Secure short packet transmission method in multi-label backscatter communication

Abstract

The invention relates to a safe short packet transmission method in multi-label backscatter communication, namely a safe short packet transmission method based on maximizing the minimum secrecy rate in all Internet of Things nodes in a short packet BackCom network. The method is realized by jointly optimizing the transmission power of a special energy source, the packet length of a short packet and the power reflection coefficient (PRC) of a backscattering label, a closed expression of the optimal PRC and the transmission power is derived, and an original problem is allowed to be decomposed into two manageable sub-problems. In order to effectively solve the sub-problems, an effective algorithm for solving the non-convex optimization problem by using continuous convex approximation SCA is provided, a theoretical basis is laid for designing a safe and energy-saving BackCom system, and particularly, the algorithm is customized for Internet of Things applications with strict delay and reliability requirements. The framework is easily adapted to other wireless communication scenarios with similar constraints.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a secure short packet transmission method in multi-label backscatter communication and a short packet BackCom network. Background Art

[0002] Backscatter communication (BackCom) has emerged as a promising solution for supporting large-scale low-power wireless connectivity in the Internet of Things (IoT). In backscatter communication, each IoT node splits the incident radio frequency (RF) signal into two parts based on its power reflection coefficient (PRC) [1]. One part is used for low-power information transmission, while the remaining part is collected at the IoT node to extend its service life.

[0003] Backscatter communication has been widely studied in IoT scenarios, where IoT nodes perform long-packet communication (LPC) and the packet length is infinite [2]–[4]. However, with the emergence of IoT applications that require the transmission of short packets, short-packet BackCom has emerged. Unlike LPC, short-packet communication (SPC) is affected by non-zero packet error probability (PEP), and its achievable rate cannot be described by Shannon capacity. Due to these differences, it is necessary to re-evaluate and optimize the performance of short-packet BackCom. For performance analysis, reference [5] studies the block error rate (BLER) and outage probability of BackCom in an environment with mixed long and short packets. Reference [6] analyzes the BLER performance of AMBC-NOMA-assisted vehicular transmission systems under high mobility and channel estimation errors. Reference [7] further extends the analysis of HARQ-assisted BackCom networks with mixed packet lengths. Reference [8] studied the relay-assisted cooperative BackCom system and derived closed-form expressions for the BLER and outage probability of the improved SIC decoding. Reference [9] focused on the relay-assisted short packet BackCom system and proposed an optimal power allocation strategy to minimize the BLER. In addition, reference

[10] conducted a random geometry-based analysis of the wireless powered asynchronous BackCom network and studied the performance of sparse short packets. Reference

[11] developed a general information theory framework, studied the STAR-RIS enhanced echo communication system, and derived the corresponding BLER upper bound. For performance optimization, reference

[12] proposed a joint optimization of packet length and reflection coefficient to minimize the error probability (EP). Reference

[13] solved a reliability-oriented resource allocation problem by jointly optimizing energy transmission power, packet length, and reflection coefficient, aiming to minimize the maximum error probability.

[0004] While these studies have achieved significant progress in optimizing throughput and error rates, physical layer security (PLS) in short-packet BackCom systems remains underexplored. The unique vulnerabilities of BackCom's passive operation and the limited security capacity imposed by finite packet lengths remain unaddressed. Existing PLS methods, designed for traditional wireless systems, fail to account for these limitations and are unable to ensure secure and reliable communications in next-generation IoT networks. Summary of the Invention

[0005] To solve the above problems, the present invention provides a secure short packet transmission method in multi-tag backscatter communication, which can significantly improve the data security during the communication process and the stability and effectiveness of short packet transmission, making it more in line with the application scenario requirements of IoT.

[0006] According to a first aspect of an embodiment of the present invention, a method for secure short packet transmission in multi-tag backscatter communication is provided, comprising the following steps:

[0007] Given a short packet length n k , the packet error rate of the legitimate user Bob and the information leakage rate of illegal user Eve The rate C at which approximate confidentiality can be achieved s,k expression;

[0008] Jointly optimize the transmission power P0 of the energy source and the short packet length n k , the power reflection coefficient β of each IoT node k ,construct the optimization problem of the overall security performance of the network;

[0009] According to the power reflection coefficient β of each IoT node k and the closed-form expression of the transmission power P0 of the optimal energy source, decoupling the optimization problem into two convex optimization subproblems;

[0010] For the two convex optimization subproblems, an iterative algorithm based on continuous convex approximation is used to solve them;

[0011] By comparing the optimal solutions of the subproblems, the minimum safety capacity between all node pairs is maximized.

[0012] Based on the above solution, the given short packet length n k , the packet error rate of the legitimate user Bob and the information leakage rate of illegal user Eve The rate C at which approximate confidentiality can be achieved s,k The steps of the expression include:

[0013]

[0014] in, and is the channel dispersion of the legitimate user Bob, is the channel dispersion of the illegal user Eve.

[0015] On the basis of the above scheme, the transmission power P0 of the energy source and the short packet length n k , the power reflection coefficient β of each IoT node k , the steps of constructing the optimization problem of the overall network security performance, where the optimization problem is:

[0016]

[0017] 0≤P0≤P max

[0018] Among them, C s,k represents the safety rate of the kth node, P c,k is the constant power consumption of the kth backscatter tag when executing BackCom; + represents the set of non-negative integers; P max is the maximum transmission power allowed by the energy source; in P1, is an energy causality constraint, ensuring that the total energy consumed by the kth backscatter tag does not exceed its harvested energy, constrained 0≤β k ≤1 and 0≤P0≤P max Limitations are imposed on the power reflection coefficient and energy transfer power, respectively.

[0019] Based on the above scheme, the power reflection coefficient β of each IoT node is k and the closed-form expression of the transmission power P0 of the optimal energy source, decoupling the optimization problem into two convex optimization subproblems, including:

[0020] S31: Consider the case of high signal-to-noise ratio, where the channel dispersion function can be approximated as The received signal strength r of the kth user can be k Approximately

[0021] S32: Derive the optimal power reflection coefficient β k , as shown below:

[0022]

[0023] S33: By changing the β of formula (8) k =1 and Replace with P1, and change β k =1 and Substitute separately In the example, according to the constraint 0≤β k ≤1 can be derived from the constraint and These two sub-problems can be simplified as follows:

[0024]

[0025]

[0026] 0≤P0≤P max

[0027]

[0028] in, is the feasibility condition limit of the power reflection coefficient;

[0029]

[0030]

[0031] 0≤P0≤P max

[0032]

[0033] in, is the limit of the total number of users N, is the feasibility condition limit of the power reflection coefficient.

[0034] Based on the above solution, step S33 further includes converting the original optimization problem into a convex optimization problem by a continuous relaxation method, specifically including:

[0035] Set P0=P max Substitute P2 and P3 and set the constraints n in k Relaxing to continuous variables can express the original optimization problem as follows:

[0036]

[0037]

[0038]

[0039] and

[0040]

[0041]

[0042]

[0043] By introducing λ and θ to express P 2-1 and P 3-1 in These two problems can be transformed into:

[0044]

[0045] st C s,k (n k )≥λ,

[0046]

[0047]

[0048] and

[0049]

[0050] Based on the above solution, the steps of solving the two convex optimization subproblems using an iterative algorithm based on continuous convex approximation specifically include:

[0051]

[0052]

[0053] in, and They represent the initial packet length distribution of the two sub-problems, ξ represents the maximum tolerance of the algorithm, C optim Represents the final optimal value achieved by the algorithm, t max and l max Indicates the maximum number of iterations for the two subproblems.

[0054] On the basis of the above scheme, the transmission power P0 of the energy source and the short packet length n k , the power reflection coefficient β of each IoT node k ,The steps of constructing the optimization problem of the overall network security performance include:

[0055] The minimum safety capacity between all nodes is maximized by jointly optimizing the short packet length n of all nodes and the power reflection coefficient β of all nodes, where n={n1,…,n K} and β={β1,…,β K}.

[0056] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0057] This application takes into account the inherent limited packet length effect of short packet transmission and proposes a comprehensive system model of BackCom involving multiple nodes. Under the constraints of energy, packet length and PRC, a multi-objective optimization problem is proposed to maximize the minimum confidentiality capacity; at the same time, an effective algorithm using continuous convex approximation (SCA) to solve non-convex optimization problems is proposed. The algorithm iteratively transforms the original problem into a series of convex subproblems to ensure convergence to the local optimal solution. This application lays the foundation for the design of secure and energy-saving BackCom systems, especially tailored for IoT applications with strict delay and reliability requirements. The framework is easily adapted to other wireless communication scenarios with similar constraints. Finally, simulations not only verify that the proposed algorithm converges very quickly, but also outperforms the baseline scheme in terms of confidentiality rate fairness compared to the baseline scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0059] Figure 1 is a system model of a short packet BackCom network according to an exemplary embodiment;

[0060] Figure 2 is a relationship diagram showing algorithm convergence according to an exemplary embodiment;

[0061] Figure 3 is a fairness comparison diagram of algorithms according to an exemplary embodiment;

[0062] Figure 4 is a graph showing a relationship between an average security rate and a packet length at different transmission power levels according to an exemplary embodiment;

[0063] Figure 5 FIG. 4 is a diagram showing a relationship between an average confidentiality rate and a maximum transmission power according to an exemplary embodiment. DETAILED DESCRIPTION

[0064] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0065] This application studies the physical layer security of short packet backscatter communication networks, in which multiple Internet of Things (IoT) nodes backscatter their respective information to legitimate recipients, while eavesdroppers attempt to intercept these transmissions. To achieve secure short packet transmission among IoT nodes, this application proposes a problem to maximize the minimum confidentiality rate among all IoT nodes by jointly optimizing the transmit power (TP) of a dedicated energy source, the short packet length, and the power reflection coefficient (PRC) of each IoT node. To solve this problem, this application first derives closed-form expressions for the optimal PRC and TP for each node, which enables the application to decompose the original problem into two subproblems. Then, this application proposes an iterative algorithm based on continuous convex approximation to efficiently solve these subproblems, thereby solving the original optimization problem. Simulation results show that the proposed algorithm converges quickly and outperforms the baseline scheme in terms of confidentiality rate fairness.

[0066] Figure 1 An embodiment of the short packet backscatter communication network of the present invention is shown.

[0067] The short packet backscatter communication network consists of an energy source, K backscatter tags, a legitimate user Bob, and an illegal user Eve. In this network, the energy source transmits energy signals to the K tags. The tags backscatter their information to the legitimate users, while the illegal users attempt to eavesdrop on the information. Specifically, the entire transmission block consisting of N channel usage is divided into K sub-phases. The parameter n is k represents the number of channels used in the kth sub-stage, satisfying the constraint To improve the total captured energy at each tag, the kth node performs short packet BackCom in the kth subphase and performs energy harvesting (EH) in the remaining block. This application assumes that all channels follow quasi-static block fading. Since the eavesdropper is actually a legitimate user from the previous communication phase, the channel state information (CSI) from the tag to the eavesdropper can be obtained. The method of obtaining CSI of the remaining link can be found in previous studies [2],

[14] .

[0068] Assume 0≤β k ≤1 represents the PRC at the kth node. Based on this, each node uses a portion of the received energy signal to backscatter its own information while collecting the remaining energy. Therefore, the total energy captured by the kth node during the entire transmission period is given by Given, where P0 is the transmission power of the energy source, hk is the channel gain from the energy source to the backscatter node, T s is the symbol period, and η is the energy collection efficiency. The received signals at Bob and Eve can be expressed as and where s and s k denote the information from the energy source and the kth backscattering node respectively. The residual channel gain is expressed as follows:

[0069] 1) g0 and f0 are the channel gains from the energy source to Bob and Eve, respectively.

[0070] 2)g k and f k denote the channel gains from the backscattering node to Bob and Eve, respectively.

[0071] The additive white Gaussian noise (AWGN) at Bob and Eve is given by and express, means the mean is 0 and the variance is σ 2 Therefore, the signal-to-noise ratio (SNR) of the signal used to decode the kth tag at Bob can be expressed as Similarly, the signal-to-interference-plus-noise ratio (SINR) of the k-th tag’s signal at Eve can be expressed as For information backscatter, Bob not only receives the backscatter signal but also the energy signal. Due to perfect CSI, the energy signal at Bob can be completely eliminated, as he has prior knowledge of the channel gains and energy signals involved. In contrast, for Eve, since she primarily eavesdrops on the backscatter signal and lacks prior knowledge of the energy signal, the energy signal acts as interference.

[0072] An embodiment of a secure short packet transmission method in multi-tag backscatter communication of the present invention.

[0073] S1: given short packet length n k , Bob's packet error rate and the information leakage rate of illegal user Eve The rate C at which approximate confidentiality can be achieved s,k expression;

[0074] Specifically

[0075] in, and is the channel dispersion of the legitimate user Bob, is the channel dispersion of the illegal user Eve;

[0076] S2: Jointly optimize the transmission power P0 of the energy source and the short packet length n k , the power reflection coefficient β of each IoT node k ,construct the optimization problem of the overall security performance of the network (maximizing the minimum security capacity between all nodes);

[0077]

[0078]

[0079]

[0080]

[0081] 0≤P0≤P max

[0082] Among them, P c,k is the constant power consumption of the kth backscatter tag when performing BackCom; represents the set of non-negative integers; P max is the maximum transmission power allowed by the energy source; in P1, is the energy causality constraint, which ensures that the total energy consumed by the kth backscatter tag does not exceed its harvested energy, constrained 0≤β k ≤1 and 0≤P0≤P max impose limits on the transmission power of PRC and energy sources respectively;

[0083] The above method maximizes the minimum security capacity between all nodes by jointly optimizing the short packet length n and PRCβ, where n={n1,…,n K} and β={β1,…,β K};

[0084] S3: Optimal power reflection coefficient β through each IoT node k and the transmission power P0 of the optimal energy source, decoupling the optimization problem into two convex optimization subproblems;

[0085] Specifically,

[0086] S31: Consider the case of high signal-to-noise ratio, where the channel dispersion function can be approximated as You can use r k Approximately

[0087] S32: Derive the optimal power reflection coefficient β k , as shown below:

[0088]

[0089] S33: By changing the β of formula (8) k =1 and Substituting P1, the two subproblems can be simplified as follows:

[0090]

[0091]

[0092] 0≤P0≤P max

[0093]

[0094] and

[0095]

[0096]

[0097] 0≤P0≤P max

[0098]

[0099] S4: For the two convex optimization subproblems in step S3, an iterative algorithm based on continuous convex approximation is used to solve them. By comparing the optimal solutions of the subproblems, the minimum safety capacity between all node pairs is maximized.

[0100] Based on the above embodiments and preferred implementations, a specific embodiment is provided as follows:

[0101] 1. Maximize the minimum confidentiality rate among all IoT nodes

[0102] In this section, considering the inherent finite packet length effect of short packet transmission, this application proposes a comprehensive system model of BackCom involving multiple nodes; under the constraints of energy, packet length and PRC, a multi-objective optimization problem is proposed to maximize the minimum confidentiality capacity; an effective algorithm for solving non-convex optimization problems using successive convex approximation (SCA) is proposed; the algorithm iteratively transforms the original problem into a series of convex subproblems, ensuring convergence to a local optimal solution.

[0103] A. Formulation of the Optimization Problem

[0104] Different from long packet communication, short packet communication is affected by Bob’s packet error rate and Eve’s information leakage rate. Based on previous research

[15] -

[17] , given a packet length n k , Bob's packet error rate and Eve's information leakage rate The approximate achievable rate of confidentiality can be approximated as

[18] :

[0105]

[0106] in and In order to enhance the security of the network under consideration, this paper proposes an optimization problem to maximize the minimum security capacity between all nodes by jointly optimizing the short packet length n and PRCβ, where n={n1,…,n K} and β={β1,…,β K}.

[0107] Based on (7), the optimization problem is formulated as:

[0108]

[0109]

[0110]

[0111]

[0112] 0≤P0≤P max

[0113] Among them, P c,k is the constant power consumption of the kth backscatter tag when performing BackCom; represents the set of non-negative integers; P max is the maximum transmission power allowed by the energy source. In P1, is an energy causality constraint that ensures that the total energy consumed by the kth backscatter tag does not exceed its harvested energy. Constraint 0≤β k ≤1 and 0≤P0≤P max Limitations are imposed on the transmission power of PRC and energy sources respectively.

[0114] B.P1 solution

[0115] Obviously, P1 is non-convex and intractable, because C s,k The function in brings P0, β k and n k The coupling relationship between them.

[0116] To simplify the problem, this application considers the case of high signal-to-noise ratio, where the channel dispersion function can be approximated as i∈{R,E}. Therefore, this application can be r k Approximately

[0117] Lemma 1: This application derives the optimal power reflection coefficient β k , as shown below:

[0118]

[0119] By transforming β of (8) k =1 and Substituting P1, the two subproblems can be simplified as follows:

[0120]

[0121]

[0122] 0≤P0≤P max

[0123]

[0124] and

[0125]

[0126]

[0127] 0≤P0≤P max

[0128]

[0129] Lemma 2: This application proves that P0=P max Always true, no matter or β k =1.

[0130] Set P0=P max Substitute P2 and P3 and set the constraints n in k Relaxing to continuous variables can express the original optimization problem as:

[0131]

[0132]

[0133]

[0134] and

[0135]

[0136]

[0137]

[0138] By introducing λ and θ to express P 2-1 and P 3-1 in These two problems can be transformed into:

[0139]

[0140] st C s,k (n k )≥λ,

[0141]

[0142]

[0143] and

[0144]

[0145] st C s,k (n k )≥θ,

[0146]

[0147]

[0148] However, P 2-2 and P 3-2 Constraint C in s,k (n k )≥λ and C s,k (n k )≥θ is nonlinear, and these constraints can be further processed to simplify the optimization problem.

[0149] Lemma 1 is proved as follows:

[0150] Proof: For β k Taking the derivative and simplifying, we get

[0151]

[0152] in and Since M3>0, only Δ1 needs to be considered. By simplifying Δ1, the present application obtains Since the denominator of Δ1 is always greater than zero, this application only needs to consider the numerator, which is expressed as Δ1′.

[0153] To ensure secure information transmission, Thus we get Substituting it into Δ1′, we get

[0154]

[0155] Therefore, this application can be concluded This shows that C s,k About Beta k Monotonically increasing.

[0156] According to the constraints in P1 and 0≤β k ≤1, β can be derived k The closed-form expression is Finally, we conclude:

[0157]

[0158] Lemma 2 is proved as follows:

[0159] For P2, β k =1Substitute into C s,k ,get Take C s,k Taking the derivative of P0, we get:

[0160]

[0161] because Therefore, Δ2 needs to be considered. By simplifying Δ2, the present application obtains

[0162]

[0163] Since the denominator of this expression is greater than zero, this application only needs to consider the numerator. Let the numerator of Δ2 be Δ2′. After simplification, this application obtains To ensure secure information transmission, Thus we get Substituting it into Δ2′, we get:

[0164]

[0165] From (13), we can see that Therefore, C s,k Monotonically increasing with respect to P0. In addition, based on the P2 constraint 0≤P0≤P max , we can derive P0=P max .

[0166] For P3, And taking the derivative with respect to P0, we get:

[0167]

[0168] in because Therefore, only Δ3 is considered. After taking the common denominator of Δ3, the denominator is Therefore, this application only needs to analyze the molecule, which is expressed as Δ3′. Substituting Δ3′ and simplifying, we get:

[0169]

[0170] From the conditions This application can deduce Nh k ηP0-n k P c,k > 0. In addition, since it is known that and This application can conclude that Δ3′>0. From (15) we know Therefore, C s,k Monotonically increasing with respect to P0. In addition, based on the constraint 0≤P0≤P max , we can derive P0=P max .

[0171] C. Algorithm Design

[0172] For nonlinear constraints in optimization problems, SCA can be used to achieve effective approximation. 2-2 , based on the constraint C s,k (n k )≥λ, this application can derive the expression in For nonlinear constraints This application can be applied at the tth iteration point Surrounding items Perform a first-order Taylor expansion on to simplify the approximation. at The Taylor expansion of can be expressed as Substituting it into the original constraint, this application obtains Therefore, the linear approximation constraint becomes:

[0173]

[0174] Similarly, for problem P 3-2 The nonlinear constraint C in s,k (n k )≥θ, this application can apply the corresponding SCA method to convert it into a linear form:

[0175]

[0176] Specifically, the package manager The confidentiality rate of the kth backscatter tag can be expressed as The parameters are defined as: exist Calculate C s,k About n k The first-order derivative of can be expressed as:

[0177]

[0178]

[0179]

[0180] It is obvious from (16) and (17) that the original optimization problem is already a tractable convex problem. As shown in the algorithm, the process starts with parameter initialization, where and They represent the initial packet length distribution of the two sub-problems, ξ represents the maximum tolerance of the algorithm, C optim Represents the final optimal value achieved by the algorithm, t max and l max represents the maximum number of iterations for the two subproblems. CVX can be used to efficiently solve the approximate problem in each SCA iteration. By comparing the optimal solutions to the subproblems, the optimal solution to the entire problem can be obtained.

[0181] 2. Simulation

[0182] This embodiment conducts simulation experiments in the following simulation scenarios. In this section, the performance of the proposed solution is evaluated through computer simulation. The system parameters are set as follows: K = 4, P max =10dBm, N=500, P c,k =4μW, η=0.7. The distance configuration is: d 0,0 and d 0,1 The distances from the energy source to Bob and Eve are 55m and 65m respectively. 1,k , d 2,k and d 3,k They represent the distance from the energy source to the backscatter node, the distance from the backscatter node to Bob, and the distance from the backscatter node to Eve. 1,1 =16m,d 1,2 =17m,d 1,3 =18m,d 1,4 =19m,d 2,1 =45m,d 2,2 =46m,d 2,3 =48m,d 2,4 =49m,d3,1 =55m,d 3,2 =56m,d 3,3 =58m,d 3,4 =59m. The path loss exponent is set to 3.

[0183] To demonstrate the superiority of the proposed scheme in terms of safety rate, it is compared with the scheme that maximizes the minimum node safety capacity of long packet BackCom (LPC-BackCom).

[0184] Figure 2 The correctness and convergence of the proposed algorithm are demonstrated. It can be observed that the results of the proposed algorithm and the exhaustive search method are very close, but there is a slight difference between the two due to the lack of approximation in the exhaustive search method. The algorithm converges after only two iterations, and the safety rate increases with the increase of .

[0185] Figure 3 The fairness of the proposed algorithm is demonstrated. The comparison scheme focuses on maximizing the total throughput. It can be observed that the fairness of the proposed scheme remains high under different noise levels, while the safety rate of each node in the comparison scheme shows significant variation.

[0186] Figure 4 The chart shows the average security rate as a function of packet length at different transmission power levels. It can be observed that the security rates of both the proposed scheme and the LPC-BackCom scheme increase with increasing packet length and transmission power. Across various transmission power levels, the proposed scheme consistently outperforms the comparison scheme. Preliminary analysis indicates that the difference between the two schemes decreases with increasing transmission power.

[0187] Figure 5 The chart shows the variation of the average security rate with transmission power for different packet lengths. It can be observed that the security rate increases with increasing packet length and transmission power. Furthermore, the difference in security rate between the proposed scheme and the LPC-BackCom scheme gradually decreases. At low transmission power, the proposed scheme outperforms the LPC-BackCom scheme. However, as transmission power increases, the performance gap between the two schemes gradually narrows, but the proposed scheme consistently outperforms the comparison scheme.

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[0209] This paper studies the security of short-packet BackCom networks and proposes a resource allocation problem based on maximum-min fairness, aiming to maximize the minimum confidentiality rate of all backscatter tags. This goal is achieved by jointly optimizing the transmission power of dedicated energy, the packet length of short packets, and the PRC of backscatter tags. Closed-form expressions for the optimal PRC and transmission power are derived, allowing the original problem to be decomposed into two manageable subproblems. To efficiently solve these subproblems, an iterative algorithm based on SCA is proposed. Simulation results demonstrate the effectiveness of this algorithm in improving network security and fairness.

[0210] Each embodiment in this specification is described in a related manner. The same or similar parts between the embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences from other embodiments. The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A secure short packet transmission method in multi-tag backscatter communication, characterized in that: The steps include: Given a short packet length n k , the packet error rate of the legitimate user Bob and the information leakage rate of illegal user Eve The rate C at which approximate confidentiality can be achieved s,k expression; Jointly optimize the transmission power P0 of the energy source and the short packet length n k and the power reflection coefficient β of each IoT node k ,construct the optimization problem of the overall security performance of the network; According to the power reflection coefficient β of each IoT node k and the closed-form expression of the transmission power P0 of the optimal energy source, decoupling the optimization problem into two convex optimization subproblems; For the two convex optimization subproblems, an iterative algorithm based on continuous convex approximation is used to solve them; By comparing the optimal solutions of the subproblems, the minimum safety capacity between all node pairs is maximized.

2. The secure short packet transmission method in multi-tag backscatter communication according to claim 1, characterized in that: The given short packet length n k , the packet error rate of the legitimate user Bob and the information leakage rate of illegal user Eve The rate C at which approximate confidentiality can be achieved s,k The steps of the expression include: in, and is the channel dispersion of the legitimate user Bob, is the channel dispersion of the illegal user Eve.

3. The method for secure short packet transmission in multi-tag backscatter communication according to claim 1, wherein: The combined optimization of the transmission power P0 of the energy source and the short packet length n k , the power reflection coefficient β of each IoT node k , the steps of constructing the optimization problem of the overall network security performance, where the optimization problem is: 0≤β k ≤1, 0≤P0≤P max Among them, C s,k represents the safety rate of the kth node, P c,k is the constant power consumption of the kth backscatter tag when performing BackCom; represents the set of non-negative integers; P max is the maximum transmission power allowed by the energy source; in P1, is an energy causality constraint, ensuring that the total energy consumed by the kth backscatter tag does not exceed its harvested energy, constrained 0≤β k ≤1 and 0≤P0≤P max Limitations are imposed on the power reflection coefficient and energy transfer power, respectively.

4. The method for secure short packet transmission in multi-tag backscatter communication according to claim 3, wherein: The power reflection coefficient β of each IoT node k and the closed-form expression of the transmission power P0 of the optimal energy source, decoupling the optimization problem into two convex optimization subproblems, including: S31: Consider the case of high signal-to-noise ratio, where the channel dispersion function can be approximated as i∈{R,E}, the received signal strength r of the kth user can be k Approximately S32: Derive the optimal power reflection coefficient β k , as shown below: S33: By changing the β of formula (8) k =1 and Replace with P1, and change β k =1 and Substitute separately In the equation, according to the constraint 0≤β k ≤1 can be derived from the constraint and These two sub-problems can be simplified as follows: 0≤P0≤P max in, is the feasibility condition limit of the power reflection coefficient; 0≤P0≤P max in, is the limit of the total number of users N, is the feasibility condition limit of the power reflection coefficient.

5. The method for secure short packet transmission in multi-tag backscatter communication according to claim 4, characterized in that: Step S33 also includes converting the original optimization problem into a convex optimization problem by a continuous relaxation method, which specifically includes: Set P0=P max Substitute P2 and P3 and set the constraints n in k Relaxing to continuous variables can express the original optimization problem as follows: and By introducing λ and θ to express P 2-1 and P 3-1 in These two problems can be transformed into: s.t C s,k (n k )≥λ, and s.t C s,k (n k )≥θ, 6. The method for secure short packet transmission in multi-tag backscatter communication according to claim 5, characterized in that: The steps of solving the two convex optimization subproblems using an iterative algorithm based on continuous convex approximation specifically include: in, and They represent the initial packet length distribution of the two sub-problems, ξ represents the maximum tolerance of the algorithm, C optim Represents the final optimal value achieved by the algorithm, t max and l max Indicates the maximum number of iterations for the two subproblems.

7. The method for secure short packet transmission in multi-tag backscatter communication according to claim 1, wherein: The combined optimization of the transmission power P0 of the energy source and the short packet length n k , the power reflection coefficient β of each IoT node k ,The steps of constructing the optimization problem of the overall network security performance include: The minimum safety capacity between all nodes is maximized by jointly optimizing the short packet length n of all nodes and the power reflection coefficient β of all nodes, where n={n1,…,n K } and β={β1,…,β K }.

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