TWI732350B - Resource allocation method and data control center based on genetic algorithm - Google Patents

Abstract

The disclosure provides a resource allocation method and a data control center based on a genetic algorithm. The method includes: determining, at a t-th time point, whether a communication environment in which the plurality of secondary user devices are located is changed, wherein a plurality of transmission links exist between the secondary user devices; in response to determining that the communication environment at the t-th time point has changed, generating a candidate chromosome set corresponding to the t-th time point according to the historical candidate chromosome set; performing a multi-objective optimization algorithm on the candidate chromosome set to allocate an optimal power and an optimal band for each transmission link; controlling the secondary user device to communicate according to the optimal power and the optimal band corresponding to each transmission link.

Description

Resource allocation method and data control center based on genetic algorithm

The present invention relates to a communication resource allocation mechanism, and in particular to a resource allocation method and data control center based on genetic algorithm.

Please refer to FIG. 1, which is a schematic diagram of a conventional communication system architecture. As shown in FIG. 1, the communication system 100 includes a data control center 110, a primary user device 120, a primary base station 125, a secondary user device 130, and a secondary base station 135. In FIG. 1, the primary base station 125 (e.g., eNB) can allow the primary user device 120 (e.g., a general mobile phone) to implement communication functions based on a licensed spectrum, and the secondary base station 125 can be used for secondary users The device 130 implements a communication function based on an unlicensed spectrum. Alternatively, the secondary user device 130 may also perform, for example, Bluetooth or other similar communication behaviors based on the unauthorized spectrum.

With the rise of Internet of Things (IoT)-related industries in recent years, the demand for wireless transmission of IoT electronic devices has increased, which has also led to the power of IoT. Sources and spectrum resources are increasingly scarce. In order to make the use of the above resources more flexible, there is an IoT technology based on cognitive radio (CR) in the prior art.

In the scenario of FIG. 1, the data control center 110 can learn from the data provided by the main user device 120 based on the concept of spectrum leasing which free sub-channels (or frequency bands) are currently available, and then can use it accordingly. These resources are allocated to CR-based IoT devices (such as the devices located in the dashed circle in FIG. 1) for communication through a certain resource allocation method.

Related existing resource allocation methods can be roughly divided into the following types of practices: (1) Find a balance point to solve through iterative calculation; (2) Convert the problem into a convex optimization problem, and use the correlation Algorithm solution. However, the above method is a single objective optimization, so it cannot solve the situation where multiple objective functions may contradict each other.

On the other hand, there are also documents discussing multi-objective optimization in the related prior art. The methods can be divided into the following categories: (1) Convert the multi-objective optimization problem into a single-objective optimization problem, such as ε Constraint (ε-constraint) method. However, the best solution performance obtained by this method is easily affected by the value of ε, so its performance is not stable; (2) Solving by multi-objective evolutionary algorithms, such as SPEA-II and NSGA-II methods. However, such an approach cannot ensure that a feasible solution is produced during operation, resulting in poor convergence performance.

In addition, the related resource allocation method in the prior art only considers the static communication environment, so it is not suitable for the actual dynamic communication environment. Specifically, the prior art assumes that relevant system parameters (such as the number of available channels, etc.) do not change over time, but These system parameters should actually change over time. In other words, as long as the system parameters change, the prior art must recalculate to find the best solution at the moment, resulting in poor convergence efficiency.

In view of this, the present invention provides a resource allocation method and a data control center based on genetic algorithm, which can be used to solve the above technical problems.

The present invention provides a resource allocation method based on genetic algorithm, which is suitable for managing a data control center of multiple secondary user devices. The method includes: judging at the t-th time point whether a communication environment in which the multiple secondary user devices are located has changed, wherein there are multiple transmission links between the multiple secondary user devices, and t is A positive integer greater than 1; in response to determining that the communication environment at the t-th time point has changed, a candidate chromosome set corresponding to the t-th time point is generated based on at least one historical candidate chromosome set, wherein the candidate chromosome set includes a plurality of Candidate chromosomes, where each candidate chromosome includes a candidate energy and a candidate frequency band corresponding to each transmission link; a multi-objective optimization algorithm is performed on the set of candidate chromosomes to allocate an optimal energy and a maximum to each transmission link Optimal frequency band; controlling the multiple secondary user devices to communicate according to the optimal energy and optimal frequency band corresponding to each transmission link.

The invention provides a data control center that allocates resources based on genetic algorithm, which is used to manage multiple secondary user devices. The data control center includes a transceiver and a processor. The transceiver receives a plurality of data indicating a plurality of idle frequency bands from a plurality of main user devices. The processor is coupled to the receiver and is configured to: based on the plurality of data Estimate a communication environment in which the plurality of primary user devices and the plurality of secondary user devices are located; determine at the t-th time point whether the communication environment in which the plurality of secondary user devices are located has changed, wherein There are multiple transmission links between multiple secondary user devices, and t is a positive integer greater than 1; in response to determining that the communication environment at the t-th time point has changed, a set of at least one historical candidate chromosome corresponding to A candidate chromosome set at the t-th time point, where the candidate chromosome set includes multiple candidate chromosomes, and each candidate chromosome includes a candidate energy source and a candidate frequency band corresponding to each transmission link; perform a multi-objective optimization on the candidate chromosome set The optimization algorithm is used to allocate an optimal energy source and an optimal frequency band to each transmission link; and control the multiple secondary user devices to communicate according to the optimal energy and optimal frequency band corresponding to each transmission link.

In order to make the above-mentioned features and advantages of the present invention more comprehensible, the following specific embodiments are described in detail in conjunction with the accompanying drawings.

100: Communication system

110, 210: Data Control Center

120, 220: main user device

125: main base station

130, 230: secondary user device

135: Secondary Base Station

1~10: Node

212: Transceiver

214: processor

510, 520: member chromosomes

610, 610a, 620, 620a: reference chromosome

S710~S740: steps

P _{( i,j )} : energy

c _{( i,j )} : frequency band

Γ ( t,k )| _{t =1} : chromosome set

Γ' ( t,k )| _{t =1} : the first set

Γ" ( t,k )| _{t =1} : the second set

Γ''' ( t,k )| _{t =1} , Γ''' ( t,k _max )| _{t =1} : the third set

x ₁ ( t )| _{t =1} ~ x ₅₀ ( t )| _{t =1} , x ₁ ' ( t )| _{t =1} ~ x ₅₀ ' ( t )| _{t =1} , x ₁ " ( t )| _{t =1} ~ x ₅₀ " ( t )| _{t =1} , x ₁ ''' ( t )| _{t =1} ~ x ₅₀ ''' ( t )| _{t =1} , x ^* ( t )| _{t =1} : chromosome

M( t ): Available spectrum set

L : Transmission link collection

F : Data stream collection

Figure 1 is a schematic diagram of a conventional communication system architecture.

Fig. 2 is a schematic diagram of a system according to an embodiment of the present invention.

FIG. 3 is a schematic diagram of energy and spectrum allocation among secondary user devices according to an embodiment of the present invention.

FIG. 4 is a schematic diagram of an application scenario of a multi-object optimization algorithm according to the first embodiment of the present invention.

Fig. 5 is a schematic diagram of generating a shuffled version according to the first embodiment of the present invention Figure.

Fig. 6 is a schematic diagram of adjusting the first reference chromosome according to the first embodiment of the present invention.

Fig. 7 is a flowchart of a resource allocation method based on a genetic algorithm according to a second embodiment of the present invention.

Fig. 8 is a functional block diagram of a data control center according to an embodiment of the present invention.

Please refer to FIG. 2, which is a schematic diagram of a system according to an embodiment of the present invention. Roughly speaking, in this embodiment, at the t-th time point (t is a positive integer greater than or equal to 1), the main user device 220 can provide the data control center 210 with the measured available spectrum set M(t) . In an embodiment, the available spectrum set M(t) may include, for example, one or more idle sub-channels or frequency bands, and the total number thereof may be referred to as the number of available channels, but it may not be limited thereto. The secondary user device 230 (for example, a CR device) can be used to provide the transmission link set L and the data stream set F to the data control center 210. Based on the available spectrum set M(t), the transmission link set L, and the data stream set F , the data control center 210 can execute the resource allocation method based on the genetic algorithm proposed in the present invention to allocate resources to the secondary user device 230. In the embodiment of the present invention, the data control center 210 can be used, for example, to allocate resources such as energy and frequency bands to the secondary user device 230, so that the secondary user device 230 can use the allocated energy to transmit on the allocated frequency band. .

Please refer to FIG. 3, which is a schematic diagram of energy and spectrum allocation among secondary user devices according to an embodiment of the present invention. In the scenario shown in FIG. 3, it is assumed that there are 10 secondary user devices, which respectively correspond to nodes 1-10, and can communicate with each other after energy and frequency bands are allocated by the data control center 210 of the present invention.

In this embodiment, P _{( i, j )} represents the energy used by node i to transmit signals to node j, and c _{( i , j )} represents the frequency band used by node i to transmit signals to node j. In addition, f represents a transmission data stream, and it can be seen from Figure 3 that there are transmission data streams f ₁ to f ₃ . Based on this, the nodes 1-10 can provide the above information as the transmission link set L and the data stream set F in FIG. 1 to the data control center 210, but the present invention is not limited to this. In addition, for ease of description, the transmission link between node i and node j is referred to as L _{( i, j ) below.}

Generally speaking, at each time point under consideration, the method of the present invention may first generate a chromosome set through a certain initialization operation, which may include multiple chromosomes, and each chromosome may include a link corresponding to each transmission link L _{( i, j)} of P _{(i, j)} and c _{(i, j).} After that, the method of the present invention can perform the multi-objective optimization algorithm proposed by the present invention on this chromosome set to obtain the optimal resource allocation solution corresponding to each transmission link L _{( i, j )} , that is, the most The best energy source and the best frequency band, so that the corresponding node (ie, the secondary user device) can transmit accordingly.

In the embodiment of the present invention, since the amount of historical data and communication environment corresponding to each time point may be different, the initialization operations related to each time point are also different, so that the generated chromosome set also has different patterns. The difference. Nevertheless, the same multi-objective optimization algorithm is used at each time point, and the multi-objective optimization algorithm The details of the optimization algorithm will be explained later.

In the first embodiment, at the first time point (it can be understood as t equals 1), since there is no historical data, it is impossible to determine where the secondary user device (such as nodes 1~10 in Figure 2) is located. Whether the communication environment of the data control center 210 can randomly generate an initial chromosome set, where the initial chromosome set includes a plurality of randomly generated initial chromosomes, and each initial chromosome includes the initial energy corresponding to each transmission link L _{( i, j )} And the initial frequency band. After that, the data control center 210 can perform a multi-objective optimization algorithm on the initial chromosome set to allocate the initial optimal energy and the initial optimal frequency band to each transmission link L _{( i, j ).} Then, the data control center 210 can control the secondary user device to communicate according to the initial optimal energy and the initial optimal frequency band corresponding to each transmission link L _{( i, j ).}

In order to facilitate the description of the details of the multi-objective optimization algorithm performed on the initial chromosome set at the first time point, the following description is supplemented with FIG. 4.

Please refer to FIG. 4, which is a schematic diagram of the application scenario of the multi-objective optimization algorithm according to the first embodiment of the present invention. In the present embodiment, the multi-objective optimization algorithm may include two iterations k _max (k _max is a positive integer).

First, it can be seen from Figure 4 that the randomly generated initial chromosome set Γ ( t,k )| _{t =1} can include multiple initial chromosomes x ₁ ( t )| _{t =1} ~ x ₅₀ ( t )| _{t =1} , And each initial chromosome x ₁ ( t )| _{t =1} ~ x ₅₀ ( t )| _{t =1} may include the initial energy and initial frequency band corresponding to each transmission link L _{( i,j ).}

Taking the initial chromosome x ₁ ( t )| _{t =1} as an example, it may include the initial frequency band c _1,(1,2) ( t ) corresponding to the transmission link L _(1,2) to L _(9,10) ~ c _{1 ,( 9 , 10 )} ( t ) and the initial energy P _1,(1,2) ( t ) to P _1,(9,10) ( t ) , and the initial frequency band c _1,(1,2) ( t ) ~ c _1,(9,10) ( t ) and the initial energy P _1,(1,2) ( t ) to P _1,(9,10) ( t ) are all randomly generated. In one embodiment, each initial frequency band is, for example, a value between 0 and 1, and each initial energy is, for example, between the lower limit of energy ( represented by P ^min ) and the upper limit of energy ( represented by P ^max ). The value between. Based on this, those with ordinary knowledge in the field should be able to understand the meaning represented by the structure of other initial chromosomes, and will not be repeated here.

In addition, in the embodiment of the present invention, the designer can set the initial number of chromosomes in the initial chromosome set to the required preset number according to requirements. Taking Fig. 4 as an example, it is assumed that the initial chromosome set includes a total of 50 (ie, the preset number) initial chromosomes. In addition, for ease of understanding, the present invention assumes that the preset number of chromosomes in all mentioned sets is 50, but it is not used to limit the possible embodiments of the present invention.

k

k _max )迭代操作中，資料控制中心210可對特定染色體集合採用比較選取法(tournament selection)，以產生第一集合Γ'(t,k)| _t=1。在不同的實施例中，若k為1，則上述特定染色體集合為初始染色體集合Γ(t,k)| _t=1，而若k不為1，則上述特定染色體集合可為第k-1個迭代操作所產生的結果，之後將作進一步說明。 In this embodiment, in the kth (1

k

In the _{iterative operation of k max} ), the data control center 210 may adopt a comparative selection method (tournament selection) on a specific chromosome set to generate the first set Γ′ ( t,k )| _{t =1} . In different embodiments, if k is 1, the above-mentioned specific chromosome set is the initial chromosome set Γ ( t,k )| _{t =1} , and if k is not 1, the above-mentioned specific chromosome set may be k-1 The result of this iterative operation will be further explained later.

For ease of understanding, the details of the first (that is, k is 1) iterative operation will be described below. As shown in Figure 4, the first set Γ' ( t,k )| _{t =1} includes multiple first chromosomes x ₁ ' ( t )| _{t =1} ~ x ₅₀ ' ( t )| _{t =1} . Moreover, each first chromosome x ₁ ′ ( t )| _{t =1} ~ x ₅₀ ′ ( t )| _{t =1} may include the first energy source and the first frequency band corresponding to each transmission link L _{( i,j ).}

Taking the first chromosome x ₁ ' ( t )| _{t =1} as an example, it may include the first frequency band c _1,(1,2) corresponding to the transmission link L _(1,2) to L _(9,10) ' ( t ) ~ c _1,(9,10) ' ( t ) and the first energy P _1,(1,2) ' ( t ) to P _1,(9,10) ' ( t ) . Based on this, a person with ordinary knowledge in the field should understand the meaning represented by the structure of other first chromosomes, which will not be repeated here.

As mentioned above, each first chromosome x ₁ ' ( t )| _{t =1} ~ x ₅₀ ' ( t )| _{t =1} is generated based on the comparative selection method. The following will further explain the comparative selection method. In one embodiment, after obtaining the initial chromosome set Γ ( t, k )| _{t =1} as the above-mentioned specific chromosome set, the data control center 210 may sequentially perform the following steps: (a) The first set Γ' ( t ,k )| _{t =1 is} initialized to an empty set; (b) Take out the initial chromosome set Γ ( t,k )| _{t =1} at random, the initial chromosome x ₁ ( t )| _{t =1} ~ x ₅₀ ( t )| _{Two of the t =1} are regarded as the first comparison object and the second comparison object; (c) one of the first comparison object and the second comparison object is added to the first set Γ' ( t,k )| _{t =1} , as one of the first chromosomes; (d) Repeat steps (b) and (c) until the number of first chromosomes in the first set Γ' ( t,k )| _{t =1 reaches the preset number} (E.g. 50).

In one embodiment, in the above step (c), the data control center 210 may determine the first crowded distance of the first comparison object and the second crowded distance of the second comparison object, where the first crowded distance is related to the first crowded distance. The energy usage rate, channel usage fairness, and spectrum usage rate of the comparison object, and the second congestion distance is related to the energy usage rate, channel usage fairness, and spectrum usage rate of the second comparison object. Afterwards, in response to determining that the first crowded distance is greater than the second crowded distance, the data control center 210 may add the first comparison object to the first set Γ' ( t,k )| _{t =1} , otherwise, the second comparison object may be added The first set Γ' ( t,k )| _{t =1} .

，其中x ₁(t)為第一比較對象在初始染色體集合Γ(t,k)| _t=1中的前一個初始染色體，而x ₃(t)為第一比較對象在初始染色體集合Γ(t,k)| _t=1中的次一個初始染色體。並且，L _EE 、L _Fair 、L _Spec 個別為對應於能源使用率、通道使用公平性及頻譜使用率的多個參數，並可個別表徵如下：

其中，F _EE (．)為能源使用率運算子，F _Fair (．)為通道使用公平性運算子，F _Spec (．)為頻譜使用率運算子。 In an embodiment, assuming that the first comparison object can be broadly characterized as x ( t ), the first crowded distance of the first comparison object can be characterized as:

, Where x ₁ ( t ) is the previous initial chromosome of the first comparison object in the initial chromosome set Γ ( t,k )| _{t =1} , and x ₃ ( t ) is the first comparison object in the initial chromosome set Γ ( t,k )| The next initial chromosome in _{t =1.} In addition, L _EE , L _Fair , and L _Spec are multiple parameters corresponding to energy usage rate, channel usage fairness, and spectrum usage rate, and can be individually characterized as follows:

Among them, F _EE (.) is the energy usage rate operator, F _Fair (.) is the channel usage fairness operator, and F _Spec (.) is the spectrum usage rate operator.

其中R _(i,j)(t)為傳輸鏈結L _(i,j)對應的傳輸速率，

為各傳輸鏈結L _(i,j)的平均傳輸速率，|L|為傳輸鏈結總數，|M _u (t)|為傳輸鏈結所佔用的頻譜總數。 In one embodiment, F _EE ( x ( t )) is the energy usage rate corresponding to x ( t ), F _Fair ( x ( t )) is the fairness of channel usage corresponding to x ( t ), F _Spec ( x ( t )) is the spectrum utilization rate corresponding to x ( t ). And, F _EE ( x ( t )), F _Fair ( x ( t )) and F _Spec ( x ( t )) can be calculated as follows:

Where R _{( i, j )} ( t ) is the transmission rate corresponding to the transmission link L _{( i, j ),}

Is the average transmission rate of each transmission link L _{( i, j )} , | L | is the total number of transmission links, and |M _u ( t )| is the total number of spectrum occupied by the transmission links.

Similarly, the second congestion distance of the second comparison object can also be obtained based on the above teachings, which will not be repeated here. In addition, if the first (or second) comparison object is the initial chromosome x ₁ ( t )| _{t =1} or x ₅₀ ( t )| _{t =1} (that is, the first or last initial chromosome), then the corresponding The first (or second) congestion distance of can be defined as infinite, but the present invention may not be limited to this.

After obtaining the first chromosome x ₁ ' ( t )| _{t =1} ~ x ₅₀ ' ( t )| _{t =1} in the first set Γ' ( t,k )| _{t =1} according to the above teaching, the data control center 210 can repeat the crossover mechanism or mutation mechanism on the first set Γ' ( t,k )| _{t =1} to generate the second set Γ” ( t,k )| _{t =1} , where the first set Γ'(t,k )| t =1 The two sets Γ" ( t,k )| _{t =1} can include multiple second chromosomes x ₁ " ( t )| _{t =1} ~ x ₅₀ " ( t )| _{t =1} , and each second chromosome x ₁ " ( t )| _{t =1} ~ x ₅₀ "( t )| _{t =1} includes the second energy and the second frequency band corresponding to each transmission link L _{( i, j ).}

Taking the second chromosome x ₁ " ( t )| _{t =1} as an example, it may include the second frequency band c _1,(1,2) corresponding to the transmission link L _(1,2) to L _(9,10) " ( t )~ c _1,(9,10) " ( t ) and the second energy P _1,(1,2) " ( t ) to P _1,(9,10) " ( t ) . Based on this, Those with ordinary knowledge in the field should be able to understand the meaning represented by the structure of other second chromosomes, so I will not repeat them here.

As mentioned above, each second chromosome x ₁ " ( t )| _{t =1} ~ x ₅₀ " ( t )| _{t =1} is generated based on the crossover mechanism or mutation mechanism. The following is a further description of the crossover mechanism and mutation mechanism.

In the crossover mechanism, the data control center 210 can randomly acquire two of the first chromosome x ₁ ' ( t )| _{t =1} ~ x ₅₀ ' ( t )| _{t =1} as the first member chromosome and the second member chromosome . After that, the data control center 210 can generate a third member chromosome based on the first member chromosome and the second member chromosome, and add the third member as one of the second chromosomes to the second set Γ" ( t,k )| _{t =1} .

，其中P _1,(i,j) '(t,k)為第一成員染色體中對應於傳輸鏈結L _(i,j)的第一能源，P _2,(i,j) '(t,k)為第二成員染色體中對應於傳輸鏈結L _(i,j)的第一能源，β(t,k)為一隨機變數，P ^min 為能源下限值。 In an embodiment, the second energy source corresponding to the transmission link L _{( i, j )} in the third member chromosome can be characterized as:

, Where P _{1,( i,j )} ' ( t,k ) is the first energy source corresponding to the transmission link L _{( i,j )} in the first member chromosome, P _{2 , ( i,j )} ' ( t, k ) is the first energy corresponding to the transmission link L _{( i, j )} in the second member chromosome, β ( t, k ) is a random variable, and P ^min is the lower limit of energy.

In addition, the second frequency band corresponding to the transmission link L _{( i,j )} in the third member chromosome is characterized as c _{3,( i,j )} " ( t,k ), and it is based on a random binary value In one embodiment, if the random binary value is the first logical value (for example, 0), then c _{3,( i,j ) "} ( t,k ) is defined as c _{2 , ( i,j )} ' ( t,k ), and if the random binary value is the second logical value (for example, 1), then c _{3 , ( i,j )} " ( t,k ) is defined as c _{1 , ( i,j )} ' ( t,k ), where c _{1 , ( i,j )} ' ( t,k ) is the first frequency band in the first member chromosome corresponding to the transmission link L _{( i,j )} , c _{2,( i,j )} ' ( t,k ) is the first frequency band corresponding to the transmission link L _{( i,j )} in the second member chromosome. In short, if the random binary value is the first logical value, then c _{3,( i ,j )} " ( t,k ) is set to c _{1,( i,j )} ' ( t,k ), and if the random binary value is the second logical value, then c _{3,( i,j )} " ( t,k ) is set to c _{2, ( i,j )} ' ( t,k ).

In addition, in the mutation mechanism, the data control center 210 can randomly obtain the fourth member chromosome in the first chromosome x ₁ ' ( t )| _{t =1} ~ x ₅₀ ' ( t )| _{t =1} , and randomly generate the fifth member chromosome Member chromosomes. After that, the data control center 210 can generate a sixth member chromosome based on the fourth member chromosome and the fifth member chromosome, and add the sixth member chromosome as one of the second chromosomes to the second set Γ" ( t,k )| _{t =1} .

，其中P _4,(i,j) '(t,k)為第四成員染色體中對應於傳輸鏈結L _(i,j)的第一能源，P _5,(i,j) '(t,k)為第五成員染色體中對應於傳輸鏈結L _(i,j)的第一能源， In an embodiment, the second energy source corresponding to the transmission link L _{( i, j )} in the sixth member chromosome can be characterized as:

, Where P _{4,( i,j )} ' ( t,k ) is the first energy source corresponding to the transmission link L _{( i,j )} in the fourth member chromosome, P _{5,( i,j )} ' ( t, k ) is the first energy source corresponding to the transmission link L _{( i, j ) in the fifth member chromosome,}

In addition, the second frequency band corresponding to the multiple transmission links L _{( i, j )} in the sixth member chromosome is the mixed first frequency band corresponding to the aforementioned transmission link L _{( i, j ) in the fourth member chromosome.} The shuffle version.

個進行混洗，其中|L|為傳輸鏈結L _(i,j)的總數(即，9)，

．

為地板函數。因此，資料控制中心210可從第一頻段c _(1,2) '(t)~c _(9,10) '(t)取出4個進行混洗，藉以產生第六成員染色體520中對應於各傳輸鏈結L _(i,j)的第二頻段。 Please refer to FIG. 5, which is a schematic diagram of generating a shuffled version according to the first embodiment of the present invention. In the present embodiment, it is assumed fourth member 510 having a chromosome transmission link corresponding to 9 L _{(i, j)} of the first frequency band _{c (1,2) '(t)} ~ c (9,10)' (t ), and the corresponding channels are shown in Figure 5. In this case, the data control center 210 can randomly take out the first frequency band c _(1,2) ' ( t ) ~ c _(9,10) ' ( t)

Are shuffled, where | L | is the total number of transmission links L _{( i, j )} (ie, 9),

.

Is the floor function. Therefore, the data control center 210 can take out 4 from the first frequency band c _(1,2) ' ( t ) ~ c _(9,10) ' ( t ) for shuffling, thereby generating the sixth member chromosome 520 corresponding to each The second frequency band of the transmission link L _{( i, j ).}

In an embodiment, the data control center 210 may repeatedly perform the crossover mechanism or mutation mechanism described above on the first set Γ' ( t,k )| _{t =1} until the second set Γ" ( t,k )| _{t =1} The total number of second chromosomes in the chromosome reaches a predetermined number (for example, 50), but the present invention may not be limited to this.

After obtaining the second set Γ" ( t,k )| _{t =1} (which includes the second chromosome x ₁ " ( t )| _{t =1} ~ x ₅₀ " ( t )| _{t =1} ), the data control center 210 Some of them can be adjusted according to the following instructions.

In an embodiment, the data control center 210 may adaptively adjust each second chromosome x ₁ " ( t ) in the second set Γ" ( t, k )| _{t =1} based on the transmission quality restriction (indicated by γ) | _{t =1} ~ x ₅₀ " ( t )| _{t =1} . Specifically, the data control center 210 can obtain the second chromosome x ₁ " ( t )| _{t =1} ~ x ₅₀ " ( t )| _{t =1} One of the first reference chromosomes is used as the first reference chromosome, where the first reference chromosome includes a plurality of first reference energy sources and a plurality of first reference frequency bands corresponding to each transmission link L _{( i, j ). After that, the data control center 210 may} Calculate the transmission quality (such as signal-to-interference-plus-noise ratio, SINR) of each first reference frequency band in the first reference chromosome.

，其中l _i,j 為傳輸鏈結L _(i,j)對應的通道衰減值，

為雜訊，而

為傳輸鏈結L _(i,j)所遭遇的干擾。 In an embodiment, the transmission quality of the first reference frequency band corresponding to the transmission link L _{( i, j ) can be characterized as follows:}

, Where l _i,j is the channel attenuation value corresponding to the transmission link L _{( i,j ),}

For noise, and

It is the interference encountered by the transmission link L _{( i, j ).}

After that, the data control center 210 can determine whether each of the first reference frequency bands in the first reference chromosome meets the transmission quality restriction. That is, the data control center 210 can determine whether the SINR of each first reference frequency band in the first reference chromosome is higher than γ . If so, the data control center 210 may not adjust the first reference chromosome.

On the other hand, if any one of the first reference frequency bands in the first reference chromosome does not meet the transmission quality restriction, the data control center 210 can replace that one of the first reference frequency bands with another frequency band to adjust the first reference frequency band. The chromosome, where another frequency band has a lower load than that of the first reference frequency band.

Please refer to FIG. 6, which is a schematic diagram of adjusting the first reference chromosome according to the first embodiment of the present invention. In this embodiment, it is assumed that the first reference chromosome 610 includes the 9 frequency bands c _(1,2) ( t ) ~ c _(9,10) ( t ) shown, and there are 4 frequency bands corresponding to channel 1. In FIG. 6, assuming that the frequency band c _(1,2) ( t ) does not meet the transmission quality restriction, the data control center 210 can replace the frequency band c _(1,2) ( t ) with another frequency band accordingly. In this embodiment, since channel 3 only corresponds to two frequency bands and the load is small, the data control center 210 can change the frequency band c _(1,2) ( t ) from corresponding to channel 1 to corresponding to channel 3. In this way, the adjusted first reference chromosome 610a can be generated.

Then, the data control center 210 can determine whether each of the first reference frequency bands in the adjusted first reference chromosome 610a meets the transmission quality restriction. If so, the data control center 210 may use the adjusted first reference chromosome 610a as one of the adjusted second chromosomes and keep it in the second set Γ" ( t,k )| _{t =1} .

In another embodiment, if any one of the first reference frequency bands in the adjusted first reference chromosome 610a still does not meet the transmission quality restriction, the data control center 210 may use the second chromosome x ₁ " ( t )| _{t =1} ~ x ₅₀ " ( t )| _{t =1} and the other one replaces the first reference chromosome 610a.

In this embodiment, it is assumed that the first reference chromosome 610a is replaced with the first reference chromosome 620. After that, the data control center 210 may swap the first specific part of the first reference frequency band in the first reference chromosome 620 and the second specific part of the first reference frequency band one-to-one to adjust the first reference chromosome 620, where The first specific part corresponds to the first channel, the second specific part corresponds to the second channel, and the first specific part and the second specific part have the same number of channels.

In FIG. 6, the first specific part of the first reference chromosome 620 corresponds to channel 4 (the number of channels is 2), and the second specific part, for example, corresponds to channel 6 (the number of channels is also 2). In this case, the data control center 210 can adjust the first specific part and the second specific part one-to-one to generate the adjusted first reference chromosome 620a. After that, the data control center 210 may use the adjusted first reference chromosome 620a as one of the adjusted second chromosomes and keep it in the second set Γ" ( t,k )| _{t =1} .

In one embodiment, after obtaining the first set Γ' ( t,k )| _{t =1} (which includes the first chromosome x ₁ ' ( t )| _{t =1} ~ x ₅₀ ' ( t )| _{t =1} ) And after the adjusted second set Γ" ( t,k )| _{t =1} (which includes the second chromosome x ₁ " ( t )| _{t =1} ~ x ₅₀ " ( t )| _{t =1} ), data control The center 210 can eliminate a part of them according to the following teaching to generate the third set Γ''' ( t,k )| _{t =1} corresponding to the first (ie, k is 1) iterative operation. The third set Γ''' ( t,k )| _{t =1} includes multiple third chromosomes x ₁ ''' ( t )| _{t =1} ~ x ₅₀ ''' ( t )| _{t =1} , and each third chromosome x ₁ ''' ( t )| _{t =1} ~ x ₅₀ ''' ( t )| _{t =1} includes the third energy and the third frequency band corresponding to each transmission link L _{( i,j ), as shown in Figure 4} .

A third chromosome _{x 1 '''(t)} | t = 1 , for example, which may include a transmission link corresponding to the L _(1,2) to L _(9,10) of the third frequency band c _{1, (1, 2)} ''' ( t )~ c _1,(9,10) ''' ( t ) and the third energy P _1,(1,2) ''' ( t ) to P _1,(9,10) ' '' ( t ). Based on this, those with ordinary knowledge in the field should be able to understand the meaning represented by the structure of other third chromosomes, which will not be repeated here.

In one embodiment, the elimination mechanism implemented by the data control center 210 is as follows. First, the data control center 210 can obtain the first chromosome x ₁ ' ( t )| _{t =1} ~ x ₅₀ ' ( t )| _{t =1} and the second chromosome x ₁ " ( t )| _{t =1} ~ x ₅₀ " ( t )| _{t =1} dominated chromosomes, and weed out dominated chromosomes. In different embodiments, the data control center 210 can, for example, find the above-mentioned dominating chromosomes based on a general method of obtaining dominance solutions, and the details are not repeated here.

Then, the data control center 210 can determine whether the total number of eliminated first chromosomes and second chromosomes exceeds a preset number (for example, 50). If not, the data control center 210 can use the first chromosome and the second chromosome as the third chromosome in the third set Γ''' ( t,k )| _{t =1} .

On the other hand, if the total number of the eliminated first chromosome and second chromosome exceeds the preset number, the data control center 210 can use an archive update method to eliminate the first chromosome and the second chromosome with lower congestion. At least one of the distances, until the total number of remaining first chromosomes and second chromosomes does not exceed the preset number. In this embodiment, the manner of calculating the congestion distance can refer to the description in the previous embodiment, which will not be repeated here. Thereafter, the data center 210 can control the remaining first and second chromosomal chromosome as a third set Γ '''(t, k ) | t = third chromosome _{1 x 1''' (t} ) | t = 1 ~ x ₅₀ ''' ( t )| _{t =1} .

Based on the above teachings, those with ordinary knowledge in the field should be able to understand how the data control center 210 generates a correspondence based on the initial chromosome set Γ ( t,k )| _{t =1 at the first time point (ie, t is equal to 1)} The third set of the first iterative operation in the multi-objective optimization algorithm Γ''' ( t,k )| _{t =1} .

k _max )迭代操作時，資料控制中心210可重複執行以上教示的內容，藉以產生對應於第k個迭代操作的第三集合Γ'''(t,k)| _t=1。 Cheng earlier, the multi-objective optimization algorithm according to the present invention comprises a _max iterations k, thus the k-th (1 <k

k _max ) During an iterative operation, the data control center 210 can repeatedly execute the content taught above, thereby generating a third set Γ''' ( t,k )| _{t =1} corresponding to the k-th iterative operation.

Specifically, at the kth time point, the data control center 210 may obtain the third set Γ''' ( t,k -1)| _{t =1} corresponding to the k-1th time point as the specific consideration Chromosome collection. After that, the data control center 210 can sequentially execute comparison selection method, crossover mechanism, mutation mechanism, adjustment and elimination mechanism, etc. based on this specific chromosome set to generate a third set corresponding to the k-th iterative operation Γ''' ( t,k )| _{t =1} . Based on this, when the upper limit of the number of iterations is reached (ie, k = k _max ), the corresponding third set can be characterized as Γ'' ( t, k _max )| _{t =1} .

In one embodiment, after generating Γ''' ( t, k _max )| _{t =1} at the first time point, the data control center 210 may make the best selection based on multiple objective functions (knee selection) method from Γ '''(t, k max) | t = 1 to find the optimal initial chromosome x corresponding to the time point 1 ^{_{* (t) | t = 1}} .

受限於(subject to)：P ^min

P _(i,j)(t)

P ^max；SINR _(i,j)(t)

γ。 In one embodiment, the above-mentioned optimal selection method may include the following steps, for example. First, the data center 210 may be controlled based on the operation of the corresponding iteration k _max third set Γ '''(t, k max) | _{t = 1} chromosome third estimation function associated with energy usage objective, channel utilization fairness The objective function and the spectrum utilization rate are a maximum energy utilization rate, an optimal channel utilization fairness, and a maximum spectrum utilization rate of the objective function. The above-mentioned energy utilization rate objective function, channel utilization fairness objective function, and spectrum utilization rate objective function can be characterized as follows:

Subject to: P ^min

P _{( i,j )} ( t )

P ^max ; SINR _{( i,j )} ( t )

γ .

Thereafter, the data center 210 may be controlled based on the highest energy use, the best channel utilization fairness and maximum spectrum utilization in a third set of k _max Γ iteration corresponding to the operation _{'''(t, k max} ) | t = 1 Find the best initial chromosome x ^* ( t )| _{t =1} from the third chromosome of, where the best initial chromosome x ^* ( t )| _{t =1} has the lowest reference difference. The lowest reference difference is characterized as the sum of a first difference, a second difference, and a third difference. The above first difference is the difference between the best initial chromosome x ^* ( t )| _{t =1} and the highest energy usage rate, and the second difference is the best initial chromosome x ^* ( t ) | _{t = 1} corresponds to the difference between the fairness of one channel use and the fairness of the best channel, and the third difference is the best initial chromosome x ^* ( t ) | _{t =1} corresponds to a spectrum use rate and The difference between the highest spectrum usage rates.

，其中L _EE '、L _Fair '及L _Spec '分別表徵如下：

In an embodiment, the optimal initial chromosome x ^* ( t )| _{t =1} can be represented by the following formula:

, Where L _EE ' , L _Fair ' and L _Spec ' are characterized as follows:

After obtaining the best initial chromosome x ^* ( t )| _{t =1} , the data control center 210 can calculate the initial value of each transmission link L _{( i,j )} in the best initial chromosome x ^* ( t )| _{t =1} The energy and initial frequency band set the best energy and best frequency band for each transmission link L _{( i, j ).}

Taking Figure 4 as an example, assuming that the content of the optimal initial chromosome x ^* ( t )| _{t =1} is shown in Figure 4, for the transmission link L _(1,2) , the data control center 210 can transfer the transmission link The optimal energy and the optimal frequency band of the node L _(1,2) are respectively set as the optimal initial chromosome x ^* ( t )| _{t =1} corresponding to the initial energy c _{11 of the} transmission link L _{(1,2), ( 1,2)} ( t ) and the initial frequency band P _{11, (1,2)} ( t ). In addition, for the transmission link L ₍₉ , 10), the data control center 210 can set the optimal energy and the optimal frequency band of the transmission link L _{(9, 10)} as the optimal initial chromosome x ^* ( t ), respectively | _{t =1} corresponds to the initial energy c _11,(9,10) ( t ) of the transmission link L _(9,10 ) and the initial frequency band P _11,(9,10) ( t ).

In this way, each secondary user device (such as nodes 1-10 in FIG. 2) can communicate with the best energy usage rate, channel usage fairness, and spectrum usage rate at the first time point.

In the second embodiment, at other t-th time points (t in the second embodiment is a positive integer greater than 1), since there is historical data at the previous time point, it is also possible to determine the secondary user device (For example, nodes 1~10 in Fig. 2) Whether the communication environment is changed, so the present invention can adopt the initialization process different from the first embodiment to generate a set of chromosomes that can be used for the multi-object optimization algorithm ( Hereinafter referred to as candidate chromosome set). In addition, to differentiate from the operation at the first time point, x ^* ( t ) in the second embodiment is called the best candidate chromosome.

Please refer to FIG. 7, which is a flowchart of a resource allocation method based on a genetic algorithm according to a second embodiment of the present invention, which can be executed by the data control center 210 in FIG. 2.

First, in step S710, the data control center 210 may determine whether the communication environment of the secondary user device (such as nodes 1-10 in FIG. 2) is changed at the t-th time point. In one embodiment, the data control center 210 may obtain the first available channel number corresponding to the communication environment at the t-th time point, and obtain the second available channel corresponding to the communication environment at the t-1 time point Quantity. After that, the data control center 210 can determine whether the first available channel number is the same as the second available channel number. If so, the data control center 210 can determine that the communication environment at the t-th time point has not changed, otherwise, it can determine that the communication environment at the t-th time point has changed. In short, as long as the number of available channels at the t-th time point is different from the number of available channels at the t-1th time point, the data control center 210 determines that the communication environment where the secondary user device is located has changed.

In one embodiment, if the communication environment at the t-th time point has not changed, the data control center 210 can obtain the best historical allocation for each transmission link L _{( i,j ) at the t-1th time point.} Energy and the best frequency band in history, and set the best energy and best frequency band for each transmission link L _{( i, j ) accordingly.} In short, the data control center 210 can directly use the resource allocation result at the t-1 time point. That is, the data control center 210 may set the best candidate chromosome x ^* ( t ) corresponding to the t-th time point as the best candidate chromosome x ^* ( t -1) corresponding to the t-1 time point, And the set of candidate chromosomes Γ ( t, k ) corresponding to the t-th time point is set to Γ''' ( t -1, k _max ) (that is, the set of Γ''' ( t -1, k _max ) All the contents are copied to the candidate chromosome set Γ ( t, k )).

On the other hand, in step S720, in response to determining that the communication environment at the t-th time point has changed, the data control center 210 can generate the candidate chromosome set Γ ( t) corresponding to the t-th time point based on the historical candidate chromosome set. ,k ), where the set of candidate chromosomes Γ ( t,k ) may include multiple candidate chromosomes x ₁ ( t ) ~ x ₅₀ ( t ).

In an embodiment, the aforementioned set of historical candidate chromosomes includes the first set of historical candidate chromosomes Γ''' ( t -1 , _kmax ) corresponding to the t-1th time point. In this case, the data control center 210 may initialize the candidate chromosome set Γ ( t,k ) corresponding to the t-th time point to an empty set.

After that, the data control center 210 can determine whether t is greater than T, where T is a predetermined number of time points (for example, 10), and the concept can be understood as determining whether there is sufficient historical data at present. If not, it means that the historical data is not enough, so the candidate chromosome set Γ ( t,k ) can be constructed in a semi-random manner. Otherwise, it can be understood that the historical data is sufficient, so the candidate chromosome set Γ ( t ,k ).

In one embodiment, if t is not greater than T, the data control center 210 can construct the candidate chromosome set Γ ( t, k ) in the semi-random manner described above. Specifically, the data control center 210 can obtain the first historical candidate chromosome set Γ''' ( t -1 , k _max ) corresponding to the t-1 th time point, where the first historical candidate chromosome set Γ''' ( t -1 , _kmax ) includes multiple first historical candidate chromosomes. After that, the data control center 210 can copy a part (for example, half) of the first historical candidate chromosome to the set of candidate chromosomes Γ ( t, k ) as the first part of the candidate chromosome. In addition, the data control center 210 can also randomly generate the second part of the candidate chromosome to construct the candidate chromosome set Γ ( t,k ) corresponding to the t-th time point in cooperation with the first part of the candidate chromosome.

In another embodiment, if t is greater than T, the data control center 210 can perform the above prediction method to construct the candidate chromosome set Γ ( t, k ). In this embodiment, the aforementioned set of historical candidate chromosomes may further include a second set of historical candidate chromosomes Γ''' ( t -2 , _kmax ) corresponding to the t-2th time point.

After that, the data control center 210 can adaptively generate candidates based on the first historical candidate chromosome set Γ''' ( t -1 , k _max ) and the second historical candidate chromosome set Γ''' ( t -2 , k _max ) A third part of the chromosome. In an embodiment, the third part of the candidate chromosome may include a plurality of predicted chromosomes, and each predicted chromosome includes c _{( i, j )} ( t ) and P _{( i, j} ) corresponding to the transmission link L _{( i, j ) )} ( t ), where c _{( i,j )} ( t ) is the predicted frequency band corresponding to the transmission link L _{( i,j )} , and P _{( i,j )} ( t ) corresponds to L _{( i,j )} Forecast energy.

。舉例而言，資料控制中心210可基於前幾 個時間點的c _(i,j)(t)來建構上述第一自迴歸模型，而其相關細節可參照習知與自迴歸模型相關的文獻，於此不另贅述。 In an embodiment, the method for the data control center 210 to generate the above-mentioned third part may be as follows. First, data control center 210 may be associated with the construction of _{c (i, j) (t} ) of a first autoregressive model and associated data to estimate _{c (i, j) (t} ) a first centroid, wherein The first centroid is represented as

. For example, the data control center 210 can construct the above-mentioned first autoregressive model based on c _{( i, j )} ( t ) at the previous several time points, and the related details can refer to the literature related to the conventional autoregressive model. I will not repeat them here.

，其為介於0及1之間的一連續型變數。 Afterwards, the data control center 210 can obtain c _{( i, j )} ( t -1), and map c _{( i, j )} ( t -1) to a spectrum reference based on the communication environment at the t-1 time point A continuous variable within a range (for example, between 0 and 1). For example, suppose that the communication environment at the t-1 time point can be characterized as the corresponding number of available channels (which can be expressed as |M( t -1)|), and c _{( i,j )} ( t -1) can be Belongs to M( t -1). In this case, the mapped c _{( i, j )} ( t -1) can be characterized as

, Which is a continuous variable between 0 and 1.

)修正為一第一副本，其中第一副本表徵為

，且第一偏移值為

及

之間的第一歐氏距離。 Then, the data control center 210 may change the above-mentioned continuous variable (ie,

) Is amended as a first copy, where the first copy is represented by

, And the first offset value is

and

The first Euclidean distance between.

After that, the data control center 210 may calculate a first frequency band reference value based on the first centroid and the first copy, and determine whether the first frequency band reference value falls within the spectrum reference range (for example, between 0 and 1). In this embodiment, the first frequency band reference value can be characterized as the sum of the first centroid and the first copy.

In an embodiment, if the first frequency band reference value falls within the spectrum reference range (for example, between 0 and 1), the data control center 210 may define c _{( i, j )} ( t ) as the first frequency band Reference value, otherwise, c _{( i, j )} ( t ) can be defined as a second frequency band reference value. In an embodiment, the second frequency band reference value can be characterized as: c _{( i, j )} ( t -1) + r ₁ ( t ) (1- c _{( i, j )} ( t -1)), where r ₁ ( t ) is the first random value that falls within the reference range of the spectrum.

。舉例而言，資料控制中心210可基於前幾個時間點的P _(i,j)(t)來建構上述第二自迴歸模型，而其相關細節可參照習知與自迴歸模型相關的文獻，於此不另贅述。 Further, data control center 210 may construct a second autoregressive model associated with _{P (i, j) (t} ) , and the associated data to estimate _{P (i, j) (t} ) a second centroid, Where the second centroid is represented by

. For example, the data control center 210 can construct the above-mentioned second autoregressive model based on P _{( i,j )} ( t ) at the previous several time points, and the related details can refer to the literature related to the conventional autoregressive model. I will not repeat them here.

，且第二偏移值為

及

之間的一第二歐氏距離。 After that, the data control center 210 can obtain P _{( i, j )} ( t -1), and modify it into a second copy based on a second offset value, where the second copy is represented by

, And the second offset value is

and

A second Euclidean distance between.

Then, the data control center 210 may calculate a first energy reference value based on the second center of mass and the second copy, and determine whether the first energy reference value falls within an energy reference range (for example, between P ^min and P ^max ) Inside. In this embodiment, the first energy reference value can be characterized as the sum of the second center of mass and the second copy.

If the first energy reference value falls within the energy reference range, the data control center 210 can define P _{( i, j )} ( t ) as the first energy reference value, otherwise it can define P _{( i, j )} ( t ) It is a second energy reference value. In an embodiment, the second energy reference value can be characterized as: P _{( i,j )} ( t -1) + r ₂ ( t )(1- P _{( i,j )} ( t -1)), where r ₂ ( t ) is a second random value falling within the energy reference range.

After generating the third part of the candidate chromosome according to the above teachings, the data control center 210 can randomly generate a fourth part of the candidate chromosome to construct the candidate chromosome set Γ corresponding to the t-th time point in cooperation with the third part of the candidate chromosome. ( t,k ).

After constructing the candidate chromosome set Γ ( t, k ) corresponding to the t-th time point according to the above teachings, it can be understood that the initialization operation at the t-th time point has been completed. Therefore, in step S730, a multi-objective optimization algorithm can be performed on the set of candidate chromosomes to allocate the best energy and the best frequency band to each transmission link L _{( i, j ).}

k

k _max )；對第一集合重複執行交叉機制或變異機制，以產生第二集合；基於傳輸品質限制適應性地調整第二集合中的各第二染色體；淘汰第一集合及第二集合中的第一染色體及第二染色體的一部分，以產生對應於第k個迭代操作的第三集合，其中若k為1，則特定染色體集合為候選染色體集合，若k不為1，則特定染色體集合為第k-1個迭代操作對應的第三集合；在產生第k _max 個迭代操作對應的第三集合之後，基於多個目標函數而以最佳選取法從第k _max 個迭代操作對應的第三集合找出最佳候選染色體x ^*(t)；依據最佳候選染色體x ^*(t)中各傳輸鏈結L _(i,j)的候選能源及候選頻段設定各傳輸鏈結L _(i,j)的最佳能源及最佳頻段。以上各步驟的細節可參照先前實施例中的說明，於此不另贅述。 In the present embodiment, comprises a multi-objective optimization algorithm iterations k _max operation, k _max is a positive integer, and the set of candidate chromosomes step multiobjective optimization algorithm comprises: k-th iteration operation , The comparative selection method is adopted for the specific chromosome set to generate the first set (1

k

k _max ); repeat the crossover mechanism or mutation mechanism on the first set to generate the second set; adaptively adjust each second chromosome in the second set based on the transmission quality restriction; eliminate the first set and the second set A part of the first chromosome and the second chromosome to generate the third set corresponding to the k-th iterative operation. If k is 1, the specific chromosome set is the candidate chromosome set, and if k is not 1, the specific chromosome set is a third set of the k-1 iterations corresponding to an operation; after generating a third set of k _max iterations corresponding to a plurality of objective function based method to select the best k _max from the operation corresponding to the third iteration Collect and find the best candidate chromosome x ^* ( t ); set each transmission link L _{( i, j )} according to the candidate energy and candidate frequency band of each transmission link L _{( i, j} ) in the best candidate chromosome x ^* ( t) ₎ The best energy and best frequency band. For the details of the above steps, please refer to the description in the previous embodiment, which will not be repeated here.

)，故本發明的資料控制中心210可依據所述第t個時間點的通訊環境將各傳輸鏈結L _(i,j)的候選頻段轉 換為離散數值。 In addition, in an embodiment, since the candidate frequency band of each transmission link L _{( i,j )} in the best candidate chromosome x ^* ( t ) may be a continuous variable falling within the aforementioned spectrum reference range (for example, the previously disclosed

), therefore, the data control center 210 of the present invention can convert the candidate frequency bands of each transmission link L _{( i, j ) into discrete values according to the communication environment at the t-th time point.}

For example, assuming that the communication environment at the t-th time point (corresponding to M( t )) corresponds to the number of first available channels (which can be expressed as |M( t )|), the data control center 210 may assign each transmission chain The candidate frequency band of the junction L _{( i, j )} is multiplied by the first available channel number to generate a reference frequency band value corresponding to each transmission link L _{( i, j ).} Thereafter, the data center 210 may control each transmission link L _{(i, j)} of the reference frequency value Rounds, each transmission link to generate L _{(i, j)} of the discrete values. After that, the data control center 210 can set the optimal frequency band of each transmission link L _{( i, j )} to a corresponding discrete value. In addition, the data control center 210 may set the optimal energy of each transmission link L _{( i, j )} as the corresponding candidate energy.

In short, the content of the multi-objective optimization algorithm executed at the t-th time point is roughly the same as the multi-objective optimization algorithm executed at the first time point, except that the content of the multi-objective optimization algorithm executed at the first time point is roughly the same. The multi-objective optimization algorithm of is executed based on the initial chromosome set Γ ( t,k )| _{t =1} , but the multi-objective optimization algorithm executed at the t-th time point is based on corresponding to the t-th The candidate chromosome set Γ ( t,k ) at the time point is executed.

After that, in step S740, the data control center 210 may control the plurality of secondary user devices (for example, nodes 1 to 10 in FIG. 2) according to the best energy and the best energy corresponding to each transmission link L _{( i, j ).} Optimal frequency band for communication.

It can be seen from the above that at other time points than the first time point, the resource allocation method based on the genetic algorithm proposed by the present invention can adopt different initialization operations in response to changes in the communication environment, and then generate accordingly Can be used to perform multi-objective optimization algorithm chromosome set candidate chromosome set Γ ( t, k ). In some embodiments, if the communication environment has changed, the method of the present invention can also generate the candidate chromosome set at the current time point based on the previous historical candidate chromosome set. Compared with the conventional method that only considers the static communication environment, the present invention takes the dynamic communication environment into consideration, so that the best candidate chromosomes that are more in line with the actual situation can be found. In addition, because the present invention is based on a multi-objective optimization algorithm to find the best candidate chromosomes, each secondary user device can use the best energy usage rate at the t-th time point (t is greater than 1). Channel use fairness and spectrum utilization rate for communication.

Please refer to FIG. 8, which is a functional block diagram of a data control center according to an embodiment of the present invention. In this embodiment, the data control center 210 may include a transceiver 212 and a processor 214.

The transceiver 212 may include at least a transmitter circuit, a receiver circuit, an analog-to-digital (A/D) converter, a digital-to-analog (D/A) converter, Low noise amplifier (LNA), mixer, filter, matching circuit, transmission line, power amplifier (PA), one or more antenna units and local storage media components, but not limited to these , To provide wireless access to the data control center 210 in FIG. 8.

The above-mentioned receiver circuit may include functional units to perform operations such as low-noise amplification, impedance matching, frequency mixing, down-frequency conversion, filtering, and amplification. The above-mentioned transmitter circuit may include functional units to perform operations such as amplification, impedance matching, frequency mixing, up-frequency conversion, filtering, power amplification, and the like. The A/D converter or D/A converter is configured to convert the analog signal format to a digital signal format during the upstream signal processing, and to convert the digital signal format to the analog signal format during the downstream signal processing formula.

The processor 214 is coupled to the transceiver 212, and can be a general purpose processor, a special purpose processor, a traditional processor, a digital signal processor, multiple microprocessors, one or more combined digital signal processing Microprocessor, controller, microcontroller, Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), any other types of integrated circuits at the core of the device , State machines, processors based on Advanced RISC Machine (ARM) and similar products.

In an embodiment of the present invention, the processor 214 can implement the resource allocation method based on the genetic algorithm proposed in the present invention by accessing software modules and program codes, and the related details can refer to the description in the previous embodiment. I will not repeat them here.

In summary, the resource allocation method and data control center based on genetic algorithm proposed by the present invention can first generate a chromosome set through a certain initialization process at each time point under consideration, and then perform the calculation of the chromosome set. The multi-objective optimization algorithm proposed by the invention is to obtain the optimal resource allocation solution corresponding to each transmission link, so that the corresponding node (ie, the secondary user device) can perform transmission accordingly.

In the first embodiment, at the first time point, since there is not yet any historical data, and it is impossible to determine whether the communication environment of the secondary user device has changed, the data control center of the present invention can randomly generate an initial chromosome set , And based on the multi-objective optimization algorithm to find the best initial chromosome.

In the second embodiment, at the t-th time point (t>1), the data control center of the present invention can determine the way to generate the candidate chromosome set based on whether the communication environment has changed and whether it has sufficient historical data (for example, Full copy, semi-random method, prediction method, etc.). After that, the data control center of the present invention can perform a multi-objective optimization algorithm on the generated candidate chromosome set to find the best candidate chromosome corresponding to the t-th time point.

In this way, considering the dynamic communication environment, the present invention allows each secondary user device to communicate with the best energy usage rate, channel usage fairness, and spectrum usage rate at each point in time. Moreover, since the multi-objective optimization algorithm of the present invention includes a crossover mechanism, a mutation mechanism, and an adjustment mechanism, it can ensure that a feasible solution is found, thereby improving the convergence performance.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention. Anyone with ordinary knowledge in the relevant technical field can make some changes and modifications without departing from the spirit and scope of the present invention. The protection scope of the present invention shall be subject to those defined by the attached patent application scope.

S710~S740: Steps

Claims (23)

A resource allocation method based on genetic algorithm is suitable for managing a data control center of multiple secondary user devices. The method includes: determining at the t-th time point whether the secondary user devices are in a communication environment Change, where there are multiple transmission links between the secondary user devices, and t is a positive integer greater than 1, including: obtaining a first available channel number corresponding to the communication environment at the t-th time point ; Obtain a second available channel number corresponding to the communication environment at the t-1 time point; in response to determining that the first available channel number is different from the second available channel number, it is determined at the t-th time point The communication environment has changed, otherwise it is determined that the communication environment at the t-th time point has not changed; in response to the determination that the communication environment at the t-th time point has changed, it is generated based on at least one historical candidate chromosome set A candidate chromosome set corresponding to the t-th time point, wherein the candidate chromosome set includes a plurality of candidate chromosomes, wherein each candidate chromosome includes a candidate energy source and a candidate frequency band corresponding to each transmission link; The chromosome set executes a multi-objective optimization algorithm to allocate an optimal energy and an optimal frequency band to each transmission link; controls the secondary user devices according to the optimal energy and the corresponding transmission link The best frequency band for communication. According to the method described in item 1 of the scope of patent application, wherein the communication environment at the t-th time point is determined to have not changed, and the method further includes: obtaining a previous pair of data at the t-1 time point A historical best energy and a historical best frequency band allocated by the transmission link, and the best energy and the best frequency band of each transmission link are set accordingly. The method described in item 1 of the scope of the patent application further includes: at the first time point, randomly generating an initial chromosome set, wherein the initial chromosome set includes a plurality of randomly generated initial chromosomes, and each of the initial chromosomes includes An initial energy source and an initial frequency band corresponding to each transmission link; performing the multi-objective optimization algorithm on the initial chromosome set to allocate an initial optimal energy source and an initial optimal frequency band to each transmission link; The secondary user devices are controlled to communicate according to the initial optimal energy and the initial optimal frequency band corresponding to each transmission link. The method according to claim 1, wherein the at least one historical candidate chromosome set includes a first historical candidate chromosome set corresponding to the t-1 time point, and is generated based on the at least one historical candidate chromosome set The step of the candidate chromosome set corresponding to the t-th time point includes: initializing the candidate chromosome set corresponding to the t-th time point to an empty set; judging whether t is greater than T, where T is a preset The value of the time point; in response to the determination that t is not greater than T, the first historical candidate chromosome set corresponding to the t-1 time point is obtained, wherein the first historical candidate chromosome set includes A plurality of first historical candidate chromosomes; copy a part of the first historical candidate chromosomes to the set of candidate chromosomes as the first part of the candidate chromosomes; randomly generate a second part of the candidate chromosomes to The set of candidate chromosomes corresponding to the t-th time point is constructed in cooperation with the first part of the candidate chromosomes. The method according to item 4 of the scope of patent application, wherein the at least one historical candidate chromosome set further includes a second historical candidate chromosome set corresponding to the t-2th time point, and in response to the determination that t is greater than T, the The method further includes: adaptively generating a third part of the candidate chromosomes according to the first historical candidate chromosome set and the second historical candidate chromosome set; randomly generating a fourth part of the candidate chromosomes to match the The third part of the candidate chromosomes cooperatively constructs the set of candidate chromosomes. For the method described in item 5 of the scope of patent application, the transmission link between the i-th secondary user device and the j-th secondary user device among the secondary user devices is represented by L _{( i, j )} , the third part of the candidate chromosomes includes a plurality of predicted chromosomes, each of the predicted chromosomes includes c _{( i, j )} ( t ) and P _{( i, j )} ( t ) corresponding to L _{( i, j)} , Where c _{( i,j )} ( t ) is a predicted frequency band corresponding to L _{( i,j )} , and P _{( i,j )} ( t ) is a predicted energy corresponding to L _{( i,j ); where} The step of adaptively generating the third part of the candidate chromosomes based on the first historical candidate chromosome set and the second historical candidate chromosome set includes: constructing a first part associated with c _{( i,j )} ( t ) The autoregressive model is used to estimate a first centroid associated with c _{( i, j )} ( t ), where the first centroid is represented by

; Obtain c _{( i, j )} ( t -1), and map c _{( i, j )} ( t -1) to be within a spectrum reference range according to the communication environment at the t-1 th time point A continuous variable of; based on a first offset value, the continuous variable is corrected to a first copy, wherein the first copy is characterized as

, And the first offset value is

and

Calculate a first frequency band reference value based on the first centroid and the first copy, and determine whether the first frequency band reference value falls within the spectrum reference range; if so, add c _{( i,j )} ( t ) is defined as the first frequency band reference value, otherwise, c _{( i,j )} ( t ) is defined as a second frequency band reference value; the construction is related to P _{( i,j )} ( t ) A second autoregressive model of, and to estimate a second center of mass associated with P _{( i,j )} ( t ), where the second center of mass is represented by

; Obtain P _{( i, j )} ( t -1), and modify it to a second copy based on a second offset value, where the second copy is characterized as

, And the second offset value is

and

Calculate a first energy reference value based on the second centroid and the second copy, and determine whether the first energy reference value falls within an energy reference range; if so, set P _{( i, j )} ( t ) is defined as the first energy reference value, otherwise, P _{( i, j )} ( t ) is defined as a second energy reference value. For the method described in item 6 of the scope of patent application, the second frequency band reference value is characterized as: c _{( i,j )} ( t -1)+ r ₁ ( t )(1- c _{( i,j )} ( t -1)) where r ₁ ( t ) is a first random value falling within the spectrum reference range; where the second energy reference value is characterized by: P _{( i,j )} ( t -1) + r ₂ ( t )(1- P _{( i,j )} ( t -1)) where r ₂ ( t ) is a second random value falling within the energy reference range. The method as defined in claim 1, item range, wherein the multi-objective optimization algorithm comprises two iterations k _max, k _max is a positive integer, and the set of candidate chromosomes of the optimization algorithm performs multi-target The steps include: in the k-th iterative operation, a comparison selection method is adopted for a specific chromosome set to generate a first set, wherein the first set includes a plurality of first chromosomes, and each first chromosome includes a corresponding A first energy source and a first frequency band of the transmission link, and 1

k

k _max ; repeating a crossover mechanism or a mutation mechanism on the first set to generate a second set, wherein the second set includes a plurality of second chromosomes, and each second chromosome includes a link corresponding to each of the transmission links A second energy source and a second frequency band in the second set; adaptively adjust each of the second chromosomes in the second set based on a transmission quality restriction; eliminate the first chromosomes and the first chromosomes in the first set and the second set A part of the second chromosomes to generate a third set corresponding to the k-th iterative operation, wherein the third set includes a plurality of third chromosomes, and each third chromosome includes a link corresponding to each transmission link A third energy source and a third frequency band, where if k is 1, the specific chromosome set is the candidate chromosome set, and if k is not 1, the specific chromosome set is the one corresponding to the k-1th iterative operation third set; after generation of the third set of k _max th iteration corresponding operation, based on a plurality of objective function to find the optimum set from the third iteration of k _max corresponding to the operation to select an optimum method Candidate chromosomes; according to the candidate energy source and the candidate frequency band of each transmission link in the best candidate chromosome, the optimal energy source and the optimal frequency band of each transmission link are set. The method according to item 8 of the scope of patent application, wherein the step of using the comparison selection method for the candidate chromosome set to generate the first set includes: (a) initializing the first set to an empty set; (b) Randomly take out two of the candidate chromosomes in the set of candidate chromosomes as a first comparison object and a second comparison object; (c) one of the first comparison object and the second comparison object Add the first set as one of the first chromosomes; (d) repeat steps (b) and (c) until the number of the first chromosomes in the first set reaches a preset number. The method according to claim 9, wherein the step of adding one of the first comparison object and the second comparison object to the first set includes: determining a first congestion of the first comparison object Distance and a second crowded distance of the second comparison object; in response to determining that the first crowded distance is greater than the second crowded distance, the first crowded distance The comparison object is added to the first set; otherwise, the second comparison object is added to the first set. The method according to claim 10, wherein the first congestion distance is associated with an energy usage rate, a channel usage fairness, and a spectrum usage rate of the first comparison object. The method described in item 11 of the scope of patent application, wherein the first comparison object is represented by x ( t ), and the first crowded distance is represented by:

, Where x ₁ ( t ) is the previous candidate chromosome of the first comparison object in the set of candidate chromosomes, and x ₃ ( t ) is the next candidate chromosome of the first comparison object in the set of candidate chromosomes; wherein , L _EE , L _Fair , and L _Spec are multiple parameters corresponding to the energy usage rate, the fairness of the channel usage, and the spectrum usage rate, and are individually characterized as follows:

Among them, Γ ( t,k ) represents the set of candidate chromosomes; where F _EE (.) is the energy usage rate operator, F _Fair (.) is the channel usage fairness operator, and F _Spec (.) is the spectrum usage rate calculation child. For the method described in item 8 of the scope of patent application, the transmission link between the i-th secondary user device and the j-th secondary user device among the secondary user devices is represented by L _{( i, j )} , and the crossover mechanism includes: randomly obtaining a first member chromosome and a second member chromosome among the first chromosomes; generating a third member chromosome based on the first member chromosome and the second member chromosome, and combining The third member is added to the second set as one of the second chromosomes; wherein, the second energy source corresponding to L _{( i,j ) in the third member chromosome is represented by:}

, Where P _{1,( i,j )} ' ( t,k ₎ is the first energy source corresponding to L _{( i,j} ) in the first member chromosome, P _{2 , ( i,j )} ' ( t,k ) Is the first energy source corresponding to L _{( i,j )} in the second member chromosome , β ( t,k ) is a random variable, and P ^min is the lower limit of energy; where, the third member chromosome corresponds to The second frequency band in L _{( i,j )} is characterized as c _{3,( i,j )} " ( t,k ), and it is determined by a random binary value, where if the random binary value is the first A logical value, then c _{3,( i,j )} " ( t,k ) is defined as c _{2 , ( i,j )} ' ( t,k ), and if the random binary value is a second logical value, Then c _{3,( i,j )} " ( t,k ) is defined as c _{1,( i,j )} ' ( t,k ), where c _{1,( i,j )} ' ( t,k ) is the In the first member chromosome corresponding to the first frequency band of L _{( i,j )} , c _{2,( i,j )} ' ( t,k ) is the second member chromosome corresponding to the first frequency band of L _{( i,j )} The first frequency band. For the method described in item 8 of the scope of patent application, the transmission link between the i-th secondary user device and the j-th secondary user device among the secondary user devices is represented by L _{( i, j )} , and the mutation mechanism includes: randomly obtaining a fourth member chromosome among the first chromosomes, and randomly generating a fifth member chromosome; generating a sixth member chromosome based on the fourth member chromosome and the fifth member chromosome , And add the sixth member chromosome as one of the second chromosomes to the second set; wherein, the second energy source corresponding to L _{( i,j ) in the sixth member chromosome is characterized as:}

, Where P _{4,( i,j )} ' ( t,k ₎ is the first energy source corresponding to L _{( i,j} ) in the fourth member chromosome , P _{5 , ( i,j )} ' ( t,k ) Is the first energy source corresponding to L _{( i, j )} in the fifth member chromosome , β ( t, k ) is a random variable, and P ^min is the lower limit of energy; wherein, the sixth member chromosome corresponds to The second frequency bands in a plurality of L _{( i,j )} are a shuffled version of the first frequency bands in the fourth member chromosome corresponding to the respective L _{( i,j ).} The method according to item 8 of the scope of patent application, wherein the step of adaptively adjusting each of the second chromosomes in the second set based on the transmission quality restriction The steps include: obtaining one of the second chromosomes as a first reference chromosome, wherein the first reference chromosome includes a plurality of first reference energy sources and a plurality of first reference frequency bands corresponding to the transmission links; Whether each of the first reference frequency bands in the first reference chromosome all meets the transmission quality restriction; in response to determining that each of the first reference frequency bands in the first reference chromosome meets the transmission quality restriction, the first reference is not adjusted chromosome. For example, the method according to item 15 of the scope of patent application, wherein in response to determining that one of the first reference frequency bands does not meet the transmission quality restriction, the method further includes: replacing the one of the first reference frequency bands with Another frequency band to adjust the first reference chromosome, wherein the other frequency band has a lower load than the one of the first reference frequency bands; determine each of the first reference chromosomes after adjustment Whether a reference frequency band satisfies the transmission quality restriction; in response to determining that each of the first reference frequency bands in the updated first reference chromosome satisfies the transmission quality restriction, the adjusted first reference chromosome is used as the adjusted first reference chromosome One of the second chromosomes remains in the second set. According to the method described in item 16 of the scope of the patent application, in response to determining that each of the first reference frequency bands in the updated first reference chromosome does not meet the transmission quality restriction, the method further includes: Replace the first reference chromosome with another one of the second chromosomes; a first specific part of the first reference frequency bands in the first reference chromosome and a second reference frequency band in the first reference frequency bands The specific parts are swapped one-to-one to adjust the first reference chromosome, wherein the first specific part corresponds to a first channel, the second specific part corresponds to a second channel, and the first specific part and The second specific part has the same number of channels; the adjusted first reference chromosome is retained in the second set as one of the adjusted second chromosomes. For example, the method according to claim 8, wherein the step of eliminating the first chromosomes and the part of the second chromosomes in the first set and the second set includes: obtaining the first chromosomes and At least one matching chromosome among the second chromosomes is eliminated, and the at least one matching chromosome is eliminated; determining whether the total number of the first chromosomes and the second chromosomes exceeds a predetermined number; in response to determining the first chromosomes And the total number of the second chromosomes does not exceed the preset number, and the first chromosomes and the second chromosomes are used as the third chromosomes in the third set. For example, the method described in item 18 of the scope of patent application, wherein in response to determining that the total number of the first chromosomes and the second chromosomes exceeds the preset number, the method further includes: using a file library update method to eliminate the The first chromosomes and the second chromosomes At least one of the remaining first chromosomes and the second chromosomes does not exceed the preset number; take the remaining first chromosomes and the second chromosomes as The third chromosomes in the third set. For the method described in item 8 of the scope of patent application, the objective functions include an energy usage objective function, a channel usage fairness objective function, and a spectrum usage objective function, and the objective function is based on the objective functions. step good method to select the best candidate to find the set of chromosomes from the third iteration of k _max operation corresponds comprising: the plurality of third chromosome based on the third iteration k _max is set corresponding to an operation associated energy estimates Utilization rate objective function, channel use fairness objective function and spectrum utilization rate objective function of a highest energy usage rate, an optimal channel usage fairness and a highest spectrum usage rate; based on the highest energy usage rate, the best the plurality of channels using the fairness and the third chromosome maximum spectrum utilization in the third iteration of k _max corresponding set of operations to find the best candidate chromosome, wherein the chromosome best candidate having a lowest reference difference , The lowest reference difference is characterized as the sum of a first difference, a second difference, and a third difference; wherein the first difference is an energy usage rate corresponding to the best candidate chromosome and the highest energy The difference between the usage rates, the second difference is the difference between the fairness of a channel corresponding to the best candidate chromosome and the fairness of the best channel, and the third difference is the best The difference between a spectrum usage rate corresponding to the candidate chromosome and the highest spectrum usage rate. For the method described in item 8 of the scope of patent application, the candidate frequency band of each transmission link is a continuous variable falling within a spectrum reference range, and is based on the transmission link of each transmission link in the best candidate chromosome The step of setting the best energy and the best frequency band of each transmission link for the candidate energy and the candidate frequency band includes: converting the candidate frequency band of each transmission link according to the communication environment at the t-th time point Is a discrete value; the best frequency band of each transmission link is set as the corresponding discrete value; the best energy of each transmission link is set as the corresponding candidate energy. The method according to item 21 of the scope of patent application, wherein the communication environment at the t-th time point corresponds to a first number of available channels, and each transmission is performed according to the communication environment at the t-th time point. The step of converting the candidate frequency band of the link into the discrete value includes: multiplying the candidate frequency band of each transmission link by the first available channel number to generate a reference frequency band value corresponding to each transmission link; The reference frequency band value of each transmission link is unconditionally rounded to generate the discrete value of each transmission link. A data control center that allocates resources based on a genetic algorithm is used to manage multiple secondary user devices, including: a transceiver that receives multiple data indicating multiple idle frequency bands from multiple primary user devices; and a processor , Coupled to the transceiver and configured to: Based on the data, estimate a communication environment in which the primary user devices and the secondary user devices are located; determine whether the communication environment in which the secondary user devices are located has changed at the t-th time point, wherein the secondary user devices There are multiple transmission links between user devices, and t is a positive integer greater than 1, including: obtaining a first available channel number corresponding to the communication environment at the t-th time point; obtaining the t-1th A second number of available channels corresponding to the communication environment at a point in time; in response to determining that the number of first available channels is different from the number of second available channels, it is determined that the communication environment has changed at the t-th point in time , Otherwise, it is determined that the communication environment at the t-th time point has not changed; in response to the determination that the communication environment at the t-th time point has changed, a set of at least one historical candidate chromosome corresponding to the t-th A set of candidate chromosomes at a time point, wherein the set of candidate chromosomes includes a plurality of candidate chromosomes, and each of the candidate chromosomes includes a candidate energy source and a candidate frequency band corresponding to each transmission link; performing a multi-target on the candidate chromosome set Optimized algorithm to allocate an optimal energy and an optimal frequency band to each transmission link; control the secondary user devices to communicate according to the optimal energy and the optimal frequency band corresponding to each transmission link .

Images