Resource Allocation Method for Maximizing the Secure Rate of Cooperative NOMA Systems under IQ Imbalance Conditions

Abstract

The present invention claims protection for a resource allocation method for maximizing the security rate of a cooperative NOMA system under IQ imbalance conditions, belonging to the field of security rate resource allocation in NOMA systems. It aims to consider the situation of eavesdroppers existing in the actual communication system and maximize the security rate through optimization. However, several variables of the original problem are mutually coupled, which is a non-convex problem. Therefore, by means of variable substitution, the original non-convex optimization problem is equivalently transformed into a convex optimization problem. On this basis, an effective resource allocation strategy is proposed, a system model for maximizing the security rate is established. After simplifying the original problem through variable substitution, it is decomposed into three sub-problems for optimization based on the block coordinate descent algorithm. The successive convex approximation (SCA) is used to transform the original non-convex optimization problem into a convex optimization problem, and an algorithm with two-layer iterative optimization inside and outside is proposed. The applicability and superiority of the proposed algorithm are verified by simulation.

Description

Technical Field

[0001] The present invention belongs to the field of secure rate resource allocation in NOMA systems. Specifically, it relates to a resource allocation method that jointly optimizes secure rate, power, and time under the condition of meeting QoS in a wireless-powered NOMA network. Background Art

[0002] With the advent of the 5G era and the upsurge of the Internet of Everything, the development of communication technology has opened a new chapter. Nowadays, the hottest technologies such as virtual reality, high-definition video, and smart home have gradually entered the public's vision. People's demand for wireless networks is also increasing day by day. These new demands mean higher spectral efficiency, lower latency, and a more secure communication environment, which have had a huge impact on traditional communication technologies. Researchers have proposed Non-orthogonal Multiple Access (NOMA) technology to address these challenges. In addition, the Internet of Everything will inevitably lead to the access of a large number of physical components. The defects of these low-cost physical components will cause IQ (In-phase and Quadrature-phase) imbalance in the radio frequency (RF) front end, which will degrade the performance of the system.

[0003] Mobile communication technology has developed rapidly in the past two decades. Especially with the rapid development of the Internet of Things (IoT) technology, the demand for high-speed data transmission and large-scale mobile terminal access has shown rapid growth. The demand for the number of connected devices in large-scale IoT systems is also increasing continuously. In addition, emerging technologies such as the current popular virtual reality, high-definition video, and smart home all benefit from the development of mobile communication technology. "Interconnection of all things" is constantly evolving towards "intelligent connection of all things". Obviously, the existing relatively complete cellular network is insufficient to meet the requirements of massive device access, high-speed data transmission, more bandwidth, low-latency quality of service, and low interference. The 5th Generation (5G) and 6th Generation (6G) mobile communication technologies, as the current new generation of emerging communication technologies, will provide new impetus for the continuous expansion and development of communication networks. Especially since 2020, commercial 5G networks have begun to be widely deployed globally. During the "14th Five-Year Plan" period, 5G technology, as a key part of new infrastructure, will continue to promote the digitalization process of traditional infrastructure in China and empower the coordinated development of multiple fields such as smart cities, intelligent manufacturing, healthcare, agriculture, materials, and energy. However, with the emergence of new requirements such as intelligent connection of all things and holographic network technology, 5G may still be unable to meet the actual needs in future network applications. It can be foreseen that communication technology will surely reach a new peak. Moreover, 6G technology has already attracted extensive research and attention in the academic community and is planned to be deployed after 2030 to support more diverse communication scenarios.

[0004] Therefore, in order to cope with future flexible and complex communication scenarios and maintain communication security, the next-generation communication technology must necessarily find new breakthrough points and improve the spectral efficiency (SE) utilization rate, as well as enhance user fairness (UF) and communication security from the underlying architecture design. However, due to its single mutually orthogonal resource block (RB), the traditional orthogonal multiple access (OMA) technology has greatly reduced the spectral efficiency and is already difficult to meet the growing service requirements. Researchers have proposed to solve the above problems through non-orthogonal multiple access (NOMA) technology. Different from that each orthogonal resource block in OMA can only serve one user, NOMA allows multiple users to share the same resource block, including the power domain, time domain, frequency domain, and code domain, and uses successive interference cancellation (SIC) technology at the receiving end to eliminate the serial interference between users. It can also adjust the power allocation scheme according to the channel state information (CSI) of the users. The NOMA technology can improve the spectral utilization rate and meet the requirements such as massive user access. Based on the above advantages of the NOMA technology and its great prospect for meeting the higher spectral efficiency required by the massive access demand caused by the future Internet of Everything, NOMA has become a current research hotspot.

[0005] Considering the impact of IQ imbalance caused by the inherent defects of radio frequency transceiver devices in actual communication systems, in order to further improve the sum rate and security rate of the C-NOMA system under IQ imbalance conditions, a reasonable resource allocation strategy is designed to maximize the system performance. And through the analysis and collation of relevant research at home and abroad, it is found that most of the current cooperative NOMA systems assume ideal IQ balance conditions, rarely consider the impact of IQ imbalance on the system, have less analysis of multi-user scenarios, and lack research from the perspective of resource allocation. Therefore, the resource allocation problem of the cooperative NOMA system for multi-user scenarios under IQ imbalance conditions is further studied. In this paper, to ensure the maximization of the system sum rate, a user number control algorithm is introduced on the basis of optimizing the base station power allocation factor, an optimization problem is established under the constraint of ensuring the minimum requirements (Quality of Service, QoS) of users, and the impacts of the base station (Base Station, BS) power, relay power and location on the system sum rate under different IQ imbalance degrees are analyzed. On this basis, the security rate optimization problem of the system is studied. By introducing an eavesdropper to eavesdrop on the users' information, to maximize the security rate of the system, while optimizing the power allocation coefficient, the transmit power of the base station and the transmit power of the relay are optimized. Finally, the impact of IQ imbalance on the system performance and the impact of changing the node transmit power on the security rate are simulated. Summary of the Invention

[0006] The present invention aims to solve the above problems of the prior art. A method is proposed. The technical solution of the present invention is as follows:

[0007] A resource allocation method for maximizing the security rate of a cooperative NOMA system under IQ imbalance conditions, an eavesdropper is added to the model to study the security rate of the system. The whole system consists of a base station, an AF relay and multiple downlink users U n (n = 1,..., N), and the IQ imbalance existing at the base station, users and eavesdropper is fully considered. It includes the following steps:

[0008] 101. Consider a resource allocation model for maximizing the security rate of a cooperative NOMA system under an IQ imbalance condition, and determine whether there is a feasible solution to the maximization model.

[0009] 102. After simplifying the original problem through variable substitution, it is decomposed into three sub-problems for optimization based on the block coordinate descent algorithm. The successive convex approximation (SCA) is used to transform the original non-convex optimization problem into a convex optimization problem. The variable substitution means introducing auxiliary variables to simplify and replace the original objective function, and at the same time adding constraints containing the introduced variables;

[0010] 103. Then, the interior point method is used to solve the convex optimization problem after equivalent transformation in step 102, and the base station transmission power P is solved. S And the relay transmission power P R , and the maximum security rate under all constraint conditions is obtained, solving the problem of the maximum security rate of the system that meets the requirements.

[0011] First, the entire communication process is completed in two time slots, and the channels between nodes also follow Rayleigh fading. Without loss of generality, it is assumed that the channel state information is known, |h1| 2 ≤…≤|h N | 2 .

[0012] In the first time slot, the NOMA protocol is used to superimpose and send signals to the relay. Considering the IQ imbalance at the sending end, the signal received by the relay is expressed as:

[0013]

[0014] where h S represents the channel gain of the base station to relay link; g T and φ T represent the amplitude and phase imbalance factors of the base station sending end respectively; P S represents the transmission power of the base station; a n represents the total power ratio factor allocated to different users by the base station through power multiplexing technology, and is used to distinguish different users; represents the background noise at the relay.

[0015] In the second time slot, the relay amplifies and forwards the received y R signal, and the amplification factor G is expressed as:

[0016] In the formula, P R represents the transmission power of the relay node. The eavesdropper eavesdrops on the signal amplified and forwarded by the relay. In the ideal state, the signal received by user U n is expressed as:

[0017] where h n represents the channel gain of the relay to user link; represents the background noise at user U n . However, due to the IQ imbalance at the receiving end, the signal received by user n is expressed as:

[0018]

[0019] Similarly, the signal intercepted by the eavesdropper, since the interference signal sent by the base station can be expressed as:

[0020]

[0021] In the above formula, h e represents the channel gain of the relay-to-eavesdropper link; represents the background noise at the eavesdropper; g R and φ R represent the amplitude and phase imbalances at the receiving end respectively. Without loss of generality, when there is a perfect match, g R =1, φ R =0°, at this time u r =1, v r =0. According to the SIC technology, in the case of known perfect CSI, the signal-to-noise ratio of user U n is expressed as:

[0022]

[0023] Substitute the amplification factor G and further simplify it by variable substitution as:

[0024]

[0025] D=(u r | 2 +|v r | 2 )

[0026] Further simplify it to:

[0027] where, X n =D|h s | 2 (u t | 2 +|v t | 2 );

[0028] Similarly, for the eavesdropper, its signal-to-interference-plus-noise ratio SINR is expressed as:

[0029] Substitute G and further simplify it to:

[0030] A e =|u r u t h e h S +v r v t he h S | 2 ,

[0031] C e =|u r +v r | 2 |h e 2 ,D=|u r | 2 +|v r | 2

[0032] The rates of user n and the rate of the eavesdropper can be expressed as:

[0033]

[0034] The secure rate of the user is defined as where [·] + denotes

[0035] The objective function for maximizing the system secure rate is:

[0036]

[0037] C3:a n ≥0, n = 1, ..., N

[0038]

[0039] where C1 represents the maximum transmit power constraint of the BS; C2 represents the maximum transmit energy constraint condition of the relay; C3 represents the constraint of the non - negative power allocation factor; C4 represents the total transmit power constraint condition of the BS; 1 / 2 represents two time slots. For simplicity, the + is omitted in the following equations because the secure rate has a minimum value of 0.

[0040] Subsequently, for the given P n (n = 1, ..., N), the power allocation coefficient a is optimized. First, expand the expression of the secure rate of the nth user in the objective function and introduce the exponential variable S and P R . It is: as:

[0041]

[0042] It can be seen that it is composed of four types of functions. Also, since the type of function is a convex function with respect to u i , then The type is a concave function, so here we mainly deal with the first and third functions. First, This is a about u i The convex function of is expanded into a linear function by the first-order Taylor to obtain a lower bound.

[0043]

[0044] Similarly, continue processing Expand its first-order Taylor expansion into a linear function to obtain a lower bound.

[0045]

[0046] Further simplify the above formula:

[0047]

[0048] Therefore, the original problem is transformed into

[0049]

[0050] In the formula, About U i Linear function of -ω(P S ,P R ,u i ) is about u i Concave function of ; About U i Linear function of -θ(P S ,P R ,u i ) is about u i Therefore, the objective function is a joint concave function, and maximizing a concave function is a convex optimization problem. Then, the constraint C1 is processed as

[0051] The lower level set of a convex function is a convex set. At this point, the original problem is transformed into the following optimization problem:

[0052]

[0053] At a given P S , P R Under the condition of, it is obvious that this is a convex optimization problem, and the iterative solution is Substitute and find the answer

[0054] Secondly, optimize P S , the specific steps are:

[0055] For 3 we get and the given P RFurther solving, the objective function is expanded into a combination of four 's, and this type of function is a concave function with respect to x k .

[0056]

[0057] Introduce variables to substitute and simplify the above formula:

[0058]

[0059] For , perform processing on it, and perform the first-order Taylor expansion at with respect to P S to obtain a lower bound:

[0060]

[0061] Then process and perform the first-order Taylor expansion at with respect to P S to obtain a lower bound:

[0062]

[0063] The original optimization problem is transformed into:

[0064]

[0065] In the above formula, given P R , , is a concave function with respect to P S ; is a linear function with respect to P S ; is a concave function with respect to P S ; is a linear function with respect to P S . Therefore, this optimization problem is a convex optimization problem.

[0066] Optimize P R , and the specific steps are as follows:

[0067] For the obtained and further solve. As can be seen from the above, the objective function is expanded into a combination of four 's, and on the basis of the previous ones, each function is simplified in turn.

[0068]

[0069] Similarly, process and perform it at Perform a first-order Taylor expansion at this point for P R to obtain a lower bound.

[0070]

[0071] Then process Perform a first-order Taylor expansion of it at for P R to obtain a lower bound.

[0072]

[0073] The original optimization problem is then transformed into:

[0074]

[0075] In the above formula, given the case of is a concave function with respect to P R ; is a linear function with respect to P R ; is a concave function with respect to P R ; is a linear function with respect to P R . Therefore, this optimization problem is a convex optimization problem.

[0076] Then update η * , and the specific steps are as follows: According to the optimal values of the power allocation optimized by the first three sub-problems Optimal transmit power of the base station Optimal transmit power of the relay Update η * .

[0077] The advantages and beneficial effects of the present invention are as follows:

[0078] The present invention adds the case of IQ imbalance under the original extensive perfect row-to conditions, and at the same time introduces eavesdroppers to optimize the physical layer security rate. The security rate maximization is studied in a cooperative NOMA system under an IQ imbalance condition. Since the original problem of maximizing the security rate is a non-convex problem, it is a difficult problem to give the optimal solution.

[0079] Under the condition of considering the addition of eavesdroppers and using NOMA decoding, while ensuring the minimum communication rate requirements of legitimate users, in step 102, the original non-convex problem is transformed into a convex optimization problem by adopting SCA. Variable substitution is used to simplify and substitute the original objective function by introducing auxiliary variables, and at the same time, constraints containing the introduced variables are added. Then, the interior point method is used to solve the convex optimization problem equivalent to step 102, and the base station transmission power P is solved. S And the relay transmission power P R , and the maximum security rate under all constraint conditions is obtained, solving the problem of the maximum security rate of the system that meets the requirements.

[0080] The present invention has the advantages of low polynomial complexity and simple solution method compared with other traditional NOMA systems based on wireless energy transfer. It guarantees the security rate of legitimate users from the perspective of the physical layer. Innovatively, the IQ imbalance state in actual production is considered at the same time, making the present invention more in line with the actual situation. The present invention is suitable for NOMA systems based on wireless energy transfer under IQ imbalance conditions, and has good feasibility and practicality. Brief Description of the Drawings

[0081] Figure 1 is the NOMA system model based on wireless energy transfer under IQ imbalance conditions provided by the preferred embodiment of the present invention;

[0082] Figure 2 is the maximum security rate of the present invention and the comparison method under different maximum transmission powers of the base station;

[0083] Figure 3 is the maximum security rate of the present invention and the comparison method under different relay positions;

[0084] Figure 4 is the maximum security rate of the present invention and the comparison method under different maximum transmission powers of the relay.

[0085] Figure 5 represents the flowchart of the resource allocation method for maximizing the security rate of the cooperative NOMA system under IQ imbalance conditions of the present invention. Detailed Embodiments

[0086] Next, the technical solutions in the embodiments of the present invention will be clearly and detailedly described in conjunction with the drawings in the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention.

[0087] The technical solution of the present invention to solve the above technical problems is:

[0088] Figure 2-4Resource Allocation Method for NOMA System Based on Wireless Energy Transfer under IQ Imbalance Conditions. It includes the following steps:

[0089] Step 1: Calculate the feasibility of the problem, and it is guaranteed that there must be a feasible solution in the constraints of the optimization problem;

[0090] Step 2: The entire communication process is completed in two time slots, and the channels between nodes also follow Rayleigh fading. Without loss of generality, assume that the channel state information is known, |h1| 2 ≤…≤|h N | 2 .

[0091] In the first time slot, the NOMA protocol is used to superimpose and send signals to the relay. Considering the IQ imbalance at the transmitter,

[0092] In the second time slot, the relay amplifies and forwards the received y R signal

[0093] Step 3: Based on the objective function of maximizing the secrecy rate of the cooperative NOMA system under IQ imbalance conditions:

[0094]

[0095] C3: a n ≥0, n = 1,..., N

[0096]

[0097] where, C1 represents the maximum transmit power constraint of the BS; C2 represents the maximum transmit energy constraint condition of the relay; C3 represents the constraint of the non - negative power allocation factor; C4 represents the total transmit power constraint condition of the BS; 1 / 2 represents two time slots. For simplicity, the + is omitted in the following expressions because the secrecy rate has a minimum value of 0.

[0098] Step 4: Optimize the power allocation coefficient a n (n = 1,..., N)

[0099] For the given P S and P R , first expand the secrecy rate expression of the nth user in the objective function and introduce the exponential variable

[0100] Transform the original problem into

[0101]

[0102] wherein, is about ui is a linear function of; -ω(P S , P R , u i ) is a concave function of u i ; is a linear function of u i ; -θ(P S , P R , u i ) is a concave function of u i . Therefore, the objective function is a jointly concave function, and maximizing a concave function is a convex optimization problem. Next, dealing with the constraint C1 gives The lower level set of a convex function is a convex set. Thus, the original problem is transformed into the following optimization problem:

[0103]

[0104] Given P S , P R , obviously this is a convex optimization problem, and iterative solution gives Substituting to find

[0105] Step 5: Optimize P S , and this step specifically involves further solving for the obtained in Step 4 and the given P R . Then the original optimization problem is transformed into:

[0106]

[0107] In the above formula, given P R , , is a concave function of P S ; is a linear function of P S ; is a concave function of P S ; is a linear function of P S . Therefore, this optimization problem is a convex optimization problem.

[0108] Step 6: Optimize P R , and the specific steps involve further solving for the and obtained in Step 4 and Step 5. As can be seen from Step 4, the objective function expands into a combination of four . On this basis, each function is simplified sequentially.

[0109]

[0110] The original optimization problem is transformed into:

[0111]

[0112] In the above formula, given , is a concave function with respect to P R ; is a linear function with respect to P R ; is a concave function with respect to P R ; is a linear function with respect to P R . Therefore, this optimization problem is a convex optimization problem.

[0113] Step 7: Update η * , and the specific steps are

[0114]

[0115] According to the optimal values of the power allocation optimized by the first three sub-problems The optimal transmit power of the base station transmit power The optimal transmit power of the relay Update η * .

[0116] In this example, Figure 1 is a preferred embodiment provided by the present invention based on a cooperative NOMA system model under an IQ imbalance condition. In the figure, the base station transmits signals to the relay node through wireless transmission, and the relay node uses the collected signals to re-transmit information to multiple users and eavesdroppers. Figure 2 Describes the variation relationship of the security rates of two algorithms under different maximum transmit powers of the base station and different degrees of IQ imbalance. It can be seen from the figure that the security rate increases to a certain extent as the base station power increases, but finally levels off. On the one hand, the channel capacity of the system reaches the peak, and on the other hand, the existence of eavesdroppers causes the security rate to reach the peak faster. And as the degree of IQ imbalance increases, the performance of both algorithms will decrease. When the IQ imbalance is relatively serious, the increase in base station power has a small performance gain for the system. In addition, the proposed C-NOMA algorithm is superior to the traditional C-OMA algorithm in terms of the performance of the security rate; Figure 3The influence of two algorithms on the system security rate by changing the position of the cooperative relay is analyzed. By changing the relay position, the channel gain from the user to the relay and the channel gain of the eavesdropper are adjusted. The size of the system security rate under different relay positions and different IQ imbalance degrees is compared to find an optimal relay position. It can be seen that due to the existence of the eavesdropper, both algorithms tend to increase the distance between the eavesdropper and the relay, and the position of the relay tends to be more in the direction of the base station, while C-OMA tends to be more in the direction of the user. And when the transmit power of the base station is 30 dBm and the transmit power of the relay is 20 dBm. When the relay is moved from (-5, 0) to (15, 0) m, the system performance decreases by about 17%. Figure 4 The influence of two algorithms on the system security rate by changing the position of the cooperative relay is analyzed. By changing the relay position, the channel gain from the user to the relay and the channel gain of the eavesdropper are adjusted. The size of the system security rate under different relay positions and different IQ imbalance degrees is compared to find an optimal relay position. It can be seen that due to the existence of the eavesdropper, both algorithms tend to increase the distance between the eavesdropper and the relay, and the position of the relay tends to be more in the direction of the base station, while C-OMA tends to be more in the direction of the user. And when the transmit power of the base station is 30 dBm and the transmit power of the relay is 20 dBm. When the relay is moved from (-5, 0) to (15, 0) m, the system performance decreases by about 17%.

[0117] The systems, devices, modules or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0118] It should also be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, commodity or device including the said element.

[0119] The above embodiments should be understood as being only for illustrative purposes of the present invention and not for limiting the scope of protection of the present invention. After reading the content described in the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A resource allocation method for maximizing the security rate of a cooperative NOMA system under IQ imbalance conditions, characterized in that, It includes the following steps:

101. Initialize the maximum base station power, maximum relay power, three noises, and the number of users. Establish a mathematical model for the original security rate optimization problem and find that the original optimization problem is a non-convex problem; 102. After simplifying the original problem through variable substitution, decompose it into three sub-problems for optimization based on the block coordinate descent algorithm. Use SCA to transform the original non-convex optimization problem into a convex optimization problem. Variable substitution means introducing auxiliary variables to simplify and replace the original objective function, and at the same time adding constraints containing the introduced variables; 103. Then, the interior point method is used to solve the convex optimization problem after the equivalent transformation in step 102, and the base station transmission power P is solved. S And the relay transmission power P R , the maximum security rate under all constraints is obtained, and resource allocation is performed. The entire communication process is completed in two time slots, and the channels between nodes also follow Rayleigh fading. Assuming that the channel state information is known, |h1| 2 ≤…≤|h N | 2 ; where h1, h N respectively represent the channel gains from the relay to each user; In the first time slot, signals are transmitted in superposition using the NOMA protocol to the relay, x s represents the superposed transmitted signal, P S represents the transmission power of the base station, a n represents the total power ratio factor allocated by the base station to different users through power multiplexing technology, and different users are distinguished by this, x n represents the signals of each user, n and N respectively represent the nth user and the total number of users. Considering the IQ imbalance at the transmitting end, in the second time slot, the relay amplifies and forwards the received y R signal The mathematical model for the security rate problem of the cooperative NOMA system under the original IQ imbalance condition is established, and the maximized objective function is: C3:a n ≥0, n = 1, ..., N Among them, a J , P R represents the transmission power R of the relay node n , respectively represent the rate of the nth user and the rate of its eavesdropper, respectively represent the maximum base station transmission power and relay power, a i represents a non - negative power allocation factor; C1 represents the maximum transmission power constraint of the BS; C2 represents the maximum transmission energy constraint condition of the relay; C3 represents the constraint of the non - negative power allocation factor; C4 represents the total transmission power constraint condition of the BS; The use of SCA to transform the original non-convex optimization problem into a convex optimization problem specifically includes: Optimize the power distribution coefficient a n (n = 1,..., N); For a given P S and P R , first expand the security rate expression of the nth user in the objective function and introduce the exponential variable u n to represent the nth user; Transform the original problem into wherein, is a linear function with respect to u i ; u i respectively represent the iterative and original unbalanced channels; -ω(P S , P R , u i ) is a concave function with respect to u i ; is a linear function with respect to u i ; -θ(P S , P R , u i ) is a concave function with respect to u i ; thus the objective function is a joint concave function, and maximizing a concave function is a convex optimization problem. Then, dealing with the constraint C1 gives the lower level set of a convex function is a convex set; thus, the original problem is transformed into the following optimization problem: Given P S , P R , obviously this is a convex optimization problem, and the iterative solution gives Substitute and solve for Optimize P S , specifically, for the obtained and the given P R further solve, and the original optimization problem is transformed into: In the above formula, given P R , , the following holds: is a concave function with respect to P S ; is a linear function with respect to P S ; is a concave function with respect to P S ; is a linear function with respect to P S ; thus, this optimization problem is a convex optimization problem; Optimize P S , and the specific steps are as follows: For the obtained and the given P R Solve further, and the objective function expands into a combination of four The function of this type is a concave function with respect to x k ; Introduce variables for substitution and simplification in the above formula: Process and perform a first-order Taylor expansion on it at with respect to P S to obtain a lower bound: Then process at it in for P at S perform a first-order Taylor expansion to obtain a lower bound: The original optimization problem is transformed into: where, for a given P R , , the following holds is a concave function with respect to P S ; is a linear function with respect to P S ; is a concave function with respect to P S ; is a linear function with respect to P S ; thus, this optimization problem is a convex optimization problem.

2. The resource allocation method for maximizing the security rate of a cooperative NOMA system under IQ imbalance conditions according to claim 1, wherein, For P R The optimizations specifically include: For the obtained and Solve further. The objective function expands into a combination of four and simplify each function in turn; The original optimization problem is transformed into: In the above formula, A n represents the decoding order. Given , is a concave function with respect to P R ; is a linear function with respect to P R ; is a concave function with respect to P R ; is a linear function with respect to P R ; therefore, this optimization problem is a convex optimization problem. Update η * where η * represents the optimized safety rate. The specific steps are as follows Optimal value of power allocation optimized according to the first three sub - problems Optimal transmit power of the base station Optimal transmit power of the relay Update η * 。

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