CN113784390B - Wireless communication system for realizing dynamic distribution of network load - Google Patents
Wireless communication system for realizing dynamic distribution of network load Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/08—Load balancing or load distribution
- H04W28/09—Management thereof
- H04W28/0925—Management thereof using policies
- H04W28/0942—Management thereof using policies based on measured or predicted load of entities- or links
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
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Abstract
The invention discloses a wireless communication system for realizing dynamic network load distribution, which comprises a channel load estimation module, a channel load estimation evaluation module and a dynamic network load threshold adjustment module, wherein the channel load estimation module is used for pre-estimating 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 coefficient of the true value of the channel load estimation according to the statistical information of the physical layer business data 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 adjustment module synthesizes the current channel load estimated value and the information of the number of the remaining waiting transmission packets of the transmission queues of each priority of the current node, and dynamically adjusts the network load threshold of the low-priority service. According to the network load estimation and the distribution characteristics of each service, the invention dynamically adjusts the network load threshold of each service and improves the service throughput of the aviation self-organizing network.
Description
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, has the characteristics of quick networking, good expansibility, strong self-healing property and the like, and a link access control protocol determines how each user in the self-organizing network shares network resources according to needs. The statistical priority multiple access protocol sets network load threshold values of corresponding services according to the busy degree of network load and the communication service priority demands of different characteristics, and can use network resources to transmit the corresponding services only when the network load estimated value is smaller than the network load threshold values.
Network load dynamic allocation is one of the important components of the link access control protocol. The network load estimation is the basis for judging the link access, and reflects the busy and idle degree of resources occupied by the aviation self-organizing network in a certain time. Therefore, how to accurately estimate the network load in real time directly affects the primary success rate of high-priority service access; meanwhile, due to uneven distribution of the priority services, the low-priority services can be retracted for a long time by adopting a fixed network load threshold, and fairness of each user 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 network load and confidence assessment of network load estimation amount and realizes dynamic adjustment of network load threshold based on service characteristics; the real-time performance and the accuracy of the network load estimation are solved; and meanwhile, according to the network load estimation quantity 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 at being realized by the following technical scheme:
a wireless communication system for realizing dynamic network load distribution comprises a channel load estimation module, a channel load estimation evaluation module and a dynamic network load threshold 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 coefficient of the true value of the channel load estimation according to the statistical information of the physical layer business data 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 adjustment module synthesizes the current channel load estimated value and the information of the number of the remaining waiting transmission packets of the transmission queues of each priority of the current node, and dynamically adjusts the network load threshold 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 powered on and initialized, the channel load estimation module acquires relevant parameters of channel load estimation as elements in a state transition matrix;
102 Counting the data packet information received by the physical layer of the communication system and the data packet information actually transmitted by the link layer, and taking the data packet information and the link layer as the channel load estimated value of the last moment;
103 Predicting a real-time channel load pre-estimation value at the current transmission time through a state equation, and using the real-time channel load pre-estimation value as a predicted value x' (n) of channel load estimation at the current time, 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 previous time.
104 Updating the covariance correction value by using a covariance prediction equation, and updating the correction coefficient by using a correction coefficient updating equation;
the covariance prediction equation is:
P'(n)＝A·P(n-1)·A Τ +Q(n-1)，
wherein P' (n) is a covariance correction value, Q is a state error, and P (n-1) is a covariance correction value of the previous cycle;
the correction coefficient update equation is:
H(n)＝C·P'(n-1)·C Τ ·[C·P'(n-1)C Τ +R] -1 ，
wherein H (n) is a correction coefficient, C is an observation matrix, and R is an observation error;
105 Using the correction equation, the updated correction coefficient H (n) at the current moment, the predicted value x' (n) of the channel load estimation at the current moment and the network layer observed value y (n) of the channel load at the current moment to obtain a correction value, and using the correction value as a true value of the real-time channel load estimation;
wherein, 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 Statistics of physical layer business data information of the communication system;
202 Analyzing the difference between the network layer observation value and the physical layer observation value to obtain the confidence coefficient of the channel load estimation value;
203 The true value of the real-time channel load estimation is adjusted according to the confidence coefficient, and the current channel load estimation value is obtained.
Preferably, the dynamic network load threshold adjustment module is implemented by the following program steps:
301 Counting the number of the data packets transmitted and received by the current communication network and the number of the remaining waiting packets to be transmitted of the transmission queues of each priority of the current node;
302 Judging whether the current node has network congestion caused by a large number of low-priority data packets in a transmission queue according to the current channel load estimated value;
303 If congestion occurs in the data packet with low priority, detecting the number of the remaining waiting packets with the last priority;
304 If the number of the remaining waiting sending packets of the previous priority is zero, dynamically adjusting the threshold value of the current priority;
305 Detecting congestion status of the priority in real time, and if no congestion status exists, adjusting the threshold value of the priority back to the original threshold value state;
steps 301 to 305 are repeated at regular intervals.
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 updated rapidly and timely, so that the collision of data packets and repeated transmission processes are avoided or reduced, and meanwhile, the high-priority packets are ensured to be transmitted with lower time delay and higher access success rate;
2) The method and the device can integrate channel load conditions in the communication network and the information of the number of waiting transmission packets of the transmission queues with various priorities, dynamically adjust the priority threshold value of the congestion data packet on the premise of ensuring the transmission of the data packet with high priority, reduce unnecessary rollback of the data packet with low priority, and improve the fairness of the system.
Drawings
Fig. 1 is a flow chart of channel load estimation for a communication network.
Fig. 2 is a flow chart of a communication network channel load estimation evaluation.
FIG. 3 is a flow chart for dynamically adjusting priority threshold policies.
Fig. 4 is a schematic diagram of a wireless communication system implementing dynamic allocation of network load.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples
The wireless communication system for realizing dynamic network load distribution comprises a channel load estimation module, a channel load estimation evaluation module and a dynamic network load threshold adjustment module.
The channel load estimation module carries out real-time pre-estimation on the channel load through a state equation, then corrects the pre-estimation of the channel load by utilizing the actual channel load observed by the physical layer at the last moment, thereby calculating a corrected channel load estimation value, and finally corrects the corrected channel load estimation by combining with the periodical network layer observation value to obtain a true value of the real-time channel load estimation. See fig. 1. The channel load estimation module calculates the true value of the real-time channel load estimation by the following main steps:
101 After the wireless communication system is powered on and initialized, the channel load estimation module acquires relevant parameters of channel load estimation as elements in a state transition matrix, such as communication modes, speed and the like of the wireless communication system, which are related to a transmission load threshold.
102 The data packet information received by the physical layer of the communication system and the data packet information actually transmitted by the link layer are counted and used as the channel load estimated value of the last moment.
103 Predicting a real-time channel load pre-estimation value at the current transmission time through a state equation, and using the real-time channel load pre-estimation value as a predicted value x' (n) of channel load estimation at the current time, 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 previous time.
104 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:
P'(n)＝A·P(n-1)·A t +Q(n-1)，
where P' (n) is the covariance correction value, Q is the state error, and P (n-1) is the covariance correction value for the previous cycle.
The correction coefficient update equation is:
H(n)＝C·P'(n-1)·C Τ ·[C·P'(n-1)C Τ +R] -1 ，
wherein H (n) is a correction coefficient, C is an observation matrix, and R is an observation error.
105 Using the correction equation, the updated correction coefficient H (n) at the current time, the predicted value x' (n) of the channel load estimation at the current time and the network layer observed value y (n) of the channel load at the current time to obtain a correction value, and using the correction value as a true value of the real-time channel load estimation.
Wherein, the correction equation is x (n) =x '(n) +h (n) · [ y (n) -c·x' (n) ].
The channel load estimate estimation evaluation module design is shown in fig. 2. The method comprises the following specific steps of calculating the confidence coefficient of the true value of the channel load estimation according to the statistical information of the physical layer business data of the communication system, and then adjusting the true value of the channel load estimation:
201 Statistics of physical layer business data information of the communication system;
202 Analyzing the difference between the network layer observation value and the physical layer observation value to obtain the confidence coefficient of the channel load estimation value;
203 The true value of the real-time channel load estimation is adjusted according to the confidence coefficient, and the current channel load estimation value is obtained.
The dynamic network load threshold adjustment module design is shown in fig. 3. The method comprises the following specific steps of dynamically adjusting the threshold value of each emission priority to improve the service throughput of the aviation self-organizing network by integrating the current channel load estimated value and the number information of the remaining waiting transmission packets of the transmission queue of each priority of the current node:
301 Counting the number of the data packets transmitted and received by the current communication network and the number of the remaining waiting packets to be transmitted of the transmission queues of each priority of the current node;
302 Judging whether the current node has network congestion caused by a large number of low-priority data packets in a transmission queue according to the current channel load estimated value;
303 If congestion occurs in the data packet with low priority, detecting the number of the remaining waiting packets with the last priority;
304 If the number of the remaining waiting sending packets of the previous priority is zero, dynamically adjusting the threshold value of the current priority;
305 Detecting congestion status of the priority in real time, and if no congestion status exists, adjusting the threshold value of the priority back to the original threshold value state;
steps 301 to 305 are repeated at regular intervals.
Claims (2)
1. The utility model provides a wireless communication system that realization network load dynamic allocation, includes channel load estimation module, channel load estimation evaluation module and dynamic network load threshold adjustment module, its 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 the real-time channel load estimation, and the true value is realized through the following program steps:
101 After the wireless communication system is powered on and initialized, the channel load estimation module acquires relevant parameters of channel load estimation as elements in a state transition matrix;
102 Counting the data packet information received by the physical layer of the communication system and the data packet information actually transmitted by the link layer, and taking the data packet information and the link layer as the channel load estimated value of the last moment;
103 Predicting a real-time channel load pre-estimation value at the current transmission time through a state equation, and using the real-time channel load pre-estimation value as a predicted value x' (n) of channel load estimation at the current time, namely:
x'(n)＝A·x(n-1)，
wherein A is a state transition matrix, and x (n-1) is channel load estimation at the last moment;
104 Updating the covariance correction value by using a covariance prediction equation, and updating the correction coefficient by using a correction coefficient updating equation;
the covariance prediction equation is:
P'(n)＝A·P(n-1)·A Τ +Q(n-1)，
wherein P' (n) is a covariance correction value, Q is a state error, and P (n-1) is a covariance correction value of the previous cycle;
the correction coefficient update equation is:
H(n)＝C·P'(n-1)·C Τ ·[C·P'(n-1)C Τ +R] -1 ，
wherein H (n) is a correction coefficient, C is an observation matrix, and R is an observation error;
105 Using the correction equation, the updated correction coefficient H (n) at the current moment, the predicted value x' (n) of the channel load estimation at the current moment and the network layer observed value y (n) of the channel load at the current moment to obtain a correction value, and using the correction value as a true value of the real-time channel load estimation;
wherein, the correction equation is x (n) =x '(n) +h (n) · [ y (n) -c·x' (n) ];
the channel load estimation evaluation module calculates the confidence coefficient of the true value of the channel load estimation according to the statistical information of the physical layer business data 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 adjustment module synthesizes the current channel load estimated value and the information of the number of remaining waiting transmission packets of the transmission queue of each priority of the current node, dynamically adjusts the network load threshold of the low-priority service on the premise of ensuring the transmission of the high-priority service, and is realized by the following program steps:
301 Counting the number of the data packets transmitted and received by the current communication network and the number of the remaining waiting packets to be transmitted of the transmission queues of each priority of the current node;
302 Judging whether the current node has network congestion caused by a large number of low-priority data packets in a transmission queue according to the current channel load estimated value;
303 If congestion occurs in the data packet with low priority, detecting the number of the remaining waiting packets with the last priority;
304 If the number of the remaining waiting sending packets of the previous priority is zero, dynamically adjusting the threshold value of the current priority;
305 Detecting congestion status of the priority in real time, and if no congestion status exists, adjusting the threshold value of the priority back to the original threshold value state;
steps 301 to 305 are repeated at regular intervals.
2. A wireless communication system for implementing dynamic allocation of network load according to claim 1, wherein the channel load estimate evaluation module is implemented by the following program steps:
201 Statistics of physical layer business data information of the communication system;
202 Analyzing the difference between the network layer observation value and the physical layer observation value to obtain the confidence coefficient of the channel load estimation value;
203 The true value of the real-time channel load estimation is adjusted according to the confidence coefficient, and the current channel load estimation value is obtained.
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