A method and device for satellite-ground cooperative handover decision in a fusion network

Abstract

The application provides a star-ground cooperation switching decision method in a fusion network, comprising the following steps: collecting switching decision attribute state data of a ground network and a satellite network; performing candidate network node screening according to the decision attribute state data to obtain a candidate network node set; calculating switching overhead of each candidate network node in the candidate network node set through fuzzy learning to obtain switching overhead values of the candidate network nodes; selecting an optimal target switching network node according to the switching overhead values; and triggering a switching process according to the obtained optimal target switching network node. The method provided by the application performs fuzzy processing on different network type performance measurement parameters of the satellite network and the ground network in the same dimension, thereby obtaining the same standard network switching performance measurement index for different network types, and realizing the star-ground cooperation switching decision of the fusion network.

Description

Technical Field

[0001] This invention belongs to the field of mobile communication technology. Background Technology

[0002] With the rapid development and deployment of terrestrial mobile networks and satellite communication technologies, satellite-terrestrial convergence (STERC) offers the advantage of wide coverage, meeting the communication needs of remote areas and compensating for the shortcomings of terrestrial networks, thus gradually becoming a research hotspot. In STERC network design, the satellite constellation is characterized by low orbital altitude and a large number of satellites. The coverage areas of each low-orbit satellite overlap to some extent. Combined with the high-speed motion of satellites orbiting the Earth, STERC networks exhibit highly dynamic characteristics. To ensure continuous communication, terminals need to frequently switch networks to maintain service continuity. Due to the differences in performance measurement standards between terrestrial and satellite networks, how to measure the switching performance of different network types under the same dimension, thereby obtaining a common standard for switching metrics across different network types, and aiming to reduce the impact of STERC network topological heterogeneity on STERC switching performance, is a problem that urgently needs to be solved. Summary of the Invention

[0003] The present invention aims to at least partially solve one of the technical problems in the related art.

[0004] Therefore, the purpose of this invention is to propose a satellite-ground cooperative handover decision-making method in a converged network, which can be used to realize satellite-ground cooperative handover in a converged network.

[0005] To achieve the above objectives, a first aspect of the present invention proposes a satellite-to-ground cooperative handover decision-making method in a converged network, comprising:

[0006] Collect the status of switching decision attributes between terrestrial and satellite networks;

[0007] Based on the decision attribute state, candidate networks are filtered to obtain a set of candidate network nodes;

[0008] The switching cost of the candidate network node set is calculated using fuzzy learning to obtain an accurate value of the switching cost;

[0009] The optimal destination switching network is obtained based on the precise value of the switching cost, and the switching is performed based on the optimal destination switching network.

[0010] In addition, the satellite-to-ground cooperative handover decision-making method in a fused network according to the above embodiments of the present invention may also have the following additional technical features:

[0011] Furthermore, in one embodiment of the present invention, the step of performing network filtering based on the decision attribute state to obtain a candidate network node set includes:

[0012] Using the idea of ​​evolutionary algorithm, candidate network nodes are non-dominatedly sorted with candidate network state as the optimization objective. The network state includes, but is not limited to, latency, bandwidth and packet loss rate.

[0013] The service weights are adjusted according to the user's service type. The candidate satellite network nodes and candidate ground network nodes are screened using strong dominance relationships. If the strong dominance relationship is not satisfied, the optimal solution set in the non-dominance sorting is sorted according to the target with the largest service weight adjustment to obtain the candidate network nodes that meet the user's service requirements.

[0014] Furthermore, in one embodiment of the present invention, the fuzzy learning calculates the switching overhead of the candidate network node set to obtain the optimal target switching network, including:

[0015] The network types of the source network and the destination network are determined, and the time cost is calculated for satellite-to-ground handover scenarios respectively; wherein the network types include satellite networks and terrestrial networks, and the satellite-to-ground handover scenarios include satellite-to-ground handover, ground-to-satellite handover, satellite-to-satellite handover, and ground-to-ground handover.

[0016] Furthermore, in one embodiment of the present invention, the step of determining the network types of the source network and the destination network, and calculating the time overhead for satellite-to-ground handover scenarios, further includes:

[0017] To determine whether the terminal's current access source network node is a satellite network, if the source network node is a satellite network and the candidate network node is also a satellite network, the handover delay is calculated based on the hop count between the source satellite network node and the candidate satellite network node. The handover delay calculation formula is as follows:

[0018]

[0019] Among them, t i，sat Let ti be the handover delay for the i-th satellite, t0 be the transmission delay from the candidate satellite network node to the terminal, and ti be the transmission delay. i，trans Let be the transmission delay from the source satellite network node to the candidate satellite network node, and n be the number of hops required to reach the candidate satellite network node. The processing latency for relay satellite node s includes resource scheduling latency and data processing latency;

[0020] When the source network node is a satellite network and the candidate network node is a terrestrial network, the handover delay is calculated based on the distance between the source satellite network node and the candidate terrestrial network node. The handover delay calculation formula is as follows:

[0021] t i，ground =t0+t i，trans ,

[0022] Among them, ti，ground Let ti be the handover delay of the i-th terrestrial network, and t0 be the transmission delay from the candidate terrestrial network node to the terminal. i，trans The transmission delay from the source satellite network node to the candidate ground network node;

[0023] When the source network node is a terrestrial network and the candidate network node is a satellite network, the handover delay calculation formula is expressed as:

[0024] t i，sat =t0+t i，trans ,

[0025] Among them, t i，sat Let ti be the handover delay of the i-th satellite network, t0 be the transmission delay from the satellite network to the terminal, and ti be the transmission delay from the satellite network to the terminal. i，trans The transmission delay from the source ground network node to the candidate satellite network node;

[0026] When both the source network node and the candidate network node are terrestrial networks, the handover delay is calculated based on the hop count between the source terrestrial network node and the candidate terrestrial network node. The handover delay calculation formula can be expressed as:

[0027]

[0028] Among them, t i，ground Let ti be the handover delay of the i-th terrestrial network, and t0 be the transmission delay from the candidate terrestrial network node to the terminal. i，trans The transmission delay from the source ground network node to the candidate ground network node. This refers to the processing delay of the relay ground network node s.

[0029] Furthermore, in one embodiment of the present invention, the calculation of the switching overhead of the plurality of candidate network nodes through fuzzy learning further includes:

[0030] The switching cost parameters of candidate network nodes are fuzzified using a membership function to obtain a first fuzzy value; wherein, the switching cost parameters of candidate network nodes include time cost, cost cost, and candidate network node performance;

[0031] Fuzzy inference is performed on the first fuzzy value through fuzzy inference of satellite-ground cooperation handover overhead, and a second fuzzy value is obtained by introducing satellite-ground cooperation handover mechanism.

[0032] The second fuzzy value is defuzzified using the centroid method to obtain the switching cost value of the candidate network node.

[0033] Furthermore, in one embodiment of the present invention, the step of fuzzifying the switching cost parameters of candidate network nodes using a membership function includes:

[0034] The switching overhead parameters of candidate network nodes are described using a fuzzy language set, and the switching overhead parameters are mapped to values ​​in the interval [0, 1] through membership functions; wherein, the performance membership functions of satellite candidate network nodes and ground candidate network nodes are respectively expressed as:

[0035]

[0036]

[0037] Among them, s i (θ) is the elevation angle membership function of candidate satellite network i, s i (m) is the membership function of the number of idle channels in candidate satellite network i, s i (τ) is the visible time membership function of candidate satellite network i, s i (r) is the signal strength membership function of candidate satellite network i, t i (r) is the signal strength membership function of candidate ground network i, t i (l) is the load membership function of candidate ground network i, t i (c) represents the membership function of the coverage area of ​​candidate terrestrial network i, s(θ) represents the mean membership value of the elevation angle of candidate satellite network, s(m) represents the mean membership value of the number of idle channels of candidate satellite network, s(τ) represents the mean membership value of the visible time of candidate satellite network, s(r) represents the mean membership value of the signal strength of candidate satellite network, s(r) represents the mean membership value of the signal strength of candidate terrestrial network, t(l) represents the mean membership value of the load of candidate terrestrial network, and t(c) represents the mean membership value of the coverage area of ​​candidate terrestrial network. The satellite and terrestrial network performance coefficients are used to measure the proportion of overhead in the performance parameters of the fused network; satellite elevation angle performance coefficients. Satellite Remaining Channel Number Coefficient Satellite network signal strength coefficient Terrestrial network signal strength coefficient Ground network load factor Terrestrial network coverage coefficient

[0038] To achieve the above objectives, a second aspect of the present invention provides a satellite-to-ground cooperative handover decision-making device in a converged network, comprising the following modules:

[0039] The data acquisition module is used to collect the status of the switching decision attributes between the terrestrial network and the satellite network;

[0040] The filtering module is used to filter candidate networks based on the decision attribute state to obtain a set of candidate network nodes;

[0041] The calculation module is used to calculate the switching cost of the candidate network node set through fuzzy learning to obtain the switching cost value;

[0042] The decision module is used to select the optimal destination switching network node based on the switching cost value, and trigger the switching based on the obtained optimal destination switching network node.

[0043] To achieve the above objectives, a third aspect of the present invention provides a computer device, characterized in that it includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements a satellite-ground cooperative handover decision-making method in a converged network as described above.

[0044] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements a satellite-ground cooperative handover decision-making method in a converged network as described above.

[0045] The satellite-ground cooperative handover decision-making method proposed in this invention performs fuzzy processing on different network performance measurement parameters of satellite and ground networks under the same dimension, thereby obtaining the same standard network handover performance measurement index for different network types, thus realizing satellite-ground cooperative handover in the converged network. During the handover decision-making process, the method first collects performance parameters from both satellite and ground networks through a data acquisition and analysis module. These collected parameters are then input into a candidate network node selection module, which selects a set of candidate network nodes based on user service requirements. If the candidate network node set includes both ground and satellite networks, the process proceeds to the satellite-ground cooperative handover decision-making module. This module first calculates the handover time cost, then fuzzifies the performance parameters of ground and satellite network nodes to obtain a membership measurement function. It then infers the fuzzy handover cost of the candidate destination network node through fuzzy value reasoning and defuzzifies it, thereby obtaining precise values ​​of the handover cost for different types of candidate destination network nodes. Finally, based on the handover cost, the optimal handover scheme for the next time step is determined and the handover is executed. If the candidate network node set does not simultaneously contain both terrestrial and satellite networks, the system enters the regular network handover decision module to determine the handover scheme for the next moment and execute the handover. Attached Figure Description

[0046] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0047] Figure 1 This is a flowchart illustrating a satellite-ground cooperative handover decision-making method in a converged network provided by an embodiment of the present invention.

[0048] Figure 2 This is a schematic diagram of a satellite-to-ground cooperative switching function provided in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of a satellite-to-ground cooperative handover decision-making method provided in an embodiment of the present invention;

[0050] Figure 4 This is a flowchart of a satellite-to-ground collaborative handover decision-making process provided in an embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram of a satellite-ground cooperative handover decision-making device in a fused network provided in an embodiment of the present invention. Detailed Implementation

[0052] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0053] The satellite-ground cooperative handover decision-making method in a converged network according to an embodiment of the present invention is described below with reference to the accompanying drawings.

[0054] Figure 1 This is a flowchart illustrating a satellite-ground cooperative handover decision-making method in a converged network, as provided in an embodiment of the present invention.

[0055] like Figure 1 As shown, the satellite-to-ground cooperative handover decision-making method in this fused network includes the following steps:

[0056] S101: Collect the status of switching decision attributes between terrestrial network and satellite network;

[0057] S102: Filter candidate networks based on decision attribute states to obtain a set of candidate network nodes;

[0058] S103: The switching cost of the candidate network node set is calculated by fuzzy learning to obtain the accurate value of the switching cost;

[0059] S104: Based on the switching cost value, obtain the optimal destination network node for switching, and trigger the switching based on the optimal destination network node.

[0060] This invention proposes a satellite-to-ground cooperative handover decision-making method and apparatus that integrates network communication, such as... Figure 2As shown, this method first collects the handover decision attributes of the satellite network, including the number of idle channels, signal strength, satellite elevation angle, and line-of-sight time, as well as the received signal strength and network load of the ground network. The number of idle channels reflects the satellite's load status; the elevation angle reflects the satellite channel quality; and the line-of-sight time reflects the remaining time the satellite can provide services to the terminal. Then, candidate network nodes are screened based on the acquired network parameters, and different service weights for network parameters such as bandwidth, latency, and packet loss rate are set according to the user's service type to filter candidate satellite networks and candidate ground networks.

[0061] Furthermore, in one embodiment of the present invention, network filtering is performed based on decision attribute states to obtain a candidate network node set, including:

[0062] Using the idea of ​​evolutionary algorithm, the candidate network nodes are non-dominatedly sorted with the candidate network state as the optimization objective. The network state includes, but is not limited to, latency, bandwidth, and packet loss rate.

[0063] The service weights are adjusted according to the user's service type. The candidate satellite network and candidate terrestrial network are screened using strong dominance relationships. When the strong dominance relationship is not met, the optimal solution set in the non-dominated sorting is sorted according to the target with the largest service weight adjustment to obtain the candidate network nodes that meet the user's service requirements.

[0064] Specifically, the candidate network node set is constructed by categorizing services into real-time services, multimedia services, reliability services, and routine services based on user service types. Real-time services include voice and video conferencing, requiring extremely low latency; multimedia services include live video streaming, requiring extremely high bandwidth; reliability services include interactive experiences and VR, requiring extremely low packet loss rates; routine services include email and file transfer, with no special requirements for network parameters. α, β, and γ are set as service weights for latency, bandwidth, and packet loss rate, respectively, with an initial value of 1; T, M, and PLR represent the required latency, bandwidth, and packet loss rate for routine services, respectively. Ti, Mi, and PLRi represent the latency, bandwidth, and packet loss rate of the i-th network node, respectively. The candidate network node selection module first uses an evolutionary algorithm to perform a non-dominated sorting of candidate network nodes, with latency, bandwidth, and packet loss rate as optimization objectives. Then, the service weights are adjusted according to the user service type. After adjustment, the user's required latency, bandwidth, and packet loss rate are αT, βM, and γPLR, respectively. Strong dominance relationships are used to select candidate satellite networks and candidate terrestrial networks, i.e., satisfying:

[0065] (αT≤T i )∧(βM≤M i )∧(γPLR≤PLR i ),

[0066] When a strong dominance relationship is not satisfied, the optimal solution set in the non-dominated sorting is sorted according to the objective with the largest business weight adjustment to obtain candidate network nodes that meet the user's business needs.

[0067] like Figure 3 As shown, the handover types in the satellite-to-ground cooperative handover decision-making method include satellite-to-ground handover, ground-to-satellite handover, satellite-to-satellite handover, and ground-to-ground handover. If the candidate network nodes include both satellite and ground network nodes, due to the differences in performance measurement standards and parameters between satellite and ground network nodes, the satellite-to-ground cooperative handover decision-making method introduces fuzzy learning to calculate network handover overhead in order to evaluate the performance of these two different types of networks and determine the optimal destination handover network node. This allows for the measurement of different network performance indicators on the same fuzzy dimension. The satellite-to-ground cooperative handover decision-making module determines the network type of the source and candidate network nodes, calculates the time overhead for each of the four satellite-to-ground handover scenarios, calculates the handover overhead using the performance parameters of different network types, determines the optimal destination handover network node, and executes the handover. This method ensures that the terminal can select the optimal network from multiple access points for access while meeting service QoS requirements.

[0068] Furthermore, in one embodiment of the present invention, the optimal target switching network is obtained by calculating the switching overhead of the candidate network node set through fuzzy learning, including:

[0069] Determine the network type of the source network and candidate network nodes, and calculate the time cost for satellite-to-ground handover scenarios respectively; where network types include satellite networks and terrestrial networks, and satellite-to-ground handover scenarios include satellite-to-ground handover, ground-to-satellite handover, satellite-to-satellite handover, and ground-to-ground handover.

[0070] Specifically, the satellite-ground cooperative handover decision-making method calculates the handover costs of candidate network nodes to improve terminal satisfaction. The handover cost decision includes time overhead, financial overhead, and the performance of the destination network. Time overhead primarily considers handover latency, including end-to-end latency, data processing latency, and resource scheduling latency; financial overhead is the network access fee. The performance parameters of candidate network nodes include the number of idle channels, signal strength, satellite elevation angle, and line-of-sight of the satellite network, as well as the received signal strength, network load, and coverage of the ground network. The satellite-ground cooperative handover decision-making process first determines whether the terminal's current access source network node is a satellite network. If the source network is a satellite network, it then determines whether the candidate network nodes are also satellite networks.

[0071] Furthermore, in one embodiment of the present invention, determining the network types of the source network and the destination network, and calculating the time overhead for satellite-to-ground handover scenarios respectively, further includes:

[0072] To determine whether the terminal's current access source network node is a satellite network, if both the source and candidate network nodes are satellite networks, the handover delay is calculated based on the hop count between the source and candidate satellite network nodes. The handover delay calculation formula is as follows:

[0073]

[0074] Among them, t i,sat Let ti be the handover delay for the i-th satellite, t0 be the transmission delay from the candidate satellite network node to the terminal, and ti be the transmission delay. i,trans Let be the transmission delay from the source satellite network node to the candidate satellite network node, and n be the number of hops required to reach the candidate satellite network node. The processing latency for relay satellite node s includes resource scheduling latency and data processing latency;

[0075] When the source network node is a satellite network and the candidate network node is a terrestrial network, the handover delay is calculated based on the distance between the source satellite network node and the candidate terrestrial network node. The handover delay calculation formula is as follows:

[0076] t i,ground =t0+t i,trans ,

[0077] Among them, t i,ground Let ti be the handover delay of the i-th terrestrial network, and t0 be the transmission delay from the candidate terrestrial network node to the terminal. i,trans The transmission delay from the source satellite network node to the candidate ground network node;

[0078] When the source network node is a terrestrial network and the candidate network node is a satellite network, the handover delay calculation formula is expressed as:

[0079] t i,sat =t0+t i,trans ,

[0080] Among them, t i,sat Let ti be the handover delay of the i-th satellite network, t0 be the transmission delay from the candidate satellite network node to the terminal, and ti be the transmission delay. i,trans The transmission delay from candidate ground network nodes to candidate satellite network nodes;

[0081] When both the source network node and the candidate network node are terrestrial networks, the handover delay is calculated based on the hop count between the source terrestrial network node and the candidate terrestrial network node. The handover delay calculation formula can be expressed as:

[0082]

[0083] Among them, ti,ground Let ti be the handover delay of the i-th terrestrial network, and t0 be the transmission delay from the candidate terrestrial network node to the terminal. i,trans The transmission delay from the source ground network node to the candidate ground network node. This refers to the processing delay of the relay ground network node s.

[0084] After obtaining parameters such as time overhead, cost overhead, and target network performance, the satellite-ground cooperative handover decision-making method fuzzifies the network node performance parameters. Then, it obtains fuzzy handover overhead values ​​for candidate network nodes through fuzzy inference of handover overhead. Finally, a defuzzification module processes the fuzzy handover overhead values ​​to obtain the actual handover overhead value. The functional flowchart of the satellite-ground cooperative handover decision-making method is shown below. Figure 4 As shown.

[0085] Due to the differences in performance parameters between satellite networks and terrestrial networks, it is necessary to fuzzify the performance parameters of different types of networks separately to obtain a unified network performance evaluation standard. Network parameter fuzzification involves fuzzifying satellite network handover decision parameters such as the number of idle channels, satellite network signal strength, satellite visibility time, satellite elevation angle, handover delay, and cost for satellite network nodes; and fuzzifying terrestrial network handover decision parameters such as received signal strength, network load, handover delay, and cost for terrestrial network nodes.

[0086] Furthermore, in one embodiment of the present invention, calculating the switching overhead of multiple candidate network nodes through fuzzy learning further includes:

[0087] The switching cost parameters of candidate network nodes are fuzzified using a membership function to obtain the first fuzzy value; wherein, the switching cost parameters of candidate network nodes include time cost, cost cost, and candidate network node performance.

[0088] Fuzzy inference is performed on the first fuzzy value through fuzzy inference of satellite-ground cooperation handover overhead, and the second fuzzy value is obtained by entering the satellite-ground cooperation handover mechanism.

[0089] The second fuzzy value is defuzzified using the centroid method to obtain the switching cost value of the candidate network node.

[0090] Specifically, this method describes parameter values ​​using a fuzzy language set and maps them to values ​​in the [0, 1] interval through a membership function. The method uses the eroded input membership function to output the membership function values ​​of each candidate network node. To measure the handover delay of different handover types between satellite and terrestrial networks on the same dimension, the satellite-terrestrial cooperative handover decision method introduces a satellite-terrestrial cooperative handover delay coefficient ω. When the candidate network node is a satellite network... When the candidate network node is a terrestrial network Where d s d represents the distance between satellite candidate network nodes and users. t This represents the distance between ground candidate network nodes and users. The average distance between the satellite candidate network and the user. Let be the average distance between ground candidate network nodes and users. First, the formula for calculating the membership function of the delay overhead t is given below. The membership function of the satellite network's delay overhead t is s. i The formula for calculating (t) is shown below.

[0091]

[0092] Where t is the candidate satellite network switching delay. Let t be the average candidate satellite network handover delay. Similarly, the delay overhead membership function t for the ground network can be obtained. i The formula for calculating (t) is shown below.

[0093]

[0094] The triangular membership function formula for cost f is as follows: The membership function formulas for cost t of satellite networks and terrestrial networks are also shown.

[0095]

[0096]

[0097] Here, f1 and f2 represent the lower and upper bounds of the cost that the user can bear, respectively. If the cost is less than the lower bound f1, the cost is 0; if the cost is greater than the upper bound f2, the cost is 1. These are the cost weights for satellite networks and terrestrial networks, respectively, where f s For the cost of satellite networks, f t Costs for terrestrial networks.

[0098] Secondly, this invention will focus on providing the membership function of candidate network node performance, thereby obtaining the network performance membership function. The membership function calculation formula for the satellite elevation angle θ is as follows:

[0099]

[0100] The membership function for the number of satellite idle channels *m* is calculated as follows, where M... total Represents the total number of satellite channels

[0101]

[0102] The membership function of satellite visibility time τ is calculated as follows, where τ1 is the lower bound of the visibility time threshold. If it is lower than the threshold τ1, the satellite service time is short and the handover cost fuzzy value is 1. τ2 is the upper bound of the satellite visibility time threshold. If it is higher than the threshold τ2, the satellite service time is long and the handover cost fuzzy value is 0.

[0103]

[0104] The satellite signal strength r and the ground signal strength r are calculated using the same membership function as follows, where s1 is the lower bound of the signal strength threshold. If the signal strength is below the threshold s1, the handover overhead ambiguity value is 1. s2 is the upper bound of the signal strength threshold. If the signal strength is above the threshold s2, the handover overhead ambiguity value is 0.

[0105]

[0106] The membership function of the ground network load l is calculated as follows, where l1 is the lower bound of the network load threshold. If the load is below the threshold l1, the fuzzy value of the handover cost is 0. l2 is the upper bound of the network load threshold. If the load is above the threshold l2, the fuzzy value of the handover cost is 1.

[0107]

[0108] Similarly, the membership function of candidate ground network signal strength can be obtained.

[0109] The membership function of the terrestrial network coverage area c is calculated as follows, where R is the radius of the terrestrial network coverage area and c is the distance between the user's location and the terrestrial network.

[0110]

[0111] After obtaining the membership functions of the performance parameters of candidate network nodes, this invention merges the network performance parameters to obtain the performance membership functions of satellite candidate network nodes and ground candidate network nodes respectively.

[0112] The process of reasoning about the first fuzzy value through switching overhead fuzzy inference includes:

[0113] Select the attribute with the smallest information entropy among all attributes of the first fuzzy value as the root node; if all instances in a node are of the same type, then a leaf node is generated; if all attributes in a node have been used, then a leaf node is generated; otherwise, the above process is recursively repeated to generate a decision tree.

[0114] The decision tree is converted into production rules; fuzzy rules are constructed based on the fuzzy logic level of the parameters; and through fuzzy inference, the fuzzy values ​​of the delay overhead, cost overhead, satellite network performance, and terrestrial network performance of the candidate target networks are finally obtained.

[0115] Furthermore, in one embodiment of the present invention, the switching cost parameters of candidate network nodes are fuzzified using a membership function, including:

[0116] The switching overhead parameters of candidate network nodes are described using a fuzzy language set, and the switching overhead parameters are mapped to values ​​in the interval [0, 1] through membership functions; wherein, the performance membership functions of satellite candidate network nodes and ground candidate network nodes are respectively expressed as:

[0117]

[0118]

[0119] Among them, s i (θ) is the elevation angle membership function of candidate satellite network i, s i (m) is the membership function of the number of idle channels in candidate satellite network i, s i (τ) is the visible time membership function of candidate satellite network i, s i (r) is the signal strength membership function of candidate satellite network i, t i (r) is the signal strength membership function of candidate ground network i, t i (l) is the load membership function of candidate ground network i, t i (c) represents the membership function of the coverage area of ​​candidate terrestrial network i, s(θ) represents the mean membership value of the elevation angle of candidate satellite network, s(m) represents the mean membership value of the number of idle channels of candidate satellite network, s(τ) represents the mean membership value of the visible time of candidate satellite network, s(r) represents the mean membership value of the signal strength of candidate satellite network, s(r) represents the mean membership value of the signal strength of candidate terrestrial network, t(l) represents the mean membership value of the load of candidate terrestrial network, and t(c) represents the mean membership value of the coverage area of ​​candidate terrestrial network. The satellite and terrestrial network performance coefficients are used to measure the proportion of overhead in the performance parameters of the fused network; satellite elevation angle performance coefficients. Satellite Remaining Channel Number Coefficient Satellite network signal strength coefficient Terrestrial network signal strength coefficient Ground network load factor Terrestrial network coverage coefficient

[0120] After obtaining the latency overhead membership function, cost overhead membership function, and network performance membership function of the candidate satellite network node and the candidate ground network node based on the above content, the fuzzy inference of the handover overhead is performed by inferring the fuzzy value of the handover overhead through a fuzzy language set.

[0121] First, the attribute with the smallest information entropy among all attributes is selected as the root node. If all instances in a node are of the same type, a leaf node is generated. If all attributes in a node have been used, a leaf node is generated; otherwise, the above process is recursively repeated. The generated tree is converted into production rules. Fuzzy rules are constructed based on the fuzzy logic level of the parameters. Through fuzzy inference, the fuzzy values ​​of delay overhead s(t) and t(t), cost overhead s(f) and t(f), satellite network performance fuzzy value Fs, and ground network performance fuzzy value Ft of the candidate destination network are finally obtained. The defuzzification module of the satellite-ground cooperation handover decision defuzzifies the fuzzy values ​​of handover overhead obtained after fuzzy inference using the centroid method and obtains the handover overhead values ​​of the candidate destination network. The handover overhead values ​​of the candidate satellite network and the ground network are respectively expressed as follows:

[0122] Hs i =s i (t)+s i (f)+Fs i ,

[0123] Ht i =t i (t)+t i (f)+Ft i ,

[0124] Among them, Hs i Ht represents the cost of satellite network handover. i The method obtains the handover cost value for the ground network. After obtaining the handover cost value, it sorts the accurate handover cost values ​​of candidate destination networks to obtain the target handover network node with the highest priority. The handover module then selects the target handover network node with the lowest handover cost value and performs the handover.

[0125] The satellite-ground cooperative handover decision-making method proposed in this invention performs fuzzy processing on different network performance measurement parameters of satellite and ground networks under the same dimension, thereby obtaining the same standard network handover performance measurement index for different network types, thus realizing satellite-ground cooperative handover in the converged network. During the handover decision-making process, the method first collects performance parameters from both satellite and ground networks through a data acquisition and analysis module. These collected parameters are then input into a candidate network node screening module, which selects a set of candidate network nodes based on user service requirements. If the candidate network node set includes both ground and satellite network nodes, the process proceeds to the satellite-ground cooperative handover decision-making module. This module first calculates the handover time cost, then fuzzifies the performance parameters of the ground and satellite network nodes to obtain a membership measurement function. It then infers the fuzzy handover cost values ​​of the candidate destination networks by reasoning from the fuzzy handover cost values ​​and defuzzifies them, thereby obtaining the handover cost values ​​for different types of candidate destination networks. Finally, based on these handover cost values, the optimal handover scheme for the next time step is determined and the handover is executed. If the candidate network node set does not simultaneously contain both terrestrial and satellite networks, the system enters the regular network handover decision module to determine the handover scheme for the next moment and execute the handover.

[0126] To achieve the above embodiments, the present invention also proposes a satellite-ground cooperative handover decision-making device in a converged network.

[0127] Figure 5 This is a schematic diagram of a satellite-ground cooperative handover decision-making device in a converged network, provided in an embodiment of the present invention.

[0128] like Figure 5 As shown, the satellite-to-ground collaborative handover decision-making device in this fused network includes: a data acquisition module 100, a filtering module 200, a calculation module 300, and a decision-making module 400, wherein...

[0129] The data acquisition module is used to collect the status of the switching decision attributes between the terrestrial network and the satellite network;

[0130] The filtering module is used to filter candidate networks based on decision attribute states to obtain a set of candidate network nodes.

[0131] The calculation module is used to calculate the switching cost of the candidate network node set through fuzzy learning to obtain the switching cost value;

[0132] The decision module is used to determine the optimal destination network based on the switching cost value, and then perform the switching based on the optimal destination network.

[0133] To achieve the above objectives, a third aspect of the present invention provides a computer device, characterized in that it includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the satellite-ground cooperative handover decision method in the fused network as described above.

[0134] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the satellite-ground cooperative handover decision-making method in a converged network as described above.

[0135] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0136] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0137] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A decision-making method for satellite-ground cooperative handover in a converged network, characterized in that, Includes the following steps: Collect the status of switching decision attributes between terrestrial and satellite networks; Based on the switching decision attribute status, candidate network nodes are filtered to obtain a set of candidate network nodes; The switching overhead of each candidate network node in the candidate network node set is calculated by fuzzy learning to obtain the switching overhead value of the candidate network node; wherein, the switching overhead parameters of the candidate network node include time overhead, cost overhead and candidate network node performance. The optimal destination switching network node is selected based on the switching cost value of the candidate network nodes, and the switching is triggered based on the obtained optimal destination switching network node. The switching cost parameters of candidate network nodes are fuzzified using a membership function, including: The switching overhead parameters of candidate network nodes are described using a fuzzy language set, and the switching overhead parameters are mapped to values ​​in the interval [0, 1] through membership functions; wherein, the performance membership functions of satellite candidate network nodes and ground candidate network nodes are respectively expressed as: in, Let be the elevation angle membership function of candidate satellite network i. Let be the membership function of the number of idle channels for candidate satellite network i. Let i be the visible temporal membership function of the candidate satellite network. Let i be the signal strength membership function of the candidate satellite network. Let be the signal strength membership function of candidate ground network i. Let be the load membership function of candidate ground network i. Let be the membership function of the coverage area of ​​candidate ground network i. The mean elevation angle membership of the candidate satellite network. The mean membership degree of the number of idle channels in the candidate satellite network. The mean of the visible time membership of the candidate satellite network. The mean membership degree of the candidate satellite network signal strength. The mean membership degree of candidate terrestrial network signal strength. The mean membership degree of candidate terrestrial network load. This represents the mean membership degree of the candidate terrestrial network coverage area. The satellite and terrestrial network performance coefficients are used to measure the proportion of overhead in the performance parameters of the fused network; satellite elevation angle performance coefficients. Satellite Remaining Channel Number Coefficient Satellite network signal strength coefficient , Terrestrial network signal strength coefficient Ground network load factor Terrestrial network coverage coefficient .

2. The method according to claim 1, characterized in that, The step of filtering network nodes based on the decision attribute state to obtain a candidate network node set includes: Using the idea of ​​evolutionary algorithm, the candidate network node states are used as the optimization objective to perform non-dominated sorting of the candidate network nodes, wherein the network node states include, but are not limited to, latency, bandwidth, and packet loss rate. The service weights are adjusted according to the user's service type. The candidate satellite network nodes and candidate ground network nodes are screened using strong dominance relationships. If the strong dominance relationship is not satisfied, the optimal solution set in the non-dominance sorting is sorted according to the target with the largest service weight adjustment to obtain the candidate network nodes that meet the user's service requirements.

3. The method according to claim 1, characterized in that, The step of calculating the switching cost value of the candidate network node set through fuzzy learning to obtain the optimal destination switching network node includes: Determine the network types of the source network node and the destination network node, and calculate the time cost for satellite-to-ground handover scenarios respectively; wherein the network types include satellite networks and terrestrial networks, and the satellite-to-ground handover scenarios include satellite-to-ground handover, ground-to-satellite handover, satellite-to-satellite handover, and ground-to-ground handover.

4. The method according to claim 3, characterized in that, The determination of the network types of the source network node and the candidate network node, and the calculation of time overhead for satellite-to-ground handover scenarios, also include: To determine whether the terminal's current access source network node is a satellite network, if both the source and candidate network nodes are satellite networks, the handover delay is calculated based on the hop count between the source and candidate satellite network nodes. The handover delay calculation formula is as follows: in, Let be the switching delay for the i-th satellite. The transmission delay from the destination satellite to the terminal. The transmission delay from the source network node to the candidate network node is denoted as . This represents the number of hops required from the source satellite node to the candidate satellite network node. The processing latency for the k-th relay satellite node includes resource scheduling latency and data processing latency; When the source network node is a satellite network and the candidate network node is a terrestrial network, the handover delay is calculated based on the distance between the source satellite node and the terrestrial network node. The handover delay calculation formula is as follows: in, Let be the handover delay of the i-th terrestrial network. The transmission delay from the candidate ground network node to the terminal. The transmission delay from the source network node to the candidate network node; When the source network node is a terrestrial network and the candidate network node is a satellite network, the handover delay calculation formula is expressed as: in, Let i be the handover delay for the i-th satellite network. For the transmission delay from the satellite network to the terminal, The transmission delay from the source network node to the candidate network node; When both the source network node and the candidate network node are terrestrial networks, the handover delay is calculated based on the hop count between the source terrestrial network node and the candidate terrestrial network node. The handover delay calculation formula can be expressed as: in, Let be the handover delay of the i-th terrestrial network. The transmission delay from the candidate ground network node to the terminal. Let be the transmission delay from the source network node to the candidate network node, and s be the number of hops required from the source ground network node to the candidate ground network node. This represents the processing delay of the k-th relay ground network node.

5. The method according to claim 1, characterized in that, The calculation of the switching cost of multiple candidate network nodes through fuzzy learning also includes: The switching cost parameters of candidate network nodes are fuzzified using a membership function to obtain the first fuzzy value; Fuzzy inference is performed on the first fuzzy value through fuzzy inference of satellite-ground cooperation handover overhead, and a second fuzzy value is obtained by introducing satellite-ground cooperation handover mechanism. The second fuzzy value is defuzzified using the centroid method to obtain the switching cost value of the candidate network node.

6. A satellite-to-ground cooperative handover decision-making device in a converged network, characterized in that, The apparatus is used to implement the satellite-to-ground cooperative handover decision-making method in a fused network as described in any one of claims 1-5, and the apparatus includes the following modules: The data acquisition module is used to collect the switching decision attribute status of terrestrial and satellite networks; The filtering module is used to filter the network based on the decision attribute state to obtain a set of candidate network nodes; The calculation module is used to calculate the switching cost of the candidate network node set through fuzzy learning to obtain the switching cost value; The decision module is used to obtain the optimal destination switching network based on the switching cost value, and to perform the switching based on the optimal destination switching network.

7. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the satellite-ground cooperative handover decision method in a converged network as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the satellite-ground cooperative handover decision-making method in the converged network as described in any one of claims 1-5.

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