Millimeter wave Internet of Vehicles secure transmission method, system and device based on intelligent reflecting surface, and medium

Abstract

The invention discloses a millimeter wave Internet of Vehicles secure transmission method, system and device based on an intelligent reflecting surface, and a medium. The method comprises the following steps: constructing a millimeter wave Internet of Vehicles downlink communication scene assisted by an intelligent reflecting surface RIS; establishing a millimeter wave channel model; modeling and analyzing the key link distance; designing an association strategy RAA according to the real-time channel state; obtaining a signal to interference plus noise ratio (SINR) of a typical user and an eavesdropper in combination with interference brought by an intelligent reflector RIS; comparing the connection outage probability COP and the security outage probability SOP in the scene with or without the assistance of the intelligent reflecting surface RIS, and evaluating the performance gain of the intelligent reflecting surface RIS in the aspect of improving the link stability and safety; the system, the equipment and the medium are used for implementing the method. According to the method, the problem of insufficient integration of millimeter waves and the RIS in the Internet of Vehicles is solved, and when direct link deterioration is caused by barrier shielding, the RIS can be utilized to enhance the coverage stability and optimize the secrecy performance, so that the reliability and the safety of Internet of Vehicles communication are ensured in a complex road scene.

Description

Technical Field

[0001] The present invention belongs to the field of physical layer security technology, and in particular relates to a millimeter wave vehicle network security transmission method, system, equipment and medium based on an intelligent reflective surface. Background Art

[0002] With the innovation and development of wireless communication technology, advanced communication technologies have made vehicle connectivity possible, and the Internet of Vehicles (IoV) has emerged, achieving excellent features such as road safety, passenger infotainment, and transportation system optimization. However, with the development of IoV, there is a growing demand for low-latency, high-throughput communication architectures. This makes millimeter wave (mmWave) systems, with their abundant spectrum resources, a key technology to meet these needs and push IoV communications into a new stage.

[0003] Millimeter-wave communications, with their abundant spectrum resources and wide bandwidth, offer advantages over sub-6GHz bands in 5G communications applications. However, their application still faces significant challenges in communication security. Compared to sub-6GHz bands, millimeter-wave communications exhibit new characteristics such as shorter transmission distance, high directivity, different propagation patterns, and sensitivity to blocking effects. These characteristics pose new requirements for ensuring the security of millimeter-wave communications.

[0004] The information-theoretical foundations of physical layer security were studied in the last century. This security technology, developed from information theory, protects system security at the physical layer, encompassing hardware, software, and data transmission. It primarily leverages the inherent characteristics of communication channels and the differences between legitimate primary channels and eavesdropped channels to ensure communication system security. Unlike traditional encryption and decryption methods, physical layer security does not rely on the computing resources of communication devices, thereby reducing system complexity and conserving energy. Due to the broadcast nature of wireless communications, physical layer security in wireless communication networks has received significant attention in recent years. Physical layer security leverages the inherent characteristics of communication channels to protect information from eavesdropping. Physical layer security has proven effective in protecting millimeter wave communications.

[0005] In the context of connected vehicle communications, physical layer security has become a research hotspot in recent years. Due to the open nature of wireless channels, millimeter-wave connected vehicle communications still face serious security vulnerabilities, which can lead to information leakage and endanger lives. Low-cost physical layer security (PLS) technology addresses the latency-sensitivity of connected vehicle communications by leveraging the random nature of wireless channels to design secure transmission schemes, significantly improving security. Several studies have explored security risks in millimeter-wave connected vehicle communications and employed physical layer security techniques to improve performance. Furthermore, previous researchers have proposed minimum distance and maximum power correlation schemes for millimeter-wave on-vehicle uplink transmission and analyzed the reliability and confidentiality of the two schemes.

[0006] Reconfigurable smart surfaces (RIS), a promising technology, are attracting increasing attention in the field of secure communications. RIS can expand network coverage and overcome the high path loss of millimeter-wave systems. RIS consists of numerous inexpensive passive reflective elements that can be intelligently controlled to adjust phase shifts, steer the transmitted signal beam toward the desired user, avoid obstacles, and achieve robust line-of-sight (LOS) transmission, reducing information leakage and improving communication security. The advantages of RIS make it a compelling option for enhancing in-vehicle network connectivity, driving significant research interest in combining PLS technology with RIS to enhance communication security. Consequently, several studies have analyzed the PLS performance of RIS-assisted connected vehicle networks and derived closed-form expressions for the system's confidentiality.

[0007] However, the aforementioned studies failed to consider the integration of millimeter wave and RIS in vehicle networks while taking into account the randomness of roads, congestion, and vehicle density under real-world conditions. The two most relevant technical solutions to this invention are: theoretical analysis techniques for communication performance in millimeter wave vehicle networks; and techniques for using RIS to assist vehicles in transmission in planar networks. Overall, these two similar technical solutions primarily focus on the invention of theoretical analysis methods and have not yet addressed the design of association schemes in RIS-assisted millimeter wave vehicle networks. The main reasons are: the unique location characteristics of vehicles bring computational complexity to theoretical analysis, and the introduction of RIS further complicates the analysis. Therefore, how to select appropriate association schemes for typical users in RIS-assisted millimeter wave vehicle networks; and how to use RIS technology to ensure link connection reliability while improving link security are key issues that need to be addressed.

[0008] In summary, the existing technology still has the following deficiencies:

[0009] (1) Existing technologies mostly discuss the benefits of millimeter wave and RIS applications in the Internet of Vehicles scenario separately, without integrating millimeter wave and smart reflective surface technology in the Internet of Vehicles. There is a lack of a framework for Internet of Vehicles channel modeling and safety performance analysis that combines millimeter wave and RIS.

[0010] (2) Existing technologies improve communication reliability through fixed link association or single RIS deployment, but lack the design of an association mechanism for multi-RIS collaboration and dynamic link switching, making it difficult to deal with sudden interruptions of millimeter wave links caused by obstacles or vehicle movement.

[0011] (3) Existing technologies for analyzing the security of Internet of Vehicles are mostly based on static scenario analysis, without combining random geometry theory to quantify the impact of road distribution, base station density, and vehicle density on physical layer security, resulting in a deviation between performance evaluation and real dynamic scenarios.

[0012] Patent application publication number CN119298952A discloses a high-frequency communication system and deployment optimization method assisted by multiple intelligent reflectors. By optimizing multiple RIS (intelligent reflectors) to assist the MIMO (multiple-input, multiple-output) system, communication performance in complex environments is improved. The system addresses the problem of blocked base station-user links by utilizing single RIS reflections to reduce path attenuation and introducing an orthogonal deployment strategy to ensure independent propagation paths and maximize system capacity. However, because it uses a fixed topology optimization model, it does not consider the random spatial distribution characteristics of the base station-user-RIS, and ignores the link switching mechanism and security threats in dynamic blocking environments, resulting in scenario adaptability issues. Summary of the Invention

[0013] In order to overcome the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide a millimeter-wave vehicle network security transmission method, system, equipment and medium based on intelligent reflecting surfaces. The present invention studies the problem of secure transmission of confidential information in the presence of malicious eavesdroppers in the millimeter-wave vehicle network communication system, and proposes an association strategy RAA based on real-time channel status design, so that typical vehicles can dynamically select direct association or intelligent reflecting surface RIS assisted association strategy according to the real-time channel status, enhance line-of-sight transmission, and thus enhance the security of confidential information transmission process; this method solves the problem of insufficient integration of millimeter waves and intelligent reflecting surfaces RIS in the vehicle network, and can use intelligent reflecting surfaces RIS to enhance coverage stability and optimize confidentiality performance when the direct link deteriorates due to obstacle obstruction, thereby ensuring the reliability and security of vehicle network communication in complex road scenarios, so that the service quality and security of millimeter-wave vehicle network communication are guaranteed.

[0014] In order to achieve the above object, the technical solution adopted by the present invention is:

[0015] A millimeter wave vehicle network security transmission method based on a smart reflective surface includes the following steps:

[0016] Step 1: Based on randomly distributed users and base stations (BSs), an eavesdropper node is introduced to construct a millimeter-wave vehicle network downlink communication scenario assisted by a smart reflective surface (RIS).

[0017] Step 2: Based on the intelligent reflector RIS-assisted millimeter-wave vehicle network downlink communication scenario constructed in step 1, establish a millimeter-wave channel model;

[0018] Step 3: Based on the millimeter wave channel model established in Step 2 and Poisson process theory, model and analyze the key link distances to obtain the probability density functions (PDFs) of the distances from a typical user to its nearest directly connected base station (B1), from the typical user to its nearest smart reflector (RIS), and from the smart reflector (RIS) to the nearest RIS-assisted base station (B2).

[0019] Step 4: Based on the millimeter wave channel model established in step 2 and the probability density functions (PDFs) of the distances from a typical user to its nearest directly connected base station B1, from the typical user to its nearest smart reflector RIS, and from the smart reflector RIS to the nearest smart reflector RIS-assisted base station B2 obtained in step 3, an association strategy (RAA) is designed according to the real-time channel state. The channel state includes line-of-sight (LoS) and non-line-of-sight (NLoS). The association strategy (RAA) includes direct association and smart reflector RIS-assisted association strategies. Specifically, if the direct link is line-of-sight (LoS) and its quality is better than the smart reflector RIS-assisted association link, direct association is used. If the direct link is non-line-of-sight (NLoS) or its quality is insufficient, the smart reflector RIS-assisted association link is enabled to enhance signal coverage through smart reflection.

[0020] Step 5: Based on the intelligent reflector RIS-assisted mmWave vehicle network downlink communication scenario constructed in Step 1 and the RAA association strategy designed in Step 4 based on the real-time channel state, combined with the interference caused by the intelligent reflector RIS, the signal-to-interference-and-noise ratio (SINR) of the typical user and eavesdropper is obtained.

[0021] Step 6: Based on the intelligent reflector RIS-assisted millimeter-wave vehicle network downlink communication scenario constructed in Step 1, combined with the RAA association strategy designed based on the real-time channel state in Step 4 and the signal-to-interference-and-noise ratio (SINR) of typical users and eavesdroppers obtained in Step 5, the performance gain of the intelligent reflector RIS in improving link stability and security is evaluated by comparing the connection interruption probability (COP) and security interruption probability (SOP) in the scenarios with and without the assistance of the intelligent reflector RIS.

[0022] The process of step one is as follows:

[0023] Construct a millimeter-wave vehicle network downlink communication scenario assisted by an intelligent reflective surface RIS, and model the location of the base station BS as a density of λ B Homogeneous Poisson point process Φ B , the road is modeled as having density λ L Poisson line process PLPΦ L , the intelligent reflective surface RIS and the eavesdropper position are described as point processes Φ driven by Poisson line processes PLP R and Φ E , that is, the road position is generated by the Poisson line process PLP, and the intelligent reflective surface RIS and the eavesdropper on each road obey the density μ R and μ e An independent one-dimensional Poisson point process PPP is proposed. The typical user UE0 is located at the coordinate origin. After establishing the association scheme between the typical user UE0 and the base station BS, a downlink transmission scheme is designed; that is, the millimeter-wave vehicle network downlink communication scenario assisted by the intelligent reflecting surface RIS is obtained.

[0024] The process of step 2 is as follows:

[0025] 2.1 Directional Beamforming

[0026] The sector model is used to approximate the gain effect of the antenna array. In the sector model, the antenna gain of the base station BS transmitting the confidential signal is expressed as:

[0027]

[0028] Among them, G I ,G A and θ represent the main lobe, side lobe gain and main lobe width respectively, Pr I It represents the probability that the signal is transmitted from the main lobe of the transmitter and received at the receiver, Pr A represents the probability that the signal is emitted from the side lobe of the transmitting end and received at the receiving end. The vehicle and the eavesdropper Eves use omnidirectional antennas to receive confidential signals. The antenna gains of the vehicle and the eavesdropper Eves are represented by G V and G e , each base station BS is preset to adjust its main lobe direction according to the position of the legal transmitting vehicle to align with the legal receiving vehicle, and all transmitting nodes are set to transmit with the same power P I Perform signal transmission;

[0029] 2.2 Building a blocking model

[0030] Define p L (r) = e -β·r and p N (r) = 1 - e -β·r is the probability that the link at distance r is line-of-sight LoS or non-line-of-sight NLoS, where β represents the blocking density. All parameters or variables related to the blocking state are identified by the subscript i∈{L,N}, where L and N identify the line-of-sight LoS link and the non-line-of-sight NLoS link, respectively;

[0031] 2.3 Constructing the millimeter wave channel model

[0032] The propagation loss generated by the signal during propagation is expressed as the path loss function Indicates that, where α i is the path loss index related to the path blocking state, C i is the path loss intercept. In the millimeter-wave vehicle network downlink communication scenario assisted by the intelligent reflector RIS constructed in step 1, there are two types of links: one is a direct link, and the other is an associated link assisted by the intelligent reflector RIS. The path loss of the direct link is expressed as: Where λ is the wavelength, r d is the distance between the directly connected base station B1 and the typical user UE0, αj is the path loss exponent, j∈{L,N}, is the path loss intercept of the direct link; assuming that the typical user UE0 and the smart reflecting surface RIS assisted base station B2 are both located in the far field of the selected auxiliary smart reflecting surface RIS, and the smart reflecting surface RIS is placed on a higher building on the roadside, the link from the smart reflecting surface RIS to the typical vehicle and the base station BS is line-of-sight LoS, and the path loss of the smart reflecting surface RIS assisted associated link is expressed as the “product of distance”, that is, Among them, d x and d y represents the length and width of the basic unit of the intelligent reflector RIS, r s is the distance between the typical user UE0 and the selected smart reflective surface RIS, r b is the distance between the selected intelligent reflecting surface RIS and the base station BS, is the path loss intercept of the RIS-assisted associated link;

[0033] Assume that the wireless channel of the direct link experiences independent d The Nakagami-m decays and is represented by independent and identically distributed random variables represents the channel gain, and the signal power from the directly connected base station BS received at a typical vehicle is in represents the channel gain of the wireless channel between a typical vehicle and the associated directly connected base station B1;

[0034] For the smart reflector RIS-assisted correlation link, when N is large enough, the small-scale attenuation at the optimal phase is approximately:

[0035] h R,T ≈N 2 μ 2

[0036] in, m a is the shape parameter of the upper-middle Nakagami-m distribution, and N represents the number of cells in the RIS;

[0037] For the base station BS j Undesired signal, j∈Φ B / o When N is large enough, the small-scale fading of the smart reflector RIS-assisted correlation link under the OPS scheme is approximately h R,t ~Gamma(1,N), the signal power received by the typical vehicle from the intelligent reflecting surface RIS assisted base station BS is where h R,oIt represents the channel gain of the wireless channel between a typical vehicle and the associated intelligent reflecting surface RIS auxiliary base station B2.

[0038] The process of step three is as follows:

[0039] Based on the millimeter wave channel model established in step 2, the distance r between the directly connected base station B1 and the typical user UE0 is d , the distance r between the selected intelligent reflector surface RIS and the base station BS b The distance r between the typical user UE0 and the selected smart reflective surface RIS s To obtain the distance distribution, we first calculate the distance statistics between the typical vehicle and the associated base station BS of the association scheme, and use the nearest distance access strategy to select the cellular base station to which the legal vehicle is connected, where CDF represents the distance from a typical user UE0 to its nearest directly connected base station B1; CDF represents the cumulative distribution function of the distance from the typical user UE0 to its nearest smart reflection surface RIS; The cumulative distribution function CDF represents the distance from the auxiliary smart reflecting surface RIS to the nearest smart reflecting surface RIS-assisted base station B2; the cumulative distribution function CDF of the distance from the typical user UE0 to its nearest directly connected base station B1, the cumulative distribution function CDF of the distance from the typical user UE0 to its nearest smart reflecting surface RIS, and the cumulative distribution function CDF of the distance from the auxiliary smart reflecting surface RIS to the nearest smart reflecting surface RIS-assisted base station B2 are derived to obtain the probability density functions PDF of the distances from the typical user to its nearest directly connected base station B1, from the typical user to its nearest smart reflecting surface RIS, and from the smart reflecting surface RIS to the nearest smart reflecting surface RIS-assisted base station B2, which are respectively: and

[0040] The process of the fourth step is as follows:

[0041] Using the millimeter wave channel model established in step 2, the maximum average received power of the direct link and the link assisted by the intelligent reflector RIS are obtained as follows: I G I G V L d (r d ), P I G I G V N 2 μ 2 L R (r s ,r b ), for line-of-sight LoS signals, if L d (rd )>N 2 μ 2 L R (r s ,r b ), the typical vehicle is connected to the nearest directly connected base station B1, that is, the typical vehicle is associated with the base station BS that can provide it with the maximum average received power in the long term; otherwise, the typical vehicle selects the nearest smart reflecting surface RIS on the same road and connects to the nearest smart reflecting surface RIS-assisted base station B2; for non-line-of-sight (NLoS) signals, the vehicle connects to the base station B2 assisted by the smart reflecting surface RIS;

[0042] The probability A of a typical vehicle connecting to the direct base station B1 d and the probability A of intelligent reflective surface RIS assisting B2 a They are:

[0043]

[0044] The r obtained in step 3 d 、r s and r b The probability density function PDF of and Substituting into the above formula we get:

[0045]

[0046] A d =1-A d ,

[0047] in,

[0048] The process of step five is as follows:

[0049] First, the signal-to-interference-and-noise ratio (SINR) at a typical user is determined. In the millimeter-wave vehicle-to-vehicle downlink communication scenario assisted by the intelligent reflector (RIS) constructed in step 1, the association strategy (RAA) designed in step 4 based on the real-time channel state has different SINRs for different association scenarios. For the direct link association scheme proposed in step 4, the SINR at the receiver is expressed as:

[0050]

[0051] For the base station B2 assisted by the smart reflector RIS, the signal-to-interference-and-noise ratio (SINR) at the receiver is given by:

[0052]

[0053] in, represents the aggregate interference from main lobes, side lobes and RIS reflections generated by information signals transmitted by other base stations on a typical vehicle, where r b,o is the distance from other base stations to typical vehicles, r b,R and r R,o They represent the distances from other base stations to the intelligent reflecting surface RIS located on the same road as the typical vehicle, and the distance from the intelligent reflecting surface RIS to the typical vehicle, respectively. is Gaussian white noise;

[0054] The signal to interference plus noise ratio (SINR) of the i-th eavesdropper is expressed as follows:

[0055]

[0056] in, represents the signal-to-interference-and-noise ratio (SINR) of the i-th eavesdropper eavesdropping from the directly connected base station B1, represents the signal-to-interference-and-noise ratio (SINR) of the i-th eavesdropper eavesdropping from the intelligent reflecting surface RIS assisted base station B2, represents the signal-to-interference-and-noise ratio (SINR) of the i-th eavesdropper eavesdropping from the intelligent reflecting surface RIS, where is the distance from the directly connected base station B1 to the i-th eavesdropper Eve, is the distance from the intelligent reflecting surface RIS assisted base station B2 to the i-th eavesdropper Eve, is the distance from the intelligent reflecting surface RIS to the i-th eavesdropper Eve, and ξ e represents the distance between the eavesdropper Eve at point e and the intersection of the straight line through the origin and its perpendicular line, where I e Represents the total interference of confidential signals transmitted by all base stations in the entire vehicle network to eavesdropping vehicles, according to Divided into two parts, It is interference from a smart reflective surface (RIS) located on the same road as the eavesdropping vehicle. It is the interference from other base stations to the eavesdropping vehicle; is the distance from other base stations to the eavesdropping vehicle, and They represent the distances from other base stations to the intelligent reflecting surface RIS on the same road as the eavesdropping vehicle, and the distance from the intelligent reflecting surface RIS to the eavesdropping vehicle, respectively. represents the channel gain of the wireless channel between the eavesdropper Eve and the directly connected base station B1 associated with the typical vehicle, It represents the channel gain of the wireless channel between the eavesdropper Eve and the intelligent reflecting surface RIS auxiliary base station B2 associated with the typical vehicle.

[0057] The process of step six is as follows:

[0058] 6.1 Calculating the Connection Interruption Probability COP

[0059] Based on the intelligent reflector RIS-assisted millimeter-wave vehicle network downlink communication scenario constructed in step 1, combined with the RAA association strategy designed in step 4 based on the real-time channel status and the signal-to-interference-and-noise ratio (SINR) of typical users and eavesdroppers obtained in step 5, the connection interruption probability (COP) is calculated as:

[0060]

[0061] Among them, for the RAA plan, The calculation is divided into two parts, the probability of direct connection and the probability of intelligent reflective surface RIS assistance Communication also meets and where β t is the SINR interruption threshold, and the connection interruption probability COP of the direct link and the RIS-assisted link is calculated as:

[0062]

[0063] In the above formula In the above formula, step (a) is derived from the tight lower bound of the gamma random variable;

[0064]

[0065] In the above formula In the above formula, approximation (c) is obtained by h when M→∞ f →1 is derived from the facts;

[0066] in, is the Laplace transform of interference, which is calculated as follows:

[0067]

[0068] Among them, k∈{I,A},

[0069]

[0070] in, Indicates the road where a typical vehicle is located;

[0071] 6.2 Calculating the Safety Interruption Probability SOP

[0072] First, the theoretical expression of the safety interruption probability SOP is given:

[0073]

[0074] Among them, β e A threshold for determining a confidentiality interruption event: when the signal interference ratio received by any eavesdropping vehicle is greater than the threshold, a confidentiality interruption event is considered to have occurred;

[0075] For the millimeter wave vehicle network downlink communication scenario, the overall safety interruption probability SOP is expressed as:

[0076]

[0077] in, and represents the safety interruption probability SOP when a typical vehicle selects a direct link and a smart reflective surface RIS assisted link, and Expressed as:

[0078]

[0079] The analysis of the security interruption probability (SOP) consists of three parts: the probability of eavesdropping on the direct connection of the directly connected base station B1, the probability of eavesdropping on the direct connection of the intelligent reflecting surface (RIS) assisted base station B2, and the probability of eavesdropping on the signal of the intelligent reflecting surface (RIS). The three eavesdropping probabilities are expressed as follows:

[0080]

[0081] in

[0082]

[0083] in

[0084]

[0085] in,

[0086] Where L(·) is the Laplace transform of the interference, which is calculated as follows:

[0087]

[0088] in, θ2 is and The angle between

[0089]

[0090] Taking the expectation of the variables in the general formula, we get:

[0091]

[0092] in, where r e is the distance from the eavesdropping vehicle to the origin, ξ L is the vertical distance from the road to the origin, and ξ e represents the distance between Eve at e and the intersection of the line through the origin and its perpendicular line, and θ3 is r s With r b The angle between

[0093] The final safety interruption probability SOP is expressed as:

[0094]

[0095] in, represents the set of all roads in the mmWave VIZ downlink communication scenario assisted by intelligent reflective surface (RIS), ξ R represents the distance between the smart reflective surface RIS located on l0 and a typical vehicle.

[0096] The present invention also provides a millimeter wave vehicle network security transmission system based on a smart reflective surface, comprising:

[0097] The communication scenario construction module is used to implement a millimeter-wave vehicle network downlink communication scenario assisted by a smart reflector (RIS) based on randomly distributed users and base stations (BSs), introduce eavesdropper nodes, and build a smart reflector (RIS)-assisted millimeter-wave vehicle network downlink communication scenario.

[0098] The millimeter wave channel model establishment module is used to implement the millimeter wave vehicle network downlink communication scenario based on the assistance of intelligent reflective surface (RIS) and establish the millimeter wave channel model;

[0099] The key link distance modeling and analysis module is used to model and analyze key link distances based on the millimeter wave channel model and Poisson process theory, and obtain the probability density functions (PDFs) of the distances from a typical user to its nearest directly connected base station B1, from a typical user to its nearest smart reflector RIS, and from the smart reflector RIS to the nearest smart reflector assisted base station B2.

[0100] The RAA association strategy design module is used to implement an association strategy based on the millimeter wave channel model and the probability density function (PDF) of the distance from a typical user to its nearest directly connected base station B1, from the typical user to its nearest smart reflector RIS, and from the smart reflector RIS to the nearest smart reflector RIS-assisted base station B2. According to the real-time channel status, the RAA association strategy is designed. The channel status includes line-of-sight (LoS) and non-line-of-sight (NLoS). Specifically, if the direct link is LoS and its quality is better than the smart reflector RIS-assisted association link, direct association is used. If the direct link is non-line-of-sight (NLoS) or its quality is insufficient, the smart reflector RIS-assisted association link is enabled to enhance signal coverage through intelligent reflection.

[0101] The Signal-to-Interference-and-Noise Ratio (SINR) acquisition module is used to implement millimeter-wave vehicle-to-vehicle downlink communication scenarios assisted by intelligent reflectors (RIS) and the RAA association strategy designed based on real-time channel conditions. It combines the interference caused by the intelligent reflectors (RIS) to obtain the SINR of typical users and eavesdroppers.

[0102] The Smart Reflecting Surface (RIS) performance evaluation module is used to implement millimeter-wave vehicle-to-vehicle downlink communication scenarios assisted by RIS. By combining the RAA association strategy designed based on real-time channel status and the signal-to-interference-and-noise ratio (SINR) of typical users and eavesdroppers, the module evaluates the performance gains of RIS in improving link stability and security by comparing the connection interruption probability (COP) and security interruption probability (SOP) in scenarios with and without RIS assistance.

[0103] The present invention also provides a millimeter wave vehicle network security transmission device based on an intelligent reflective surface, comprising:

[0104] Memory: a computer-readable device storing a computer program for the above-mentioned millimeter-wave vehicle network security transmission method based on a smart reflective surface;

[0105] Processor: used to implement the millimeter wave vehicle network security transmission method based on intelligent reflecting surface when executing the computer program.

[0106] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the millimeter-wave vehicle network security transmission method based on an intelligent reflecting surface.

[0107] Compared with the prior art, the present invention has the following beneficial effects:

[0108] 1. This invention introduces intelligent reflective surface (RIS) technology into the millimeter-wave Internet of Vehicles (MMW) downlink communication scenario. By assisting in establishing a high-quality reflective link, namely, an intelligent reflective surface (RIS)-assisted associated link, it significantly enhances connection stability in conditions of obstructed line of sight, effectively overcoming the link congestion problem caused by obstruction in high-frequency communications, thereby providing solid protection for communication reliability and physical layer security.

[0109] 2. The present invention actively applies intelligent reflecting surface (RIS) technology to the physical layer security design of millimeter-wave vehicle networks. It proposes an association strategy (RAA) to enhance the physical layer security of millimeter-wave vehicle networks. This strategy dynamically switches between direct links and intelligent reflecting surface (RIS)-assisted association links, and utilizes the reflected signals of the intelligent reflecting surface (RIS) to interfere with potential eavesdropping links, thereby reducing the eavesdropper's signal-to-interference-and-noise ratio (SINR), thereby enhancing physical layer security during communication.

[0110] 3. This invention uses random geometry theory to model and analyze the RIS-assisted association mechanism, constructing a spatial distribution model that includes base stations, vehicle users, RIS nodes, and eavesdroppers. Specifically, Poisson process theory is used to model the distribution of base stations, vehicle users, RIS nodes, and eavesdroppers, as well as to model and analyze critical link distances. This results in a closed-form probabilistic expression for the system's security performance, enabling quantitative assessment of physical layer security effectiveness without actual deployment, significantly reducing the economic and time costs required for assessment.

[0111] In summary, this invention utilizes three key technical approaches: intelligent reflector (RIS) anti-blocking technology, a dynamic link selection mechanism, and geometric modeling and analysis using Poisson process theory. This system has been developed to create a highly reliable and secure communication system suitable for millimeter-wave vehicle-to-vehicle downlink communication scenarios. This system not only enhances link stability and eavesdropping resistance in complex and dynamic environments, but also enables quantitative assessment of security performance without the need for field deployment, significantly reducing development and verification costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] Figure 1 It is a flow chart of the implementation method of the present invention.

[0113] Figure 2 This is a schematic diagram comparing the effects of the association strategy design proposed in the present invention with and without RIS assistance in an embodiment of the present invention.

[0114] Figure 3 This is a schematic diagram comparing the effects of the association strategy design scheme proposed in the embodiment of the present invention with and without RIS assistance. DETAILED DESCRIPTION

[0115] The technical solution adopted by the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0116] like Figure 1 As shown, the present invention is implemented as follows: a millimeter-wave vehicle network security transmission method based on an intelligent reflecting surface, which first models the vehicle network communication scenario assisted by the intelligent reflecting surface, and uses random geometry theory to model the road as a Poisson line process PLP; the base station is modeled as a Poisson point process PPP; the user, RIS (located on the building next to the road), and the eavesdropper are all modeled as point processes driven by the Poisson line process PLP. When downlink transmission is carried out between a typical user and its associated base station, there are multiple malicious eavesdroppers (Eves) performing non-collusive eavesdropping. In this scenario, the present invention considers two association situations, namely the direct association between the typical user and the base station or the association assisted by the intelligent reflecting surface RIS. When the link between the typical user and the base station is a line-of-sight link, the link is selected by comparing the direct association strategy with the intelligent reflecting surface RIS-assisted association strategy. In the case of line-of-sight links, a typical user and base station use a closest-distance association strategy based on line-of-sight (LoS). Considering that smart reflective surfaces (RISs) are often deployed on tall buildings, the present invention employs a LoS-based closest-distance association strategy when a typical user chooses to associate with a service-providing smart reflective surface (RIS). Specifically, the typical user connects to the nearest smart reflective surface (RIS) via a LoS link, which then connects to the nearest base station via a LoS link. The probability density function of the distance between the typical user and the associated base station, the probability density function of the association distance between the typical user and the smart reflective surface (RIS), and the probability density function of the association distance between the smart reflective surface (RIS) and the base station are further calculated. This strategy can significantly enhance the communication quality of legitimate links. When the path is non-line-of-sight (NLoS), a smart reflective surface (RIS)-assisted association strategy is used to enhance line-of-sight transmission. This results in the proposed association strategy. The introduction of the smart reflective surface (RIS) in this connected vehicle scenario helps enhance the flexibility and stability of the association strategy. However, the deployment of the smart reflective surface (RIS) will cause some interference to the eavesdropper, Eve, in the scenario. By analyzing the signal-to-interference-and-noise ratio (SIR) of typical users and eavesdroppers (Eve), we can obtain the overall network's connection interruption probability (COP) and security interruption probability (SOP). By comparing the impact of intelligent reflective surface (RIS) assistance on the millimeter-wave Internet of Vehicles (Millimeter-wave Internet of Vehicles), we can ultimately achieve a millimeter-wave Internet of Vehicles (Millimeter-wave Internet of Vehicles) communication network with high stability, strong flexibility, high security, ultra-large capacity, and ultra-low latency services. Furthermore, the secure transmission method for millimeter-wave Internet of Vehicles based on intelligent reflective surfaces includes the following steps:

[0117] Step 1: Based on randomly distributed users and base stations (BSs), an eavesdropper node is introduced to construct a millimeter-wave vehicle network downlink communication scenario assisted by a smart reflector (RIS). This lays the foundation for the system architecture and node roles for subsequent modeling and analysis.

[0118] The process of step one is as follows:

[0119] Model the communication system and clarify the communication process: When an emergency occurs, the base station transmits confidential signals to legitimate vehicles through the downlink, while surrounding eavesdropping vehicles will passively eavesdrop on the confidential signals. Construct a millimeter-wave vehicle network downlink communication scenario assisted by an intelligent reflective surface (RIS), and model the location of the base station (BS) as a density of λ. B Homogeneous Poisson point process Φ B , the road is modeled as having density λ L Poisson line process PLPΦ L Due to the limitation of the road, the intelligent reflective surface RIS and the eavesdropper position are described as point processes Φ driven by the Poisson line process PLP. R and Φ E , that is, the road position is generated by the Poisson line process PLP, and the intelligent reflective surface RIS and the eavesdropper on each road obey the density μ R and μ e For the convenience of theoretical analysis, the typical user UE0 is located at the origin of the coordinate system. After establishing the association scheme between the typical user UE0 and the base station BS, the downlink transmission scheme is designed; that is, the millimeter-wave vehicle network downlink communication scenario assisted by the intelligent reflecting surface RIS is obtained.

[0120] Step 2: Based on the intelligent reflector RIS-assisted millimeter-wave vehicle network downlink communication scenario constructed in step 1, establish a millimeter-wave channel model;

[0121] The process of step 2 is as follows:

[0122] 2.1 Directional Beamforming

[0123] To overcome the severe path loss of millimeter-wave signals, all base stations (BSs) in the millimeter-wave vehicle network downlink communication scenario are equipped with highly directional antenna arrays. A sector model is used to approximate the gain effect of the antenna array. In this sector model, the antenna gain of the base station (BS) transmitting a confidential signal is expressed as:

[0124]

[0125] Among them, G I ,G A and θ represent the main lobe, side lobe gain and main lobe width respectively, Pr I It represents the probability that the signal is transmitted from the main lobe of the transmitter and received at the receiver, PrA represents the probability that the signal is emitted from the side lobe of the transmitting end and received at the receiving end. The vehicle and the eavesdropper Eves use omnidirectional antennas to receive confidential signals. The antenna gains of the vehicle and the eavesdropper Eves are represented by G V and G e , each base station BS is preset to adjust its main lobe direction according to the position of the legal transmitting vehicle to achieve perfect alignment with the legal receiving vehicle. In addition, all transmitting nodes are set to transmit with the same power P I Perform signal transmission;

[0126] 2.2 Building a blocking model

[0127] During the communication process, all communication channels are millimeter wave channels. Since millimeter wave communication is very sensitive to blocking, line-of-sight LoS links and non-line-of-sight NLoS links have different transmission characteristics. In order to identify and mitigate their blocking effects in different transmission processes, the following blocking model is adopted. Define p L (r) = e -β·r and p N (r) = 1 - e -β·r is the probability that the link at distance r is line-of-sight LoS or non-line-of-sight NLoS, where β represents the blocking density. For ease of expression, all parameters or variables related to the blocking state are identified by the subscript i∈{L,N}, where L and N represent the line-of-sight LoS link and the non-line-of-sight NLoS link, respectively;

[0128] 2.3 Constructing the millimeter wave channel model

[0129] The millimeter wave's susceptibility to blocking is reflected by small-scale fading and path propagation loss. Due to the millimeter wave blocking effect, the propagation loss generated by the signal during propagation is expressed by the path loss function. Indicates that, where α i is the path loss index related to the path blocking state, C i is the path loss intercept. In the millimeter-wave vehicle network downlink communication scenario assisted by the intelligent reflector RIS constructed in step 1, there are two types of links: one is a direct link, and the other is an associated link assisted by the intelligent reflector RIS. The path loss of the direct link is expressed as: Where λ is the wavelength, r d is the distance between the directly connected base station B1 and the typical user UE0, α j is the path loss exponent, j∈{L,N}, is the path loss intercept of the direct link; assuming that the typical user UE0 and the smart reflecting surface RIS assisted base station B2 are both located in the far field of the selected auxiliary smart reflecting surface RIS, and the smart reflecting surface RIS is placed on a higher building on the roadside, the link from the smart reflecting surface RIS to the typical vehicle and the base station BS is line-of-sight LoS, and the path loss of the smart reflecting surface RIS assisted associated link is expressed as the “product of distance”, that is, Among them, d x and d y represents the length and width of the basic unit of the intelligent reflector RIS, r s is the distance between the typical user UE0 and the selected smart reflective surface RIS, r b is the distance between the selected intelligent reflecting surface RIS and the base station BS, is the path loss intercept of the RIS-assisted associated link;

[0130] By considering the generalized fading environment, it is assumed that the wireless channel of the direct link experiences independent d The Nakagami-m decays and is represented by independent and identically distributed random variables represents the channel gain, and the signal power from the directly connected base station BS received at a typical vehicle is in represents the channel gain of the wireless channel between a typical vehicle and the associated directly connected base station B1;

[0131] For the smart reflector RIS-assisted correlation link, when N is large enough, the small-scale attenuation at the optimal phase is approximately:

[0132] h R,T ≈N 2 μ 2

[0133] in, m a is the shape parameter of the upper-middle Nakagami-m distribution, and N represents the number of cells in the RIS;

[0134] For the base station BS j Undesired signal, j∈Φ B / o When N is large enough, the small-scale fading of the smart reflector RIS-assisted correlation link under the OPS scheme is approximately h R,t ~Gamma(1,N), the signal power received by the typical vehicle from the intelligent reflecting surface RIS assisted base station BS is where h R,o It represents the channel gain of the wireless channel between a typical vehicle and the associated intelligent reflecting surface RIS auxiliary base station B2.

[0135] Step 3: Based on the millimeter wave channel model established in Step 2 and Poisson process theory, the key link distances are modeled and analyzed. The probability density functions (PDFs) of the distances from a typical user to their nearest directly connected base station B1, from the typical user to their nearest smart reflector RIS, and from the smart reflector RIS to the nearest smart reflector assisted base station B2 are obtained. This PDF quantifies the distance distribution and provides a mathematical basis for link quality assessment and association strategy optimization.

[0136] The process of step three is as follows:

[0137] In order to facilitate the analysis of the connection interruption probability COP and security interruption probability SOP of the physical layer of the Internet of Vehicles, based on the millimeter wave channel model established in step 2, the distance r between the direct base station B1 and the typical user UE0 is calculated. d , the distance r between the selected intelligent reflector surface RIS and the base station BS b The distance r between the typical user UE0 and the selected smart reflective surface RIS s To obtain the distance distribution, we first calculate the distance statistics between the typical vehicle and the associated base station BS of the association scheme, and use the nearest distance access strategy to select the cellular base station to which the legal vehicle is connected, where CDF represents the distance from a typical user UE0 to its nearest directly connected base station B1; CDF represents the cumulative distribution function of the distance from the typical user UE0 to its nearest smart reflection surface RIS; The cumulative distribution function CDF represents the distance from the auxiliary smart reflecting surface RIS to the nearest smart reflecting surface RIS-assisted base station B2; the cumulative distribution function CDF of the distance from the typical user UE0 to its nearest directly connected base station B1, the cumulative distribution function CDF of the distance from the typical user UE0 to its nearest smart reflecting surface RIS, and the cumulative distribution function CDF of the distance from the auxiliary smart reflecting surface RIS to the nearest smart reflecting surface RIS-assisted base station B2 are derived to obtain the probability density functions PDF of the distances from the typical user to its nearest directly connected base station B1, from the typical user to its nearest smart reflecting surface RIS, and from the smart reflecting surface RIS to the nearest smart reflecting surface RIS-assisted base station B2, which are respectively: and

[0138] Step 4: Based on the millimeter wave channel model established in step 2 and the probability density functions (PDFs) of the distances from a typical user to its nearest directly connected base station B1, from the typical user to its nearest smart reflector RIS, and from the smart reflector RIS to the nearest smart reflector RIS-assisted base station B2 obtained in step 3, an association strategy (RAA) is designed according to the real-time channel state. The channel state includes line-of-sight (LoS) and non-line-of-sight (NLoS). The association strategy (RAA) includes direct association and smart reflector RIS-assisted association strategies. Specifically, if the direct link is line-of-sight (LoS) and its quality is better than the smart reflector RIS-assisted association link, direct association is used. If the direct link is non-line-of-sight (NLoS) or its quality is insufficient, the smart reflector RIS-assisted association link is enabled to enhance signal coverage through smart reflection.

[0139] The process of the fourth step is as follows:

[0140] When the direct link is weakened due to obstruction, in order to provide a stable connection for the Internet of Vehicles, the maximum average received power of the direct link and the link assisted by the intelligent reflector RIS can be obtained by using the millimeter wave signal model obtained in step 2: P I G I G V L d (r d ), P I G I G V N 2 μ 2 L R (r s ,r b ), for line-of-sight LoS signals, if L d (r d )>N 2 μ 2 L R (r s ,r b ), the typical vehicle is connected to the nearest directly connected base station B1, that is, the typical vehicle is associated with the base station BS that can provide it with the maximum average received power in the long term; otherwise, the typical vehicle selects the nearest smart reflecting surface RIS on the same road to connect to the nearest smart reflecting surface RIS-assisted base station B2; for non-line-of-sight NLoS signals, the vehicle connects to the base station B2 assisted by the smart reflecting surface RIS, using the smart reflecting surface RIS to enhance connection stability and security.

[0141] The probability A of a typical vehicle connecting to the direct base station B1 d and the probability A of intelligent reflective surface RIS assisting B2 a They are:

[0142]

[0143] The r obtained in step 3 d 、r s and r b The probability density function PDF of and Substituting into the above formula we get:

[0144]

[0145] A a =1-A d ,

[0146] in,

[0147] Step 5: After the dynamic link selection mechanism is established, the interference capability of the reflected signal introduced by the intelligent reflector (RIS) on the eavesdropper's link is further analyzed. Based on the intelligent reflector-assisted millimeter-wave vehicle network downlink communication scenario constructed in Step 1 and the RAA association strategy designed in Step 4 based on real-time channel status, combined with the interference caused by the intelligent reflector, the signal-to-interference-and-noise ratio (SINR) model is used to evaluate the interference intensity and impact of the reflected signal from the intelligent reflector on the eavesdropping link. The SINR of a typical user and eavesdropper is obtained, providing a theoretical expression for evaluating network performance and serving as a reference for subsequent performance evaluation.

[0148] The process of step five is as follows:

[0149] Analysis of the signal-to-interference-and-noise ratio of typical users and eavesdroppers;

[0150] First, the signal-to-interference-and-noise ratio (SINR) at a typical user is determined. In the millimeter-wave vehicle-to-vehicle downlink communication scenario assisted by the intelligent reflector (RIS) constructed in step 1, the association strategy (RAA) designed in step 4 based on the real-time channel state has different SINRs for different association scenarios. For a direct link, the SINR at the receiver is expressed as:

[0151]

[0152] For the base station B2 assisted by the smart reflector RIS, the signal-to-interference-and-noise ratio (SINR) at the receiver is given by:

[0153]

[0154] in, represents the aggregate interference from main lobes, side lobes and RIS reflections generated by information signals transmitted by other base stations on a typical vehicle, where rb,o is the distance from other base stations to typical vehicles, r b,R and r R,o They represent the distances from other base stations to the intelligent reflecting surface RIS located on the same road as the typical vehicle, and the distance from the intelligent reflecting surface RIS to the typical vehicle, respectively. is Gaussian white noise;

[0155] The signal to interference plus noise ratio (SINR) of the i-th eavesdropper is expressed as follows:

[0156]

[0157] in, represents the signal-to-interference-and-noise ratio (SINR) of the i-th eavesdropper eavesdropping from the directly connected base station B1, represents the signal-to-interference-and-noise ratio (SINR) of the i-th eavesdropper eavesdropping from the intelligent reflecting surface RIS assisted base station B2, represents the signal-to-interference-and-noise ratio (SINR) of the i-th eavesdropper eavesdropping from the intelligent reflecting surface RIS, where is the distance from the directly connected base station B1 to the i-th eavesdropper Eve, is the distance from the intelligent reflecting surface RIS assisted base station B2 to the i-th eavesdropper Eve, is the distance from the intelligent reflecting surface RIS to the i-th eavesdropper Eve, and ξ e represents the distance between the eavesdropper Eve at point e and the intersection of the straight line through the origin and its perpendicular line, where I e Represents the total interference of confidential signals transmitted by all base stations in the entire vehicle network to eavesdropping vehicles, according to Divided into two parts, It is interference from a smart reflective surface (RIS) located on the same road as the eavesdropping vehicle. It is the interference from other base stations to the eavesdropping vehicle; is the distance from other base stations to the eavesdropping vehicle, and They represent the distances from other base stations to the intelligent reflecting surface RIS on the same road as the eavesdropping vehicle, and the distance from the intelligent reflecting surface RIS to the eavesdropping vehicle, respectively. represents the channel gain of the wireless channel between the eavesdropper Eve and the directly connected base station B1 associated with the typical vehicle, It represents the channel gain of the wireless channel between the eavesdropper Eve and the intelligent reflecting surface RIS auxiliary base station B2 associated with the typical vehicle.

[0158] Step 6: Based on the intelligent reflector RIS-assisted millimeter-wave vehicle network downlink communication scenario constructed in Step 1, combined with the association strategy RAA designed according to the real-time channel status in Step 4 and the theoretical expression of the eavesdropping link signal-to-interference and noise ratio (SINR) obtained in Step 5, by comparing the connection interruption probability (COP) and security interruption probability (SOP) in the scenarios with and without the assistance of the intelligent reflector RIS, the performance gain of the intelligent reflector RIS in improving link stability and security is evaluated, thereby achieving highly reliable and secure communication in the millimeter-wave vehicle network with ultra-low latency and ultra-large capacity requirements.

[0159] The process of step six is as follows:

[0160] Perform performance analysis on mmWave vehicle-to-vehicle communication scenarios assisted by intelligent reflective surfaces (RIS), including connection interruption probability (COP) and safety interruption probability (SOP), and evaluate the effectiveness of the designed association scheme.

[0161] 6.1 Calculating the Connection Interruption Probability COP

[0162] Based on the intelligent reflector RIS-assisted millimeter-wave vehicle network downlink communication scenario constructed in step 1, combined with the RAA association strategy designed in step 4 based on the real-time channel status and the signal-to-interference-and-noise ratio (SINR) of typical users and eavesdroppers obtained in step 5, the connection interruption probability (COP) is calculated as:

[0163]

[0164] Among them, for the RAA scheme, since there are two association methods, The calculation is divided into two parts, the probability of direct connection and the probability of intelligent reflective surface RIS assistance To ensure reliable transmission, communication must also meet and where β t is the SINR interruption threshold, and the connection interruption probability COP of the direct link and the RIS-assisted link is calculated as:

[0165]

[0166] In the above formula In the above formula, step (a) is derived from the tight lower bound of the gamma random variable;

[0167]

[0168] In the above formula In the above formula, approximation (c) is obtained by h when M→∞ f →1 is derived from the facts;

[0169] in, is the Laplace transform of interference, which is calculated as follows:

[0170]

[0171] Among them, k∈{I,A},

[0172]

[0173] in, Indicates the road where a typical vehicle is located;

[0174] 6.2 Calculating the Safety Interruption Probability SOP

[0175] First, the theoretical expression of the safety interruption probability SOP is given:

[0176]

[0177] Among them, β e A threshold for determining a confidentiality interruption event: when the signal interference ratio received by any eavesdropping vehicle is greater than the threshold, a confidentiality interruption event is considered to have occurred;

[0178] For the millimeter wave vehicle network downlink communication scenario, the overall safety interruption probability SOP is expressed as:

[0179]

[0180] in, and represents the safety interruption probability SOP when a typical vehicle selects a direct link and a smart reflective surface RIS assisted link, and Expressed as:

[0181]

[0182] The analysis of the security interruption probability (SOP) consists of three parts: the probability of eavesdropping on the direct connection of the directly connected base station B1, the probability of eavesdropping on the direct connection of the intelligent reflecting surface (RIS) assisted base station B2, and the probability of eavesdropping on the signal of the intelligent reflecting surface (RIS). The three eavesdropping probabilities are expressed as follows:

[0183]

[0184] in

[0185]

[0186] in

[0187]

[0188] in,

[0189] Where L(·) is the Laplace transform of the interference, which is calculated as follows:

[0190]

[0191] in, θ2 is and The angle between

[0192]

[0193] Taking the expectation of the variables in the general formula, we get:

[0194]

[0195] in, where r e is the distance from the eavesdropping vehicle to the origin, ξ L is the vertical distance from the road to the origin, and ξ e represents the distance between Eve at e and the intersection of the line through the origin and its perpendicular line, and θ3 is r s With r b The angle between

[0196] The final safety interruption probability SOP is expressed as:

[0197]

[0198] in, represents the set of all roads in the mmWave VIZ downlink communication scenario assisted by intelligent reflective surface (RIS), ξ R represents the distance between the smart reflective surface RIS located on l0 and a typical vehicle.

[0199] Experimental analysis

[0200] 1. Experimental conditions

[0201] The exact size of the communication nodes was ignored during the experiment. By selecting an appropriate node density, we simulated a real-world intelligent reflector (RIS)-assisted millimeter-wave vehicle network downlink communication scenario with a typical node size. All simulations were conducted at a millimeter-wave carrier operating frequency of 28 GHz. The parameters in the theoretical expressions of the connection interruption probability (COP) and the safety interruption probability (SOP) were set to G I =15dB, G A =-15dB,θ=π / 20,αL =2,α N =3,m d =4,m a =4, M=2, dx=dy=0.01m, λ=0.002m, N=100, P I =10dBw. In addition, the unit of node density is / m2.

[0202] 2. Experimental content

[0203] Simulation experiments were carried out using MATLAB 2021 software to simulate the expressions of the connection interruption probability COP and the safety interruption probability SOP, and to verify the relationship between the connection interruption probability COP and the safety interruption probability SOP and the signal-to-interference-and-noise ratio (SINR) threshold for different roads, base stations BS and smart reflective surface RIS densities. The connection interruption probability COP and the safety interruption probability SOP of the proposed association strategy RAA design were compared with those of the design without RIS assistance to verify the effectiveness of the smart reflective surface RIS technology in the physical layer security design of millimeter-wave vehicle networks.

[0204] 3. Experimental results

[0205] Figure 2 The results of the proposed RAA design comparing the connection outage probability (COP) with and without RIS assistance are shown, which shows the relationship between the connection outage probability (COP) and the signal-to-interference-plus-noise ratio (SINR) threshold βt for different road, base station (BS) and smart reflector surface (RIS) densities. Figure 2 The horizontal axis is the value of SINR threshold βt, and the vertical axis is the connection interruption probability COP. In general, as the SINR threshold βt increases, the connection interruption probability COP increases. t As the density of base stations (BSs) and roads increases, the overall COP (connection interruption probability) also increases. However, the COP of the proposed association strategy (RAA) is lower than that of the scheme without RIS because the intelligent reflective surfaces (RIS) enhance legitimate signal reception and communication stability, thereby improving safety. The relationship between base station density, road density, and intelligent reflective surface (RIS) density and the connection interruption probability is also studied. A higher density of intelligent reflective surfaces (RIS) increases interference and affects signal quality. Furthermore, the figure shows that simply increasing the density of base stations (BSs) and roads also leads to an increase in the overall COP because the interference received by a typical vehicle increases, affecting the quality of the received signal.

[0206] Figure 3 The results of the proposed RAA design with and without RIS assistance are shown, which shows the relationship between the SOP and the SINR threshold βe for different road, base station BS and smart reflector surface densities. Figure 3 The horizontal axis is the value of SINR threshold βe, and the vertical axis is the safe outage probability SOP. In general, the safe outage probability SOP increases with the SINR threshold βe. e The proposed scheme effectively reduces the security interruption probability SOP compared to the scheme without smart reflector RIS. This is because the smart reflector RIS introduces effective interference against eavesdropping and enhances security. In addition, the relationship between base station density, road density, and smart reflector RIS density and the security interruption probability is studied. A higher smart reflector RIS density further reduces the security interruption probability SOP, indicating its effectiveness in combating eavesdropping. At the same time, a larger base station BS density leads to a smaller security interruption probability SOP because the interference to the eavesdropper Eve increases. An increase in road density increases the security interruption probability SOP, which shows that denser road construction is detrimental to confidentiality performance.

[0207] In millimeter-wave connected vehicle communication scenarios assisted by intelligent reflective surfaces (RIS), machine learning-based solutions can currently be used to achieve secure transmission at the physical layer. However, since machine learning requires a large amount of computation to train the model, which takes a certain amount of time, and the large number of highly mobile nodes in the connected vehicle have high requirements for communication latency, the above-mentioned methods cannot be effectively applied to millimeter-wave connected vehicle scenarios for the time being.

[0208] Currently, there is no research on using intelligent reflective surfaces (RIS) to enhance connection stability in millimeter-wave connected vehicle scenarios, thereby providing guarantees for communication security. The present invention can use intelligent reflective surfaces (RIS) to overcome the adverse effects of blockage during communication, thereby enhancing system security.

[0209] This paper analyzes the performance of a millimeter-wave vehicle-to-vehicle communication network assisted by intelligent reflective surfaces (RIS). Using stochastic geometry analysis, the paper theoretically analyzes the designed correlation scheme and provides a theoretical evaluation expression for safety performance. This theoretical expression, derived from this analysis, allows for an estimate of safety performance without the need for actual deployment, significantly saving both time and money.

[0210] The present invention also provides a millimeter wave vehicle network security transmission system based on a smart reflective surface, comprising:

[0211] The communication scenario construction module is used to implement the randomly distributed users and base stations (BS) in step 1, introduce eavesdropper nodes, and build a millimeter-wave vehicle network downlink communication scenario assisted by a smart reflector (RIS).

[0212] A millimeter wave channel model establishment module is used to implement the millimeter wave vehicle network downlink communication scenario assisted by the intelligent reflector RIS constructed in step 1 in step 2 and establish a millimeter wave channel model;

[0213] The key link distance modeling and analysis module is used to implement the millimeter wave channel model established in step 2 in step 3 and use Poisson process theory to model and analyze the key link distances, thereby obtaining the probability density functions (PDFs) of the distances from a typical user to its nearest directly connected base station B1, from a typical user to its nearest smart reflector RIS, and from the smart reflector RIS to the nearest smart reflector assisted base station B2.

[0214] An association strategy RAA design module is used to implement the probability density function PDF of the distance from a typical user to its nearest directly connected base station B1, from the typical user to its nearest smart reflector RIS, and from the smart reflector RIS to the nearest smart reflector RIS-assisted base station B2 obtained in step 3 based on the millimeter wave channel model established in step 4. Based on the real-time channel status, an association strategy RAA is designed. The channel status includes line-of-sight LoS and non-line-of-sight NLoS. The association strategy RAA includes direct association and smart reflector RIS-assisted association strategies. Specifically, if the direct link is line-of-sight LoS and its quality is better than the smart reflector RIS-assisted association link, direct association is adopted. If the direct link is non-line-of-sight NLoS or its quality is insufficient, the smart reflector RIS-assisted association link is enabled to enhance signal coverage through smart reflection.

[0215] The Signal-to-Interference-and-Noise Ratio (SINR) acquisition module is used to implement the millimeter-wave vehicle network downlink communication scenario based on the intelligent reflector RIS constructed in step 1 in step 5 and the RAA association strategy designed in step 4 based on the real-time channel state, combined with the interference caused by the intelligent reflector RIS, to obtain the SINR of typical users and eavesdroppers;

[0216] The intelligent reflector surface (RIS) performance evaluation module is used to implement the millimeter-wave vehicle network downlink communication scenario assisted by the intelligent reflector surface (RIS) constructed in step 1 in step 6. Combined with the RAA association strategy designed based on the real-time channel status in step 4 and the signal-to-interference-and-noise ratio (SINR) of typical users and eavesdroppers obtained in step 5, the module evaluates the performance gain of the intelligent reflector surface (RIS) in improving link stability and security by comparing the connection interruption probability (COP) and security interruption probability (SOP) in scenarios with and without the assistance of the intelligent reflector surface (RIS).

[0217] The present invention also provides a millimeter wave vehicle network security transmission device based on an intelligent reflective surface, comprising:

[0218] Memory: a computer-readable device storing a computer program for the above-mentioned millimeter-wave vehicle network security transmission method based on a smart reflective surface;

[0219] Processor: used to implement the millimeter wave vehicle network security transmission method based on intelligent reflecting surface when executing the computer program.

[0220] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the millimeter-wave vehicle network security transmission method based on an intelligent reflecting surface.

Claims

1. A millimeter wave vehicle network security transmission method based on a smart reflective surface, characterized in that: The following steps are involved: Step 1: Based on randomly distributed users and base stations (BSs), an eavesdropper node is introduced to construct a millimeter-wave vehicle network downlink communication scenario assisted by a smart reflective surface (RIS). Step 2: Based on the intelligent reflector RIS-assisted millimeter-wave vehicle network downlink communication scenario constructed in step 1, establish a millimeter-wave channel model; Step 3: Based on the millimeter wave channel model established in Step 2 and Poisson process theory, model and analyze the key link distances to obtain the probability density functions (PDFs) of the distances from a typical user to its nearest directly connected base station (B1), from the typical user to its nearest smart reflector (RIS), and from the smart reflector (RIS) to the nearest RIS-assisted base station (B2). Step 4: Based on the millimeter wave channel model established in step 2 and the probability density functions (PDFs) of the distances from a typical user to its nearest directly connected base station B1, from the typical user to its nearest smart reflector RIS, and from the smart reflector RIS to the nearest smart reflector RIS-assisted base station B2 obtained in step 3, an association strategy (RAA) is designed according to the real-time channel state. The channel state includes line-of-sight (LoS) and non-line-of-sight (NLoS). The association strategy (RAA) includes direct association and smart reflector RIS-assisted association strategies. Specifically, if the direct link is line-of-sight (LoS) and its quality is better than the smart reflector RIS-assisted association link, direct association is used. If the direct link is non-line-of-sight (NLoS) or its quality is insufficient, the smart reflector RIS-assisted association link is enabled to enhance signal coverage through smart reflection. Step 5: Based on the intelligent reflector RIS-assisted mmWave vehicle network downlink communication scenario constructed in Step 1 and the RAA association strategy designed in Step 4 based on the real-time channel state, combined with the interference caused by the intelligent reflector RIS, the signal-to-interference-and-noise ratio (SINR) of the typical user and eavesdropper is obtained. Step 6: Based on the intelligent reflector RIS-assisted millimeter-wave vehicle network downlink communication scenario constructed in Step 1, combined with the RAA association strategy designed based on the real-time channel state in Step 4 and the signal-to-interference-and-noise ratio (SINR) of typical users and eavesdroppers obtained in Step 5, the performance gain of the intelligent reflector RIS in improving link stability and security is evaluated by comparing the connection interruption probability (COP) and security interruption probability (SOP) in the scenarios with and without the assistance of the intelligent reflector RIS.

2. The millimeter wave vehicle network security transmission method based on a smart reflective surface according to claim 1 is characterized in that: The process of step one is as follows: The location of the base station BS is modeled as a density of λ B Homogeneous Poisson point process Φ B , the road is modeled as having density λ L Poisson line process PLPΦ L , the intelligent reflective surface RIS and the eavesdropper position are described as point processes Φ driven by Poisson line processes PLP R and Φ E , that is, the road position is generated by the Poisson line process PLP, and the intelligent reflective surface RIS and the eavesdropper on each road obey the density μ R and μ e An independent one-dimensional Poisson point process PPP is proposed. The typical user UE0 is located at the coordinate origin. After establishing the association scheme between the typical user UE0 and the base station BS, a downlink transmission scheme is designed; that is, the millimeter-wave vehicle network downlink communication scenario assisted by the intelligent reflecting surface RIS is obtained.

3. The millimeter wave vehicle network security transmission method based on a smart reflective surface according to claim 1 is characterized in that: The process of step 2 is as follows: 2.1 Directional Beamforming The sector model is used to approximate the gain effect of the antenna array. In the sector model, the antenna gain of the base station BS transmitting the confidential signal is expressed as: Among them, G I ,G A and θ represent the main lobe, side lobe gain and main lobe width respectively, Pr I It represents the probability that the signal is transmitted from the main lobe of the transmitter and received at the receiver, Pr A represents the probability that the signal is emitted from the side lobe of the transmitting end and received at the receiving end. The vehicle and the eavesdropper Eves use omnidirectional antennas to receive confidential signals. The antenna gains of the vehicle and the eavesdropper Eves are represented by G V and G e , each base station BS is preset to adjust its main lobe direction according to the position of the legal transmitting vehicle to align with the legal receiving vehicle, and all transmitting nodes are set to transmit with the same power P I Perform signal transmission; 2.2 Building a blocking model Define p L (r) = e -β·r and p N (r) = 1 - e -β·r is the probability that the link at distance r is line-of-sight LoS or non-line-of-sight NLoS, where β represents the blocking density. All parameters or variables related to the blocking state are identified by the subscript i∈{L,N}, where L and N identify the line-of-sight LoS link and the non-line-of-sight NLoS link, respectively; 2.3 Constructing the millimeter wave channel model The propagation loss generated by the signal during propagation is expressed as the path loss function Indicates that α i is the path loss index related to the path blocking state, C i is the path loss intercept. In the millimeter-wave vehicle network downlink communication scenario assisted by the intelligent reflector RIS constructed in step 1, there are two types of links: one is a direct link, and the other is an associated link assisted by the intelligent reflector RIS. The path loss of the direct link is expressed as: Where λ is the wavelength, r d is the distance between the directly connected base station B1 and the typical user UE0, α j is the path loss exponent, j∈{L,N}, is the path loss intercept of the direct link; assuming that the typical user UE0 and the smart reflecting surface RIS assisted base station B2 are both located in the far field of the selected auxiliary smart reflecting surface RIS, and the smart reflecting surface RIS is placed on a higher building on the roadside, the link from the smart reflecting surface RIS to the typical vehicle and the base station BS is line-of-sight LoS, and the path loss of the smart reflecting surface RIS assisted associated link is expressed as the "product of distances", that is Among them, d x and d y represents the length and width of the basic unit of the intelligent reflector RIS, r s is the distance between the typical user UE0 and the selected smart reflective surface RIS, r b is the distance between the selected intelligent reflecting surface RIS and the base station BS, is the path loss intercept of the RIS-assisted associated link; Assume that the wireless channel of the direct link experiences independent d The Nakagami-m decays and is represented by independent and identically distributed random variables represents the channel gain, and the signal power from the directly connected base station BS received at a typical vehicle is in represents the channel gain of the wireless channel between a typical vehicle and the associated directly connected base station B1; For the smart reflector RIS-assisted correlation link, when N is large enough, the small-scale attenuation at the optimal phase is approximately: h R,T ≈N 2 m 2 in, m a is the shape parameter of the upper-middle Nakagami-m distribution, and N represents the number of cells in the RIS; For the base station BS j Undesired signal, j∈Φ B / o When N is large enough, the small-scale fading of the smart reflector RIS-assisted correlation link under the OPS scheme is approximately h R,t ~Gamma(1,N), the signal power received by the typical vehicle from the intelligent reflecting surface RIS assisted base station BS is where h R,o It represents the channel gain of the wireless channel between a typical vehicle and the associated intelligent reflecting surface RIS auxiliary base station B2.

4. The method for secure transmission of millimeter wave vehicle networks based on intelligent reflective surfaces according to claim 1, characterized in that: The process of step three is as follows: Based on the millimeter wave channel model established in step 2, the distance r between the directly connected base station B1 and the typical user UE0 is d , the distance r between the selected intelligent reflector surface RIS and the base station BS b The distance r between the typical user UE0 and the selected smart reflective surface RIS s To obtain the distance distribution, we first calculate the distance statistics between the typical vehicle and the associated base station BS of the association scheme, and use the nearest distance access strategy to select the cellular base station to which the legal vehicle is connected, where CDF represents the distance from a typical user UE0 to its nearest directly connected base station B1; CDF represents the cumulative distribution function of the distance from the typical user UE0 to its nearest smart reflection surface RIS; The cumulative distribution function CDF represents the distance from the auxiliary smart reflecting surface RIS to the nearest smart reflecting surface RIS-assisted base station B2; the cumulative distribution function CDF of the distance from the typical user UE0 to its nearest directly connected base station B1, the cumulative distribution function CDF of the distance from the typical user UE0 to its nearest smart reflecting surface RIS, and the cumulative distribution function CDF of the distance from the auxiliary smart reflecting surface RIS to the nearest smart reflecting surface RIS-assisted base station B2 are derived to obtain the probability density functions PDF of the distances from the typical user to its nearest directly connected base station B1, from the typical user to its nearest smart reflecting surface RIS, and from the smart reflecting surface RIS to the nearest smart reflecting surface RIS-assisted base station B2, which are respectively: and 5. The millimeter wave vehicle network security transmission method based on a smart reflective surface according to claim 1 is characterized in that: The process of the fourth step is as follows: Using the millimeter wave channel model established in step 2, the maximum average received power of the direct link and the link assisted by the intelligent reflector RIS are obtained as follows: I G I G V L d (r d )P I G I G V N 2 μ 2 L R (r s ,r b ), for line-of-sight LoS signals, if L d (r d )>N 2 μ 2 L R (r s ,r b ), the typical vehicle is connected to the nearest directly connected base station B1, that is, the typical vehicle is associated with the base station BS that can provide it with the maximum average received power for a long time; Otherwise, the typical vehicle selects the nearest smart reflective surface RIS on the same road to connect to the nearest smart reflective surface RIS-assisted base station B2; for non-line-of-sight (NLoS) signals, the vehicle connects to base station B2 assisted by the smart reflective surface RIS; The probability A of a typical vehicle connecting to the direct base station B1 d and the probability A of intelligent reflective surface RIS assisting B2 a They are: The r obtained in step 3 d 、r s and r b The probability density function PDF of and Substituting into the above formula we get: in, 6. The millimeter wave vehicle network security transmission method based on a smart reflective surface according to claim 1 is characterized in that: The process of step five is as follows: First, the signal-to-interference-and-noise ratio (SINR) at a typical user is determined. In the millimeter-wave vehicle-to-vehicle downlink communication scenario assisted by the intelligent reflector (RIS) constructed in step 1, the association strategy (RAA) designed in step 4 based on the real-time channel state has different SINRs for different association scenarios. For the direct link association scheme proposed in step 4, the SINR at the receiver is expressed as: For the base station B2 assisted by the smart reflector RIS, the signal-to-interference-and-noise ratio (SINR) at the receiver is given by: in, represents the aggregate interference from main lobes, side lobes and RIS reflections generated by information signals transmitted by other base stations on a typical vehicle, where r b,o is the distance from other base stations to typical vehicles, r b,R and r R,o They represent the distances from other base stations to the intelligent reflecting surface RIS located on the same road as the typical vehicle, and the distance from the intelligent reflecting surface RIS to the typical vehicle, respectively. is Gaussian white noise; The signal to interference plus noise ratio (SINR) of the i-th eavesdropper is expressed as follows: in, represents the signal-to-interference-and-noise ratio (SINR) of the i-th eavesdropper eavesdropping from the directly connected base station B1, represents the signal-to-interference-and-noise ratio (SINR) of the i-th eavesdropper eavesdropping from the intelligent reflecting surface RIS assisted base station B2, represents the signal-to-interference-and-noise ratio (SINR) of the i-th eavesdropper eavesdropping from the intelligent reflecting surface RIS, where is the distance from the directly connected base station B1 to the i-th eavesdropper Eve, is the distance from the intelligent reflecting surface RIS assisted base station B2 to the i-th eavesdropper Eve, is the distance from the intelligent reflecting surface RIS to the i-th eavesdropper Eve, and ξ e represents the distance between the eavesdropper Eve at point e and the intersection of the straight line through the origin and its perpendicular line, where I e Represents the total interference of confidential signals transmitted by all base stations in the entire vehicle network to eavesdropping vehicles, according to Divided into two parts, It is interference from a smart reflective surface (RIS) located on the same road as the eavesdropping vehicle. It is the interference from other base stations to the eavesdropping vehicle; is the distance from other base stations to the eavesdropping vehicle, and They represent the distances from other base stations to the intelligent reflecting surface RIS on the same road as the eavesdropping vehicle, and the distance from the intelligent reflecting surface RIS to the eavesdropping vehicle, respectively. represents the channel gain of the wireless channel between the eavesdropper Eve and the directly connected base station B1 associated with the typical vehicle, It represents the channel gain of the wireless channel between the eavesdropper Eve and the intelligent reflecting surface RIS auxiliary base station B2 associated with the typical vehicle.

7. The millimeter wave vehicle network security transmission method based on a smart reflective surface according to claim 1 is characterized in that: The process of step six is as follows: 6.1 Calculating the Connection Interruption Probability COP Based on the intelligent reflector RIS-assisted millimeter-wave vehicle network downlink communication scenario constructed in step 1, combined with the RAA association strategy designed in step 4 based on the real-time channel status and the signal-to-interference-and-noise ratio (SINR) of typical users and eavesdroppers obtained in step 5, the connection interruption probability (COP) is calculated as: Among them, for the RAA plan, The calculation is divided into two parts, the probability of direct connection and the probability of intelligent reflective surface RIS assistance Communication also meets and where β t is the SINR interruption threshold, and the connection interruption probability COP of the direct link and the RIS-assisted link is calculated as: In the above formula In the above formula, step (a) is derived from the tight lower bound of the gamma random variable; In the above formula In the above formula, approximation (c) is obtained by h when M→∞ f →1 is derived from the facts; in, is the Laplace transform of interference, which is calculated as follows: Among them, k∈{I,A}, in, Indicates the road where a typical vehicle is located; 6.2 Calculating the Safety Interruption Probability SOP First, the theoretical expression of the safety interruption probability SOP is given: Among them, β e A threshold for determining a confidentiality interruption event: when the signal interference ratio received by any eavesdropping vehicle is greater than the threshold, a confidentiality interruption event is considered to have occurred; For the millimeter wave vehicle network downlink communication scenario, the overall safety interruption probability SOP is expressed as: in, and represents the safety interruption probability SOP when a typical vehicle selects a direct link and a smart reflective surface RIS assisted link, and Expressed as: The analysis of the security interruption probability (SOP) consists of three parts: the probability of eavesdropping on the direct connection of the directly connected base station B1, the probability of eavesdropping on the direct connection of the intelligent reflecting surface (RIS) assisted base station B2, and the probability of eavesdropping on the signal of the intelligent reflecting surface (RIS). The three eavesdropping probabilities are expressed as follows: in in in, Where L(·) is the Laplace transform of the interference, which is calculated as follows: in, θ2 is and The angle between Taking the expectation of the variables in the general formula, we get: in, where r e is the distance from the eavesdropping vehicle to the origin, ξ L is the vertical distance from the road to the origin, and ξ e represents the distance between Eve at e and the intersection of the line through the origin and its perpendicular line, and θ3 is r s With r b The angle between The final safety interruption probability SOP is expressed as: in, represents the set of all roads in the mmWave VIZ downlink communication scenario assisted by intelligent reflective surface (RIS), ξ R represents the distance between the smart reflective surface RIS located on l0 and a typical vehicle.

8. A millimeter wave vehicle network security transmission system based on a smart reflective surface based on the method according to any one of claims 1 to 7, characterized in that: include: The communication scenario construction module is used to implement a millimeter-wave vehicle network downlink communication scenario assisted by a smart reflector (RIS) based on randomly distributed users and base stations (BSs), introduce eavesdropper nodes, and build a smart reflector (RIS)-assisted millimeter-wave vehicle network downlink communication scenario. The millimeter wave channel model establishment module is used to implement the millimeter wave vehicle network downlink communication scenario based on the assistance of intelligent reflective surface (RIS) and establish the millimeter wave channel model; The key link distance modeling and analysis module is used to model and analyze key link distances based on the millimeter wave channel model and Poisson process theory, and obtain the probability density functions (PDFs) of the distances from a typical user to its nearest directly connected base station B1, from a typical user to its nearest smart reflector RIS, and from the smart reflector RIS to the nearest smart reflector assisted base station B2. The RAA association strategy design module is used to implement an association strategy based on the millimeter wave channel model and the probability density function (PDF) of the distance from a typical user to its nearest directly connected base station B1, from the typical user to its nearest smart reflector RIS, and from the smart reflector RIS to the nearest smart reflector RIS-assisted base station B2. According to the real-time channel status, the RAA association strategy is designed. The channel status includes line-of-sight (LoS) and non-line-of-sight (NLoS). The RAA association strategy includes direct association and smart reflector RIS-assisted association strategies. Specifically, if the direct link is line-of-sight (LoS) and its quality is better than the smart reflector RIS-assisted association link, direct association is used. If the direct link is non-line-of-sight (NLoS) or its quality is insufficient, the smart reflector RIS-assisted association link is enabled to enhance signal coverage through smart reflection. The Signal-to-Interference-and-Noise Ratio (SINR) acquisition module is used to implement millimeter-wave vehicle-to-vehicle downlink communication scenarios assisted by intelligent reflectors (RIS) and the RAA association strategy designed based on real-time channel status. It combines the interference caused by the intelligent reflectors (RIS) to obtain the SINR of typical users and eavesdroppers. The Smart Reflecting Surface (RIS) performance evaluation module is used to implement millimeter-wave vehicle-to-vehicle downlink communication scenarios assisted by RIS. By combining the RAA association strategy designed based on real-time channel status and the signal-to-interference-and-noise ratio (SINR) of typical users and eavesdroppers, the module evaluates the performance gains of RIS in improving link stability and security by comparing the connection interruption probability (COP) and security interruption probability (SOP) in scenarios with and without RIS assistance.

9. A millimeter wave vehicle network security transmission device based on a smart reflective surface, characterized in that: include: Memory: a computer-readable device storing a computer program for a millimeter-wave vehicle network security transmission method based on a smart reflective surface according to any one of claims 1 to 7; Processor: used to implement the millimeter wave vehicle network security transmission method based on intelligent reflecting surface as described in any one of claims 1-7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, can implement a millimeter-wave vehicle network security transmission method based on an intelligent reflecting surface as described in any one of claims 1 to 7.

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