CN106851694B - Dynamic optimal network selection method and device for heterogeneous network - Google Patents

Dynamic optimal network selection method and device for heterogeneous network Download PDF

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CN106851694B
CN106851694B CN201710169515.8A CN201710169515A CN106851694B CN 106851694 B CN106851694 B CN 106851694B CN 201710169515 A CN201710169515 A CN 201710169515A CN 106851694 B CN106851694 B CN 106851694B
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network
value
data packet
time
heterogeneous
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CN106851694A (en
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程良伦
黄振杰
王涛
佘爽
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Guangdong University of Technology
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Guangdong University of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • 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

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Abstract

The embodiment of the invention discloses a dynamic optimal network selection method and a device for a heterogeneous network. After the exploration phase is completed, the algorithm starts a learning phase, when a new data packet arrives, the probability of success of data transmission of each network is calculated through behaviors before learning, the heterogeneous network node calculates a new Q value by using the optimal network of the previous phase, then the Q value is compared with the Q value of the other network of the previous phase, and finally the network with the larger Q value is selected for data packet forwarding.

Description

Dynamic optimal network selection method and device for heterogeneous network
Technical Field
The present invention relates to the field of heterogeneous network selection, and in particular, to a method and an apparatus for dynamically selecting an optimal network for a heterogeneous network.
Background
Heterogeneous networks distribute load through different network technologies to improve overall performance of the network and provide support for high-density coverage area cost, dense, large-area or temporary change events. But the main challenges faced by current heterogeneous network selection include: the explosive growth of mobile data traffic leads to a continuous increase in network load; the utilization rate of network resources is low; different types of network evolution make the network structure become more and more complex and the network coordination efficiency is not good. Therefore, how to optimize the selection of network transmission technology is a major challenge facing heterogeneous networks.
In order to deal with the low overall network capacity brought by the selection of the heterogeneous network transmission technology, the low utilization rate and the poor network coordination efficiency cause the performance of the heterogeneous network to be unexpected. The existing heterogeneous network transmission technologies mainly include: the micro network in the micro cellular network improves the frequency reuse through lower transmitting power; heterogeneous networks employ wireless features to distribute data between networks; network capacity is increased by splitting data; the vehicle-mounted network elects the node as the gateway to transmit the data packet, and because different networks adopt different standards and data formats, the methods need the heterogeneous network to exchange control information among different networks through a new mechanism, so that the method has the problems of higher time delay, higher energy consumption and incapability of better solving the problems of low capacity, low efficiency and poor cooperativity.
Therefore, it is a technical problem to be solved by those skilled in the art to provide a method and an apparatus capable of solving the problems of high time delay, high energy consumption, low capacity, low efficiency, poor cooperativity, and the like in the heterogeneous network transmission technology.
Disclosure of Invention
The embodiment of the invention provides a dynamic optimal network selection method and device for a heterogeneous network, which are combined with a reinforcement learning theory to optimize the selection of a transmission technology and provide a new reinforcement learning algorithm to allow each node to learn previous operations to improve the network performance.
The embodiment of the invention provides a dynamic optimal network selection method for a heterogeneous network, which comprises the following steps:
s1: acquiring a parameter Exp ═ true, a flag scalar flag w ═ true and a counting variable Expcount ═ 0;
s2: after acquiring the data packet, judging whether the parameter Exp is true, if so, executing S3, otherwise, executing S7;
s3: judging whether the flag scalar flag w is true, if so, executing S4, otherwise, executing S5;
s4: selecting a second network to send a data packet, making flag w equal to a flag, and then executing S6;
s5: selecting to use the first network to send the data packet, and making flag w true, and then executing S6;
s6: adding one to the counting variable Expcount, judging whether the counting variable Expcount is larger than a preset threshold value, if so, making Exp be equal to a flase, comparing the Q values obtained by calculating the first network and the second network through a preset formula, making a larger Q value be a first Q value and a smaller Q value be a second Q value, selecting the network corresponding to the first Q value as the current optimal network, and executing S2, if not, executing S2;
s7: and after a new data packet is obtained, calculating the current optimal network through a preset formula to obtain a third Q value, comparing the second Q value with the third Q value to obtain a maximum Q value, and selecting the network corresponding to the maximum Q value to send the data packet.
Preferably, the preset formula is:
Q(ti)＝(1-α)Q(ti-1)+α[SR(ti-1-ti)+CQ(ti)-Q(ti-1)]
wherein, Q (t)i) At a time tiProbability of access to network channel by heterogeneous node α learning rate SR (t)i-1-ti) From time t for a heterogeneous nodei-1To time tiThe success rate of data packet transmission between; CQ (t)i) At a time tiThe network channel quality.
Preferably, the heterogeneous node is at time ti-1To time tiSuccess rate SR (t) of data packet transmission betweeni-1-ti) The calculation formula of (2) is as follows:
SR(ti-1-ti)＝ST(ti-1-ti)/TT(ti-1-ti)
wherein, ST (t)i-1-ti) From time t for a heterogeneous nodei-1To time tiThe number of successful transmissions of the data packet of (a); TT (t)i-1-ti) From t for heterogeneous nodesi-1To tiThe total number of data packet transmissions during the period.
Preferably, said at time tiNetwork channel quality CQ (t)i) The calculation formula of (2) is as follows:
CQ(ti)＝R(ti)/Rmax
wherein R (t)i) For heterogeneous nodes at time tiThe transmission rate of the network; rmaxIs the maximum transmission rate that the network transmission technology can support.
Preferably, an embodiment of the present invention further provides a dynamic optimal network selection apparatus for a heterogeneous network, including:
the acquisition unit is used for acquiring a parameter Exp ═ true, a flag scalar flag w ═ true and a counting variable Expcount ═ 0;
the first judging unit is used for judging whether the parameter Exp is true or not after the data packet is obtained, if so, the second judging unit is triggered, and if not, the second calculating unit is triggered;
the second judgment unit is used for judging whether the scalar quantity flag W is true, if so, the first selection unit is triggered, and if not, the second selection unit is triggered;
the first selection unit is used for selecting the second network to send the data packet, making flag W equal to the flag, and triggering the first calculation unit;
the second selection unit is used for selecting to use the first network to send the data packet, making flag W true and triggering the first calculation unit;
the first calculation unit is used for adding one to the counting variable Expcount, judging whether the counting variable Expcount is larger than a preset threshold value, if so, making Exp equal to flase and comparing the Q values obtained by calculating the first network and the second network through a preset formula, making the larger Q value be the first Q value and the smaller Q value be the second Q value, selecting the network corresponding to the first Q value as the current optimal network, triggering the first judgment unit, and if not, triggering the first judgment unit;
and the second calculation unit is used for calculating the current optimal network to obtain a third Q value through a preset formula after acquiring the new data packet, comparing the second Q value with the third Q value to obtain a maximum Q value, and selecting the network corresponding to the maximum Q value to transmit the data packet.
Preferably, the preset formula is:
Q(ti)＝(1-α)Q(ti-1)+α[SR(ti-1-ti)+CQ(ti)-Q(ti-1)]
wherein, Q (t)i) At a time tiProbability of access to network channel by heterogeneous node α learning rate SR (t)i-1-ti) From time t for a heterogeneous nodei-1To time tiThe success rate of data packet transmission between; CQ (t)i) At a time tiThe network channel quality.
Preferably, the heterogeneous node is at time ti-1To time tiSuccess rate SR (t) of data packet transmission betweeni-1-ti) The calculation formula of (2) is as follows:
SR(ti-1-ti)＝ST(ti-1-ti)/TT(ti-1-ti)
wherein, ST (t)i-1-ti) From time t for a heterogeneous nodei-1To time tiThe number of successful transmissions of the data packet of (a); TT (t)i-1-ti) From t for heterogeneous nodesi-1To tiThe total number of data packet transmissions during the period.
Preferably, said at time tiNetwork channel quality CQ (t)i) The calculation formula of (2) is as follows:
CQ(ti)＝R(ti)/Rmax
wherein R (t)i) For heterogeneous nodes at time tiThe transmission rate of the network; rmaxIs the maximum transmission rate that the network transmission technology can support.
According to the technical scheme, the embodiment of the invention has the following advantages:
the embodiment of the invention provides a dynamic optimal network selection method and a device for a heterogeneous network, wherein the dynamic optimal network selection method for the heterogeneous network comprises the following steps: s1: acquiring a parameter Exp ═ true, a flag scalar flag w ═ true and a counting variable Expcount ═ 0; s2: after acquiring the data packet, judging whether the parameter Exp is true, if so, executing S3, otherwise, executing S7; s3: judging whether the flag scalar flag w is true, if so, executing S4, otherwise, executing S5; s4: selecting a second network to send a data packet, making flag w equal to a flag, and then executing S6; s5: selecting to use the first network to send the data packet, and making flag w true, and then executing S6; s6: adding one to the counting variable Expcount, judging whether the counting variable Expcount is larger than a preset threshold value, if so, making Exp be equal to a flase, comparing the Q values obtained by calculating the first network and the second network through a preset formula, making a larger Q value be a first Q value and a smaller Q value be a second Q value, selecting the network corresponding to the first Q value as the current optimal network, and executing S2, if not, executing S2; s7: and after a new data packet is obtained, calculating the current optimal network through a preset formula to obtain a third Q value, comparing the second Q value with the third Q value to obtain a maximum Q value, and selecting the network corresponding to the maximum Q value to send the data packet. The embodiment of the invention combines a reinforcement learning theory, learns the previous network state based on the parameters of each network, optimizes the selection of the transmission technology, improves the network capacity, reduces the expenditure, and solves the problems of low efficiency and poor cooperativity.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a schematic flowchart of a dynamic optimal network selection method for a heterogeneous network according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a dynamic optimal network selection apparatus for a heterogeneous network according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a dynamic optimal network selection method and device for a heterogeneous network, which are combined with a reinforcement learning theory to optimize the selection of a transmission technology and provide a new reinforcement learning algorithm to allow each node to learn previous operations to improve the network performance.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, 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 invention.
Referring to fig. 1, an embodiment of a dynamic optimal network selection method for a heterogeneous network according to the present invention includes:
101. acquiring a parameter Exp ═ true, a flag scalar flag w ═ true and a counting variable Expcount ═ 0;
and acquiring a preset parameter Exp ═ true, a flag scalar flag w ═ true, and a counting variable Expcount ═ 0.
102. After acquiring the data packet, judging whether the parameter Exp is true, if so, executing 103, and if not, executing 107;
after the data packet is acquired, it is determined whether the parameter Exp is true, if yes, step 103 is executed, and if not, step 107 is executed.
103. Judging whether the flag scalar flag w is true, if so, executing 104, and if not, executing 105;
after determining that the parameter Exp is true, it is determined whether the scalar flag w is true, if so, step 104 is executed, and if not, step 105 is executed.
104. Selecting a second network to send a data packet, making flag W equal to a flag, and then executing 106;
after the flag w is determined to be true, the second network is selected to transmit the packet, and the flag w is set to be flase, and then step 106 is executed.
105. Selecting to use the first network to send the data packet, and making flag w true, and then executing 106;
after the flag w is determined to be the flag, the first network is selected to transmit the packet, and the flag w is set to be true, and then step 106 is executed.
106. Adding one to a counting variable Expcount, judging whether the counting variable Expcount is larger than a preset threshold value, if so, making Exp equal to a flase, comparing the Q values obtained by calculating a first network and a second network through a preset formula, making a larger Q value be a first Q value and a smaller Q value be a second Q value, selecting a network corresponding to the first Q value as a current optimal network, and executing 102, if not, executing 102;
107. and after a new data packet is obtained, calculating the current optimal network through a preset formula to obtain a third Q value, comparing the second Q value with the third Q value to obtain a maximum Q value, and selecting the network corresponding to the maximum Q value to send the data packet.
And after judging that Exp is equal to the flash, obtaining a new data packet, calculating the current optimal network through a preset formula to obtain a third Q value, comparing the second Q value with the third Q value to obtain a maximum Q value, and selecting the network corresponding to the maximum Q value to send the data packet.
Further, the preset formula is as follows:
Q(ti)＝(1-α)Q(ti-1)+α[SR(ti-1-ti)+CQ(ti)-Q(ti-1)]
wherein, Q (t)i) At a time tiProbability of access to network channel by heterogeneous node α learning rate SR (t)i-1-ti) From time t for a heterogeneous nodei-1To time tiThe success rate of data packet transmission between; CQ (t)i) At a time tiThe network channel quality.
Further, the heterogeneous node is at time ti-1To time tiSuccess rate SR (t) of data packet transmission betweeni-1-ti) The calculation formula of (2) is as follows:
SR(ti-1-ti)＝ST(ti-1-ti)/TT(ti-1-ti)
wherein, ST (t)i-1-ti) From time t for a heterogeneous nodei-1To time tiThe number of successful transmissions of the data packet of (a); TT (t)i-1-ti) From t for heterogeneous nodesi-1To tiThe total number of data packet transmissions during the period.
Further, at time tiNetwork channel quality CQ (t)i) The calculation formula of (2) is as follows:
CQ(ti)＝R(ti)/Rmax
wherein R (t)i) For heterogeneous nodes at time tiThe transmission rate of the network; rmaxIs the maximum transmission rate that the network transmission technology can support.
In order to facilitate understanding, a specific application scenario is described below as an application of the dynamic optimal network selection method for a heterogeneous network, where the application scenario includes:
① algorithm parameters are first initialized with parameter preset value ti＝0；Exp＝true；FlagW＝true；Expcount＝0；Q(ti)＝0；CQ(ti)＝0；SR(ti-1-ti)＝0。
② entering into exploration phase, where different networks are used to send specific number of packets, a flag variable (flag w) is used to indicate which network is used, two networks are trained alternately by using packets through standard variable, then a count variable (Expcount) is used to control the number of explorations required to be completed in this phase, when each packet is used, Expcount is added by 1, when Expcount reaches threshold, Exp is set to false, then Q values of two networks are calculated respectively, the current optimal network is found out, and the exploration phase is completed.
③ after the exploration phase, because Exp is false, the algorithm starts a learning phase, in which when a new packet arrives, the heterogeneous network node calculates a new Q value using the optimal network of the previous phase, then compares the Q value with the Q value of another network of the previous phase, and finally selects the network with a larger Q value to forward the packet.
Note that, in step ②:
a. the formula for calculating the Q value is as follows:
Q(ti)＝(1-α)Q(ti-1)+α[SR(ti-1-ti)+CQ(ti)-Q(ti-1)]
wherein, Q (t)i) Is represented at time tiProbability of a node accessing a network channel α is the learning rate, SR (t)i-1-ti) Is the node at ti-1State of to tiSuccess rate of packet transmission since the state of (a); CQ (t)i) Is at a point in time tiThe network channel quality.
b、SR(ti-1-ti) The calculation formula of (a) is as follows:
SR(ti-1-ti)＝ST(ti-1-ti)/TT(ti-1-ti)
wherein, ST (t)i-1-ti) Is the node from ti-1To tiNumber of successful transmissions of data packets of, TT (t)i-1-ti) Is the node at ti-1To tiThe total number of data packet transmissions during the period.
c、CQ(ti) The calculation formula of (a) is as follows:
CQ(ti)＝R(ti)/Rmax
wherein R (t)i) Is the node at tiTransmission rate of time network, RmaxIs the maximum transmission rate that the network transmission technology can support.
Referring to fig. 2, an embodiment of a dynamic optimal network selection apparatus for a heterogeneous network according to the present invention includes:
an obtaining unit 201, configured to obtain a parameter Exp ═ true, a flag scalar flag w ═ true, and a count variable Expcount ═ 0;
a first determining unit 202, configured to determine whether the parameter Exp is true after the data packet is acquired, if yes, trigger a second determining unit 203, and if not, trigger a second calculating unit 207;
a second determining unit 203, configured to determine whether the scalar flag w is true, if yes, trigger the first selecting unit 204, and if not, trigger the second selecting unit 205;
a first selecting unit 204, configured to select to send a packet using the second network, and trigger the first calculating unit 206 by setting flag w to be a flag;
a second selecting unit 205, configured to select to send a data packet using the first network, and trigger the first calculating unit 206 by setting flag w to true;
the first calculating unit 206 is configured to add one to the count variable Expcount, determine whether the count variable Expcount is greater than a preset threshold, if so, make Exp equal to flase, compare the Q values obtained by calculating the first network and the second network through a preset formula, make a larger Q value be the first Q value, make a smaller Q value be the second Q value, select a network corresponding to the first Q value as the current optimal network, and trigger the first determining unit 202, and if not, trigger the first determining unit 202;
and the second calculating unit 207 is configured to calculate the current optimal network through a preset formula to obtain a third Q value after obtaining the new data packet, compare the second Q value with the third Q value to obtain a maximum Q value, and select a network corresponding to the maximum Q value to send the data packet.
Further, the preset formula is as follows:
Q(ti)＝(1-α)Q(ti-1)+α[SR(ti-1-ti)+CQ(ti)-Q(ti-1)]
wherein, Q (t)i) At a time tiProbability of access to network channel by heterogeneous node α learning rate SR (t)i-1-ti) From time t for a heterogeneous nodei-1To time tiThe success rate of data packet transmission between; CQ (t)i) At a time tiThe network channel quality.
Further, the heterogeneous node is at time ti-1To time tiSuccess rate SR (t) of data packet transmission betweeni-1-ti) The calculation formula of (2) is as follows:
SR(ti-1-ti)＝ST(ti-1-ti)/TT(ti-1-ti)
wherein, ST (t)i-1-ti) From time t for a heterogeneous nodei-1To time tiThe number of successful transmissions of the data packet of (a); TT (t)i-1-ti) From t for heterogeneous nodesi-1To tiThe total number of data packet transmissions during the period.
Further, at time tiNetwork channel quality CQ (t)i) The calculation formula of (2) is as follows:
CQ(ti)＝R(ti)/Rmax
wherein R (t)i) For heterogeneous nodes at time tiThe transmission rate of the network; rmaxIs the maximum transmission rate that the network transmission technology can support.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (8)

1. A method for dynamically selecting an optimal network for a heterogeneous network, comprising:
s1: acquiring a parameter Exp ═ true, a flag scalar flag w ═ true and a counting variable Expcount ═ 0;
s2: after acquiring the data packet, judging whether the parameter Exp is true, if so, executing S3, otherwise, executing S7;
s3: judging whether the flag scalar flag w is true, if so, executing S4, otherwise, executing S5;
s4: selecting a second network to send a data packet, making flag w equal to a flag, and then executing S6;
s5: selecting to use the first network to send the data packet, and making flag w true, and then executing S6;
s6: adding one to the counting variable Expcount, judging whether the counting variable Expcount is larger than a preset threshold value, if so, making Exp be equal to a flase, comparing the Q values obtained by calculating the first network and the second network through a preset formula, making a larger Q value be a first Q value and a smaller Q value be a second Q value, selecting the network corresponding to the first Q value as the current optimal network, and executing S2, if not, executing S2;
s7: and after a new data packet is obtained, calculating the current optimal network through a preset formula to obtain a third Q value, comparing the second Q value with the third Q value to obtain a maximum Q value, and selecting the network corresponding to the maximum Q value to send the data packet.
2. The dynamic optimal network selection method for heterogeneous networks according to claim 1, wherein the preset formula is:
Q(ti)＝(1-α)Q(ti-1)+α[SR(ti-1-ti)+CQ(ti)-Q(ti-1)]
wherein, Q (t)i) At a time tiProbability of access to network channel by heterogeneous node α learning rate SR (t)i-1-ti) From time t for a heterogeneous nodei-1To time tiThe success rate of data packet transmission between; CQ (t)i) At a time tiThe network channel quality.
3. The method of claim 2, wherein the heterogeneous node selects at time ti-1To time tiSuccess rate SR (t) of data packet transmission betweeni-1-ti) The calculation formula of (2) is as follows:
SR(ti-1-ti)＝ST(ti-1-ti)/TT(ti-1-ti)
wherein, ST (t)i-1-ti) From time t for a heterogeneous nodei-1To time tiThe number of successful transmissions of the data packet of (a); TT (t)i-1-ti) From t for heterogeneous nodesi-1To tiThe total number of data packet transmissions during the period.
4. The method of claim 3, wherein the time t is a time of a dynamic optimal network selection for heterogeneous networksiNetwork channel quality CQ (t)i) The calculation formula of (2) is as follows:
CQ(ti)＝R(ti)/Rmax
wherein R (t)i) For heterogeneous nodes at time tiThe transmission rate of the network; rmaxIs the maximum transmission rate that the network transmission technology can support.
5. A dynamic optimal network selection apparatus for heterogeneous networks, comprising:
the acquisition unit is used for acquiring a parameter Exp ═ true, a flag scalar flag w ═ true and a counting variable Expcount ═ 0;
the first judging unit is used for judging whether the parameter Exp is true or not after the data packet is obtained, if so, the second judging unit is triggered, and if not, the second calculating unit is triggered;
the second judgment unit is used for judging whether the scalar quantity flag W is true, if so, the first selection unit is triggered, and if not, the second selection unit is triggered;
the first selection unit is used for selecting the second network to send the data packet, making flag W equal to the flag, and triggering the first calculation unit;
the second selection unit is used for selecting to use the first network to send the data packet, making flag W true and triggering the first calculation unit;
the first calculation unit is used for adding one to the counting variable Expcount, judging whether the counting variable Expcount is larger than a preset threshold value, if so, making Exp equal to flase and comparing the Q values obtained by calculating the first network and the second network through a preset formula, making the larger Q value be the first Q value and the smaller Q value be the second Q value, selecting the network corresponding to the first Q value as the current optimal network, triggering the first judgment unit, and if not, triggering the first judgment unit;
and the second calculation unit is used for calculating the current optimal network to obtain a third Q value through a preset formula after acquiring the new data packet, comparing the second Q value with the third Q value to obtain a maximum Q value, and selecting the network corresponding to the maximum Q value to transmit the data packet.
6. The dynamic optimal network selection apparatus for heterogeneous networks according to claim 5, wherein the preset formula is:
Q(ti)＝(1-α)Q(ti-1)+α[SR(ti-1-ti)+CQ(ti)-Q(ti-1)]
wherein, Q (t)i) At a time tiProbability of access to network channel by heterogeneous node α learning rate SR (t)i-1-ti) From time t for a heterogeneous nodei-1To time tiThe success rate of data packet transmission between; CQ (t)i) At a time tiThe network channel quality.
7. The apparatus of claim 6, wherein the heterogeneous node selects at time ti-1To time tiSuccess rate SR (t) of data packet transmission betweeni-1-ti) The calculation formula of (2) is as follows:
SR(ti-1-ti)＝ST(ti-1-ti)/TT(ti-1-ti)
wherein, ST (t)i-1-ti) For a heterogeneous nodeTime ti-1To time tiThe number of successful transmissions of the data packet of (a); TT (t)i-1-ti) From t for heterogeneous nodesi-1To tiThe total number of data packet transmissions during the period.
8. The apparatus of claim 7, wherein the time t is a dynamic optimal network selectioniNetwork channel quality CQ (t)i) The calculation formula of (2) is as follows:
CQ(ti)＝R(ti)/Rmax
wherein R (t)i) For heterogeneous nodes at time tiThe transmission rate of the network; rmaxIs the maximum transmission rate that the network transmission technology can support.
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