A method for analyzing performance of intelligent reflecting surface assisted communication for V2X network

Abstract

The application discloses a kind of intelligent reflecting surface assisted communication performance analysis method for V2X network, in the communication network model based on millimeter wave, when the transmission link between two vehicles is blocked because of obstacle, RIS will be selected to assist communication, by combining the transmission model in two scenarios of V2V direct communication and RIS assisted V2V communication (realized in directional mode), a method for deducing and calculating the transmission performance of the whole communication system based on random geometry, distance distribution and interference distribution is proposed. Using this method, the communication interruption probability of system vehicles under the specific deployment setting of RIS can be accurately evaluated, and the influence of some system parameters on network performance is clearly reflected.

Description

Technical Field

[0001] This invention relates to the field of RIS-assisted collaborative V2V communication technology, and in particular to a method for analyzing the communication performance of intelligent reflective surfaces for V2X networks. Background Technology

[0002] With the rapid development of the new generation of technological revolution centered on the Internet and artificial intelligence, and the rapid increase in the number of private cars, intelligentization and connectivity have become important directions for automotive development. Next-generation mobile communication systems (6G) have higher requirements than 5G: ultra-high data rates, ultra-high energy efficiency, global coverage connectivity, high reliability, and low latency. As an important application of 6G, vehicle-to-everything (V2X) communication also needs to meet the requirements of high data rates, low-latency transmission, and high reliability. RIS, as an innovative technology, can be used to achieve sustainable growth in the capacity of future wireless networks.

[0003] A Resonant Radio Array (RIS) consists of numerous low-cost passive components and can be programmed in software to adjust the phase and amplitude of the incident signal, thereby improving signal quality at the receiver. When the incident signal is reflected at the RIS, it generates an additional phase shift, guiding the signal in the desired direction. RIS components can reflect, refract, and scatter radio signals, thus counteracting the effects of multipath fading. By utilizing the characteristics of RIS, the performance of millimeter-wave-based communication systems can be improved at a more economical cost. Therefore, research on the performance of RIS-based communication networks is of great significance.

[0004] Stochastic geometry theory can effectively characterize the random distribution of node location information on a large scale in a network. When network nodes exhibit random distribution within a certain area, the Poisson point process (PPP) and the hard core point process (MHCP) can accurately describe the random distribution characteristics of nodes and provide conditions for obtaining mathematical analytical solutions to network performance indicators. Existing research has begun to focus on the application of RIS in vehicular network communication, but in the communication model, only a single RIS, a single source, and a single destination are studied. That is, the RIS is deployed in a fixed location, without considering the randomness of the RIS location distribution, and ignoring the impact of interference. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a smart reflective surface-assisted communication performance analysis method for V2X networks. This method can not only reveal the performance indicators of the communication network system and the impact of network parameters on the probability of communication interruption, but also achieve better network performance through an optimized RIS deployment scheme.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] A method for analyzing the communication performance of intelligent reflective surfaces for V2X networks, proposed according to the present invention, includes the following steps:

[0008] Consider a one-way straight road with a Smart Reflective Surface (RIS) deployed on one side. Vehicles on the road follow a PPP distribution. The communication scenario includes a pair of communicating vehicles and a serving RIS. The pair of vehicles consists of a sending user (UT) and a receiving vehicle (UR), with a fixed communication distance (L) between UT and UR. When there are no vehicles within the communication path L between UT and UR, the channel between UT and UR is a LOS channel, and the vehicles communicate directly; this is the direct communication mode. When there are vehicles within the communication path L between UT and UR, the channel between UT and UR is an NLOS channel, and UT selects the nearest RIS for auxiliary communication; this is the RIS-assisted communication mode.

[0009] The 1D homogeneous Poisson point process HPPP is used to model the vehicle position distribution on the road. This Poisson point process is defined as Φ V The density of the Poisson point process is λ. V Simultaneously, the one-dimensional Matérn hard core point process MHCP is used to model the location distribution of RIS, and this hard core point process is defined as Φ. R The density of hard core point processes is λ. R The RIS includes N reflective elements, which connect the UT to the RIS via a link channel h. TS RIS to UR link channel h SR Defined as h respectively TS =[h TS,1 ,…,h TS,n ,…,h TS,N ]、h SR =[h SR,1 ,…,h SR,n ,…,h SR,N ], where h TS,n h represents the channel between the nth element of UT and RIS. SR,n Let n represent the channel between the nth element of RIS and UR, where n = 1, 2, 3…N;

[0010] Assume that the small-scale fading of the links from UT to RIS, RIS to UR, and UT to UR is independent and they all follow the Nakagami-m distribution. The small-scale fading of the interference signal is modeled using the Rayleigh distribution.

[0011] Calculate the signal-to-interference-plus-noise ratio (SINR1) in direct communication mode and the SINR2 in RIS-assisted communication mode; based on the SINR1 in direct communication mode and the SINR2 in RIS-assisted communication mode, calculate the interruption probability in direct communication mode. Interruption probability in RIS-assisted communication mode

[0012] according to The overall network outage probability is calculated;

[0013] in,

[0014]

[0015] Among them, P r (*) represents the probability calculation, τ is the pre-given SINR threshold, n1 is the accumulated variable, and m is the fading parameter from channel UT to UR. The Laplace transform of the disturbance distribution, r is the transmit power of the UT in direct communication mode. TR The distance between UT and UR is represented by α, which is the path loss parameter, and N0 represents the variance of zero-mean complex Gaussian noise.

[0016] As a further optimization scheme for the intelligent reflective surface-assisted communication performance analysis method for V2X networks described in this invention, the Laplace transform of the interference distribution is as follows: β(·,·) denotes the Beta function, and s is a variable.

[0017] As a further optimization scheme for the intelligent reflective surface-assisted communication performance analysis method for V2X networks described in this invention,

[0018]

[0019] Where, r TR This represents the communication distance between UT and UR, where I represents aggregated interference. P I h is the transmission power of the interference signal. i d represents the small-scale fading between UR and the i-th interfering vehicle. i Let UR represent the communication distance between the i-th interfering vehicle and the UR, and N0 represent the variance of the zero-mean complex Gaussian noise. S represents the transmit power of the UT in direct communication mode. D Let denoted as UR, and α be the path loss parameter. This is a small-scale fading phenomenon;

[0020]

[0021] Among them, S R * P represents the signal power received at UR. T η represents the transmit power of the signal at UT in RIS-assisted communication mode, and η represents a fixed amplitude reflection coefficient.

[0022] As a further optimization scheme for the intelligent reflective surface-assisted communication performance analysis method for V2X networks described in this invention,

[0023] The received signal power at UR is The natural base, j The optimal phase shift θ for each reflecting element is in imaginary units. n * =-(φ n +ω n ), φn n and ω n φ is uniformly distributed on (-π,π] n ω n They represent h respectively TS,n and h SR,n phase, θ n =[0,2π), θ n η represents the adjustable phase of the nth reflective element on the RIS; n ∈[0,1],η n Represents the amplitude reflection coefficient. Assume that the reflection amplitude of each reflecting element is equal, that is, for the nth element, η = 1 / n. n =η, then the optimal received signal power at UR simplifies to:

[0024] As a further optimization scheme for the intelligent reflective surface-assisted communication performance analysis method for V2X networks described in this invention,

[0025]

[0026]

[0027] in: and This indicates the small-scale fading of the UT-RIS and RIS-UR channels, r TS r represents the communication distance of the UT-RIS link. SR Let l be the communication distance of the RIS-UR link, and l(*) be the path loss function.

[0028] As a further optimization scheme for the intelligent reflective surface-assisted communication performance analysis method for V2X networks described in this invention,

[0029]

[0030] Where m1 and m2 represent the small-scale fading parameters from channel UT to RIS and from RIS to UR, respectively. Both are represented as factorial exponentiation operations, with intermediate variables. intermediate variables K u-1 (·) denotes the second-order modified Bessel function of order u-1. Let k represent the cumulative distribution function of SINR2. n Let f represent the nth accumulated variable, where n = 1, ..., N; Ω1 and Ω2 represent the scale parameters of the small-scale fading nakagami-m distributions from channel UT to RIS and from RIS to UR; Г(·) represents the gamma function; and y represents the interference variable. The interference is approximated as a gamma distribution with shape parameter θ and scale parameter k. ρ (r) represents the probability density function with respect to the nearest distance r between UT and RIS.

[0031] As a further optimization scheme for the intelligent reflective surface-assisted communication performance analysis method for V2X networks described in this invention,

[0032] ρ(·) represents the refined probability of 1D MHCP, t represents the integration variable, and d h λ represents the hard core distance. p The density of the original Poisson point process that generates the MHCP process is represented; the disturbance distribution is approximated as a variable that follows a gamma distribution with parameters k and θ.

[0033] As a further optimization scheme for the intelligent reflective surface-assisted communication performance analysis method for V2X networks described in this invention, the probability P of the direct transmission mode occurring is obtained by utilizing the Poisson distribution correlation formula. TR,L The probability P of RIS-assisted transfer mode occurring TR,N ;in,

[0034] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0035] Compared with existing vehicle communication networks based on smart reflective surfaces with fixed RIS positions, this invention uses a random geometric method to model the position distribution of RIS and derives the relevant distance distribution. It also derives the distribution of aggregation interference in the communication network, making the performance analysis results more valuable. Attached Figure Description

[0036] Figure 1aThis is a diagram of a RIS-assisted communication scenario. Figure 1b This is a diagram of a direct communication scenario.

[0037] Figure 2 This is a flowchart of the present invention.

[0038] Figure 3 This is a simulation diagram of the effect of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] In vehicle-to-everything (V2V) communication networks, random interference is unavoidable. Considering that base stations are not considered in V2V communication, and that RIS (Radio Router Interference System) is a passive device that does not actively generate interference, this invention only considers the interference caused by random vehicles on the road to V2V communication. In this invention, aggregated interference from multiple random vehicles is described as the sum of random variables. Under two transmission modes, the distribution of random interference is derived by transforming the Laplace expression and approximating a gamma distribution, respectively.

[0041] Figure 1a This is a diagram of a RIS-assisted communication scenario. Figure 1b This is a diagram of a direct communication scenario. Figure 2 This is a flowchart of the present invention. A method for analyzing the communication performance of intelligent reflective surfaces for V2X networks is described below:

[0042] Consider a one-way straight road with a Smart Reflective Surface (RIS) deployed on one side. Vehicles on the road follow a PPP distribution. The communication scenario involves a pair of communicating vehicles and a serving RIS. The pair of vehicles are the sending user (UT) and the receiving vehicle (UR), with a fixed communication distance L between UT and UR. When there are no vehicles within the communication path L between UT and UR, the channel between UT and UR is a LOS channel, and the vehicles communicate directly; this is the direct communication mode. When there are vehicles within the communication path L between UT and UR, the channel between UT and UR is an NLOS channel, and UT selects the nearest RIS for auxiliary communication; this is the RIS-assisted communication mode. Interference received at UR comes from other vehicles, denoted as R in the vicinity of UT. c There are no interfering vehicles inside;

[0043] The 1D homogeneous Poisson point process HPPP is used to model the vehicle position distribution on the road, and the Poisson point process is defined as Φ. V The density of the Poisson point process is λ. V Simultaneously, the one-dimensional Matérn hard core point process MHCP is used to model the location distribution of RIS, and this hard core point process is defined as Φ.R The density of hard core point processes is λ. R The RIS includes N reflective elements, which connect the UT to the RIS via a link channel h. TS RIS to UR link channel h SR Defined as h respectively TS =[h TS,1 ,…,h TS,n ,…,h TS,N ]、h SR =[h SR,1 ,…,h SR,n ,…,h SR,N ], where h TS,n h represents the channel between the nth element of UT and RIS. SR,n Let n represent the channel between the nth element of RIS and UR, where n = 1, 2, 3…N;

[0044]

[0045]

[0046] in: and This indicates the small-scale fading of the UT-RIS and RIS-UR channels, r TS r represents the communication distance of the UT-RIS link. SR Let l be the communication distance of the RIS-UR link, and l(*) be the path loss function.

[0047] Assume that the small-scale fading of the links from UT to RIS, RIS to UR, and UT to UR is independent and they all follow the Nakagami-m distribution. The small-scale fading of the interference signal is modeled using the Rayleigh distribution.

[0048] Calculate the signal-to-interference-plus-noise ratio (SINR1) in direct communication mode and the SINR2 in RIS-assisted communication mode;

[0049]

[0050] Where, r TR This represents the communication distance between UT and UR, where I represents aggregated interference. P I h is the transmission power of the interference signal. i d represents the small-scale fading between UR and the i-th interfering vehicle. i Let UR represent the communication distance between the i-th interfering vehicle and the UR, and N0 represent the variance of the zero-mean complex Gaussian noise. S represents the transmit power of the UT in direct communication mode. DLet denoted as UR, and α be the path loss parameter. This is a small-scale fading phenomenon;

[0051]

[0052] Among them, S R * P represents the signal power received at UR. T η represents the transmit power of the signal at UT in RIS-assisted communication mode, and η represents a fixed amplitude reflection coefficient.

[0053] The received signal power at UR is The natural base, j The optimal phase shift θ for each reflecting element is in imaginary units. n * =-(φ n +ω n ), φ n and ω n φ is uniformly distributed on (-π,π] n ω n They represent h respectively TS,n and h SR,n phase, θ n =[0,2π), θ n η represents the adjustable phase of the nth reflective element on the RIS; n ∈[0,1],η n Represents the amplitude reflection coefficient. Assume that the reflection amplitude of each reflecting element is equal, that is, for the nth element, η = 1 / n. n =η, then the optimal received signal power at UR simplifies to:

[0054] The interruption probability in direct communication mode is calculated based on the signal-to-interference-plus-noise ratio (SINR1) in direct communication mode and the SINR2 in RIS-assisted communication mode. Interruption probability in RIS-assisted communication mode

[0055] in,

[0056]

[0057] Among them, P r (*) represents the probability calculation, τ is the pre-given SINR threshold, n1 is the accumulated variable, and m is the fading parameter from channel UT to UR. The Laplace transform of the disturbance distribution, r is the transmit power of the UT in direct communication mode. TRThe distance between UT and UR is represented by α, which is the path loss parameter, and N0 represents the variance of zero-mean complex Gaussian noise.

[0058] The Laplace transform of the interference distribution is: β(·,·) denotes the Beta function, and s is a variable.

[0059]

[0060] Where m1 and m2 represent the small-scale fading parameters from channel UT to RIS and from RIS to UR, respectively. Both are represented as factorial exponentiation operations, with intermediate variables. intermediate variables K u-1 (·) denotes the second-order modified Bessel function of order u-1. Let k represent the cumulative distribution function of SINR2. n Let f represent the nth accumulated variable, where n = 1, ..., N; Ω1 and Ω2 represent the scale parameters of the small-scale fading nakagami-m distributions from channel UT to RIS and from RIS to UR; Γ(·) represents the gamma function; and y represents the interference variable. The interference is approximated as a gamma distribution with shape parameter θ and scale parameter k. ρ (r) represents the probability density function with respect to the nearest distance r between UT and RIS.

[0061] ρ(·) represents the refined probability of 1D MHCP, t represents the integration variable, and d h λ represents the hard core distance. p The density of the original Poisson point process that generates the MHCP process is represented; the disturbance distribution is approximated as a variable that follows a gamma distribution with parameters k and θ.

[0062] according to The overall network outage probability is calculated;

[0063] By using the Poisson distribution correlation formula, the probability P of the direct transmission mode occurring can be obtained. TR,L The probability P of RIS-assisted transfer mode occurring TR,N ;in,

[0064] Figure 3This is the outage probability distribution function of a RIS-based V2V communication network system, with the vertical axis representing the outage probability and the horizontal axis representing vehicle density. First, the image shows a comparison between Monte Carlo simulation results and theoretical analysis results; the two curves are largely consistent, verifying the correctness of the theoretical derivation. Second, the image shows that the system outage probability increases with increasing vehicle density at different thresholds, and that the outage probability also increases with increasing signal-to-interference-plus-noise ratio (SNR) threshold. Finally, the simulation results provide some insights and support for the performance analysis of vehicle-to-everything (V2X) communication networks. For example, when the vehicle density is 0.1, the system outage probability increases rapidly compared to before; therefore, the vehicle density should ideally be controlled to be less than 0.1.

[0065] Considering all vehicles are located on a one-way straight road, the RIS is deployed on one side of the road, where the vehicles follow a one-dimensional PPP distribution and the RIS follows a one-dimensional MHCP distribution. The communication scenario includes a pair of communicating vehicles and a serving RIS, where the communication distance between the UT and UR is fixed. When there are obstructing vehicles on the communication path between the sending and receiving vehicles, the direct communication link will be blocked. In this case, the communication between the UT and UR is non-line-of-sight, and a suitable RIS needs to be selected for auxiliary communication. Conversely, when there are no obstructing vehicles between the UT and UR, the communication method is line-of-sight, and direct transmission can be used. In the RIS-assisted transmission mode, a suitable RIS selection mechanism is required. In this invention, the RIS closest to the UT is selected as the serving RIS. The probabilities of the two transmission modes can be derived from the relevant theories of the 1D PPP distribution. In both transmission modes, the UR will receive interference from other vehicles on the road (at a certain distance from the UR). This interference is modeled as aggregate interference in the derivation process. For the main signal transmission channels in the system network, such as UT-UR, UT-RIS, and RIS-UR, nakagami-m is used for modeling, and Rayleigh distribution is used for modeling the channels of interference signals. Based on the above system model, to obtain the interruption probability of the entire communication network, it is necessary to obtain the interruption probability in direct transmission mode and the interruption probability in RIS-assisted mode respectively. Then, based on the probability of the existence of the two transmission modes, the overall network interruption probability is calculated. At the same time, Monte Carlo simulation is required to obtain the distribution of the overall network interruption probability.

[0066] In a simulation test environment, a V2V communication network is simulated. Different transmission modes are adopted depending on whether there are obstructing vehicles between the communicating vehicles. Several RIS (Real-Integrated Vehicles) conforming to a 1D MHCP distribution are randomly deployed along the roadside, while interfering vehicles are randomly deployed along the road. Assume the road length is 1000m, the UT (Underground Vehicle) is located at the origin, and the communication distance between the UT and UR is r. TR=L=100, vehicles within 500m of UR will not interfere with it, transmission power Number of RIS components N=32, hard core distance d h =100, interference power P I =5, noise variance N0=10 -10 RIS density λ p =0.02. Figure 3 It can be seen that the derived analytical solution for the interruption probability is highly consistent with the numerical solution obtained through numerous Monte Carlo simulations. Furthermore, under different thresholds, the system interruption probability increases with increasing vehicle density, and also increases with increasing signal-to-interference-plus-noise ratio (SINNR) threshold.

[0067] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for analyzing the communication performance of intelligent reflective surfaces assisted by V2X networks, characterized in that, Includes the following steps: Consider a one-way straight road with a Smart Reflective Surface (RIS) deployed on one side. Vehicles on the road follow a PPP distribution. The communication scenario includes a pair of communicating vehicles and a serving RIS. The pair of vehicles consists of a sending user (UT) and a receiving vehicle (UR), with a fixed communication distance (L) between UT and UR. When there are no vehicles within the communication path L between UT and UR, the channel between UT and UR is a LOS channel, and the vehicles communicate directly; this is the direct communication mode. When there are vehicles within the communication path L between UT and UR, the channel between UT and UR is an NLOS channel, and UT selects the nearest RIS for auxiliary communication; this is the RIS-assisted communication mode. The 1D homogeneous Poisson point process HPPP is used to model the vehicle position distribution on the road. This Poisson point process is defined as follows: The density of the Poisson point process is Simultaneously, the one-dimensional Matérn hard core point process MHCP is used to model the location distribution of RIS, and this hard core point process is defined as... The density of the hard core process is The RIS includes N reflective elements, which form the link channel from UT to RIS. RIS to UR link channel Defined respectively , ,in, This represents the channel between the nth element of UT and RIS. This represents the channel between the nth element of the RIS and the UR, where n = 1, 2, 3…N; Assume that the small-scale fading of the links from UT to RIS, RIS to UR, and UT to UR is independent and they all follow the Nakagami-m distribution. The small-scale fading of the interference signal is modeled using the Rayleigh distribution. Calculate the signal-to-interference-plus-noise ratio in direct communication mode Signal-to-interference-plus-noise ratio in RIS-assisted communication mode Based on the signal-to-interference-plus-noise ratio in direct communication mode Signal-to-interference-plus-noise ratio in RIS-assisted communication mode Calculate the interruption probability in direct communication mode respectively. Interruption probability in RIS-assisted communication mode ; according to , The overall network outage probability is calculated. in, ； in, For probability calculation, For a pre-defined SINR threshold, For cumulative variables, The fading parameters for the channel from UT to UR. The Laplace transform of the disturbance distribution, The transmit power of the UT in direct communication mode. This indicates the communication distance between UT and UR. For path loss parameters, This represents the variance of zero-mean complex Gaussian noise; The Laplace transform of the interference distribution is: , This represents the Beta function. As a variable; ； in, These represent the small-scale fading parameters from channel UT to RIS and from RIS to UR, respectively. , Both are represented as factorial exponentiation operations, with intermediate variables. intermediate variables , This represents the modified Bessel function of the second kind of order u-1. express The cumulative distribution function, Let n be the nth accumulated variable, where n = 1, ..., N. , The scale parameter representing the small-scale fading nakagami-m distribution from channel UT to RIS and from RIS to UR. Represents the gamma function. Representing the disturbance variable, the disturbance is approximated by a shape parameter of... Scale parameters are The gamma distribution; Let r be the probability density function relating the nearest distance r between UT and RIS; , , Let t represent the refined probability of 1D MHCP, and t represent the integral variable. Indicates the hard core distance. The density of the original Poisson point process that generates the MHCP process is represented; the disturbance distribution is approximated as following a parameter k and The variables of the gamma distribution.

2. The method for analyzing the communication performance of intelligent reflective surfaces for V2X networks according to claim 1, characterized in that, ； in, This indicates the communication distance between UT and UR. For aggregation interference, , The transmission power of the interference signal, This represents the small-scale fading between UR and the i-th interfering vehicle. This represents the communication distance between UR and the i-th interfering vehicle. The variance of zero-mean complex Gaussian noise is represented. The transmit power of the UT in direct communication mode. The signal power received at UR. For path loss parameters, This is a small-scale fading phenomenon; ； in, The signal power received at UR. This indicates the transmit power of the signal at UT in RIS-assisted communication mode. This represents a fixed amplitude reflection coefficient.

3. The method for analyzing the communication performance of intelligent reflective surfaces for V2X networks according to claim 2, characterized in that, The received signal power at UR is e is the natural base. The optimal phase shift for each reflecting element is represented by the imaginary unit. , and exist Evenly distributed on top , They represent and phase, , Represents the adjustable phase of the nth reflective element on the RIS; , This represents the amplitude reflection coefficient. Assuming the reflection amplitude is equal for each reflecting element, that is, for the nth element... Then the optimal received signal power at UR can be simplified to: .

4. The method for analyzing the communication performance of intelligent reflective surfaces for V2X networks according to claim 1, characterized in that, ； ； in: and This indicates small-scale fading in UT-RIS and RIS-UR channels. The communication distance of the UT-RIS link. The communication distance of the RIS-UR link. This is the path loss function.

5. The method for analyzing the communication performance of intelligent reflective surfaces for V2X networks according to claim 1, characterized in that, By using the Poisson distribution correlation formula, the probability of the direct transmission mode occurring can be obtained. The probability of RIS-assisted transfer mode occurring ;in, .

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