Power allocation method for energy harvesting cognitive wireless network based on non-cooperative game

Abstract

This invention proposes a power allocation method for a non-cooperative game-based cognitive wireless network for energy harvesting, comprising the following steps: Step S1: During a time interval of τT within a time slot T, the primary user transmitter PT transmits data to the primary user receiver PR via an antenna, and each secondary user receiver SU is equipped with an omnidirectional antenna for energy harvesting to collect energy from the PU radio frequency signal; Step S2: During the data transmission process of a time interval of (1-τ)T within a time slot T, a defined minimum signal-to-noise ratio threshold and an interference power threshold I for PU are set; Step S3: During the time interval of (1-τ)T within a time slot T, SU... i (i = 1, 2, ..., n) Power allocation is performed through a non-cooperative game; Step S4: Solve for SU within a time interval (1 - τ)T of a time slot T. i The utility function (i = 1, 2, ..., n) eventually reaches Nash equilibrium, maximizing the throughput of secondary users and the revenue of primary and secondary users. This invention improves the throughput and revenue of secondary users by finding the optimal power allocation strategy in energy harvesting cognitive radio networks while satisfying all constraints.

Description

Technical Field

[0001] This invention relates to the field of cognitive radio technology, and in particular to a power allocation method for energy harvesting cognitive wireless networks based on non-cooperative game theory. Background Technology

[0002] With the rapid development of technology, the number of wireless communication devices has increased dramatically, leading to a shortage of wireless spectrum resources. Cognitive radio, a technology that can fully utilize idle spectrum resources, can sense the external communication environment and self-adjust according to changes in the environment, thereby achieving intelligent communication. Its development can alleviate the problem of electromagnetic spectrum resource shortage to some extent. Therefore, cognitive radio technology has become a hot research direction in next-generation wireless communication.

[0003] Cognitive radio networks (CNRs) are wireless communication networks that can intelligently learn and perceive information about their surrounding network environment, and adjust parameters (such as power, modulation techniques, and data rates) in the process of interacting with the environment. CNRs can detect holes in the electromagnetic spectrum and effectively utilize specific idle spectrum, thus alleviating the scarcity of electromagnetic spectrum resources. Secondary users, while ensuring communication quality, are controlled by the primary user to access specific idle spectrum, resulting in higher spectrum resource utilization for the primary user and higher overall system capacity of the CNR.

[0004] Furthermore, energy supply presents certain challenges in cognitive radio networks. Traditionally, secondary users in cognitive radio networks are often powered by batteries, requiring periodic charging or replacement. This limits the performance of cognitive radio networks. Therefore, applying energy harvesting technology to cognitive radio networks is a necessary consideration to address this issue.

[0005] Energy harvesting is a technology that enables continuous power supply to energy-constrained devices, allowing energy to be harvested from the natural environment without supervision. Energy harvesting technologies can extract energy from natural resources such as sunlight, wind, geothermal energy, and tides, as well as radio frequency (RF) resources. However, harvesting from natural resources often requires specialized electrical equipment, while RF resource harvesting can be easily achieved by multiplexing antennas from communication equipment, making it more readily applicable. Here, we will primarily consider RF energy harvesting. RF energy harvesting technology allows secondary users to collect energy from the primary user's RF signals and use this energy for data transmission. This solves the problem of data interruption due to insufficient energy during data transmission for secondary users and can also reduce energy consumption in cognitive radio networks to some extent. Therefore, introducing energy harvesting technology into cognitive radio networks can not only fully utilize spectrum resources but also partially solve the energy supply problem.

[0006] In cognitive radio networks, due to competition among secondary users, each user will try to maximize their own benefit by increasing their transmission power during the electromagnetic spectrum resource allocation process. However, each secondary user's transmission power interferes with other neighboring secondary users. Blindly increasing each other's transmission power will increase network interference without necessarily achieving a high signal-to-noise ratio. Therefore, a reasonable power allocation mechanism can reduce interference generated by secondary users, improve system throughput, save power to extend terminal lifespan, and improve network performance. Game theory is an effective mathematical analysis tool that can effectively solve the competition problem of power allocation among secondary users. Summary of the Invention

[0007] This invention proposes a power allocation method for energy harvesting cognitive wireless networks based on non-cooperative game theory. By finding the optimal power allocation strategy in energy harvesting cognitive wireless networks, the throughput and revenue of secondary users are improved while satisfying all constraints.

[0008] The present invention adopts the following technical solution.

[0009] A power allocation method for energy harvesting cognitive wireless networks based on non-cooperative game theory, for use in radio networks, includes the following steps;

[0010] Step S1: During a time interval τT within a time slot T, the primary user transmitter PT transmits data to the primary user receiver PR via its antenna. Each secondary user receiver SU is equipped with an omnidirectional antenna for energy harvesting to collect energy from the PU radio frequency signal, thereby mitigating energy consumption during data transmission. The energy obtained is E. i ;

[0011] Step S2: During the data transmission process within a time slot T and a time period of (1-τ)T, set a defined minimum signal-to-noise ratio threshold. Interference power threshold I of PU;

[0012] Step S3: Within a time interval (1-τ)T of a time slot T, SU i (i = 1, 2, ..., n) Power is allocated through a non-cooperative game, and a utility function is defined with the transmission power as the cost function;

[0013] Step S4: Solve for SU within a time interval (1-τ)T of time slot T. i The utility function of (i = 1, 2, ..., n) eventually reaches Nash equilibrium. Under the Nash equilibrium solution, the optimal power allocation strategy is obtained, thereby maximizing the throughput of secondary users and the revenue of primary and secondary users.

[0014] The radio network is an underlay cognitive radio. Step S1 specifically involves: within a time slot T and a period τT, PT transmits data to PR via a directional antenna. Each SU is equipped with a separate omnidirectional antenna for energy harvesting to collect energy from the PU radio frequency signal to alleviate energy consumption during data transmission. The obtained energy is E. i :E i =μP T |β i | 2 τT(1,2,...,n), where μ represents the energy harvesting efficiency, β i For energy harvesting gain, P T This indicates the transmit power of the PT. This step sets up a pair of primary users and two secondary users.

[0015] The data transmission process in step S2 involves no reflection or the reflection is negligible, including the following steps;

[0016] Step S21: Within a time interval (1-τ)T of a time slot T, when SU i The signal-to-noise ratio (SNR) of (i = 1, 2, ..., n) is greater than a certain minimum SNR threshold. When this time is reached, the data is considered to have been successfully transmitted, that is:

[0017] Then SU i The signal-to-noise ratio (SNR) at the receiver (i = 1, 2, ..., n) is defined as:

[0018] Where, σ 2 p represents the power of Gaussian white noise. i SU i The transmission power, hi SU i Link gain with secondary user access point (SBS), h i Defined as A is a constant, determined by decay, d i SU i The distance between the source and the SBS, the fading factor m is a constant;

[0019] Let the above formula be

[0020] SU i Interference received;

[0021] Step S22: Within a time interval (1-τ)T of a time slot T, SU i While ensuring PU communication quality, the spectrum resources of the PU are shared. The PU sets an interference power threshold I, and the SU... i If data transmission occurs within the PU interference threshold I, and there is no reflection during data transmission, then the interference constraint condition of the system can be expressed as:

[0022] Among them, g i SU i The link gain to PU can be obtained from this inequality:

[0023]

[0024] From the above formula, it can be seen that if SU i The transmit power p i Exceeded maximum transmission power This will affect the normal communication of the PU, and thus the SU i The transmit power must be reduced or the spectrum segment must be withdrawn to ensure the communication quality of the PU.

[0025] Step S3 specifically involves: within a time interval (1-τ)T of a time slot T, SU i Power allocation is performed through a non-cooperative game approach, and a utility function is defined with transmission power as the cost function, including the following steps;

[0026] Step S31: Within a time slot T and a time interval of (1-τ)T, the power allocation problem of energy harvesting cognitive radio networks is treated as a repeated non-cooperative game process. A utility function model for power allocation is proposed for this non-cooperative game model. In the utility function model, Γ represents the non-cooperative game, N = {1,2,...,n} represents the number of sub-users, and A... i SU i strategy space A i ∈[0,pmax ], p max For SU i Maximum transmit power, μ i (p i ,p -i ) represents the payoff in this game, where p -i Let represent the set of transmit power of all secondary users except the i-th secondary user; therefore, the non-cooperative game-theoretic power allocation model of this energy harvesting cognitive radio network can be defined as Γ. i = <N,A,{μ i (p i ,p -i Formula 5;

[0027] Step S32: Within a time interval (1-τ)T of a time slot T, SU i Choosing an optimal strategy yields the Nash equilibrium, i.e. Due to SU i Higher transmission power leads to greater interference with other nodes and consumes more energy. Therefore, the utility function is defined with transmission power as the cost function, i.e.:

[0028]

[0029] Where b is the number of information bits contained in a data packet of size F bits, r is the data transmission rate, and f(γ) i ) is the efficiency function, defined as f(γ) i )=(1-2Pe) F Formula 7,

[0030] in The bit error rate (BER) depends on the channel conditions and interference from other network links; c(p i )=λ*p i This represents a cost function based on transmit power, where λ represents a price adjustment factor. The existence of this cost function prompts SU i The selection is subject to the constraint of the globally optimal transmit power.

[0031] Step S4 specifically involves solving SU within a (1-τ)T time interval of time slot T. i The utility function eventually reaches Nash equilibrium. Under the Nash equilibrium solution, the optimal power allocation strategy is obtained to maximize throughput and revenue, including the following steps:

[0032] Step S41: From SU i The strategy space set A of the transmit power i ∈[0,p max Find an optimal transmission power. This allows the defined utility function to reach its maximum value; to obtain the optimal transmit power, it must be verified that the model has a unique Nash equilibrium point.

[0033] The verification method is as follows: Based on the definition of the supermodel game model, firstly, calculate the emission power p from the utility function expression. i The first derivative, i.e.

[0034]

[0035] Then p j Find the first-order partial derivative.

[0036]

[0037] in:

[0038]

[0039]

[0040] When γ i When ≥2lnF, Therefore, it can be deduced that

[0041] From the above derivation, it can be seen that the utility function of the game model belongs to the supermodel game; according to the Topkis fixed point theorem, all supermodel games have a unique Nash equilibrium point, so the existence and uniqueness of the Nash equilibrium point of this model are proved.

[0042] Step S42: To solve for the Nash equilibrium solution of this model. Find the transmit power p from the utility function expression. i The first derivative is obtained.

[0043]

[0044] According to the maximum value theory, setting its partial derivative expression to 0, we get:

[0045]

[0046] Solving the above equation yields:

[0047]

[0048] Using Newton's iteration method, we obtain SU i The iterative formula for transmit power is:

[0049]

[0050] Step S43: Substitute the power iteration value of each iteration into the following formula to obtain the corresponding throughput until the iteration stops. At this point, the maximum throughput can be obtained.

[0051]

[0052] Where W represents the bandwidth of the channel.

[0053] Step S44: Substitute the power iteration value of each iteration into the following formula to obtain the corresponding benefit until the iteration stops. At this point, the maximum benefit can be obtained.

[0054]

[0055] This invention introduces energy harvesting technology into cognitive radio networks to construct energy-harvesting cognitive radio networks, providing a solution to the energy supply problem for information transmission in these systems, building upon existing methods. A non-cooperative game-based power allocation method is employed, and it is proven that this game model conforms to the characteristics of a supermodel game, thus demonstrating that the model possesses a unique Nash equilibrium. Subsequently, the optimal transmit power is determined through Newton's iteration method. Simulation results show that, after introducing energy harvesting, the proposed solution improves the throughput of secondary users and the revenue of both primary and secondary users.

[0056] Compared with the prior art, the present invention has the following advantages: it can find the optimal power allocation strategy in energy harvesting cognitive radio networks and improve the throughput and revenue of secondary users while satisfying all constraints. Attached Figure Description

[0057] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0058] Appendix Figure 1 This is a schematic diagram of a cognitive radio network model according to an embodiment of the present invention;

[0059] Appendix Figure 2 This is a schematic diagram of the time slot allocation frame according to an embodiment of the present invention;

[0060] Appendix Figure 3 This is a schematic diagram of the process for solving the optimal transmission power in an embodiment of the present invention;

[0061] Appendix Figure 4 This is a schematic diagram illustrating the relationship between the throughput per user and the number of iterations when the price adjustment factor λ = 0.05, according to an embodiment of the present invention.

[0062] Appendix Figure 5 This is a schematic diagram illustrating the relationship between the throughput per user and the number of iterations when the price adjustment factor λ = 0.1, according to an embodiment of the present invention.

[0063] Appendix Figure 6 This is a schematic diagram illustrating the relationship between user revenue and iteration number when the price adjustment factor λ = 0.05 and λ = 0.1, according to an embodiment of the present invention.

[0064] Appendix Figure 7 This is a schematic diagram illustrating the relationship between the main user's revenue and the number of iterations when the price adjustment factors λ = 0.05 and λ = 0.1, according to an embodiment of the present invention. Detailed Implementation

[0065] like Figures 1 to 3 As shown, a power allocation method for energy harvesting cognitive wireless networks based on non-cooperative game theory, used in radio networks, includes the following steps;

[0066] Step S1: During a time interval τT within a time slot T, the primary user transmitter PT transmits data to the primary user receiver PR via its antenna. Each secondary user receiver SU is equipped with an omnidirectional antenna for energy harvesting to collect energy from the PU radio frequency signal, thereby mitigating energy consumption during data transmission. The energy obtained is E. i ;

[0067] Step S2: During the data transmission process within a time slot T and a time period of (1-τ)T, set a defined minimum signal-to-noise ratio threshold. Interference power threshold I of PU;

[0068] Step S3: Within a time interval (1-τ)T of a time slot T, SU i (i = 1, 2, ..., n) Power is allocated through a non-cooperative game, and a utility function is defined with the transmission power as the cost function;

[0069] Step S4: Solve for SU within a time interval (1-τ)T of time slot T. i The utility function of (i = 1, 2, ..., n) eventually reaches Nash equilibrium. Under the Nash equilibrium solution, the optimal power allocation strategy is obtained, thereby maximizing the throughput of secondary users and the revenue of primary and secondary users.

[0070] The radio network is an underlay cognitive radio. Step S1 specifically involves: within a time slot T and a period τT, PT transmits data to PR via a directional antenna. Each SU is equipped with a separate omnidirectional antenna for energy harvesting to collect energy from the PU radio frequency signal to alleviate energy consumption during data transmission. The obtained energy is E. i :E i =μP T |β i | 2 τT(1,2,...,n), where μ represents the energy harvesting efficiency, β iFor energy harvesting gain, P T This indicates the transmit power of the PT. This step sets up a pair of primary users and two secondary users.

[0071] The data transmission process in step S2 involves no reflection or the reflection is negligible, including the following steps;

[0072] Step S21: Within a time interval (1-τ)T of a time slot T, when SU i The signal-to-noise ratio (SNR) of (i = 1, 2, ..., n) is greater than a certain minimum SNR threshold. When this time is reached, the data is considered to have been successfully transmitted, that is:

[0073] Then SU i The signal-to-noise ratio (SNR) at the receiver (i = 1, 2, ..., n) is defined as:

[0074] Where, σ 2 p represents the power of Gaussian white noise. i SU i The transmission power, h i SU i Link gain with secondary user access point (SBS), h i Defined as A is a constant, determined by decay, d i SU i The distance between the source and the SBS, the fading factor m is a constant;

[0075] Let the above formula be

[0076] SU i Interference received;

[0077] Step S22: Within a time interval (1-τ)T of a time slot T, SU i While ensuring PU communication quality, the spectrum resources of the PU are shared. The PU sets an interference power threshold I, and the SU... i If data transmission occurs within the PU interference threshold I, and there is no reflection during data transmission, then the interference constraint condition of the system can be expressed as:

[0078] Among them, g i SU i The link gain to PU can be obtained from this inequality:

[0079]

[0080] From the above formula, it can be seen that if SU iThe transmit power p i Exceeded maximum transmission power This will affect the normal communication of the PU, and thus the SU i The transmit power must be reduced or the spectrum segment must be withdrawn to ensure the communication quality of the PU.

[0081] Step S3 specifically involves: within a time interval (1-τ)T of a time slot T, SU i Power allocation is performed through a non-cooperative game approach, and a utility function is defined with transmission power as the cost function, including the following steps;

[0082] Step S31: Within a time slot T and a time interval of (1-τ)T, the power allocation problem of energy harvesting cognitive radio networks is treated as a repeated non-cooperative game process. A utility function model for power allocation is proposed for this non-cooperative game model. In the utility function model, Γ represents the non-cooperative game, N = {1,2,...,n} represents the number of sub-users, and A... i SU i strategy space A i ∈[0,p max ], p max For SU i Maximum transmit power, μ i (p i ,p -i ) represents the payoff in this game, where p -i Let represent the set of transmit power of all secondary users except the i-th secondary user; therefore, the non-cooperative game-theoretic power allocation model of this energy harvesting cognitive radio network can be defined as Γ. i = <N,A,{μ i (p i ,p -i Formula 5;

[0083] Step S32: Within a time interval (1-τ)T of a time slot T, SU i Choosing an optimal strategy yields the Nash equilibrium, i.e. Due to SU i Higher transmission power leads to greater interference with other nodes and consumes more energy. Therefore, the utility function is defined with transmission power as the cost function, i.e.:

[0084]

[0085] Where b is the number of information bits contained in a data packet of size F bits, r is the data transmission rate, and f(γ) i ) is the efficiency function, defined as f(γ) i )=(1-2Pe) F Formula 7,

[0086] in The bit error rate (BER) depends on the channel conditions and interference from other network links; c(p i )=λ*p i This represents a cost function based on transmit power, where λ represents a price adjustment factor. The existence of this cost function prompts SU i The selection is subject to the constraint of the globally optimal transmit power.

[0087] Step S4 specifically involves solving SU within a (1-τ)T time interval of time slot T. i The utility function eventually reaches Nash equilibrium. Under the Nash equilibrium solution, the optimal power allocation strategy is obtained to maximize throughput and revenue, including the following steps:

[0088] Step S41: From SU i The strategy space set A of the transmit power i ∈[0,p max Find an optimal transmission power. This allows the defined utility function to reach its maximum value; to obtain the optimal transmit power, it must be verified that the model has a unique Nash equilibrium point.

[0089] The verification method is as follows: Based on the definition of the supermodel game model, firstly, calculate the emission power p from the utility function expression. i The first derivative, i.e.

[0090]

[0091] Then p j Find the first-order partial derivative.

[0092]

[0093] in:

[0094]

[0095]

[0096] When γ i When ≥2lnF, Therefore, it can be deduced that

[0097] From the above derivation, it can be seen that the utility function of the game model belongs to the supermodel game; according to the Topkis fixed point theorem, all supermodel games have a unique Nash equilibrium point, so the existence and uniqueness of the Nash equilibrium point of this model are proved.

[0098] Step S42: To solve for the Nash equilibrium solution of this model. Find the transmit power p from the utility function expression. i The first derivative is obtained.

[0099]

[0100] According to the maximum value theory, setting its partial derivative expression to 0, we get:

[0101]

[0102] Solving the above equation yields:

[0103]

[0104] Using Newton's iteration method, we obtain SU i The iterative formula for transmit power is:

[0105]

[0106] Step S43: Substitute the power iteration value of each iteration into the following formula to obtain the corresponding throughput until the iteration stops. At this point, the maximum throughput can be obtained.

[0107]

[0108] Where W represents the bandwidth of the channel.

[0109] Step S44: Substitute the power iteration value of each iteration into the following formula to obtain the corresponding benefit until the iteration stops. At this point, the maximum benefit can be obtained.

[0110]

[0111] like Figures 4 to 7 As shown, the simulation results are verified in this example:

[0112] Figure 4 and Figure 5 This indicates that when λ = 0.05 and λ = 0.1, the maximum throughput of the secondary user changes slightly before and after the addition of energy harvesting. This is because, with energy harvesting, compared to the traditional method, more energy is available for data transmission under the same conditions, resulting in higher throughput.

[0113] Figure 6 and Figure 7This indicates that when λ = 0.05 and λ = 0.1, there are slight changes in the benefits for primary and secondary users before and after adding energy harvesting. This is because, with energy harvesting, compared to the traditional scheme, more energy is available for data transmission under the same conditions, resulting in higher throughput and thus higher benefits for the primary user. Similarly, with the secondary user achieving higher throughput, the interference to the primary user is also greater, thus the penalty for interference is also greater, and therefore the primary user's benefit is higher.

[0114] In summary, it can be seen that the introduction of energy harvesting improves the throughput of secondary users and the revenue of both primary and secondary users. Therefore, this paper proposes to combine energy harvesting technology with cognitive radio to form an energy harvesting cognitive radio network. Employing non-cooperative game-theoretic power allocation, this paper provides a solution to the energy supply problem for system information transmission, thereby improving the throughput of secondary users and the revenue of both primary and secondary users.

[0115] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A power allocation method for energy harvesting cognitive wireless networks based on non-cooperative game theory, used in radio networks, characterized by: Includes the following steps; Step S1: In a time slot of During the time period, the primary user transmitter (PT) transmits data to the primary user receiver (PR) via its antenna, and each secondary user receiver... Equipped with an omnidirectional antenna for energy harvesting to collect energy from the PU radio frequency signal, thus mitigating energy consumption during data transmission, the obtained energy is ; Step S2: In a time slot of During the data transmission process over a given time period, a specific minimum signal-to-noise ratio threshold is set. Interference power threshold of PU ; Step S3: In a time slot of During the time period, , Power allocation is performed through non-cooperative game theory, and a utility function is defined with transmission power as the cost function. Step S4: In a time slot of Solve within the time period The utility function eventually reaches Nash equilibrium. Under the Nash equilibrium solution, the optimal power allocation strategy is obtained, thereby maximizing the throughput of secondary users and the revenue of primary and secondary users. The radio network is an underlay cognitive radio, and step S1 specifically involves: in a time slot of During the time period, the PT transmits data to the PR via a directional antenna, each A separate omnidirectional antenna is used to harvest energy from the PU radio frequency signal, thus mitigating energy consumption during data transmission. The obtained energy is... : ,in, Indicates energy harvesting efficiency. For energy harvesting gain, This indicates the transmit power of the PT. This step sets up a pair of primary users and two secondary users. The radio network is an underlay cognitive radio, and there is no reflection or the reflection is negligible during the data transmission process in step S2, including the following steps; Step S21: In a time slot of During the time period, when The signal-to-noise ratio is greater than a certain defined minimum signal-to-noise ratio threshold. When the data is successfully transmitted, that is: , but The signal-to-noise ratio at the receiver is defined as: Formula 1; in, This represents the power of Gaussian white noise. express The transmission power, express Link gain between the secondary user access point (SBS) and the secondary user access point (SBS). Defined as , It is a constant, determined by decay. express Distance from SBS, fading factor It is a constant; Let the above formula be Formula 2, express Interference received; Step S22: In a time slot of During the time period, While ensuring PU communication quality, the PU's spectrum resources are shared, and the PU sets an interference power threshold. , Less than the PU interference power threshold Under the premise that data transmission occurs without reflection during data transmission, the interference constraint condition of the system is expressed as: Formula 3; in, express The link gain to PU can be obtained from Equation 3: Formula 4; From the above formula, we can see that if Transmission power Exceeded maximum transmission power This will affect the normal communication of the PU. The transmit power must be reduced or the spectrum segment must be withdrawn to ensure the communication quality of the PU; The radio network is an underlay cognitive radio, and step S3 specifically involves: in a time slot of During the time period, Power allocation is performed through a non-cooperative game approach, and a utility function is defined with transmission power as the cost function, including the following steps; Step S31: In a time slot of Within a given timeframe, the power allocation problem in energy harvesting cognitive radio networks is treated as a recurring non-cooperative game process. A utility function model for power allocation is proposed for this non-cooperative game model. In this utility function model, This indicates a non-cooperative game. Indicates the number of secondary users. express strategy space , for Maximum transmission power, This represents the payout from this game, among which... Indicates except the first The set of transmit power from all secondary users other than the primary user; therefore, the non-cooperative game-theoretic power allocation model of this energy harvesting cognitive radio network can be defined as follows: Formula 5; Step S32: In a time slot of During the time period, Choosing an optimal strategy yields the Nash equilibrium, i.e. ;because Higher transmission power leads to greater interference with other nodes and consumes more energy. Therefore, the utility function is defined with transmission power as the cost function, i.e.: Formula Six; in, It is the size of The number of information bits contained in a bit data packet. For the data transmission rate, It is the efficiency function, defined as Formula 7, in The bit error rate depends on the channel conditions and interference from other network links. This indicates that the cost function is based on the transmit power. This represents the price adjustment factor; the existence of the cost function prompts... The selection is subject to factors constrained by the globally optimal transmit power; The radio network is an underlay cognitive radio, and step S4 specifically involves a time slot. of Solve within the time period The utility function eventually reaches Nash equilibrium. Under the Nash equilibrium solution, the optimal power allocation strategy is obtained to maximize throughput and revenue, including the following steps: Step S41: From The strategy space of the transmit power Find an optimal transmission power This allows the defined utility function to reach its maximum value; to obtain the optimal transmit power, it must be verified that the model has a unique Nash equilibrium point. The verification method is as follows: Based on the definition of the supermodel game model, firstly, calculate the transmission power from the utility function expression. The first derivative, i.e. Formula 8; Again Find the first-order partial derivative. Formula Nine; in: Formula 10; Formula 11; when hour, Therefore, it can be deduced that Formula 12; From the above derivation, it can be seen that the utility function of the game model belongs to the supermodel game; according to the Topkis fixed point theorem, all supermodel games have a unique Nash equilibrium point, so the existence and uniqueness of the Nash equilibrium point of this model are proved. Step S42: To solve for the Nash equilibrium solution of this model. Calculate the transmit power from the utility function expression. The first derivative is obtained. Formula Thirteen; According to the maximum value theory, setting its partial derivative expression to 0, we get: Formula Fourteen; Solving the above equation yields: Formula 15; Using Newton's iteration method, we obtain The iterative formula for transmit power is: Formula Sixteen; Step S43: Substitute the power iteration value of each iteration into the following formula to obtain the corresponding throughput until the iteration stops. At this point, the maximum throughput can be obtained. Formula 17; in, Indicates the bandwidth of the channel; Step S44: Substitute the power iteration value of each iteration into the following formula to obtain the corresponding benefit until the iteration stops. At this point, the maximum benefit can be obtained. Formula 18.

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