Intelligent metasurface assisted uplink non-orthogonal multiple access covert communication method

Abstract

The application provides a kind of intelligent metasurface assisted uplink non-orthogonal multiple access covert communication method, belongs to the field of covert communication security.In order to improve the spectrum efficiency, in the process of sending public information to the base station by the public user with varying power, the covert user sends covert information to the base station at irregular intervals.With the help of intelligent metasurface assistance, not only can the detection of the listener be interfered, but also the transmission rate of covert information can be improved.Based on the system model in the application, the application proposes a method of jointly optimizing the transmission power of the covert user and the phase shift matrix of the intelligent metasurface, which can achieve covert communication while improving the transmission rate of covert information.

Description

Technical Field

[0001] This invention belongs to the field of covert communication security and relates to a method for improving the transmission rate of covert information with the assistance of a smart metasurface. More specifically, this invention, through the combined effect of public user cover and a smart metasurface, improves the transmission rate of covert information while meeting the covert user's need for concealment. Background Technology

[0002] From traditional wired communication to today's commercially available fifth-generation mobile communication technology, communication mechanisms have undergone a tremendous transformation, from analog to digital, and from intelligent voice transmission to the transmission of various information types such as images and text. This has greatly promoted social change and technological innovation. Wireless communication transmits signals via radio electromagnetic waves, which not only increases the speed of information transmission but also enhances the flexibility of information reception methods.

[0003] Ensuring communication security has always been a crucial research topic in the field of communications. While wireless communication transmits signals via radio waves, it also presents significant security risks. These risks manifest in two ways: First, radio waves can propagate in all directions, allowing malicious or unauthorized users to receive electromagnetic radiation and process the signals. Second, electromagnetic waves have strong penetrating power, making it easy for information to be eavesdropped on and stolen during wireless communication.

[0004] With the rapid development of the mobile internet and the Internet of Things, an increasing amount of data is being transmitted via wireless communication. Due to the broadcast nature of wireless communication, critical privacy data, particularly military secrets, medical and health information, government secrets, and personal privacy, faces a severe challenge of privacy breaches. Eavesdropping and sabotage can cause incalculable losses. To address the privacy and security issues of wireless communication, covert communication, as a physical layer information security technology, fundamentally protects privacy by deceiving eavesdroppers into determining whether a user has sent a message. However, covert communication imposes strict limitations on the transmission power of covert users, severely impacting their communication performance. To achieve efficient utilization of limited spectrum resources and meet the quality of service requirements for multiple users, non-orthogonal multiple access (NOAMI) technology effectively improves user communication performance by allocating the same time-frequency resource block to multiple users.

[0005] Among the currently published patents for covert communication assisted by reconfigurable intelligent surfaces (RIS), the following methods are mainly provided: covert communication is achieved by combining a drone with an intelligent metasurface when the location information of the eavesdropper is unknown or the noise uncertainty model is unknown; covert communication is achieved by using jamming from a jammer and reflection from the intelligent metasurface; covert communication is achieved by using an intelligent metasurface to assist a multiple-input multiple-output system; and covert communication is achieved by using an intelligent metasurface to assist in the process based on channel uncertainty and noise uncertainty models. While these methods have some innovative aspects, they also have the following problems: First, wireless communication spectrum resources are becoming increasingly scarce, and most of these methods are designed for single-user downlink communication scenarios, which cannot adapt to future multi-user communication scenarios; second, these methods do not consider the interference effect of the phase shift uncertainty of the intelligent metasurface on the eavesdropper.

[0006] With the rapid development of short videos, virtual reality, and high-definition live streaming, uplink communication performance will become crucial. Therefore, this invention proposes an intelligent metasurface-assisted communication transmission method for uplink non-orthogonal multiple access communication scenarios. Summary of the Invention

[0007] This invention proposes a method for covert uplink nonorthogonal multiple access communication assisted by a smart metasurface. Under the cover of a smart metasurface and a public user, the covert user can achieve covert communication. The specific scheme is as follows: Figure 1 Based on this system model, this invention provides a method for jointly optimizing the transmit power of covert users and the phase shift of intelligent metasurfaces, thereby improving the transmission rate of covert information.

[0008] The specific technical solution of the present invention is as follows:

[0009] A smart metasurface-assisted uplink nonorthogonal multiple access covert communication method is characterized by: jointly utilizing the uncertainties of the public user's transmit power and the phase shift of the smart metasurface to reduce the detection performance of eavesdroppers while improving the transmission rate of covert information; the steps of this method are as follows:

[0010] Step 1: Construct the system model, specifically:

[0011] 1) In an uplink communication scenario, a public user continuously sends public information x to the base station. c Meanwhile, covert users, aided by intelligent metasurfaces, periodically send covert information x to the base station. a Meanwhile, the eavesdropper determines whether the covert user has sent information by detecting the communication environment; since the covert user is a remote user, the direct transmission link from the covert user to the base station and the eavesdropper is not considered.

[0012] 2) A quasi-static block fading channel model based on distance path loss is adopted, i.e., h ar h rb h rw These represent the channel fading coefficients between the covert user, the base station, and the eavesdropper and the RIS, respectively, with corresponding distances d and d. ar d rb and d rw h cw and h cb Let d represent the channel fading coefficients between the public user and the eavesdropper and the base station, respectively, with corresponding distances d and d. cw and d cb All channel fading coefficients are independently and identically distributed according to a complex Gaussian distribution with a mean of 0 and a variance of 1; both the base station and the eavesdropper are subjected to a complex Gaussian distribution with a mean of 0 and a variance of σ0. 2 The effect of additive white Gaussian noise;

[0013] Step 2: Introduce intelligent metasurfaces to assist in covert communication, specifically:

[0014] 1) Based on the system model and channel model in step one, the received signal of the base station is:

[0015]

[0016] Where y b [i] represents the received signal of the base station in the i-th symbol period, Θ represents the phase shift matrix of the smart metasurface, and x a [i] and x c [i] represent the transmitted signals of the hidden user and the public user in the i-th symbol period, respectively, P a and P c r represents the transmission power of the covert user and the public user, respectively. b [i] represents the additive white Gaussian noise at the base station in the i-th symbol period, L(d ar ), L(d rb ), L(d cb ) represent the path loss between the covert user, the base station, and the eavesdropper and the RIS, respectively, and H represents the conjugate transpose of the vector;

[0017] 2) The base station uses a serial interference cancellation method to decode information; it first decodes the information of public users, and then decodes the information of hidden users; the signal-to-interference-plus-noise ratio (SIR) γ of the base station decoding the information of public users is [not specified]. c for:

[0018]

[0019] in |g cb | 2 and |gab | 2 These represent the channel gain between the public user and the base station, and between the hidden user and the base station, respectively; the signal-to-noise ratio γ of the base station decoding the hidden user's information after the public user's information is decoded. a for:

[0020]

[0021] Therefore, the rate R achievable by public users c And the rate R achievable by covert users a They are respectively:

[0022] R c =log2(1+γ) c (4)

[0023] R a =log2(1+γ) a (5)

[0024] Step 3: Utilize the statistical characteristics of the public user's varying transmission power and the phase shift of the smart metasurface to interfere with the eavesdropper's detection. Specifically:

[0025] 1) To determine whether the covert user has sent a message, the eavesdropper uses a binary hypothesis testing principle to make a decision; based on the system model and channel model in step one, the eavesdropper's received signal is:

[0026]

[0027] Where y w [i] represents the signal received by the listener in the i-th symbol period, r w [i] represents the additive white Gaussian noise at the listener end in the i-th symbol period, L(d cw ), L(d rw H1 and H2 represent the path losses between the public user, the smart metasurface, and the listener, respectively, and H0 and H1 represent the null hypothesis and the non-null hypothesis, respectively.

[0028] 2) The eavesdropper uses a radiometer to detect the signal it receives and, based on its decision criterion, determines the average power T of the received signal. w The signal is compared with a decision threshold τ to ultimately determine whether the covert user sent or did not send a message; among these factors are the eavesdropper's decision criteria and the average power T of the received signal. w They are represented as follows:

[0029]

[0030] and

[0031]

[0032] Among them, D0 and D1 are binary decisions made by the eavesdropper to determine whether the hidden user has transmitted information;

[0033] 3) The eavesdropper uses the total false detection probability ξ to represent its detection performance, where ξ is defined as:

[0034] ξ=α+β (9)

[0035] Where α=Pr(D1|H0) is the false alarm probability and β=Pr(D0|H1) is the false alarm probability; the uncertainty of the public user's transmission power and the uncertainty of the phase shift of the smart metasurface are used to jointly interfere with the detection of the eavesdropper;

[0036] Step 4: Use variable decomposition and iterative optimization methods to improve the rate of covert information transmission, specifically:

[0037] 1) To achieve covert communication while improving the transmission rate of covert information, based on the system model, the goal is to maximize the transmission rate of covert information under the common constraints of the maximum transmit power of the covert user, the covertness requirements, and the reflection phase of each unit of the smart metasurface. This optimization problem is expressed as:

[0038]

[0039]

[0040]

[0041] ξ * ≥1-ε (10d)

[0042] θ n ∈[0,2π),n=1,2,...,N (10e)

[0043] in θ represents the maximum transmission power of the covert user. n Let N represent the phase of the nth unit cell of the smart metasurface, where N represents the number of smart metasurfaces. ε is a definite, sufficiently small positive number;

[0044] 2) Decompose the above optimization problem into two subproblems. In each subproblem, fix one variable and optimize the other variable, specifically as follows:

[0045] 2.1) First, fix Θ, and then consider P. a The optimization problem is expressed as:

[0046]

[0047]

[0048]

[0049] ξ * ≥1-ε (11d)

[0050] From formula (11c), we can see that, make From formula (11d), we know that ξ * It is about variable P a The function is a monotonically decreasing function; therefore, when the equality of formula (11d) holds, the transmission power of the covert user is at its maximum, and this maximum value is denoted as . Since the objective function is about variable P a The function is a monotonically increasing function; therefore, the maximum transmission power of the covert user is...

[0051] 2.2) Fix P a The optimization problem, which optimizes variable Θ, can be expressed as:

[0052]

[0053] stθ n ∈[0,2π),n=1,2,...,N (12b)

[0054] Where h an and h nb Let represent the channel fading coefficients between the nth cell of the smart metasurface and the covert user and the base station, respectively; similarly, the objective function is a monotonically increasing function with respect to the variable Θ, therefore, when When θ reaches its maximum value, the objective function also reaches its maximum value; according to the phase optimization principle of intelligent metasurfaces, when θ... n =2π-arg(h) an )-arg(h nb )hour, Find the maximum value; determine P a Once the optimal values ​​of Θ and Θ are determined, the optimal rate of covert information transmission can be obtained.

[0055] The advantages of this invention are:

[0056] 1) This invention improves spectrum utilization: public users and covert users send information to the base station simultaneously in a non-orthogonal multiple access manner, and the base station first decodes the information of the public users. This method improves spectrum utilization while providing cover.

[0057] 2) This invention improves the transmission rate of covert information: the intelligent metasurface utilizes its phase shift characteristics to optimize based on the channel state information of the legitimate communication link, which not only assists covert users in transmitting information but also interferes with the detection of eavesdroppers. Attached Figure Description

[0058] Figure 1 A model diagram of an uplink nonorthogonal multiple access covert communication system assisted by a smart metasurface;

[0059] Figure 2 The above is a simulation result diagram of the method proposed in this invention;

[0060] Figure 3 To conceal the impact of the user's transmit power on the eavesdropper's detection performance;

[0061] Figure 4 The impact of the maximum transmit power of public users on eavesdropper detection performance;

[0062] Figure 5 The impact of the number of reflection units in the RIS on the eavesdropper detection performance;

[0063] Figure 6 The effect of the number of RIS reflection units on the rate of covert information transmission. Detailed Implementation

[0064] To better understand the technical solution proposed in this invention, this section will provide specific implementation steps and illustrate them with experimental results and accompanying drawings. The specific steps of this embodiment include:

[0065] Step 1: Constructing the system model:

[0066] 1) The system model in this invention is as follows: Figure 1 As shown, in this embodiment, all nodes are configured as single antennas, the number of reflection elements of the RIS is set to N=64, and its phase shift matrix is... The covert user is a user located far from the base station, while the public user is a user located near the base station. Therefore, the covert user must use the assistance of a smart metasurface to establish communication with the base station, while the public user can communicate directly with the base station. In this embodiment, the coordinates of the covert user, the base station, the public user, the RIS, and the eavesdropper are (0,0), (100,0), (100,-5), (50,10), and (90,-5), respectively.

[0067] 2) In this invention, the fading coefficients of all channels, i.e., h ar h rb h rw h cw and h cb All signals follow a complex Gaussian distribution with a mean of 0 and a variance of 1. The corresponding distances can be obtained from the location information of the aforementioned elements. In this embodiment, the signals sent by the covert user and the public user are both Gaussian signals with a mean of 0 and a variance of 1; the variance of the additive white Gaussian noise at the base station and the eavesdropper end is... The corresponding path loss model is expressed as: L(d)=d -α , where α is the path loss coefficient, α = 2.7.

[0068] Step 2: Introduce intelligent metasurfaces to assist in covert communication, specifically:

[0069] 1) Based on the system model and channel model in step one, the received signal of the base station is:

[0070]

[0071] 2) In uplink communication, the decoding order of the serial interference cancellation method is to decode the user information with good channel quality first. Therefore, the base station first decodes the signal of the common user, and then decodes the signal of the hidden user; the signal-to-interference-plus-noise ratio γ of the base station decoding the signal of the common user is... c for:

[0072]

[0073] After the public user's signal is decoded, the base station decodes the covert user's signal with a signal-to-noise ratio γ. a for:

[0074]

[0075] Therefore, according to Shannon's formula, the rate R achievable by public users is... c And the rate R achievable by covert users a They are respectively:

[0076] R c =log2(1+γ) c (4)

[0077] R a =log2(1+γ) a (5)

[0078] Step 3: Utilize the statistical characteristics of the public user's varying transmission power and the phase shift of the smart metasurface to interfere with the eavesdropper's detection. Specifically:

[0079] 1) Based on the system model and channel model in step one, the receiver's received signal is:

[0080]

[0081] To determine whether a hidden user has sent a message, the eavesdropper uses a binary hypothesis testing principle. The eavesdropper uses a radiometer to detect the received signal and makes a decision based on its decision criterion; whereby the eavesdropper's decision criterion and the average power of the received signal are expressed as follows:

[0082]

[0083] and

[0084]

[0085] 2) The eavesdropper uses the total false detection probability ξ to represent its detection performance, where ξ is defined as:

[0086] ξ=α+β (9)

[0087] This invention utilizes the uncertainty of the public user's transmit power and the uncertainty of the phase shift of the smart metasurface to jointly interfere with the detection of eavesdroppers; in this system model, the transmit power P of the public user... c exist The above follows a uniform distribution, and its probability density function is:

[0088]

[0089] Where P c max The maximum transmission power of the public user is represented by x, which is a random variable. The phase shift matrix of the intelligent metasurface is designed and optimized based on the channel state information of the legitimate communication link, which leads to the eavesdropper being unable to accurately obtain the phase shift information of the intelligent metasurface, i.e., |h in formula (8). ar Θh rw | 2 It is also a random variable, and follows a complex Gaussian distribution with a mean of 0 and a variance of N;

[0090] 3) By definition, the false alarm probability and false negative probability of Warden are expressed as follows:

[0091]

[0092]

[0093] in From formulas (9) and (10), the listener's ξ is expressed as:

[0094]

[0095] The prerequisite for covert communication is that the eavesdropper cannot correctly determine whether the covert user has sent a message, that is, ensuring that the minimum value of the eavesdropper ξ is infinitely close to 1. Analysis shows that the optimal detection threshold for the eavesdropper is τ. * =ρ1, at this time the minimum total DEP of Warden is

[0096] Step 4: Use variable decomposition and iterative optimization methods to improve the rate of covert information transmission, specifically:

[0097] 1) The optimization problem in this embodiment can be expressed as:

[0098]

[0099]

[0100]

[0101] ξ * ≥1-ε (14d)

[0102] θ n ∈[0,2π),n=1,2,...,N (14e)

[0103] Because the objective function contains a variable P a The coupling problem between P and Θ cannot be solved directly. Analysis of its constraints reveals that P... a Θ exists only in formulas (14b), (14c), and (14d), and Θ exists only in formula (14e). Therefore, this embodiment decomposes the above optimization problem into two sub-problems and uses an alternating iterative optimization method to obtain the optimal solutions for the two variables sequentially, specifically:

[0104] 2.1) First, fix Θ and solve for the optimal P. a The objective problem is expressed as:

[0105]

[0106]

[0107] P c max μ2>Nμ1 (15c)

[0108] ξ * ≥1-ε (15d)

[0109] To maximize the rate of covert information transmission, it is necessary to find the maximum covert user transmission power under the covertness constraint. Based on this, this embodiment analyzes the constraint conditions: From formula (15c), it can be seen that... Recorded as From formula (15d), we know that ξ * It is about variable P a The monotonically decreasing function, when the equality of formula (15d) holds, has the maximum transmission power of the concealed user, denoted as . Therefore, the maximum transmission power of the covert user is

[0110] 2.2) Secondly, fix P aThe objective problem, which optimizes variable Θ, can be represented as:

[0111]

[0112] stθ n ∈[0,2π),n=1,2,...,N (16b)

[0113] Similarly, R a It is a monotonically increasing function of Θ; because Therefore, according to the phase optimization principle of intelligent metasurfaces, when θ n =2π-arg(h) an )-arg(h nb )hour, To obtain the maximum value.

[0114] Results verification and analysis:

[0115] Figure 2 The system simulation results demonstrate consistency with the method proposed in this invention; it also shows that the total false detection probability of the eavesdropper first increases and then decreases as τ increases.

[0116] Figure 3 This paper demonstrates the impact of the transmit power of a covert user on the eavesdropper detection performance under the proposed method, the orthogonal multiple access method, and the non-RIS uncertain non-orthogonal multiple access method. First, it can be seen that the orthogonal multiple access method cannot achieve covert communication. Second, compared to the non-RIS uncertain non-orthogonal multiple access method, the proposed method can further reduce the eavesdropper's detection performance, thereby achieving a better covert effect.

[0117] Figure 4 This study demonstrates the impact of the public user's maximum transmit power on the eavesdropper's detection performance. As the public user's maximum transmit power increases, the minimum total false detection probability for the eavesdropper also gradually increases, indicating that a higher maximum transmit power from the public user provides better concealment. The proposed scheme offers better concealment compared to schemes without RIS phase shift uncertainty.

[0118] Figure 5 This paper demonstrates the impact of the number of RIS reflection units on the eavesdropper detection performance in the proposed method. As the number of RIS reflection units increases, the minimum total false detection probability of the eavesdropper decreases, meaning that increasing the number of RIS reflection units improves the eavesdropper's detection capability. Furthermore, the figure shows that when the number of RIS reflection units is fixed, increasing the transmit power of the covert user improves the eavesdropper's detection performance.

[0119] Figure 6This paper demonstrates the impact of the proposed method and the RIS arbitrary phase reflection method on the rate of covert information transmission. Under the same covert constraints, the proposed method provides a better rate of covert information transmission; moreover, the rate of covert information transmission gradually increases as the covert constraints decrease.

Claims

1. A method for intelligent metasurface-assisted uplink non-orthogonal multiple access covert communication, characterized in that: The joint use of public user transmission power and smart surface phase shift uncertainty reduces the detection performance of the listener while improving the transmission rate of covert information; the method steps are as follows: Step one: build a system model, specifically: 1) In an uplink communication scenario, the public user always sends public information x to the base station c , while the covert user occasionally sends covert information x to the base station with the assistance of the intelligent metasurface a ; at the same time, the listener judges whether the covert user sends information by detecting the communication environment; since the covert user is a far user, the direct transmission link of the covert user to the base station and the listener is not considered; 2) a quasi-static block-fading channel model based on distance path loss, i.e., h ar , h rb , and h rw represent the channel fading coefficients between the hidden user, the base station and the warden and the RIS, respectively, with corresponding distances d ar , d rb , and d rw ; h cw and h cb represent the channel fading coefficients between the public user and the warden to the base station, respectively, with corresponding distances d cw and d cb ; all channel fading coefficients are independently and identically distributed as complex Gaussian with mean 0 and variance 1; Both the base station and the listener are subject to additive white Gaussian noise with mean 0 and variance σ2. Step two: introduce smart surface assisted covert communication, specifically: 1) According to the system model and channel model in step one, the received signal of the base station is: Where y b [i] represents the received signal of the base station in the i-th symbol period, Θ represents the phase shift matrix of the smart metasurface, and x a [i] and x c [i] represent the transmitted signals of the hidden user and the public user in the i-th symbol period, respectively, P a and P c r represents the transmission power of the covert user and the public user, respectively. b [i] represents the additive white Gaussian noise at the base station in the i-th symbol period, L(d ar ), L(d rb ), L(d cb ) represent the path loss between the covert user, the base station, and the eavesdropper and the RIS, respectively, and H represents the conjugate transpose of the vector; 2) The base station uses a serial interference cancellation method to decode information; It decodes the information of the common user first, and then decodes the information of the concealed user; the signal-to-interference-noise ratio γ of the base station decoding the information of the common user c is: wherein |g cb | 2 and |g ab | 2 respectively represent the channel gain between the public user and the base station, the channel gain between the covert user and the base station; the signal-to-noise ratio γ for the base station to decode the information of the covert user after the information of the public user is decoded a is: Thus, the rate R achievable by the public user c and the rate R achievable by the hidden user a are respectively: R c = log2(1 + γ c ) (4) R a = log2(1 + γ a ) (5) Step three: use the statistical characteristics of the public user's changing transmission power and the smart surface phase shift to interfere with the listener's detection, specifically: 1) In order to determine whether the covert user has transmitted information, the listener uses the binary hypothesis testing principle to make a decision; according to the system model and channel model in step one, the received signal of the listener is: where y w [i] represents the received signal of the listener at the i-th symbol period, r w [i] represents the additive white Gaussian noise at the i-th symbol period of the listener side, L(d cw ), L(d rw ) represent the path loss between the common user, the intelligent metasurface and the listener, H0 and H1 represent the zero hypothesis and the non-zero hypothesis, respectively; 2) The listener uses a radiometer to detect its received signal and decides according to its decision criterion, i.e. the average power T of its received signal w In comparison with the decision threshold τ, it is finally determined whether the hidden user has sent information or not; wherein the decision criterion of the listener and the average power T of the received signal w are respectively represented as: And Where D0 and D1 are the binary decisions made by the listener to determine whether the covert user has transmitted information; 3) The listener uses the total error detection probability ξ to represent its detection performance, ξ is defined as: ξ=α+β (9) Where α=Pr(D1|H0) is the false alarm probability, β=Pr(D0|H1) is the missed detection probability; the uncertainty of the public user's transmission power and the uncertainty of the smart surface phase shift are used to interfere with the listener's detection; Step four: use the variable decomposition iterative optimization method to improve the transmission rate of covert information, specifically: 1) In order to achieve covert communication while improving the transmission rate of covert information, based on the system model, maximize the transmission rate of covert information under the joint constraints of the maximum transmission power of the covert user, the demand for concealment, and the reflection phase of each unit of the smart surface, the optimization problem is represented as: ξ * ≥1-ε (10d) θ n ∈ [0, 2π), n = 1, 2,..., N (10e) wherein represents the maximum transmit power of the hidden user, θ n is the phase of the nth unit of the smart metasurface, N represents the number of smart metasurfaces, ε is a determined positive number small enough; 2) The above optimization problem is decomposed into two sub-problems, in each sub-problem one variable is fixed and the other variable is optimized, specifically: 2.1) First fix Θ, about P a The optimization problem is expressed as: ξ * ≥1-ε (11d) From equation (11c), we have Let From equation (11d), we have * is a monotonically decreasing function of P a ; therefore, when equation (11d) is equal, the maximum transmission power of the covert user is Since the objective function is a monotonically increasing function of P a , the maximum transmission power of the covert user is 2.2) Fix P a , the optimization variable Θ, the optimization problem can be expressed as: s.t. θ n ∈ [0, 2π), n = 1, 2,..., N (12b) where h an and h nb respectively represent the channel fading coefficients between the nth unit of the intelligent metasurface and the concealed user and the base station; similarly, the objective function is a monotonically increasing function of variable Θ, thus, when reaches the maximum value, the objective function also reaches the maximum value; according to the phase optimization principle of the intelligent metasurface, when θ n = 2π-arg(h an )-arg(h nb ), reaches the maximum value; after the optimal values of P a and Θ are determined, the optimal concealed information transmission rate can be obtained.

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