Low-frequency satellite spectrum allocation and gateway access optimization method based on evolutionary game

Abstract

The invention discloses a low-frequency satellite spectrum allocation and gateway access optimization method based on an evolutionary game. The method comprises the following steps: constructing a low-orbit satellite Internet of Things system model and a channel model thereof; establishing a hierarchical game spectrum domain anti-interference model, modeling a leader sub-game into a spectrum allocation matching game model, and modeling a follower sub-game into a gateway access evolutionary game model; and calculating a gateway access strategy of the sensor user based on the gateway access evolutionary game model, solving stable matching between the spectrum and the gateway by using the spectrum allocation matching game model, and outputting a gateway spectrum allocation strategy. According to the invention, the problem of spectrum resource allocation optimization in the low earth orbit satellite Internet of Things can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication networks, and particularly relates to a method for optimizing low-frequency satellite spectrum allocation and gateway access based on evolutionary game, which is particularly applicable to dynamic spectrum management and efficient communication decision-making in large-scale sensor user scenarios. Background Art

[0002] Due to the small size, low cost, power limitation and resource limitation of Internet of Things (IoT) nodes, interference attack is one of the most common harmful technologies, which may endanger the security of transmitted data. As a new type of satellite communication system, the Low-Earth Orbit (LEO) Internet of Things has many advantages, such as wide coverage, short delay time, large capacity, etc. In the field of low-orbit satellite Internet of Things, the reasonable allocation of spectrum resources is particularly important. With the continuous growth of the number of users, the low-orbit satellite Internet of Things system faces a major challenge: how to meet the access needs of all users, especially for those users with high priority or high requirements for service quality. Simply relying on the method of enhancing satellite coverage cannot fundamentally solve the problem of system expansion, and it is necessary to reasonably optimize the satellite spectrum resources to ensure the good operation of the network. In the existing decision-making framework, it is assumed that all communication users are in an absolutely rational state. When the cognitive ability of the users participating in the game is not perfect and the obtained environmental information cannot be error-free all the time, the selected strategy will deviate from the rational strategy and cannot always follow the principle of utility maximization. When users are boundedly rational, evolutionary game can be used to model the system, and users can continuously modify and improve their strategies through imitation and learning during the strategy evolution process until reaching an evolutionary equilibrium.

[0003] It is worth noting that the existing research ignores the synergy in multi-user scenarios, and the advantages of cooperative communication have not been effectively developed. In addition, most of the existing multi-domain anti-interference decision-making methods construct a wider decision-making space by permuting and combining the strategies of multiple domains. However, this method may be too complex and inefficient, so it is necessary to design a more efficient multi-domain anti-interference decision-making method. At the same time, traditional game theory methods assume that users are all absolutely rational and rely highly on information acquisition. Therefore, it is of great significance to study the strategy evolution process of irrational users. Summary of the Invention

[0004] The purpose of the present invention is to propose a method for optimizing low-frequency satellite spectrum allocation and gateway access based on evolutionary game, which can solve the problem of optimizing the allocation of spectrum resources in low-orbit satellite Internet of Things.

[0005] To achieve the above technical purpose, the technical solution adopted by the present invention is as follows:

[0006] An optimization method for low-frequency satellite spectrum allocation and gateway access based on evolutionary game, the method comprising:

[0007] S1, constructing a low-earth orbit satellite Internet of Things system model and its channel model;

[0008] S2, for the dynamic evolutionary process of low-earth orbit satellite spectrum allocation and UAV gateway access, from the perspective of decision space stratification, establishing a hierarchical game spectrum domain anti-interference model; constructing utility functions for sensor users and UAV gateways, modeling the leader sub-game as a spectrum allocation matching game model, and modeling the follower sub-game as a gateway access evolutionary game model;

[0009] S3, calculating the gateway access strategy of sensor users based on the gateway access evolutionary game model, and using the spectrum allocation matching game model to solve the stable matching of spectrum and gateway based on the matching preference and satisfaction function, and outputting the gateway spectrum allocation strategy.

[0010] Further, in step S1, the low-earth orbit satellite Internet of Things system model includes a sensor user set a UAV gateway set and a low-earth orbit satellite set The data of the low-earth orbit satellite and the UAV gateway are from the same operator, and the total bandwidth B allowed to be used by the operator sum is allocated to the UAV gateway-low-earth orbit satellite transmission link; it is assumed that orthogonal frequency division multiplexing is used between UAV gateways, and the spectrum resources occupied by UAV gateway m are wherein, represents the set of satellite allocable bandwidths, and b l represents the spectrum resources occupied by the l-th UAV access gateway, and the service fee charged by UAV gateway m is c m ∈C = {c1, c2,..., c M}.

[0011] Further, in step S1, the channel model of the low-earth orbit satellite Internet of Things includes a sensor-UAV channel model and a UAV-low-earth orbit satellite channel model;

[0012] The sensor-UAV channel model is a Rayleigh fading channel, and the channel gain H n，m is defined as: where d n，m , η, β~exp(υ) respectively represent the distance from sensor user n to UAV gateway m, the path fading factor, and the Rayleigh fading coefficient; the sensor-UAV channel model is expressed as The signal-to-dry ratio of the signal received by the UAV gateway from the user is The channel capacity received by the UAV gateway is C n，m = b mlog2(1 + γ n，m ); where n′ represents the sensor user n′, f(n, n′) represents the interference factor function between the sensor user n and the sensor user n′, p represents the transmission power of the sensor user, and σ 2 represents the noise power variance of the received signal;

[0013] The UAV - low - earth - orbit satellite channel model is modeled as a Rice channel, and the channel gain is defined as H m，I ; In the UAV - low - earth - orbit channel model, the signal - to - noise ratio of the signal received by the satellite from the user is expressed as The channel capacity of the signal received by the low - earth - orbit satellite from the UAV gateway m is C m，l = b m log2(1 + γ m，l ); where p′ represents the transmission power of the UAV gateway.

[0014] Furthermore, step S2 further includes:

[0015] Construct a hierarchical evolutionary game model, where the low - earth - orbit satellite is the leader, allocating available frequency bands according to the transmission requirements of the UAV gateway, and the sensor users are the followers, choosing the UAV gateway to access;

[0016] Regard the game of the followers as a sub - game of the gateway selection of bounded - rational sensor users; in the process of the sub - game of the gateway selection of sensor users, the sensor users observe the average revenue of all sensor users, compare it with the revenue obtained by their own strategies, and adopt the strategy with higher revenue in the next round of the game. The sensor users continuously repeat the dynamic strategy adjustment process until reaching the evolutionary equilibrium state, and output the gateway access strategy of the sensor users;

[0017] Regard the game of the leader as a sub - game of the spectrum allocation of the low - earth - orbit satellite; in the process of the sub - game of the spectrum allocation of the low - earth - orbit satellite, for the spectrum allocation problem of the UAV gateway, use the deferred acceptance algorithm to find a stable matching. After the gateway selection strategy of the sensor users is given, the UAV gateway formulates its own transmission requirements, seeks a stable matching to improve its own frequency - usage satisfaction, and outputs the gateway spectrum allocation strategy; the optimization goal of the gateway is to obtain more revenue while ensuring the communication conditions of the sensor users.

[0018] Furthermore, the specific evolutionary game problem of the game of the followers is expressed as: participants, strategies, population, population state, and revenue; among them, the participants of the game are the set N of all sensor users; the strategy set of the sensor users is all the UAV gateways in the area, expressed as The participants in the same area form a population; the population state x m represents the proportion of choosing the strategy m, x m = k m / N, where k m is the number of sensor users choosing strategy m, and there is The utility function of sensor user n choosing to access the UAV gateway m is expressed as the value obtained by subtracting the fee paid to the access node from the communication rate obtained, π m = αc m - βp m , where α and β represent the coefficients for weighing the communication speed and the payment cost, and p m represents the fee that the sensor user needs to pay to access the UAV gateway m; then the optimization goal of the sensor user is expressed as:

[0019]

[0020] where π n and a n represent the revenue and the strategy taken by sensor user n respectively.

[0021] The replicator dynamics equation is used to model and analyze the strategy adjustment process of sensor users to reflect the rate of change of sensor users' strategies, where γ is the learning rate, used to control the speed of sensor users' strategy adjustment, and π n (t) represents the revenue of sensor user n at time t, represents the average revenue of all sensor users at time t, is the change rate of the proportion of the group adopting strategy m in the population; reaching the evolutionary equilibrium means that the solution of the evolutionary game of sensor user gateway selection is a fixed point of the replicator dynamics equation, and the rate of change of sensor users' strategies is zero;

[0022] By solving the stability at x m (t) * is obtained.

[0023] Furthermore, the specific matching game problem of the leader game is expressed as: participants, strategies, preferences, matching, utility function; among them, the participants are the two parties of the matching, divided into the UAV gateway set and the set of available frequency bands of low-earth orbit satellites In the matching game process, the strategy of UAV gateway m is all available frequency bands of low-earth orbit satellites, For any two available frequency bands b1 ≠ b2, if b1 > mb2, then for UAV gateway m, the preference for choosing frequency band b1 is better than b2; if UAV gateway m chooses frequency band b1, a set of matching links m - b1 is formed; the optimization goal of the gateway is to obtain more revenue under the condition of ensuring the communication conditions of sensor users, and its utility function is defined as

[0024] For the drone gateway m, the satisfaction Q of selecting the available frequency band l m，l is expressed as: where C m，l is the expected throughput of the drone gateway in the matching link m-b1, and C' ml is the actual throughput of the drone gateway in the matching link m-b1, and λ m is the transmission priority of the drone gateway.

[0025] Furthermore, in step S3, the gateway access strategy of the sensor users calculated based on the gateway access evolutionary game model includes the following steps:

[0026] S301, Initialize the sensor users to randomly select gateway access, input the set of sensor users the set of gateways and the gateway spectrum allocation strategy

[0027] S302, Calculate the current revenue π of each sensor user according to the formula m ;

[0028] S303, Calculate the average revenue

[0029] S304, For the sensor users who select the gateway, judge the size of their current utility and the average utility. If the sensor user changes the strategy to select other gateway m' to access with a probability of γ, otherwise the sensor user maintains the current selection;

[0030] S305, Repeat steps S302 to S304 until the convergence condition is met or the maximum number of iterations is reached, and output the gateway access strategy a of the sensor users n .

[0031] Furthermore, in step S3, the process of solving the stable matching of the spectrum and the gateway using the spectrum allocation matching game model and outputting the gateway spectrum allocation strategy includes the following steps:

[0032] S311, Input the set of available frequency bands the set of gateways and the gateway access strategy a of the sensor users n ;

[0033] S312, Calculate the satisfaction of each matching link. The drone gateways and the available frequency bands are sorted according to the satisfaction respectively, and their respective preference lists are established;

[0034] S313, Each drone gateway sends a matching request to its most preferred frequency band

[0035] S314. Each frequency band selects its most preferred UAV gateway from the received matching requests and rejects the requests of the remaining UAV gateways.

[0036] S315. The rejected UAV gateway sends a matching request to the most preferred frequency band among the frequency bands that have not rejected it.

[0037] S316. Each frequency band selects its most preferred UAV gateway from the received matching requests and rejects the requests of the remaining UAV gateways.

[0038] S317. Repeat steps S315 to S316.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] The low-frequency satellite spectrum allocation and gateway access optimization method based on evolutionary game of the present invention aims at the problem of high frequency coordination complexity in the large-scale uplink access user scenario of the air (low-orbit satellite)-space (UAV)-ground (sensor) network. Considering the characteristics of bounded rationality and incomplete information of sensor users, the dynamic evolutionary process of low-orbit satellite spectrum allocation and UAV gateway access is studied. First, from the perspective of decision space stratification, a hierarchical frequency domain anti-interference model is established, and the utility functions of sensor users and UAV gateways are constructed. Second, the leader sub-game is modeled as a spectrum allocation matching game model, and the matching preference and satisfaction function are defined. Then, the follower sub-game is modeled as a gateway access evolutionary game model, and an anti-interference gateway access algorithm based on evolutionary game with extremely small information interaction volume is proposed. Finally, the simulation results show that the proposed scheme can effectively improve the network throughput in the low-orbit satellite Internet of Things scenario, achieve fast decision-making with less information interaction volume in the large-scale user scenario, and ensure the information transmission effect of sensor users and the spectrum utilization rate of low-orbit satellites. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of a low-orbit satellite Internet of Things scenario involved in an embodiment of the present invention;

[0042] Figure 2 It is a schematic diagram of the structure of a hierarchical evolutionary game model involved in an embodiment of the present invention;

[0043] Figure 3 It is a schematic diagram of the planar distribution of sensor users and gateways involved in an embodiment of the present invention;

[0044] Figure 4 It is a schematic diagram of the convergence curve of the proportion of selected gateway users with the number of games involved in an embodiment of the present invention;

[0045] Figure 5Schematic diagram of the convergence curve of the average user utility involved in the embodiments of the present invention with respect to the number of games

[0046] Figure 6 Schematic diagram of the matching situation between available frequency bands and UAV gateways involved in the embodiments of the present invention

[0047] Figure 7 Schematic diagram of the replicator dynamic phase plane of the evolutionary game involved in the embodiments of the present invention

[0048] Figure 8 Schematic diagram of the comparison of the satisfaction of UAV gateways under different frequency band allocation algorithms involved in the embodiments of the present invention

[0049] Figure 9 Schematic diagram of the comparison of the network average throughput under different algorithms involved in the embodiments of the present invention Detailed implementation manners

[0050] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0051] The present invention discloses an optimization method for low-frequency satellite spectrum allocation and gateway access based on evolutionary game, and the method includes:

[0052] S1, constructing a low-earth orbit satellite Internet of Things system model and its channel model;

[0053] S2, for the dynamic evolution process of low-earth orbit satellite spectrum allocation and UAV gateway access, starting from the perspective of decision space stratification, establishing a hierarchical game spectrum domain anti-interference model; constructing utility functions for sensor users and UAV gateways, modeling the leader sub-game as a spectrum allocation matching game model, and modeling the follower sub-game as a gateway access evolutionary game model;

[0054] S3, calculating the gateway access strategy of sensor users based on the gateway access evolutionary game model, and using the spectrum allocation matching game model to solve the stable matching of spectrum and gateway based on the matching preference and satisfaction function, and outputting the gateway spectrum allocation strategy.

[0055] This embodiment is based on Figure 1 the low-earth orbit satellite Internet of Things scenario shown Figure 2 which is the hierarchical evolutionary game model structure of the present invention. An optimization method for low-frequency satellite spectrum allocation and gateway access based on evolutionary game of the present invention includes the steps:

[0056] (1) Constructing a low-earth orbit satellite Internet of Things system model and its channel model;

[0057] 1) Constructing a low-earth orbit satellite Internet of Things system model;

[0058] As Figure 1As shown, the system considers an industrial area lacking basic communication facilities. The Internet of Things sensors are responsible for collecting environmental information around the factory. For sensor users with only a single antenna, they can choose to send the collected information to the drone gateway. The gateway nodes aggregate and transmit the data from the users, and complete the information transmission to the low-earth orbit satellites through cooperative forwarding. These low-earth orbit satellites can temporarily store the received data and finally transmit it to the ground data center.

[0059] Considering the random distribution of the drone gateways and sensor users, the random set tool is used to model them as a Poisson distribution. Assume that there is a circular area with a radius of R and a density of λ u Sensor users subject to a two-dimensional Poisson distribution, the number of users can be expressed as N = λ u πR 2 , and the user set is expressed as The drone gateway hovers at a height of h in the air, and its horizontal position follows a Poisson distribution with a density of λ g , and the number of gateways is M = λ g πR 2 , and the drone set is expressed as At the same time, L low-frequency satellites provide communication services for this area, and the low-earth orbit satellite set is expressed as The low-earth orbit satellites and the drone gateways belong to the same operator, and the total available bandwidth B of the operator sum is allocated to the drone gateway - low-earth orbit satellite transmission link. Assume that orthogonal frequency division multiplexing is used between the drone gateways, and the spectrum resource occupied by drone gateway m is Among them, represents the set of satellite allocable bandwidth, and b l represents the spectrum resource occupied by the l-th drone accessing the gateway. The service fee charged by drone gateway m is c m ∈C = {c1, c2,..., c M}.

[0060] 2) Construct the sensor-drone channel model and the drone-low-earth orbit satellite channel model

[0061] Since the sensor-drone channel can be regarded as a Rayleigh fading channel, the channel gain H n，m is defined as: Among them, d n，m , η, β~exp(υ) represent the distance from sensor user n to drone gateway m, the path fading factor, and the Rayleigh fading coefficient respectively. Among them, d n，m follows the probability distribution function:

[0062]

[0063] Since the LOS component is more prominent in the communication between UAVs and low-earth orbit satellites, the UAV-low-earth orbit satellite channel can be modeled as a Rice channel, and the channel gain is defined as H m，I , and its probability density function is:[[]]

[0064]

[0065] where K is the Rice fading factor, representing the ratio of the power of the dominant component to the average power of the scattered component, and I0(·) is the modified Bessel function of the first kind of order zero.[[]]

[0066] The signal sent by the sensor user to the UAV gateway will be interfered by the signals of other sensor users that choose the same strategy, which is expressed as where d n′，m represents the distance between user n′ and UAV gateway m. At this time, the signal-to-interference-plus-noise ratio (SINR) of the signal received by the UAV gateway from the user is According to the Shannon formula, the channel capacity received by the UAV gateway is C n，m = b m log2(1 + γ n，m ); where n′ represents sensor user n′, f(n, n′) represents the interference factor function between sensor user n and sensor user n′, p represents the transmission power of the sensor user, and σ 2 represents the noise power variance of the received signal.[[]]

[0067] When communicating with the low-earth orbit satellite, the UAV gateway accesses the satellite through a non-interfering steering point beam, and sends the collected information to the satellite through a directional horn antenna in a forwarding manner with a power of p′. Then, the signal-to-noise ratio of the signal received by the satellite from the user can be expressed as: The channel capacity of the signal received by the satellite from UAV gateway m is C m，l = b m log2(1 + γ m，l ); where p′ represents the transmission power of the UAV gateway.[[]]

[0068] (2) For the dynamic evolution process of low-earth orbit satellite spectrum allocation and UAV gateway access, from the perspective of decision space stratification, a hierarchical game spectrum domain anti-interference model is established; the utility functions of sensor users and UAV gateways are constructed, the leader sub-game is modeled as a spectrum allocation matching game model, and the follower sub-game is modeled as a gateway access evolution game model;

[0069] A hierarchical evolutionary game model is constructed, where the low-earth orbit satellite is the leader, allocating available frequency bands according to the transmission requirements of the UAV gateway, and the sensor users are the followers, choosing to access the UAV gateway. The hierarchical evolutionary game framework is as Figure 2As shown in the figure. Each sensor user can only select one UAV gateway to access at the same time, and it is assumed that sensor users are all selfish, that is, sensor users hope to select the best UAV gateway to maximize their own rewards. Each UAV gateway wants to attract more sensor users to increase its own income on the premise of meeting the load conditions. Each sensor user needs to select an appropriate UAV gateway to maximize the utility determined by the expected transmission rate and price, while the low-earth orbit satellite needs to determine the size of the bandwidth allocation to the UAV gateway by considering the sensor user access status and its income of the UAV gateway. At the same time, the proportion of sensor users selecting the same UAV gateway and the available spectrum bandwidth allocated by the satellite to the base station will affect the income of the UAV gateway, and thus affect the satellite's spectrum allocation decision.

[0070] 1) Follower gateway selection evolutionary game model;

[0071] In the hierarchical game model framework, the follower game is a sub-game of gateway selection for sensor users with bounded rationality. The specific evolutionary game problem can be described as follows:

[0072] Participants: Each sensor user in the service area can choose to access among multiple UAV gateways. The participants in the game are the set N of all sensor users;

[0073] Strategies: The strategy set of sensor users is all the UAV gateways in the area, which can be expressed as

[0074] Population: The participants in the same area form a population, that is, all sensor users with the same strategy;

[0075] Population state: The population state is expressed as the proportion of choosing a certain strategy, that is, x m =k m / N, where k m is the number of sensor users choosing strategy m, and there is

[0076] Payoff: Given the population state, the payoff is used to quantify the satisfaction of each participant adopting a certain strategy. The payoff of sensor users is defined as a linear function of the expected channel capacity and the access price. The utility function of sensor user n choosing to access UAV gateway m can be expressed as the value obtained by subtracting the fee paid to the access node from the communication rate obtained, that is, π m =αc m -βp m . Among them, α and β are coefficients for weighing the communication rate and the payment cost, and p m represents the fee that the sensor user needs to pay to access UAV gateway m; therefore, the optimization goal of the sensor user can be expressed as:

[0077]

[0078] Among them, π n and a n respectively represent the benefit and the strategy adopted by the sensor user n.

[0079] During the evolution process, the sensor user observes the average benefit of all sensor users, compares it with the benefit obtained by its own adopted strategy, and adopts a strategy that can obtain a higher benefit in the next round of game. The sensor user continuously repeats this dynamic strategy adjustment process until it reaches the evolutionary equilibrium state. The replicator dynamic equation is used to model and analyze the strategy adjustment process of the sensor user, reflecting the rate of change of the sensor user's strategy, that is Among them, γ is the learning rate, which is used to control the speed of the sensor user's strategy adjustment, π n (t) represents the benefit of the sensor user n at time t, represents the average benefit of all sensor users at time t, is the change rate of the proportion of the group adopting strategy m in the population.

[0080] The evolutionary equilibrium is the solution of the evolutionary game selected by the sensor user gateway, which is a fixed point of the replicator dynamic equation. At this time, the benefits of all sensor users are the same, that is, the rate of change of the sensor user's strategy is zero Therefore, no sensor user will deviate from the strategy to obtain a higher benefit. The evolutionary equilibrium of the present invention is obtained through numerical calculation, by solving The obtained x m (t) * The stability at. By calculating the eigenvalues of the Jacobian matrix corresponding to the replicator dynamic equation, when all eigenvalues have negative real parts, x m (t) * is stable.

[0081] 2) Leader spectrum allocation matching game;

[0082] In the hierarchical game, the leader game is the low-earth orbit satellite spectrum allocation sub-game. The specific matching game problem can be described as follows:

[0083] Participants: The two parties of the match can be divided into the set of drone gateways and the set of available frequency bands of low-earth orbit satellites

[0084] Strategy: During the matching game process, the strategy of the drone gateway m is all available frequency bands of low-earth orbit satellites, that is

[0085] Preference: For any two available frequency bands If \(b1\neq b2\) and \(b1 > mb2\), then for the drone gateway \(n\), the preferred frequency band is \(b1\) over \(b2\).

[0086] Matching: If the drone gateway \(m\) selects the frequency band \(b1\), then a matching link \(m - b1\) is formed.

[0087] Utility function: The optimization goal of the gateway is to obtain more benefits while ensuring the communication conditions of sensor users. Its utility function can be defined as

[0088] Introduce a satisfaction function to quantify the matching satisfaction degree between the drone gateway and the available frequency bands. At the same time, the preference lists of both matching parties are determined by the satisfaction function. For the drone gateway \(m\), the satisfaction \(Q\) of its selection of the available frequency band \(l\) m，l is expressed as:

[0089]

[0090] where \(C\) m，l is the expected throughput of the drone gateway of the matching link \(m - b1\), \(C'\) m，l is the actual throughput of the drone gateway of the matching link \(m - b1\), and \(\lambda\) m is the transmission priority of the drone gateway.

[0091] (3) Calculate the gateway access strategy of sensor users based on the gateway access evolutionary game model. Based on the matching preference and satisfaction function, use the spectrum allocation matching game model to solve the stable matching of the spectrum and the gateway, and output the gateway spectrum allocation strategy;

[0092] 1) Anti-interference gateway selection evolutionary algorithm;

[0093] The present invention proposes a low-earth orbit satellite spectrum allocation and gateway access algorithm based on hierarchical evolutionary game for the spectrum allocation and gateway access problems in the low-earth orbit satellite Internet of Things scenario. Among them, the utility information of sensor users in the region is maintained by a central controller, and the average user utility is broadcast by the central controller to each sensor user. The gateway access decision of each sensor user is based on the current benefit and the average benefit of all sensor users in the same region. The complexity of the gateway access algorithm of sensor users can be expressed as \(O(N*M*T)\). The gateway access strategy of sensor users calculated based on the gateway access evolutionary game model includes the following steps:

[0094] S301, Initialize the sensor users to randomly select gateway access, input the sensor user set gateway set and the gateway spectrum allocation strategy

[0095] S302, Calculate the current benefit \(\pi\) of each sensor user according to the formulam ;

[0096] S303, Calculate the average revenue

[0097] S304, For the sensor users who select the gateway, determine the magnitude relationship between their current utility and the average utility. If the sensor user changes the strategy to select other gateway m' for access with a probability of γ, otherwise the sensor user maintains the current selection;

[0098] S305, Repeat steps S302 to S304 until the convergence condition is met or the maximum number of iterations is reached, and output the gateway access strategy a of the sensor user n .

[0099] 2) Spectrum allocation deferred acceptance algorithm;

[0100] For the spectrum allocation problem of UAV gateways, the present invention uses the deferred acceptance algorithm to find a stable matching. After the sensor user gateway selection strategy is given, the UAV gateway formulates its own transmission requirements and seeks a stable matching to improve its satisfaction with frequency usage. The complexity of this algorithm is O(M 2 ). Using the spectrum allocation matching game model to solve the stable matching of spectrum and gateway, the process of outputting the gateway spectrum allocation strategy includes the following steps:

[0101] S311, Input the set of available frequency bands the set of gateways and the sensor user gateway access strategy a n ;

[0102] S312, Calculate the satisfaction of each matching link, and the UAV gateway and the available frequency bands are sorted according to the satisfaction respectively to establish their respective preference lists;

[0103] S313, Each UAV gateway sends a matching request to its most preferred frequency band

[0104] S314, Each frequency band selects its most preferred UAV gateway from the received matching requests and rejects the requests of the remaining UAV gateways

[0105] S315, The rejected UAV gateway sends a matching request to the most preferred frequency band among the frequency bands that have not rejected it

[0106] S316, Each frequency band selects its most preferred UAV gateway from the received matching requests and rejects the requests of the remaining UAV gateways

[0107] S317, Repeat steps S315 to S316.

[0108] (4) Through numerical simulation and analysis, the effectiveness of the described algorithm is verified, and the influence of user density on the model is analyzed;

[0109] The numerical simulation parameters of the present invention are set as follows:

[0110] Figure 2 A schematic diagram of the planar distribution of the positions of sensor users and UAV gateways is given. The horizontal and vertical coordinates respectively represent the x - coordinate and y - coordinate of the projections of sensor users and UAV gateways on the ground. It is assumed that in the low - earth - orbit satellite Internet of Things system model, there is a circular area with a radius of R = 1 km, in which there are 200 sensor users and 9 UAV gateways. The hovering height is h = 200 m, and the positions of sensor users and UAVs both satisfy two - dimensional Poisson distribution. The satellite height is 500 km, and the available frequency band set is The throughput of the transmission requirements of sensor users satisfies a random distribution in the range of [50, 100] Mbps.

[0111] The analysis based on the simulation results is as follows:

[0112] Figure 4 It shows the convergence curve of the proportion of sensor users selecting each gateway with the number of games, demonstrating the changing trend of the proportion of sensor users selecting different gateways according to the sensor user gateway access evolution algorithm. The results show that the evolutionary game algorithm converges to the evolutionary stable strategy when the number of iterations is about 60 times, indicating that the algorithm has a relatively fast convergence speed. Through evolutionary game, sensor users select the base station with the highest comprehensive benefit for themselves to connect. Each base station serves a certain proportion of sensor users, avoiding the situation that too many users are connected to a certain base station, which leads to a decline in transmission rate and performance, and improving the utilization efficiency of the base station bandwidth.

[0113] Figure 5 It shows the convergence curve of the average utility of sensor users with the number of games. As the number of games increases, the average utility of sensor users has been increasing and finally converges to a stable state when the number of iterations is about 60 times, indicating the effectiveness of the proposed algorithm.

[0114] Figure 6 It shows the matching situation between available frequency bands and UAV gateways. Among them, the darker the color of the color block, the larger the bandwidth of the frequency band. Combining Figure 5 It can be seen that the proportion of sensor users selecting UAV gateways 8 and 9 is relatively large, the transmission requirements of sensor users are relatively high, and they are allocated a larger available bandwidth; at the same time, the proportion of sensor users selecting UAV gateways 5 and 7 is relatively small, the transmission requirements of sensor users are relatively low, and they are allocated a smaller available bandwidth, thus proving the rationality of the matching results.

[0115] Figure 7It shows the replicator dynamic phase plane of the UAV gateway when the number \(M = 3\). Under the given spectrum allocation conditions, it can be seen that the sensor user gateway selects the adaptive direction of evolution. Given an initial gateway selection ratio, as the direction of the evolution arrow, it will eventually reach the evolutionarily stable strategy, proving the existence of the evolutionary equilibrium.

[0116] To verify the effectiveness of the proposed spectrum allocation deferred acceptance algorithm, this algorithm is compared with the random selection algorithm and the proportional allocation algorithm. Figure 8 It gives the comparison curve of the UAV gateway satisfaction under different frequency band allocation algorithms. From Figure 8 the following conclusions can be drawn:

[0117] 1) Under different numbers of sensor users, the UAV gateway satisfaction under the matching algorithm is better than the other two algorithms, proving the superiority of the matching algorithm;

[0118] 2) As the number of sensor users increases, the overall UAV gateway satisfaction decreases. This is because as the user demand increases, the transmission demand of the UAV gateway increases, and the limited frequency band resources are difficult to meet its demand.

[0119] To evaluate the anti-interference performance of the algorithm, the algorithm is compared with the optimal response algorithm, the random selection algorithm, the scheme that only optimizes the gateway access, and the scheme that only optimizes the spectrum allocation under different numbers of sensor users. Figure 9 It gives the comparison curve of the network expected throughput under different algorithms. The abscissa represents the number of different sensor users, and the ordinate is the network expected throughput. The following conclusions can be drawn from the figure:

[0120] 1) Under the condition of limited available frequency bands, as the number of sensor users increases, the network expected throughput continuously decreases. This is because as the number of sensor users increases, the mutual interference among sensor users will increase, thus reducing the expected throughput;

[0121] 2) Compared with the scheme that only optimizes the gateway access and the scheme that only optimizes the spectrum allocation, the algorithm can greatly improve the network expected throughput and can greatly enhance the network expected throughput in the case of sensor users with different densities.

[0122] 3) The network average throughput of the algorithm is close to about 80% of that under the optimal response algorithm (i.e., the optimal NE) strategy. More importantly, the algorithm does not require sensor users to obtain global information. Therefore, it has lower complexity and faster decision-making speed.

[0123] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript, etc.

[0124] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions run by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0125] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are run on the computer or other programmable device to generate a computer-implemented process, so that the instructions running on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0127] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0128] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to cover these changes and modifications.

Claims

1. A low-frequency satellite spectrum allocation and gateway access optimization method based on evolutionary game, characterized in that: The method comprises: S1, build a low-orbit satellite IoT system model and its channel model; S2, aiming at the dynamic evolution process of low-orbit satellite spectrum allocation and UAV gateway access, from the perspective of decision space stratification, a hierarchical game spectrum domain anti-interference model is established; the utility functions of sensor users and UAV gateways are constructed, the leader sub-game is modeled as a spectrum allocation matching game model, and the follower sub-game is modeled as a gateway access evolution game model; S3, based on the gateway access evolutionary game model, calculates the gateway access strategy of the sensor user. Based on the matching preference and satisfaction function, the spectrum allocation matching game model is used to solve the stable matching between the spectrum and the gateway, and the gateway spectrum allocation strategy is output.

2. The low-frequency satellite spectrum allocation and gateway access optimization method based on evolutionary game according to claim 1 is characterized in that: In step S1, the low-orbit satellite Internet of Things system model includes a sensor user set Drone Gateway Collection and low-orbit satellite collection The low-orbit satellite and drone gateway data are from the same operator, and the total bandwidth allowed by the operator is B sum Assigned to the UAV gateway-LOW earth orbit satellite transmission link; Assuming that orthogonal frequency multiplexing is used between UAV gateways, the spectrum resources occupied by UAV gateway m are in, represents the set of satellite allocatable bandwidth, b l represents the spectrum resources occupied by the lth drone access gateway, and the service fee charged by drone gateway m is c m ∈C={c1,c2,...,c M }.

3. The low-frequency satellite spectrum allocation and gateway access optimization method based on evolutionary game according to claim 2 is characterized in that: In step S1, the channel model of the low-orbit satellite Internet of Things includes a sensor-drone channel model and a drone-low-orbit satellite channel model; The sensor-UAV channel model is a Rayleigh fading channel, and the channel gain H n，m Defined as: where d n，m , η, β~exp(v) represent the distance from sensor user n to UAV gateway m, path fading factor and Rayleigh fading coefficient respectively; the sensor-UAV channel model is expressed as The signal drying ratio of the signal sent by the user received by the drone gateway is The channel capacity received by the UAV gateway is C n，m =b m log2(1+γ n，m ), where n′ represents sensor user n′, f(n, n′) represents the interference factor function between sensor user n and sensor user n′, p represents the transmission power of the sensor user, σ 2 represents the noise power variance of the received signal; The UAV-LEO satellite channel model is modeled as a Rice channel, and the channel gain is defined as H m，I ; The signal-to-noise ratio of the signal sent by the user to the satellite in the UAV-LEO channel model is expressed as The channel capacity of the signal received by the low-orbit satellite from the UAV gateway m is C m，l =b m log2(1+γ m，l ), where p′ represents the transmission power of the UAV gateway.

4. The low-frequency satellite spectrum allocation and gateway access optimization method based on evolutionary game according to claim 1, characterized in that: Step S2 further comprises: A hierarchical evolutionary game model is constructed, in which the low-orbit satellite is the leader and allocates available frequency bands according to the transmission requirements of the drone gateway, and the sensor user is the follower and chooses the drone gateway to access; The follower game is transformed into a sub-game of sensor user gateway selection with bounded rationality. In the sub-game of sensor user gateway selection, the sensor user observes the average benefits of all sensor users, compares it with the benefits obtained by its own strategy, and adopts a strategy with higher benefits in the next round of the game. The sensor user continuously repeats the dynamic strategy adjustment process until the evolutionary equilibrium state is reached, and the gateway access strategy of the sensor user is output. The leader game is transformed into a sub-game of low-orbit satellite spectrum allocation. In the sub-game of low-orbit satellite spectrum allocation, a delayed acceptance algorithm is used to find a stable match for the spectrum allocation problem of the UAV gateway. After the sensor user gateway selection strategy is given, the UAV gateway formulates its own transmission requirements, seeks a stable match to improve its own frequency satisfaction, and outputs the gateway spectrum allocation strategy. The optimization goal of the gateway is to obtain more benefits while ensuring the communication conditions of the sensor users.

5. The low-frequency satellite spectrum allocation and gateway access optimization method based on evolutionary game according to claim 4 is characterized in that: The specific evolutionary game problem of the follower game is expressed as: participants, strategies, populations, population states and benefits; the participants of the game are all sensor users set N; the strategy set of sensor users is all drone gateways in the area, expressed as Participants in the same area form a population; the population state x m Expressed as the proportion of selecting strategy m, x m =k m / N, where k m is the number of sensor users who choose strategy m, and there is The utility function of sensor user n who chooses to access UAV gateway m is expressed as the value obtained by subtracting the fee paid to the access node from the communication rate it obtains, π m =αc m -βp m , where α and β represent the coefficients for weighing the communication speed and payment cost, p m represents the fee that the sensor user needs to pay to access the drone gateway m; then the optimization goal of the sensor user is expressed as: Among them, π n and a n They represent the benefits and strategies adopted by sensor user n respectively; The replicator dynamic equation is used to model and analyze the strategy adjustment process of sensor users to reflect the rate of sensor user strategy change. Where γ is the learning rate, which is used to control the speed of sensor user policy adjustment, and π n (t) represents the benefit of sensor user n at time t, represents the average revenue of all sensor users at time t, is the rate of change of the proportion of the population that adopts strategy m; reaching evolutionary equilibrium means that the solution of the sensor user gateway selection evolutionary game is a fixed point of the replicator dynamic equation, and the rate of change of the sensor user strategy is zero; By solving The obtained x m (t) * The stability of the place.

6. The low-frequency satellite spectrum allocation and gateway access optimization method based on evolutionary game according to claim 4 is characterized in that: The specific matching game problem of the leader game is expressed as: participants, strategies, preferences, matching, and utility functions; the participants are the matching parties, which are divided into a set of drone gateways. and low-orbit satellite available frequency band collection In the matching game process, the strategy of the drone gateway m is to use all available frequency bands of low-orbit satellites. For any two available frequency bands If b1>mb2, then for UAV gateway m, the frequency band b1 is preferred over b2; if UAV gateway m selects frequency band b1, a set of matching links m-b1 is formed; the optimization goal of the gateway is to obtain more benefits under the conditions of ensuring the communication of sensor users, and its utility function is defined as For drone gateway m, its satisfaction with the available frequency band l is Q m，l It is expressed as: Among them, C m，l is the expected throughput of the UAV gateway in the matching link m-b1, C′ m，l is the actual throughput of the UAV gateway in the matching link m-b1, λ m It is the transmission priority of the drone gateway.

7. The low-frequency satellite spectrum allocation and gateway access optimization method based on evolutionary game according to claim 5, characterized in that: In step S3, calculating the gateway access strategy of the sensor user based on the gateway access evolutionary game model includes the following steps: S301, initialize sensor users to randomly select gateway access and input sensor user set Gateway Collection and Gateway Spectrum Allocation Strategy S302, calculate the current income π of each sensor user according to the formula m ; S303, calculate average return S304: for the sensor user who selects the gateway, determine the size of the current utility and the average utility. If The sensor user changes its strategy with a probability of γ and chooses another gateway m′ to access, otherwise the sensor user maintains the current choice; S305, repeating steps S302 to S304 until the convergence condition is met or the maximum number of iterations is reached, and outputting the gateway access strategy a of the sensor user n .

8. The low-frequency satellite spectrum allocation and gateway access optimization method based on evolutionary game according to claim 6, characterized in that: In step S3, the spectrum allocation matching game model is used to solve the stable matching between the spectrum and the gateway, and the process of outputting the gateway spectrum allocation strategy includes the following steps: S311, input available frequency band set Gateway Collection and sensor user gateway access policy a n ; S312, calculating the satisfaction of each matching link, sorting the drone gateways and available frequency bands according to the satisfaction, and establishing their respective preference lists; S313, each drone gateway sends a matching request to its most preferred frequency band S314, each frequency band selects its most preferred drone gateway from the received matching requests and rejects requests from other drone gateways S315, the rejected drone gateway sends a matching request to the most preferred frequency band among the frequency bands that have not rejected it S316, each frequency band selects its most preferred drone gateway from the received matching requests and rejects requests from other drone gateways S317, repeat steps S315 to S316.

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