RIS-assisted MISO system physical layer security optimization method

Abstract

The invention discloses an RIS-assisted MISO system physical layer security optimization method. A downlink MISO communication system comprising a base station, an IRS, a single user and a single eavesdropper is constructed; defining each channel gain coefficient and channel model of the MISO system; defining a reflection coefficient matrix of the IRS, and solving received signals of a user and an eavesdropper; defining signal-to-noise ratios of the user and the eavesdropper, and respectively solving channel capacities of the user and the eavesdropper; defining the safety rate of the system, and constructing an optimization problem; and finally, a base station beam forming vector and an IRS phase shift matrix are jointly optimized to maximize the system safety rate. According to the invention, the physical layer security of the wireless communication system is effectively improved, and the blank of the traditional IRS physical layer security technology in the aspect of processing full-duplex eavesdroppers is filled.

Description

Technical Field

[0001] The invention belongs to the technical field of wireless communications, and in particular relates to a RIS-assisted MISO system physical layer security optimization method. Background Art

[0002] At present, with the massive increase in the number of users and devices required to be supported, the sixth generation of mobile communications (6G) will be the focus of future researchers. In 6G networks, intelligent reflecting surfaces (IRS) have attracted widespread attention as an emerging technology. IRS consists of a large number of programmable passive reflection units, each of which can independently adjust the amplitude and phase shift of the reflected signal from the base station, thereby enhancing or weakening the signal, thereby optimizing signal transmission quality, enhancing signal coverage, and significantly improving the performance of the communication system.

[0003] However, while 6G networks bring greater information capacity, higher transmission rates, and lower transmission delays, they also bring new security challenges. If a large amount of information generated by 6G networks is stolen by malicious network activities, it may lead to serious privacy leaks and economic losses. Information confidentiality and privacy protection have become key tasks in the development of 6G networks. Physical layer security technology can effectively protect private information, but traditional physical layer security technology is mainly based on the difference in signal quality between legitimate receivers and malicious eavesdroppers, and its security effect mainly depends on the electromagnetic environment during wireless communication. As the wireless network environment becomes more and more complex, traditional physical layer security technology is difficult to ensure the security of the system.

[0004] Most of the existing work on IRS-assisted physical layer security assumes that the eavesdropper performs passive eavesdropping in half-duplex mode, but the eavesdropper may have more powerful signal processing capabilities. If there is a full-duplex eavesdropper in the network that can perform active interference and passive eavesdropping at the same time, it will bring more serious security risks. On the other hand, most research works assume that all or part of the channel state information (CSI) of the eavesdropper can be obtained, but in actual networks, the eavesdropper is an untrusted illegal user and often does not actively exchange CSI with the base station. Therefore, it is difficult for the legitimate system to obtain the instantaneous CSI of the eavesdropper, resulting in the IRS being unable to accurately adjust the channel to defend against eavesdropping, which seriously threatens the security and stability of the system transmission. Summary of the invention

[0005] The purpose of the present invention is to provide a RIS-assisted MISO system physical layer security optimization method, which utilizes IRS to resist full-duplex eavesdroppers who only know statistical CSI and protect MISO system transmission.

[0006] The technical solution adopted by the present invention is a RIS-assisted MISO system physical layer security optimization method, which is specifically implemented according to the following steps:

[0007] Step 1: construct a multi-input single-output system including a base station, an intelligent reflective surface, a user U and an eavesdropper E;

[0008] Step 2: Establish corresponding channel models according to the gain coefficients of each channel of the MISO system;

[0009] Step 3: Define the IRS reflection coefficient matrix and derive the received signal expressions of user U and eavesdropper E respectively;

[0010] Step 4: Calculate the signal-to-noise ratio at user U and eavesdropper E respectively, and solve the transmission capacity of user U and eavesdropper E according to Shannon's theorem;

[0011] Step 5: Establish an objective function with the system information transmission security rate as the target, set up multiple constraints and construct an optimization problem;

[0012] Step 6: Jointly optimize the BS beamforming vector and the IRS phase shift matrix, and use the alternating optimization algorithm to solve the optimization problem to maximize the system information security rate.

[0013] The present invention is also characterized in that:

[0014] In step 1, the BS is configured with M antennas, the IRS is configured with N reflection units, the user U and the eavesdropper E are both configured with a single antenna, the IRS communicates with the BS through the link connected to the intelligent controller, the BS realizes real-time control of the phase shift and amplitude of the reflection unit in the IRS through the intelligent controller, and the eavesdropper E adopts the full-duplex communication working mode.

[0015] The channel in step 2 is a quasi-static flat fading channel, and the reflection channels passing through adjacent elements are independent of each other. The channel from BS to IRS is a Rice fading channel, and the other channels are Rayleigh fading channels. The channel gain coefficient vectors from BS to IRS, BS to user U, BS to eavesdropper E, IRS to user U, IRS to eavesdropper E, eavesdropper to IRS, and eavesdropper to user U are defined as: and

[0016] In step 3, the IRS reflection coefficient matrix Φ is defined as:

[0017]

[0018] In the formula, θ i∈[0,2π) and β i ∈[0,1] are the phase shift and amplitude of the i-th reflection unit of IRS, assuming that IRS has the maximum reflected power gain, that is, β i =1, i=1, 2, ..., N.

[0019] The received signals of user U and eavesdropper E in step 3 are respectively expressed as:

[0020]

[0021]

[0022] In the formula, y U represents the received signal of user U, y E represents the received signal of the eavesdropper E; represents the BS beamforming vector and satisfies the power constraint ||W||≤P BS , P BS represents the maximum transmission power of the BS; x represents the confidential user information sent by the BS, which is a random variable with zero mean and unit variance, that is, x satisfies n U With n E Respectively represent the background noise at user U and eavesdropper E, and n U With n E satisfy P E It represents the interference signal power transmitted by the full-duplex eavesdropper. Since the eavesdropper E will produce residual self-interference when the self-interference elimination is incomplete, the coefficient α∈[0,1] is used to quantify the self-interference level.

[0023] The signal-to-noise ratio of user U in step 4 is expressed as:

[0024]

[0025] The signal-to-noise ratio of the eavesdropper E is expressed as:

[0026]

[0027] In step 5, the MISO system safety rate is defined as:

[0028] R S (W, Φ) = R U -R E (6)

[0029] The optimization equation is:

[0030]

[0031] In step 6, the two optimization variables in the optimization problem are coupled to each other. The objective function in the optimization problem is a non-convex function. Therefore, an alternating optimization method is used to fix the beamforming vector W and the IRS phase shift matrix Φ respectively, thereby gradually reducing the coupling degree between the two variables until the convergence condition is reached.

[0032] The beneficial effects of the present invention are:

[0033] The RIS-assisted MISO system physical layer security optimization method of the present invention introduces a full-duplex eavesdropper model in view of the fact that most of the existing IRS-assisted physical layer security studies assume that the eavesdropper is in half-duplex mode, so that the eavesdropper can simultaneously transmit interference signals and eavesdrop on user signals, filling the deficiency of traditional IRS-assisted physical layer security technology in dealing with full-duplex eavesdroppers, making IRS-assisted physical layer security more practical, and also providing new ideas for the security design of 6G and future wireless communication networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the process of the present invention;

[0035] Figure 2 This is the downlink MISO communication system model of the present invention. DETAILED DESCRIPTION

[0036] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] Example 1

[0038] The RIS-assisted MISO system physical layer security optimization method of the present invention is as follows: Figure 1-2 As shown, the specific implementation steps are as follows:

[0039] Step 1: Build a multiple input single output (MISO) system including a base station (BS), an intelligent reflection surface (IRS), a user U and an eavesdropper E to realize physical layer secure communication of the MISO system assisted by the IRS.

[0040] In step 1, the BS is configured with M antennas, the IRS is configured with N reflection units, and the user U and the eavesdropper E are both configured with a single antenna. The IRS communicates with the BS through the link connected to the intelligent controller to coordinate transmission and exchange information. The BS uses the intelligent controller to achieve real-time control of the phase shift and amplitude of the reflection unit in the IRS. The eavesdropper E adopts a full-duplex communication working mode, that is, the eavesdropper E can simultaneously transmit interference signals and eavesdrop on the signal of user U; the BS transmits information to user U and eavesdropper E through wireless communication, and the IRS reflects the wireless signal of the BS to user U and eavesdropper E. The eavesdropper E steals the signal of user U through wireless communication and transmits interference signals to user U;

[0041] Step 2: Establish corresponding channel models according to the gain coefficients of each channel of the MISO system;

[0042] The channel in step 2 is a quasi-static flat fading channel, and the reflection channels passing through adjacent elements are independent of each other. The channel from BS to IRS is a Rice fading channel, and the other channels are Rayleigh fading channels. The channel gain coefficient vectors from BS to IRS, BS to user U, BS to eavesdropper E, IRS to user U, IRS to eavesdropper E, eavesdropper to IRS, and eavesdropper to user U are defined as: and

[0043] Step 3: Define the IRS reflection coefficient matrix and derive the received signal expressions of user U and eavesdropper E respectively;

[0044] In step 3, the IRS reflection coefficient matrix Φ is defined as:

[0045]

[0046] In the formula, θ i ∈[0,2π) and β i ∈[0, 1] are the phase shift and amplitude of the i-th reflection unit of IRS, assuming that IRS has the maximum reflected power gain, i.e., β i =1, i=1, 2, ..., N;

[0047] The received signals of user U and eavesdropper E in step 3 are respectively expressed as:

[0048]

[0049]

[0050] In the formula, y U represents the received signal of user U, yE represents the received signal of the eavesdropper E; represents the BS beamforming vector and satisfies the power constraint ||W||≤P BS , P BS represents the maximum transmission power of the BS; x represents the confidential user information sent by the BS, which is a random variable with zero mean and unit variance, that is, x satisfies n U With n E Respectively represent the background noise at user U and eavesdropper E, and n U With n E satisfy P E It represents the interference signal power transmitted by the full-duplex eavesdropper. Since the eavesdropper E will produce residual self-interference when the self-interference elimination is incomplete, the coefficient α∈[0,1] is used to quantify the self-interference level;

[0051] Step 4: Calculate the signal-to-noise ratio at user U and eavesdropper E respectively, and solve the transmission capacity of user U and eavesdropper E according to Shannon's theorem;

[0052] The signal-to-noise ratio of user U in step 4 is expressed as:

[0053]

[0054] The signal-to-noise ratio of the eavesdropper E is expressed as:

[0055]

[0056] Step 5: Establish an objective function with the system information transmission security rate as the target, set up multiple constraints and construct an optimization problem;

[0057] In step 5, the MISO system safety rate is defined as:

[0058] R S (W, Φ) = R U -R E (6)

[0059] The optimization equation is:

[0060]

[0061] Step 6: Jointly optimize the BS beamforming vector and the IRS phase shift matrix, and solve the optimization problem with the help of an alternating optimization algorithm to maximize the system information security rate;

[0062] In step 6, the two optimization variables in the optimization problem are coupled to each other. The objective function in the optimization problem is a non-convex function. Therefore, an alternating optimization method is used to fix the beamforming vector w and the IRS phase shift matrix Φ respectively, thereby gradually reducing the coupling degree between the two variables until the convergence condition is reached.

[0063] Example 2

[0064] The RIS-assisted MISO system physical layer security optimization method of the present invention is specifically as follows:

[0065] Establish a downlink MISO communication system model. The base station is equipped with M antennas, the IRS contains N reflection units, 1 single-antenna user U, and 1 single-antenna eavesdropper E, and the eavesdropper works in full-duplex mode. That is, the eavesdropper transmits interference signals and eavesdrops user signals at the same time. The IRS can communicate with the base station through a separate link provided by the intelligent controller to coordinate transmission and exchange information, and the base station can realize real-time control of the phase shift and amplitude of the reflection unit through the controller;

[0066] Assume that all channels are quasi-static flat fading channels, and the reflection channels through adjacent elements are independent of each other. The channel gain coefficient vectors from BS to IRS, BS to user, BS to eavesdropper, IRS to user, IRS to user, eavesdropper E to IRS, and eavesdropper to user are defined as, and According to the channel reciprocity, the channel gain coefficient vectors from IRS to eavesdropper and from eavesdropper to IRS satisfy The channel from the base station to the IRS is a Rice fading channel, and the other channels are Rayleigh fading channels;

[0067] The reflection coefficient matrix of IRS is defined as Among them, θ i ∈[0,2π) and β i ∈[0, 1] are the phase shift and amplitude of the i-th reflection unit of IRS respectively. Assuming that IRS has the maximum reflected power gain, let β i =1, i=1, 2, ..., N. Due to the slowly changing characteristics of the channel, without loss of generality, it is assumed that the base station can accurately obtain the legitimate channel CSI. Since the legitimate system and the eavesdropper are in a non-cooperative relationship, the eavesdropper will not actively exchange information with the legitimate system, so it is difficult for the base station to obtain the eavesdropper's instantaneous CSI. However, the base station can obtain the eavesdropper's CSI through channel estimation. From this, the received signal expression of user U is derived The received signal expression of eavesdropper E is in, represents the beamforming vector of the base station, which satisfies the power constraint ||W||≤P BS , P BS represents the maximum transmission power of the base station; x represents the confidential information sent by the base station, which is a random variable with zero mean and unit variance; n U With n E Respectively represent the background noise at user U and eavesdropper E, and n U With n E satisfy P E It represents the power of the interference signal transmitted by the full-duplex eavesdropper. Since the eavesdropper will generate residual self-interference when the self-interference elimination is incomplete, the coefficient α∈[0,1] is used to quantify the self-interference level;

[0068] Example 3

[0069] The RIS-assisted MISO system physical layer security optimization method of the present invention calculates the signal-to-noise ratio at the user and the eavesdropper respectively. The signal-to-noise ratio of the user can be calculated by step S3 as follows: The signal-to-noise ratio of the eavesdropper is According to Shannon's formula, the user's transmission capacity can be solved as The transmission capacity of the eavesdropper is

[0070] Define the system's safe rate as R S (W, Φ) = R U -R E , the transmission capacity obtained in step 4 is brought into the safe rate, and the following optimization problem can be constructed:

[0071] max W，Φ R S (W, Φ)

[0072]

[0073] From the optimization problem, we can see that the two optimization variables W and Φ in the objective function are coupled with each other, and the objective function is a non-convex function. We can use the alternating optimization method to design W and μ separately until the objective function converges.

[0074] Jointly optimize the BS beamforming vector and the IRS phase shift matrix. Fix the IRS phase shift matrix μ and optimize the beamforming vector W. The optimization problem can be expressed as:

[0075]

[0076] st||W|| 2 ≤P BS

[0077] make

[0078]

[0079]

[0080] The optimization problem can be expressed as:

[0081]

[0082] st||W|| 2 ≤P BS

[0083] Fix the beamforming vector W and optimize the phase shift matrix Φ of the IRS. The optimization problem can be expressed as:

[0084]

[0085] st|Φ i，i |=1,i∈1,2,...,N

[0086] make

[0087]

[0088] The optimization problem can be expressed as:

[0089]

[0090]

[0091] n U =diag(h IU )H BI W

[0092]

[0093]

[0094]

[0095] n E =diag(h IE )H BI W

[0096]

[0097] The optimization problem can be further simplified as:

[0098]

[0099]

[0100] The optimal values ​​of W and Φ under the conditions of fixed IRS phase shift matrix Φ and fixed beamforming vector W are solved respectively through fractional programming and semidefinite relaxation (SDR) methods, and then the optimization problem is solved by alternating iterative optimization algorithm to maximize the system information security rate.

Claims

1. RIS-assisted MISO system physical layer security optimization method, characterized in that: Follow the steps below to implement it: Step 1: construct a multi-input single-output system including a base station, an intelligent reflective surface, a user U and an eavesdropper E; Step 2: Establish corresponding channel models according to the gain coefficients of each channel of the MISO system; Step 3: Define the IRS reflection coefficient matrix and derive the received signal expressions of user U and eavesdropper E respectively; Step 4: Calculate the signal-to-noise ratio at user U and eavesdropper E respectively, and solve the transmission capacity of user U and eavesdropper E according to Shannon's theorem; Step 5: Establish an objective function with the system information transmission security rate as the target, set up multiple constraints and construct an optimization problem; Step 6: Jointly optimize the BS beamforming vector and the IRS phase shift matrix, and use the alternating optimization algorithm to solve the optimization problem to maximize the system information security rate.

2. The RIS-assisted MISO system physical layer security optimization method according to claim 1, characterized in that: In step 1, the BS is configured with M antennas, the IRS is configured with N reflection units, the user U and the eavesdropper E are both configured with a single antenna, the IRS communicates with the BS via a link connected to the intelligent controller, the BS implements real-time control of the phase shift and amplitude of the reflection unit in the IRS through the intelligent controller, and the eavesdropper E adopts a full-duplex communication working mode.

3. The RIS-assisted MISO system physical layer security optimization method according to claim 1, characterized in that: The channel in step 2 is a quasi-static flat fading channel, and the reflection channels passing through adjacent elements are independent of each other. The channel from BS to IRS is a Rice fading channel, and the other channels are Rayleigh fading channels. The channel gain coefficient vectors from BS to IRS, BS to user U, BS to eavesdropper E, IRS to user U, IRS to eavesdropper E, eavesdropper to IRS, and eavesdropper to user U are defined as: and 4. The RIS-assisted MISO system physical layer security optimization method according to claim 1, characterized in that: In step 3, the IRS reflection coefficient matrix Φ is defined as: In the formula, θ i ∈[0,2π) and β i ∈[0, 1] are the phase shift and amplitude of the i-th reflection unit of IRS, assuming that IRS has the maximum reflected power gain, i.e., β i =1, i=1, 2, ..., N.

5. The RIS-assisted MISO system physical layer security optimization method according to claim 1, characterized in that: The received signals of user U and eavesdropper E in step 3 are respectively expressed as: In the formula, y U represents the received signal of user U, y E represents the received signal of the eavesdropper E; represents the BS beamforming vector and satisfies the power constraint ||W||≤P BS , P BS represents the maximum transmission power of the BS; x represents the confidential user information sent by the BS, which is a random variable with zero mean and unit variance, that is, x satisfies n U With n E Respectively represent the background noise at user U and eavesdropper E, and n U With n E Satisfy n U , P E It represents the interference signal power transmitted by the full-duplex eavesdropper. Since the eavesdropper E will produce residual self-interference when the self-interference elimination is incomplete, the coefficient a∈[0,1] is used to quantify the self-interference level.

6. The RIS-assisted MISO system physical layer security optimization method according to claim 1, characterized in that: The signal-to-noise ratio of user U in step 4 is expressed as: The signal-to-noise ratio of the eavesdropper E is expressed as:

7. The RIS-assisted MISO system physical layer security optimization method according to claim 1, characterized in that: The MISO system safety rate in step 5 is defined as: R S (W,Φ)=R U -R E (6) The optimization equation is:

8. The RIS-assisted MISO system physical layer security optimization method according to claim 1, characterized in that: In step 6, the two optimization variables in the optimization problem are coupled to each other. The objective function in the optimization problem is a non-convex function. Therefore, an alternating optimization method is adopted to fix the beamforming vector W and the IRS phase shift matrix Φ respectively, thereby gradually reducing the coupling degree between the two variables until the convergence condition is reached.

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