CN108632906B

CN108632906B - Resource allocation device and method for wireless multi-hop network

Info

Publication number: CN108632906B
Application number: CN201710164590.5A
Authority: CN
Inventors: 吴杰; 田军
Original assignee: Fujitsu Ltd
Current assignee: Fujitsu Ltd
Priority date: 2017-03-17
Filing date: 2017-03-17
Publication date: 2021-10-22
Anticipated expiration: 2037-03-17
Also published as: CN108632906A

Abstract

The embodiment of the invention provides a resource allocation device and a resource allocation method of a wireless multi-hop network, which combine a tabu search algorithm and a genetic algorithm and design an individual coding mode with a reference table, wherein the reference table determines the generation of a channel allocation table and a path allocation table in an individual, and can prevent illegal or invalid solutions from being generated in the crossing and variation processes, thereby reducing the iteration times and obtaining the optimal solution quickly.

Description

Resource allocation device and method for wireless multi-hop network

Technical Field

The present invention relates to the field of communications technologies, and in particular, to a resource allocation apparatus and method for a wireless multi-hop network.

Background

In a wireless multi-hop network, configuring multiple interfaces for each node becomes an effective way to increase the throughput of the network. A node equipped with multiple interfaces may utilize multiple orthogonal channels for simultaneous data transmission. The method has good effect on video application with large data transmission quantity. However, in a wireless multi-hop network, a plurality of nodes may transmit simultaneously, and interference may occur between the nodes, which may affect the network throughput. Therefore, how to configure channels for nodes is a considerable problem.

In addition, there is an association between channel assignment and routing and interface number assignment. Therefore, selecting paths and configuring the number of interfaces and channels for nodes in a wireless multi-hop network becomes an NP (NP-Hard) problem. In the existing method, under the condition that one of three configurations, namely routing, interface number and channel, is determined firstly, other configurations are performed.

It should be noted that the above background description is only for the sake of clarity and complete description of the technical solutions of the present invention and for the understanding of those skilled in the art. Such solutions are not considered to be known to the person skilled in the art merely because they have been set forth in the background section of the invention.

Disclosure of Invention

The above existing method can obtain the configuration scheme of the network resources, but the method is not necessarily the optimal solution, and especially for a more complex network, the method can hardly obtain the optimal solution.

According to a first aspect of the embodiments of the present invention, there is provided a resource configuration apparatus for a wireless multi-hop network, the apparatus including: a processing unit, configured to perform elite reservation, cross processing, and mutation processing on individuals in a previous generation population to obtain a current generation population, where the individuals include a reference table, and a channel allocation table and a path allocation table generated by the reference table, the reference table represents allocation channels of links between each node and all next-hop nodes of the node in a wireless multi-hop network, the channel allocation table represents allocation channels of links between each node and a selected next-hop node of the node in the wireless multi-hop network, and the path allocation table represents a path from each node to a destination node in the wireless multi-hop network; the updating unit is used for updating the optimal solution of the objective function and a tabu table for tabu search according to the objective function value of each individual in the current generation population; and the searching unit is used for carrying out tabu searching by utilizing the updated tabu table, outputting the optimal solution when a preset condition is met, and carrying out resource allocation of the wireless multi-hop network according to the optimal solution.

According to a second aspect of the embodiments of the present invention, there is provided a resource allocation method for a wireless multi-hop network, the method including: performing elite reservation, cross processing and mutation processing on individuals in a previous generation population to obtain a current generation population, wherein the individuals comprise a reference table, and a channel allocation table and a path allocation table generated by the reference table, the reference table represents allocation channels of links of each node and all next-hop nodes of the node in a wireless multi-hop network, the channel allocation table represents allocation channels of links of each node and the selected next-hop node of the node in the wireless multi-hop network, and the path allocation table represents paths from each node to a destination node in the wireless multi-hop network; updating the optimal solution of the objective function and a tabu table for tabu search according to the objective function value of each individual in the current generation population; and performing tabu search by using the updated tabu table, outputting the optimal solution when a preset condition is met, and performing resource allocation of the wireless multi-hop network according to the optimal solution.

The invention has the beneficial effects that: by combining the tabu search algorithm and the genetic algorithm and designing an individual coding mode with a reference table, the reference table determines the generation of a channel allocation table and a path allocation table in the individual, and can prevent illegal or invalid solutions from being generated in the crossing and mutation processes, thereby reducing the iteration times and obtaining the optimal solution quickly.

Specific embodiments of the present invention are disclosed in detail with reference to the following description and drawings, indicating the manner in which the principles of the invention may be employed. It should be understood that the embodiments of the invention are not so limited in scope. The embodiments of the invention include many variations, modifications and equivalents within the spirit and scope of the appended claims.

Features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments, in combination with or instead of the features of the other embodiments.

It should be emphasized that the term "comprises/comprising" when used herein, is taken to specify the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps or components.

Drawings

The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:

fig. 1 is a schematic diagram of a resource allocation method of a wireless multi-hop network according to embodiment 1 of the present invention;

fig. 2 is a schematic diagram of a wireless multihop network according to embodiment 1 of the present invention;

FIG. 3 is a schematic diagram of the method of performing elite retention, crossover and mutation processing on individuals in the previous generation population in step 101 of FIG. 1;

fig. 4 is a schematic diagram of a neighbor link of embodiment 1 of the present invention;

FIG. 5 is a schematic illustration of the method of obtaining a reference table for a second portion of individuals in step 302 of FIG. 3;

FIG. 6 is a schematic diagram of generation of individuals in a next generation population by elite retention, crossover and mutation of individuals in a current generation population according to example 1 of the present invention;

FIG. 7 is a schematic diagram of a method of generating a candidate solution according to embodiment 1 of the present invention;

fig. 8 is a schematic diagram of a resource allocation method of a wireless multi-hop network according to embodiment 2 of the present invention;

fig. 9 is a schematic diagram of a resource allocation apparatus of a wireless multi-hop network according to embodiment 3 of the present invention;

FIG. 10 is a schematic view of a processing unit 901 according to embodiment 3 of the present invention;

fig. 11 is a schematic diagram of a second processing unit 1002 according to embodiment 3 of the present invention;

fig. 12 is a schematic hardware configuration diagram of a resource allocation apparatus of a wireless multi-hop network according to embodiment 3 of the present invention.

Detailed Description

The foregoing and other features of the invention will become apparent from the following description taken in conjunction with the accompanying drawings. In the description and drawings, particular embodiments of the invention have been disclosed in detail as being indicative of some of the embodiments in which the principles of the invention may be employed, it being understood that the invention is not limited to the embodiments described, but, on the contrary, is intended to cover all modifications, variations, and equivalents falling within the scope of the appended claims.

Example 1

Fig. 1 is a schematic diagram of a resource allocation method of a wireless multi-hop network according to embodiment 1 of the present invention. As shown in fig. 1, the method includes:

step 101: performing elite reservation, cross processing and mutation processing on individuals in a previous generation population to obtain a current generation population, wherein the individuals comprise a reference table, and a channel allocation table and a path allocation table generated by the reference table, the reference table represents allocation channels of links of each node and all next-hop nodes of the node in a wireless multi-hop network, the channel allocation table represents allocation channels of links of each node and the selected next-hop node of the node in the wireless multi-hop network, and the path allocation table represents paths from each node to a destination node in the wireless multi-hop network;

step 102: updating the optimal solution of the objective function and a tabu table for tabu search according to the objective function value of each individual in the current generation population;

step 103: and performing tabu search by using the updated tabu table, outputting the optimal solution when a preset condition is met, and performing resource allocation of the wireless multi-hop network according to the optimal solution.

It can be seen from the above embodiments that, by combining the tabu search algorithm and the genetic algorithm, and designing an individual encoding scheme with a reference table that determines the generation of the channel allocation table and the path allocation table in the individual, it is possible to prevent the generation of an illegal or invalid solution during the crossing and mutation processes, thereby reducing the number of iterations and obtaining an optimal solution faster.

In this embodiment, the wireless multi-hop network has a plurality of nodes, and in this embodiment, the wireless multi-hop network has 9 nodes as an example, which is exemplarily described. Fig. 2 is a schematic diagram of a wireless multi-hop network according to embodiment 1 of the present invention. As shown in fig. 2, 9 nodes are deployed in a deployment area, where the node 9 is a destination node and the nodes 1 to 8 are source nodes.

In the present embodiment, the population is composed of a plurality of individuals, and in the genetic algorithm, the population of the current generation is obtained by performing elite retention, crossover processing, and mutation processing on the individuals in the population of the previous generation.

In this embodiment, the individual includes a reference table representing allocation channels of links between each node and all next-hop nodes of the node in the wireless multi-hop network, and a channel allocation table and a path allocation table generated from the reference table, where the channel allocation table represents allocation channels of links between each node and a selected next-hop node of the node in the wireless multi-hop network, and the path allocation table represents paths from each node to a destination node in the wireless multi-hop network.

The following exemplarily illustrates individual reference tables, channel allocation tables, and path allocation tables for the wireless multi-hop network shown in fig. 2.

Table 1, table 2, and table 3 show a reference table (Ref _ Tab), a channel allocation table (CA _ Tab), and a Path allocation table (Path _ Tab), respectively. In this embodiment, the reference table, the channel allocation table, and the path allocation table may also be expressed in other forms, and only the corresponding parameters may be embodied.

As shown in Table 1, the non-zero values in each row represent the channel number to which the node to which the row number points may be assigned. The column number pointed to by each non-zero value represents all of the parents of the node pointed to by the row number. In the second behavior example, all nonzero values are Ref _ Tab (2, 3) ═ 7 and Ref _ Tab (2, 5) ═ 5, which indicates that all parent nodes of node 2 are node 3 and node 5, link (2, 3) is assigned channel 7, and link (2, 5) is assigned channel 5.

TABLE 1 reference table (Ref _ Tab)

	1	2	3	4	5	6	7	8	9
										1	0	10	0	10	0	0	0	0	0
2	0	0	7	0	5	0	0	0	0
										3	0	0	0	0	0	1	0	0	0
4	0	0	0	0	11	0	1	0	0
										5	0	0	0	0	0	3	0	4	0
6	0	0	0	0	0	0	0	0	7
										7	0	0	0	0	0	0	0	8	0
8	0	0	0	0	0	0	0	0	7
										9	0	0	0	0	0	0	0	0	0

After the reference table is generated, each row of nodes independently determines a path to the destination node. The source node randomly selects a column number pointed by a non-zero value in a row of the source node according to the reference table, and the selected column number on the reference table is added into a path as a next hop node of the source node on the path from the source node to the destination node. Then, the selected node continues to randomly select the next-hop node in the row where the selected node is located. The process is carried out in sequence until the path contains the destination node.

The following description will be made by taking as an example a process in which the node 1 determines a path to reach the destination node:

1) firstly, node 1 randomly selects the column number pointed by the nonzero value in the first row, if the selected column number is 4, node 4 will be the next hop node of node 1, and Ref _ Tab (1, 4) ═ 10 will be the channel number of link (1, 4);

2) node 4 randomly selects a column number in the fourth row, and if the column number is selected to be 7, node 4 selects its next hop to be 7, and Ref _ Tab (4, 7) ═ 1 will be used as the channel number of link (4, 7);

3) node 7 randomly selects a column number in the seventh row, and if the column number is selected to be 8, node 7 selects its next hop to be 8, and Ref _ Tab (7, 8) ═ 8 will be used as the channel number of link (7, 8);

4) node 8 randomly selects a column number in the eighth row, which is 9, and node 8 selects its next hop of 9, Ref _ Tab (8, 9) ═ 7, which will be the channel number of link (8, 9).

Since node 9 is the destination node, the decision process for node 1 to reach the destination node ends, with the path 1 → 4 → 7 → 8 → 9 and the corresponding

channel numbers

10, 1, 8, 7.

The Path determining process from other nodes to the destination node is the same, and after all nodes determine the Path to the destination node and the channel of each link, a channel allocation table (CA _ Tab) and a Path allocation table (Path _ Tab) are obtained as shown in tables 2 and 3. Thus, an individual including the reference table (Ref _ Tab), the channel allocation table (CA _ Tab), and the Path allocation table (Path _ Tab) is obtained.

Table 2 channel allocation table (CA _ Tab)

	1	2	3	4	5	6	7	8	9
										1	0	0	0	10	0	0	0	0	0
2	0	0	0	0	5	0	0	0	0
										3	0	0	0	0	0	1	0	0	0
4	0	0	0	0	0	0	1	0	0
										5	0	0	0	0	0	3	0	4	0
6	0	0	0	0	0	0	0	0	7
										7	0	0	0	0	0	0	0	8	0
8	0	0	0	0	0	0	0	0	7
										9	0	0	0	0	0	0	0	0	0

Table 3 Path allocation table (Path _ Tab)

	1	2	3	4	5	6	7	8	9
										1	1	4	7	8	9	0	0	0	0
2	2	5	8	9	0	0	0	0	0
										3	3	6	9	0	0	0	0	0	0
4	4	7	8	9	0	0	0	0	0
										5	5	6	9	0	0	0	0	0	0
6	6	9	0	0	0	0	0	0	0
										7	7	8	9	0	0	0	0	0	0
8	8	9	0	0	0	0	0	0	0
										9	0	0	0	0	0	0	0	0	0

In step 101 of this embodiment, elite retention, crossover processing and mutation processing are performed on individuals in the previous generation population to obtain the current generation population.

In this embodiment, the number of individuals in the current generation population is less than or equal to the number of individuals in the previous generation population.

The methods of elite retention, crossover processing, and mutation processing according to the present embodiment are exemplarily described below.

Fig. 3 is a schematic diagram of a method for performing elite retention, crossover processing and mutation processing on individuals in the previous generation population in step 101 of fig. 1. As shown in fig. 3, the method includes:

step 301: reserving a first part of individuals as elite with a first probability according to the objective function values of all the individuals in the previous generation population;

step 302: for individuals except the first part of individuals in the previous generation population, carrying out cross processing with other individuals in the previous generation population according to a second probability to obtain a reference table of a second part of individuals;

step 303: performing mutation processing on the reference table of the second part of individuals with a third probability;

step 304: generating a channel allocation table and a path allocation table of the second part of individuals according to the results of the cross processing and the mutation processing;

step 305: and forming a current generation population according to the first part of individuals and the second part of individuals.

In this embodiment, the objective function may be a multi-objective function or a single objective function, which may be determined according to at least one of a path length, the number of interfaces of each node, and interference on a link.

For example, when the objective function is a single objective function, it can be expressed as:

wherein λ is₁、λ₂And λ₃Respectively representing path length, number of interfaces and weight of interference, L_iIndicates the length of the ith path, C_jNumber of interfaces, I, representing the jth node_kIndicating the interference experienced by the kth link, P, N and M indicating the number of paths, nodes and links, respectively, P, N, M, i, j are positive integers.

For example, when the objective function is a multi-objective function, it can be expressed as:

wherein L is_iIndicates the length of the ith path, C_jNumber of interfaces, I, representing the jth node_kIndicating the interference experienced by the kth link, P, N and M indicating the number of paths, nodes and links, respectively, P, N, M, i, j are positive integers.

In this embodiment, the interference on the link may be determined according to traffic on a one-hop neighbor link of the link and interference of the link and the one-hop neighbor link caused by the degree of channel separation, and traffic on a two-hop neighbor link of the link and interference of the link and the two-hop neighbor link caused by the degree of channel separation.

For example, the interference on link i may be calculated according to the following equation (5):

wherein T represents the flow size on the link; δ represents the interference caused by the degree of channel separation; n is a radical of¹(i) And N²(i) Respectively representing a one-hop neighbor set and a two-hop neighbor set of a link i, p₁And ρ₂And the weights representing interference caused by two neighbors, wherein i is a positive integer.

Fig. 4 is a schematic diagram of a neighbor link of embodiment 1 of the present invention. As shown in fig. 4, taking the link AB as an example, the dashed link represents a one-hop neighbor link of the link AB, and the dotted link represents a two-hop neighbor link of the link AB.

In this embodiment, the objective function value of an individual means a value obtained by inputting the resource allocation method represented by the individual into the objective function.

In step 301, a first portion of the individuals is retained as elite with a first probability based on the objective function values of the individuals in the previous generation population. For example, the objective function values of all the individuals in the previous generation population are sorted in the order from small to large or from large to small, and the individuals ranked in the front with the first probability are retained as elite. That is, a part of the individuals having the smallest objective function values may be retained as elite, or a part of the individuals having the largest objective function may be retained as elite. In this embodiment, whether the individual with the smallest objective function value is to be retained as elite or the individual with the largest objective function value is to be retained as elite can be determined according to the nature of the objective function and the actual needs.

In this embodiment, reserving an individual means reserving a reference table, a channel allocation table, and a path allocation table included in the individual.

In this embodiment, the first probability may be set according to actual needs. For example, if the first probability is 0.05-0.2, 5% -20% of the individuals with the largest or smallest objective function values in the previous generation population are reserved as elite.

In step 302, for individuals in the previous generation population other than the first part of individuals, cross-processing with other individuals in the previous generation population with a second probability to obtain a reference table of the second part of individuals. That is, for each individual other than the first portion, the individual is cross-processed with the other individuals other than the individual at the second probability.

In this embodiment, the second probability can be set according to actual needs, for example, the second probability is a numerical value of 0.1 to 0.5.

Fig. 5 is a schematic diagram of a method of obtaining a reference table for a second portion of individuals in step 302 of fig. 3. As shown in fig. 5, the method includes:

step 501: when the individuals except the first part of individuals in the previous generation population are crossed with the other individuals, the channel allocation tables of the individuals and the channel allocation tables of the other individuals are crossed, and the crossed result is used as a reference table of the second part of individuals;

step 502: and when the individuals except the first part of individuals in the previous generation population are not crossed with the other individuals, taking the reference table of the individual as the reference table of the second part of individuals.

In this embodiment, the intersection processing may be performed in various ways, for example, by merging the specific row of the individual with the other rows, which are not the specific row of the individual, to form a new reference table, which is used as the reference table of the second partial individual. The following examples are given.

Table 4 shows the channel allocation table 1 of the individual 1 performing interleaving, table 5 shows the channel allocation table 2 of the individual 2 performing interleaving, and table 6 shows the reference table of the new individual obtained after interleaving. As shown in tables 4 and 5, rows 1-3, 7-8 of channel allocation table 1 for individual 1 are crossed with rows 4-6 of channel allocation table 2 for individual 2 to obtain a reference table as shown in table 6.

Table 4 channel allocation for individual 1 table 1

Table 5 channel allocation for individual 2 table 2

Table 6 reference to new individuals table 3

	1	2	3	4	5	6	7	8	9
										1	0	0	0	10	0	0	0	0	0
2	0	0	0	0	5	0	0	0	0
										3	0	0	0	0	0	1	0	0	0
4	0	0	0	0	1	0	7	0	0
										5	0	0	0	0	0	0	0	9	0
6	0	0	0	0	0	0	0	0	4
										7	0	0	0	0	0	0	0	8	0
8	0	0	0	0	0	0	0	0	7
										9	0	0	0	0	0	0	0	0	0

In step 303, mutation processing is performed on the reference table of the second portion of individuals with a third probability. For example, for each reference table of each of the second portion of individuals, each non-zero value in the reference table is mutated with a third probability.

In the present embodiment, the mutation process is, for example, to change each non-zero value in the reference table to a random number, for example, to any number of 1 to 11 when the channel number is 1 to 11.

In this embodiment, the third probability may be set according to actual needs, for example, the third probability is 0.005 to 0.02, for example, 0.01. The following is an exemplary description. Table 7 shows reference table 4 obtained by subjecting the reference table shown in table 6 to mutation processing. As shown in Table 4, each non-zero value in the reference table shown in Table 3 is mutated with a third probability, wherein "4" in row 6, column 9 is changed to "7" and the other non-zero values are unchanged.

TABLE 7 reference Table 4 obtained after mutation treatment

In this embodiment, in step 304, a channel allocation table and a path allocation table for the second subset of individuals are generated according to the results of the interleaving process and the mutation process. For example, the channel allocation table and the path allocation table of each of the second part of individuals are respectively generated according to the reference table of each of the second part of individuals with or without mutation. The method of generating the channel allocation table and the path allocation table from the reference table is the same as the method described above, and a description thereof will not be repeated.

In this embodiment, the generation of the illegal solution or invalid solution can be further prevented by obtaining a reference table of a new individual by intersecting the channel allocation tables of two individuals and generating a channel allocation table and a path allocation table of the new individual from the reference table.

In step 305, a current generation population is formed based on the first portion of individuals and the second portion of individuals.

FIG. 6 is a schematic diagram of generation of individuals in the next generation population by elite retention, crossover and mutation of individuals in the current generation population according to example 1 of the present invention. As shown in fig. 6, the reference table Ref _ Tab of the individuals in the current generation population is generated in two ways: 1) elite retention (solid arrow); 2) the intersection between the channel allocation tables CA _ Tab of two individuals in the previous generation population and the variation after the intersection (dashed arrow). The channel allocation table CA _ Tab and the Path _ Tab of the individual in the current generation population are obtained by elite reservation or are generated by the method described above from a newly generated reference table Ref _ Tab.

In this embodiment, after obtaining an individual in the population of the current generation, the individual may be used as a candidate for the optimal solution, i.e., a candidate solution. The number of candidate solutions may be set according to actual needs, and may be the same as or smaller than the number of individuals in the previous generation population.

Fig. 7 is a schematic diagram of a method of generating a candidate solution according to embodiment 1 of the present invention. As shown in fig. 7, the method includes:

step 701: selecting the ith individual of the previous generation population;

step 702: judging whether i is larger than the number of the candidate solutions, if so, entering a step 714, otherwise, entering a step 703;

step 703: judging whether the ith individual is an elite solution or not according to the first probability, if so, entering a step 708, otherwise, entering a step 704;

step 704: generating a random number Rand 1 and judging whether the random number Rand 1 is smaller than a second probability, if so, entering a step 705, otherwise, entering a step 707;

step 705: randomly selecting a jth individual (j ≠ i) from the previous generation of population;

step 706: performing cross operation on the channel allocation tables of the previous generation individuals i and j to generate a new reference table;

step 707: reserving the reference table of the ith individual as a new reference table;

step 708: reserving the individual as the ith individual of the current generation population;

step 709: generating a random number Rand 2 and judging whether the random number Rand 2 is smaller than a third probability, if so, entering a step 710, otherwise, entering a step 711;

step 710: performing mutation operation on the new reference table;

step 711: taking the new reference table as a reference table of the ith individual in the current generation group, and generating a channel distribution table and a path distribution table of the ith individual in the current generation group by using the reference table;

step 712: taking the ith individual in the current generation population as the ith candidate solution;

step 713: setting i to i + 1;

step 714: and outputting all the candidate solutions.

In the present embodiment, after the current generation population is obtained through step 101, in step 102, the optimal solution of the objective function and the tabu table for tabu search are updated according to the objective function values of the individual individuals in the current generation population.

For example, the individual with the smallest objective function value in the population of the current generation is used as the optimal solution of the objective function; and if the optimal solution is already in the tabu table, adding the second-smallest suboptimal solution with the second-smallest objective function value into the tabu table, and if the optimal solution is not in the tabu table, adding the optimal solution into the tabu table.

In step 103, a tabu search is performed by using the updated tabu table, and when a preset condition is satisfied, the optimal solution is output, and the resource allocation of the wireless multi-hop network is performed according to the optimal solution.

In the present embodiment, the method of performing tabu search may use an existing tabu search algorithm.

In this embodiment, the preset condition is that the number of iterations reaches a predetermined value, for example, the predetermined value may be set according to the number of nodes in the multi-hop wireless network.

When the preset condition is not met, repeating the

steps

101 and 102 until the preset condition is met; when the preset conditions are met, the current optimal solution is used as the resource allocation of the wireless multi-hop network, that is, the channel allocation table and the path allocation table of the optimal solution (individual) are used as the optimal resource allocation mode.

Example 2

The embodiment of the invention also provides a resource allocation method of the wireless multi-hop network. Fig. 8 is a schematic diagram of a resource allocation method of a wireless multi-hop network according to embodiment 2 of the present invention. As shown in fig. 8, the method includes:

step 801: randomly initializing a population, namely, randomly generating an initial population containing n individuals, wherein each individual contains a reference table, a channel allocation table and a path allocation table, and setting a taboo table to be null;

step 802: calculating an objective function value of each individual, recording an optimal solution and the objective function value thereof, and setting the desirability level as the objective function value of the optimal solution;

step 803: judging whether a preset condition is met, if so, entering a step 813, otherwise, entering a step 804;

step 804: performing elite retention, crossing and variation on individuals of the previous generation population to generate a current generation population, wherein the number of the individuals of the current generation population is m, and m is less than or equal to n;

step 805: calculating the objective function value of each individual in the current generation population, and simultaneously recording the optimal solution of the current generation population and the objective function value thereof;

step 806: judging whether the objective function value of the optimal solution of the current generation population meets the craving level, if so, entering a step 807, otherwise, entering a step 808;

step 807: updating the objective function value of the optimal solution with the craving level as the current generation population, and updating the optimal solution and the current solution as the optimal solution of the current generation population;

step 808: judging whether the optimal solution of the current generation population is in a tabu table, if so, entering step 809, otherwise, entering step 810;

step 809: taking the second-smallest (or second-largest) suboptimal solution of the objective function value as the current solution;

step 810: taking the optimal solution of the current generation population as a current solution;

step 811: updating the tabu table, and adding the current solution into the tabu table;

step 812: carrying out tabu search by using the updated tabu table;

step 813: and outputting an optimal solution, and performing resource allocation of the wireless multi-hop network according to the optimal solution.

In this embodiment, the specific implementation method in steps 801 to 813 is the same as that described in embodiment 1, and a description thereof is not repeated here.

Example 3

Embodiment 3 further provides a resource allocation apparatus for a wireless multi-hop network, and since the principle of the apparatus for solving the problem is similar to the methods in

embodiments

1 and 2, the specific implementation thereof may refer to the implementation of the methods in

embodiments

1 and 2, and repeated details are not repeated.

Fig. 9 is a schematic diagram of a resource allocation apparatus of a wireless multi-hop network according to embodiment 3 of the present invention. As shown in fig. 9, the apparatus 900 includes:

a processing unit 901, configured to perform elite reservation, cross processing and mutation processing on individuals in a previous generation population to obtain a current generation population, where the individuals include a reference table, and a channel allocation table and a path allocation table generated by the reference table, the reference table indicates allocation channels of links between each node and all next-hop nodes of the node in a wireless multi-hop network, the channel allocation table indicates allocation channels of links between each node and a selected next-hop node of the node in the wireless multi-hop network, and the path allocation table indicates paths from each node to a destination node in the wireless multi-hop network;

an updating unit 902, configured to update an optimal solution of the objective function and a tabu table for tabu search according to the objective function value of each individual in the current generation population;

a searching unit 903, configured to perform tabu search by using the updated tabu table, output the optimal solution when a preset condition is met, and perform resource allocation of the wireless multi-hop network according to the optimal solution.

Fig. 10 is a schematic diagram of a processing unit 901 according to embodiment 3 of the present invention. As shown in fig. 10, the processing unit 901 includes:

a first processing unit 1001, configured to reserve a first portion of individuals as elites with a first probability according to the objective function values of the individual individuals in the previous generation population;

a second processing unit 1002, configured to perform cross processing on individuals in the previous generation population except for the first part of individuals with a second probability, and obtain a reference table of the second part of individuals;

a third processing unit 1003, configured to perform mutation processing on the reference table of the second part of individuals with a third probability;

a fourth processing unit 1004, configured to generate a channel allocation table and a path allocation table for the second partial individual according to the results of the crossover processing and the mutation processing;

a fifth processing unit 1005 for composing the current generation population from the first part of individuals and the second part of individuals.

Fig. 11 is a schematic diagram of the second processing unit 1002 according to embodiment 3 of the present invention. As shown in fig. 11, the second processing unit 1002 includes:

a sixth processing unit 1101, configured to, when an individual other than the first part of individuals in the previous generation population crosses another individual, cross-process the channel allocation table of the individual with the channel allocation table of the other individual, and use the result of the cross as a reference table of the second part of individuals;

a seventh processing unit 1102, configured to, when an individual other than the first part of individuals in the previous generation population does not cross other individuals, use the reference table of the individual as the reference table of the second part of individuals.

In this embodiment, the fourth processing unit 1004 may generate the channel allocation table and the path allocation table for each of the second part of individuals according to the reference table of each of the second part of individuals with or without mutation.

Fig. 12 is a schematic hardware configuration diagram of a resource allocation apparatus of a wireless multi-hop network according to embodiment 3 of the present invention. As shown in fig. 12, the apparatus 1200 may include: an interface (not shown), a Central Processing Unit (CPU)1220 and a memory 1210; the memory 1210 is coupled to the central processor 1220. Wherein the memory 1210 may store various data; further, a program of resource configuration of the wireless multi-hop network is stored, and the program is executed under the control of the central processor 1220, and various preset values and the like are stored.

In one embodiment, the functions of the resource configuration means of the wireless multi-hop network may be integrated into the central processor 1220. Wherein the central processor 1220 may be configured to: performing elite reservation, cross processing and mutation processing on individuals in a previous generation population to obtain a current generation population, wherein the individuals comprise a reference table, and a channel allocation table and a path allocation table generated by the reference table, the reference table represents allocation channels of links of each node and all next-hop nodes of the node in a wireless multi-hop network, the channel allocation table represents allocation channels of links of each node and the selected next-hop node of the node in the wireless multi-hop network, and the path allocation table represents paths from each node to a destination node in the wireless multi-hop network; updating the optimal solution of the objective function and a tabu table for tabu search according to the objective function value of each individual in the current generation population; and performing tabu search by using the updated tabu table, outputting the optimal solution when a preset condition is met, and performing resource allocation of the wireless multi-hop network according to the optimal solution.

Wherein, the performing elite retention, cross-processing and mutation processing on the individuals in the previous generation population to obtain the current generation population may include: according to the objective function values of all individuals in the previous generation population, reserving a first part of individuals as elite with a first probability; for individuals except the first part of individuals in the previous generation population, carrying out cross processing with other individuals in the previous generation population according to a second probability to obtain a reference table of a second part of individuals; performing mutation processing on the reference table of the second part of individuals with a third probability; generating a channel allocation table and a path allocation table of the second part of individuals according to the results of the cross processing and the mutation processing; and forming the current generation population according to the first part of individuals and the second part of individuals.

The cross-processing the individuals other than the first part of individuals in the previous generation population with the other individuals in the previous generation population with the second probability to obtain the reference table of the second part of individuals may include: when the individuals except the first part of individuals in the previous generation population are crossed with the other individuals, the channel allocation tables of the individuals and the channel allocation tables of the other individuals are crossed, and the crossed result is used as a reference table of the second part of individuals; and when the individuals except the first part of individuals in the previous generation population are not crossed with the other individuals, taking the reference table of the individual as the reference table of the second part of individuals.

In another embodiment, the resource allocation apparatus of the wireless multi-hop network may be configured on a chip (not shown) connected to the central processor 1220, and the function of the resource allocation apparatus of the wireless multi-hop network may be realized by the control of the central processor 1220.

In this embodiment, the apparatus 1200 may further include: sensor 1201, transceiver 1204, power module 1205, etc.; the functions of the above components are similar to those of the prior art, and are not described in detail here. It is noted that the apparatus 1200 also does not necessarily include all of the components shown in FIG. 12; furthermore, the apparatus 1200 may also comprise components not shown in fig. 12, which can be referred to in the prior art.

An embodiment of the present invention further provides a computer-readable program, where when the program is executed in a resource allocation apparatus of a wireless multi-hop network, the program causes a computer to execute the resource allocation method of the wireless multi-hop network described in

embodiment

1 or 2 in the resource allocation apparatus of the wireless multi-hop network.

An embodiment of the present invention further provides a storage medium storing a computer-readable program, where the computer-readable program enables a computer to execute the resource allocation method of the wireless multi-hop network described in embodiment 3 in a resource allocation device of the wireless multi-hop network.

The configuration method performed in the resource configuration device of the wireless multi-hop network described in connection with the embodiments of the present invention may be directly embodied as hardware, a software module executed by a processor, or a combination of the two. For example, one or more of the functional block diagrams and/or one or more combinations of the functional block diagrams illustrated in fig. 9 may correspond to individual software modules of a computer program flow or may correspond to individual hardware modules. These software modules may correspond to the various steps shown in fig. 1 or fig. 8, respectively. These hardware modules may be implemented, for example, by solidifying these software modules using a Field Programmable Gate Array (FPGA).

A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. A storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium; or the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The software module may be stored in the memory of the mobile terminal or in a memory card that is insertable into the mobile terminal. For example, if the apparatus (e.g., mobile terminal) employs a relatively large capacity MEGA-SIM card or a large capacity flash memory device, the software module may be stored in the MEGA-SIM card or the large capacity flash memory device.

One or more of the functional block diagrams and/or one or more combinations of the functional block diagrams described with respect to fig. 9 may be implemented as a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any suitable combination thereof designed to perform the functions described herein. One or more of the functional block diagrams and/or one or more combinations of the functional block diagrams described with respect to fig. 9 may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP communication, or any other such configuration.

While the invention has been described with reference to specific embodiments, it will be apparent to those skilled in the art that these descriptions are illustrative and not intended to limit the scope of the invention. Various modifications and alterations of this invention will become apparent to those skilled in the art based upon the spirit and principles of this invention, and such modifications and alterations are also within the scope of this invention.

With regard to the embodiments including the above embodiments, the following remarks are also disclosed.

Note 1, a resource configuration apparatus of a wireless multi-hop network, the apparatus comprising:

a processing unit, configured to perform elite reservation, cross processing, and mutation processing on individuals in a previous generation population to obtain a current generation population, where the individuals include a reference table, and a channel allocation table and a path allocation table generated by the reference table, the reference table represents allocation channels of links between each node and all next-hop nodes of the node in a wireless multi-hop network, the channel allocation table represents allocation channels of links between each node and a selected next-hop node of the node in the wireless multi-hop network, and the path allocation table represents a path from each node to a destination node in the wireless multi-hop network;

the updating unit is used for updating the optimal solution of the objective function and a tabu table for tabu search according to the objective function value of each individual in the current generation population;

and the searching unit is used for carrying out tabu searching by utilizing the updated tabu table, outputting the optimal solution when a preset condition is met, and carrying out resource allocation of the wireless multi-hop network according to the optimal solution.

Supplementary note 2, the apparatus according to supplementary note 1, wherein the processing unit includes:

a first processing unit, configured to reserve a first portion of individuals as elites with a first probability according to the objective function values of the individual individuals in the previous generation population;

a second processing unit, configured to perform cross processing on individuals in the previous generation population other than the first part of individuals with a second probability, and obtain a reference table of a second part of individuals;

a third processing unit, configured to perform mutation processing on the reference table of the second part of individuals with a third probability;

a fourth processing unit, configured to generate a channel allocation table and a path allocation table for the second portion of individuals according to results of the crossover processing and the mutation processing;

a fifth processing unit for composing the current generation population from the first and second portion of individuals.

Supplementary note 3, the apparatus according to supplementary note 2, wherein the second processing unit includes:

a sixth processing unit, configured to, when an individual other than the first part of individuals in the previous generation population crosses the other individual, cross-process a channel allocation table of the individual with a channel allocation table of the other individual, and use a result of the cross as a reference table of the second part of individuals;

a seventh processing unit, configured to, when an individual other than the first part of individuals in the previous generation population does not cross the other individuals, use the reference table of the individual as the reference table of the second part of individuals.

Supplementary note 4, the apparatus according to supplementary note 3, wherein the fourth processing unit is configured to generate a channel allocation table and a path allocation table for each of the second part of individuals respectively according to a reference table for each of the second part of individuals with or without mutation.

Note 5, the apparatus according to note 1, wherein the objective function is a multi-objective function or a single objective function, and the objective function is determined according to at least one of a path length, the number of interfaces of each node, and interference on a link.

Supplementary note 6, the apparatus of supplementary note 5, wherein the interference on the link is determined according to traffic on a one-hop neighbor link of the link and interference of the link with the one-hop neighbor link due to a degree of channel separation, and traffic on a two-hop neighbor link of the link and interference of the link with the two-hop neighbor link due to a degree of channel separation.

Supplementary note 7, a resource allocation method of a wireless multi-hop network, the method comprising:

performing elite reservation, cross processing and mutation processing on individuals in a previous generation population to obtain a current generation population, wherein the individuals comprise a reference table, and a channel allocation table and a path allocation table generated by the reference table, the reference table represents allocation channels of links of each node and all next-hop nodes of the node in a wireless multi-hop network, the channel allocation table represents allocation channels of links of each node and the selected next-hop node of the node in the wireless multi-hop network, and the path allocation table represents paths from each node to a destination node in the wireless multi-hop network;

updating the optimal solution of the objective function and a tabu table for tabu search according to the objective function value of each individual in the current generation population;

and performing tabu search by using the updated tabu table, outputting the optimal solution when a preset condition is met, and performing resource allocation of the wireless multi-hop network according to the optimal solution.

Supplementary notes 8, the method according to supplementary notes 7, wherein the performing elite preservation, crossover processing and mutation processing on the individuals in the previous generation population to obtain the current generation population comprises:

reserving a first part of individuals as elite with a first probability according to the objective function values of the individuals in the previous generation population;

for individuals except the first part of individuals in the previous generation population, carrying out cross processing with other individuals in the previous generation population according to a second probability to obtain a reference table of a second part of individuals;

performing mutation processing on the reference table of the second part of individuals with a third probability;

generating a channel allocation table and a path allocation table of the second part of individuals according to the results of the cross processing and the mutation processing;

and forming the current generation population according to the first part of individuals and the second part of individuals.

Supplementary note 9, the method according to supplementary note 8, wherein the obtaining a reference table of a second part of individuals by performing a cross-processing with a second probability on the individuals other than the first part of individuals in the previous generation population comprises:

when an individual except the first part of individuals in the previous generation population is crossed with other individuals, the channel allocation table of the individual is crossed with the channel allocation tables of the other individuals, and the crossed result is used as a reference table of the second part of individuals;

and when the individuals except the first part of individuals in the previous generation population are not crossed with the other individuals, taking the reference table of the individual as the reference table of the second part of individuals.

The method according to supplementary note 10 and supplementary note 9, wherein the generating of the channel allocation table and the path allocation table for the second partial individual according to the result of the interleaving process and the mutation process includes:

and respectively generating a channel allocation table and a path allocation table of each individual in the second part of individuals according to the reference table of each individual in the second part of individuals subjected to variation or not subjected to variation.

Supplementary note 11, the method according to supplementary note 7, wherein the objective function is a multi-objective function or a single objective function, and the objective function is determined according to at least one of a path length, the number of interfaces of each node, and interference on a link.

Note 12, the method according to note 11, wherein the interference on the link is determined according to traffic on a one-hop neighbor link of the link and interference of the link with the one-hop neighbor link due to a degree of channel separation, and traffic on a two-hop neighbor link of the link and interference of the link with the two-hop neighbor link due to a degree of channel separation.

Claims

1. A resource configuration apparatus of a wireless multi-hop network, the apparatus comprising:

a searching unit for performing tabu search using the updated tabu table, outputting the optimal solution when a preset condition is satisfied, and performing resource allocation of the wireless multi-hop network according to the optimal solution,

wherein the processing unit comprises:

2. The apparatus of claim 1, wherein the second processing unit comprises:

3. The apparatus according to claim 2, wherein the fourth processing unit is configured to generate a channel allocation table and a path allocation table for each of the second portion of individuals according to a reference table of each of the second portion of individuals with or without mutation.

4. The apparatus of claim 1, wherein the objective function is a multi-objective function or a single objective function, the objective function being determined according to at least one of a path length, a number of interfaces of each node, and interference on a link.

5. A method of resource allocation for a wireless multi-hop network, the method comprising:

performing tabu search by using the updated tabu table, outputting the optimal solution when a preset condition is met, performing resource allocation of the wireless multi-hop network according to the optimal solution,

performing elite retention, cross treatment and mutation treatment on individuals in the previous generation population to obtain the current generation population, wherein the obtaining of the current generation population comprises the following steps:

6. The method of claim 5, wherein said cross-processing other individuals in the previous generation population with a second probability other than the first subset of individuals to obtain a reference table for a second subset of individuals comprises:

7. The method of claim 6, wherein generating the channel allocation table and the path allocation table for the second portion of individuals according to the results of the interleaving and mutation processes comprises:

8. The method of claim 5, wherein the objective function is a multi-objective function or a single objective function, the objective function being determined according to at least one of path length, number of interfaces of each node, and interference on a link.