FAS-RIS-based URLLC performance evaluation method and system

Abstract

The invention belongs to the field of ultra-high-reliability low-delay communication networks, and discloses an FAS-RIS-based URLLC performance evaluation method and related equipment, which realize performance analysis under a specific communication architecture by constructing a signal channel transmission model comprising a base station, RIS elements and a user equipped with an FAS port. And the channel gain is maximized by optimizing the reflection phase of the RIS element, so that the signal-to-noise ratio of a signal received by a user is obtained. Subsequently, the signal-to-noise ratio is approximated as Gaussian distribution processing to obtain statistical distribution characteristics thereof. Based on the statistical distribution, an average block error rate is calculated as a key index of performance evaluation. According to the method, an efficient and accurate performance evaluation approach is provided, the channel transmission characteristics and the signal processing technology can be comprehensively considered for the FAS-RIS-assisted URLLC network, so that the performance of the system in a complex communication environment is accurately reflected, and powerful theoretical support is provided for fusion application and performance optimization of the FAS-RIS technology.

Description

Technical Field

[0001] The present invention belongs to the technical field of ultra-high reliable and low-latency communication networks, and particularly relates to a performance evaluation method and related equipment for URLLC based on FAS-RIS. Background Art

[0002] In the field of current wireless communication networks, with the increasingly stringent requirements for communication quality in various application scenarios, ultra-high reliable and low-latency communication networks (URLLC) have received more and more attention. It aims to meet the communication requirements of near real-time and extremely high reliability in critical mission scenarios such as industrial automation control, autonomous driving, and remote medical surgery. However, achieving such communication standards faces many challenges, driving researchers to continuously explore innovative technologies and their integrated applications to break through the existing bottlenecks.

[0003] Although traditional wireless massive multiple-input multiple-output (MIMO) technology has improved communication performance to a certain extent, it has significant defects. On the one hand, when expanding the number of antennas to pursue higher performance, the cost will increase significantly, which undoubtedly limits its large-scale optimization and upgrading; on the other hand, technologies such as MIMO and reconfigurable intelligent surface (RIS) rely on fixed layouts and only achieve beam control through discrete phase adjustment, and cannot dynamically change the inherent characteristics of antennas. Key characteristics such as resonant frequency and radiation pattern are restricted and it is difficult to flexibly adapt to complex and changing communication environments. In contrast, the fluid antenna system (FAS) exhibits unique advantages. It does not require multiple antenna signal processing units. By using software to control the fluid of the antenna to flexibly shift to different ports, the signal strength at the receiving end is enhanced, achieving low-cost and high-performance communication. At the same time, the port spacing can be extremely small. Limited by the design of the antenna fluid controller, the entire antenna module is small and portable. And RIS technology, as an emerging technology with great potential in recent years, can realize operations such as signal enhancement, attenuation, and polarization by regulating the propagation path and phase of electromagnetic waves, effectively solving the interference problem between multiple users, improving the coverage rate and spectrum efficiency, and having outstanding characteristics such as high reliability, large capacity, and environment controllability. However, how to organically integrate FAS and RIS and deeply carry out performance research on the technical integration level involved in its auxiliary system has become a difficult problem for researchers. At present, from the basic principles of the FAS system and RIS-assisted communication to the channel model, new breakthroughs are urgently needed. The performance research method of the FAS-RIS-assisted URLLC network is still in the exploratory stage and lacks a mature and effective system. In particular, for URLLC based on FAS-RIS, there is a lack of an efficient and accurate performance research method.

[0004] Therefore, for ultra-high reliable and low-latency networks assisted by fluid antennas and reconfigurable intelligent surfaces, existing technical means cannot efficiently and accurately evaluate their performance. Summary of the Invention

[0005] The present invention provides a performance evaluation method and related devices for URLLC based on FAS-RIS to solve the technical problem that existing technical means cannot efficiently and accurately evaluate the performance of ultra-reliable and low-latency networks assisted by fluid antennas and intelligent surfaces.

[0006] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a performance evaluation method for URLLC based on FAS-RIS, including: Constructing a FAS-RIS-URLLC signal channel transmission model; wherein, the FAS-RIS-URLLC signal channel transmission model includes a base station, RIS elements of multiple reflection units, and a user equipped with multiple FAS ports, and the base station communicates with the user only through the RIS elements; In the FAS-RIS-URLLC signal channel transmission model, maximizing the channel gain by optimizing the reflection phase of the RIS elements to obtain the signal-to-noise ratio after the user receives the signal; Approximating the signal-to-noise ratio after the user receives the signal as a Gaussian distribution to obtain the statistical distribution of the signal-to-noise ratio; Calculating the average block error rate based on the statistical distribution of the signal-to-noise ratio to obtain the performance evaluation result according to the average block error rate.

[0007] A further improvement of the present invention is that before constructing the FAS-RIS-URLLC signal channel transmission model, it further includes: Constructing a downlink FAS-RIS-URLLC system; wherein, the downlink FAS-RIS-URLLC system includes a base station, RIS elements of multiple reflection units, and a user equipped with a single fluid antenna, and the base station sends information to the user only through the reflection of the RIS elements.

[0008] A further improvement of the present invention is that constructing the FAS-RIS-URLLC signal channel transmission model includes: Based on the downlink FAS-RIS-URLLC system, constructing a FAS-RIS-URLLC signal channel transmission model; Among them, the signal received by the user at the l th FAS port is expressed as:

[0009] In the formula, g m represents the channel coefficient from the base station to the mth RIS element; χ m,lDenote the channel coefficient from the m-th RIS element to the l -th FAS port of the user; ι m Denote the reflection phase of the m-th RIS element; d SR and d RU respectively denote the distance from the base station to the RIS element and the distance from the RIS element to the user; α Denote the path loss exponent; n l Represent the zero-mean complex Gaussian noise of the l -th port; the variance is δ 2 .

[0010] A further improvement of the present invention lies in that, in the FAS-RIS-URLLC signal channel transmission model, the channel gain is maximized by optimizing the reflection phase of the RIS element to obtain the signal-to-noise ratio after the user receives the signal, including: In the FAS-RIS-URLLC signal channel transmission model, the reflection phase of the RIS element is optimized and set, and the specific formula is as follows:

[0011] In the formula, ι m Denote the reflection phase of the m-th RIS element; g m Denote the channel coefficient from the base station to the m-th RIS element; χ m,l Denote the channel coefficient from the m-th RIS element to the l -th FAS port of the user; After optimizing the reflection phase of the RIS element, the maximization of the channel gain is achieved, where the channel parameter of the -th FAS port of the user is

[0012] In the formula, M represents the maximum number of RIS elements; According to the channel parameter of the -th FAS port of the user, the channel parameter of the optimal port is calculated

[0013] According to the channel parameter of the optimal port, the signal-to-noise ratio after the user receives the signal is calculated γ The specific formula is as follows:

[0014] In the formula, L represents the maximum number of FAS ports; ρ represents the transmission power or a parameter related to the transmission power; d SR and d RU respectively represent the distance from the base station to the RIS element and the distance from the RIS element to the user; α represents the path loss exponent.

[0015] A further improvement of the present invention lies in that the signal-to-noise ratio after the user receives the signal is approximated as a Gaussian distribution to obtain the statistical distribution of the signal-to-noise ratio, including: Using the central limit theorem to approximate the signal-to-noise ratio after the user receives the signal as a Gaussian distribution and obtain the statistical distribution of the signal-to-noise ratio. The specific formula is as follows:

[0016] In the formula, represents the signal-to-noise ratio γ 's cumulative distribution function; t represents the independent variable; H represents a constant; U represents a parameter for weighing between control accuracy and complexity; p t,b , q l both represent constants; where, ; ; t b is a constant from 1 to U; L b represents a constant from 1 to L; ρ 0 and ρ 1 respectively represent constants; E γ represents the expectation of the variable γ , ; V γ represents the variance of the variable γ, ; d SR and d RU respectively represent the distance from the base station to the RIS element and the distance from the RIS element to the user.

[0017] A further improvement of the present invention lies in that the average block error rate is calculated based on the statistical distribution of the signal-to-noise ratio, including: Combining the instantaneous block error rate and the statistical distribution of the signal-to-noise ratio to calculate the average block error rate, specifically including: Based on the URLLC standard definition, obtain the instantaneous block error rate. The specific formula of the instantaneous block error rate is as follows:

[0018] In the formula, represents the instantaneous block error rate; represents the approximate instantaneous block error rate; Q(·) represents the Gaussian function; represents the Shannon capacity; represents the channel dispersion; s represents the length of the block; N represents the number of bits of transmitted information; γ represents the signal-to-noise ratio; Based on the statistical distribution of the signal-to-noise ratio, the instantaneous block error rate is processed by taking the expectation, and combined with linear approximation and integral transformation to obtain the average block error rate. The specific formula is as follows:

[0019] where, is represented by linear approximation as:

[0020]

[0021] In the formula, represents the average block error rate; represents the statistical distribution of the signal-to-noise ratio; , , are parameters related to N and s respectively, and are specifically represented as: ; ; ; U P represents the parameter that balances the control precision and complexity; P represents the summation index; represents the cosine value calculated from p and U P ; represents the signal-to-noise ratio γ at ; where, .

[0022] A further improvement of the present invention is that after calculating the average block error rate based on the statistical distribution of the signal-to-noise ratio, it further includes: Performing a first-order Riemann approximation on the calculated average block error rate to obtain the limiting average block error rate, so as to obtain the performance evaluation result in an extreme scenario based on the limiting average block error rate.

[0023] In a second aspect, the present invention provides a performance evaluation system for FAS-RIS-based URLLC, including: A network model construction module for constructing a FAS-RIS-URLLC signal channel transmission model; wherein, the FAS-RIS-URLLC signal channel transmission model includes a base station, RIS elements of multiple reflection units, and a user equipped with multiple FAS ports, and the base station communicates with the user only through the RIS elements; A parameter optimization module, which is used to maximize the channel gain by optimizing the reflection phase of RIS elements in the FAS-RIS-URLLC signal channel transmission model, so as to obtain the signal-to-noise ratio after the user receives the signal; An approximation processing module, which is used to approximate the signal-to-noise ratio after the user receives the signal as a Gaussian distribution, so as to obtain the statistical distribution of the signal-to-noise ratio; A performance evaluation module, which is used to calculate the average block error rate based on the statistical distribution of the signal-to-noise ratio, so as to obtain the performance evaluation result according to the average block error rate.

[0024] In a third aspect, the present invention provides an electronic device, which is characterized by comprising: A memory, which is used to store a computer program; A processor, which is used to implement the steps of the above-mentioned performance evaluation method for FAS-RIS-based URLLC when executing the computer program.

[0025] In a fourth aspect, the present invention provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and is characterized in that the computer program is used to implement the steps of the above-mentioned performance evaluation method for FAS-RIS-based URLLC when executed by a processor.

[0026] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a performance evaluation method for FAS-RIS-based URLLC. By constructing a signal channel transmission model including a base station, RIS elements and a user equipped with an FAS port, the performance analysis under a specific communication architecture is realized. By optimizing the reflection phase of RIS elements to maximize the channel gain, and then obtaining the signal-to-noise ratio of the user receiving the signal. Subsequently, the signal-to-noise ratio is approximated as a Gaussian distribution for processing to obtain its statistical distribution characteristics. Based on this statistical distribution, the average block error rate is calculated as a key indicator for performance evaluation. This method provides an efficient and accurate performance evaluation approach, which can comprehensively consider the channel transmission characteristics and signal processing technologies for the FAS-RIS-assisted URLLC network, so as to accurately reflect the performance of the system in a complex communication environment, and provides a strong theoretical support for the integrated application and performance optimization of FAS-RIS technology.

[0027] Preferably, in the present invention, before constructing the performance evaluation method, a downlink FAS-RIS-URLLC system is first constructed, which provides a basis for the subsequent signal channel transmission model. This step ensures the practicability and pertinence of the evaluation method, makes the performance evaluation closer to the actual communication scenario, and provides a strong theoretical support for the application of FAS-RIS technology in URLLC.

[0028] Preferably, in the present invention, by optimizing the reflection phase of the RIS elements, the maximization of the channel gain is achieved. This optimization process improves the signal transmission efficiency and reduces the block error rate, providing an effective means to enhance the performance of the URLLC network.

[0029] Preferably, in the present invention, the central limit theorem is used to approximate the signal-to-noise ratio as a Gaussian distribution, obtaining the statistical distribution of the signal-to-noise ratio. This processing simplifies the complexity of performance evaluation, making it possible to perform performance analysis based on the statistical characteristics of the signal-to-noise ratio and providing a basis for the subsequent calculation of the average block error rate.

[0030] Preferably, in the present invention, the calculated average block error rate is subjected to the first-order Riemann approximation processing to obtain the limiting average block error rate. This processing enables the performance evaluation method to be applicable to extreme scenarios, providing a powerful tool for the performance evaluation of the FAS-RIS-assisted URLLC network under extreme conditions. Description of the Drawings

[0031] Figure 1 It is a relationship diagram of the average block error rate and the signal-to-noise ratio provided by an embodiment of the present invention; Figure 2 It is a relationship diagram of the average block error rate and the reflection unit provided by an embodiment of the present invention; Figure 3 It is a comparison diagram of the relationship between the average block error rate and the signal-to-noise ratio with and without the FAS technology provided by an embodiment of the present invention; Figure 4 It is a flowchart of a performance evaluation method for FAS-RIS-based URLLC provided by an embodiment of the present invention; Figure 5 It is a schematic structural diagram of a performance evaluation system for FAS-RIS-based URLLC provided by an embodiment of the present invention. Detailed Embodiments

[0032] To further understand the content of the present invention, the following provides a detailed description of the present invention with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and not for limiting it.

[0033] According to the background technology, currently, how to solve the performance research in terms of technical integration brought by the FAS-RIS-assisted system is challenging. New breakthroughs need to be sought from aspects such as the FAS system, the basic principles of RIS-assisted communication, and the channel model. Therefore, the performance research method for the FAS-RIS-assisted URLLC network remains a problem.

[0034] To solve the above problems, this embodiment provides a performance evaluation method for FAS-RIS-based URLLC. Based on ideal assumptions, this method calculates the theoretical performance of the average block error rate (BLER) of users in an ultra-reliable and low-latency system. Then, through simulation verification, the authenticity and effectiveness of the evaluation theory results are verified. This method can be applied to the Internet of Things system with high-reliability and low-latency requirements, and can obtain the performance evaluation results of the system under an accurate network model. Furthermore, according to the performance evaluation results, the system performance can be optimized, providing a theoretical research basis for the next-generation Internet of Things system.

[0035] This embodiment provides a performance evaluation method for FAS-RIS-based URLLC, including: Construct a FAS-RIS-URLLC signal channel transmission model; wherein, the FAS-RIS-URLLC signal channel transmission model includes a base station, RIS elements of multiple reflection units, and a user equipped with multiple FAS ports, and the base station communicates with the user only through the RIS elements; In the FAS-RIS-URLLC signal channel transmission model, maximize the channel gain by optimizing the reflection phase of the RIS elements to obtain the signal-to-noise ratio after the user receives the signal; Approximate the signal-to-noise ratio after the user receives the signal as a Gaussian distribution to obtain the statistical distribution of the signal-to-noise ratio; Calculate the average block error rate based on the statistical distribution of the signal-to-noise ratio to obtain the performance evaluation result according to the average block error rate.

[0036] The following further explains the evaluation method provided in this embodiment with reference to the accompanying drawings: Step 1. Based on the pre-constructed downlink FAS-RIS-URLLC system, establish a FAS-RIS-URLLC signal channel transmission model, specifically including: For the pre-constructed downlink FAS-RIS-URLLC system, the system includes a base station (BS) equipped with a single fixed-position antenna, a RIS with M reflection units, and a user equipped with a single fluid antenna (FA). We consider that the user receives signals only through the reflections of the RIS and assume that the user has L uniformly distributed ports, and select the most favorable port, that is, the optimal port, from the linear space size of Wλ among the L available ports, where λ represents the wavelength. For the user, the BS needs to send N bits of information to the user only through the reflections of the RIS. Therefore, the signal expression received by the user at the l th FAS port is:

[0037] In the formula, g mDenote the channel coefficient from the base station to the m-th RIS element; χ m,l Denote the channel coefficient from the m-th RIS element to the n-th FAS port of the user; l of the user; ι m Denote the reflection phase of the m-th RIS element; d SR and d RU denote the distance from the base station to the RIS element and the distance from the RIS element to the user, respectively; α Denote the path loss exponent; n l Represent the zero-mean complex Gaussian noise of the n-th port; with variance l of the user; δ 2 .

[0038] g m obeys CN (0, ω 1 ); that is, it represents a complex Gaussian distribution with a mean of 0 and a variance of ω 1 ; χ m,l obeys CN (0, ω 2 ); that is, it represents a complex Gaussian distribution with a mean of 0 and a variance of ω 2 .

[0039] Step 2: Establish an FAS-RIS-URLLC transmission protocol model, that is, optimize the RIS phase configuration and FAS port selection strategy to maximize the channel gain, specifically including: In this embodiment, the channel coefficient g m from the base station to the m-th RIS element l and the channel coefficient χ m,l from the m-th RIS element to the n-th FAS port of the user ι m are both assumed to be known; by adjusting the reflection phase ι m of the m-th RIS element, the channel gain is maximized, specifically

[0040] Therefore, the channel parameter of the n-th FAS port of the user is expressed as:

[0041] In the formula, M represents the maximum number of RIS elements.

[0042] Furthermore, the channel parameters of the optimal ports can be obtained , and the specific formula is as follows:

[0043] Therefore, the signal-to-noise ratio (SNR) after the user receives the signal γ can be expressed as:

[0044] In the formula, L represents the maximum number of FAS ports; ρ represents the transmit power or a parameter related to the transmit power; d SR and d RU represent the distance from the base station to the RIS element and the distance from the RIS element to the user, respectively; α represents the path loss exponent.

[0045] Step 3. Deduce the channel gain distribution (signal-to-noise ratio statistical distribution): Based on the central limit theorem to approximate a Gaussian distribution, obtain the cumulative distribution function (CDF) of the signal-to-noise ratio (SNR), specifically including: In an actual application scenario, generally the number M of reflection units of the RIS is very large. Therefore, the central limit theorem can be used to approximate the channel parameters of the FAS ports (i.e., the signal-to-noise ratio after the user receives the signal) as a Gaussian distribution. This processing simplifies the complexity of performance evaluation and makes it possible to perform performance analysis based on the statistical characteristics of the signal-to-noise ratio; in this embodiment, through a series of mathematical derivations, the mathematical expression form of the cumulative distribution function (CDF) of the signal-to-noise ratio γ can be obtained as:

[0046] In the formula, represents the cumulative distribution function of the signal-to-noise ratio γ ; t represents the independent variable; H represents a constant; U represents a parameter that balances the control precision and complexity; p t,b , q l both represent constants; among them, ; ; t b is a constant from 1 to U; L b represents a constant from 1 to L; ρ 0 and ρ 1 respectively represent constants; E γ represents the expectation of the variable γ , ; V γ represents the variance of the variable γ, 。

[0047] Step 4. Performance Analysis of the Network Model: Combining Shannon capacity and channel dispersion theory, derive a closed-form solution for the average block error rate (BLER), specifically including: Based on the URLLC standard definition, the instantaneous block error rate (BLER) of a user can be expressed as:

[0048] In the formula, represents the instantaneous block error rate; represents the approximate instantaneous block error rate; Q(·) represents the Gaussian function; represents the Shannon capacity; represents the channel dispersion; s represents the block length; N represents the number of transmitted information bits; γ represents the signal-to-noise ratio; Taking the expectation of the above formula, the expression for the average BLER can be obtained as:

[0049] Among them, Using linear approximation processing, we get:

[0050] After a series of simplifications, the final closed expression for the average BLER can be obtained as:

[0051] In the formula, represents the average block error rate; represents the statistical distribution of the signal-to-noise ratio; , , are parameters related to N and s, specifically expressed as: ; ; ; U P represents the parameter that controls the trade-off between accuracy and complexity; P represents the summation index; represents the cosine value calculated from p and U P ; represents the signal-to-noise ratio γ at ; among them, 。

[0052] So far, the instantaneous block error rate can be processed by taking the expectation of the statistical distribution of the signal-to-noise ratio. Combining linear approximation and integral transformation, the final average BLER can be calculated. The average BLER is used to evaluate the performance of URLLC based on FAS-RIS.

[0053] In this embodiment, a performance evaluation method under extreme conditions is also provided, that is, a performance limit analysis method under high SNR conditions, which specifically includes: Considering the case of independent and identically distributed, under the condition of high signal-to-noise ratio SNR, the cumulative distribution function (CDF) of γ can be expressed as:

[0054] In the formula, 。

[0055] Therefore, by adopting the first-order Riemann approximation, this embodiment can further obtain the expression of the average BLER under high SNR conditions as:

[0056] Based on the above formula, the limiting average block error rate under high SNR conditions can be obtained, which is used to evaluate the performance of the FAS-RIS assisted URLLC network under extreme conditions.

[0057] Exemplarily, for the performance evaluation method provided in this embodiment, specific simulation verification experiments are carried out, and the implementation process is as follows: In this embodiment, specific settings are made for the following parameters, and the parameter settings are ω 1 =ω 2 =1, W =10, α =2, d SR =20 m, d RU = 10 m.

[0058] As Figure 1 shown, in the first group of simulations, a curve graph showing the change of the average BLER with SNR is presented. It can be seen that the value of the average BLER gradually decreases as the SNR increases, which indicates that as the SNR increases, the performance gets better. Moreover, from Figure 1 it can also be seen that the evaluation result obtained by adopting this performance evaluation method is very well matched with the true value. Thus, the theoretical correctness of this method is proved.

[0059] As Figure 2 shown, in the second group of simulations, a curve graph showing the change of the average BLER with the number of reflection units M of the RIS is presented. From Figure 2 it can be seen that the value of the average BLER gradually decreases as M increases, which indicates that as M increases, the performance gets better.

[0060] As Figure 3As shown, in the third set of simulations, a graph showing the variation of the average BLER with SNR is presented. From Figure 3 , it can be seen that the system performance with the FAS technology is superior to that without the FAS technology. It is sufficient to prove that the adoption of the FAS and RIS technologies in the present invention significantly improves the system performance.

[0061] Thus, it can be seen that the performance evaluation method for URLLC based on FAS-RIS provided in this embodiment can derive a closed expression for the average block error rate of users. At the same time, the MATLAB simulation experiment can be used to verify that the theoretical results and the simulation numerical experimental results of users under this wireless network model are consistent, which proves the correctness of the theoretical derivation and further proves the efficiency and accuracy of this performance evaluation method.

[0062] Exemplarily, as Figure 4 shown, this embodiment provides a performance evaluation method for URLLC based on FAS-RIS, including: Constructing a FAS-RIS-URLLC signal channel transmission model; wherein, the FAS-RIS-URLLC signal channel transmission model includes a base station, RIS elements of multiple reflection units, and a user equipped with multiple FAS ports, and the base station communicates with the user only through the RIS elements; In the FAS-RIS-URLLC signal channel transmission model, maximizing the channel gain by optimizing the reflection phase of the RIS elements to obtain the signal-to-noise ratio after the user receives the signal; Approximating the signal-to-noise ratio after the user receives the signal as a Gaussian distribution to obtain the statistical distribution of the signal-to-noise ratio; Calculating the average block error rate based on the statistical distribution of the signal-to-noise ratio to obtain the performance evaluation result according to the average block error rate.

[0063] In this embodiment, before constructing the FAS-RIS-URLLC signal channel transmission model, it further includes: Constructing a downlink FAS-RIS-URLLC system; wherein, the downlink FAS-RIS-URLLC system includes a base station, RIS elements of multiple reflection units, and a user equipped with a single fluid antenna, and the base station sends information to the user only through the reflection of the RIS elements.

[0064] In this embodiment, the constructing of the FAS-RIS-URLLC signal channel transmission model includes: Based on the downlink FAS-RIS-URLLC system, constructing the FAS-RIS-URLLC signal channel transmission model; wherein, the signal l received by the user at the th FAS port is expressed as:

[0065] wherein, g m represents the channel coefficient from the base station to the m-th RIS element; χ m,l represents the channel coefficient from the m-th RIS element to the l n-th FAS port of the user; ι m represents the reflection phase of the m-th RIS element; d SR and d RU respectively represent the distance from the base station to the RIS element and the distance from the RIS element to the user; α represents the path loss exponent; n l represents the l n-th port of zero-mean complex Gaussian noise; the variance is δ 2 .

[0066] In this embodiment, in the FAS-RIS-URLLC signal channel transmission model, by optimizing the reflection phase of the RIS element to maximize the channel gain to obtain the signal-to-noise ratio after the user receives the signal, it includes: In the FAS-RIS-URLLC signal channel transmission model, the reflection phase of the RIS element is optimized and set, and the specific formula is as follows:

[0067] wherein, ι m represents the reflection phase of the m-th RIS element; g m represents the channel coefficient from the base station to the m-th RIS element; χ m,l represents the channel coefficient from the m-th RIS element to the l n-th FAS port of the user; After optimizing the reflection phase of the RIS element, the maximization of the channel gain is achieved, wherein the channel parameter of the n-th FAS port of the user is

[0068] wherein, M represents the maximum number of RIS elements; According to the channel parameter of the n-th FAS port of the user, the channel parameter of the optimal port is calculated, and the specific formula is as follows:

[0069] Calculate the signal-to-noise ratio (SNR) of the received signal at the user based on the channel parameters of the optimal port. γ , and the specific formula is as follows:

[0070] In the formula, L represents the maximum number of FAS ports; ρ represents the transmit power or a parameter related to the transmit power; d SR and d RU represent the distance from the base station to the RIS element and the distance from the RIS element to the user, respectively; α represents the path loss exponent.

[0071] In this embodiment, approximating the SNR of the received signal at the user as a Gaussian distribution to obtain the statistical distribution of the SNR includes: Using the central limit theorem to approximate the SNR of the received signal at the user as a Gaussian distribution and obtain the statistical distribution of the SNR. The specific formula is as follows:

[0072] In the formula, represents the cumulative distribution function of the SNR γ ; t represents the independent variable; H represents a constant; U represents a parameter that balances the control precision and complexity; p t,b , q l both represent constants; where ; ; t b is a constant from 1 to U; L b represents a constant from 1 to L; ρ 0 and ρ 1 represent constants respectively; E γ represents the expectation of the variable γ ; ; V γ represents the variance of the variable γ, .

[0073] In this embodiment, calculating the average block error rate based on the statistical distribution of the SNR includes: Combining the instantaneous block error rate and the statistical distribution of the SNR to calculate the average block error rate, which specifically includes: Based on the URLLC standard definition, obtain the instantaneous block error rate. The specific formula of the instantaneous block error rate is as follows:

[0074] In the formula, represents the instantaneous block error rate; denotes the approximate instantaneous block error rate; Q(·) denotes the Gaussian function; denotes the Shannon capacity; denotes the channel dispersion; s denotes the length of the block; N denotes the number of bits of transmitted information; γ denotes the signal-to-noise ratio; Performing an expectation operation on the instantaneous block error rate based on the statistical distribution of the signal-to-noise ratio, and combining linear approximation and integral transformation to obtain the average block error rate. The specific formula is as follows:

[0075] where, is represented by linear approximation as:

[0076]

[0077] In the formula, denotes the average block error rate; denotes the statistical distribution of the signal-to-noise ratio; , , are parameters related to N and s respectively, and are specifically represented as: ; ; ; U P denotes the parameter that balances the control precision and complexity; P denotes the summation index; denotes the value calculated from p and U P cosine value; denotes the signal-to-noise ratio γ at ; where, .

[0078] In this embodiment, after calculating the average block error rate based on the statistical distribution of the signal-to-noise ratio, it further includes: Performing a first-order Riemann approximation on the calculated average block error rate to obtain the limiting average block error rate, so as to obtain the performance evaluation result in the extreme scenario based on the limiting average block error rate.

[0079] Such as Figure 5As shown in the figure, this embodiment also provides a performance evaluation system for URLLC based on FAS-RIS, including: a network model construction module for constructing a FAS-RIS-URLLC signal channel transmission model; wherein, the FAS-RIS-URLLC signal channel transmission model includes a base station, RIS elements with multiple reflection units, and a user equipped with multiple FAS ports, and the base station communicates with the user only through the RIS elements; a parameter optimization module for maximizing the channel gain by optimizing the reflection phase of the RIS elements in the FAS-RIS-URLLC signal channel transmission model to obtain the signal-to-noise ratio after the user receives the signal; an approximation processing module for approximating the signal-to-noise ratio after the user receives the signal as a Gaussian distribution to obtain the statistical distribution of the signal-to-noise ratio; a performance evaluation module for calculating the average block error rate based on the statistical distribution of the signal-to-noise ratio to obtain a performance evaluation result according to the average block error rate.

[0080] The present invention also provides a device, including: a memory for storing a computer program; a processor for implementing the steps of the performance evaluation method for URLLC based on FAS-RIS when executing the computer program.

[0081] When the processor executes the computer program, it implements the steps of the above-mentioned performance evaluation of URLLC based on FAS-RIS, for example: constructing a FAS-RIS-URLLC signal channel transmission model; wherein, the FAS-RIS-URLLC signal channel transmission model includes a base station, RIS elements with multiple reflection units, and a user equipped with multiple FAS ports, and the base station communicates with the user only through the RIS elements; in the FAS-RIS-URLLC signal channel transmission model, maximizing the channel gain by optimizing the reflection phase of the RIS elements to obtain the signal-to-noise ratio after the user receives the signal; approximating the signal-to-noise ratio after the user receives the signal as a Gaussian distribution to obtain the statistical distribution of the signal-to-noise ratio; calculating the average block error rate based on the statistical distribution of the signal-to-noise ratio to obtain a performance evaluation result according to the average block error rate. Alternatively, when the processor executes the computer program, it implements the functions of the various modules in the above-mentioned system.

[0082] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing preset functions, and the instruction segments are used to describe the execution process of the computer program in the performance evaluation device of the FAS-RIS-based URLLC. For example, the computer program may be divided into a network model construction module, a parameter optimization module, an approximation processing module, and a performance evaluation module; the specific functions of each module are as follows: The network model construction module is used to construct a FAS-RIS-URLLC signal channel transmission model; wherein, the FAS-RIS-URLLC signal channel transmission model includes a base station, RIS elements of multiple reflection units, and a user equipped with multiple FAS ports, and the base station communicates with the user only through the RIS elements; the parameter optimization module is used to maximize the channel gain by optimizing the reflection phase of the RIS elements in the FAS-RIS-URLLC signal channel transmission model to obtain the signal-to-noise ratio after the user receives the signal; the approximation processing module is used to approximate the signal-to-noise ratio after the user receives the signal as a Gaussian distribution to obtain the statistical distribution of the signal-to-noise ratio; the performance evaluation module is used to calculate the average block error rate based on the statistical distribution of the signal-to-noise ratio to obtain the performance evaluation result according to the average block error rate.

[0083] The performance evaluation device of the FAS-RIS-based URLLC may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The performance evaluation device of the FAS-RIS-based URLLC may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above are examples of the performance evaluation device of the FAS-RIS-based URLLC, and do not constitute a limitation on the performance evaluation device of the FAS-RIS-based URLLC. It may include more components than the above, or combine some components, or different components. For example, the performance evaluation device of the FAS-RIS-based URLLC may further include input / output devices, network access devices, a bus, etc.

[0084] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center for the performance evaluation of the FAS-RIS-based URLLC, and connects various parts of the performance evaluation device of the entire FAS-RIS-based URLLC through various interfaces and lines.

[0085] The memory can be used to store the computer programs and / or modules. The processor realizes various functions of the performance evaluation device of the FAS-RIS-based URLLC by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory.

[0086] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0087] The present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the performance evaluation method of a FAS-RIS-based URLLC are realized.

[0088] If the modules / units integrated in the performance evaluation system of the FAS-RIS-based URLLC are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0089] Based on such understanding, all or part of the processes in the above-mentioned performance evaluation method of FAS-RIS-based URLLC of the present invention can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned performance evaluation method of FAS-RIS-based URLLC can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or preset intermediate form, etc.

[0090] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0091] It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0092] The present invention provides a performance evaluation method of FAS-RIS-based URLLC. Compared with the prior art, it has the following advantages: This method maximizes the channel gain by constructing a signal channel transmission model and optimizing the reflection phase of RIS elements, and then obtains the signal-to-noise ratio of the user received signal, and approximates it as a Gaussian distribution to obtain statistical characteristics. Combining the statistical distribution of the instantaneous block error rate and the signal-to-noise ratio, the average block error rate is calculated as the key index for performance evaluation. In addition, this method also considers the performance in extreme scenarios, and obtains the limit average block error rate through the first-order Riemann approximation processing, providing strong support for the performance optimization of the FAS-RIS-assisted URLLC network in a complex and changeable environment.

[0093] The above-mentioned embodiments are only one of the implementation manners capable of implementing the technical solution of the present invention. The scope of protection required by the present invention is not limited only by this embodiment, but also includes any changes, substitutions and other implementation manners that are easily conceivable by those skilled in the art within the technical scope disclosed by the present invention.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A performance evaluation method of URLLC based on FAS-RIS, characterized in that: include: Constructing a FAS-RIS-URLLC signal channel transmission model; wherein the FAS-RIS-URLLC signal channel transmission model includes a base station, a plurality of RIS elements of a reflection unit, and a user equipped with a plurality of FAS ports, and the base station communicates with the user only through the RIS element; In the FAS-RIS-URLLC signal channel transmission model, the channel gain is maximized by optimizing the reflection phase of the RIS element to obtain the signal-to-noise ratio after the user receives the signal; The signal-to-noise ratio after the user receives the signal is approximated to a Gaussian distribution to obtain a statistical distribution of the signal-to-noise ratio; The average block error rate is calculated based on the statistical distribution of the signal-to-noise ratio, so as to obtain a performance evaluation result according to the average block error rate.

2. The performance evaluation method of URLLC based on FAS-RIS according to claim 1, characterized in that: Before constructing the FAS-RIS-URLLC signal channel transmission model, the method further includes: Construct a downlink FAS-RIS-URLLC system; wherein the downlink FAS-RIS-URLLC system includes a base station, a RIS element of multiple reflection units, and a user equipped with a single fluid antenna, and the base station sends information to the user only through reflection of the RIS element.

3. The performance evaluation method of URLLC based on FAS-RIS according to claim 2, characterized in that, The constructing of the FAS-RIS-URLLC signal channel transmission model comprises: Based on the downlink FAS-RIS-URLLC system, a FAS-RIS-URLLC signal channel transmission model is constructed; Among them, users l The signal received by the FAS port It is expressed as: In the formula, g m represents the channel coefficient from the base station to the mth RIS element; χ m,l Indicates the mth RIS element to the user l The channel coefficients of the FAS ports; ι m represents the reflection phase of the mth RIS element; d SR and d RU Respectively represent the distance from the base station to the RIS element and the distance from the RIS element to the user; α represents the path loss exponent; n l Representative l The zero-mean complex Gaussian noise of the ports is δ 2 .

4. The performance evaluation method of URLLC based on FAS-RIS according to claim 1, characterized in that: In the FAS-RIS-URLLC signal channel transmission model, the channel gain is maximized by optimizing the reflection phase of the RIS element to obtain the signal-to-noise ratio after the user receives the signal, including: In the FAS-RIS-URLLC signal channel transmission model, the reflection phase of the RIS element is optimized and set. The specific formula is as follows: In the formula, ι m represents the reflection phase of the mth RIS element; g m represents the channel coefficient from the base station to the mth RIS element; χ m,l Indicates the mth RIS element to the user l The channel coefficients of the FAS ports; After optimizing the reflection phase of the RIS element, the channel gain is maximized, where the channel parameter of the user's nth FAS port is It is expressed as: Where M represents the maximum number of RIS elements; According to the channel parameters of the user's nth FAS port, calculate the channel parameters of the optimal port , the specific formula is as follows: According to the channel parameters of the optimal port, the signal-to-noise ratio of the user after receiving the signal is calculated γ , the specific formula is as follows: Where, L represents the maximum number of FAS ports; ρ represents the transmit power or a parameter related to the transmit power; d SR and d RU Respectively represent the distance from the base station to the RIS element and the distance from the RIS element to the user; α Represents the path loss exponent.

5. The performance evaluation method of URLLC based on FAS-RIS according to claim 1, characterized in that: The step of approximating the signal-to-noise ratio after the user receives the signal to a Gaussian distribution to obtain a statistical distribution of the signal-to-noise ratio includes: The central limit theorem is used to approximate the signal-to-noise ratio of the user after receiving the signal to a Gaussian distribution, and the statistical distribution of the signal-to-noise ratio is obtained. The specific formula is as follows: In the formula, Signal-to-noise ratio γ The cumulative distribution function of t is the independent variable; H is the constant; U is the parameter for the trade-off between control accuracy and complexity; p t,b ,q l All represent constants; among them, ; ;t b is a constant from 1 to U; L b represents a constant from 1 to L; ρ0 and ρ1 represent constants; E γ Representation variables γ expectations, ; V γ represents the variance of variable γ, ; d SR and d RU They represent the distance from the base station to the RIS element and the distance from the RIS element to the user respectively.

6. The performance evaluation method of URLLC based on FAS-RIS according to claim 1, characterized in that: The calculating the average block error rate based on the statistical distribution of the signal-to-noise ratio includes: Combined with the statistical distribution of the instantaneous block error rate and the signal-to-noise ratio, the average block error rate is calculated, including: Based on the URLLC standard definition, the instantaneous block error rate is obtained. The specific formula of the instantaneous block error rate is as follows: In the formula, Indicates the instantaneous block error rate; represents the approximate instantaneous block error rate; Q(·) represents the Gaussian function; represents the Shannon capacity; represents channel dispersion; s represents the length of the block; N represents the number of bits of transmitted information; γ represents the signal-to-noise ratio; Based on the statistical distribution of the signal-to-noise ratio, the instantaneous block error rate is processed by expectation operation, and the average block error rate is obtained by combining linear approximation and integral transformation. The specific formula is as follows: in, The linear approximation is: In the formula, represents the average block error rate; represents the statistical distribution of the signal-to-noise ratio; , , are parameters related to N and s respectively, specifically expressed as: ; ; ; U P The parameter representing the trade-off between control accuracy and complexity; P represents the summation index; Represented by p and U P The calculated cosine value; Signal-to-noise ratio γ exist The value at ; where .

7. The performance evaluation method of URLLC based on FAS-RIS according to claim 1, characterized in that: After the average block error rate is obtained by calculating the statistical distribution of the signal-to-noise ratio, the method further includes: The calculated average block error rate is processed by first-order Riemann approximation to obtain a limit average block error rate, so as to obtain a performance evaluation result in an extreme scenario according to the limit average block error rate.

8. A performance evaluation system of URLLC based on FAS-RIS, characterized in that: include: A network model building module, used to build a FAS-RIS-URLLC signal channel transmission model; wherein the FAS-RIS-URLLC signal channel transmission model includes a base station, a plurality of RIS elements of a reflection unit, and a user equipped with a plurality of FAS ports, and the base station communicates with the user only through the RIS element; A parameter optimization module is used to maximize the channel gain by optimizing the reflection phase of the RIS element in the FAS-RIS-URLLC signal channel transmission model to obtain the signal-to-noise ratio after the user receives the signal; An approximate processing module, used for approximating the signal-to-noise ratio after the user receives the signal to a Gaussian distribution, so as to obtain a statistical distribution of the signal-to-noise ratio; The performance evaluation module is used to calculate an average block error rate based on the statistical distribution of the signal-to-noise ratio, so as to obtain a performance evaluation result according to the average block error rate.

9. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the performance evaluation method of URLLC based on FAS-RIS according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it is used to implement the steps of the performance evaluation method of URLLC based on FAS-RIS according to any one of claims 1 to 7.

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