CN114710218A - 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

Info

Publication number
CN114710218A
CN114710218A CN202210603780.3A CN202210603780A CN114710218A CN 114710218 A CN114710218 A CN 114710218A CN 202210603780 A CN202210603780 A CN 202210603780A CN 114710218 A CN114710218 A CN 114710218A
Authority
CN
China
Prior art keywords
node
base station
communication
loss
nodes
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202210603780.3A
Other languages
Chinese (zh)
Other versions
CN114710218B (en
Inventor
邓梦璨
刘春来
刘军
韩留斌
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Jiaxian Communication Technology Co ltd
Original Assignee
Shenzhen Jiaxian Communication Equipment Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Jiaxian Communication Equipment Co ltd filed Critical Shenzhen Jiaxian Communication Equipment Co ltd
Priority to CN202210603780.3A priority Critical patent/CN114710218B/en
Publication of CN114710218A publication Critical patent/CN114710218A/en
Application granted granted Critical
Publication of CN114710218B publication Critical patent/CN114710218B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W88/00—Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
    • H04W88/08—Access point devices
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Probability & Statistics with Applications (AREA)
  • Quality & Reliability (AREA)
  • Electromagnetism (AREA)
  • Mobile Radio Communication Systems (AREA)

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; 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 progress of information technology, 5G brings great convenience to the life of people, 5G communication enables the world to become more intelligent, in the 5G technology, the design of a communication efficiency optimization method between distributed nodes and a base station is the basis of 5G fast communication, and taxis are common transportation tools in outgoing of people.
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 method for optimizing the communication efficiency between 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;
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 advantages 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.
Drawings
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 the real-time speed of the taxis, and the speed of the taxis can be used for judging whether the taxis are overspeed or not and whether the taxis are abnormally parked or not. A taxi's speed exceeding a defined speed and an abnormal stop may increase passenger risk.
All taxis are accessed to Beidou navigation, whether the taxis deviate from a designed path or not is judged, and the taxis deviate from a set route, so that the danger of passengers is increased, especially 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, the conversation between passengers and a driver is recorded, and 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 771313DEST_PATH_IMAGE001
The intensity of a signal input to a node at a certain time
Figure 949485DEST_PATH_IMAGE002
And n is the number of nodes in the coverage area of the base station.
Determining the signal intensity level around the base station, fixing the installation position of the base station, determining that the signal intensities of different areas around the base station are different due to the influence of obstacles and other reasons, wherein K-means is a K-means clustering algorithm, judging the similar relation of the K-means and the base station by calculating the distance between different samples, dividing the signal intensity level around the base station into six levels based on the K-means,
Figure 634544DEST_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.
The Beidou system is used for counting the number of vehicles in the similar area and the distance between the vehicles at the same time interval, 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 nodes to the base station is.
Giving the interference function when the node i transmits signals to the base station
Figure 313787DEST_PATH_IMAGE004
X is node i or halfAnd determining the number of the nodes in the circle with the diameter R 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 72796DEST_PATH_IMAGE005
，
Figure 749765DEST_PATH_IMAGE006
Is the number of nodes around the node at which the base station communicates to the node.
And normalizing the value calculated by the interference function to enable the value range to be positioned at [0, 1], wherein the closer to 1, the stronger the interference received by the node is.
And performing the operation on other nodes to determine the interference of all the nodes at a certain time.
And determining communication path loss of the distributed nodes and the base station based on the distance between the base station and the nodes, wherein the farther the distance between the distributed nodes and the base station is, the higher the communication loss between the nodes and the base station is. 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 984437DEST_PATH_IMAGE007
And n is the number of nodes in the base station range area.
Calculating communication loss between node and base station
Figure 811579DEST_PATH_IMAGE008
Where K is the loss excitation constant,
Figure 18569DEST_PATH_IMAGE009
is a 5G base station frequency point, C is an electromagnetic wave transmission speed, namely an optical speed,
Figure 928756DEST_PATH_IMAGE010
the node passes through the building and the vehicle when communicating with the base stationThe loss caused by other obstacles such as the housing 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 588408DEST_PATH_IMAGE011
，
Figure 484819DEST_PATH_IMAGE012
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 value of the node and the base station to ensure that the value range is positioned at [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, and determining the overall communication efficiency between the nodes and the base station based on the signal intensity level of the base station at a certain moment of the distributed nodes, the interference function and the communication loss
Figure 280737DEST_PATH_IMAGE013
，
Figure 96246DEST_PATH_IMAGE013
The calculation method of (2) is as follows:
Figure 180877DEST_PATH_IMAGE014
in the formula (I), the compound is shown in the specification,
Figure 740034DEST_PATH_IMAGE015
is the signal strength of the node communicating to the base station,
Figure 656038DEST_PATH_IMAGE016
Is the signal strength of the base station communication to the node.
Figure 517815DEST_PATH_IMAGE017
、
Figure 152058DEST_PATH_IMAGE018
Is a function of the interference of the node i,
Figure 514906DEST_PATH_IMAGE019
、
Figure 223099DEST_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 time, wherein the larger the value of the overall communication efficiency is, the better the communication quality between the node and the base station at the certain time 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 penetration of the 5G signal 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 moment is obtained
Figure 318094DEST_PATH_IMAGE021
Selecting the maximum value
Figure 501951DEST_PATH_IMAGE022
As an optimal communication performance.
In the non-overlapping area, the only whole communication efficiency is selected as the maximumGood communication efficiency
Figure 278277DEST_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 903294DEST_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 497086DEST_PATH_IMAGE025
in the formula (I), the compound is shown in the specification,
Figure 840343DEST_PATH_IMAGE026
is an interference function of a certain time node i communicates with a base station,
Figure 420360DEST_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 431041DEST_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 930155DEST_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 the mean square error and the compensation factor when the TCN is used for training
Figure 698391DEST_PATH_IMAGE030
。
The Loss function Loss is calculated and the Loss function Loss is calculated,
Figure 144416DEST_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.
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 (8)

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;
step S3: the state of the node is determined based on the TCN predicted overall communication performance of the node and the base station.
2. The optimization method for communication efficiency between a distributed node and a base station based on 5G as claimed in claim 1, wherein the step S1 is used for collecting speed, path and attitude information of all taxis in the city, accessing the taxis to Beidou navigation, collecting real-time speed and path deviation information of the taxis, collecting door opening and closing conditions and in-vehicle conversation through a door sensor and a recording system, converting the collected information into corresponding electromagnetic waves, and packaging and transmitting the information to the base station.
3. The method for optimizing communication performance between a 5G-based distributed node and a base station according to claim 2, wherein the method for determining the signal strength of the base station and the node comprises:
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) communication path losses of the distributed nodes and the base station are determined based on distances of the base station and the nodes.
4. The optimization method for communication efficiency between distributed nodes and base stations based on 5G according to claim 3, wherein the calculation method for the interference on the transmission of information from taxi nodes to base stations is as follows:
A) transmission of computing node i to base stationInterference function in signal
Figure 649929DEST_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 301490DEST_PATH_IMAGE002
In the formula
Figure 753331DEST_PATH_IMAGE003
Is the number of nodes around the node at which the base station communicates to the node.
5. The method for optimizing communication performance between a distributed node and a base station based on 5G as claimed in claim 4, wherein the step of determining the communication path loss between the distributed node and the base station is as follows:
a) calculating communication loss between node and base station
Figure 679699DEST_PATH_IMAGE004
，
Figure 228492DEST_PATH_IMAGE004
The calculation method of (2) is as follows:
Figure 723059DEST_PATH_IMAGE005
wherein K is a loss excitation constant,
Figure 724513DEST_PATH_IMAGE006
is a 5G base station frequency point, C is an electromagnetic wave transmission speed, namely an optical speed,
Figure 720150DEST_PATH_IMAGE007
is the loss caused when the node passes through other barriers such as buildings, vehicle shells and the like when communicating with the base station；
b) Calculating communication path loss when a base station communicates to a node
Figure 795554DEST_PATH_IMAGE008
，
Figure 523338DEST_PATH_IMAGE008
The calculation method of (2) is as follows;
Figure 74405DEST_PATH_IMAGE009
；
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 value of the node and the base station to enable the value range to be in [0, 1 ].
6. The method as claimed in claim 5, wherein the step S2 is used to analyze the overall communication performance between the distributed nodes and the base station, train the TCN to predict the best communication performance at the next time, and determine the overall communication performance between the nodes and the base station according to the signal strength level of the base station at a certain time of the distributed nodes, the interference function and the communication loss
Figure 545838DEST_PATH_IMAGE010
，
Figure 741327DEST_PATH_IMAGE010
The calculation method of (2) is as follows:
Figure 702330DEST_PATH_IMAGE011
in the formula (I), the compound is shown in the specification,
Figure 678376DEST_PATH_IMAGE012
is the signal strength of the node as it communicates to the base station,
Figure 625604DEST_PATH_IMAGE012
is the signal strength of the base station communication to the node.
7. The method for optimizing communication performance of 5G-based distributed nodes and base stations according to claim 6, wherein the TCN predicts the next overall communication performance according to the historical data as follows:
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 800233DEST_PATH_IMAGE013
in the formula (I), the compound is shown in the specification,
Figure 869820DEST_PATH_IMAGE014
is an interference function of a certain time node i communicates with a base station,
Figure 270846DEST_PATH_IMAGE015
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 84081DEST_PATH_IMAGE016
wherein, i is the ith communication between the node and the base station.
8. The method for optimizing communication performance between a 5G-based distributed node and a base station according to claim 7, wherein the step S3 is configured to determine the state of the node, measure the signal strength of each segment within the range according to the installation location of the base station, convert the signal strength into a signal strength level, link the signal strength level with the path information of the node, and rapidly determine the signal strength.
CN202210603780.3A 2022-05-31 2022-05-31 Distributed node and base station communication efficiency optimization method based on 5G Active CN114710218B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210603780.3A CN114710218B (en) 2022-05-31 2022-05-31 Distributed node and base station communication efficiency optimization method based on 5G

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210603780.3A CN114710218B (en) 2022-05-31 2022-05-31 Distributed node and base station communication efficiency optimization method based on 5G

Publications (2)

Publication Number Publication Date
CN114710218A true CN114710218A (en) 2022-07-05
CN114710218B CN114710218B (en) 2022-12-16

Family

ID=82176909

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210603780.3A Active CN114710218B (en) 2022-05-31 2022-05-31 Distributed node and base station communication efficiency optimization method based on 5G

Country Status (1)

Country Link
CN (1) CN114710218B (en)

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103916956A (en) * 2014-04-25 2014-07-09 南京工程学院 Optimization method for wireless transmission environment loss parameters in indoor positioning system
WO2019033228A1 (en) * 2017-08-12 2019-02-21 Zte Corporation Wireless reference transmission
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
CN111918321A (en) * 2020-07-22 2020-11-10 湖北工业大学 Mobile flow prediction method based on space-time attention convolutional network
CN112242874A (en) * 2020-06-04 2021-01-19 北京航空航天大学 Optimization variable decoupling-based unmanned aerial vehicle relay transmission efficiency optimization method
CN113453197A (en) * 2021-06-28 2021-09-28 华南师范大学 User pairing method combining mobile prediction and dynamic power
WO2021248607A1 (en) * 2020-06-10 2021-12-16 深圳大学 Deep reinforcement learning-based taxi dispatching method and system
CN114066070A (en) * 2021-11-19 2022-02-18 郑州航空工业管理学院 Weather forecasting method based on artificial intelligence and numerical calculation
CN114062993A (en) * 2021-10-20 2022-02-18 国网上海市电力公司 CVT error state prediction method based on time convolution network

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103916956A (en) * 2014-04-25 2014-07-09 南京工程学院 Optimization method for wireless transmission environment loss parameters in indoor positioning system
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
WO2019033228A1 (en) * 2017-08-12 2019-02-21 Zte Corporation Wireless reference transmission
CN112242874A (en) * 2020-06-04 2021-01-19 北京航空航天大学 Optimization variable decoupling-based unmanned aerial vehicle relay transmission efficiency optimization method
WO2021248607A1 (en) * 2020-06-10 2021-12-16 深圳大学 Deep reinforcement learning-based taxi dispatching method and system
CN111918321A (en) * 2020-07-22 2020-11-10 湖北工业大学 Mobile flow prediction method based on space-time attention convolutional network
CN113453197A (en) * 2021-06-28 2021-09-28 华南师范大学 User pairing method combining mobile prediction and dynamic power
CN114062993A (en) * 2021-10-20 2022-02-18 国网上海市电力公司 CVT error state prediction method based on time convolution network
CN114066070A (en) * 2021-11-19 2022-02-18 郑州航空工业管理学院 Weather forecasting method based on artificial intelligence and numerical calculation

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
李常贤等: "列车网络控制系统EMC干扰丢包特性研究", 《机车电传动》 *
王慧强: ""高精度室内定位研究评述及未来演进展望"", 《通信学报》 *

Also Published As

Publication number Publication date
CN114710218B (en) 2022-12-16

Similar Documents

Publication Publication Date Title
EP3694237A1 (en) Anomaly mapping by vehicular micro clouds
CN107066682A (en) Apparatus and method for road friction power in predicted link net
CN108428343A (en) A kind of more vehicle driving behavior analysis and danger early warning method and system
CN102139701B (en) Train working condition on-line monitoring method for rail transit
WO2020217457A1 (en) Communication system and base station
CN103716123A (en) Method and system for identifying packet loss and error causes in sensor network
Sliwa et al. System-of-systems modeling, analysis and optimization of hybrid vehicular traffic
CN111402631A (en) Vehicle driving-based danger early warning method and system and storage medium
WO2020217460A1 (en) Terminal and communication system
CN113096411A (en) Vehicle collision early warning method at intersection based on Internet of vehicles environmental system
CN116300973A (en) Autonomous obstacle avoidance method for unmanned mine car in complex weather
KR20160054921A (en) Interval detector using received signal strength indicator (rssi), and travel time estimating system and method having the same
CN114710218B (en) Distributed node and base station communication efficiency optimization method based on 5G
CN111355540B (en) Wireless link modeling and quality assessment method based on diffraction theory
CN111583649B (en) Method for predicting characteristic parameters of traffic flow by using RFID (radio frequency identification) space-time data
CN109887310A (en) A kind of retrograde determination method of the vehicle based on active RFID
US20230097606A1 (en) System, device, method and program that predict communication quality
Haghani et al. Automated low-cost and real-time truck parking information system.
Raiyn Classification of road traffic anomaly based on travel data analysis
CN113949473B (en) System and method for resisting co-channel interference in wireless communication of rail transit vehicle and ground
CN114631130B (en) Method for providing object messages on surrounding objects in a communication network for communication with other traffic participants
Elangovan et al. An ensembled approach to time series prediction for vehicle communication
CN110430549B (en) Data detection and restoration method for wearable network and Internet of vehicles
Todawat et al. A deep learning based approach on radar interference mitigation for autonomous vehicles
CN117880820B (en) Radio safety guarantee system and method for Internet of vehicles

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CP03 Change of name, title or address
CP03 Change of name, title or address

Address after: 518000 Room 101, building C4, Zone C, Fuhai international science and Technology Park, Zhancheng community, Fuhai street, Bao'an District, Shenzhen, Guangdong Province

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.