CN112020115A - Network access method and system based on intelligent internet - Google Patents

Network access method and system based on intelligent internet Download PDF

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Publication number
CN112020115A
CN112020115A CN202010802476.2A CN202010802476A CN112020115A CN 112020115 A CN112020115 A CN 112020115A CN 202010802476 A CN202010802476 A CN 202010802476A CN 112020115 A CN112020115 A CN 112020115A
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vehicle
state
rsu
calculating
rss
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林凡
张秋镇
黄富铿
杨峰
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GCI Science and Technology Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/30—Reselection being triggered by specific parameters by measured or perceived connection quality data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/18—Selecting a network or a communication service

Abstract

The invention discloses a network access method and a system based on intelligent internet connection, wherein the switching method comprises the following steps: calculating the predicted motion state of the vehicle at the next moment according to the current moment state of the vehicle and a self-adaptive filtering algorithm; broadcasting the predicted motion state of the next moment to the RSU in the preset range around the vehicle, and calculating to obtain the predicted RSS of the vehicle at the next moment according to the position of the vehicle in the predicted motion state of the next moment and the position of the RSU; and predicting RSS according to the next moment, and performing switching decision by utilizing deep learning to obtain the optimal RSU switching of the vehicle. The embodiment of the invention provides a network access method and a network access system based on intelligent internet connection, which can effectively reduce the time delay of switching RSUs when a vehicle is in a high-speed motion state and are beneficial to improving the network access efficiency of the intelligent internet connection.

Description

Network access method and system based on intelligent internet
Technical Field
The invention relates to the technical field of network communication, in particular to a network access method and a network access system based on intelligent internet.
Background
At present, the automobile industry has formed a consensus, and the information-based and intelligent internet of vehicles will be an important direction for future development. In the network access process of the intelligent network connection, the switching of the vehicle networking RSU (road side unit) is an extremely important ring, the existing network access method of the intelligent network connection generally realizes the switching of the RSU based on the optimization problem of Received Signal Strength (RSS), the method is simple, direct and easy to use, however, the existing network access method of the intelligent network connection only considers the received signal strength and cannot well adapt to the high-speed motion state of a vehicle, so that the time delay of switching the RSU is large and even the RSU is disconnected when the vehicle runs at a high speed, and the network access efficiency of the intelligent network connection is low.
Disclosure of Invention
The invention provides a network access method and a network access system based on intelligent networking, which aim to solve the technical problem that the prior art cannot be well adapted to the high-speed motion state of a vehicle, so that the switching time delay of an RSU (remote subscriber Unit) is longer or even the line is disconnected when the vehicle is in a high-speed running state, and the network access efficiency of the intelligent networking is lower.
The first embodiment of the invention provides a network access method based on intelligent internet connection, which comprises the following steps:
calculating a predicted motion state of the vehicle at the next moment according to the current moment state of the vehicle and a self-adaptive filtering algorithm;
broadcasting the next-time predicted motion state to the RSU in a preset range around the vehicle, and calculating to obtain the next-time predicted RSS of the vehicle according to the position of the vehicle in the next-time predicted motion state and the position of the RSU;
and predicting RSS according to the next moment, and performing switching decision by utilizing deep learning to obtain the optimal RSU switching of the vehicle.
Further, the calculating the predicted motion state of the vehicle at the next moment according to the current moment state of the vehicle and the adaptive filtering algorithm specifically includes:
calculating the motion state of the vehicle at the next moment according to the current moment state of the vehicle and a state transition matrix from the current moment to the next moment;
and calculating a self-adaptive gain, and calculating to obtain a next-time predicted motion state of the vehicle according to the self-adaptive gain and the next-time motion state of the vehicle.
Further, the broadcasting the next-time predicted motion state to the RSU in the preset range around the vehicle, and calculating the next-time predicted RSS of the vehicle according to the vehicle position of the next-time predicted motion state and the position of the RSU specifically include:
and extracting the vehicle position at the next moment in the predicted motion state at the next moment, calculating the distance between the vehicle position at the next moment and the RSU position in the preset range around the vehicle, and calculating to obtain the predicted RSS (received signal strength) of the vehicle at the next moment according to the distance.
Further, the step of predicting RSS according to the next moment and performing a switching decision by using deep learning to obtain the optimal RSU switching of the vehicle specifically includes:
setting a vehicle state set, wherein the vehicle state of the vehicle state set comprises RSS of a current service network and RSS of a neighboring network, a network ID corresponding to each RSS and the speed of a vehicle relative to a corresponding base station;
based on the current vehicle state, learning an agent and selecting to execute an action to switch the RSU, after executing the action, enabling the vehicle to enter the next state according to the state transition probability, and obtaining a feedback value of the executed action;
and obtaining a decision set based on all the feedback values, calculating an optimal solution of total expected profit feedback according to the decision set, and obtaining the optimal RSU switching of the vehicle.
Further, the obtaining of a decision set based on all the feedback values, calculating an optimal solution of total expected revenue feedback according to the decision set, and obtaining the optimal RSU switching of the vehicle specifically includes:
calculating total expected income feedback according to the income factors, the expected operation method and the feedback function corresponding to the state action on the basis of the decision set;
defining a reinforcement learning function on state-actions from the total expected revenue feedback;
and updating the reinforcement learning function according to the reinforcement learning function, the income factor and the learning rate, and when the learning rate tends to 0, obtaining the maximum reinforcement learning function as the optimal solution of the total expected income feedback to realize the optimal RSU switching of the vehicle.
A second embodiment of the present invention provides a network access system based on an intelligent internet vehicle, including:
the first calculation module is used for calculating a predicted motion state of the vehicle at the next moment according to the current moment state of the vehicle and a self-adaptive filtering algorithm;
the second calculation module is used for broadcasting the next-time predicted motion state to the RSU in the preset range around the vehicle, and calculating the next-time predicted RSS of the vehicle according to the position of the vehicle in the next-time predicted motion state and the position of the RSU;
and the switching decision module is used for predicting RSS according to the next moment, and performing switching decision by utilizing deep learning to obtain the optimal RSU switching of the vehicle.
Further, the first calculating module is specifically configured to:
calculating the motion state of the vehicle at the next moment according to the current moment state of the vehicle and a state transition matrix from the current moment to the next moment;
and calculating a self-adaptive gain, and calculating to obtain a next-time predicted motion state of the vehicle according to the self-adaptive gain and the next-time motion state of the vehicle.
Further, the second calculation module is specifically configured to:
and extracting the vehicle position at the next moment in the predicted motion state at the next moment, calculating the distance between the vehicle position at the next moment and the RSU position in the preset range around the vehicle, and calculating to obtain the predicted RSS (received signal strength) of the vehicle at the next moment according to the distance.
Further, the handover decision module is specifically configured to:
setting a vehicle state set, wherein the vehicle state of the vehicle state set comprises RSS of a current service network and RSS of a neighboring network, a network ID corresponding to each RSS and the speed of a vehicle relative to a corresponding base station;
based on the current vehicle state, learning an agent and selecting to execute an action to switch the RSU, after executing the action, enabling the vehicle to enter the next state according to the state transition probability, and obtaining a feedback value of the executed action;
and obtaining a decision set based on all the feedback values, calculating an optimal solution of total expected profit feedback according to the decision set, and obtaining the optimal RSU switching of the vehicle.
Further, the obtaining of a decision set based on all the feedback values, calculating an optimal solution of total expected revenue feedback according to the decision set, and obtaining the optimal RSU switching of the vehicle specifically includes:
calculating total expected income feedback according to the income factors, the expected operation method and the feedback function corresponding to the state action on the basis of the decision set;
defining a reinforcement learning function on state-actions from the total expected revenue feedback;
and updating the reinforcement learning function according to the reinforcement learning function, the income factor and the learning rate, and when the learning rate tends to 0, obtaining the maximum reinforcement learning function as the optimal solution of the total expected income feedback to realize the optimal RSU switching of the vehicle.
The embodiment of the invention provides a network access method and a network access system based on intelligent internet connection, which can effectively reduce the time delay of switching RSUs when a vehicle is in a high-speed motion state and avoid disconnection, and are beneficial to improving the network access efficiency of the intelligent internet connection.
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Fig. 1 is a schematic flow chart of a network access method based on an intelligent internet vehicle according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a handover decision according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a network access system based on an intelligent internet vehicle according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description of the present application, it is to be understood that the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless otherwise specified.
In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art.
Referring to fig. 1, an embodiment of the present invention provides a network access method based on an intelligent internet vehicle as shown in fig. 1, including:
s1, calculating the predicted motion state of the vehicle at the next moment according to the current moment state of the vehicle and the adaptive filtering algorithm;
the embodiment of the invention adopts the self-adaptive filtering to measure the RSS and the motion state information of the vehicle in real time.
S2, broadcasting the next-time predicted motion state to the RSU in the preset range around the vehicle, and calculating to obtain the next-time predicted RSS of the vehicle according to the position of the vehicle in the next-time predicted motion state and the position of the RSU;
and S3, forecasting RSS according to the next moment, and performing switching decision by using deep learning to obtain the optimal RSU switching of the vehicle.
The embodiment of the invention predicts the motion state of the vehicle at the next moment according to the current moment state of the vehicle and the adaptive filtering algorithm, and broadcasts the prediction result to the RSU in the preset range around the vehicle, so that the RSU can calculate the predicted RSS of the vehicle at the next moment according to the motion state of the vehicle at the next moment, and when switching the RSU, switching can be carried out in advance according to the predicted RSS at the next moment, and the switching time delay can be effectively reduced, thereby improving the network access efficiency of the intelligent internet.
Furthermore, after the RSS of the vehicle is predicted at the next moment through calculation, a deep reinforcement learning algorithm is adopted to link the current switching decision of the vehicle with the long-term benefit, and the switching decision is more accurate through continuous reinforcement learning, so that the switching efficiency of the vehicle RSU can be improved, and the network access efficiency of the intelligent networked vehicle can be further improved.
As a specific implementation manner of the embodiment of the present invention, the method for calculating the predicted motion state of the vehicle at the next time according to the current time state of the vehicle and the adaptive filtering algorithm specifically includes:
calculating the motion state of the vehicle at the next moment according to the current moment state of the vehicle and the state transition matrix from the current moment to the next moment;
in the embodiment of the invention, the vehicle motion state m at the next momentt+1And measured values z of the vehiclet+1Are respectively:
mt+1＝Φmt+wt,wt～N(0,Q) (1)
zt+1＝Hmt+vt,vt～N(0,R) (2)
wherein m ist([xtytvx,tvy,t]T) Representing the vehicle motion at time t, at mtIn, xtIs the displacement of the vehicle in the horizontal direction at time t, ytIs the displacement of the vehicle in the vertical direction at time t, vx,tIs the speed in the horizontal direction of the vehicle at time t, vy,tIs the speed of the vehicle in the vertical direction at time t, phi is the state transition matrix from the current time t to the next time t +1, zt+1Is a measured value of the vehicle, H is a measurement matrix linking the measured value and the state value, and a random number wtFor parameters related to process noise, the random number vtFor parameters related to measurement noise, wtHas a covariance matrix of Q, vtThe covariance matrix of (2) is R.
And calculating the self-adaptive gain, and calculating the predicted motion state of the vehicle at the next moment according to the self-adaptive gain and the motion state of the vehicle at the next moment.
In the embodiment of the invention, the state transition matrix and w are calculated according to the state transition matrix from the current time t to the next time t +1tThe covariance matrix of (A) is calculated for Q to obtain the vehicle error covariance prediction at the next moment
Figure RE-GDA0002736974430000061
Figure RE-GDA0002736974430000062
Figure RE-GDA0002736974430000063
Wherein the content of the first and second substances,
Figure RE-GDA0002736974430000064
the vehicle motion state prediction for the next time t + 1.
According to the next momentVehicle error covariance prediction
Figure RE-GDA0002736974430000065
Calculating to obtain self-adaptive gain, and calculating to obtain the predicted motion state m of the vehicle at the next moment according to the self-adaptive gain and the motion state of the vehicle at the next momentt+1。
Figure RE-GDA0002736974430000071
Figure RE-GDA0002736974430000072
Wherein, Kt+1The adaptive gain at time t + 1.
The embodiment of the invention calculates the predicted motion state of the vehicle at the next moment according to the adaptive gain and the motion state of the vehicle at the next moment, calculates the adaptive gain at the next moment by using the vehicle error covariance prediction at the next moment, and combines the measured value z of the vehiclet+1The predicted motion state of the vehicle at the next moment is calculated, and the influence of the error of the measured value in the actual motion of the vehicle on the predicted value is considered, so that the calculation of the predicted motion state at the next moment is more accurate, and the reliability and the accuracy of the switching of the RSU of the vehicle are improved.
As a specific implementation manner of the embodiment of the present invention, the next-time predicted motion state is broadcasted to the RSU within the preset range around the vehicle, and the next-time predicted RSS of the vehicle is calculated according to the vehicle position of the next-time predicted motion state and the position of the RSU, specifically:
and extracting the vehicle position at the next moment in the predicted motion state at the next moment, calculating the distance between the vehicle position at the next moment and the RSU position in the preset range around the vehicle, and calculating to obtain the predicted RSS (received signal strength) of the vehicle at the next moment according to the distance.
Distance d 'between vehicle position and RSU position at next moment't+1The expression is as follows:
Figure RE-GDA0002736974430000073
according to distance d't+1Average signal received strength and inter-link shadowing fading, calculating the predicted RSS at the next time:
X'RSU＝ZRSU+μRSU-10ηlogd't+1 (8)
wherein, X'RSUPredicted RSS for the vehicle; zRSUIs the shadow fading between links; mu.sRSUIs the average signal received strength; η is the propagation constant.
In the embodiment of the invention, the next moment predicted RSS of the vehicle is calculated, and when the vehicle is in a high-speed motion state, the next moment predicted RSS is utilized to advance the switching of the RSU, so that the time delay of the switching of the RSU can be effectively reduced, and the success rate of the switching of the RSU of the vehicle is improved.
As a specific implementation manner of the embodiment of the present invention, RSS is predicted according to the next moment, and a switching decision is made by deep learning to obtain the optimal RSU switching of the vehicle, which specifically includes:
setting a vehicle state set, wherein the vehicle state of the vehicle state set comprises RSS of a current service network and RSS of a neighboring network, a network ID corresponding to each RSS and the speed of a vehicle relative to a corresponding base station;
as a specific implementation, the RSS and the velocity v are divided according to size and the size grade is represented by specific numerical values, specifically: rssi∈{1,2,3,4,5}，viE { -4, -3, -2, -1,0,1,2,3,4}, it should be noted that when the speed v is negative, it indicates that the vehicle is moving toward the direction of the principle base station. The vehicle state may be expressed as S { { id { (id) }1,rss1,v1},…,{id4,rss4,v4}}. The action is denoted by α, the network number corresponding to the action is {1,2, …, N }, which represents the handover decision, and the action space can be denoted as a ═ a ∈ {1,2, …, N } }.
Based on the current vehicle state, learning an agent and selecting to execute an action to switch the RSU, after executing the action, enabling the vehicle to enter the next state according to the state transition probability, and obtaining a feedback value of the executed action;
based on the current vehicle state stBelongs to S, learns the agent and performs an action atE.g. a to perform the switching of RSUs. With probability of state transition Ps,s'(at) The vehicle is enabled to enter the next state, and a feedback value r for executing the action is obtainedt。
And obtaining a decision set based on all the feedback values, and calculating an optimal solution of total expected profit feedback according to the decision set to obtain the optimal RSU switching of the vehicle.
Based on the technical scheme, an optimal decision set pi is finally obtained through repeated learning processes in a learning agent*(s)∈A。
As a specific implementation manner of the embodiment of the present invention, a decision set is obtained based on all feedback values, an optimal solution of total expected revenue feedback is calculated according to the decision set, and optimal RSU switching of a vehicle is obtained, specifically:
calculating total expected income feedback according to the income factors, the expected operation method and the feedback function corresponding to the state action on the basis of the decision set;
as a specific implementation mode, the decision set is pi(s) epsilon A, and the total expected income feedback Vπ(s) is:
Figure RE-GDA0002736974430000081
wherein, Vπ(s) total expected revenue feedback; gamma raytE [0,1) is a revenue factor; e is the desired operator; r(s)t,x(st) ) is a feedback function for the state action.
Defining a reinforcement learning function on the state-action according to the total expected revenue feedback;
converting formula (9) to be based on optimal strategy pi*(s) form of Bayesian equation for E A:
Figure RE-GDA0002736974430000091
wherein, V*(s) feeding back an optimal solution for the total expected revenue of the current system; gamma raytIs a yield factor and gammat∈[0,1)； Ps,s'(a) A state transition probability of transitioning to s' for performing the action α at state s; r (s, a) is a feedback function corresponding to the state action; v*(s ') feeding back an optimal solution for the total expected revenue for the system state s'.
Defining a reinforcement learning function for the state-action pairs (s, α):
Figure RE-GDA0002736974430000092
under the action of the optimal strategy, continuously defining the optimal solution of the reinforcement learning function in the current state:
Figure RE-GDA0002736974430000093
wherein the content of the first and second substances,
Figure RE-GDA0002736974430000094
performing reinforcement learning function optimal solution for the current state;
Figure RE-GDA0002736974430000095
the reinforcement learning function may be updated based on each execution of the action and the environmental feedback.
And updating the reinforcement learning function according to the reinforcement learning function, the income factor and the learning rate, and when the learning rate tends to 0, obtaining the maximum reinforcement learning function as the optimal solution of the total expected income feedback to realize the optimal RSU switching of the vehicle.
In the embodiment of the present invention, the reinforcement learning function is calculated as:
Figure RE-GDA0002736974430000096
wherein the content of the first and second substances,
Figure RE-GDA0002736974430000099
for the updated reinforcement learning function;
Figure RE-GDA00027369744300000910
the original reinforcement learning function is adopted; gamma is a revenue factor; ρ is a learning rate and
Figure RE-GDA0002736974430000097
t (s, a) represents a certain state- -the number of times an action pair is accessed. Mqt(s ', a') is a reinforcement learning function that performs action a 'at state s'.
When T (s, a) goes to infinity, ρ goes to 0, at which time
Figure RE-GDA00027369744300000911
Converge to
Figure RE-GDA0002736974430000098
Through repeated learning and decision-making processes, the learner obtains an optimal decision set, and obtains a maximum reinforcement learning function as an optimal solution for the feedback of the optimal total expected income.
Please refer to fig. 2, which is a flowchart illustrating a handover decision according to an embodiment of the present invention.
The embodiment of the invention has the following beneficial effects:
the embodiment of the invention predicts the motion state of the vehicle at the next moment according to the current moment state of the vehicle and the adaptive filtering algorithm, and broadcasts the prediction result to the RSU in the preset range around the vehicle, so that the RSU can calculate the predicted RSS of the vehicle at the next moment according to the motion state of the vehicle at the next moment, and when switching the RSU, switching can be carried out in advance according to the predicted RSS at the next moment, and the switching time delay can be effectively reduced, thereby improving the network access efficiency of the intelligent internet.
Furthermore, after the RSS of the vehicle is predicted at the next moment through calculation, a deep reinforcement learning algorithm is adopted to link the current switching decision of the vehicle with the long-term benefit, and the switching decision is more accurate through continuous reinforcement learning, so that the switching efficiency of the vehicle RSU can be improved, and the network access efficiency of the intelligent networked vehicle can be further improved.
Referring to fig. 3, a second embodiment of the present invention provides a network access system based on an intelligent internet vehicle, including:
the first calculation module 10 is used for calculating a predicted motion state of the vehicle at the next moment according to the current moment state of the vehicle and an adaptive filtering algorithm;
the embodiment of the invention adopts the self-adaptive filtering to measure the RSS and the motion state information of the vehicle in real time.
The second calculating module 20 is configured to broadcast the predicted motion state at the next time to the RSU within the preset range around the vehicle, and calculate the predicted RSS at the next time of the vehicle according to the vehicle position of the predicted motion state at the next time and the position of the RSU;
and the switching decision module 30 is used for predicting the RSS at the next moment and performing switching decision by using deep learning to obtain the optimal RSU switching of the vehicle.
The embodiment of the invention predicts the motion state of the vehicle at the next moment according to the current moment state of the vehicle and the adaptive filtering algorithm, and broadcasts the prediction result to the RSU in the preset range around the vehicle, so that the RSU can calculate the predicted RSS of the vehicle at the next moment according to the motion state of the vehicle at the next moment, and when switching the RSU, switching can be carried out in advance according to the predicted RSS at the next moment, and the switching time delay can be effectively reduced, thereby improving the network access efficiency of the intelligent internet.
Furthermore, after the RSS of the vehicle is predicted at the next moment through calculation, a deep reinforcement learning algorithm is adopted to link the current switching decision of the vehicle with the long-term benefit, and the switching decision is more accurate through continuous reinforcement learning, so that the switching efficiency of the vehicle RSU can be improved, and the network access efficiency of the intelligent networked vehicle can be further improved.
As a specific implementation manner of the embodiment of the present invention, the first calculating module 10 is specifically configured to:
calculating the motion state of the vehicle at the next moment according to the current moment state of the vehicle and the state transition matrix from the current moment to the next moment;
in the embodiment of the invention, the vehicle motion state m at the next momentt+1And measured values z of the vehiclet+1Are respectively:
mt+1＝Φmt+wt,wt～N(0,Q) (1)
zt+1＝Hmt+vt,vt～N(0,R) (2)
wherein m ist([xtytvx,tvy,t]T) Representing the vehicle motion at time t, at mtIn, xtIs the displacement of the vehicle in the horizontal direction at time t, ytIs the displacement of the vehicle in the vertical direction at time t, vx,tIs the speed in the horizontal direction of the vehicle at time t, vy,tIs the speed of the vehicle in the vertical direction at time t, phi is the state transition matrix from the current time t to the next time t +1, zt+1Is a measured value of the vehicle, H is a measurement matrix linking the measured value and the state value, and a random number wtFor parameters related to process noise, the random number vtFor parameters related to measurement noise, wtHas a covariance matrix of Q, vtThe covariance matrix of (2) is R.
And calculating the self-adaptive gain, and calculating the predicted motion state of the vehicle at the next moment according to the self-adaptive gain and the motion state of the vehicle at the next moment.
In the embodiment of the invention, the state transition matrix and w are calculated according to the state transition matrix from the current time t to the next time t +1tThe covariance matrix of (A) is calculated for Q to obtain the vehicle error covariance prediction at the next moment
Figure RE-GDA0002736974430000111
Figure RE-GDA0002736974430000112
Figure RE-GDA0002736974430000121
Wherein the content of the first and second substances,
Figure RE-GDA0002736974430000122
the vehicle motion state prediction for the next time t + 1.
Vehicle error covariance prediction from next time instant
Figure RE-GDA0002736974430000123
Calculating to obtain self-adaptive gain, and calculating to obtain the predicted motion state m of the vehicle at the next moment according to the self-adaptive gain and the motion state of the vehicle at the next momentt+1。
Figure RE-GDA0002736974430000124
Figure RE-GDA0002736974430000125
Wherein, Kt+1The adaptive gain at time t + 1.
The embodiment of the invention calculates the predicted motion state of the vehicle at the next moment according to the adaptive gain and the motion state of the vehicle at the next moment, calculates the adaptive gain at the next moment by using the vehicle error covariance prediction at the next moment, and combines the measured value z of the vehiclet+1The predicted motion state of the vehicle at the next moment is calculated, and the influence of the error of the measured value in the actual motion of the vehicle on the predicted value is considered, so that the calculation of the predicted motion state at the next moment is more accurate, and the reliability and the accuracy of the switching of the RSU of the vehicle are improved.
As a specific implementation manner of the embodiment of the present invention, the second calculating module 20 is specifically configured to:
and extracting the vehicle position at the next moment in the predicted motion state at the next moment, calculating the distance between the vehicle position at the next moment and the RSU position in the preset range around the vehicle, and calculating to obtain the predicted RSS (received signal strength) of the vehicle at the next moment according to the distance.
Distance d 'between vehicle position and RSU position at next moment't+1The expression is as follows:
Figure RE-GDA0002736974430000126
according to distance d't+1Average signal received strength and inter-link shadowing fading, calculating the predicted RSS at the next time:
X'RSU＝ZRSU+μRSU-10ηlogd't+1 (8)
wherein, X'RSUPredicted RSS for the vehicle; zRSUIs the shadow fading between links; mu.sRSUIs the average signal received strength; η is the propagation constant.
In the embodiment of the invention, the next moment predicted RSS of the vehicle is calculated, and when the vehicle is in a high-speed motion state, the next moment predicted RSS is utilized to advance the switching of the RSU, so that the time delay of the switching of the RSU can be effectively reduced, and the success rate of the switching of the RSU of the vehicle is improved.
As a specific implementation manner of the embodiment of the present invention, the switching decision module 30 is specifically configured to:
setting a vehicle state set, wherein the vehicle state of the vehicle state set comprises RSS of a current service network and RSS of a neighboring network, a network ID corresponding to each RSS and the speed of a vehicle relative to a corresponding base station;
as a specific implementation, the RSS and the velocity v are divided according to size and the size grade is represented by specific numerical values, specifically: rssi∈{1,2,3,4,5}，viE { -4, -3, -2, -1,0,1,2,3,4}, it should be noted that when the speed v is negative, it indicates that the vehicle is moving toward the direction of the principle base station. The vehicle state may be expressed as S { { id { (id) }1,rss1,v1},…,{id4,rss4,v4}}. The action is denoted by α, the network number corresponding to the action is {1,2, …, N }, which represents the handover decision, and the action space may be denoted as a ═ N{a|a∈{1,2,…,N}}。
Based on the current vehicle state, learning an agent and selecting to execute an action to switch the RSU, after executing the action, enabling the vehicle to enter the next state according to the state transition probability, and obtaining a feedback value of the executed action;
based on the current vehicle state stBelongs to S, learns the agent and performs an action atE.g. a to perform the switching of RSUs. With probability of state transition Ps,s'(at) The vehicle is enabled to enter the next state, and a feedback value r for executing the action is obtainedt。
And obtaining a decision set based on all the feedback values, and calculating an optimal solution of total expected profit feedback according to the decision set to obtain the optimal RSU switching of the vehicle.
Based on the technical scheme, an optimal decision set pi is finally obtained through repeated learning processes in a learning agent*(s)∈A。
As a specific implementation manner of the embodiment of the present invention, a decision set is obtained based on all feedback values, an optimal solution of total expected revenue feedback is calculated according to the decision set, and optimal RSU switching of a vehicle is obtained, specifically:
calculating total expected income feedback according to the income factors, the expected operation method and the feedback function corresponding to the state action on the basis of the decision set; as a specific implementation mode, the decision set is pi(s) epsilon A, and the total expected income feedback Vπ(s) is:
Figure RE-GDA0002736974430000131
wherein, Vπ(s) total expected revenue feedback; gamma raytE [0,1) is a revenue factor; e is the desired operator; r(s)t,x(st) ) is a feedback function for the state action.
Defining a reinforcement learning function on the state-action according to the total expected revenue feedback;
converting formula (9) to be based on optimal strategy pi*(s) form of Bayesian equation for E A:
Figure RE-GDA0002736974430000141
wherein, V*(s) feeding back an optimal solution for the total expected revenue of the current system; gamma raytIs a yield factor and gammat∈[0,1)； Ps,s'(a) A state transition probability of transitioning to s' for performing the action α at state s; r (s, a) is a feedback function corresponding to the state action; v*(s ') feeding back an optimal solution for the total expected revenue for the system state s'.
Defining a reinforcement learning function for the state-action pairs (s, α):
Figure RE-GDA0002736974430000142
under the action of the optimal strategy, continuously defining the optimal solution of the reinforcement learning function in the current state:
Figure RE-GDA0002736974430000143
wherein the content of the first and second substances,
Figure RE-GDA0002736974430000144
performing reinforcement learning function optimal solution for the current state;
Figure RE-GDA0002736974430000145
the reinforcement learning function may be updated based on each execution of the action and the environmental feedback.
And updating the reinforcement learning function according to the reinforcement learning function, the income factor and the learning rate, and when the learning rate tends to 0, obtaining the maximum reinforcement learning function as the optimal solution of the total expected income feedback to realize the optimal RSU switching of the vehicle.
In the embodiment of the present invention, the reinforcement learning function is calculated as:
Figure RE-GDA0002736974430000146
wherein the content of the first and second substances,
Figure RE-GDA0002736974430000149
for the updated reinforcement learning function;
Figure RE-GDA00027369744300001410
the original reinforcement learning function is adopted; gamma is a revenue factor; ρ is a learning rate and
Figure RE-GDA0002736974430000147
t (s, a) represents a certain state- -the number of times an action pair is accessed. Mqt(s ', a') is a reinforcement learning function that performs action a 'at state s'.
When T (s, a) goes to infinity, ρ goes to 0, at which time
Figure RE-GDA00027369744300001411
Converge to
Figure RE-GDA0002736974430000148
Through repeated learning and decision-making processes, the learner obtains an optimal decision set, and obtains a maximum reinforcement learning function as an optimal solution for the feedback of the optimal total expected income.
Please refer to fig. 2, which is a flowchart illustrating a handover decision according to an embodiment of the present invention.
The embodiment of the invention has the following beneficial effects:
the embodiment of the invention predicts the motion state of the vehicle at the next moment according to the current moment state of the vehicle and the adaptive filtering algorithm, and broadcasts the prediction result to the RSU in the preset range around the vehicle, so that the RSU can calculate the predicted RSS of the vehicle at the next moment according to the motion state of the vehicle at the next moment, and when switching the RSU, switching can be carried out in advance according to the predicted RSS at the next moment, and the switching time delay can be effectively reduced, thereby improving the network access efficiency of the intelligent internet.
Furthermore, after the RSS of the vehicle is predicted at the next moment through calculation, a deep reinforcement learning algorithm is adopted to link the current switching decision of the vehicle with the long-term benefit, and the switching decision is more accurate through continuous reinforcement learning, so that the switching efficiency of the vehicle RSU can be improved, and the network access efficiency of the intelligent networked vehicle can be further improved.
The foregoing is a preferred embodiment of the present invention, and it should be noted that it would be apparent to those skilled in the art that various modifications and enhancements can be made without departing from the principles of the invention, and such modifications and enhancements are also considered to be within the scope of the invention.

Claims (10)

1. A network access method based on intelligent Internet connection is characterized by comprising the following steps:
calculating a predicted motion state of the vehicle at the next moment according to the current moment state of the vehicle and a self-adaptive filtering algorithm;
broadcasting the next-time predicted motion state to the RSU in a preset range around the vehicle, and calculating to obtain the next-time predicted RSS of the vehicle according to the position of the vehicle in the next-time predicted motion state and the position of the RSU;
and predicting RSS according to the next moment, and performing switching decision by utilizing deep learning to obtain the optimal RSU switching of the vehicle.
2. The network access method based on the intelligent internet vehicle as claimed in claim 1, wherein the step of calculating the predicted motion state of the vehicle at the next moment according to the current moment state of the vehicle and an adaptive filtering algorithm specifically comprises the steps of:
calculating the motion state of the vehicle at the next moment according to the current moment state of the vehicle and a state transition matrix from the current moment to the next moment;
and calculating a self-adaptive gain, and calculating to obtain a next-time predicted motion state of the vehicle according to the self-adaptive gain and the next-time motion state of the vehicle.
3. The network access method based on the intelligent internet vehicle as claimed in claim 1, wherein the next-time predicted motion state is broadcasted to the RSU within a preset range around the vehicle, and the next-time predicted RSS of the vehicle is calculated according to the vehicle position of the next-time predicted motion state and the position of the RSU, specifically:
and extracting the vehicle position at the next moment in the predicted motion state at the next moment, calculating the distance between the vehicle position at the next moment and the RSU position in the preset range around the vehicle, and calculating to obtain the predicted RSS (received signal strength) of the vehicle at the next moment according to the distance.
4. The network access method based on the intelligent internet vehicle as claimed in claim 1, wherein the RSS is predicted according to the next moment, and a switching decision is made by deep learning to obtain the optimal RSU switching of the vehicle, specifically:
setting a vehicle state set, wherein the vehicle state of the vehicle state set comprises RSS of a current service network and RSS of a neighboring network, a network ID corresponding to each RSS and the speed of a vehicle relative to a corresponding base station;
based on the current vehicle state, learning an agent and selecting to execute an action to switch the RSU, after executing the action, enabling the vehicle to enter the next state according to the state transition probability, and obtaining a feedback value of the executed action;
and obtaining a decision set based on all the feedback values, calculating an optimal solution of total expected profit feedback according to the decision set, and obtaining the optimal RSU switching of the vehicle.
5. The network access method based on the intelligent internet vehicle as claimed in claim 4, wherein a decision set is obtained based on all the feedback values, an optimal solution of total expected revenue feedback is calculated according to the decision set, and optimal RSU switching of the vehicle is obtained, specifically:
calculating total expected income feedback according to the income factors, the expected operation method and the feedback function corresponding to the state action on the basis of the decision set;
defining a reinforcement learning function on state-actions from the total expected revenue feedback;
and updating the reinforcement learning function according to the reinforcement learning function, the income factor and the learning rate, and when the learning rate tends to 0, obtaining the maximum reinforcement learning function as the optimal solution of the total expected income feedback to realize the optimal RSU switching of the vehicle.
6. The utility model provides a network access system based on intelligent networking car which characterized in that includes:
the first calculation module is used for calculating a predicted motion state of the vehicle at the next moment according to the current moment state of the vehicle and a self-adaptive filtering algorithm;
the second calculation module is used for broadcasting the next-time predicted motion state to the RSU in the preset range around the vehicle, and calculating the next-time predicted RSS of the vehicle according to the position of the vehicle in the next-time predicted motion state and the position of the RSU;
and the switching decision module is used for predicting RSS according to the next moment, and performing switching decision by utilizing deep learning to obtain the optimal RSU switching of the vehicle.
7. The network access system based on the intelligent internet vehicle as claimed in claim 6, wherein the first computing module is specifically configured to:
calculating the motion state of the vehicle at the next moment according to the current moment state of the vehicle and a state transition matrix from the current moment to the next moment;
and calculating a self-adaptive gain, and calculating to obtain a next-time predicted motion state of the vehicle according to the self-adaptive gain and the next-time motion state of the vehicle.
8. The network access system based on the intelligent internet vehicle as claimed in claim 6, wherein the second computing module is specifically configured to:
and extracting the vehicle position at the next moment in the predicted motion state at the next moment, calculating the distance between the vehicle position at the next moment and the RSU position in the preset range around the vehicle, and calculating to obtain the predicted RSS (received signal strength) of the vehicle at the next moment according to the distance.
9. The network access system based on the intelligent internet protocol as claimed in claim 6, wherein the switching decision module is specifically configured to:
setting a vehicle state set, wherein the vehicle state of the vehicle state set comprises RSS of a current service network and RSS of a neighboring network, a network ID corresponding to each RSS and the speed of a vehicle relative to a corresponding base station;
based on the current vehicle state, learning an agent and selecting to execute an action to switch the RSU, after executing the action, enabling the vehicle to enter the next state according to the state transition probability, and obtaining a feedback value of the executed action;
and obtaining a decision set based on all the feedback values, calculating an optimal solution of total expected profit feedback according to the decision set, and obtaining the optimal RSU switching of the vehicle.
10. The network access system based on the intelligent internet vehicle as claimed in claim 9, wherein a decision set is obtained based on all the feedback values, an optimal solution of total expected revenue feedback is calculated according to the decision set, and optimal RSU switching of the vehicle is obtained, specifically:
calculating total expected income feedback according to the income factors, the expected operation method and the feedback function corresponding to the state action on the basis of the decision set;
defining a reinforcement learning function on state-actions from the total expected revenue feedback;
and updating the reinforcement learning function according to the reinforcement learning function, the income factor and the learning rate, and when the learning rate tends to 0, obtaining the maximum reinforcement learning function as the optimal solution of the total expected income feedback to realize the optimal RSU switching of the vehicle.
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