CN107608803B - Social D2D relay selection method - Google Patents
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Abstract
The invention provides a social D2D relay selection method, which is used for preprocessing input data to obtain data of a required type; performing index, namely target decision weight generation calculation on the obtained data; and converting and integrating the obtained single target output quantity and the QoS condition, and solving through a distributed message transmission mechanism to obtain a final output result. The algorithm is based on the combination of a modified IFAHP method and an entropy weight method, converts a multi-objective optimization problem into a single-objective optimization problem, further converts an expression of a quality of service condition, and further solves a D2D UE-NW relay selection result in a distributed mode through a proper message passing algorithm. The invention combines the subjective preference of the VUE with hesitation degree and the objective information decision weight for consideration, thereby leading the relay selection result to obtain more comprehensive performance improvement and having higher equipment access proportion, throughput and system fairness.
Description
Technical Field
The invention relates to the field of mobile communication, in particular to a social D2D relay selection method.
Background
The D2D communication technology enables direct communication between User Equipments (UEs) without transmission or forwarding through a base station (eNB) or other devices, thereby achieving the purposes of reducing eNB load and expanding communication coverage.
UE-NW (UE-to-Network) relay is a new feature introduced by 3GPP LTE standards-making group in the D2D communication issue, has the advantage of flexible deployment, and can expand the Network coverage range without increasing the existing Network devices, so that it can be widely applied to the fields of commercial communication, public safety communication (such as earthquake, war), etc. As shown in fig. 1, a typical D2D UE-NW system includes one eNB, several Relay user equipments Relay UEs (RUEs), and several Victim UEs (VUEs, i.e., UEs requiring D2D Relay connection service). The eNB and RUE are connected via a cellular communication link, while the RUE and VUE are connected via a 3GPP specified D2D communication-dedicated sidelink. VUEs can be further divided into two types: In-Coverage VUE (IC VUE) within the Coverage of the cellular network, Out-of-Coverage VUE (OOC VUE) outside the Coverage of the cellular network.
Although the D2D UE-NW relay communication technique has the above advantages, the existing solution still has a problem of how to effectively perform the D2D relay selection function based on the conclusion that the existing 3GPP specifications are satisfied. Among these, one key issue is to perform D2D relay selection under multi-objective optimization based on what criteria and methods. In the conclusion formed in 2015 by the 3GPP RAN2 working group, the following requirements were proposed: "the VUE-selected relay UE needs to satisfy both the best link quality and other conditions specified by higher layers".
Meanwhile, with the development of social networking technology, the D2D UE increasingly presents the social attributes of the holder. The higher the social similarity between UEs, the higher the trust level, i.e. the higher the connection security. In addition, a high social similarity also often means that the VUE is more likely to retrieve interesting data content from socially close UEs. Thus, the social connection attribute of D2D has gained increasing research attention.
However, the prior art methods all suffer from different degrees of design deficiencies. In the prior art, a cluster and a corresponding cluster head with minimum time-frequency resource consumption under a reliable retransmission mechanism are obtained by iteratively updating the maximum reachable broadcast rate of a D2D link in the cluster. However, this approach ignores the social connection properties presented by D2D devices and is computationally prohibitive. In the prior art, a D2D relay selection scheme combining social and physical connection attributes is proposed, but all the schemes are executed under the assumption that the social and physical connection attributes have the same processing priority, and do not necessarily accord with the actual application scenario. On the other hand, the methods are optimization schemes under a single objective, and the situation of multi-objective optimization is not considered, so that the access performance of the UE-NW system is improved and simplified.
In addition to the above single optimization objective scheme, a multi-objective D2D relay selection scheme based on the combination of Complementary Fuzzy Analytic Hierarchy Process (CFAHP) and Mahalanobis Distance (MD) has been proposed in the prior art. However, multiple targets in this scheme do not contain social attributes. In addition, the scheme relies on subjective judgment brought by CFAHP, and ignores the favorable decision information for relay selection, which can be provided by objective data. Meanwhile, the CFAHP method is performed without considering the psychological characteristic of hesitation in the subjective fuzzy judgment of the UE. On the other hand, this approach fails to distinguish between different treatment strategies for revenue-type and attrition-type indicators.
Disclosure of Invention
The invention provides a social D2D relay selection method for improving the probability of acquiring data of a target type.
In order to achieve the technical effects, the technical scheme of the invention is as follows:
a social D2D relay selection method, comprising the steps of:
s1: preprocessing input data to obtain data of a required type;
s2: performing index, namely target decision weight generation calculation on the data in the S1;
s3: and converting and integrating the single target output quantity obtained in the S2 and the QoS condition, and solving through a distributed message transmission mechanism to obtain a final output result.
Further, the specific process of step S1 is:
let the relay user equipment RUE form a set R ═ { R ═ R1,R2,...,RNN is the number of candidate relay user equipment RUE; user equipment VUE of relay connection service forms set V ═ { V ═ V-1,v2,...,vMM is the number of VUE;
the indexes of the method which need to carry out decision weight generation calculation are as follows:
link capacity between VUE v and RUE r
Social similarity between VUE v and RUE r Sv,r: the social attribute is described by a Jacard coefficient and is defined as the proportion of the common social attribute owned by the VUE v and the RUE r to the total social attribute;
buffer size beta of RUE r endr；
VUE v acquires consumption C required for RUE r relay servicev,r: in IC scenarios, the cost required to implement relay services for VUE-excited RUEs; in an OOC scenario, the power consumption of the VUE itself is assumed;
the multiple targets form a target set of VUE v ends
The capacity, the social similarity and the cache size of the RUE end are gain indexes, and the higher the numerical value is, the better the numerical value is; on the other hand, the consumption is a loss index, and the lower the value is, the better the value is;
at the same time, a binary selection variable X is definedv,rTo indicate whether VUE v selects a candidate RUE r:
in addition to the above multiple targets, the indexes to be calculated for generating decision weight include:
QoS conditions required by the VUE end for each index;
acceptance of the RUE end, i.e. maximum number of accessible VUs Kr；
Each VUE can only access one unique RUE;
based on the above, the optimization model of the solution required by the method is as follows:
max{P1,P2,P3,-P4} (2)
the model is limited to:
wherein:
Further, the specific process of step S2 includes performing subjective preference decision weight generation and objective decision weight generation;
the subjective preference decision weight generation comprises the following steps:
1) constructing a visual fuzzy preference relationship;
2) constructing a perfect multiplicative consistency visual fuzzy relation matrix;
3) generating visual fuzzy numerical weight;
4) generating a definite number ranking value
5) Normalizing the ranking values to generate output subjective preference weights
Namely the subjective preference weight corresponding to the ith index of the VUE v end;
the objective decision weight generation comprises the steps of:
1) inputting all index values of all candidate RUEs;
2) data preprocessing;
3) obtaining objective decision weight by an entropy weight method;
4) processing a result obtained by integrating the subjective preference decision weight to generate a single target relative approximation degree sequence T of an output VUE v endv。
Further, the specific process of step S3 is:
through gammavConverting the original optimization problem model into a new optimization model with the minimum sum of solving single-target relative approximation RPD:
limited by: C1-C6 (6)
Since this optimization model containing QoS threshold conditions cannot be solved directly by existing distributed messaging algorithms, to further solve the optimization problem (5), the following indicators are introduced:
further, combining the objectives of (7) and (4), the following transformation model is obtained:
limited by:
wherein C7 is a new condition introduced to ensure that (8) and (5) are mathematically equivalent, the objective function for models (8), (5) has been combined with QoS threshold conditions such that the solution of (8) is semantically equivalent to solving for relay selection results that can simultaneously satisfy the minimum RPD and satisfy all QoS threshold conditions, since in practical communication systems condition C7 is difficult to be fully satisfied in a binary selection problem under this type of QoS constraint, C7 is omitted for model solution with distributed messaging mechanisms, to solve for (8) the desired output result, and in order to solve for (8) with distributed messaging mechanisms, the message from RUE end to e end is redefined in the tth iteration of VUE end to e end
And the message from the VUE end to the RUE end is
Where ω is a predefined damping coefficient that ensures algorithm convergence,
the constituent set V of VUs represents the Kth in the Vth remaining subset of VUs (i.e., V/{ V }) that is deletedrA minimum message size of 0 < Kr≤M,Kr∈Z+Is a predefined parameter;
because the type of the solution objective in (8) is the minimum optimization summation, correspondingly, the invention sets the solution objective of the distributed message transfer mechanism as the sum of the minimum message quantity to obtain the final iteration output:
compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the method comprises the steps of preprocessing input data to obtain data of a required type; performing index, namely target decision weight generation calculation on the obtained data; and converting and integrating the obtained single target output quantity and the QoS condition, and solving through a distributed message transmission mechanism to obtain a final output result. The algorithm is based on the combination of a Modified IFAHP (Modified IFAHP, MIFAHP) method and an entropy weight method, converts a multi-objective optimization problem into a single-objective optimization problem, further converts an expression of Quality of Service (QoS) conditions, and further solves the D2D UE-NW relay selection result in a distributed manner through a proper message transfer algorithm. The invention combines the subjective preference of the VUE band hesitation degree and the objective information decision weight for consideration, thereby leading the D2D UE-NW relay selection result to obtain more comprehensive performance improvement. Meanwhile, compared with the existing scheme, the method has higher equipment access ratio, higher throughput and higher system fairness, and is more suitable for the practical D2D UE-NW communication system.
Drawings
FIG. 1 is a typical D2D UE-NW system;
FIG. 2 is a basic process flow of the present invention;
FIG. 3 is a flow chart based on a Modified IFAHP Method (MIFAHP);
FIG. 4 is a flowchart of a target weight generation algorithm combining the IFAHP method and the entropy weight method;
FIG. 5 is a flow diagram of a solution over a distributed messaging mechanism;
fig. 6 is an IC scenario access ratio vs capacity QoS threshold;
FIG. 7 is an IC scene access ratio vs social similarity QoS threshold;
FIG. 8 is an IC scenario access ratio vs. consumption QoS threshold;
fig. 9 is an IC scenario access ratio vs cache size QoS threshold;
fig. 10 is an IC scenario access ratio vs full QoS threshold;
fig. 11 is an OOC scenario access ratio vs capacity QoS threshold;
FIG. 12 is an OOC scenario access ratio vs social similarity QoS threshold;
fig. 13 is an OOC scenario access ratio vs consumption QoS threshold;
fig. 14 is an OOC scenario access ratio vs cache QoS threshold;
fig. 15 is an OOC scenario access ratio vs full QoS threshold;
FIG. 16 is an IC scenario throughput vs full QoS threshold;
FIG. 17 is an IC scenario system fairness vs. full QoS threshold;
FIG. 18 is an OOC scenario throughput vs full QoS threshold;
fig. 19 is OOC scenario system fairness vs full QoS threshold.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
A social D2D relay selection method, comprising the steps of:
s1: preprocessing input data to obtain data of a required type;
s2: performing index, namely target decision weight generation calculation on the data in the S1;
s3: and converting and integrating the single target output quantity obtained in the S2 and the QoS condition, and solving through a distributed message transmission mechanism to obtain a final output result.
Further, the specific process of step S1 is:
let the relay user equipment RUE form a set R ═ { R ═ R1,R2,...,RNN is the number of candidate relay user equipment RUE; user equipment VUE of relay connection service forms set V ═ { V ═ V-1,v2,...,vMM is the number of VUE;
the indexes of the method which need to carry out decision weight generation calculation are as follows:
link capacity between VUE v and RUE r
Social similarity between VUE v and RUE r Sv,r: the social attribute is described by a Jacard coefficient and is defined as the proportion of the common social attribute owned by the VUE v and the RUE r to the total social attribute;
buffer size beta of RUE r endr；
VUE v acquires consumption C required for RUE r relay servicev,r: in IC scenarios, the cost required to implement relay services for VUE-excited RUEs; in an OOC scenario, the power consumption of the VUE itself is assumed;
the multiple targets form a target set of VUE v ends
The capacity, the social similarity and the cache size of the RUE end are gain indexes, and the higher the numerical value is, the better the numerical value is; on the other hand, the consumption is a loss index, and the lower the value is, the better the value is;
at the same time, a binary selection variable X is definedv,rTo indicate whether VUE v selects a candidate RUE r:
in addition to the above multiple targets, the indexes to be calculated for generating decision weight include:
QoS conditions required by the VUE end for each index;
acceptance of the RUE end, i.e. maximum number of accessible VUs Kr；
Each VUE can only access one unique RUE;
based on the above, the optimization model of the solution required by the method is as follows:
max{P1,P2,P3,-P4} (2)
the model is limited to:
wherein:
Further, the specific process of step S2 includes performing subjective preference decision weight generation and objective decision weight generation;
the subjective preference decision weight generation comprises the following steps:
1) constructing a visual fuzzy preference relationship;
2) constructing a perfect multiplicative consistency visual fuzzy relation matrix;
3) generating visual fuzzy numerical weight;
4) generating a definite number ranking value
5) Normalizing the ranking values to generate output subjective preference weights
Namely the subjective preference weight corresponding to the ith index of the VUE v end;
the objective decision weight generation comprises the steps of:
1) inputting all index values of all candidate RUEs;
2) data preprocessing;
3) obtaining objective decision weight by an entropy weight method;
4) processing a result obtained by integrating the subjective preference decision weight to generate a single target relative approximation degree sequence T of an output VUE v endv。
Further, the specific process of step S3 is:
through gammavConverting the original optimization problem modelFor solving the new optimization model with the minimum sum of relative approximation degrees RPD of the single target:
limited by: C1-C6 (6)
Since this optimization model containing QoS threshold conditions cannot be solved directly by existing distributed messaging algorithms, to further solve the optimization problem (5), the following indicators are introduced:
further, combining the objectives of (7) and (4), the following transformation model is obtained:
limited by:
wherein C7 is a new condition introduced to ensure that (8) and (5) are mathematically equivalent, the objective function for models (8), (5) has been combined with QoS threshold conditions such that the solution of (8) is semantically equivalent to solving for relay selection results that can simultaneously satisfy the minimum RPD and satisfy all QoS threshold conditions, since in practical communication systems condition C7 is difficult to be fully satisfied in a binary selection problem under this type of QoS constraint, C7 is omitted for model solution with distributed messaging mechanisms, to solve for (8) the desired output result, and in order to solve for (8) with distributed messaging mechanisms, the message from RUE end to e end is redefined in the tth iteration of VUE end to e end
And the message from the VUE end to the RUE end is
Where ω is a predefined damping coefficient that ensures algorithm convergence,
the constituent set V of VUs represents the Kth in the Vth remaining subset of VUs (i.e., V/{ V }) that is deletedrA minimum message size of 0 < Kr≤M,Kr∈Z+Is a predefined parameter;
because the type of the solution objective in (8) is the minimum optimization summation, correspondingly, the invention sets the solution objective of the distributed message transfer mechanism as the sum of the minimum message quantity to obtain the final iteration output:
to more fully illustrate the advantages of the present invention, the following description is provided to further illustrate the effectiveness and advancement of the invention, in connection with the following detailed description and related simulation results and analyses.
Assume that the system consists of one eNB, 10 randomly and uniformly distributed D2D RUEs and 30 randomly and uniformly distributed VUEs. Wherein, the maximum accessible VUE number K of each RUE endrHas a value of [4,6 ]]Are uniformly randomly generated. In practical application, KrThe specific value of (a) may be determined by the RUE according to its own condition (remaining power, security, sharing will of the user, etc.), and reported to the eNB. The VUE is further classified into IC VUE and OOC VUE according to whether coverage is available or not. The D2D link is described by a large scale loss plus a small scale fading channel. For convenience of description, the horizontal axis of the coordinates has a value of 0.5 representing the average of the index QoS values of all the candidate RUEs, and then 0.4 represents the average of 80%, and 0.6 represents the average of all the candidate RUEsAverage of 120%. The embodiment uses CRAWDA upb/hyccups (v.2016-10-17) real social network experimental data to simulate social similarity index values.
The present USARA scheme will be compared to the aforementioned typical D2D UE-NW relay selection scheme, namely: maximum Physical link capacity (Max Physical), maximum Physical link capacity (Max Physical Max Social, MPMS), Hybrid Selection Scheme (HRS), and complementary fuzzy hierarchy analysis-mahalanobis distance method (FAHP-M). In order to realize the contrast, in the simulation test, the minimum physical distance target in the MPMS method and the HRS method is equivalently converted into the maximum capacity target.
At the system input, a typical subjective fuzzy decision ordering (from high to low) is set as follows:
■ IC scenario: capacity → social similarity → consumption → cache size;
■ OOC scenario: capacity → consumption → cache size → social similarity.
As previously described, in IC scenarios, "consumption" is defined as the cost required by the VUE to excite the RUE to perform relay services; in an OOC scenario, "consumption" is defined as the power consumption of the VUE itself.
For the MIFAHP method and the FAHP-M method proposed by the invention, the implementation needs to input a predefined typical visual fuzzy input matrix and a complementary fuzzy input matrix. An example of these matrices is given in tables 1 and 2. In an actual system, the values of the elements in the matrix are selected according to the network condition.
TABLE 1 typical visual fuzzy input matrix required for MIFAHP method
(a) IC scene
| Index (I) | Capacity of | Social similarity | Consumption of | Cache size |
| Capacity of | (0.5,0.5) | (0.6,0.2) | (0.7,0.1) | (0.8,0.1) |
| Social similarity | (0.2,0.6) | (0.5,0.5) | (0.6,0.2) | (0.7,0.1) |
| Consumption of | (0.1,0.7) | (0.2,0.6) | (0.5,0.5) | (0.6,0.2) |
| Cache size | (0.1,0.8) | (0.1,0.7) | (0.2,0.6) | (0.5,0.5) |
(b) OOC scenario
| Index (I) | Capacity of | Social similarity | Consumption of | Cache size |
| Capacity of | (0.5,0.5) | (0.9,0.1) | (0.6,0.2) | (0.7,0.1) |
| Social similarity | (0.1,0.9) | (0.5,0.5) | (0.1,0.7) | (0.2,0.6) |
| Consumption of | (0.2,0.6) | (0.7,0.1) | (0.5,0.5) | (0.7,0.1) |
| Cache size | (0.1,0.7) | (0.6,0.2) | (0.1,0.7) | (0.5,0.5) |
TABLE 2 typical complementary fuzzy input matrix required for FAHP-M method
(a) IC scene
| Index (I) | Capacity of | Social similarity | Consumption of | Cache size |
| Capacity of | 0.5 | 0.6 | 0.7 | 0.8 |
| Social similarity | 0.4 | 0.5 | 0.6 | 0.7 |
| Consumption of | 0.3 | 0.4 | 0.5 | 0.6 |
| Cache size | 0.2 | 0.3 | 0.4 | 0.5 |
(b) OOC scenario
| Index (I) | Capacity of | Social similarity | Consumption of | Cache size |
| Capacity of | 0.5 | 0.9 | 0.6 | 0.7 |
| Social similarity | 0.1 | 0.5 | 0.1 | 0.2 |
| Consumption of | 0.4 | 0.9 | 0.5 | 0.7 |
| Cache size | 0.3 | 0.8 | 0.3 | 0.5 |
In fig. 6 to 9 and fig. 11 to 14, to examine one type of index, the QoS threshold is changed as an argument, and the values of the other indexes are fixed at 0.5; in fig. 9 and 14, the QoS thresholds (All thresholds) of All types of metrics are All varied as arguments. When the QoS threshold is lowered, the VUE access threshold is lowered and thus the access ratio of all schemes is increased. When the QoS threshold value is the lowest, the access proportion of each scheme reaches the maximum. As can be seen from fig. 5-14, the Proposed scheme (i.e. the deployed curves in each figure) can achieve a higher access ratio than all other schemes. This is because the solution simultaneously takes the minimum RPD and the QoS indicator phi, so that the system can obtain the ideal weighting values of all the indicators as much as possible and satisfy the QoS conditions of all the indicators as much as possible. In contrast, none of the other existing solutions can simultaneously satisfy both of the above objects.
The invention tests the throughput rate and the fairness of the system. As can be seen from fig. 16 to fig. 19, the USARA scheme proposed by the present invention can achieve higher system throughput and fairness than the existing schemes. Wherein, the advantage of the throughput rate directly benefits from the high access ratio performance of the scheme. In addition, the optimization process of the USARA scheme considers the QoS conditions of all the type indexes, rather than focusing on the QoS conditions of only a single or partial type index, thereby enabling the system to achieve higher fairness.
Particularly, compared with the FAHP-M, the scheme refers to the VUE subjective preference weight and also performs more comprehensive RUE relay selection judgment by means of the objective decision weight provided by the index data. In addition, in the data normalization preprocessing stage, different processing modes are adopted for the gain type indexes and the loss type indexes so as to reflect preference trends of different types of indexes.
The same or similar reference numerals correspond to the same or similar parts;
the positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (1)
1. A social D2D relay selection method is characterized by comprising the following steps:
s1: preprocessing input data to obtain data of a required type;
s2: performing index, namely target decision weight generation calculation on the data in the S1;
s3: converting and integrating the single target output quantity obtained in the S2 and the QoS condition, and further solving through a distributed message transmission mechanism to obtain a final output result;
the specific process of step S1 is:
let the relay user equipment RUE form a set R ═ { R ═ R1,R2,...,RNN is the number of candidate relay user equipment RUE; user equipment VUE of relay connection service forms set V ═ { V ═ V-1,v2,...,vMM is the number of VUE;
the indexes of the method which need to carry out decision weight generation calculation are as follows:
link capacity between VUE v and RUE r
Social similarity between VUE v and RUE r Sv,r: the social attribute is described by a Jacard coefficient and is defined as the proportion of the common social attribute owned by the VUE v and the RUE r to the total social attribute;
buffer size beta of RUE r endr；
VUE v acquires consumption C required for RUE r relay servicev,r: in IC scenarios, the cost required to implement relay services for VUE-excited RUEs; in an OOC scenario, the power consumption of the VUE itself is assumed;
the multiple targets form a target set of VUE v ends
The capacity, the social similarity and the cache size of the RUE end are gain indexes, and the higher the numerical value is, the better the numerical value is; on the other hand, the consumption is a loss index, and the lower the value is, the better the value is;
at the same time, a binary selection variable X is definedv,rTo indicate whether VUE v selects a candidate RUE r:
in addition to the above multiple targets, the indexes to be calculated for generating decision weight include:
QoS conditions required by the VUE end for each index;
acceptance of the RUE end, i.e. maximum number of accessible VUs Kr；
Each VUE can only access one unique RUE;
based on the above, the optimization model of the solution required by the method is as follows:
max{P1,P2,P3,-P4} (2)
the model is limited to:
wherein:
the specific process of step S2 includes performing subjective preference decision weight generation and objective decision weight generation;
the subjective preference decision weight generation comprises the following steps:
1) constructing a visual fuzzy preference relationship;
2) constructing a perfect multiplicative consistency visual fuzzy relation matrix;
3) generating visual fuzzy numerical weight;
4) generating a determined number sequencing numerical value;
5) normalizing the ranking values to generate an output subjective preference weight λ'i ,vI.e. the subjective preference weight corresponding to the ith index of the VUE v end;
the objective decision weight generation comprises the steps of:
1) inputting all index values of all candidate RUEs;
2) data preprocessing;
3) obtaining objective decision weight by an entropy weight method;
4) processing a result obtained by integrating the subjective preference decision weight to generate a single target relative approximation degree sequence T of an output VUE v endv；
The specific process of step S3 is:
through gammavConverting the original optimization problem model into a new optimization model with the minimum sum of solving single-target relative approximation RPD:
limited by: C1-C6 (6)
Since this optimization model containing QoS threshold conditions cannot be solved directly by existing distributed messaging algorithms, to further solve the optimization problem (5), the following indicators are introduced:
further, combining the objectives of (7) and (4), the following transformation model is obtained:
limited by:
wherein C7 is a new condition introduced to ensure that (8) and (5) are mathematically equivalent, the objective function for models (8), (5) has been combined with QoS threshold conditions such that the solution of (8) is semantically equivalent to solving for relay selection results that can simultaneously satisfy the minimum RPD and satisfy all QoS threshold conditions, since in practical communication systems condition C7 is difficult to be fully satisfied in a binary selection problem under this type of QoS constraint, C7 is omitted for model solution with distributed messaging mechanisms, to solve for (8) the desired output result, and in order to solve for (8) with distributed messaging mechanisms, the message from RUE end to e end is redefined in the tth iteration of VUE end to e end
And the message from the VUE end to the RUE end is
Where ω is a predefined damping coefficient that ensures algorithm convergence,
the constituent set V of VUs represents the Kth in the Vth remaining subset of VUs (i.e., V/{ V }) that is deletedrA minimum message size, and 0<Kr≤M,Kr∈Z+Is a predefined parameter;
because the type of the solution objective in (8) is the minimum optimization summation, correspondingly, the invention sets the solution objective of the distributed message transfer mechanism as the sum of the minimum message quantity to obtain the final iteration output:
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| CN110213775B (en) * | 2019-05-24 | 2021-01-05 | 吉林大学 | D2D relay selection method based on social attraction mobile model |
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