阅读说明：本技术 实现网络负载动态分配的无线通信系统 (Wireless communication system for realizing network load dynamic distribution ) 是由 张亦居 李金喜 杨志明 于 2021-09-13 设计创作，主要内容包括：本发明公开了一种实现网络负载动态分配的无线通信系统,包括信道负载估计模块、信道负载估计评估模块和动态网络负载阈值调整模块,信道负载估计模块用于实时对信道负载进行预估计,得到实时信道负载估计的真值；信道负载估计评估模块根据通信系统物理层业务数据的统计信息计算出信道负载估计的真值的置信率,再对信道负载估计的真值进行调整,得到当前的信道负载估计值；动态网络负载阈值调整模块综合当前的信道负载估计值与当前节点各个优先级的发送队列剩余等待发送包数量信息,动态调整低优先业务的网络负载阀值。本发明根据网络负载估计量和各业务的分布特性,动态调整各业务的网络负载阀值,提高航空自组织网络的业务吞吐量。(The invention discloses a wireless communication system for realizing dynamic distribution of network load, which comprises a channel load estimation module, a channel load estimation evaluation module and a dynamic network load threshold value adjustment module, wherein the channel load estimation module is used for pre-estimating the channel load in real time to obtain a true value of real-time channel load estimation; the channel load estimation evaluation module calculates the confidence rate of the true value of the channel load estimation according to the statistical information of the service data of the physical layer of the communication system, and then adjusts the true value of the channel load estimation to obtain the current channel load estimation value; and the dynamic network load threshold value adjusting module integrates the current channel load estimated value and the number information of the remaining packets waiting to be transmitted of the transmission queue of each priority of the current node, and dynamically adjusts the network load threshold value of the low-priority service. According to the method, the network load threshold value of each service is dynamically adjusted according to the network load estimator and the distribution characteristics of each service, and the service throughput of the aviation self-organizing network is improved.)

1. A wireless communication system for realizing network load dynamic distribution comprises a channel load estimation module, a channel load estimation evaluation module and a dynamic network load threshold value adjustment module, and is characterized in that:

the channel load estimation module is used for pre-estimating the channel load in real time to obtain a true value of real-time channel load estimation;

the channel load estimation evaluation module calculates the confidence rate of the true value of the channel load estimation according to the statistical information of the service data of the physical layer of the communication system, and then adjusts the true value of the channel load estimation to obtain the current channel load estimation value;

the dynamic network load threshold value adjusting module integrates the current channel load estimated value and the number information of the remaining packets waiting to be sent of the sending queue of each priority of the current node, and dynamically adjusts the network load threshold value of the low-priority service on the premise of ensuring the transmission of the high-priority service.

2. The wireless communication system of claim 1, wherein the channel load estimation module is implemented by the following steps:

101) after the wireless communication system is electrified and initialized, the channel load estimation module acquires relevant parameters of channel load estimation as elements in a state transition matrix;

102) counting data packet information received by a physical layer of a communication system and actually transmitted data packet information of a link layer, wherein the data packet information is used as a channel load estimation value at the last moment;

103) predicting the real-time channel load pre-estimation value at the current sending time through a state equation to serve as a predicted value x' (n) of the current time channel load estimation, namely:

x'(n)＝A·x(n-1)，

where A is the state transition matrix and x (n-1) is the channel load estimate at the last time instant.

104) Updating the covariance correction value by using a covariance prediction equation updating equation, and updating the correction coefficient by using a correction coefficient updating equation;

the covariance prediction equation is as follows:

P'(n)＝A·P(n-1)·A^T+Q(n-1)，

wherein P' (n) is the covariance correction, Q is the state error, and P (n-1) is the covariance correction of the previous cycle;

the correction coefficient update equation is:

H(n)＝C·P'(n-1)·C^T·[C·P'(n-1)C^T+R]^-1，

wherein H (n) is a correction coefficient, C is an observation matrix, and R is an observation error;

105) obtaining a correction value by using a correction equation, the updated correction coefficient H (n) of the current time, the predicted value x' (n) of the current time channel load estimation and the network layer observation value y (n) of the current time channel load, and taking the correction value as a true value of the real-time channel load estimation;

the correction equation is x (n) ═ x '(n) + h (n) · [ y (n) -C · x' (n) ].

3. The wireless communication system of claim 1, wherein the channel load estimation evaluation module is implemented by the following steps: :

201) counting service data information of a physical layer of a communication system;

202) analyzing the difference between the network layer observed value and the physical layer observed value to obtain the confidence rate of the channel load estimated value;

203) and adjusting the true value of the real-time channel load estimation according to the confidence rate to obtain the current channel load estimation value.

4. The wireless communication system of claim 1, wherein the dynamic network load threshold adjustment module is implemented by the following steps:

301) counting the quantity of data packets to be transmitted and received of the current communication network and the quantity of packets to be transmitted which remain in a transmission queue of each priority of the current node;

302) judging whether a network congestion phenomenon caused by a large number of low-priority data packets in a sending queue exists in a current node or not according to a current channel load estimation value;

303) if the data packet with the low priority has the congestion phenomenon, detecting the number of the remaining packets waiting to be sent of the previous priority;

304) if the number of the remaining packets waiting for being sent of the previous priority is zero, dynamically adjusting the threshold value of the current priority;

305) detecting the congestion condition of the priority in real time, and adjusting the threshold value of the priority back to the original threshold value state if no congestion condition exists;

and repeating the steps 301 to 305 according to a certain interval period.

Technical Field

The invention belongs to the field of aviation self-organizing networks, and relates to a wireless communication system for realizing dynamic distribution of network loads.

Background

The aviation self-organizing network does not depend on preset infrastructure, and has the characteristics of quick networking, good expansibility, strong self-healing property and the like, wherein the link access control protocol determines how each user in the self-organizing network shares network resources as required. According to the busy degree of the network load and the priority requirements of communication services with different characteristics, the statistical priority multiple access protocol sets the network load threshold value of corresponding services aiming at the services with different priorities, and only when the network load estimated value is smaller than the network load threshold value, the network resource can be used for transmitting the corresponding services.

Dynamic allocation of network load is one of the important components of the link access control protocol. The network load estimator is a basis for judging link access and reflects the busy and idle degree of resources occupied by the aeronautical ad hoc network within a certain time. Therefore, how to accurately estimate the network load in real time directly influences the once success rate of the high-priority service access; meanwhile, due to the fact that the priority services are not uniformly distributed, long-time backspacing of low-priority services can be achieved by adopting a fixed network load threshold value, and fairness of users in the self-organizing network is reduced.

Disclosure of Invention

The invention aims to provide a wireless communication system for realizing dynamic distribution of network load, which realizes real-time dynamic estimation of the network load and confidence evaluation of network load estimator, and realizes dynamic adjustment of a network load threshold value based on service characteristics; the real-time performance and the accuracy of the network load estimator are solved; and meanwhile, according to the network load estimator and the distribution characteristics of each service, the network load threshold value of each service is dynamically adjusted, and the service throughput of the aviation self-organizing network is improved.

The invention aims to be realized by the following technical scheme:

a wireless communication system for realizing network load dynamic distribution comprises a channel load estimation module, a channel load estimation evaluation module and a dynamic network load threshold value adjustment module;

the channel load estimation module is used for pre-estimating the channel load in real time to obtain a true value of real-time channel load estimation;

the channel load estimation evaluation module calculates the confidence rate of the true value of the channel load estimation according to the statistical information of the service data of the physical layer of the communication system, and then adjusts the true value of the channel load estimation to obtain the current channel load estimation value;

the dynamic network load threshold value adjusting module integrates the current channel load estimated value and the number information of the remaining packets waiting to be sent of the sending queue of each priority of the current node, and dynamically adjusts the network load threshold value of the low-priority service on the premise of ensuring the transmission of the high-priority service.

Preferably, the channel load estimation module is implemented by the following program steps:

101) after the wireless communication system is electrified and initialized, the channel load estimation module acquires relevant parameters of channel load estimation as elements in a state transition matrix;

102) counting data packet information received by a physical layer of a communication system and actually transmitted data packet information of a link layer, wherein the data packet information is used as a channel load estimation value at the last moment;

103) predicting the real-time channel load pre-estimation value at the current sending time through a state equation to serve as a predicted value x' (n) of the current time channel load estimation, namely:

x'(n)＝A·x(n-1)，

where A is the state transition matrix and x (n-1) is the channel load estimate at the last time instant.

104) Updating the covariance correction value by using a covariance prediction equation updating equation, and updating the correction coefficient by using a correction coefficient updating equation;

the covariance prediction equation is as follows:

P'(n)＝A·P(n-1)·A^T+Q(n-1)，

wherein P' (n) is the covariance correction, Q is the state error, and P (n-1) is the covariance correction of the previous cycle;

the correction coefficient update equation is:

H(n)＝C·P'(n-1)·C^T·[C·P'(n-1)C^T+R]^-1，

wherein H (n) is a correction coefficient, C is an observation matrix, and R is an observation error;

105) obtaining a correction value by using a correction equation, the updated correction coefficient H (n) of the current time, the predicted value x' (n) of the current time channel load estimation and the network layer observation value y (n) of the current time channel load, and taking the correction value as a true value of the real-time channel load estimation;

the correction equation is x (n) ═ x '(n) + h (n) · [ y (n) -C · x' (n) ].

Preferably, the channel load estimation evaluation module is implemented by the following program steps: :

201) counting service data information of a physical layer of a communication system;

202) analyzing the difference between the network layer observed value and the physical layer observed value to obtain the confidence rate of the channel load estimated value;

203) and adjusting the true value of the real-time channel load estimation according to the confidence rate to obtain the current channel load estimation value.

Preferably, the dynamic network load threshold adjustment module is implemented by the following program steps:

301) counting the quantity of data packets to be transmitted and received of the current communication network and the quantity of packets to be transmitted which remain in a transmission queue of each priority of the current node;

302) judging whether a network congestion phenomenon caused by a large number of low-priority data packets in a sending queue exists in a current node or not according to a current channel load estimation value;

303) if the data packet with the low priority has the congestion phenomenon, detecting the number of the remaining packets waiting to be sent of the previous priority;

304) if the number of the remaining packets waiting for being sent of the previous priority is zero, dynamically adjusting the threshold value of the current priority;

305) detecting the congestion condition of the priority in real time, and adjusting the threshold value of the priority back to the original threshold value state if no congestion condition exists;

and repeating the steps 301 to 305 according to a certain interval period.

Effects of the invention

Compared with the prior art, this patent has following characteristics:

1) the channel load condition in the current communication network can be rapidly and timely updated, so that the collision of data packets and repeated sending processes are avoided or reduced, and high-priority packets are transmitted with lower time delay and higher access success rate;

2) the method can synthesize the channel load condition in the communication network and the quantity information of the waiting-to-send packets of the sending queues of all priorities, dynamically adjust the priority threshold of the congested data packets on the premise of ensuring the transmission of the data packets with high priorities, reduce unnecessary backspacing of the data packets with low priorities and improve the fairness of the system.

Drawings

Fig. 1 is a flow chart of channel load estimation in a communication network.

Fig. 2 is a flow diagram of a communication network channel load estimation evaluation.

Figure 3 is a flow diagram of a dynamically adjusting priority threshold policy.

Fig. 4 is a schematic structural diagram of a wireless communication system implementing dynamic allocation of network load.

Detailed Description

The invention is explained in more detail below with reference to the figures and examples

The wireless communication system for implementing network load dynamic allocation shown in this embodiment includes a channel load estimation module, a channel load estimation evaluation module, and a dynamic network load threshold adjustment module.

The channel load estimation module pre-estimates the channel load in real time through a state equation, corrects the pre-estimation of the channel load by using the actual channel load observed by a physical layer at the previous moment so as to calculate a corrected channel load estimation value, and finally corrects the corrected channel load estimation value by combining with periodic network layer observation values to obtain a true value of the real-time channel load estimation. As shown in fig. 1. The channel load estimation module calculates a true value of the real-time channel load estimation through the following main steps:

101) after the wireless communication system is powered on and initialized, the channel load estimation module obtains relevant parameters of channel load estimation as elements in a state transition matrix, for example, a communication mode, a rate and the like of the wireless communication system are related to a transmission load threshold.

102) And counting the data packet information received by the physical layer of the communication system and the data packet information actually sent by the link layer as the channel load estimation value at the last moment.

103) Predicting the real-time channel load pre-estimation value at the current sending time through a state equation to serve as a predicted value x' (n) of the current time channel load estimation, namely:

x'(n)＝A·x(n-1)，

where A is the state transition matrix and x (n-1) is the channel load estimate at the last time instant.

104) And updating the covariance correction value by using the covariance prediction equation, and updating the correction coefficient by using the correction coefficient updating equation.

The covariance prediction equation is as follows:

P'(n)＝A·P(n-1)·A^T+Q(n-1)，

where P' (n) is the covariance correction, Q is the state error, and P (n-1) is the covariance correction of the previous cycle.

The correction coefficient update equation is:

H(n)＝C·P'(n-1)·C^T·[C·P'(n-1)C^T+R]^-1，

wherein H (n) is a correction coefficient, C is an observation matrix, and R is an observation error.

105) And obtaining a correction value by using the correction equation, the updated correction coefficient H (n) of the current time, the predicted value x' (n) of the current time channel load estimation and the network layer observation value y (n) of the current time channel load, and taking the correction value as a true value of the real-time channel load estimation.

The correction equation is x (n) ═ x '(n) + h (n) · [ y (n) -C · x' (n) ].

The channel load estimation evaluation module design is shown in fig. 2. According to the statistical information of the physical layer service data of the communication system, the confidence rate of the truth value of the channel load estimation is calculated, and then the truth value of the channel load estimation is adjusted, wherein the method comprises the following specific steps:

201) counting service data information of a physical layer of a communication system;

202) analyzing the difference between the network layer observed value and the physical layer observed value to obtain the confidence rate of the channel load estimated value;

203) and adjusting the true value of the real-time channel load estimation according to the confidence rate to obtain the current channel load estimation value.

The dynamic network load threshold adjustment module design is shown in fig. 3. The method comprises the following steps of integrating the current channel load estimation value and the number information of the remaining packets waiting to be sent of the sending queue of each priority of the current node, dynamically adjusting the threshold value of each sending priority, and improving the service throughput of the aeronautical self-organizing network, wherein the method comprises the following specific steps:

301) counting the quantity of data packets to be transmitted and received of the current communication network and the quantity of packets to be transmitted which remain in a transmission queue of each priority of the current node;

302) judging whether a network congestion phenomenon caused by a large number of low-priority data packets in a sending queue exists in a current node or not according to a current channel load estimation value;

303) if the data packet with the low priority has the congestion phenomenon, detecting the number of the remaining packets waiting to be sent of the previous priority;

304) if the number of the remaining packets waiting for being sent of the previous priority is zero, dynamically adjusting the threshold value of the current priority;

305) detecting the congestion condition of the priority in real time, and adjusting the threshold value of the priority back to the original threshold value state if no congestion condition exists;

and repeating the steps 301 to 305 according to a certain interval period.