A Robust Resource Scheduling Method for Cognitive Backscatter Communication Oriented to Information Security

Abstract

The present invention relates to a robust resource scheduling method for cognitive backscatter communication for information security, belonging to the field of the Internet of Things. The method includes the following steps: S1: Establish a transmission model for a multi-user cognitive backscatter communication system; S2: Considering the constraints such as the quality of service of cognitive users, reflection coefficient, energy harvesting, transmission time, interference power, etc., construct a resource scheduling problem for maximizing the throughput of the cognitive backscatter communication system; S3: Considering spectrum sensing errors and bounded channel uncertainties, establish a multi-variable coupled non-linear robust resource scheduling model; S4: Use the worst-case criterion, successive convex approximation, and alternating optimization method to transform the original problem into a deterministic convex optimization problem, and use convex optimization tools to solve it to obtain the scheduling methods for transmit power, transmission time, and reflection coefficient. The present invention can effectively improve the transmission security and robustness of the passive Internet of Things system, and has high practical value.

Description

Technical Field

[0001] The present invention belongs to the field of the Internet of Things, and relates to a robust resource scheduling method for cognitive backscatter communication for information security. Background Art

[0002] In order to solve the problems of limited battery capacity and low network operating life in low-power Internet of Things, backscatter communication has emerged. However, with the increase in the number of Internet of Things nodes, there is a problem of spectrum shortage in the backscatter communication network. To address this problem, cognitive radio is introduced into the backscatter communication technology to form a new type of cognitive backscatter communication mode. Cognitive backscatter communication has the dual advantages of improved spectrum efficiency and reduced energy consumption. The cognitive backscatter communication system consists of a primary system and a secondary system. The secondary system shares the spectrum resources of the primary users, effectively improving the spectrum efficiency of the communication system. On the premise of ensuring the communication quality of the primary users, the secondary system uses the radio frequency signals of the primary system for wireless power supply and modulates its own signals on the radio frequency source signals to achieve low-power reflection communication.

[0003] Although many existing methods have studied cognitive backscatter communication, they are completed under ideal assumptions such as perfect spectrum sensing, perfect channel state information, and no information leakage, ignoring the adverse effects of actual factors such as spectrum misdetection probability, channel uncertainty, and user eavesdropping. At the same time, it is also unrealistic to assume that all nodes or transceivers work in a stable ideal operating state (i.e., perfect hardware conditions). However, in an actual backscatter communication system, the use of low-cost hardware components by equipment manufacturers to reduce deployment costs may cause the system to suffer serious hardware impairments. These hardware impairments may lead to a further decline in system performance. Therefore, for the convenience of practical engineering applications, there is an urgent need for a method that can improve the robustness of cognitive backscatter communication systems. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a robust resource scheduling method for cognitive backscatter communication for information security, which solves the problems that existing methods for studying cognitive backscatter communication are completed under ideal assumptions such as perfect spectrum sensing, perfect channel state information, and no information leakage, while ignoring the adverse effects of actual factors such as spectrum misdetection probability, channel uncertainty, and user eavesdropping, and assuming that all nodes or transceivers work in a stable ideal operating state, thus being inconvenient for practical engineering applications. The present invention converts the system performance optimization problem into a deterministic convex optimization problem by using the worst-case criterion, successive convex approximation, and alternating optimization methods, and obtains the scheduling methods for transmit power, transmission time, and reflection coefficient, effectively improving the transmission security and robustness of the passive Internet of Things system, thus facilitating practical engineering applications.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A robust resource scheduling method for cognitive backscatter communication for information security, the method comprising the following steps:

[0007] S1: Establish a transmission model for a multi-user cognitive backscatter communication system;

[0008] S2: Considering the quality of service, reflection coefficient, energy harvesting, transmission time, and interference power constraints of cognitive users, construct a throughput maximization resource scheduling problem for the cognitive backscatter communication system;

[0009] S3: Considering spectrum sensing errors and bounded channel uncertainties, convert the throughput maximization resource scheduling problem in S2 into a multi-variable coupled non-linear robust resource allocation problem;

[0010] S4: Use the worst-case criterion, successive convex approximation, and alternating optimization method to convert the non-linear robust resource allocation problem in S3 into a deterministic convex optimization problem, and solve it using convex optimization tools to obtain the scheduling methods for transmit power, transmission time, and reflection coefficient.

[0011] Further, in S1, the multi-user cognitive backscatter communication system includes: a primary base station, a primary receiver, a cognitive backscatter user, an information receiver, and an eavesdropper;

[0012] The primary base station transmits signals to the cognitive backscatter user, the eavesdropper, and the primary receiver, and the cognitive backscatter user transmits signals to the eavesdropper and the primary receiver;

[0013] The cognitive backscatter user is sequentially provided with a spectrum sensing module, a radio frequency energy harvesting module, a backscatter circuit, and an active transmission circuit;

[0014] The cognitive backscatter user uses time division multiple access for data transmission during the transmission time, and the entire transmission time frame T includes a reflection transmission stage T B and an active transmission stage T A ;

[0015] During the reflection transmission stage, the primary receiver transmits data on the authorized spectrum;

[0016] During the active transmission stage, the primary receiver stops transmitting on the authorized spectrum;

[0017] The cognitive backscatter user uses the spectrum sensing module to access the primary user spectrum resources, and are the times for the k-th cognitive backscatter user to perform backscattering and active transmission respectively, satisfying and

[0018] Let \(K\) denote the number of cognitive backscatter users.

[0019] Furthermore, in step S1, by considering that the received residual hardware impairments at the cognitive backscatter users and the cognitive information receiver will distort the desired received signals, the established signal transmission model specifically includes: Represent the transmitted signal of the \(k\) -th cognitive backscatter user as:

[0020]

[0021] where \(x\) P represents the transmitted signal of the macro - base station, \(c\) k is the signal of the \(k\) -th cognitive backscatter user, satisfying \(\beta\) k is the reflection coefficient of the \(k\) -th cognitive backscatter user, \(f\) k represents the channel gain from the macro - base station to the \(k\) -th cognitive backscatter user, is the distortion noise caused by hardware impairments, and represents the hardware impairment level parameter at the \(k\) -th cognitive backscatter user;

[0022] During when the \(k\) -th cognitive backscatter user performs backscattering, the signals received by the cognitive information receiver and the eavesdropper are respectively:

[0023]

[0024] where \(f\) P and \(f\) E respectively represent the channel gains from the macro - base station to the cognitive information receiver and the eavesdropper, \(h\) k and \(g\) k respectively represent the channel gains from the \(k\) -th cognitive backscatter user to the cognitive information receiver and the eavesdropper, \(n\) B,R is the distortion noise caused by hardware impairments, and \(CN(\ )\) represents the complex Gaussian distribution, \(P_0\) represents the total transmitted power of the macro - base station, \(\kappa\) R represents the hardware impairment level parameter at the cognitive information receiver, \(n\) U and \(n\) E represent the Gaussian white noises at the cognitive information receiver and the eavesdropper, satisfying and represent the variances of the noises at the user side, represents the variance of the noise at the eavesdropper side;

[0025] During the throughputs of the \(k\) -th cognitive backscatter user and the eavesdropper are respectively:

[0026]

[0027] Among them, and respectively represent the rates of the k-th cognitive backscatter user and the eavesdropper in the reflection transmission stage;

[0028] Within T B the secrecy rate of the k-th cognitive backscatter user is:

[0029]

[0030] When the k-th cognitive backscatter user performs backscattering within the remaining cognitive backscatter users perform energy harvesting. The energy introduced by the hardware impairment distortion noise and the Gaussian white noise is much smaller than the energy harvesting introduced by the useful information part. Ignoring the energy harvesting introduced by the hardware impairment and the noise, the total energy harvested by the k-th cognitive backscatter user is:

[0031]

[0032] Among them, η represents the energy harvesting coefficient. Within if the cognitive backscatter user detects through spectrum sensing that the primary receiver does not use the authorized spectrum, then the cognitive backscatter user uses the energy harvested in the reflection transmission stage and actively transmits data to the cognitive information receiver through time division multiple access. When the cognitive backscatter user performs active transmission, the signals received by the cognitive information receiver and the eavesdropper are:

[0033]

[0034] Among them, p k is the transmission power of the k-th cognitive backscatter user in the active transmission stage; and n A,R are the hardware impairments at the k-th cognitive backscatter user and the cognitive information receiver respectively, satisfying and Then within the throughputs of the k-th cognitive backscatter user and the eavesdropper are respectively expressed as:

[0035]

[0036] Among them, and respectively represent the rates of the k-th cognitive backscatter user and the eavesdropper in the active transmission stage. Similarly, within the secrecy rate satisfied by the k-th cognitive backscatter user is:

[0037]

[0038] The interference temperature model is proposed to describe the interference magnitude at the primary receiver side, so that the normal communication of the primary user is not affected when sharing resources; the interference generated by the k-th cognitive backscatter user to the m-th primary receiver satisfies the following constraint:

[0039]

[0040] where g k,m is the channel gain from the k-th cognitive backscatter user to the m-th primary receiver, is the maximum interference threshold generated by the k-th cognitive backscatter user to the m-th primary receiver;

[0041] The total energy consumption of the k-th cognitive backscatter user is:

[0042]

[0043] where and respectively represent the circuit consumption of the k-th cognitive backscatter user during backscatter and active transmission.

[0044] Furthermore, in S2, considering the quality of service, reflection coefficient, energy harvesting, transmission time, and interference power constraint conditions of cognitive users, a resource scheduling problem for maximizing the throughput of a cognitive backscatter communication system is constructed, including:

[0045] Under perfect channel state information, the resource allocation problem for maximizing the throughput of the secondary system, including:

[0046]

[0047] C7: 0 ≤ β k ≤ 1 (15)

[0048] where and represent the minimum secrecy rate thresholds of the k-th cognitive backscatter user in the reflection transmission and active transmission phases, C1 represents the minimum secrecy rate constraint of the k-th cognitive backscatter user in the reflection transmission phase, C2 represents the minimum secrecy rate constraint of the k-th cognitive backscatter user in the active transmission phase, C3 represents the energy collection and consumption constraint of the k-th cognitive backscatter user, C4 represents the maximum interference constraint of the k-th cognitive backscatter user to the m-th primary receiver, C5 represents the time allocation constraint in the reflection transmission phase, C6 represents the time allocation constraint in the active transmission phase, and C7 represents the range of the reflection coefficient of the k-th cognitive backscatter user.

[0049] Furthermore, in S3, considering the spectrum sensing error and bounded channel uncertainty, a non-linear robust resource allocation problem with multivariable coupling is established, specifically as follows:

[0050] Define q fa and q md as the false alarm probability and the miss detection probability of the cognitive backscatter user, respectively, and q o as the probability that the primary receiver occupies the authorized spectrum; considering the influence of the spectrum sensing error, the true throughput of the cognitive backscatter user is expressed as:

[0051]

[0052] where is the probability that the authorized spectrum is occupied and the cognitive backscatter user senses that the authorized spectrum is occupied; is the probability that the authorized spectrum is idle and the cognitive backscatter user senses that the authorized spectrum is idle;

[0053] The interference constraint generated by the k-th cognitive backscatter user on the m-th primary receiver is re-expressed as:

[0054]

[0055] where represents the probability that the authorized spectrum is occupied and the cognitive backscatter user senses that the authorized spectrum is idle;

[0056] The energy collected and consumed by the k-th cognitive backscatter user is re-expressed as:

[0057]

[0058] Establish the following bounded wiretap channel uncertainty model:

[0059]

[0060] where and represent the channel uncertainty sets, and represent the channel estimation gains, Δg k and Δf E represent the channel estimation errors, and δ E represent the corresponding upper bounds of the channel estimation errors;

[0061] Then, the robust resource allocation problem P2 corresponding to P1 is:

[0062]

[0063] s.t.C5, C6, C7

[0064]

[0065] Among them, C8 represents the set of uncertainty parameters.

[0066] Furthermore, in the step S4, the problem constructed in S3 is transformed into a deterministic convex optimization form and solved, specifically including:

[0067] Using the worst-case principle continuous convex approximation method to handle the channel uncertainty in P2 and convert it into a deterministic problem, specifically:

[0068]

[0069] Combined with the uncertainty set, there is:

[0070]

[0071] For Δf E and Δg k coupling problem, let Using the Taylor expansion of the binary function, we get:

[0072]

[0073] The constraint condition is transformed into:

[0074]

[0075] Among them,

[0076] The constraint condition is transformed into:

[0077]

[0078] Among them, and

[0079]

[0080] The following deterministic optimization problem is obtained:

[0081]

[0082] s.t.C5, C6, C7

[0083]

[0084] To handle non-convexity, introduce auxiliary variables:

[0085]

[0086] a k ≤β k P0f k h k (30)

[0087]

[0088] wherein, a k ,a k ,c k and d k are slack variables;

[0089] Using the continuous convex approximation method and Taylor series expansion, it is:

[0090]

[0091] wherein, and are slack variables, and are respectively and the iteration values of the previous time; It is equivalent to:

[0092]

[0093]

[0094] wherein, φ k ,ξ k ,v k ,ζ k , and are slack variables, and are respectively and the iteration values of the previous time;

[0095] Define, and Then the problem after conversion is expressed as:

[0096]

[0097] Using the alternating optimization method, P4 is divided into two sub-problems:

[0098] Sub-problem 1: Fix and Solve for βk , p k , Λ and Θ;

[0099] Sub - problem 2: Fix β k , p k , Λ and Θ are solved and

[0100] Fix and Based on P4, the following sub - problem P4 - 1 is obtained:

[0101]

[0102] Fix β k , p k , Λ and Θ, based on P4, the following sub - problem P4 - 2 is obtained:

[0103]

[0104] Both P4 - 1 and P4 - 2 are convex problems, and are solved using the Matlab convex optimization toolbox - CVX;

[0105] By using the convex optimization tool to solve the two sub - problems, the transmission power, transmission time, reflection coefficient, i.e., the transmission scheme, are obtained.

[0106] Furthermore, when the increases, the reflection coefficient and transmission power of the cognitive backscatter user increase, and the system throughput increases.

[0107] The beneficial effects of the present invention are as follows: By using the worst - case criterion, successive convex approximation, and alternating optimization methods, the system performance optimization problem is transformed into a deterministic convex optimization problem. Compared with non - robust algorithms, the algorithm of the present invention has better robustness and effectively improves the transmission security and robustness of the passive Internet of Things system, thus facilitating practical engineering applications.

[0108] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0110] Figure 1 is the flow chart of the present invention;

[0111] Figure 2 It is a flow chart for solving the iterative-based robust resource allocation algorithm;

[0112] Figure 3 It is a graph showing the relationship between the interference outage probability and the channel error of the present invention, traditional robust algorithms without hardware impairment, traditional perfect spectrum sensing robust algorithms, and traditional non-robust algorithms. Specific embodiments

[0113] The following uses specific specific examples to illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0114] Among them, the drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and should not be construed as a limitation to the present invention; in order to better illustrate the embodiments of the present invention, some components in the drawings will be omitted, enlarged, or reduced, which do not represent the dimensions of actual products; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0115] In the drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc. indicating the orientation or positional relationship, they are based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the drawings are only for illustrative purposes and should not be construed as a limitation to the present invention. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0116] Please refer to Figures 1 to 3 , which is a robust resource scheduling method for cognitive backscatter communication for information security, mainly including the following parts:

[0117] The present invention proposes a robust resource scheduling method for cognitive backscatter communication for information security. As Figure 1 shown, the method includes the following content:

[0118] S1: Establish a transmission model for a multi-user cognitive backscatter communication system;

[0119] S2: Consider the constraint conditions such as the quality of service of cognitive users, reflection coefficient, energy harvesting, transmission time, interference power, etc., and construct a resource scheduling problem for maximizing the throughput of the cognitive system;

[0120] S3: Consider the spectrum sensing error and bounded channel uncertainty, and establish a multi-variable coupled non-linear robust resource allocation model;

[0121] S4: Use the worst-case criterion, successive convex approximation and alternating optimization method to transform the original problem into a deterministic convex optimization problem, and use convex optimization tools to solve it to obtain the scheduling methods of transmit power, transmission time, and reflection coefficient.

[0122] S1: Establish a transmission model for a multi-user cognitive backscatter communication system, including:

[0123] M primary receivers, K cognitive backscatter users, a cognitive information receiver (Cognitive Information Receiver), and an eavesdropper, and the user set is defined as Assume that both the transmitting and receiving ends of the devices are equipped with single antennas, and each cognitive backscatter user is equipped with a spectrum sensing module, a radio frequency energy harvesting module, a backscatter circuit, and an active transmission circuit. To reduce the mutual interference between cognitive backscatter users, the cognitive backscatter users all use time division multiple access for data transmission during the transmission time. The entire transmission time frame T is divided into two phases, namely, the reflection transmission phase T B and the active transmission phase T A , and T = T B +T A . Specifically, within T B , the primary receivers are active and transmit data on the authorized spectrum; within T A , the primary receivers are inactive and stop transmitting on the authorized spectrum. The cognitive backscatter users use spectrum sensing to access the primary user spectrum resources. The cognitive backscatter users use spectrum sensing to access the primary user spectrum resources. and are the times for the k-th cognitive backscatter user to perform backscattering and active transmission respectively, satisfying and

[0124] Considering that both the cognitive backscatter users and the cognitive information receiver will be affected by residual hardware impairments, and the residual hardware impairments will distort the desired received signal, the transmitted signal of the k-th cognitive backscatter user is expressed as

[0125]

[0126] Among them, x P represents the transmission signal of the macro base station, and c k is the signal of the k-th cognitive backscatter user, satisfying β k is the reflection coefficient of the k-th cognitive backscatter user, and f k represents the channel gain from the macro base station to the k-th cognitive backscatter user, is the distortion noise caused by hardware impairment, and represents the hardware impairment level parameter at the k-th cognitive backscatter user.

[0127] During when the k-th cognitive backscatter user performs backscattering, the signals received by the cognitive information receiver and the eavesdropper are respectively

[0128]

[0129] Among them, f P and f E respectively represent the channel gains from the macro base station to the cognitive information receiver and the eavesdropper, h k and g k respectively represent the channel gains from the k-th cognitive backscatter user to the cognitive information receiver and the eavesdropper, n B,R is the distortion noise caused by hardware impairment, and CN() represents the complex Gaussian distribution, P0 represents the total transmission power of the macro base station, κ R represents the hardware impairment level parameter at the cognitive information receiver. n U and n E represent the Gaussian white noise at the cognitive information receiver and the eavesdropper, satisfying and represent the variance of the noise at the user, represents the variance of the noise at the eavesdropper.

[0130] During the throughputs of the k-th cognitive backscatter user and the eavesdropper are respectively

[0131]

[0132] Among them, and respectively represent the rates of the k-th cognitive backscatter user and the eavesdropper in the reflection transmission stage.

[0133] At T BInside, to ensure the quality of user services for legitimate cognitive backscatter users, the secrecy rate that the k-th cognitive backscatter user needs to meet is

[0134]

[0135] When the k-th cognitive backscatter user performs backscattering within the remaining cognitive backscatter users perform energy harvesting. The energy harvesting introduced by hardware impairment distortion noise and Gaussian white noise is much smaller than the energy harvesting introduced by the useful information part. Therefore, for the convenience of calculation, the energy harvesting introduced by hardware impairment and noise is ignored, and the total energy harvested by the k-th cognitive backscatter user is

[0136]

[0137] where η represents the energy harvesting coefficient. Within if the cognitive backscatter users detect that the primary receiver does not use the authorized spectrum through spectrum sensing, the cognitive backscatter users use the energy harvested during the reflection transmission stage and actively transmit data to the cognitive information receiver through time division multiple access. When the cognitive backscatter users perform active transmission, the signals received by the cognitive information receiver and the eavesdropper are

[0138]

[0139] where p k is the transmit power of the k-th cognitive backscatter user during the active transmission stage; and n A,R are the hardware impairments at the k-th cognitive backscatter user and the cognitive information receiver respectively, satisfying and Then within the throughputs of the k-th cognitive backscatter user and the eavesdropper are respectively expressed as

[0140]

[0141] where and respectively represent the rates of the k-th cognitive backscatter user and the eavesdropper during the active transmission stage. Similarly, within the secrecy rate that the k-th cognitive backscatter user needs to meet is

[0142]

[0143] During the reflection transmission phase, the primary receiver occupies the licensed spectrum, and when the cognitive backscatter user performs backscattering, it will cause interference to the primary receiver. However, during the active transmission phase, the primary receiver does not occupy the licensed spectrum, so it will not cause interference to the primary receiver. To describe the magnitude of the interference at the primary receiver, the interference temperature model is proposed. The interference temperature model is mainly to ensure that the normal communication of the primary user is not affected when sharing resources. The interference generated by the k-th cognitive backscatter user on the m-th primary receiver needs to satisfy the following constraints

[0144]

[0145] where g k,m is the channel gain from the k-th cognitive backscatter user to the m-th primary receiver, is the maximum interference threshold generated by the k-th cognitive backscatter user on the m-th primary receiver.

[0146] The total energy consumption of the k-th cognitive backscatter user is

[0147]

[0148] where and represent the circuit consumption of the k-th cognitive backscatter user during backscattering and active transmission, respectively.

[0149] S2: Considering the constraints such as the quality of service of cognitive users, reflection coefficient, energy harvesting, transmission time, interference power, etc., construct a resource scheduling problem for maximizing the throughput of the cognitive system, including:

[0150] Under perfect channel state information, the resource allocation problem for maximizing the throughput of the secondary system, including:

[0151]

[0152] where and represent the minimum secrecy rate thresholds of the k-th cognitive backscatter user during the reflection transmission and active transmission phases, C1 represents the minimum secrecy rate constraint of the k-th cognitive backscatter user during the reflection transmission phase, C2 represents the minimum secrecy rate constraint of the k-th cognitive backscatter user during the active transmission phase, C3 represents the energy collection and consumption constraint of the k-th cognitive backscatter user, C4 represents the maximum interference constraint of the k-th cognitive backscatter user on the m-th primary receiver, C5 represents the time allocation constraint during the reflection transmission phase, C6 represents the time allocation constraint during the active transmission phase, and C7 represents the range of the reflection coefficient of the k-th cognitive backscatter user.

[0153] S3: Considering the spectrum sensing error and bounded channel uncertainty, establish a multi-variable coupled non-linear robust resource allocation model, including:

[0154] In an actual cognitive backscatter network, there will be spectrum sensing errors, resulting in the fact that cognitive backscatter users' use of spectrum sensing to judge whether the licensed spectrum is occupied by the primary receiver is not completely accurate. Spectrum sensing errors will lead to lower transmission efficiency of cognitive backscatter users, increased interference power to the primary receiver, and unnecessary energy consumption. Define q fa and q md as the false alarm probability and missed detection probability of cognitive backscatter users, and q o as the probability that the primary receiver occupies the licensed spectrum. Considering the influence of spectrum sensing errors, the true throughput of cognitive backscatter users is re-expressed as

[0155]

[0156] where is the probability that the licensed spectrum is occupied and the cognitive backscatter user senses that the licensed spectrum is occupied. is the probability that the licensed spectrum is idle and the cognitive backscatter user senses that the licensed spectrum is idle. The interference constraint generated by the k-th cognitive backscatter user to the m-th primary receiver is re-expressed as

[0157]

[0158] where represents the probability that the licensed spectrum is occupied and the cognitive backscatter user senses that the licensed spectrum is idle.

[0159] The energy collected and consumed by the k-th cognitive backscatter user is re-expressed as

[0160]

[0161] In an actual cognitive backscatter communication network, the position of the eavesdropper in the network is random and has no cooperation with the primary base station, making it impossible to accurately estimate the relevant channel state information. Therefore, consider the following bounded eavesdropping channel uncertainty model

[0162]

[0163] where, and represent the channel uncertainty set, and represent the channel estimation gains respectively, and Δg k and Δf E represent the channel estimation errors, and δ ERepresents the upper bound of the corresponding channel estimation error.

[0164] The robust resource allocation problem P2 corresponding to P1 is as follows:

[0165]

[0166] Among them, C8 represents the set of uncertainty parameters.

[0167] S4: Use the worst-case criterion, successive convex approximation, and alternating optimization method to convert the original problem into a deterministic convex optimization problem, and use convex optimization tools to solve it to obtain the scheduling methods for transmit power, transmission time, and reflection coefficient, specifically including:

[0168] S41: Use the worst-case criterion and successive convex approximation method to handle the channel uncertainty in P2 and convert it into a deterministic problem, specifically including:

[0169] The problem is a non-convex optimization with parameter perturbation and is difficult to solve directly. Based on this, for the uncertainty in, there is:

[0170]

[0171] Combined with the uncertainty set, there is For the Δf E and Δg k coupling problem in, let Using the Taylor expansion of a binary function, we can get:

[0172]

[0173] Among them, o(Δf E , Δg k ) is a higher-order infinitesimal. Therefore can be approximated as:

[0174]

[0175] Therefore, the constraint condition can be converted to:

[0176]

[0177] Among them, Similarly, the constraint condition can be transformed into:

[0178]

[0179] Among them, and

[0180] Obtain the following deterministic optimization problem

[0181]

[0182] Although P3 is a deterministic optimization problem, the coupling relationship of the optimization variables still makes the above problem unsolvable. To handle the non-convexity, introduce auxiliary variables

[0183]

[0184] where a k ,a k ,c k and d k are slack variables;

[0185] The problem is still non-convex. Using the continuous convex approximation method and Taylor series expansion, approximate it as

[0186]

[0187] where and are slack variables, and are respectively and the previous iteration values. Similarly, can be equivalent to

[0188]

[0189]

[0190] where φ k ,ξ k ,ν k ,ζ k , and are slack variables, and are respectively and the previous iteration values.

[0191] Define and Then the problem after transformation is expressed as

[0192]

[0193] S42: Convert the deterministic problem into two sub - problems using alternating optimization to handle the existing coupled variable constraints, specifically including:

[0194] Due to the existence of coupled variables (e.g., coupled with β k ), the objective function is still non - convex. The alternating optimization method is used to divide P4 into two sub - problems. Sub - problem 1: Fix and and solve for β k , p k , Λ and Θ; Sub - problem 2: Fix β k , p k , Λ and Θ, and solve for and

[0195] Fix and Based on P4, the following sub - problems are obtained

[0196]

[0197] Fix β k , p k , Λ and Θ, and based on P4, the following sub - problems are obtained

[0198]

[0199] It can be seen that both P4 - 1 and P4 - 2 are convex problems and can be directly solved using the Matlab convex optimization toolbox - CVX. Moreover, an iterative - based robust resource allocation algorithm and the algorithm solution process shown in Figure 2 are proposed.

[0200] The iterative - based robust resource allocation algorithm is as follows:

[0201] Initialize the system parameters:

[0202]

[0203] Define the algorithm convergence accuracy η > 0 and the maximum number of outer - layer iterations L max ; Initialize the outer - layer iteration count l = 0;

[0204] (1) while or l ≤ L max , do;

[0205] (2) Define the iteration count l = l + 1;

[0206] (3) Given the values of and , calculate and

[0207] (4) Fix and Calculate according to P4-2 and

[0208] (5) Update throughput

[0209] (6) end while;

[0210] (7) Output β k , p k

[0211] S43: Solve the two sub-problems using convex optimization tools to obtain the transmission power, transmission time, reflection coefficient, i.e., the transmission scheme, specifically including:

[0212] Both P4-1 and P4-2 are convex optimization problems. Therefore, the present invention directly uses convex optimization tools to solve these two sub-problems to obtain the transmission power, transmission time, reflection coefficient, i.e., the transmission scheme. Users in the cognitive backscatter communication network can perform information transmission according to the transmission scheme.

[0213] 1) Simulation conditions

[0214] In this section, simulations are carried out to verify the effectiveness of the proposed algorithm. It is assumed that all users are randomly distributed within a circle with a radius of 10m, and the channel model is h = κd -α , where represents the fading coefficient [4]. d represents the distance between the transceiver, and α ∈ [2, 5] represents the path loss exponent. The path loss factors from the primary base station to the cognitive backscatter user and the cognitive information receiver are both 2. The path loss factors from the cognitive backscatter user to the primary receiver, cognitive information receiver, and eavesdropper are 3, 2, and 3.5 respectively. Other simulation parameters are: M = 2, K = 2, P0 = 1W, T A = 0.5, T B = 0.5, q o ∈ [0, 1], q md ∈ [0.01, 0.05], q fa ∈ [0.05, 0.1], L max = 10 5 η = 10 -5 , and the upper limit of the channel estimation error is [0, 0.15].

[0215] 2) Simulation results

[0216] FromFigure 3 It can be seen that as increases, the system throughput of all algorithms increases. This is because as increases, the reflection coefficient and transmission power of cognitive backscatter users also increase, thereby increasing the system throughput. In addition, compared with the perfect spectrum sensing throughput maximization algorithm and the ideal hardware robust security throughput maximization algorithm, the throughput of the proposed algorithm in this paper is the lowest. This is because when users overcome the influence of spectrum sensing errors and hardware impairments, it will lead to a decrease in the throughput of cognitive backscatter users, thereby reducing the system throughput.

[0217] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the present technical solution, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A robust resource scheduling method for cognitive backscatter communication towards information security, characterized in that: The method includes the following steps: S1: Establish a transmission model for a multi-user cognitive backscatter communication system; Among them, the multi-user cognitive backscatter communication system includes: a primary base station, a primary receiver, a cognitive backscatter user, an information receiver, and an eavesdropper; The master base station transmits signals to the cognitive backscatter user, the eavesdropper, and the master receiver, and the cognitive backscatter user transmits signals to the eavesdropper and the master receiver; the cognitive backscatter user is sequentially provided with a spectrum sensing module, a radio frequency energy harvesting module, a backscatter circuit, and an active transmission circuit; the cognitive backscatter user uses time division multiple access for data transmission during the transmission time, and the entire transmission time frame T includes a reflection transmission stage T B and an active transmission stage T A ; during the reflection transmission stage, the master receiver transmits data on the authorized spectrum; during the active transmission stage, the master receiver stops transmitting on the authorized spectrum; the cognitive backscatter user uses the spectrum sensing module to access the primary user spectrum resource, and are the times for the k-th cognitive backscatter user to perform backscattering and active transmission respectively, satisfying and K represents the number of cognitive backscatter users; By considering that the residual hardware impairments suffered by the cognitive backscatter user and the cognitive information receiver will distort the desired received signal, the established signal transmission model specifically includes: expressing the transmitted signal of the k-th cognitive backscatter user as: where x P denotes the transmission signal of the primary base station, c k is the signal of the k-th cognitive backscatter user, satisfying β k is the reflection coefficient of the k-th cognitive backscatter user, f k represents the channel gain from the primary base station to the k-th cognitive backscatter user, is the distortion noise caused by hardware impairment, and represents the hardware impairment level parameter at the k-th cognitive backscatter user; Within when the k-th cognitive backscatter user performs backscattering, the signals received by the cognitive information receiver and the eavesdropper are respectively: where, f P and f E represent the channel gains from the primary base station to the cognitive information receiver and the eavesdropper respectively, h k and g k represent the channel gains from the k-th cognitive backscatter user to the cognitive information receiver and the eavesdropper respectively, n B,R is the distortion noise caused by hardware impairments, and CN() represents the complex Gaussian distribution, P0 represents the total transmit power of the primary base station, κ R represents the hardware impairment level parameter at the cognitive information receiver, n U and n E represent the additive white Gaussian noise at the cognitive information receiver and the eavesdropper respectively, satisfying represents the variance of the noise at the user side, represents the variance of the noise at the eavesdropper side; Within the throughput of the k-th cognitive backscatter user and the eavesdropper are respectively: Among them, and respectively represent the rates of the k-th cognitive backscatter user and the eavesdropper in the reflection transmission stage; Within T B The secrecy rate of the k-th cognitive backscatter user is given by: When the k-th cognitive backscatter user performs backscattering within , the remaining cognitive backscatter users perform energy harvesting. The energy introduced by the hardware impairment distortion noise and the Gaussian white noise is much smaller than the energy harvesting introduced by the useful information part. Ignoring the energy harvesting introduced by the hardware impairment and the noise, the total energy harvested by the k-th cognitive backscatter user is: where η represents the energy harvesting coefficient within If the cognitive backscatter user detects that the primary receiver does not use the licensed spectrum through spectrum sensing within , the cognitive backscatter user uses the energy harvested during the reflection transmission phase and actively transmits data to the cognitive information receiver through time division multiple access. When the cognitive backscatter user performs active transmission, the signals received by the cognitive information receiver and the eavesdropper are: where p k is the transmission power of the k-th cognitive backscatter user in the active transmission phase; and n A,R are the hardware impairments at the k-th cognitive backscatter user and the cognitive information receiver, respectively, satisfying and Then, in the throughputs of the k-th cognitive backscatter user and the eavesdropper are respectively expressed as: Among them, and respectively represent the rates of the k-th cognitive backscatter user and the eavesdropper in the active transmission phase. Similarly, within the secrecy rate satisfied by the k-th cognitive backscatter user is: A jamming temperature model is proposed to describe the magnitude of interference at the primary receiver end, so that when sharing resources, the normal communication of the primary user is not affected; the interference generated by the k-th cognitive backscatter user on the m-th primary receiver satisfies the following constraint: where \(g\) k,m is the channel gain from the \(k\)-th cognitive backscatter user to the \(m\)-th primary receiver, is the maximum interference threshold generated by the \(k\)-th cognitive backscatter user to the \(m\)-th primary receiver; The corresponding total energy consumption of the k-th cognitive backscatter user is: Among them, and respectively represent the circuit power consumption when the k-th cognitive backscatter user performs backscattering and active transmission S2: Considering the constraints of the quality of service, reflection coefficient, energy harvesting, transmission time, and interference power of the cognitive user, construct a throughput maximization resource scheduling problem for the cognitive backscatter communication system, including: Under perfect channel state information, the resource allocation problem for maximizing the throughput of the secondary system, including: wherein, and represent the minimum secrecy rate threshold of the k-th cognitive backscatter user in the reflection transmission and active transmission phases, C1 represents the minimum secrecy rate constraint of the k-th cognitive backscatter user in the reflection transmission phase, C2 represents the minimum secrecy rate constraint of the k-th cognitive backscatter user in the active transmission phase, C3 represents the energy collection and consumption constraint of the k-th cognitive backscatter user, C4 represents the maximum interference constraint of the k-th cognitive backscatter user on the m-th primary receiver, C5 represents the time allocation constraint in the reflection transmission phase, C6 represents the time allocation constraint in the active transmission phase, and C7 represents the range of the reflection coefficient of the k-th cognitive backscatter user; S3: Considering the spectrum sensing error and bounded channel uncertainty, convert the throughput maximization resource scheduling problem in S2 into a multi-variable coupled non-linear robust resource allocation problem; S4: Use the worst-case criterion, successive convex approximation, and alternating optimization method to convert the non-linear robust resource allocation problem in S3 into a deterministic convex optimization problem, and use a convex optimization tool to solve it to obtain the scheduling methods for the transmit power, transmission time, and reflection coefficient.

2. The robust resource scheduling method for cognitive backscatter communication for information security according to claim 1, characterized in that: In S3, considering the spectrum sensing error and bounded channel uncertainty, establish a multi-variable coupled non-linear robust resource allocation problem, specifically: Define q fa and q md as the false alarm probability and the misdetection probability of the cognitive backscatter user, and q o as the probability that the primary receiver occupies the authorized spectrum; considering the impact of spectrum sensing errors, the true throughput of the cognitive backscatter user is expressed as: where is the probability that the licensed spectrum is occupied and the cognitive backscatter user perceives that the licensed spectrum is occupied; is the probability that the licensed spectrum is idle and the cognitive backscatter user perceives that the licensed spectrum is idle; The interference constraint generated by the k-th cognitive backscatter user on the m-th primary receiver is re-expressed as: Among them, represents the probability that the licensed spectrum is occupied and the cognitive backscatter user perceives the licensed spectrum as idle; The energy collected and consumed by the k-th cognitive backscatter user is re-expressed as: Establish the following bounded eavesdropping channel uncertainty model: Among them, and represent the channel uncertainty set, and represent the channel estimation gains, Δg k and Δf E represent the channel estimation errors, and δ E represent the corresponding upper bounds of the channel estimation errors; Then, the robust resource allocation problem P2 corresponding to P1 is: Among them, C8 represents the set of uncertainty parameters.

3. A robust resource scheduling method for cognitive backscatter communication for information security according to claim 2, characterized in that: Convert the problem constructed in S3 into a deterministic convex optimization form and solve it, specifically including: Using the worst-case principle successive convex approximation method to handle the channel uncertainty in P2 and convert it into a deterministic problem, specifically: Combined with the uncertainty set, there is: For the Δf E and Δg k coupling problem, let Using the Taylor expansion of a binary function, we get: Constraints Convert to: Among them, Constraints Convert to: Among them, and Obtain the following deterministic optimization problem: To handle the non-convexity, auxiliary variables are introduced: a k ≤β k P0f k h k (30) Among them, a k , a k , c k and d k are slack variables; Adopt the successive convex approximation method and Taylor series expansion as: wherein, and are slack variables, and are respectively and the iteration values of the previous time; is equivalent to: Among them, φ k , ξ k , ν k , ζ k , and are slack variables, and are respectively and the iteration values of the previous time; Definition And The problem after conversion is represented as: Use the alternating optimization method to divide P4 into two sub-problems: Sub-problem 1: Fix and Solve for β k , p k , Λ and Θ; Sub-problem 2: Fix β k , p k , solve for Λ and Θ and Fixed and Based on P4, the following sub-question P4-1 is obtained: Fix β k , p k , Λ and Θ, the following sub-problem P4-2 is obtained based on P4: Both P4-1 and P4-2 are convex problems, and are solved using the Matlab convex optimization toolbox - CVX; Use a convex optimization tool to solve the two sub-problems to obtain the transmit power, transmission time, reflection coefficient, that is, the transmission scheme.

4. A robust resource scheduling method for cognitive backscatter communication for information security according to claim 3, characterized in that: When the increases, the reflection coefficient and transmission power of the cognitive backscatter user increase, and the system throughput increases.

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