CN114710218B - Distributed node and base station communication efficiency optimization method based on 5G - Google Patents

Distributed node and base station communication efficiency optimization method based on 5G Download PDF

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CN114710218B
CN114710218B CN202210603780.3A CN202210603780A CN114710218B CN 114710218 B CN114710218 B CN 114710218B CN 202210603780 A CN202210603780 A CN 202210603780A CN 114710218 B CN114710218 B CN 114710218B
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邓梦璨
刘春来
刘军
韩留斌
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Abstract

The invention discloses a distributed node and base station communication efficiency optimization method based on 5G, which comprises the following steps: step S1: acquiring node information, determining signal strength based on RSSI, analyzing an interference function, and determining the overall communication loss between a node and a base station; step S2: determining the optimal overall communication efficiency, and training the TCN to predict the optimal communication efficiency at the next moment according to the node movement path; and step S3: the state of the node is determined based on the TCN predicted overall communication performance of the node and the base station. According to the invention, the state of the node is determined through the integral communication efficiency of the taxi node and the base station predicted by the TCN, and the signal intensity level is linked with the path information of the node, so that the network load when the node transmits information to the base station is effectively reduced, the management and control of the taxi are optimized, and the possibility of accidents is reduced.

Description

Distributed node and base station communication efficiency optimization method based on 5G
Technical Field
The application relates to the technical field of communication, in particular to a distributed node and base station communication efficiency optimization method based on 5G.
Background
With the development and the progress of information technology, 5G brings great convenience to the life of people, the world becomes more intelligent through 5G communication, in the 5G technology, the communication efficiency optimization method design between distributed nodes and a base station is the basis of 5G fast communication, taxis are common transportation tools used by people in outgoing, however, the use of the taxis has more defects, accidents often occur, and great harm and influence are caused.
The taxi is actually used and needs to be effectively controlled, the drip-taxi is taken as a design scene, the speed, the path information, the posture and the sound information of the taxi are collected, the taxi in the whole city is taken as a distributed node for receiving and sending information, the taxi meets the conditions that the signal is weak, the base station covers an overlapped area, too many taxis and other interferences exist in the same adjacent area in the moving process, and the information transmitted from the node to the base station generates larger network load.
Disclosure of Invention
Aiming at the problems, the invention provides a communication efficiency optimization method of a distributed node and a base station based on 5G, which comprises the following steps:
step S1: acquiring node information, determining signal strength based on RSSI, analyzing an interference function, and determining the overall communication loss between a node and a base station;
step S2: determining the optimal overall communication efficiency, and training the TCN to predict the optimal communication efficiency at the next moment according to the node movement path;
and step S3: the state of the node is determined based on the TCN predicted overall communication performance of the node and the base station.
Has the beneficial effects that:
the invention determines the state of the node through the overall communication efficiency of the taxi node and the base station predicted by TCN, the signal intensity level is linked with the path information of the node, the node moves to the position, the node can feel the signal level intensity of the position and quickly determine the signal intensity so as to make corresponding adjustment in time, thereby effectively reducing the network load when the node transmits information to the base station, being beneficial to optimizing the management and control of the taxi and reducing the possibility of accidents.
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Fig. 1 is a flowchart of a method for optimizing communication performance between a 5G-based distributed node and a base station according to the present invention.
Detailed Description
In order to make the invention more comprehensible to those skilled in the art, the invention is described below with reference to an embodiment and the accompanying drawings, in which fig. 1 is referred to.
In order to realize the content, the invention designs a communication efficiency optimization method of a distributed node and a base station based on 5G, which comprises the following steps:
step S1: acquiring node information, determining signal strength based on RSSI, analyzing an interference function, and determining the overall communication loss between a node and a base station;
the taxi speed, path and attitude information of all taxis in the city are collected, all taxis are connected to the Beidou navigation system and used for collecting real-time taxi speeds, and the taxi speeds can be used for judging whether the taxis are over-speed or not and whether the taxis are abnormally stopped or not. Taxis with speeds exceeding a defined speed and improper stops can increase passenger hazards.
All taxis are accessed to the Beidou navigation system, whether the taxis deviate from the designed path or not is judged, the taxis deviate from the set route, and the danger of passengers can be increased, particularly for driving to areas with weak base station signals and remote places.
All taxis are additionally provided with a taxi door sensor for sensing the opening and closing state of a taxi door, and when a passenger takes the taxi, the taxi door is opened and closed abnormally, so that the safety coefficient of the passenger is reduced.
A recording system is installed in the vehicle to record the conversation between passengers and drivers, and the signals are packaged and transmitted to the base station and the terminal.
And converting all the information collected above into corresponding electromagnetic waves, and packaging and transmitting the information to the base station. Each car is considered a node, and all vehicles in the city constitute distributed nodes and transmit communications to the base station.
The RSSI is a positioning technique for measuring the distance between a signal point and a receiving point according to the strength of the received signal and further performing positioning calculation according to corresponding data.
Determining the signal strength of a base station and nodes based on RSSI, installing RSSI at each taxi node for detecting the signal strength received and transmitted by the node, and recording the strength of the signal output by the node within a period of time
Figure DEST_PATH_IMAGE001
The intensity of a signal input to a node at a certain time
Figure 933265DEST_PATH_IMAGE002
And n is the number of nodes in the coverage area of the base station.
Determining signal strength etc. around a base stationThe signal intensity levels around the base station are divided into six levels based on K-means by calculating the distance between different samples and judging the similar relation of the K-means clustering algorithm,
Figure DEST_PATH_IMAGE003
rank1 signal is strongest, and so on rank6 signal is weakest. The manner of grading is determined by an operator based on the parameters of the environment surrounding the base station.
And counting the number of vehicles and the distance between the vehicles in the similar area at the same time interval by using a Beidou system, wherein the more the number of the vehicle nodes is and the closer the node distance is, the greater the interference on information transmission from the taxi node to the base station is.
Giving the interference function when the node i transmits signals to the base station
Figure 81350DEST_PATH_IMAGE004
And x is the number of nodes in the circle of which the radius is R of the node i, and the number of the nodes is determined according to the Beidou system. This equation indicates that the larger the number of nodes around the node i, the stronger the interference caused to the node i.
Calculating an interference function for base station to node communication
Figure DEST_PATH_IMAGE005
，
Figure 496151DEST_PATH_IMAGE006
Is the number of nodes around the node at which the base station communicates to the node.
The value calculated by the interference function is normalized to be in a value range of [0,1], and the closer to 1, the stronger the interference received by the node.
The above operation is performed for other nodes, and the interference of all nodes at a certain time is determined.
Determining communication path loss for distributed nodes and base stations based on distances between the base stations and the nodes, the distributed nodes andthe farther the distance between the base stations, the higher the communication loss between the node and the base station. In the invention, the base station is fixed, and the distributed nodes are all moving, so the communication loss between the nodes and the base station changes at any time. Linear distance between acquisition base station and each node based on Beidou system
Figure DEST_PATH_IMAGE007
And n is the number of nodes in the base station range area.
Calculating communication loss between node and base station
Figure 517327DEST_PATH_IMAGE008
Where K is the loss excitation constant,
Figure DEST_PATH_IMAGE009
is a 5G base station frequency point, C is an electromagnetic wave transmission speed, namely an optical speed,
Figure 17579DEST_PATH_IMAGE010
the loss caused by the fact that the node passes through other obstacles such as buildings, car shells and the like when communicating with the base station can be generally found out according to the frequency points of the base station.
Calculating communication path loss when a base station communicates to a node
Figure DEST_PATH_IMAGE011
，
Figure 773218DEST_PATH_IMAGE012
Calculating the communication path loss of each node, determining the communication loss between the node and the base station at a certain moment, and normalizing the communication loss value between the node and the base station to make the value range thereof be [0,1]]The closer to 1, the stronger the loss between the node and the base station communication.
Step S2: determining the optimal overall communication efficiency, and training the TCN to predict the optimal communication efficiency at the next moment according to the node movement path;
analyzing the overall communication efficiency between the distributed nodes and the base station based onDetermining the overall communication efficiency between the node and the base station by the signal intensity level of the base station at a certain moment of the distributed node, the interference function and the communication loss
Figure DEST_PATH_IMAGE013
，
Figure 308104DEST_PATH_IMAGE013
The calculation method of (2) is as follows:
Figure 703445DEST_PATH_IMAGE014
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE015
is the signal strength of the node communicating to the base station,
Figure 894255DEST_PATH_IMAGE016
Is the signal strength of the base station communication to the node.
Figure DEST_PATH_IMAGE017
、
Figure 180880DEST_PATH_IMAGE018
Is a function of the interference of the node i,
Figure DEST_PATH_IMAGE019
、
Figure 553961DEST_PATH_IMAGE020
is the path loss of the communication between the node and the base station.
And performing overall communication efficiency on all the distributed nodes at a certain moment, wherein the larger the value of the overall communication efficiency is, the better the communication quality between the nodes and the base station at the certain moment is. The calculated U is normalized so that its value range is [0,1].
Thus, the overall communication performance between the node and the base station is evaluated.
Since the distributed nodes are mostly in motion and the base station is fixed at a certain position, the best communication efficiency of the communication between the nodes and the base station is determined.
The frequency point of the 5G base station is very high, meanwhile, the 5G signal penetration is poor, and the coverage area is usually hundreds of meters, so that the number of the 5G base stations is large, and the coverage of the base stations is covered with a lot of overlapping areas.
And determining the optimal overall communication performance of the 5G base station in the repeated coverage area based on the overall communication performance between the node and the base station analyzed in the first section.
In the overlapping area, the overall communication efficiency of the node i and the three base stations at a certain time is obtained
Figure 635049DEST_PATH_IMAGE021
Selecting the maximum value
Figure 578735DEST_PATH_IMAGE022
As an optimal communication performance.
Selecting the only whole communication performance as the best communication performance in the non-overlapping area
Figure DEST_PATH_IMAGE023
。
In the first section, the Beidou system selects the optimal motion trail for the node, the node obtains an overall communication efficiency according to the motion trail and information transmitted and received to the base station once, and the information is obtained in n-time communication
Figure 419783DEST_PATH_IMAGE024
。
In the invention, the motion trail of the node is fixed, but the node encounters other nodes in motion and is random, so that the TCN is adopted to predict the next overall communication efficiency according to historical data.
The reliable link quality M of communication between a communication node i and a base station at a certain time is determined, and the calculation method of M is as follows:
Figure DEST_PATH_IMAGE025
in the formula (I), the compound is shown in the specification,
Figure 866945DEST_PATH_IMAGE026
is an interference function of a certain time node i communicates with a base station,
Figure DEST_PATH_IMAGE027
the larger the value of reliable link quality is, the more reliable the communication between the node and the base station is.
Recording reliable link quality for n communications of a node with a base station
Figure 368202DEST_PATH_IMAGE028
. The reliable link quality is normalized and summed to 1, resulting in the compensation factor C.
Training a TCN network, wherein the TCN is a time domain convolution network and consists of expanded and causal 1D convolution layers with the same input and output length, and the obtained nodes are communicated with the base station for n times to obtain historical data
Figure DEST_PATH_IMAGE029
The previous part of the sequence is input into the TCN as a signature sequence and the next value of the sequence is used as a tag to enable the TCN to learn the next predicted value of the current sequence pattern and repeat the training.
The Loss function Loss is mean square error and compensation factor when TCN is used for training
Figure 330342DEST_PATH_IMAGE030
。
The Loss function Loss is calculated and the Loss function Loss is calculated,
Figure DEST_PATH_IMAGE031
and i is the ith communication between the node and the base station.
The purpose of the compensation factor adopted by Loss is that reliable link quality is an important parameter for describing the quality of communication between the node and the base station, so that the reliable link quality is selected as the compensation factor of the Loss function, and the wireless of the TCN is close to an actual value in prediction.
And step S3: determining the state of the node based on the TCN predicted overall communication efficiency of the node and the base station;
and measuring the signal strength of each road section in the range according to the installation position of the base station, finally converting the signal strength into a signal strength grade, and repeating the steps for all the base stations. The signal strength level is linked with the path information of the node, the node moves to the position, the node can sense the signal strength level of the position, and the signal strength can be rapidly determined.
For remote areas with poor signals, the position of the taxi node is sent to a working end of a worker, so that corresponding adjustment can be made in time.

Claims (1)

1. The distributed node and base station communication efficiency optimization method based on 5G is characterized by comprising the following steps:
step S1: acquiring node information, determining signal strength based on RSSI, analyzing an interference function, and determining the overall communication loss between a node and a base station;
step S2: determining the optimal overall communication efficiency, and training the TCN to predict the optimal communication efficiency at the next moment according to the node movement path;
and step S3: determining the state of the node based on the TCN predicted overall communication efficiency of the node and the base station;
the step S1 is used for acquiring the speed, path and attitude information of all taxis in the city, accessing the taxis to Beidou navigation, acquiring the real-time speed and path deviation information of the taxis, acquiring the opening and closing conditions of the taxi door and conversation in the taxi through a taxi door sensor and a recording system, converting the acquired information into corresponding electromagnetic waves, and packaging and transmitting the information to a base station;
the step S1 further includes:
1) Installing RSSI (received signal strength indicator) at a node on each taxi, and recording the strength of a signal output by the node within a period of time and the strength of a signal input to the node within a certain period of time;
2) Determining the signal intensity level around the base station, and dividing the signal intensity level around the base station based on K-means;
3) Counting the number of vehicles in the similar area and the distance between the vehicles at the same time interval by using a Beidou system, and calculating the interference on information transmission from taxi nodes to a base station;
4) Determining communication path loss of the distributed nodes and the base station based on the distance between the base station and the nodes;
the calculation method for the interference on the information transmission from the taxi node to the base station is as follows:
a) Calculating interference function when node i transmits signal to base station
Figure 212261DEST_PATH_IMAGE001
Where x is the number of nodes in the circle of node i by radius R;
b) Calculating an interference function for base station to node communication
Figure DEST_PATH_IMAGE002
In the formula
Figure 270347DEST_PATH_IMAGE003
Is the number of nodes around the node when the base station communicates to the node;
the steps of judging the communication path loss of the distributed node and the base station are as follows:
a) Calculating communication loss between node and base station
Figure DEST_PATH_IMAGE004
，
Figure 955144DEST_PATH_IMAGE004
The calculation method of (2) is as follows:
Figure 288037DEST_PATH_IMAGE005
wherein K is a loss excitation constant,
Figure DEST_PATH_IMAGE006
is the frequency point of the 5G base station, C is the transmission speed of the electromagnetic wave, namely the speed of light, L is the linear distance between the node and the base station,
Figure 243354DEST_PATH_IMAGE007
loss caused when a node passes through a building, a vehicle shell or other barriers when communicating with a base station;
b) Calculating communication path loss when a base station communicates to a node
Figure DEST_PATH_IMAGE008
，
Figure 970877DEST_PATH_IMAGE008
The calculation method of (2) is as follows;
Figure 441172DEST_PATH_IMAGE009
(ii) a Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE010
indicating the straight-line distance between the base station and the node,
Figure 781018DEST_PATH_IMAGE011
represents the loss caused when the base station passes through a building, a vehicle shell or other barriers when communicating with the node;
c) Calculating the communication path loss of each node, determining the communication loss of the node and the base station at a certain moment, and normalizing the communication loss values of the node and the base station to enable the value range to be [0,1];
the step S2 is used for analyzing the overall communication efficiency between the distributed nodes and the base station, training the TCN to predict the optimal communication efficiency at the next moment, and determining the overall communication efficiency between the nodes and the base station according to the signal intensity level of the base station at a certain moment of the distributed nodes, the interference function and the communication loss
Figure DEST_PATH_IMAGE012
，
Figure 354956DEST_PATH_IMAGE012
The calculation method of (2) is as follows:
Figure 551582DEST_PATH_IMAGE013
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE014
is the signal strength of the node as it communicates to the base station,
Figure 446857DEST_PATH_IMAGE014
is the signal strength of the base station to node communication;
the method for predicting the next overall communication efficiency by the TCN according to the historical data comprises the following steps:
i) determining reliable link quality M of communication between a communication node i and a base station, wherein the calculation method of M is as follows:
Figure 885667DEST_PATH_IMAGE015
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE016
is an interference function of a certain time node i communicates with a base station,
Figure 549997DEST_PATH_IMAGE017
is its communication path loss;
II) recording the reliable link quality of n times of communication between the node and the base station, carrying out normalization processing on the reliable link quality, and adding the normalized reliable link quality to 1 to obtain a compensation factor C;
III) training a TCN network, inputting the previous part of the historical data of n-time communication between the node and the base station into the TCN as a characteristic sequence, and using the next value of the sequence as a label to enable the TCN to learn the next predicted value in the current sequence mode;
IV) calculating a Loss function Loss, wherein the Loss calculation method comprises the following steps:
Figure DEST_PATH_IMAGE018
wherein, i is the ith communication between the node and the base station;
and the step S3 is used for determining the state of the node, measuring the signal intensity of each road section in the range according to the installation position of the base station, converting the signal intensity into a signal intensity grade, linking the signal intensity grade with the path information of the node, and rapidly determining the signal intensity.
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* Cited by examiner, † Cited by third party
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CN109390660A (en) * 2017-08-04 2019-02-26 川升股份有限公司 Applied to the multiple antennas electronic device development system under multi-path environment

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* Cited by examiner, † Cited by third party
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Patentee after: Shenzhen Jiaxian Communication Technology Co.,Ltd.

Address before: 518100 Room 101, building C4, Zone C, Fuhai international science and Technology Park, Zhancheng community, Fuhai street, Bao'an District, Shenzhen, Guangdong Province

Patentee before: Shenzhen Jiaxian communication equipment Co.,Ltd.