed will cause the network scheduling decision to be outdated and unable to cope with the continuously dynamically changing network environment. At the same time, the satellite payload is limited. In addition to power and frequency resources, satellite computing resources are also very valuable. Although the speed at which the deep reinforcement learning method that completes offline training converges to the optimal solution can adapt to the dynamically changing network environment, it requires a large amount of computing resources. Therefore, the optimization method needs to have a faster convergence speed and a lower computing resource occupancy rate in order to be deployed in the actual beam-hopping low-orbit satellite communication system.

[0080] Therefore, the purpose of the present invention is to solve the BHP scheduling and power allocation problems in the beam-hopping low-orbit satellite communication system by comprehensively considering the user's business needs and delay requirements on the basis of the beam-hopping low-orbit satellite communication system. To this end, the issues that need to be focused on are: 1) The power resources on the satellite are limited, and the distribution of user business needs in different areas is uneven, and the levels vary greatly. Therefore, in order to meet this demand, the satellite needs to reasonably allocate power to each beam to avoid wasting resources; 2) The mechanism by which the satellite serves users through beam hopping will lead to the accumulation of data packet queuing delays in the buffer. Therefore, it is necessary to schedule the BHP in a certain order to meet the user's demand for delay performance and ensure the user's communication experience.

[0081] To this end, the present invention discloses a method for joint beam hopping pattern scheduling and power allocation for LEO satellites, the main idea of ​​which is: firstly, construct a system model of the downlink of a beam hopping low-orbit satellite communication system, and then construct a decision variable for BHP scheduling; make full use of the influence of the BHP scheduling order on the user queuing delay, and construct a joint BHP scheduling and power allocation optimization problem under the delay constraint with the maximization of minimum service satisfaction as the optimization goal, that is, construct the optimization problem in the system as an optimization problem of maximizing minimum service satisfaction with a delay upper bound constraint; set a distance threshold for avoiding co-channel interference (CCI) between beams, and reduce the solution space scale of BHP scheduling, that is, by setting the distance threshold between the beams enabled at the same time. The value limit is used to reduce the size of the feasible solution space of BHP scheduling. This preprocessing can effectively accelerate the convergence speed of BHP scheduling decisions. Considering the large amount of computing resources required for the optimal solution of BHP scheduling and power allocation, the original complex optimization problem is split, and a two-stage beam-hopping pattern scheduling and power allocation joint decision optimization method based on weight search is proposed: first, the BHP scheduling method based on weight search is used to make BHP scheduling decisions in each BHTS to obtain the suboptimal solution of BHP scheduling, and then the original optimization problem is converted into the corresponding power allocation sub-problem in each BHTS to obtain the optimal solution of power allocation in each BHTS; the above decision is executed BHTS by BHTS in the entire beam-hopping period to realize joint BHP scheduling and power allocation.

[0082] A LEO satellite joint beam hopping pattern scheduling and power allocation method of the present invention specifically comprises the following steps:

[0083] Step 1: Construct the system model of the downlink of the beam-hopping low-orbit satellite communication system;

[0084] Construct a downlink communication scenario for a beam-hopping low-orbit satellite communication system, such as Figure 1As shown. For any satellite in the LEO satellite constellation, the maximum number of beams it can generate at the same time is M. There are a total of N cells within the coverage area of ​​the satellite. The maximum number of beams M is much smaller than the number of cells N within the satellite coverage area, that is, M<<N. and To represent the set of all satellite beams and all cells in this communication scenario. The beams generated by the satellite are all staring beams. When the satellite passes overhead, the beam will continue to track the N cells and provide stable services to users in each cell. There are U n Mobile users, U n ≥1, the user distribution model is modeled using the real population density. The total number of users in the system is Use and represents the set of users in the nth cell and all users in the entire system. Assume that for any user Its average traffic intensity is r u , in bps. Due to user traffic demand d u Independent of the user's distribution, assuming that the longitude and latitude coordinates of user u are (λ u ,φ u ), then the service demand of cell n can be equivalently modeled as:

[0085] r n ={(λ u ,φ u ),r u}

[0086] The scheduling of beams is performed in units of beam hopping time slots (BHTS). In a given BHTS, a beam can only illuminate one cell, and a cell can only be illuminated by the beam, so as to ensure that there is no overlap between the cells illuminated by the beams and avoid waste of beam resources. The method of combining a limited number of beams to illuminate the cells within the satellite coverage is called a beam-hopping pattern (BHP). The mechanism of satellite scheduling BHP is as follows: Figure 2 shown.

[0087] For a given N cells and M beams, there are a total of N! / (M!(NM)!) BHPs. The set of all these BHPs is expressed as Assuming that the number of BHTSs in a beam hopping period is S and the time span of each BHTS is T, the set of all scheduled enabled BHPs can be defined for each beam hopping period as follows:

[0088]

[0089] Where i is the index of BHTS, 1≤i≤S. i For satellites at BHTS i from the collection The BHP selected and dispatched is i The definition is as follows:

[0090]

[0091] in It is used to indicate the nth cell in BHP B i A binary variable indicating whether the is illuminated. Indicates that the nth cell is not illuminated at BHTS i. This means that the nth cell is illuminated by a beam at BHTS i, providing services for users in the cell.

[0092] In this scenario, the satellite transmitting antenna gain at the i-th BHTS The calculation formula is as follows:

[0093]

[0094] in Indicates the maximum transmission gain of the satellite transmitting antenna, It represents the angle between the vector of the satellite transmitting antenna pointing to the user u direction and the vector of the main lobe direction of the beam at the i-th BHTS, represents the normalized antenna gain radiation pattern of the satellite, which is calculated as follows:

[0095]

[0096] where θ represents the angle between the vector of the satellite transmitting antenna pointing in the direction of the user and the vector of the main lobe direction of the beam, f represents the operating frequency of the satellite, α represents the radius of the circular aperture antenna, c represents the speed of light, and J1(·) represents the first-order Bessel function of the first kind with respect to the variables in the brackets.

[0097] The carrier-to-interference-to-noise ratio of the downlink between the satellite and user u The calculation formula is as follows:

[0098]

[0099] in and They represent the carrier-to-interference ratio and carrier-to-noise ratio of the downlink between the satellite and user u, respectively. The calculation formula is as follows:

[0100]

[0101] in represents the effective isotropic radiated power of user u in the i-th BHTS; It represents the ratio of antenna gain to noise temperature T; k represents the Boltzmann constant, which is usually -228.6dBW / K / Hz; PL FS Represents free space loss. Due to the long distance between the satellite and the user, this part of the loss is the main loss in the satellite-to-ground link. A Represents the atmospheric loss caused by cloud and rain fading; PL SM Indicates the additional gain margin reserved to compensate for the shadow fading caused by multipath and obstacle blocking in the satellite-to-ground link; PL SL represents flicker loss; PL AD Indicates various additional losses; Indicates the bandwidth that user u can access, in dBHz; The calculation of the value is affected by the transmission power allocated by the satellite to each user, and its calculation formula is as follows:

[0102]

[0103] in represents the transmission power allocated by the satellite to user u in the i-th BHTS, L c represents the line loss of the satellite transmitting antenna, G T Indicates the transmission gain of the satellite transmitting antenna. Satellites are allocated to all users in the entire network. The set of transmit powers is as follows:

[0104]

[0105] According to the above formula, the power allocation of all BHTSs in the entire beam hopping period can be deduced as follows:

[0106]

[0107] For the convenience of calculation, the satellite's system bandwidth is evenly distributed to each user, and the satellite uses full-band multiplexing technology between the generated beams. Each beam can access the satellite's full system bandwidth, so it can be obtained in represents the system bandwidth obtained by user u, B W represents the system bandwidth of the satellite. Therefore, the channel capacity of the downlink established between the i-th BHTS satellite and user u can be calculated by the Shannon formula as follows:

[0108]

[0109] Thus, it can be calculated that in BHTSi, the satellite beam The throughput provided by illuminated cell n is as follows:

[0110]

[0111] Step 2: Construct the joint BHP dispatch and power allocation optimization problem;

[0112] For cell n, service satisfaction is defined as the total amount of data provided by the satellite to the cell within a beam hopping cycle. Divide by the total volume of services required by the cell R n =r n ×T×S. As of the i-th BHTS, the total amount of data provided by the satellite to cell n The calculation formula is as follows:

[0113]

[0114] The specific optimization problem OP1 is modeled as follows:

[0115]

[0116] The constraint (a) indicates that the number of beams enabled by the satellite at the same time cannot exceed the maximum number M; the constraint (b) indicates the decision variable is a binary variable; constraint (c) represents a decision variable The element B i Only from the collection Constraint (d) indicates that the sum of the power values ​​allocated to each user cannot exceed the total power P of the satellite payload. total ; Constraint (e) strictly constrains the timing relationship of BHP, ensuring that the BHP scheduling result can meet the user's requirements for delay performance. It represents the cumulative waiting delay of cell n at the end time of BHTS i, which is defined as follows:

[0117]

[0118] Where i represents the index of the current BHTS, i l Indicates the BHTS index when the cell was last illuminated by the beam. represents the minimum delay that cell n can accept, D u represents the delay acceptable to user u. Constraint (f) ensures that the total amount of data provided by the satellite to each cell is Do not exceed the total volume of services required by the community to avoid wasting resources.

[0119] Step 3: Reduce the size of the solution space for BHP scheduling;

[0120] Assuming that the radius of the cell covered by the satellite is R, after interference analysis, it can be found that when the center distance between two cells is 3R or more, the CCI between the two beams covering the two cells can be ignored. Therefore, the number of alternative BHPs can be reduced by setting the distance threshold. The specific operation is as follows: First, set the distance threshold d lim =3R, then the distance d between the centers of the cell n within the satellite coverage and the other cells n' except the cell is calculated. n,n′ , n≠n′. If d n,n′ ≤d lim This means that cell n and cell n' are geographically adjacent. Finally, a BHP that does not have an adjacent cell and has a beam illuminating it is defined as an effective BHP. Assuming that after preprocessing, there are K effective BHPs waiting to be scheduled and activated, the set of effective BHPs is It is expressed as follows:

[0121]

[0122] Among them k For this collection The kth valid BHP in:

[0123] Β k =[β k,1 ,β k,2 ,…,β k,n ,…,β k,N ],β k,n ∈{0,1}

[0124] where β k,n = 0 means that within the kth effective BHP, no beam illuminates cell n; on the contrary, when β k,n When =1, it means that within the kth effective BHP, there is a beam illuminating cell n.

[0125] Step 4: BHP scheduling based on weight search;

[0126] Solving the optimization problem OP1 directly is too complicated, so the present invention proposes a BHP scheduling solution based on weight search, which is used to quickly complete the BHP scheduling decision in each BHTS. For the BHP scheduling problem, in order to meet the two constraints (e) and (f) in the optimization problem OP1, the kth effective BHPΒ k The scheduling weight W in BHTSi i k The calculation formula is as follows:

[0127]

[0128] where β k,n ∈Β k , It represents the cumulative traffic provided by the satellite to cell n by the end of BHTS(i-1). When i=1, In addition, ψ(n) represents the penalty weight function when calculating the scheduling weight, and its calculation formula is as follows:

[0129]

[0130] when When ψ(n) is infinite, we can get the effective BHPΒ k The scheduling weight W i k is also infinite, and the BHP should be immediately scheduled and activated in BHTS i. For a given BHTS i, the specific operation of determining the scheduling of the BHP in the BHTS is as follows:

[0131] First, for all the communities Calculate the corresponding Then, all valid BHPs are traversed and the scheduling priority W is calculated for each valid BHP. i k , and continuously update the current maximum weight W in the iteration max Update the BHP B enabled by the schedule at the same time i is the scheduling weight and W max Equal effective BHP. After completing the traversal, the final B i That is, the BHP that is scheduled and enabled in the current BHTS i. The algorithm pseudo code is as follows:

[0132]

[0133]

[0134] Step 5: Solve the corresponding power allocation sub-problem for each BHTS;

[0135] In the corresponding BHTS i, the BHP B scheduled and activated by the BHTS is determined in step 4. i Finally, the power allocation subproblem decomposed from the original optimization problem can be modeled as follows:

[0136]

[0137] B i =Βk ,1≤i≤S, (b)

[0138]

[0139] For this type of fractional optimization problem, the optimization problem is transformed using a quadratic transformation, and the transformed optimization problem is solved. After the quadratic transformation, the optimization problem OP2 can be equivalent to the following form:

[0140]

[0141] B i =Β k ,1≤i≤S, (b)

[0142]

[0143] Among them, constraint (a) indicates that the number of beams enabled at the same time cannot exceed the maximum number of beams; constraint (b) indicates that the BHP scheduled to be enabled at the current moment must be a valid BHP and is obtained based on the output of step 4; constraint (c) indicates that the sum of the power allocated to each user cannot exceed the total power of the satellite; constraint (d) indicates that the total throughput sent to each cell by the current BHTS does not exceed its remaining demand; constraint (e) indicates that the value of the new objective function z can be obtained through the auxiliary variable y n Constraint (f) indicates that the satellite's transmission power will only be allocated to users in the cells illuminated by the beam in the BHP currently scheduled and enabled by the BHTS, and users not within the coverage of any beam will not receive power resources. n It is an auxiliary variable introduced when performing secondary transformation. For a given allocated power sequence P i ,y n The calculation formula is as follows:

[0144]

[0145] Therefore, for each BHP B determined in the BHTS obtained in step 4, i , P can be obtained by solving OP3 i and a solution for z. Then, the solved P i Substituting into the above formula for y n The value of is updated, and the above steps are repeated until the value of z converges, and then P can be obtained. iThe optimal solution. Combining the above step 5 with step 4, the present invention comprehensively considers the historical scheduling of each cell and the queuing delay it has experienced, decomposes and analyzes the original complex optimization problem, reduces the difficulty of problem solving, and proposes a two-stage beam hopping pattern scheduling and power allocation joint decision method based on weighted search. The algorithm pseudo code of this method is as follows:

[0146]

[0147]

[0148] The present invention proposes a two-stage beam hopping pattern scheduling and power allocation joint decision method based on weight search. First, the carrier-to-noise-and-interference ratio (CNIR) of the user's downlink is analyzed, and the size of the BHP scheduling solution space is reduced by interference avoidance, the scale of problem solving is reduced, and the speed of problem solving is accelerated. Secondly, a scheduling weight calculation method for a given BHP is defined, and a suboptimal solution for BHP scheduling is obtained by a weight search method. The suboptimal solution is used to approximate the optimal solution of the BHP scheduling decision variable, and the BHP activation sequence is scheduled to meet the user's delay performance requirements. Finally, in each beam hopping time slot (BHTS), a maximization minimum ratio subproblem corresponding to the BHTS is established to allocate satellite power to meet the user's throughput performance requirements.

[0149] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for LEO satellite joint beam hopping pattern scheduling and power allocation, characterized in that: The following steps are involved: Step 1: Construct the system model of the downlink of the beam-hopping low-orbit satellite communication system and the decision variables of BHP scheduling; Step 2: Construct a joint BHP scheduling and power allocation optimization problem with the goal of maximizing the minimum business satisfaction under delay constraints; Step 3: Set the distance threshold to avoid co-channel interference between beams and reduce the solution space size of BHP scheduling; Step 4: The BHP scheduling method based on weighted search makes BHP scheduling decisions in each BHTS to obtain the suboptimal solution of BHP scheduling, and then converts the original optimization problem into the corresponding power allocation sub-problem in each BHTS to obtain the optimal solution of power allocation in each BHTS; Step 5: Solve the corresponding power allocation sub-problem for each BHTS during the entire beam hopping period.

2. The LEO satellite joint beam hopping pattern scheduling and power allocation method according to claim 1, characterized in that: The specific operation process of step one is as follows: In the downlink communication scenario of the beam-hopping low-orbit satellite communication system, the maximum number of beams generated by any satellite at the same time is M. There are N cells within the coverage of any satellite, and M<<N. The beams generated by the satellite are all staring beams. When the satellite passes overhead, the beams generated by the satellite will continue to track the N cells. U n Mobile users, U n ≥1, represents the set of all cells in this communication scenario; the total number of users in the system is Use and represents the set of users in the nth cell and all users in the entire system; assuming that for any user Its average traffic intensity is r u , in bps; due to user traffic demand d u Independent of the user's distribution, assuming that the longitude and latitude coordinates of user u are (λ u ,φ u ), then the service demand of cell n is equivalently modeled as: r n ={(λ u ,f u ),r u } For a given N cells and M beams, there are N! / (M!(NM)!) BHPs. The set of all BHPs is expressed as Assuming that the number of BHTSs in a beam hopping period is S and the time span of each BHTS is T, the set of all scheduled enabled BHPs for each beam hopping period is defined as: Where i is the index of BHTS, 1≤i≤S; B i For satellites in BHTSi from the collection The BHP selected and dispatched; In this communication scenario, the satellite transmitting antenna gain at the i-th BHTS The calculation formula is: in Indicates the maximum transmission gain of the satellite transmitting antenna, It represents the angle between the vector of the satellite transmitting antenna pointing to the user u direction and the vector of the main lobe direction of the beam at the i-th BHTS, represents the normalized antenna gain radiation pattern of the satellite; The carrier-to-interference-to-noise ratio of the downlink between the satellite and user u The calculation formula is: in and They represent the carrier-to-interference ratio and carrier-to-noise ratio of the downlink between the satellite and user u, respectively. The calculation formula is: in represents the effective isotropic radiated power of user u in the i-th BHTS; represents the ratio of antenna gain to noise temperature T; k represents the Boltzmann constant; PL FS Represents free space loss; PL A Represents the atmospheric loss caused by cloud and rain fading; PL SM Indicates the additional gain margin reserved to compensate for the shadow fading caused by multipath and obstacle blocking in the satellite-to-ground link; PL SL Indicates flicker loss; PL AD Indicates various additional losses; Indicates the bandwidth that user u can access; Satellites are allocated to all users The set of transmit powers is: According to the above formula, the power allocation of all BHTSs in the entire beam hopping period is derived as follows: Assuming that the satellite's system bandwidth is evenly distributed to each user, and the satellite uses full-band multiplexing technology between the generated beams, each beam can access the satellite's full system bandwidth, thus obtaining represents the system bandwidth obtained by user u, B W represents the system bandwidth of the satellite; the channel capacity of the downlink established between the i-th BHTS satellite and user u is calculated by the Shannon formula: In the BHTSi satellite beam The throughput provided by illuminated cell n is:

3. The LEO satellite joint beam hopping pattern scheduling and power allocation method according to claim 2, characterized in that: B i is defined as: in It is used to indicate the nth cell in BHP B i A binary variable indicating whether the is illuminated; Indicates that the nth cell is not illuminated in BHTSi. This means that the nth cell is illuminated by a beam in BHTSi, providing services to users in the cell.

4. The LEO satellite joint beam hopping pattern scheduling and power allocation method according to claim 2, characterized in that: Normalized antenna gain radiation pattern of the satellite The calculation formula is as follows: where θ represents the angle between the vector of the satellite transmitting antenna pointing in the direction of the user and the vector of the main lobe direction of the beam, f represents the operating frequency of the satellite, α represents the radius of the circular aperture antenna, c represents the speed of light, and J1(·) represents the first-order Bessel function of the first kind with respect to the variables in the brackets.

5. The LEO satellite joint beam hopping pattern scheduling and power allocation method according to claim 1, characterized in that: In step 2, the service satisfaction is defined as the total amount of data provided by the satellite to cell n within a beam hopping cycle. Divide by the total volume of services required by the cell R n =r n ×T×S; As of the i-th BHTS, the total amount of data provided by the satellite to cell n The calculation formula is: The specific optimization problem OP1 is modeled as follows: The constraint (a) indicates that the number of beams enabled by the satellite at the same time cannot exceed the maximum number M; the constraint (b) indicates the decision variable is a binary variable; constraint (c) represents a decision variable The element B i Only from the collection Constraint (d) indicates that the sum of the power values ​​allocated to each user cannot exceed the total power P of the satellite payload. total ; Constraint (e) constrains the timing relationship of BHP to ensure that the BHP scheduling result can meet the user's requirements for delay performance, where It represents the cumulative waiting delay of cell n at the end of BHTSi, which is defined as: Where i represents the index of the current BHTS, i l Indicates the BHTS index when the cell was last illuminated by the beam; represents the minimum delay that cell n can accept, D u Indicates the delay that user u can accept; Constraint (f) ensures that the total amount of data provided by the satellite to each cell is Do not exceed the total volume of services required by the community.

6. The LEO satellite joint beam hopping pattern scheduling and power allocation method according to claim 1, characterized in that: In step 3, assuming that the radius of the cell covered by the satellite is R, when the center distance between the two cells is 3R or more, the CCI between the two beams covering the two cells is negligible, and the number of candidate BHPs is reduced by setting the distance threshold: First, set the distance threshold d lim =3R, then the distance between the centers of cell n within the satellite coverage and other cells n' except this cell is calculated. If d n,n′ ≤d lim This means that cell n and cell n' are geographically adjacent. Finally, a BHP that does not have adjacent cells and has a beam illuminating it is defined as a valid BHP. Assuming that after preprocessing, there are K valid BHPs waiting to be scheduled and activated, the set of valid BHPs is for: Among them k For this collection The kth valid BHP in: B k =[β k,1 ,b k,2 ,…,b k,n ,…,b k,N ],b k,n ∈{0,1} where β k,n = 0 means that within the kth effective BHP, no beam illuminates cell n; when β k,n When =1, it means that within the kth effective BHP, there is a beam illuminating cell n.

7. The LEO satellite joint beam hopping pattern scheduling and power allocation method according to claim 5, characterized in that: In step 4, for the BHP scheduling problem, in order to satisfy the two constraints (e) and (f) in the optimization problem OP1, the kth effective BHPΒ k The scheduling weight W in BHTSi i k The calculation formula is: where β k,n ∈Β k , It represents the cumulative traffic provided by the satellite to cell n by the end of BHTS(i-1). When i=1, ψ(n) represents the penalty weight function when calculating the scheduling weight; For a given BHTSi, when determining the BHP to be scheduled within the BHTS, first all cells Calculate the corresponding Then, all valid BHPs are traversed and the scheduling priority W is calculated for each valid BHP. i k , and continuously update the current maximum weight W in the iteration max Update the BHP B enabled by the schedule at the same time i is the scheduling weight and W max Equal effective BHP; after completing the traversal, the final B i That is, the BHP that is scheduled and enabled at the current BHTSi.

8. The LEO satellite joint beam hopping pattern scheduling and power allocation method according to claim 7, characterized in that: In step 5, the power allocation sub-problem decomposed from the original optimization problem is modeled as: B i =B k ,1≤i≤S, (b) The power allocation subproblem is transformed using the secondary transformation, and the transformed optimization problem is solved; after the secondary transformation, the optimization problem OP2 is equivalent to: B i =B k ,1≤i≤S, (b) Among them, constraint (a) indicates that the number of beams enabled at the same time cannot exceed the maximum number of beams; constraint (b) indicates that the BHP scheduled to be enabled at the current moment must be a valid BHP; constraint (c) indicates that the sum of the power allocated to each user cannot exceed the total power of the satellite; constraint (d) indicates that the total throughput sent to each cell by the current BHTS does not exceed its remaining demand; constraint (e) indicates that the value of the new objective function z is obtained through the auxiliary variable y n Obtained; Constraint (f) indicates that the satellite's transmission power will only be allocated to users in the cell illuminated by the beam in the BHP currently scheduled and enabled by the BHTS, and users not within the coverage of any beam will not receive power resources; z and y n is an auxiliary variable introduced during the secondary transformation. For a given allocated power sequence P i ,y n The calculation formula is: For each BHTS obtained in step 4, the BHP B i , P can be obtained by solving OP3 i and a solution of z; the solved P i Substituting into the above formula for y n The value of is updated, and the above steps are repeated until the z value converges, thus obtaining P at this time i The optimal solution of .

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