CN115696354B - High-speed rail mobile communication system network coverage method based on improved particle swarm - Google Patents
High-speed rail mobile communication system network coverage method based on improved particle swarm Download PDFInfo
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Abstract
The invention relates to a high-speed railway mobile communication system network coverage method based on improved particle swarms. The method comprises the following steps: step 1, dividing a high-speed rail to-be-covered area by a grid method; step 2, calculating the distance between the sensor and the grid to be covered; step 3: designing an improved particle swarm mobile communication position coverage optimization algorithm; step 4: designing a high-speed rail mobile communication position coverage optimization algorithm for improving a particle swarm; and 5, optimizing the sensor node by using an improved particle swarm algorithm according to the position information fed back by the high-speed rail. According to the invention, the inertia weight and acceleration constant of the particle swarm are improved, the capability of the particle swarm algorithm to jump out of a local optimal solution in the later iteration stage is enhanced, and the optimization of the high-speed railway mobile communication network is completed.
Description
Technical Field
The invention relates to the field of network communication, in particular to a network coverage method of a high-speed railway mobile communication system based on improved particle swarms.
Background
Since the advent of high-speed railways, the development of the railways is rapid worldwide, the development of the railways in China is rapid in recent years, the operation mileage of the high-speed railways is increased year by year, and the growth speed is faster and faster. With the productive operation of a large number of high-speed rail trains, the number of passengers traveling for a long time on the high-speed rail is increasing, and the communication demand on the high-speed rail trains during traveling is becoming stronger.
Network coverage is an important criterion for evaluating a wireless sensor network. The primary way to improve network coverage is to arrange sensor terminal nodes on a large scale, which is not only expensive, but also communication blockage may be caused by excessive sensor terminals in the area. So the mobile sensor terminal is widely used at present, but the mobile sensor terminal position is optimized, the mobile length is reduced, the network coverage rate of the mobile sensor terminal is enabled to be larger, and the mobile sensor terminal network development is still another difficult problem.
The invention provides a high-speed rail mobile communication system network coverage method based on improved particle swarm, which is applied to the problem of optimizing network coverage rate by a particle swarm algorithm, expands ideas and methods for optimizing the problems of optimizing configuration of a mobile sensor terminal, planning urban traffic lines and the like, can bring certain economic benefit and social benefit, and is beneficial to strengthening national economic construction and promoting development of novel information technology.
Disclosure of Invention
In order to solve the problems, the invention simulates the behavior of searching foods randomly by a bird group on the basis of an improved particle swarm algorithm, and provides a network coverage method of a high-speed railway mobile communication system based on an improved particle swarm. To achieve this object:
Step 1, dividing a high-speed rail to-be-covered area by a grid method, and collecting longitude and latitude information of a to-be-detected range, a monitoring radius of a mobile sensor and a communication radius;
Step 2, calculating the distance between the sensor and the grid to be covered, and calculating the distance between the sensor node and the point of the area to be covered according to the divided grid;
step 3: the improved particle swarm mobile communication position coverage optimization algorithm is designed, and the defect that the particle swarm algorithm is easy to fall into local optimum is overcome;
Step 4: designing a high-speed rail mobile communication position coverage optimization algorithm of an improved particle swarm, and solving scheduling optimization by using the improved particle swarm algorithm to obtain a scheduling scheme of the high-speed rail mobile communication;
And 5, optimizing the sensor nodes by using an improved particle swarm algorithm according to the position information fed back by the high-speed rail, planning a communication system network, and switching communication sites.
Further, the process of dividing the coverage area of the high-speed rail by using the grid method in the step 1 can be expressed as follows:
And (3) the position information returned by the high-speed rail is to be covered into a rectangular area with the length of 10 and the width of 200, the coverage area is divided into 5 multiplied by 100 grids by a grid method, 200 mobile wireless sensor terminal nodes are placed in the required coverage area, the monitoring radius of a single mobile wireless sensor terminal node is set to be 2, and the communication radius is set to be 4.
Further, the process of calculating the distance between the sensor and the mesh to be covered in step 2 may be expressed as follows:
In the area to be monitored, the position of any sensor node i is denoted as (x i,yi), and then a target point (x j,yj) is selected, the distance between the sensor and the target point is denoted as d= (x i-xj)2+(yi-yj)2), and the probability that the target point can be perceived by the sensor node is:
the coverage area of the sensor node is an area with the current position of the sensor as a circle center and the detection distance R as a radius. The joint probability G that the target point in the area to be monitored needs to be covered by the sensor node is:
G＝1-Σ(1-p) (2)
Where p is the probability that the target point can be detected by the sensor node, G is the probability that all nodes detect the target point, and if the joint monitoring probability is greater than the threshold, the point is considered to be covered.
Further, the process of designing the improved particle swarm algorithm in step 3 may be expressed as follows:
step 3.1: the updating modes of the inertia weight coefficient omega and the acceleration constant eta 1、η2 in the particle swarm algorithm are improved, and the improved updating algorithm is as follows:
ω＝ωmax-0.02tan(2πIteration/MaxIteration) (4)
η1＝0.5ω2+0.5 (5)
η2＝1-ω2 (6)
Wherein ω max is the maximum inertial weight set to 0.94; iteration is the current Iteration number; maxIteration is the maximum number of iterations;
the improved particle swarm mobile communication position coverage optimization algorithm is as follows:
step 3.2: initializing a population
Position initialization: z= unifmd (Q, S i)T (7)
Initializing the speed: v= uniffnd (V, V) T (8)
Uniffnd functions are random numbers which are uniformly distributed, Q is a random number with high-speed rails and high-speed rails, S i is a random number of a sensor node, and V is a random number of speed;
step 3.3: determining a fitness function:
y＝∑σG (9)
wherein σ is a weight coefficient, and G is a joint probability;
Step 3.4: calculating the fitness value of particles, determining the self-fitness value of each particle in the population according to the fitness function, updating the individual extremum P id, the global extremum P gd, the inertia weight omega and the acceleration constant eta 1、η2, and recording the individual extremum position P cbest and the global extremum position P cgbest;
step 3.5: entering an iteration loop, setting the maximum iteration number of 1000, and updating the particle speed and the position by the following steps
Vid＝ωvid(t)+η1rand()[Pid-Zid(t)]+η2rand()[Pgd-Zid(t)] (10)
Zid(t+1)＝Zid(t)+vid(t+1) (11)
Where t is the number of iterations, V id (t) represents the speed of the ith particle in the d-th dimension in the t iterations, V id (t+1) represents the speed of the ith particle in the d-th dimension in the t+1 iterations, Z id (t) represents the position of the ith particle in the d-th dimension in the t iterations, Z id (t+1) represents the position of the ith particle in the d-th dimension in the t+1 iterations, and rand is a random number of 0 to 1;
Step 3.6: and after the iteration is finished, obtaining an optimal optimization result, and outputting a global extremum P gd and a global extremum position P cgbest of the population, namely an optimal solution.
The invention relates to a high-speed railway mobile communication system network coverage method based on improved particle swarm, which has the beneficial effects that:
1. according to the invention, on the basis of improving a particle swarm algorithm, the capacity of jumping out of a local optimal solution in the later iteration stage of the particle swarm algorithm is enhanced by improving the inertia weight and the acceleration constant of the particle swarm;
2. according to the invention, the communication system is accurately planned through an improved particle swarm algorithm, and the network layout is reasonably carried out, so that the network quality can be ensured to be optimal;
3. the invention re-programs the mobile sensor terminal nodes, thereby more effectively expanding the effective coverage area of the network and improving the monitoring capability and communication capability of the sensor network;
4. The invention provides an important technical means for communication system configuration and can bring certain economic benefit and social benefit.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of the improved particle swarm algorithm of the present invention.
Detailed Description
The invention provides a high-speed railway mobile communication system network coverage method based on improved particle swarms, which aims at re-planning mobile sensor terminal nodes so as to more effectively enlarge the effective coverage area of a network and improve the monitoring capability and communication capability of the sensor network, wherein fig. 1 is a flow chart of the invention, and the invention is further described below with reference to the accompanying drawings and the specific embodiments:
Step 1, dividing a high-speed rail to-be-covered area by a grid method, and collecting longitude and latitude information of a to-be-detected range, a monitoring radius of a mobile sensor and a communication radius;
And (3) the position information returned by the high-speed rail is to be covered into a rectangular area with the length of 10 and the width of 200, the coverage area is divided into 5 multiplied by 100 grids by a grid method, 200 mobile wireless sensor terminal nodes are placed in the required coverage area, the monitoring radius of a single mobile wireless sensor terminal node is set to be 2, and the communication radius is set to be 4.
Step 2, calculating the distance between the sensor and the grid to be covered, and calculating the distance between the sensor node and the point of the area to be covered according to the divided grid;
In the area to be monitored, the position of any sensor node i is denoted as (x i,yi), and then a target point (x j,yj) is selected, the distance between the sensor and the target point is denoted as d= (x i-xj)2+(yi-yj)2), and the probability that the target point can be perceived by the sensor node is:
the coverage area of the sensor node is an area with the current position of the sensor as a circle center and the detection distance R as a radius. The joint probability G that the target point in the area to be monitored needs to be covered by the sensor node is:
G＝1-Σ(1-p) (2)
Where p is the probability that the target point can be detected by the sensor node, G is the probability that all nodes detect the target point, and if the joint monitoring probability is greater than the threshold, the point is considered to be covered.
Step 3: the improved particle swarm mobile communication position coverage optimization algorithm is designed, and the defect that the particle swarm algorithm is easy to fall into local optimum is overcome;
step 3.1: the updating modes of the inertia weight coefficient omega and the acceleration constant eta 1、η2 in the particle swarm algorithm are improved, and the improved updating algorithm is as follows:
ω＝ωmax-0.02tan(2πIteration/MaxIteration) (4)
η1＝0.5ω2+0.5 (5)
η2＝1-ω2 (6)
Wherein ω max is the maximum inertial weight set to 0.94; iteration is the current Iteration number; maxIteration is the maximum number of iterations;
the improved particle swarm mobile communication position coverage optimization algorithm is as follows:
step 3.2: initializing a population
Position initialization: z= unifrnd (Q, S i)T (7)
Initializing the speed: v= unifrnd (V, V) T (8)
Unifrnd functions are random numbers which are uniformly distributed, Q is a random number with high-speed rails and high-speed rails, S i is a random number of a sensor node, and V is a random number of speed;
step 3.3: determining a fitness function:
y＝∑σG (9)
wherein σ is a weight coefficient, and G is a joint probability;
Step 3.4: calculating the fitness value of particles, determining the self-fitness value of each particle in the population according to the fitness function, updating the individual extremum Pid, the global extremum P gd, the inertia weight omega and the acceleration constant eta 1、η2, and recording the individual extremum position P cbest and the global extremum position P cgbest;
step 3.5: entering an iteration loop, setting the maximum iteration number of 1000, and updating the particle speed and the position by the following steps
Vid＝ωvid(t)+η1rand()[Pid-Zid(t)]+η2rand()[Pgd-Zid(t)] (10)
Zid(t+1)＝Zid(t)+vid(t+1) (11)
Where t is the number of iterations, V id (t) represents the speed of the ith particle in the d-th dimension in the t iterations, V id (t+1) represents the speed of the ith particle in the d-th dimension in the t+1 iterations, Z id (t) represents the position of the ith particle in the d-th dimension in the t iterations, Z id (t+1) represents the position of the ith particle in the d-th dimension in the t+1 iterations, and rand is a random number of 0 to 1;
Step 3.6: and after the iteration is finished, obtaining an optimal optimization result, and outputting a global extremum P gd and a global extremum position P cgbest of the population, namely an optimal solution.
Step 4: designing a high-speed rail mobile communication position coverage optimization algorithm of an improved particle swarm, and solving scheduling optimization by using the improved particle swarm algorithm to obtain a scheduling scheme of the high-speed rail mobile communication;
And 5, optimizing the sensor nodes by using an improved particle swarm algorithm according to the position information fed back by the high-speed rail, planning a communication system network, and switching communication sites.
The above description is only of the preferred embodiment of the present invention, and is not intended to limit the present invention in any other way, but is intended to cover any modifications or equivalent variations according to the technical spirit of the present invention, which fall within the scope of the present invention as defined by the appended claims.
Claims (3)
1. The network coverage method of the high-speed railway mobile communication system based on the improved particle swarm comprises the following specific steps of:
Step 1, dividing a high-speed rail to-be-covered area by a grid method, and collecting longitude and latitude information of a to-be-detected range, a monitoring radius of a mobile sensor and a communication radius;
Step 2, calculating the distance between the sensor and the grid to be covered, and calculating the distance between the sensor node and the point of the area to be covered according to the divided grid;
step 3: the improved particle swarm mobile communication position coverage optimization algorithm is designed, and the defect that the particle swarm algorithm is easy to fall into local optimum is overcome;
The process of designing the improved particle swarm algorithm in step 3 may be expressed as follows:
step 3.1: the updating modes of the inertia weight coefficient omega and the acceleration constant eta 1、η2 in the particle swarm algorithm are improved, and the improved updating algorithm is as follows:
ω＝ωmax-0.02tan(2πIteration/MaxIteration) (4)
η1＝0.5ω2+0.5 (5)
η2＝1-ω2 (6)
Wherein ω max is the maximum inertial weight set to 0.94; iteration is the current Iteration number; maxIteration is the maximum number of iterations;
the improved particle swarm mobile communication position coverage optimization algorithm is as follows:
step 3.2: initializing a population
Position initialization: z= unifrnd (Q, S i)T (7)
Initializing the speed: v= unifrnd (V, V) T (8)
Unifrnd functions are random numbers which are uniformly distributed, Q is a random number with high iron and high iron, si is a random number of a sensor node, and V is a random number of speed;
step 3.3: determining a fitness function:
y＝∑σG (9)
wherein σ is a weight coefficient, and G is a joint probability;
Step 3.4: calculating the fitness value of particles, determining the self-fitness value of each particle in the population according to the fitness function, updating the individual extremum P id, the global extremum P gd, the inertia weight omega and the acceleration constant eta 1、η2, and recording the individual extremum position P cbest and the global extremum position P cgbest;
step 3.5: entering an iteration loop, setting the maximum iteration number of 1000, and updating the particle speed and the position by the following steps
Vid＝ωvid(t)+η1rand()[Pid-Zid(t)]+η2rand()[Pgd-Zid(t)] (10)
Zid(t+1)＝Zid(t)+vid(t+1) (11)
Where t is the number of iterations, V id (t) represents the speed of the ith particle in the d-th dimension in the t iterations, V id (t+1) represents the speed of the ith particle in the d-th dimension in the t+1 iterations, Z id (t) represents the position of the ith particle in the d-th dimension in the t iterations, Z id (t+1) represents the position of the ith particle in the d-th dimension in the t+1 iterations, and rand is a random number of 0 to 1;
Step 3.6: after iteration is finished, an optimal optimization result is obtained, and a global extremum P gd and a global extremum position P cgbest of the population are output, namely an optimal solution;
Step 4: designing a high-speed rail mobile communication position coverage optimization algorithm of an improved particle swarm, and solving scheduling optimization by using the improved particle swarm algorithm to obtain a scheduling scheme of the high-speed rail mobile communication;
And 5, optimizing the sensor nodes by using an improved particle swarm algorithm according to the position information fed back by the high-speed rail, planning a communication system network, and switching communication sites.
2. The improved particle swarm-based network coverage method of the high-speed rail mobile communication system according to claim 1, characterized in that: the process of dividing the coverage area of the high-speed rail by using a grid method in the step 1 can be expressed as follows:
And (3) the position information returned by the high-speed rail is to be covered into a rectangular area with the length of 10 and the width of 200, the coverage area is divided into 5 multiplied by 100 grids by a grid method, 200 mobile wireless sensor terminal nodes are placed in the required coverage area, the monitoring radius of a single mobile wireless sensor terminal node is set to be 2, and the communication radius is set to be 4.
3. The improved particle swarm-based network coverage method of the high-speed rail mobile communication system according to claim 1, characterized in that: the process of calculating the distance between the sensor and the mesh to be covered in step 2 can be expressed as follows:
In the area to be monitored, the position of any sensor node i is denoted as (x i,yi), and then a target point (x j,yj) is selected, the distance between the sensor and the target point is denoted as d= (x i-xj)2+(yi-yj)2), and the probability that the target point can be perceived by the sensor node is:
The coverage area of the sensor node is an area taking the current position of the sensor as a circle center and the detection distance R as a radius, and if the target point in the area to be monitored needs to be covered by the sensor node, the joint probability G that the target point can be monitored by the sensor is:
G＝1-∑(1-p) (2)
Where p is the probability that the target point can be detected by the sensor node, G is the probability that all nodes detect the target point, and if the joint monitoring probability is greater than the threshold, the point is considered to be covered.
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| CN113905389A (en) * | 2021-10-27 | 2022-01-07 | 湖北文理学院 | Wireless sensor network coverage method based on particle swarm optimization imperial butterfly algorithm |
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| CN103249055A (en) * | 2013-04-25 | 2013-08-14 | 中山大学 | Binary particle swarm algorithm based layered dispatching method for nodes of wireless sensor network |
| WO2018072351A1 (en) * | 2016-10-20 | 2018-04-26 | 北京工业大学 | Method for optimizing support vector machine on basis of particle swarm optimization algorithm |
| CN111065103A (en) * | 2019-12-11 | 2020-04-24 | 哈尔滨工程大学 | Multi-objective optimization wireless sensor network node deployment method |
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