CN105007601A - Network selection method and a network selection device of heterogeneous network - Google Patents

Abstract

The invention relates to a network selection method and a network selection device of a heterogeneous network, and belongs to the technical field of mobile communication. The network selection method comprises the steps of S1, acquiring N types of network parameters with the correlation being less than a preset threshold for each candidate network of M candidate networks, wherein the M and the N are integers which are greater than 1; S2, determining a minimum value and a maximum value of a service quality parameter corresponding to each network parameter of the N types of network parameters in the M candidate networks, wherein the service quality parameter is the product of the network parameter and a weight of the network parameter; S3, determining a sorting value of each candidate network of the M candidate networks by using attributes of each network parameter of the N types of network parameters and the determined maximum value and the determined minimum value; S4, selecting a candidate network to act as user equipment (UE) to access to the network according to the determined sorting value. According to the invention, the sum of the weights of the network parameters with the correlation being too high can be reduced, thereby enabling weight distribution of the network parameters to be more objective, thus improving the accuracy of network selection, and being capable of improving the performance of the network in the aspect of load balance.

Description

A kind of network selecting method of heterogeneous network and device

Technical field

The invention belongs to mobile communication technology field, relate to a kind of network selecting method and device of heterogeneous network.

Background technology

Along with commercialization and the widespread deployment of 4G network, user can enjoy data service more and more at a high speed.The development of mobile Internet is the main drive of mobile communication.The class of business of future mobile communications and demand will be abundanter on existing basis.Can optimally meet the different business demand of user without any a kind of wireless access technology, so in order to maximize user experience quality, the network merging these isomeries provides better service to be the development trend of next generation network to user simultaneously.

In the mobile communication system in future, multiple wireless interface people network exists simultaneously, mutually supplements, is integrating seamlessly in unified heterogeneous network environment.The feature of multiple wireless access technology comprises: bandwidth, coverage, reliability, expense etc.How according to above-mentioned characteristic parameter, the optimal network meeting user's QoS demand is selected to become one of study hotspot in recent years fast and accurately.

Summary of the invention

In view of this, the object of the present invention is to provide a kind of network selecting method and device of heterogeneous network, for improving the accuracy and the load balancing of network that network selects.

For achieving the above object, the invention provides following technical scheme:

A network selecting method for heterogeneous network, comprises the following steps:

S1: to each candidate network in M candidate network, obtains the N kind network parameter of the degree of association lower than predetermined threshold, M and N be greater than 1 integer;

S2: to determine in described N kind network parameter minimum value in a described M candidate network of QoS parameter that often kind of network parameter is corresponding and maximum, described QoS parameter is the product of the weight of described network parameter and described network parameter;

S3: the attribute utilizing often kind of network parameter in described N kind network parameter, and the maximum determined and minimum value, determine the ranking value of each candidate network in a described M candidate network;

S4: according to the ranking value determined, selects the network that a candidate network accesses as user equipment (UE).

Further, described step S1 specifically comprises:

S11: to the P kind network parameter of each candidate network in M candidate network, determine the degree of association of any two network parameters, P is the integer being more than or equal to N;

S12: remove the degree of association higher than any one network parameter in two kinds of network parameters of described predetermined threshold, obtain described N kind network parameter.

Further, to the P kind network parameter of each candidate network in M candidate network in described S11, determine the degree of association of any two network parameters, comprising:

Be the bigger the better described in being divided into by described P kind network parameter shape parameter and described the smaller the better shape parameter, wherein, the network parameter of the shape parameter that is the bigger the better described in belonging to is larger, more useful to network utility, the network parameter belonging to described the smaller the better shape parameter is less, more useful to network utility;

To determine to be the bigger the better described in belonging in described P kind network parameter the normalized value of network parameter of shape parameter;

Determine the normalized value belonging to the network parameter of described the smaller the better shape parameter in described P kind network parameter;

Utilize the normalized value of described P kind network parameter, determine the degree of association of any two network parameters.

Further, described step S3 specifically comprises the following steps:

S31: determine that described N kind network parameter intermediate value is larger, is the shape parameter that is the bigger the better to the attribute of the more useful parameter of network utility, and determines that described N kind network parameter intermediate value is less, is the smaller the better shape parameter to the attribute of the more useful parameter of network utility;

S32: utilize attribute in described N kind network parameter for described in be the bigger the better the maximum of QoS parameter corresponding to the network parameter of shape parameter, with the minimum value that attribute is the QoS parameter that the network parameter of described the smaller the better shape parameter is corresponding, determine optimum network parameter;

S33: utilize attribute in described N kind network parameter for described in be the bigger the better the minimum value of QoS parameter corresponding to the network parameter of shape parameter, with the maximum that attribute is the QoS parameter that the network parameter of described the smaller the better shape parameter is corresponding, determine the poorest network parameter;

S34: utilize described optimum network parameter, the poorest described network parameter, determines the ranking value of each candidate network in a described M candidate network.

Further, describedly utilize optimum network parameter, the poorest network parameter, determine the ranking value of each candidate network in a described M candidate network, comprising:

Determine the N kind network parameter of each candidate network in a described M candidate network and the Distance geometry of optimum network parameter and the distance of the poorest network parameter;

According to the distance determined, obtain the ranking value of each candidate network in a described M candidate network.

Present invention also offers a kind of network selection apparatus of heterogeneous network, comprise screening unit, comparing unit, determining unit, selected cell;

Described screening unit be used for each candidate network in M candidate network is screened, obtain the N kind network parameter of the degree of association lower than predetermined threshold, M and N be greater than 1 integer;

Described comparing unit is for determining in described N kind network parameter minimum value in a described M candidate network of QoS parameter that often kind of network parameter is corresponding and maximum, and described QoS parameter is the product of the weight of described network parameter and described network parameter;

Described determining unit is for utilizing the attribute of often kind of network parameter in described N kind network parameter, and the maximum determined and minimum value, determines the ranking value of each candidate network in a described M candidate network;

Described selected cell is used for the ranking value according to determining, selects the network that a candidate network accesses as user equipment (UE).

Further, described screening unit is used for:

To the P kind network parameter of each candidate network in M candidate network, determine the degree of association of any two network parameters, P is the integer being more than or equal to N;

Remove the degree of association higher than any one network parameter in two kinds of network parameters of described predetermined threshold, obtain described N kind network parameter.

Further, described screening unit is used for:

Be the bigger the better described in being divided into by described P kind network parameter shape parameter and described the smaller the better shape parameter, wherein, the network parameter of the shape parameter that is the bigger the better described in belonging to is larger, more useful to network utility, the network parameter belonging to described the smaller the better shape parameter is less, more useful to network utility;

To determine to be the bigger the better described in belonging in described P kind network parameter the normalized value of network parameter of shape parameter;

Determine the normalized value belonging to the network parameter of described the smaller the better shape parameter in described P kind network parameter;

Utilize the normalized value of described P kind network parameter, determine the degree of association of any two network parameters.

Further, described determining unit is used for:

Determining that described N kind network parameter intermediate value is larger, be the shape parameter that is the bigger the better, and determine that described N kind network parameter intermediate value is less to the attribute of the more useful parameter of network utility, is the smaller the better shape parameter to the attribute of the more useful parameter of network utility;

Utilize attribute in described N kind network parameter for described in be the bigger the better the maximum of QoS parameter corresponding to the network parameter of shape parameter, with the minimum value that attribute is the QoS parameter that the network parameter of described the smaller the better shape parameter is corresponding, determine optimum network parameter;

Utilize attribute in described N kind network parameter for described in be the bigger the better the minimum value of QoS parameter corresponding to the network parameter of shape parameter, with the maximum that attribute is the QoS parameter that the network parameter of described the smaller the better shape parameter is corresponding, determine the poorest network parameter;

Utilize described optimum network parameter, the poorest described network parameter, determine the ranking value of each candidate network in a described M candidate network.

Further, described determining unit is used for:

Determine the N kind network parameter of each candidate network in a described M candidate network and the Distance geometry of optimum network parameter and the distance of the poorest network parameter;

According to the distance determined, obtain the ranking value of each candidate network in a described M candidate network.

Beneficial effect of the present invention is: the present invention screens network parameter, for two parameters that the degree of association is too high, removes arbitrary parameter, and only retain a parameter, therefore, the degree of association of the network parameter after screening is low.The network parameter after screening is utilized to carry out network selection, the weight sum of the too high network parameter of the degree of association can be reduced, make the weight allocation of network parameter more objective, and then improve the accuracy of network selection, and the performance of network in load balancing can be improved.

Accompanying drawing explanation

In order to make object of the present invention, technical scheme and beneficial effect clearly, the invention provides following accompanying drawing and being described:

Fig. 1 is the schematic diagram of the heterogeneous network being applicable to the method that network is selected in the embodiment of the present invention;

The flow chart of the network selecting method of a kind of heterogeneous network that Fig. 2 provides for the embodiment of the present invention;

Fig. 3 is the detail flowchart of step 101 in the embodiment of the present invention;

Fig. 4 is the detail flowchart of step 103 in the embodiment of the present invention;

The schematic diagram of a kind of network selection apparatus that Fig. 5 provides for the embodiment of the present invention.

Embodiment

The embodiment of the present invention provides a kind of network selecting method and device of heterogeneous network, for improving the accuracy degree that network is selected, and improves the performance of network in load balancing.By screening network parameter, for two parameters that the degree of association is too high, remove arbitrary parameter, only retain a parameter, therefore, the degree of association of the network parameter after screening is low.The network parameter after screening is utilized to carry out network selection, the weight sum of the too high network parameter of the degree of association can be reduced, make the weight allocation of network parameter more objective, and then improve the accuracy of network selection, and improve the performance of Network Load Balance aspect.

Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described in detail.

Fig. 1 is the schematic diagram of the heterogeneous network being applicable to the method that network is selected in the embodiment of the present invention.Heterogeneous network shown in Fig. 1 comprises following four kinds of networks: LTE network (that is: 4G network), 3G network, WIMAX network, wlan network.The embodiment of the present invention, in heterogeneous network, how for UE selects optimum network to carry out the problem accessed, provides a kind of method of accurate selection network, and ensures the load balancing of network.

The flow chart of the network selecting method of a kind of heterogeneous network that Fig. 2 provides for the embodiment of the present invention.The method comprises the following steps:

Step 101: to each candidate network in M candidate network, obtains the N kind network parameter of the degree of association lower than predetermined threshold, M and N be greater than 1 integer;

Step 102: to determine in described N kind network parameter minimum value in a described M candidate network of QoS parameter that often kind of network parameter is corresponding and maximum, described QoS parameter is the product of the weight of described network parameter and described network parameter;

Step 103: the attribute utilizing often kind of network parameter in described N kind network parameter, and the maximum determined and minimum value, determine the ranking value of each candidate network in a described M candidate network;

Step 104: according to the ranking value determined, selects the network that a candidate network accesses as user equipment (UE).

Wherein, M candidate network composition heterogeneous network.M candidate network can comprise the LTE network shown in Fig. 1,3G network, WIMAX network, wlan network, can also comprise other wireless networks such as 1G network, 2G network.

The network parameter of each candidate network has multiple, such as: the ability to shoulder economically of network delay, the network bandwidth, message transmission rate, packet loss, fail safe, network price, user itself and user are to the sensitivity etc. of network price.

First perform step 101, the network parameter of each candidate network is screened, selects the P kind network parameter of the degree of association lower than predetermined threshold.With reference to the detail flowchart that figure 3, Fig. 3 is step 101 in the embodiment of the present invention.Step 101 comprises the following steps:

Step 1011: to the P kind network parameter of each candidate network in M candidate network, determine the degree of association of any two network parameters, P is the integer being more than or equal to N;

Step 1012: remove the degree of association higher than any one network parameter in two kinds of network parameters of described predetermined threshold, obtain described N kind network parameter.

Consider that often kind of network parameter in P kind network parameter may be abstract fuzzy indicator, so before execution step 1011, first P kind network parameter can be carried out de-fuzzy by fuzzy theory.Deblurring method-gravity model appoach can be used to carry out de-fuzzy to P kind network parameter.

After the P kind network parameter of each candidate network carries out de-fuzzy in M candidate network, consider that the dimension of different types of network parameter is different, cause not there is comparativity between P kind network parameter.So need to be normalized to the P kind network parameter of each candidate network in M candidate network.

In the process be normalized, consider the attribute of often kind of network parameter in P kind network parameter, that is: in P kind network parameter, the attribute of the network parameter of some kind is the shape parameter that is the bigger the better, and the attribute of the network parameter of some kind is the smaller the better type network parameter, so according to the attribute of often kind of network parameter in P kind network parameter, different formula is adopted to be normalized respectively to the network parameter belonging to the type of being the bigger the better and the network parameter that belongs to the smaller the better type.That is:

Be the bigger the better described in being divided into by described P kind network parameter shape parameter and described the smaller the better shape parameter, wherein, the network parameter of the shape parameter that is the bigger the better described in belonging to is larger, more useful to network utility, the network parameter belonging to described the smaller the better shape parameter is less, more useful to network utility;

To determine to be the bigger the better described in belonging in described P kind network parameter the normalized value of network parameter of shape parameter;

Determine the normalized value belonging to the network parameter of described the smaller the better shape parameter in described P kind network parameter.

Particularly, first divide according to the attribute of P kind network parameter, P kind network parameter is divided into two types: be the bigger the better shape parameter and the smaller the better shape parameter.Wherein, the shape parameter that is the bigger the better refers to: the numerical value of network parameter is larger, more useful to network utility.Such as: internet security, message transmission rate etc.The shape parameter that is the bigger the better refers to: the numerical value of network parameter is less, more useful to network utility.Such as: network delay, packet loss etc.

For the network parameter belonging to the shape parameter that is the bigger the better in P kind network parameter, following formula is adopted to be normalized:

$b_{ij}=\frac{a_{ij}-{\mathrm{threshold}}_{ij}}{{\max }_{ij}-{\mathrm{threshold}}_{ij}}$

Wherein, a _ijrepresent the initial data of the jth kind network parameter of i-th candidate network in M candidate network; b _ijrepresent the normalized value of the jth kind network parameter of i-th candidate network in M candidate network, span is 0 to 1; max _ijrepresent the maximum threshold of the jth kind network parameter of i-th candidate network in M candidate network, if the value of jth kind network parameter is max _ij, then the useful degree of jth kind network parameter to network utility reaches maximum; Threshold _ijrepresent the minimum threshold of the jth kind network parameter of i-th candidate network in M candidate network, if the value of jth kind network parameter is threshold _ij, then jth kind network parameter has an impact to network utility.

For the network parameter belonging to the smaller the better shape parameter in P kind network parameter, following formula is adopted to be normalized:

$b_{ij}=\frac{{\mathrm{threshold}}_{ij}-a_{ij}}{{\mathrm{threshold}}_{ij}-{\min }_{ij}}$

Wherein, a _ijrepresent the initial data of the jth kind network parameter of i-th candidate network in M candidate network; b _ijrepresent the normalized value of the jth kind network parameter of i-th candidate network in M candidate network, span is 0 to 1; min _ijrepresent the minimum threshold of the jth kind network parameter of i-th candidate network in M candidate network, if the value of jth kind network parameter is min _ij, then the useful degree of jth kind network parameter to network utility reaches maximum; Threshold _ijrepresent the maximum threshold of the jth kind network parameter of i-th candidate network in M candidate network, if the value of jth kind network parameter is threshold _ij, then jth kind network parameter has an impact to network utility.

After carrying out de-fuzzy process and normalized successively to P kind network parameter, utilize the normalized value of P kind network parameter, determine the degree of association of any two network parameters.

First according to the degree of association in following formulae discovery P kind network parameter between any two network parameters:

${\mathrm{\γ}}_{ij}=\frac{1}{m}\underset{k=1}{\overset{m}{\Σ}}\frac{\underset{i}{\min }\underset{j}{\min }\left|b_{ki}-b_{kj}\right|+\mathrm{\ρ}\underset{i}{\max }\underset{j}{\max }\left|b_{ki}-b_{kj}\right|}{\left|b_{ki}-b_{kj}\right|+\mathrm{\ρ}\underset{i}{\max }\underset{j}{\max }\left|b_{ki}-b_{kj}\right|}$

Wherein, m represents the number of parameter; b _ijrepresent the normalized value of the jth kind network parameter of i-th candidate network in M candidate network, span is 0 to 1; γ _ijrepresent the degree of association between i-th kind of network parameter and jth kind network parameter in P kind network parameter; ρ is resolution ratio, is taken as 0.5 herein.

Then perform step 1012, for two network parameters of the degree of association higher than predetermined threshold, remove arbitrary network parameter, only retain a network parameter, after screening, obtain N kind network parameter.Wherein, predetermined threshold can artificially set or software set as required.The accuracy requirement selected network is higher, arranges less by predetermined threshold.

After execution of step 101, perform step 102.QoS parameter in step 102 is the product of the weight of network parameter and network parameter.

For the different business that UE initiates, the weighted of consolidated network parameter.The business that UE initiates can be divided into four large kinds: session service, Streaming Media class business, interaction service and background class traffic.

After calculating in N kind network parameter QoS parameter corresponding to often kind of network parameter, owing to there being M candidate network, so for often kind of network parameter, there is M QoS parameter, maximum and minimum value can be determined from M QoS parameter.

Following execution step 103 is the detail flowchart of step 103 in the embodiment of the present invention with reference to figure 4, Fig. 4.Step 103 comprises:

Step 1031: determine that described N kind network parameter intermediate value is larger, is the shape parameter that is the bigger the better to the attribute of the more useful parameter of network utility, and determines that described N kind network parameter intermediate value is less, is the smaller the better shape parameter to the attribute of the more useful parameter of network utility;

Step 1032: utilize attribute in described N kind network parameter for described in be the bigger the better the maximum of QoS parameter corresponding to the network parameter of shape parameter, with the minimum value that attribute is the QoS parameter that the network parameter of described the smaller the better shape parameter is corresponding, determine optimum network parameter;

Step 1033: utilize attribute in described N kind network parameter for described in be the bigger the better the minimum value of QoS parameter corresponding to the network parameter of shape parameter, with the maximum that attribute is the QoS parameter that the network parameter of described the smaller the better shape parameter is corresponding, determine the poorest network parameter;

Step 1034: determine the N kind network parameter of each candidate network in a described M candidate network and the Distance geometry of optimum network parameter and the distance of the poorest network parameter;

Step 1035: according to the distance determined, obtains the ranking value of each candidate network in a described M candidate network.

Particularly, be that the be the bigger the better network parameter of shape parameter should get maximum when calculating optimum network parameter for attribute, should minimum value be got when the poorest network parameter of calculating.Be that the network parameter of the smaller the better shape parameter should get minimum value when calculating optimum network parameter for attribute, should maximum be got when the poorest network parameter of calculating.

After execution of step 102, the maximum of often kind of network parameter in M candidate network and minimum value are determined in N kind network parameter, so can in conjunction with the attribute of often kind of network parameter in N kind network parameter, utilize the maximum and minimum value determined, calculate very thick optimum network parameter and the poorest network parameter.

Then, utilize the mathematical formulae of Euclidean distance, calculate the N kind network parameter of each candidate network and the distance G of optimum network parameter ₊, and calculate the N kind network parameter of each candidate network and the distance G of the poorest network parameter _-.

Finally, the approach degree P of each candidate network in M candidate network is determined according to following formula:

$P=\frac{G_{-}}{G_{-}+G_{+}}$

Using the approach degree P that calculates as ranking value, sort to M candidate network, the candidate network sequence that P value is larger is more forward; In contrast, the candidate network sequence that P value is less more rearward.

Finally, perform step 104, according to the ranking value of M candidate network, select the network that a candidate network accesses as UE.

Based on the inventive concept identical with the method that the network that the embodiment of the present invention provides is selected, the embodiment of the present invention additionally provides the device that a kind of network is selected, with reference to the schematic diagram of a kind of network selection apparatus that figure 5, Fig. 5 provides for the embodiment of the present invention.The device that network is selected comprises:

Screening unit 51, for each candidate network in M candidate network, obtain the N kind network parameter of the degree of association lower than predetermined threshold, M and N be greater than 1 integer;

Comparing unit 52, for determining in described N kind network parameter minimum value in a described M candidate network of QoS parameter that often kind of network parameter is corresponding and maximum, described QoS parameter is the product of the weight of described network parameter and described network parameter;

Determining unit 53, for utilizing the attribute of often kind of network parameter in described N kind network parameter, and the maximum determined and minimum value, determine the ranking value of each candidate network in a described M candidate network;

Selected cell 54, for according to the ranking value determined, selects the network that a candidate network accesses as user equipment (UE).

Optionally, described screening unit 51 for:

To the P kind network parameter of each candidate network in M candidate network, determine the degree of association of any two network parameters, P is the integer being more than or equal to N;

Remove the degree of association higher than any one network parameter in two kinds of network parameters of described predetermined threshold, obtain described N kind network parameter.

Optionally, described screening unit 51 for:

Be the bigger the better described in being divided into by described P kind network parameter shape parameter and described the smaller the better shape parameter, wherein, the network parameter of the shape parameter that is the bigger the better described in belonging to is larger, more useful to network utility, the network parameter belonging to described the smaller the better shape parameter is less, more useful to network utility;

To determine to be the bigger the better described in belonging in described P kind network parameter the normalized value of network parameter of shape parameter;

Determine the normalized value belonging to the network parameter of described the smaller the better shape parameter in described P kind network parameter;

Utilize the normalized value of described P kind network parameter, determine the degree of association of any two network parameters.

Optionally, described determining unit 53 for:

Determining that described N kind network parameter intermediate value is larger, be the shape parameter that is the bigger the better, and determine that described N kind network parameter intermediate value is less to the attribute of the more useful parameter of network utility, is the smaller the better shape parameter to the attribute of the more useful parameter of network utility;

Utilize attribute in described N kind network parameter for described in be the bigger the better the maximum of QoS parameter corresponding to the network parameter of shape parameter, with the minimum value that attribute is the QoS parameter that the network parameter of described the smaller the better shape parameter is corresponding, determine optimum network parameter;

Utilize attribute in described N kind network parameter for described in be the bigger the better the minimum value of QoS parameter corresponding to the network parameter of shape parameter, with the maximum that attribute is the QoS parameter that the network parameter of described the smaller the better shape parameter is corresponding, determine the poorest network parameter;

Utilize described optimum network parameter, the poorest described network parameter, determine the ranking value of each candidate network in a described M candidate network.

Optionally, described determining unit 53 for:

Determine the N kind network parameter of each candidate network in a described M candidate network and the Distance geometry of optimum network parameter and the distance of the poorest network parameter;

According to the distance determined, obtain the ranking value of each candidate network in a described M candidate network.

The one or more technical schemes provided in the embodiment of the present invention, at least have following technique effect or advantage:

The one or more technical schemes provided in the embodiment of the present invention, at least have following technique effect or advantage:

The embodiment of the present invention is screened network parameter, for two parameters that the degree of association is too high, removes arbitrary parameter, and only retain a parameter, therefore, the degree of association of the network parameter after screening is low.The network parameter after screening is utilized to carry out network selection, the weight sum of the too high network parameter of the degree of association can be reduced, make the weight allocation of network parameter more objective, and then improve the accuracy of network selection, and be better than traditional algorithm in load balancing.

What finally illustrate is, above preferred embodiment is only in order to illustrate technical scheme of the present invention and unrestricted, although by above preferred embodiment to invention has been detailed description, but those skilled in the art are to be understood that, various change can be made to it in the form and details, and not depart from claims of the present invention limited range.

Claims (10)

1. a network selecting method for heterogeneous network, is characterized in that: comprise the following steps:

S1: to each candidate network in M candidate network, obtains the N kind network parameter of the degree of association lower than predetermined threshold, M and N be greater than 1 integer;

S2: to determine in described N kind network parameter minimum value in a described M candidate network of QoS parameter that often kind of network parameter is corresponding and maximum, described QoS parameter is the product of the weight of described network parameter and described network parameter;

S3: the attribute utilizing often kind of network parameter in described N kind network parameter, and the maximum determined and minimum value, determine the ranking value of each candidate network in a described M candidate network;

S4: according to the ranking value determined, selects the network that a candidate network accesses as user equipment (UE).

2. the network selecting method of a kind of heterogeneous network according to claim 1, is characterized in that: described step S1 specifically comprises:

S11: to the P kind network parameter of each candidate network in M candidate network, determine the degree of association of any two network parameters, P is the integer being more than or equal to N;

S12: remove the degree of association higher than any one network parameter in two kinds of network parameters of described predetermined threshold, obtain described N kind network parameter.

3. the network selecting method of a kind of heterogeneous network according to claim 2, is characterized in that: to the P kind network parameter of each candidate network in M candidate network in described S11, determine the degree of association of any two network parameters, comprising:

Be the bigger the better described in being divided into by described P kind network parameter shape parameter and described the smaller the better shape parameter, wherein, the network parameter of the shape parameter that is the bigger the better described in belonging to is larger, more useful to network utility, the network parameter belonging to described the smaller the better shape parameter is less, more useful to network utility;

To determine to be the bigger the better described in belonging in described P kind network parameter the normalized value of network parameter of shape parameter;

Determine the normalized value belonging to the network parameter of described the smaller the better shape parameter in described P kind network parameter;

Utilize the normalized value of described P kind network parameter, determine the degree of association of any two network parameters.

4. the network selecting method of a kind of heterogeneous network according to any one of claim 1 to 3, is characterized in that: described step S3 specifically comprises the following steps:

S31: determine that described N kind network parameter intermediate value is larger, is the shape parameter that is the bigger the better to the attribute of the more useful parameter of network utility, and determines that described N kind network parameter intermediate value is less, is the smaller the better shape parameter to the attribute of the more useful parameter of network utility;

S32: utilize attribute in described N kind network parameter for described in be the bigger the better the maximum of QoS parameter corresponding to the network parameter of shape parameter, with the minimum value that attribute is the QoS parameter that the network parameter of described the smaller the better shape parameter is corresponding, determine optimum network parameter;

S33: utilize attribute in described N kind network parameter for described in be the bigger the better the minimum value of QoS parameter corresponding to the network parameter of shape parameter, with the maximum that attribute is the QoS parameter that the network parameter of described the smaller the better shape parameter is corresponding, determine the poorest network parameter;

S34: utilize described optimum network parameter, the poorest described network parameter, determines the ranking value of each candidate network in a described M candidate network.

5. the network selecting method of a kind of heterogeneous network according to claim 4, is characterized in that: describedly utilize optimum network parameter, the poorest network parameter, determines the ranking value of each candidate network in a described M candidate network, comprising:

Determine the N kind network parameter of each candidate network in a described M candidate network and the Distance geometry of optimum network parameter and the distance of the poorest network parameter;

According to the distance determined, obtain the ranking value of each candidate network in a described M candidate network.

6. a network selection apparatus for heterogeneous network, is characterized in that: comprise screening unit, comparing unit, determining unit, selected cell;

Described screening unit be used for each candidate network in M candidate network is screened, obtain the N kind network parameter of the degree of association lower than predetermined threshold, M and N be greater than 1 integer;

Described comparing unit is for determining in described N kind network parameter minimum value in a described M candidate network of QoS parameter that often kind of network parameter is corresponding and maximum, and described QoS parameter is the product of the weight of described network parameter and described network parameter;

Described determining unit is for utilizing the attribute of often kind of network parameter in described N kind network parameter, and the maximum determined and minimum value, determines the ranking value of each candidate network in a described M candidate network;

Described selected cell is used for the ranking value according to determining, selects the network that a candidate network accesses as user equipment (UE).

7. the network selection apparatus of a kind of heterogeneous network according to claim 6, is characterized in that: described screening unit is used for:

To the P kind network parameter of each candidate network in M candidate network, determine the degree of association of any two network parameters, P is the integer being more than or equal to N;

Remove the degree of association higher than any one network parameter in two kinds of network parameters of described predetermined threshold, obtain described N kind network parameter.

8. the network selection apparatus of a kind of heterogeneous network according to claim 7, is characterized in that: described screening unit is used for:

Be the bigger the better described in being divided into by described P kind network parameter shape parameter and described the smaller the better shape parameter, wherein, the network parameter of the shape parameter that is the bigger the better described in belonging to is larger, more useful to network utility, the network parameter belonging to described the smaller the better shape parameter is less, more useful to network utility;

To determine to be the bigger the better described in belonging in described P kind network parameter the normalized value of network parameter of shape parameter;

Determine the normalized value belonging to the network parameter of described the smaller the better shape parameter in described P kind network parameter;

Utilize the normalized value of described P kind network parameter, determine the degree of association of any two network parameters.

9. the network selection apparatus of a kind of heterogeneous network according to any one of claim 6 to 8, is characterized in that: described determining unit is used for:

Determining that described N kind network parameter intermediate value is larger, be the shape parameter that is the bigger the better, and determine that described N kind network parameter intermediate value is less to the attribute of the more useful parameter of network utility, is the smaller the better shape parameter to the attribute of the more useful parameter of network utility;

Utilize attribute in described N kind network parameter for described in be the bigger the better the maximum of QoS parameter corresponding to the network parameter of shape parameter, with the minimum value that attribute is the QoS parameter that the network parameter of described the smaller the better shape parameter is corresponding, determine optimum network parameter;

Utilize attribute in described N kind network parameter for described in be the bigger the better the minimum value of QoS parameter corresponding to the network parameter of shape parameter, with the maximum that attribute is the QoS parameter that the network parameter of described the smaller the better shape parameter is corresponding, determine the poorest network parameter;

Utilize described optimum network parameter, the poorest described network parameter, determine the ranking value of each candidate network in a described M candidate network.

10. the network selection apparatus of a kind of heterogeneous network according to claim 9, is characterized in that: described determining unit is used for:

Determine the N kind network parameter of each candidate network in a described M candidate network and the Distance geometry of optimum network parameter and the distance of the poorest network parameter;

According to the distance determined, obtain the ranking value of each candidate network in a described M candidate network.