Multi-beam interference avoidance method and system based on attractor selection model

Abstract

The invention relates to a multi-beam interference avoidance method and system based on an attractor selection model. The method comprises the following steps: S1, establishing a system model of a coverage relationship between multiple beams and a ground cell; s2, constructing a business arrival model; s3, establishing a queuing theory model according to data volume characteristics in the cell; s4, analyzing interference among multiple beams; s5, converting a multi-beam interference avoidance problem into an algorithm problem of an attractor selection model; s6, constructing a beam interference avoidance model based on an attractor selection model; and S7, beam control. According to the method, the problems that the overall performance is reduced and beam transmission is interfered due to interference generated among multiple beams of the low-orbit satellite are solved, the method is more reasonable and flexible, the throughput and time delay of the system can be effectively optimized, and the overall performance of the system is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of satellite communications, and in particular relates to a multi-beam interference avoidance method and system based on an attractor selection model. Background Art

[0002] Satellite communication technology plays a key role in modern communication networks, especially in covering remote areas and oceans where traditional terrestrial networks are difficult to reach. With the growing global demand for high-speed Internet and data transmission, satellite communication technology is also evolving. Multi-beam technology enables each beam to accurately cover a specific small area on the earth's surface, which not only meets the communication needs of high-density users, but also makes resource allocation more accurate. However, the widespread application of multi-beam technology also makes inter-beam interference face greater challenges. On the one hand, with the expansion of satellite communication systems and the increase in user demand, the complexity and cost of interference management are also increasing. On the other hand, the limited frequency resources lead to the frequency planning strategy must strike a balance between resource utilization efficiency and interference control, which requires further optimization and innovation. Therefore, it is necessary to combine other methods to avoid inter-beam interference. Based on this, the present invention designs a multi-beam interference avoidance method and system based on an attractor selection model, which reduces inter-beam interference while ensuring its fairness and improving system throughput. Summary of the invention

[0003] In view of the above situation, the present invention proposes a multi-beam interference avoidance method and system based on an attractor selection model, and determines the optimal beam transmission scheme through a queue theory model analysis with priority and limited weights. The present invention solves the above problems existing in the prior art to avoid interference in the existing low-orbit satellite multi-beam system, and improves the throughput within the beam while ensuring fairness between cells.

[0004] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0005] A multi-beam interference avoidance method based on an attractor selection model comprises the following steps:

[0006] S1. Build system model: Build a system model of the coverage relationship between multi-beams and ground cells;

[0007] S2, constructing a service arrival model for users within the system based on geographical terrain;

[0008] S3. Establish and analyze the queuing theory model based on the data volume characteristics in the community;

[0009] S4. Analyze the interference problem between the main lobe and side lobe of the antenna when generating multiple beams;

[0010] S5. Convert the multi-beam interference avoidance problem into an algorithmic problem of the attractor selection model, that is, express the multi-beam interference avoidance problem using the algorithm of the attractor selection model;

[0011] S6. Construct a beam interference avoidance model based on attractor selection model;

[0012] S7. Perform beam control according to the algorithm calculation of the attractor selection model.

[0013] As a preferred solution, in step S1, for building a system model, establishing a relationship coverage type between multi-beams and ground cells includes:

[0014] The single satellite multi-beam model is used as the system model, and the single satellite communication system is selected to include low-orbit satellites, gateway stations and ground cells. The antenna selects a planar uniform phased array antenna array, which can randomly generate K beams, expressed as K = {k|k = 1, 2,, K}, covering N ground cells, expressed as N = {n|n = 1, 2, ..., N}, where K <N。

[0015] As a preferred solution, in step S2, a service arrival model is established, which specifically includes:

[0016] Based on the geographical differences of ground cells, a service arrival model is established. The service volume is obtained by weighting the three factors of terrain, development, and time. At time t, the device deployment density of grid i is defined as:

[0017]

[0018] Among them, S i represents the area of ​​grid i, m represents the number of geographical environment types contained in the grid, and ρ j It represents the equipment deployment density corresponding to geographical environment type j, which is obtained by weighting according to the terrain and development conditions, and setting corresponding weights for different types of terrain and development conditions. i,j It represents the area occupied by geographical environment type j in grid i. The flow in the system follows a Poisson distribution with an arrival rate of λ.

[0019] As a preferred solution, in step S3, the queuing theory model is analyzed, specifically including:

[0020] S31. The satellite can provide N queues to store traffic arriving from N cells, and the queue storage is limited. Since the waiting time of each data packet in the queue is different, assuming that the data packet waits for more than the survival time T dl Will be abandoned later.

[0021] S32, the data packets in the cell are divided into two priorities, high and low, and the data packets of different priorities are subject to independent parameters λ iPoisson distribution (λ i is the average arrival rate of the i-th level user, i=1,2).

[0022] S33, based on step S32, a limited priority parameter n is set. After entering the queue, a high priority data packet can jump the queue, but in order to ensure that the discarded data packets are as few as possible, a limited parameter n is set to limit the number of high priority data packets jumping the queue, that is, only n high priority data packets are allowed to jump the queue within the time period T to avoid too many low priority data packets being discarded.

[0023] S34. Based on step S33, the average waiting time of different priority data packets when queuing in the cell is calculated as follows:

[0024]

[0025] Among them, W H is the average queuing waiting time of high priority packets, W L is the average queuing waiting time of low priority packets, λ i is the average arrival rate of the i-th level user, μ is the average service rate of the information, T is the time period, and n is the number of high priority data packets processed by queue interruption.

[0026] S35. According to the principle that a data packet will be discarded after the waiting time in the queue exceeds the survival time, the average delay of the overall queue of the cell takes into account the probability of being discarded. The effective average waiting delays of high-priority and low-priority data packets are:

[0027]

[0028] Among them, P d,H , P d,L are the probabilities of high and low priority packets being discarded, respectively;

[0029] S36. For each cell n, the overall delay is expressed as the sum of the effective waiting delays of high-priority and low-priority data packets, that is:

[0030]

[0031] in, is the average waiting delay of high priority nodes, is the average waiting delay of low priority nodes, P d,H , P d,L are the probabilities of high and low priority packets being discarded, respectively.

[0032] As a preferred solution, in step S4, interference analysis is performed, specifically including:

[0033] S41, the side lobe or main lobe of each spot beam overlaps with the main lobe of other beams in space, causing interference. Therefore, when the same-frequency beam performs link communication, in addition to the required signal, the main lobe or side lobe interference signal of the adjacent beam will also be received.

[0034] S42: Based on step S41, the carrier-to-interference ratio C / I of the terminal received signal is used to quantify the inter-beam interference:

[0035]

[0036] Where m is the number of beams that cause interference, G t (θ0) is the desired beam transmit antenna gain, θ0 is the transmit angle of the desired beam, G t,I (θ i ) is the antenna gain of other interference beams, θ i is the angle at which the desired user deviates from the antenna axis of interference beam i.

[0037] As a preferred solution, in step S5, the multi-beam interference avoidance problem is converted into an algorithm problem of an attractor selection model, which specifically includes:

[0038] S51: Based on step S4, a beam interference avoidance model based on attractor selection is used. The number of available beams for ground users in the entire single-satellite coverage scenario is K. The decision state vector when selecting beam i to be turned on or off is m i (1≤i≤K), the biological cell attractor selection model is applied to the field of satellite communications, and the cell attractor selection model is extended from the two-dimensional state space to the multi-dimensional state space, and the attractor selection model is established as follows:

[0039]

[0040] Among them, m i represents the network state variables of beam i when it is turned on or off, such as access throughput, average access delay and other access performance; the cell activity characterization parameter α is one of the core parameters of the decision, which is used to characterize the comprehensive optimization performance of the decision solution; S(α) and D(α) are the increase rate and decrease rate of the state variables of the beam, representing the improvement and decrease speed of system performance; η i (i∈{1,2,…,K}) is the noise of the external environment, representing the changes in the external network environment.

[0041] S52. The problem of the present invention can be established as solving the cell attractor selection model under the drive of the cell activity parameter α to obtain the state vector of the equilibrium solution, and finally taking the model corresponding to the maximum component in the state vector as the target system j * ,Right now:

[0042]

[0043] As a preferred solution, in step S6, a beam interference avoidance model is constructed, which specifically includes:

[0044] S61. Since the attractor value cannot be obtained directly, the parameter h is constructed i To reflect the overall performance of the system when the beam is turned on or off, and to establish a conversion relationship with the attractor value. According to the system model of step S1, the service arrival model generated in step S2 and the queue theory model generated in step S3 are introduced into the system model. At the same time, combined with the analysis in step S4, the interference ratio C / I and the intra-beam throughput TP when the beam is turned on are considered in the optimization decision. total The parameter h is constructed based on factors such as the maximum waiting delay difference TD of the terrestrial cell. i .

[0045] S62. At time t, after receiving the three performance characteristic feedbacks of each beam, the satellite performs weighted calculation on the performance index of beam i through the multi-attribute decision method, which is specifically expressed as follows:

[0046]

[0047] Among them, ω1, ω2, and ω3 represent the weight coefficients of these three key factors.

[0048] S63. Use the Sigmoid utility function to map the performance parameters after beam selection to the network activity factor to ensure the consistency between the two, that is, the better the performance of the beam selected by the satellite, the higher its real-time network activity factor. At time t, the instantaneous network activity factor of beam i is calculated by the following formula:

[0049]

[0050] Preferably, step S61 is specifically as follows:

[0051] S611. First, when calculating the interference between beams, the positions between beams are determined based on the center points of different beams generated by the phased array to determine which beam main lobes or side lobes will cause interference to the desired beam. The carrier-to-interference ratio C / I is used to quantify the interference between beams. Assuming that the transmit power of each antenna and the gain of the receiving antenna are the same, and the loss in the channel is also the same, the carrier-to-interference ratio is:

[0052]

[0053] Where m is the number of beams that cause interference, G t (θ0) is the desired beam transmit antenna gain, θ0 is the transmit angle of the desired beam, G t,I (θ i) is the satellite antenna gain of interference beam i, θ i is the angle at which the desired user deviates from the antenna axis of interference beam i;

[0054] S612, throughput TP within the beam total The channel capacity and the total number of queued packets in the cell are obtained:

[0055]

[0056] Where T represents the long-term performance of T time slots, N is the number of cells, and d t,n is the total flow stored in queue n in time slot t, C t,n is the channel capacity of the beam;

[0057] S613: Use the maximum waiting delay difference TD of the ground cell to indicate fairness between cells:

[0058]

[0059] Among them, τ t,n is the average queue delay of cell n in time slot t.

[0060] As a preferred solution, in step S7, the load balancing problem in the multi-beam is solved according to the change of the attractor value. According to the transformation of the problem and the calculation of the attractor selection model, beam control is performed, which specifically includes:

[0061] Driven by the attractor selection algorithm, the system can find a suitable attractor selection scheme to maximize the system activity and temporarily stabilize the system state. In this state, the impact of noise on the overall state of the system can be ignored, thus deriving the target scheduling strategy. When selecting to turn beam scheduling on or off, options with relatively high attractor values ​​will be given priority to achieve overall beam scheduling benefits and maximize performance while ensuring system stability.

[0062] The present invention also discloses a multi-beam interference avoidance system based on an attractor selection model, which is used to execute the above method and includes the following modules:

[0063] System model building module: builds a system model of the coverage relationship between multi-beams and ground cells;

[0064] Business arrival model building module: builds the business arrival model of users in the system based on geographical terrain;

[0065] Queuing theory model building module: build a queuing theory model based on the data volume characteristics in the community;

[0066] Interference problem analysis module: analyzes the interference problem between the main lobe and side lobe of the antenna when generating multiple beams;

[0067] Multi-beam interference avoidance problem conversion module: the multi-beam interference avoidance problem is expressed using the attractor selection model algorithm;

[0068] Beam interference avoidance model construction module: construct a beam interference avoidance model based on the attractor selection model;

[0069] Beam control module: performs beam control based on the algorithm calculation of the attractor selection model.

[0070] Compared with the prior art, the present invention has the following technical effects:

[0071] (1) The present invention adopts the queuing theory model to build a queuing model in the cell, simulates the queuing of data packets in the cell, realizes limited differentiated services by distinguishing priorities, and analyzes the performance indicators of the communication system, such as delay and throughput. Through reasonable design and scheduling, the present invention can optimize communication efficiency, reduce the delay difference between cells, and improve the fairness of the system.

[0072] (2) The preferred solution of the present invention adopts a heuristic computing model - the attractor selection model. This model maps the solution of a specific problem into the network characteristics and system state of the cell attractor selection model by designing a reasonable mathematical model, and associates the dynamically changing conditions and the quantitative performance of the solution with the cell activity, providing a new framework for studying optimization, control and decision-making problems in dynamic environments. The cell attractor selection model is applied to multi-beam interference avoidance. This mechanism can perform beam scheduling selection based on the feedback data received by the beam, so that the system performance can be improved while adapting to the dynamically changing environment. By selecting a suitable attractor and generating a beam network in a stable state, the system performance can be further improved and the communication efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A flowchart of a multi-beam interference avoidance method based on an attractor selection model according to a preferred embodiment of the present invention;

[0074] Figure 2 A model diagram based on queuing theory involved in a preferred embodiment of the present invention;

[0075] Figure 3 A model diagram based on an attractor selection model according to a preferred embodiment of the present invention;

[0076] Figure 4 The present invention is a block diagram of a multi-beam interference avoidance system based on an attractor selection model according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0077] The following describes the embodiments of the present invention through specific embodiments, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0078] like Figure 1 As shown, this embodiment proposes a multi-beam interference avoidance method based on an attractor selection model, which combines a queuing theory model and an attractor selection model. First, a system model is constructed based on the relationship between multi-beam generation and ground cell coverage, and a service arrival model is established based on the geographical terrain. The satellite is responsible for monitoring the data volume of each cell in the system, establishing a queuing theory model with limited priority, and analyzing the interference problem between the main lobe and side lobe of the antenna in the satellite multi-beam model. Next, a beam interference avoidance model is constructed based on the attractor selection model, and the multi-beam interference avoidance problem is converted into an algorithmic problem of the attractor selection model. By calculating the attractor selection model, the satellite controls the beam, and realizes the opening and closing of the multi-beam according to the change of the attractor value, thereby effectively avoiding interference.

[0079] This embodiment provides a multi-beam interference avoidance method based on an attractor selection model, which is specifically as follows:

[0080] S1. Establish a system model of the coverage relationship between multi-beams and ground cells. The details are as follows:

[0081] A single satellite multi-beam model is used as the system model. The single satellite communication system includes a low-orbit satellite, a gateway station and a ground cell. The antenna selection sets a planar uniform phased array antenna array, which can randomly generate K beams, expressed as K = {k|k = 1, 2, ..., K}, covering N ground cells, expressed as N = {n|n = 1, 2, ..., N}, where K <N。

[0082] S2. Construct a service arrival model for users in the system based on geographical terrain. Specifically, a service arrival model is established based on geographical terrain differentiation. The service volume is obtained by weighting the three factors of terrain, development, and time. At time t, the device deployment density of grid i is defined as:

[0083]

[0084] Among them, S i represents the area of ​​grid i, m represents the number of geographical environment types contained in the grid, ρ j represents the device deployment density corresponding to geographical environment type j, S i,jRepresents the area occupied by geographic environment type j in grid i.

[0085] S3. Establish a queuing theory model based on the data volume characteristics in the community.

[0086] This embodiment uses a queuing model. Figure 2 As shown in the figure, by using this model to queue the data packets in each cell, the average delay of each cell in the satellite coverage area can be calculated. The details are as follows:

[0087] S31. The satellite provides N queues to store traffic arriving from N cells. The waiting time of each data packet in the queue is different. Suppose the data packet waits for more than the survival time T dl will be abandoned later;

[0088] S32. Differentiate the priority of the request data packet and the corresponding queue. According to the tolerance to delay, the data packets are divided into two different levels: high and low (1, 2). Assume that each arriving task has a maximum tolerable delay i For some tasks with extremely high latency requirements, these tasks must be performed in a relatively long time. i Get a response within a small delay. Set a threshold TH1. If a task's response is less than TH1, it is given a high priority (1), otherwise it is given a low priority (2). And packets of different priorities are subject to independent parameters λ i Poisson distribution, λ i is the average arrival rate of the i-th level user, i=1,2;

[0089] S33, setting a limited priority parameter n, a high priority data packet can jump the queue after entering the queue, but in order to ensure that the discarded data packets are as few as possible, a limited parameter n is set to limit the number of high priority data packets jumping the queue, that is, only n high priority data packets are allowed to jump the queue within the time period T, so as to avoid too many low priority data packets being discarded;

[0090] S34, calculate the average waiting time of different priority data packets after queuing in the cell according to the queuing model. The data packets of different priorities are subject to independent parameters λ i Poisson distribution (λ i is the average arrival rate of the i-th level user, i = 1, 2), but for the entire big data information processing system, it does not distinguish between level 1 and level 2 packets. The information processing system is a data packet arriving according to a Poisson distribution with parameter λ (λ = λ1 + λ2). Assuming that the processing time of a beam in the system is S, the average processing time is, μ is the average service rate of information. Specifically:

[0091] S341. The waiting time of the high priority data packet can be obtained according to the set queuing model and the characteristics given by the high priority:

[0092]

[0093] Among them, λ i is the average arrival rate of the i-th level user, and μ is the average service rate of the information.

[0094] S342, using the Pollaczek-Khinchine (PK) formula to calculate the waiting time of the low priority data packet. According to the priority rule, the average waiting time of the low priority data packet needs to take into account the impact of the high priority data packet jumping the queue, that is:

[0095]

[0096] Among them, λ i is the average arrival rate of the i-th level user, μ is the average service rate of the information, T is the time period, and n is the number of high priority data packets processed by queue interruption.

[0097] S343, based on step S341 and step S342, the average waiting time of different priority data packets after queuing in the cell is calculated as:

[0098]

[0099] Among them, W H is the average queuing waiting time of high priority packets, W L is the average queuing waiting time of low priority packets, λ i is the average arrival rate of the i-th level user, μ is the average service rate of information, and T is the time period;

[0100] S35. According to the principle that a data packet will be discarded after its waiting time in the queue exceeds the survival time, the average delay of the overall queue of the cell takes into account the probability of being discarded to calculate the effective average waiting delay of high-priority and low-priority data packets; specifically:

[0101] S351. When the waiting time exceeds the survival time, the packet will be discarded. Therefore, the probability of high and low priority packets being discarded is the probability that the waiting time exceeds the survival time:

[0102]

[0103] Among them, P d,H , P d,L are the probability of high and low priority packets being discarded, T dl The life cycle is set.

[0104] S352, combined with the discarding principle of step S351, the average waiting delay of high and low priority data packets is:

[0105]

[0106] Among them, P d,H , P d,L are the probabilities of high and low priority packets being discarded, respectively.

[0107] S36. For each cell n, the overall delay can be expressed as the sum of the effective waiting delays of high-priority and low-priority data packets, that is:

[0108]

[0109] in, is the average waiting delay of high priority nodes, is the average waiting delay of low priority nodes, P d,H , P d,L are the probabilities of high and low priority packets being discarded, respectively.

[0110] S4. Analyze the interference between the main lobe and side lobe of the antenna when generating multiple beams. Specifically include:

[0111] S41, the side lobes of each spot beam overlap with the main lobes of other beams in space. When the same-frequency beams perform link communication, they will receive the main lobe or side lobe interference signal of the adjacent beam;

[0112] S42, using the carrier-to-interference ratio C / I of the terminal received signal to quantify the inter-beam interference:

[0113]

[0114] Where m is the number of beams that cause interference, G t (θ0) is the desired beam transmit antenna gain, θ0 is the transmit angle of the desired beam, G t,I (θ i ) is the satellite antenna gain of interference beam i, θ i is the angle at which the desired user deviates from the antenna axis of interference beam i.

[0115] S5. Convert the multi-beam interference avoidance problem into an algorithmic problem of attractor selection model. Specifically include:

[0116] S51. The number of available beams for ground users in the entire single-satellite coverage scenario is K. The decision state vector for selecting beam i to be turned on or off is m i (1≤i≤K), the cell attractor selection model is extended from the two-dimensional state space to the multi-dimensional state space, and the cell attractor selection model is established as:

[0117]

[0118] Among them, m i represents the network state variable of beam i when it is turned on or off; the cell activity characterization parameter α is used to characterize the comprehensive optimization performance of the decision solution; S(α) and D(α) are the increase rate and decrease rate of the state variable of the beam, respectively; η i (i∈{1,2,…,K}) is the noise of the external environment;

[0119] S52, driven by the cell activity parameter α, solve the cell attractor selection model to obtain the state vector of the equilibrium solution, and finally take the model corresponding to the largest component in the state vector as the target system j * ,Right now:

[0120]

[0121] S6. Construct a beam interference avoidance model based on the attractor selection model.

[0122] This embodiment is based on the attractor selection model. Figure 3 As shown, with attractor value α i Indicates the pros and cons of the current access scheme. Establishes beam scheduling driven by the cell activity parameter α. Solve the cell attractor selection model to obtain the state vector of the equilibrium solution, and finally take the model corresponding to the largest component in the state vector as the target system. Feedback to the beam scheduling at the next moment to maximize throughput and minimize latency while avoiding interference. Specifically includes the following steps:

[0123] S61. Since the attractor value cannot be obtained directly, the parameter h is constructed i To reflect the overall performance of the system when the beam is turned on or off, and to establish a conversion relationship with the attractor value; according to the system model of step S1, the service arrival model generated in step S2 and the queuing theory model generated in step S3 are introduced into the system model, and combined with the analysis in step S4, the interference ratio C / I and the intra-beam throughput TP when the beam is turned on are considered in the optimization decision. total The maximum waiting delay difference TD of the ground cell is taken into consideration to construct the parameter h i Specific:

[0124] S611. Based on the system model, the carrier-to-interference ratio C / I is used to quantify the interference between beams. At the same time, when calculating the interference between beams, the positions between beams are determined based on the center points of different beams generated by the phased array to determine which beam main lobes or side lobes will cause interference to the desired beam. This step is specific to:

[0125] S6111. According to the system model, the expected signal power received by the user is:

[0126]

[0127] Among them, P t is the power of the signal sent by the desired beam, G t (θ0) desired beam transmit antenna gain, G r (θ0) is the desired signal receiving antenna gain, λ is the wavelength, and d is the distance between the transmitting antenna and the receiving antenna.

[0128] Similarly, the interference signal power that the user is expected to accept is:

[0129]

[0130] Among them, P t,i is the satellite signal power of interference beam i, G t,i (θ i ) is the satellite antenna gain of interference beam i, θ i is the angle at which the desired user deviates from the antenna axis of the interfering beam i, is the receiving antenna gain of the interference signal sent by beam i, is the angle at which the direction of the interference signal sent by beam i deviates from the axis of the receiving antenna, d i is the distance between the transmitting antenna and the receiving antenna.

[0131] S6112. In multi-beam satellite communication scenarios, most interference occurs as multiple interference sources affecting the same receiver, namely:

[0132]

[0133] Where m is the number of beams that cause interference, P r,i is the interference signal power that the desired user accepts.

[0134] S6113. Based on steps S6111 and S6112, the carrier-to-interference ratio of the signal received by the terminal is:

[0135]

[0136] S6114. Assuming that the transmit power of each antenna and the gain of the receiving antenna are the same, and the loss in the channel is also the same, the above formula can be simplified to:

[0137]

[0138] S612, throughput TP within the beam total The channel capacity and the total number of queued packets in the cell are obtained.

[0139] S6121. The interference plus noise signal (SINR) of cell n can be expressed as:

[0140]

[0141] Wherein, the noise power is N0 and the bandwidth is BW.

[0142] Based on SINR, the channel capacity can be calculated as:

[0143] C t,n =x t,n W log2(1+SINR n ) (twenty three)

[0144] Among them, x t,n Indicates whether the cell has beam service. t,n =1 indicates that the cell is served by the beam, otherwise it is not served.

[0145] S6122. The satellite can provide N finite queues to store the traffic arriving at each cell. The traffic arriving at time slot t is represented by L t = {l t,n |n∈N} means, where l t,n is the arrival flow of unit n at time slot t, which follows a Poisson distribution with an arrival rate of λ. The total flow stored in the queue at time slot t is expressed as D t ={d t,n |n∈N}, where d t,n is the total flow stored in queue n in time slot t.

[0146] S6123. Based on step S6121 and step S6122, the data throughput of cell n in time slot t can be expressed as:

[0147] Th t.n =min{C t,n ,d t,n} (twenty four)

[0148] It can be deduced that the overall long-term throughput of the satellite is:

[0149]

[0150] Wherein, T represents the long-term performance of T time slots, and N is the number of cells.

[0151] S613, maximum waiting delay difference TD of the terrestrial cell;

[0152] The overall delay of cell n can be expressed as the sum of the effective waiting delays of high-priority and low-priority data packets, that is:

[0153]

[0154] in, is the average waiting delay of high priority nodes, is the average waiting delay of low priority nodes, P d,H , P d,L are the probabilities of high and low priority packets being discarded, respectively.

[0155] The fairness between cells is expressed by the delay between cells as:

[0156]

[0157] Among them, τ t,n is the average queue delay of cell n in time slot t.

[0158] S62. At time t, after the satellite receives the three performance characteristics feedback of each beam, the performance index h of beam i is determined by the multi-attribute decision method. i For weighted calculation, the expression is as follows:

[0159]

[0160] Among them, ω1, ω2, and ω3 represent the weight coefficients of these three key factors.

[0161] S63, using the Sigmoid utility function to select the beam parameter h i Mapped onto network activity factors;

[0162] The better the overall performance of the satellite's selected beam, the higher its real-time network activity factor. At time t, the instantaneous network activity factor of beam i is calculated by the following formula:

[0163]

[0164] S7. Implement load balancing in multiple beams based on changes in attractor values; specifically:

[0165] Driven by the attractor selection algorithm, the system can find a suitable attractor selection scheme to maximize the system activity and temporarily stabilize the system state. At this time, the impact of noise on the overall state of the system can be ignored, thereby obtaining the target scheduling strategy. When the beam scheduling is turned on or off, the option with a higher attractor value will be given priority to maximize the beam performance while ensuring the stability of the system.

[0166] like Figure 4 As shown, this embodiment discloses a multi-beam interference avoidance system based on an attractor selection model, which is used to execute the above method embodiment, and includes the following modules:

[0167] System model building module: builds a system model of the coverage relationship between multi-beams and ground cells;

[0168] Business arrival model building module: builds the business arrival model of users in the system based on geographical terrain;

[0169] Queuing theory model building module: build a queuing theory model based on the data volume characteristics in the community;

[0170] Interference problem analysis module: analyzes the interference problem between the main lobe and side lobe of the antenna when generating multiple beams;

[0171] Multi-beam interference avoidance problem conversion module: the multi-beam interference avoidance problem is expressed using the attractor selection model algorithm;

[0172] Beam interference avoidance model construction module: construct a beam interference avoidance model based on the attractor selection model;

[0173] Beam control module: performs beam control based on the algorithm calculation of the attractor selection model.

[0174] For other contents of this embodiment, please refer to the above method embodiment.

[0175] The above description is only a detailed description of the preferred embodiments and principles of the present invention. For ordinary technicians in this field, according to the ideas provided by the present invention, there will be changes in the specific implementation methods, and these changes should also be regarded as the protection scope of the present invention.

Claims

1. A multi-beam interference avoidance method based on an attractor selection model, characterized in that The following steps are involved: S1. Establish a system model of the coverage relationship between multi-beams and ground cells; S2, constructing a service arrival model for users within the system based on geographical terrain; S3. Establish a queuing theory model based on the data volume characteristics in the cell; S4. Analyze the interference problem between the main lobe and side lobe of the antenna when generating multiple beams; S5. The algorithmic problem of converting the multi-beam interference avoidance problem into an attractor selection model; S6. Construct a beam interference avoidance model based on attractor selection model; S7. Perform beam control according to the algorithm calculation of the attractor selection model.

2. According to claim 1, a multi-beam interference avoidance method based on attractor selection model is characterized in that: Step S1 is specifically as follows: A single satellite multi-beam model is used as the system model. The single satellite communication system includes a low-orbit satellite, a gateway station and a ground cell. The antenna selection sets a planar uniform phased array antenna array, which can randomly generate K beams, expressed as K = {k|k = 1, 2,, K}, covering N ground cells, expressed as N = {n|n = 1, 2,, N}, where K <N。 3. The multi-beam interference avoidance method based on attractor selection model according to claim 2, characterized in that: In step S2, based on geographical terrain differentiation, a service arrival model is established. The service volume is obtained by weighting the three factors of terrain, development, and time. At time t, the device deployment density of grid i is defined as: Among them, S i represents the area of ​​grid i, m represents the number of geographical environment types contained in the grid, and ρ j represents the device deployment density corresponding to geographical environment type j, S i,j Represents the area occupied by geographic environment type j in grid i.

4. The multi-beam interference avoidance method based on attractor selection model according to claim 3 is characterized in that: Step S3 specifically includes: S31. The satellite provides N queues to store traffic arriving from N cells. The waiting time of each data packet in the queue is different. Suppose the data packet waits for more than the survival time T dl will be abandoned later; S32, the data packets in the cell are divided into two priorities, high and low, and the data packets of different priorities are subject to independent parameters λ i Poisson distribution, λ i is the average arrival rate of the i-th level user, i=1,2; S33, setting a limited priority parameter n; S34, calculating the average waiting time of different priority data packets after queuing in the cell: Among them, W H is the average queuing waiting time of high priority packets, W L is the average queuing waiting time of low priority packets, λ i is the average arrival rate of the i-th level user, μ is the average service rate of information, and T is the time period; S35. According to the principle that a data packet will be discarded after the waiting time in the queue exceeds the survival time, the average delay of the overall queue of the cell takes into account the probability of being discarded. The effective average waiting delays of high-priority and low-priority data packets are: Among them, P d,H , P d,L are the probabilities of high and low priority packets being discarded, respectively; S36. For each cell n, the overall delay is expressed as the sum of the effective waiting delays of high-priority and low-priority data packets, that is: in, is the average waiting delay of high priority nodes, is the average waiting delay of low priority nodes, P d,H , P d,L are the probabilities of high and low priority packets being discarded, respectively.

5. The multi-beam interference avoidance method based on attractor selection model according to claim 4 is characterized in that: Step S4 specifically includes: S41, the side lobes of each spot beam overlap with the main lobes of other beams in space. When the same-frequency beams perform link communication, they will receive the main lobe or side lobe interference signal of the adjacent beam; S42, using the carrier-to-interference ratio C / I of the terminal received signal to quantify the inter-beam interference: Where m is the number of beams that cause interference, G t (θ0) is the desired beam transmit antenna gain, θ0 is the transmit angle of the desired beam, G t,I (θ i ) is the satellite antenna gain of interference beam i, θ i is the angle at which the desired user deviates from the antenna axis of interference beam i.

6. The multi-beam interference avoidance method based on attractor selection model according to claim 5, characterized in that: Step S5 specifically includes: S51. The number of available beams for ground users in the entire single-satellite coverage scenario is K. The decision state vector for selecting beam i to be turned on or off is m i (1≤i≤K), the cell attractor selection model is extended from the two-dimensional state space to the multi-dimensional state space, and the cell attractor selection model is established as: Among them, m i represents the network state variable of beam i when it is turned on or off; the cell activity characterization parameter α is used to characterize the comprehensive optimization performance of the decision solution; S(α) and D(α) are the increase rate and decrease rate of the state variable of the beam, respectively; η i (i∈{1,2,…,K}) is the noise of the external environment; S52, driven by the cell activity parameter α, solve the cell attractor selection model to obtain the state vector of the equilibrium solution, and finally take the model corresponding to the largest component in the state vector as the target system j * ,Right now:

7. The multi-beam interference avoidance method based on attractor selection model according to claim 6, characterized in that: Step S6 specifically includes: S61, according to the system model of step S1, the service arrival model generated in step S2 and the queuing theory model generated in step S3 are introduced into the system model, and combined with the analysis in step S4, the interference ratio C / I and the intra-beam throughput TP when the beam is turned on are considered in the optimization decision. total The maximum waiting delay difference TD of the ground cell is taken into consideration to construct the parameter h i ; S62. At time t, after the satellite receives the three performance characteristics feedback of each beam, the performance index h of beam i is determined by the multi-attribute decision method. i For weighted calculation, the expression is as follows: Among them, ω1, ω2, and ω3 represent the weight coefficients of the three factors; S63. Use the Sigmoid utility function to map the performance parameters after beam selection to the network activity factor. At time t, the instantaneous network activity factor of beam i is calculated by the following formula:

8. The multi-beam interference avoidance method based on attractor selection model according to claim 7, characterized in that: Step S61 is specifically as follows: S611. Use the carrier-to-interference ratio C / I to quantify the interference generated between beams. Assuming that the transmit power of each antenna and the gain of the receiving antenna are the same, and the loss in the channel is also the same, the carrier-to-interference ratio is: Where m is the number of beams that cause interference, G t (θ0) is the desired beam transmit antenna gain, θ0 is the transmit angle of the desired beam, G t,I (θ i ) is the satellite antenna gain of interference beam i, θ i is the angle at which the desired user deviates from the antenna axis of interference beam i; S612, throughput TP within the beam total The channel capacity and the total number of queued packets in the cell are obtained: Where T represents the long-term performance of T time slots, N is the number of cells, and d t,n is the total flow stored in queue n in time slot t, C t,n is the channel capacity of the beam; S613: Use the maximum waiting delay difference TD of the ground cell to indicate fairness between cells: Among them, τ t,n is the average queue delay of cell n in time slot t.

9. The multi-beam interference avoidance method based on attractor selection model according to claim 8, characterized in that: Step S7 specifically includes: Driven by the algorithm of the attractor selection model, an attractor selection scheme that meets the set conditions is found; when beam scheduling is turned on or off, options with high attractor values ​​are given priority.

10. A multi-beam interference avoidance system based on an attractor selection model, used to execute the method according to any one of claims 1 to 9, characterized in that Includes the following modules: System model building module: builds a system model of the coverage relationship between multi-beams and ground cells; Business arrival model building module: builds the business arrival model of users in the system based on geographical terrain; Queuing theory model building module: build a queuing theory model based on the data volume characteristics in the community; Interference problem analysis module: analyzes the interference problem between the main lobe and side lobe of the antenna when generating multiple beams; Multi-beam interference avoidance problem conversion module: the multi-beam interference avoidance problem is expressed using the attractor selection model algorithm; Beam interference avoidance model construction module: construct a beam interference avoidance model based on the attractor selection model; Beam control module: performs beam control based on the algorithm calculation of the attractor selection model.

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