A CR-NOMA resource allocation scheme combining Hungarian algorithm and genetic algorithm

Abstract

The present invention discloses a CR-NOMA resource allocation scheme that combines a Hungarian algorithm and a genetic algorithm, and relates to wireless communication technology. The scheme comprises decomposing a constructed optimization problem P0 into a secondary user channel matching problem P1 and a secondary user power allocation problem P2; solving the secondary user channel matching problem P1 using the Hungarian algorithm, designing a secondary user pairing clustering strategy to obtain an optimal channel matching matrix, and solving the secondary user power allocation problem P2 using an adaptive genetic algorithm to obtain an optimal power allocation scheme; iteratively executing the above steps until the adaptive genetic algorithm reaches a maximum number of iterations, and outputting the optimal channel matching scheme and the optimal power allocation scheme for the secondary user. The present invention reduces the complexity of problem solving by decomposing the optimization problem P0 into the secondary user channel matching problem P1 and the secondary user power allocation problem P2; using an adaptive genetic algorithm to solve the secondary user power allocation problem P2, and improving global search capability by dynamically adjusting crossover and mutation probabilities.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a CR-NOMA resource allocation scheme combining a Hungarian algorithm and a genetic algorithm. Background Art

[0002] With the advent of the "Internet of Everything" era, the number of wireless terminals has exploded, and mobile networks are placing increasing demands on spectrum resources. How to efficiently provide multiple access (MA) for a large number of terminals within limited spectrum resources has become a key issue facing wireless networks.

[0003] Fifth-generation mobile communications introduce a new power-domain Non-Orthogonal Multiple Access (NOMA) technology. This technology reuses power resources to support simultaneous co-frequency transmission by multiple users and employs a more complex Serial Interference Cancellation (SIC) technique to demodulate multiple accesses. Cognitive radio (CR) technology is considered an effective solution to the problem of insufficient spectrum resources. It allows secondary users (SUs) to share the licensed spectrum resources of primary users (PUs) without unduly impacting the transmission performance of primary users (PUs), supporting flexible and dynamic data transmission by terminals. It also facilitates the random transmission of short packets by large-scale IoT terminals. Given the flexible wireless spectrum resources provided by CR and the flexible and efficient multiple access capabilities of NOMA, combining the two is expected to significantly improve wireless spectrum utilization, thereby resolving the conflict between insufficient spectrum resources and the surging demand for random access by large-scale terminals.

[0004] Currently, CR-NOMA research is underway, primarily focusing on designing more efficient resource allocation schemes to improve access efficiency and system throughput. This research primarily focuses on small-scale CR terminal scenarios. Limited research has been conducted on CR-NOMA for random access of large-scale IoT terminals. Furthermore, existing research often uses highly complex exhaustive methods or low-performance sequential matching methods to handle secondary user pairing. Therefore, it is necessary to research efficient NOMA transmission optimization schemes for large-scale CR terminal scenarios to improve CR-NOMA transmission performance. Summary of the Invention

[0005] In view of the problems existing in the existing CR-NOMA resource allocation scheme, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is how to design an efficient CR-NOMA resource allocation scheme that can simultaneously optimize user-channel matching and power allocation, maximize system throughput while satisfying various system constraints, and has low computational complexity and is suitable for practical systems.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, the embodiment of the present invention provides a CR-NOMA resource allocation solution combining the Hungarian algorithm and the genetic algorithm, which includes: constructing an optimization problem in the CR-NOMA downlink system , and the optimization problem Decomposed into secondary user channel matching problem and secondary user power allocation problem ; Use the Hungarian algorithm to solve the secondary user channel matching problem , design a secondary user pairing clustering strategy to obtain the optimal channel matching matrix; based on the optimal channel matching matrix, use an adaptive genetic algorithm to solve the secondary user power allocation problem , get the optimal power allocation solution; iterative execution solves the secondary user channel matching problem and secondary user power allocation problem steps until the adaptive genetic algorithm reaches the maximum number of iterations and outputs the optimal channel matching solution and optimal power allocation solution for the secondary user.

[0009] As a preferred solution of the CR-NOMA resource allocation scheme of the combined Hungarian algorithm and genetic algorithm of the present invention, wherein: the CR-NOMA downlink system includes a primary base station PBS and a cognitive base station SBS; the primary base station PBS includes several primary users PU, where the number of channels is M and the channel bandwidth is , the channel set is expressed as The cognitive base station SBS adopts the Underlay method to share the channel of the primary user PU and transmits downlink signals to N secondary users SU. The secondary user set is , the total downlink transmission power is , ,in, Indicates the Sub-users SU, is the transmit power allocated by the cognitive base station SBS to channel m; the construction process of the optimization problem includes the following steps: using NOMA technology to share the same channel m for signal transmission, and dividing N secondary users SU into m clusters of K, where the set of optional power levels of the mth cluster is ; Calculate the cognitive base station SBS through the channel m with power level To secondary users The downlink achievable rate of the sent signal is as follows:

[0010] ;

[0011] Where, is the downlink achievable rate of the nth secondary user SU in the kth group on channel m, is the received signal-to-interference-and-noise ratio of the n-th secondary user SU in the k-th group on channel m. The specific formula is as follows:

[0012] ;

[0013] Where, is the cognitive base station SBS to the mth channel The gain of each secondary user SU, is the Rayleigh fading factor of the nth secondary user SU on channel m, is the path loss, is the path loss function, is the cognitive base station SBS to the mth channel The distance between the secondary users SU, is the cognitive base station SBS to the mth channel The additive white Gaussian noise power of each secondary user SU, is the kth power level in the mth cluster, is the power level The total transmit power on channel m The proportion of , ,for and , .

[0014] As a preferred solution of the CR-NOMA resource allocation scheme combining the Hungarian algorithm and the genetic algorithm described in the present invention, it also includes calculating the total downlink throughput of the cognitive base station SBS to the secondary user based on the downlink achievable rate , the specific formula is as follows:

[0015] ;

[0016] Where, is the channel matching relationship variable of the nth secondary user SU on channel m, , , is the kth power level in the mth cluster The downlink power level selection variable of the nth secondary user SU is: , then the cognitive base station SBS does not transmit the signal of the nth secondary user SU through channel m, if , then the cognitive base station SBS sends a signal to the nth secondary user SU through channel m, if , then the cognitive base station SBS does not select the kth power level in the mth cluster The signal sent by the nth secondary user SU, if , then the cognitive base station SBS selects the kth power level in the mth cluster The transmission signal of the nth secondary user SU;

[0017] According to the total downlink throughput, taking the maximization of the secondary user throughput as the objective function, 、 、 、 、 、 as well as As the constraint condition, establish the optimization problem , and the optimization problem Decomposed into secondary user channel matching problem and secondary user power allocation problem .

[0018] in, The constraint condition is that each secondary user SU can only occupy one sub-channel. As the constraint that each secondary user SU can only select one power level on each channel, For each channel, the power level is proportional to the constraint conditions. is the constraint condition for the ordering relationship of the K power levels on each channel, is the constraint condition of the total transmit power of the cognitive base station SBS, is the interference constraint condition for the primary user, is the constraint condition of the minimum transmission rate of the secondary user SU.

[0019] As a preferred solution of the CR-NOMA resource allocation solution combining the Hungarian algorithm and the genetic algorithm described in the present invention, wherein: the optimization problem The relevant formula is as follows:

[0020] ;

[0021] Where, is the upper limit of the transmit power, is the maximum interference tolerance of the primary user PU on channel m, is the lower limit of the rate of the nth secondary user SU on channel m.

[0022] The secondary user channel matching problem The relevant formula is as follows:

[0023] ;

[0024] The secondary user power allocation problem The relevant formula is as follows:

[0025] .

[0026] As a preferred solution of the CR-NOMA resource allocation solution combining the Hungarian algorithm and the genetic algorithm described in the present invention, wherein: the secondary user channel matching problem The solution is as follows: the gain of any channel m to N secondary users SU is , sorted in descending order, and ; Using Hungarian algorithm to extract the sub-user set Select two users to pair into user cluster m and perform power domain NOMA transmission on channel m, perform secondary user pairing clustering and user power level selection strategy within the cluster; from the secondary user set of channel m The current unpaired minimum gain is selected as the weak user of channel m , and calculate the achievable rate of weak user pairing ; Traverse all channels and get the weak users matched on each channel , and will be added to the set of users in the matched channel In; from the secondary user set of channel m Remove users with matching channels and obtain the pruned strong user set ; According to the strong user set Choose a strong user , calculate the optimal strong user and weak user pairing achievable rate; traverse the strong user set , get the total rate of user clusters after pairing on the channel, and construct the initial channel benefit matrix ; For the initial channel benefit matrix Optimize and obtain the optimal channel matching matrix; according to the optimal channel matching matrix, use the adaptive genetic algorithm to solve the secondary user power allocation problem , and obtain the initial power allocation scheme.

[0027] As a preferred solution of the CR-NOMA resource allocation solution combining the Hungarian algorithm and the genetic algorithm described in the present invention, wherein: the secondary user power allocation problem The solution is as follows: the cognitive base station SBS randomly generates S groups of M-dimensional power allocation coefficient vectors As the initial population, each vector represents a power allocation scheme; based on the power allocation scheme and the optimal channel matching matrix, the total system throughput is calculated as the fitness function of the population species According to the fitness function , use the roulette wheel method to select the new generation population, retain the individuals with the highest fitness as the new generation population; perform a two-point crossover operation on the new generation population, and the crossover probability is dynamically adjusted according to the state of the current new generation population; perform a mutation operation on the new generation population after the two-point crossover operation, and the mutation probability is dynamically adjusted according to the state of the current new generation population; repeat the above steps until the number of iterations of the adaptive genetic algorithm is Reaching the maximum number of selected algebras , select the individual with the highest fitness from the final population as the optimal NOMA power allocation coefficient vector on the channel, and output the optimal power allocation solution.

[0028] As a preferred solution of the CR-NOMA resource allocation solution combining the Hungarian algorithm and the genetic algorithm described in the present invention, wherein: the fitness function The specific formula is as follows:

[0029]

[0030] ;

[0031] in, In the population individual s, when the power distribution coefficient is , the pairing of the nth secondary user SU and channel m, if , then the nth secondary user SU is multiplexed on channel m, if , then the nth secondary user SU is not multiplexed on channel m, is the total rate of the user cluster when the secondary user SU is multiplexed on channel m.

[0032] In the second aspect, the embodiment of the present invention provides a CR-NOMA resource allocation method combining the Hungarian algorithm and the genetic algorithm, which includes: an optimization problem construction and decomposition module for constructing an optimization problem in the CR-NOMA downlink system. , and the optimization problem Decomposed into secondary user channel matching problem and secondary user power allocation problem ; Secondary user channel matching module, used to solve the secondary user channel matching problem using the Hungarian algorithm , design a secondary user pairing clustering strategy to obtain the optimal channel matching matrix; the secondary user power allocation module is used to solve the secondary user power allocation problem using an adaptive genetic algorithm based on the optimal channel matching matrix , get the optimal power allocation solution; output module, used to iteratively solve the secondary user channel matching problem and secondary user power allocation problem steps until the adaptive genetic algorithm reaches the maximum number of iterations and outputs the optimal channel matching solution and optimal power allocation solution for the secondary user.

[0033] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the CR-NOMA resource allocation scheme combining the Hungarian algorithm and the genetic algorithm as described in the first aspect of the present invention are implemented.

[0034] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the CR-NOMA resource allocation scheme combining the Hungarian algorithm and the genetic algorithm as described in the first aspect of the present invention are implemented.

[0035] The beneficial effects of the present invention are: Decomposed into secondary user channel matching problem and secondary user power allocation problem , reducing the complexity of problem solving; using the Hungarian algorithm to solve the secondary user channel matching problem Compared with the exhaustive method, the computational complexity is significantly reduced while ensuring the optimality of the matching results; an adaptive genetic algorithm is used to solve the secondary user power allocation problem. By dynamically adjusting the crossover and mutation probabilities, the risk of falling into local optimal solutions can be effectively avoided, and the global search capability can be improved. By iteratively executing the Hungarian algorithm and the adaptive genetic algorithm, the joint optimization of channel matching and power allocation can be achieved, while ensuring the convergence of the algorithm and maximizing the system throughput. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0037] Figure 1 This is a CR-NOMA system diagram of the CR-NOMA resource allocation solution that combines the Hungarian algorithm and the genetic algorithm in Example 1.

[0038] Figure 2This is a downlink CR-NOMA system resource allocation flowchart of the CR-NOMA resource allocation solution that combines the Hungarian algorithm and the genetic algorithm in Example 1.

[0039] Figure 3 This is a user matching flowchart of the CR-NOMA resource allocation solution that combines the Hungarian algorithm and the genetic algorithm in Example 1.

[0040] Figure 4 This is a Hungarian-adaptive genetic algorithm flow chart of the CR-NOMA resource allocation solution combining the Hungarian algorithm and the genetic algorithm in Example 1.

[0041] Figure 5 Example 2 is a simulation result diagram of the Hungarian algorithm solving the channel-secondary user matching scheme in the CR-NOMA resource allocation scheme that combines the Hungarian algorithm and the genetic algorithm.

[0042] Figure 6 Example 2 is a simulation result diagram of the adaptive genetic algorithm solving the power allocation scheme in the CR-NOMA resource allocation scheme that combines the Hungarian algorithm and the genetic algorithm. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0044] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0046] Example 1

[0047] Reference Figures 1 to 4 , which is the first embodiment of the present invention, provides a CR-NOMA resource allocation scheme combining the Hungarian algorithm and the genetic algorithm, including:

[0048] S1: Optimization Problems Built on CR-NOMA Downlink System , and the optimization problem Decomposed into secondary user channel matching problem and secondary user power allocation problem .

[0049] Specifically, such as Figure 1 As shown, the CR-NOMA downlink system includes a primary base station PBS and a cognitive base station SBS; the primary base station PBS includes several primary users PU, where the number of channels is M and the bandwidth of each channel is , the channel set is expressed as .

[0050] Furthermore, the cognitive base station SBS adopts the Underlay method to share the primary user channel and transmit downlink signals to N secondary users SU. The secondary user set is , Indicates the users; the total downlink transmission power of the secondary base station SBS is , ,in is the transmit power allocated by the secondary base station to channel m.

[0051] Furthermore, NOMA technology is used to share the same channel m for signal transmission, and N secondary user SUs are divided into Q clusters with K SUs in each group, where the optional power level set of the mth cluster is .

[0052] Specifically, the optional power level set of the mth cluster is defined as ,in is the kth power level in the mth cluster, is the power level The total transmit power on channel m The proportion of , ,for and , .

[0053] Furthermore, the construction process of the optimization problem includes the following steps: calculating the cognitive base station SBS through the channel m with a power level To secondary users The downlink achievable rate of the sent signal is as follows:

[0054] ;

[0055] Where, is the downlink achievable rate of the nth secondary user SU in the kth group on channel m, is the received signal-to-interference-and-noise ratio of the n-th secondary user SU in the k-th group on channel m. The specific formula is as follows:

[0056] ;

[0057] Where, is the cognitive base station SBS to the mth channel The gain of each secondary user SU, is the Rayleigh fading factor of the nth secondary user SU on channel m, is the path loss, is the path loss function, is the cognitive base station SBS to the mth channel The distance between the secondary users SU, is the cognitive base station SBS to the mth channel The additive white Gaussian noise power of each secondary user SU, is the kth power level in the mth cluster.

[0058] Furthermore, based on the downlink achievable rate, the total downlink throughput from the cognitive base station SBS to the secondary user is calculated. , the specific formula is as follows:

[0059] ;

[0060] Where, is the channel matching relationship variable of the nth secondary user SU on channel m, , , is the kth power level in the mth cluster The downlink power level selection variable of the nth secondary user SU is: , then the cognitive base station SBS does not transmit the signal of the nth secondary user SU through channel m, if , then the cognitive base station SBS sends a signal to the nth secondary user SU through channel m, if , then the cognitive base station SBS does not select the kth power level in the mth cluster The signal sent by the nth secondary user SU, if , then the cognitive base station SBS selects the kth power level in the mth cluster The transmitted signal of the nth secondary user SU.

[0061] Specifically, according to the total downlink throughput, the secondary user throughput maximization is taken as the objective function. 、 、 、 、 、 as well as As the constraint condition, establish the optimization problem , and the optimization problem Decomposed into secondary user channel matching problem and secondary user power allocation problem .

[0062] It should be noted that The constraint condition is that each secondary user SU can only occupy one sub-channel. As the constraint that each secondary user SU can only select one power level on each channel, For each channel, the power level is proportional to the constraint conditions. is the constraint condition for the ordering relationship of the K power levels on each channel, is the constraint condition of the total transmit power of the cognitive base station SBS, is the interference constraint condition for the primary user, is the constraint condition of the minimum transmission rate of the secondary user SU.

[0063] Furthermore, the optimization problem The relevant formula is as follows:

[0064] ;

[0065] Where, is the upper limit of the transmit power, is the maximum interference tolerance of the primary user PU on channel m, is the lower limit of the rate of the nth secondary user SU on channel m.

[0066] Furthermore, the secondary user channel matching problem The relevant formula is as follows:

[0067] ;

[0068] Specifically, the secondary user power allocation problem The relevant formula is as follows:

[0069] .

[0070] S2: Solve the secondary user channel matching problem using the Hungarian algorithm , design the secondary user pairing clustering strategy and obtain the optimal channel matching matrix.

[0071] Specifically, it is assumed that the cognitive base station SBS knows the status information of all downlink channels. Secondary user channel matching problem The solution is as follows: the gain of any channel m to N secondary users SU is , sorted in descending order, and .

[0072] Furthermore, the Hungarian algorithm is used to extract Two users are selected to pair into user cluster m and power domain NOMA transmission is performed on channel m, and secondary user pairing clustering and intra-cluster user power level selection strategy are performed.

[0073] It should be noted that the number of secondary users and power levels in each cluster of users, i.e., on each channel, are , the total number of channels available in the system .use represents the user pair of cluster m carried on channel m, where the cognitive base station SBS to user The channel attenuation coefficient satisfy Corresponding to the user cluster, the set of optional power levels on channel m is To ensure that users can successfully detect downlink signals through SIC, weak users Allocate a larger transmit power level For strong users Assign a smaller transmit power level , represents the users in cluster m The power allocation factor.

[0074] Furthermore, from the secondary user set of channel m The current unpaired minimum gain is selected as the weak user of channel m , and calculate the achievable rate of weak user pairing ; Traverse all channels and get the weak users matched on each channel , and will be added to the set of users in the matched channel In; from the secondary user set of channel m Remove users with matching channels and obtain the pruned strong user set .

[0075] It should be noted that since the secondary user uses SIC technology to receive the downlink NOMA signal, the weak user The user's signal will be demodulated first from the received signal, and the strong user signal paired with it will be regarded as noise.

[0076] Specifically, the achievable rate The specific formula is as follows:

[0077] ;

[0078] Where, is the channel attenuation coefficient from the cognitive base station SBS to the user, For SBS to users The additive white Gaussian noise power on the downlink channel m.

[0079] Further, according to the strong user set Choose a strong user , calculate the optimal strong user and weak user pairing achievable rate; traverse the strong user set , , get the total rate of user clusters after pairing on the channel , , construct the initial channel benefit matrix .

[0080] It should be noted that ,by As a benefit matrix The element in the mth row and nth column, from which the initial benefit matrix of the Hungarian algorithm is obtained .

[0081] Furthermore, the specific formula for the achievable rate of the optimal strong user and weak user pairing is as follows:

[0082] ;

[0083] Where, is the channel attenuation coefficient from the cognitive base station SBS to the user, For SBS to users The additive white Gaussian noise power on the downlink channel m.

[0084] Specifically, computing strong users In any channel weak users on the channel Paired into user cluster m, the achievable rate of user cluster m for:

[0085] ;

[0086] In the formula, the first item in the brackets is the user In the channel The achievable rate on the channel, the second term is Weak users matched on The achievable rate; 、 For SBS Xeon users The channel attenuation coefficient and the additive white Gaussian noise power on the downlink channel m; 、 is the weak user on SBS to channel m The channel attenuation coefficient and the additive white Gaussian noise power on the downlink channel m.

[0087] Furthermore, the initial channel benefit matrix Optimize and obtain the optimal channel matching matrix; Perform row simplification and column simplification operations, that is, subtract the minimum number of each row from the row and the minimum number of each column from the column to obtain a new benefit matrix , each row and column of the new benefit matrix has at least one 0 element; use the minimum horizontal and vertical lines to cover the new benefit matrix All 0 elements in; find the new benefit matrix The minimum number not covered by horizontal and vertical lines in ; New benefit matrix Subtract all rows not covered by horizontal and vertical lines The operation is performed on the covered column. The optimal channel matching matrix is ​​obtained by ; Check the new benefit matrix Whether the total number of horizontal and vertical lines covering 0 elements in the matrix reaches half of the matrix dimension M, that is, M / 2.

[0088] Furthermore, if the new benefit matrix If the total number of horizontal and vertical lines covering 0 elements in does not reach half of the matrix dimension M, repeat the above steps until the above conditions are met; if the new benefit matrix If the total number of horizontal and vertical lines covering 0 elements in the matrix reaches half of the matrix dimension M, the Hungarian algorithm converges and outputs the optimal channel matching matrix , which is the current optimal user-channel matching matrix ,in For strong users Multiplexed Channel , For strong users Unmultiplexed channels .like ,user That is, a strong user on channel m , which is paired with the weak user .

[0089] Specifically, according to the optimal channel matching matrix, an adaptive genetic algorithm is used to solve the secondary user power allocation problem. , and obtain the initial power allocation scheme.

[0090] S3: Based on the optimal channel matching matrix, an adaptive genetic algorithm is used to solve the secondary user power allocation problem. , and obtain the optimal power allocation solution.

[0091] Specifically, the secondary user power allocation problem The solution is as follows: the cognitive base station SBS randomly generates S groups of power allocation coefficient vectors with a length of M , As the initial population, each vector represents a power allocation scheme; is the chromosome of individual s, M is the number of channels, S is the population size, is the mth gene of individual s, that is, the secondary user power allocation factor in the mth cluster occupying channel m. To meet the constraint C1 of the optimization problem P0, The initial value is a uniformly distributed random number in the interval (0.5, 1).

[0092] It should be noted that the initial value of the population matrix is , the initial value of the number of iterations is g, and the maximum number of iterations is .

[0093] Furthermore, the chromosome of individual s in the population , , as the secondary user power allocation coefficient vector of all current M channels, based on the power allocation scheme and the optimal channel matching matrix , calculate the total system throughput as the fitness function of the population , the specific formula is as follows:

[0094]

[0095] ;

[0096] in, In the population individual s, when the power distribution coefficient is , the pairing of the nth secondary user SU and channel m, if , then the nth secondary user SU is multiplexed on channel m, if , then the nth secondary user SU is not multiplexed on channel m, is the total rate of the user cluster when the secondary user SU is multiplexed on channel m.

[0097] Furthermore, according to the fitness function , use the roulette wheel method to select S new generation populations, participate in subsequent mutations, and retain the individuals with the highest fitness as the new generation population. The specific method of selecting population chromosomes is: regard the continuous [0,1] interval as a roulette wheel, and the individual The position interval on the roulette wheel is ( , ], repeat the roulette wheel selection S times, each time generating a [0,1] uniformly distributed random number, if the random number falls in the interval ( , ], individual s is selected as the individual in the new population; before the start of this population selection, the chromosome with the largest fitness value in the population is selected as the optimal chromosome and directly used as the last individual in the new population.

[0098] It should be noted that the probability of any individual in the population being selected is equal to its fitness divided by the sum of the fitness of all individuals, that is,

[0099] .

[0100] Specifically, a two-point crossover operation is performed on the new generation population, and the crossover probability is dynamically adjusted according to the current state of the new generation population; the probability of crossover in each population individual is Calculated as:

[0101] ;

[0102] Where, and are the maximum fitness and average fitness of the current population, i.e., all individuals in the population before crossover. is the maximum fitness of the two population individuals that have mutated, and is a constant in the interval [0,1].

[0103] It should be noted that the two-point crossover method operates as follows: traverse the parent population, select two parent individuals s and s+1 each time, generate a [0,1] uniformly distributed random number for them, and if the random number is greater than the current crossover probability , the two do not perform gene crossover; if the random number is less than the current crossover probability , two genes are randomly selected on the chromosomes of the two to be exchanged, forming two new parent individuals.

[0104] Furthermore, the new generation population after the two-point crossover operation is mutated, and the mutation probability is dynamically adjusted according to the current state of the new generation population; the probability of gene mutation in all individuals of the population after gene crossover is calculated. for:

[0105] ;

[0106] Where, and are the maximum fitness and average fitness of the current population, i.e., all individuals in the population before mutation. is the fitness of the individual s that undergoes mutation, and is a constant in the interval [0,1].

[0107] It should be noted that the mutation operation method is as follows: a [0,1] uniformly distributed random number is generated for each parent individual after crossover. If the random number is greater than the probability of gene mutation , the individual does not perform gene mutation; if the random number is less than the gene mutation probability , randomly select a gene point on the chromosome of the individual as the mutation point, randomly generate a normally distributed random number with a mean of 0 and a variance of 0.1 as the mutation amount for this gene, add the mutation amount to the current value of the mutation point gene, and generate a new offspring individual.

[0108] Furthermore, the above steps are repeated until the number of iterations of the adaptive genetic algorithm reaches Reaching the maximum number of selected algebras , select the individual with the highest fitness from the final population as the optimal NOMA power allocation coefficient vector on the channel, and output the optimal power allocation solution.

[0109] S4: Iterative execution solves the secondary user channel matching problem and secondary user power allocation problem steps until the adaptive genetic algorithm reaches the maximum number of iterations and outputs the optimal channel matching solution and optimal power allocation solution for the secondary user.

[0110] Furthermore, this embodiment also provides a CR-NOMA resource allocation system combining the Hungarian algorithm and the genetic algorithm, including: an optimization problem construction and decomposition module for constructing the optimization problem in the CR-NOMA downlink system , and the optimization problem Decomposed into secondary user channel matching problem and secondary user power allocation problem ; Secondary user channel matching module, used to solve the secondary user channel matching problem using the Hungarian algorithm , design a secondary user pairing clustering strategy to obtain the optimal channel matching matrix; the secondary user power allocation module is used to solve the secondary user power allocation problem using an adaptive genetic algorithm based on the optimal channel matching matrix , get the optimal power allocation solution; output module, used to iteratively solve the secondary user channel matching problem and secondary user power allocation problem steps until the adaptive genetic algorithm reaches the maximum number of iterations and outputs the optimal channel matching solution and optimal power allocation solution for the secondary user.

[0111] This embodiment also provides a computer device, which is suitable for the CR-NOMA resource allocation scheme that combines the Hungarian algorithm and the genetic algorithm, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the CR-NOMA resource allocation scheme that combines the Hungarian algorithm and the genetic algorithm proposed in the above embodiment.

[0112] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0113] This embodiment also provides a storage medium on which a computer program is stored, which, when executed by a processor, implements the following steps: constructing an optimization problem in the CR-NOMA downlink system , and the optimization problem Decomposed into secondary user channel matching problem and secondary user power allocation problem ; Use the Hungarian algorithm to solve the secondary user channel matching problem , design a secondary user pairing clustering strategy to obtain the optimal channel matching matrix; based on the optimal channel matching matrix, use an adaptive genetic algorithm to solve the secondary user power allocation problem , get the optimal power allocation solution; iterative execution solves the secondary user channel matching problem and secondary user power allocation problem steps until the adaptive genetic algorithm reaches the maximum number of iterations and outputs the optimal channel matching solution and optimal power allocation solution for the secondary user.

[0114] In summary, the present invention solves the optimization problem by Decomposed into secondary user channel matching problem and secondary user power allocation problem , reducing the complexity of problem solving; using the Hungarian algorithm to solve the secondary user channel matching problem Compared with the exhaustive method, the computational complexity is significantly reduced while ensuring the optimality of the matching results; an adaptive genetic algorithm is used to solve the secondary user power allocation problem. By dynamically adjusting the crossover and mutation probabilities, the risk of falling into local optimal solutions can be effectively avoided, and the global search capability can be improved. By iteratively executing the Hungarian algorithm and the adaptive genetic algorithm, the joint optimization of channel matching and power allocation can be achieved, while ensuring the convergence of the algorithm and maximizing the system throughput.

[0115] Example 2

[0116] Reference Figure 5~Figure 6 This is the second embodiment of the present invention. This embodiment provides a CR-NOMA resource allocation scheme that combines the Hungarian algorithm and the genetic algorithm. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0117] Specifically, such as Figure 5 As shown in the figure, the parameter settings are: the number of primary users M = 5, the number of secondary users N = 10, and each cluster has 2 users. The available bandwidth B = 50MHz, the noise variance The power allocation coefficient within each user cluster is , the power value of cognitive base station SBS is [1:1:10] w.

[0118] Further, such as Figure 6 As shown in the figure, the parameter settings are: the number of primary users M = 5, the number of secondary users N = 10, and each cluster has 2 users. The available bandwidth B = 50MHz, the noise variance Crossover probability is 0.6, and the mutation probability is 0.1; crossover probability dynamic adjustment parameter ; , dynamic adjustment parameters of mutation probability ; , the power value of cognitive base station SBS is [1:1:10] w.

[0119] Furthermore, observe Figure 5 and Figure 6 It can be seen that under the dimension of cognitive base station (SBS) power, the CR-NOMA system using the Hungarian channel allocation scheme of this invention can achieve higher throughput than sequential matching and random matching. The Hungarian Adaptive Genetic Algorithm (H-AGA) proposed in this scheme also improves system throughput compared to the Hungarian Genetic Algorithm (H-GA) and optimizes the allocation of channel resources within the system.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A CR-NOMA resource allocation scheme combining the Hungarian algorithm and the genetic algorithm, characterized by: include, Optimization Problems Built on CR-NOMA Downlink System , and the optimization problem Decomposed into secondary user channel matching problem and secondary user power allocation problem ; Use the Hungarian algorithm to solve the secondary user channel matching problem , design the secondary user pairing clustering strategy to obtain the optimal channel matching matrix; Based on the optimal channel matching matrix, an adaptive genetic algorithm is used to solve the secondary user power allocation problem. , get the optimal power allocation scheme; Iterative execution solves the secondary user channel matching problem and secondary user power allocation problem steps until the adaptive genetic algorithm reaches the maximum number of iterations and outputs the optimal channel matching solution and optimal power allocation solution for the secondary user.

2. The CR-NOMA resource allocation scheme combining the Hungarian algorithm and the genetic algorithm according to claim 1 is characterized in that: The CR-NOMA downlink system includes a primary base station PBS and a cognitive base station SBS; the primary base station PBS includes several primary users PU, where the number of channels is , the channel bandwidth is , the channel set is expressed as The cognitive base station SBS adopts the Underlay method to share the channel of the primary user PU and transmits downlink signals to N secondary users SU. The secondary user set is , the total downlink transmission power is , ,in, Indicates the Sub-users SU, is the transmit power allocated by the cognitive base station SBS to channel m; the construction process of the optimization problem includes the following steps: NOMA technology is used to share the same channel m for signal transmission, and N secondary user SUs are divided into m clusters with K SUs in each group. The optional power level set of the mth cluster is ; Calculate the cognitive base station SBS through the channel m with power level To secondary users The downlink achievable rate of the sent signal is as follows: ； Where, is the downlink achievable rate of the nth secondary user SU in the kth group on channel m, is the received signal-to-interference-and-noise ratio of the n-th secondary user SU in the k-th group on channel m. The specific formula is as follows: ； Where, is the cognitive base station SBS to the mth channel The gain of each secondary user SU, is the Rayleigh fading factor of the nth secondary user SU on channel m, is the path loss, is the path loss function, is the cognitive base station SBS to the mth channel The distance between the secondary users SU, is the cognitive base station SBS to the mth channel The additive white Gaussian noise power of each secondary user SU, is the kth power level in the mth cluster, is the power level The total transmit power on channel m The proportion of , ,for and , .

3. The CR-NOMA resource allocation scheme combining the Hungarian algorithm and the genetic algorithm according to claim 2 is characterized by: Also includes, Based on the downlink achievable rate, calculate the total downlink throughput from the cognitive base station SBS to the secondary user , the specific formula is as follows: ； Where, is the channel matching relationship variable of the nth secondary user SU on channel m, , , is the kth power level in the mth cluster The downlink power level selection variable of the nth secondary user SU is: , then the cognitive base station SBS does not transmit the signal of the nth secondary user SU through channel m, if , then the cognitive base station SBS sends a signal to the nth secondary user SU through channel m, if , then the cognitive base station SBS does not select the kth power level in the mth cluster The signal sent by the nth secondary user SU, if , then the cognitive base station SBS selects the kth power level in the mth cluster The transmission signal of the nth secondary user SU; According to the total downlink throughput, taking the maximization of the secondary user throughput as the objective function, 、 、 、 、 、 as well as As the constraint condition, establish the optimization problem , and the optimization problem Decomposed into secondary user channel matching problem and secondary user power allocation problem ; in, The constraint condition is that each secondary user SU can only occupy one sub-channel. As the constraint that each secondary user SU can only select one power level on each channel, For each channel, the power level is proportional to the constraint conditions. is the constraint condition for the ordering relationship of the K power levels on each channel, is the constraint condition of the total transmit power of the cognitive base station SBS, is the interference constraint condition for the primary user, is the constraint condition of the minimum transmission rate of the secondary user SU.

4. The CR-NOMA resource allocation scheme combining the Hungarian algorithm and the genetic algorithm according to claim 3 is characterized by: The optimization problem The relevant formula is as follows: ； Where, is the upper limit of the transmit power, is the maximum interference tolerance of the primary user PU on channel m, is the lower limit of the rate of the nth secondary user SU on channel m; The secondary user channel matching problem The relevant formula is as follows: ； The secondary user power allocation problem The relevant formula is as follows: 。 5. The CR-NOMA resource allocation scheme combining the Hungarian algorithm and the genetic algorithm according to claim 4 is characterized in that: The secondary user channel matching problem The solution is as follows: The gain of transmitting any channel m to N secondary users SU is , sorted in descending order, and ; Hungarian algorithm is used to extract the sub-user set Select two users to pair into user cluster m and perform power domain NOMA transmission on channel m, perform secondary user pairing clustering and intra-cluster user power level selection strategy; From the set of secondary users of channel m The current unpaired minimum gain is selected as the weak user of channel m , and calculate the achievable rate of weak user pairing ; Traverse all channels and get the weak users matched on each channel , and will be added to the set of users in the matched channel middle; From the set of secondary users of channel m Remove users with matching channels and obtain the pruned strong user set ; According to the strong user set Choose a strong user , calculate the achievable rate of the optimal strong user and weak user pairing; Traverse the strong user collection , get the total rate of user clusters after pairing on the channel, and construct the initial channel benefit matrix ; For the initial channel benefit matrix Perform optimization to obtain the optimal channel matching matrix; According to the optimal channel matching matrix, an adaptive genetic algorithm is used to solve the secondary user power allocation problem. , and obtain the initial power allocation scheme.

6. The CR-NOMA resource allocation scheme combining the Hungarian algorithm and the genetic algorithm according to claim 5 is characterized by: The secondary user power allocation problem The solution is as follows: The cognitive base station SBS randomly generates S groups of power allocation coefficient vectors with a length of M As the initial population, the elements represents the power allocation factor in the mth channel, and each vector represents a power allocation scheme in M ​​channels; Based on the power allocation scheme and the optimal channel matching matrix, the total system throughput is calculated as the fitness function of each species in the group ; According to the fitness function , use the roulette wheel method to select the new generation population, and retain the individuals with the highest fitness as the new generation population; Performing a two-point crossover operation on the new generation population, with the crossover probability dynamically adjusted according to the current state of the new generation population; Perform mutation operation on the new generation population after the two-point crossover operation, and the mutation probability is dynamically adjusted according to the status of the current new generation population; Repeat the above steps until the number of iterations of the adaptive genetic algorithm reaches Reaching the maximum number of selected algebras , select the individual with the highest fitness from the final population as the optimal NOMA power allocation coefficient vector on the channel, and output the optimal power allocation solution.

7. The CR-NOMA resource allocation scheme combining the Hungarian algorithm and the genetic algorithm according to claim 6 is characterized in that: The fitness function The specific formula is as follows: ； in, In the population individual s, when the power distribution coefficient is , the pairing of the nth secondary user SU and channel m, if , then the nth secondary user SU is multiplexed on channel m, if , then the nth secondary user SU is not multiplexed on channel m, is the total rate of the user cluster when the secondary user SU is multiplexed on channel m.

8. A CR-NOMA resource allocation scheme combining a Hungarian algorithm and a genetic algorithm, based on the CR-NOMA resource allocation scheme combining a Hungarian algorithm and a genetic algorithm according to any one of claims 1 to 7, characterized in that: Also includes, Optimization problem construction and decomposition module, used to construct optimization problems in CR-NOMA downlink system , and the optimization problem Decomposed into secondary user channel matching problem and secondary user power allocation problem ; The secondary user channel matching module is used to solve the secondary user channel matching problem using the Hungarian algorithm , design the secondary user pairing clustering strategy to obtain the optimal channel matching matrix; A secondary user power allocation module is used to solve the secondary user power allocation problem using an adaptive genetic algorithm based on the optimal channel matching matrix. , get the optimal power allocation scheme; Output module, used to iteratively solve the secondary user channel matching problem and secondary user power allocation problem steps until the adaptive genetic algorithm reaches the maximum number of iterations and outputs the optimal channel matching solution and optimal power allocation solution for the secondary user.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the CR-NOMA resource allocation scheme combining the Hungarian algorithm and the genetic algorithm are implemented in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the CR-NOMA resource allocation scheme combining the Hungarian algorithm and the genetic algorithm are implemented.

Images