Antenna Quantity Configuration Optimization Method and System for RIS-Assisted Millimeter-Wave Communication System

Abstract

The present invention discloses a method and system for optimizing the number of antennas in a RIS-assisted millimeter-wave communication system. The method establishes a RIS-assisted millimeter-wave downlink communication system model and a corresponding channel model, determines the received signal-to-noise ratio (SNR) at the receiving end, ensures that the SNR is greater than a set threshold, and obtains the probability of successful transmission. The probability of successful transmission is expressed as a function of the number of antennas. Based on the effect of the number of antennas on the probability of successful transmission, the number of antennas that maximizes the probability of successful transmission is determined, and a numerical solution is used to optimize the number of antennas. The present invention utilizes a RIS-assisted millimeter-wave communication system model for transmission over a Nakagami‑m channel. By balancing the change in the probability of successful transmission caused by changes in the number of antennas, the method maximizes the probability of successful transmission.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a method and system for optimizing the configuration of the number of antennas in a RIS-assisted millimeter wave communication system. Background Art

[0002] Millimeter-wave communications are being widely researched for use in emerging fifth-generation (5G) mobile communication networks and other areas. Their wider bandwidth provides higher data rates and capacity than current levels. However, due to their high frequency, millimeter-waves are subject to higher path loss and are more susceptible to blockage. To improve the millimeter-wave channel environment, reconfigurable smart reflectors (RIS) have been introduced as relays to assist in communication. To enhance spectral and energy transmission efficiency, uniform linear arrays are deployed at base stations to improve signal transmission directionality and antenna gain. However, as the number of antennas increases, deployment costs also increase. Furthermore, due to the narrowing of the antenna main lobe, it is difficult to find a close RIS for communication, thus failing to achieve the optimal transmission success probability.

[0003] Antenna gain increases with the number of antennas, thereby improving the received signal-to-noise ratio (SNR). However, in RIS-assisted communication systems, the reflective surface is a key factor in increasing the probability of successful transmission. A narrower main lobe causes the associated RIS to be farther away from the base station, increasing path loss in the reflection channel and, in turn, reducing the received SNR. Existing research has not considered the impact of the number of antennas on RIS selection, focusing solely on the signal enhancement effect of antenna gain. Clearly, this model does not accurately reflect the current state of technological development.

[0004] At the same time, in terms of antenna configuration, Massive Multiple Input Multiple Output (MIMO) technology is currently widely used, and research on RIS is becoming increasingly in-depth. However, some existing articles have not conducted in-depth research on the mutual influence between the two and cannot accurately reflect the current status of technological development. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the deficiencies in the above-mentioned prior art and provide a method and system for optimizing the configuration of the number of antennas in a RIS-assisted millimeter-wave communication system. The method utilizes the changes in antenna gain and main lobe width caused by changes in the number of antennas to find the optimal number of antennas to maximize the probability of successful transmission, thereby solving the technical problem that the existing optimized antenna number configuration cannot balance antenna gain and main lobe width to achieve the optimal probability of successful transmission.

[0006] The present invention adopts the following technical solutions:

[0007] The present invention is directed to a method for optimizing the number of antennas configured in a RIS-assisted millimeter wave communication system, comprising the following steps:

[0008] S1. Establish a RIS-assisted millimeter wave downlink communication system model and a corresponding channel model to determine the received signal-to-noise ratio at the receiving end;

[0009] S2. Based on the received signal-to-noise ratio obtained in step S1, ensure that the received signal-to-noise ratio is greater than a set threshold, and obtain the transmission success probability;

[0010] S3. Express the transmission success probability obtained in step S2 as a function of the number of antennas. Based on the impact of the number of antennas on the transmission success probability, the number of antennas that maximizes the transmission success probability is determined, and a numerical solution is used to optimize the configuration of the number of antennas.

[0011] Specifically, step S1 is as follows:

[0012] S101. Establish a RIS-assisted millimeter-wave downlink communication system model. The base station is equipped with a uniform linear array with N0 antennas. The user is equipped with a single receiving antenna. RIS is used for auxiliary communication. Each RIS has K reflective elements.

[0013] S102: Based on the millimeter wave downlink communication system model obtained in step S101, the main lobe direction of the uniform linear array is set to align with the user, and a flat-top antenna model is adopted to simplify the antenna gain.

[0014] S103, according to the flat-top antenna model obtained in step S102, setting an association rule that the base station communicates with the RIS closest to it in the main lobe;

[0015] S104. According to the association rule obtained in step S103, for a communication link, determine whether the link is a line-of-sight link or a non-line-of-sight link according to whether the link is blocked;

[0016] S105: Establish a channel model based on the line-of-sight link or non-line-of-sight link determined in step S104, assuming that the small-scale fading of the channel is Nakagami-m fading, and the baseband equivalent channels of base station-user, base station-RIS, and RIS-user are h d 、h i and h r , determine the received signal-to-noise ratio Γ at the user according to the received signal power at the user.

[0017] Furthermore, in step S105, the received signal-to-noise ratio Γ at the user is:

[0018]

[0019] Among them, P0 represents the transmission power of a single antenna, δ 2 represents the noise power at the user, |H| is the amplitude gain of the composite channel, and G is the main lobe gain.

[0020] Specifically, step S2 is as follows:

[0021] S201, determining a joint probability density function of a distance r1 associated with the RIS to the base station and a distance r2 associated with the RIS to the user;

[0022] S202, obtaining a shape parameter a and an inverse scale parameter b of a channel power gain distribution through parameter matching;

[0023] S203: The probability that the received signal-to-noise ratio is greater than the set threshold is used as the transmission success probability. Determine the transmission success probability based on the results obtained in step S201 and step S202

[0024] Furthermore, in step S201, the joint probability density function Specifically:

[0025]

[0026]

[0027] in, is the conditional probability density function of r2 given r1, is the probability density function of r1, λ RIS is the density, θ0 is the main lobe width, and r is the fixed value of the distance between the base station and the user.

[0028] Furthermore, in step S203, the transmission success probability for:

[0029]

[0030] Among them, |H i | is the composite channel amplitude gain of case i, a i is the shape parameter of case i, b i is the inverse scale parameter of case i, is the probability of the line-of-sight situation in case i, is the transmission success probability of case i, r is the fixed value of the distance between the base station and the user, T is the receiving signal-to-noise ratio threshold, P0 is the transmission power of a single antenna, G is the main lobe gain, is a term in the probability density function of the Gamma distribution, δ 2 is the noise power at the user.

[0031] Specifically, in step S3, the derivative of the transmission success probability with respect to the number of antennas N0 is expressed as follows:

[0032]

[0033] in, is the transmission success probability, is the probability of the line-of-sight situation in case i, is the joint probability density function of r1 and r2, is an item in the probability density function of the Gamma distribution, Γ(·) is the Gamma function, θ is the angle away from the main lobe alignment direction, P0 is the transmit power of a single antenna, N0 is the number of antennas, T is the receive signal-to-noise ratio threshold, δ 2 is the noise power at the user, a i is the shape parameter of case i, b i is the inverse scale parameter of case i.

[0034] In a second aspect, an embodiment of the present invention provides a system for optimizing the number of antennas configured in a RIS-assisted millimeter wave communication system, including:

[0035] The receiving module establishes the RIS-assisted millimeter wave downlink communication system model and the corresponding channel model to determine the received signal-to-noise ratio at the receiving end;

[0036] The probability module makes the received signal-to-noise ratio greater than a set threshold according to the received signal-to-noise ratio obtained by the receiving module, and obtains the probability of successful transmission;

[0037] The optimization module expresses the transmission success probability obtained by the probability module as a function of the number of antennas. Based on the impact of the number of antennas on the transmission success probability, the number of antennas that maximizes the transmission success probability is determined, and a numerical solution is used to optimize the configuration of the number of antennas.

[0038] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned method for optimizing the configuration of the number of antennas in a RIS-assisted millimeter wave communication system are implemented.

[0039] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, comprising a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for optimizing the configuration of the number of antennas in a RIS-assisted millimeter wave communication system.

[0040] Compared with the prior art, the present invention has at least the following beneficial effects:

[0041] This invention addresses the optimization of antenna configuration for RIS-assisted millimeter-wave communication systems. As the number of antennas increases, their main lobes narrow and antenna gain increases. In the RIS-assisted communication model, this dynamic relationship prevents the base station from finding a nearby RIS, resulting in a non-monotonically increasing probability of successful transmission with increasing antenna numbers, creating a trade-off. Therefore, a general RIS-assisted millimeter-wave communication system model is proposed. An expression for the probability of successful transmission is derived based on the theory of random geometry. Finally, a method for optimizing the number of antennas to maximize the probability of successful transmission is proposed. Simulation results demonstrate that this method achieves superior success rates with fewer antennas compared to a larger number, thus achieving better results at a lower cost.

[0042] Furthermore, by establishing a RIS-assisted millimeter-wave downlink communication system model and the corresponding channel model, the received signal-to-noise ratio (SNR) at the receiving end was determined. The SNR is a key indicator of system performance and a necessary quantity for determining the probability of successful transmission. The uniform linear array installed at the base station provides a certain power gain to the transmitted signal. By adjusting the signal phase shift through the phase shift controller on the reflecting surface, the reflective element enhances the signal amplitude. Taking into account the influence of obstacles, the link may be line-of-sight or non-line-of-sight, resulting in different large-scale and small-scale fading factors, and thus different power gains for the signal, which is consistent with actual communication conditions.

[0043] Furthermore, the received signal-to-noise ratio (SNR) at the user end is an important indicator that not only measures performance but also contributes to the calculation of the probability of successful transmission. The SNR is defined as the ratio of the received signal power to the noise.

[0044] Furthermore, signal propagation in a wireless environment is subject to large-scale fading, which is strongly correlated with communication distance. The longer the communication distance, the more the signal is weakened, thus affecting the overall channel power gain and received signal-to-noise ratio (SNR). Therefore, determining the communication distances for the entire communication process is crucial: the distance from the base station to the user, the distance from the base station to the associated RIS, and the distance from the associated RIS to the user. These three distances can be determined using the association rule in step S103, thereby deriving the shape parameter a and inverse scale parameter b that characterize the channel power gain distribution. Finally, the expectation of the joint probability density function of these distances is calculated to yield the final transmission success probability.

[0045] Furthermore, communication distance will greatly affect large-scale fading. It should be noted that the distance r1 from the base station to the associated RIS and the distance r2 from the associated RIS to the user are not independent of each other, but are correlated. That is, r1 will change due to changes in r2, and vice versa. Therefore, it is necessary to solve their joint probability density function to reflect this correlation. The specific solution process is as follows: given that the angle between r and r1 is uniformly distributed, the cosine theorem can be used to derive the conditional probability density function of r2 given r1. Multiplying it by the probability density function of r1 gives the joint probability density function of r1 and r2

[0046] Furthermore, the transmission success probability is a key performance metric. The higher the transmission success probability, the better the system performance. The transmission success probability is defined as the probability that the received signal-to-noise ratio (SNR) exceeds a set threshold. Once the received SNR is determined through analysis, the transmission success probability can be derived. This is essentially solving the distribution function of the Gamma distribution. It should be noted that the different line-of-sight / non-line-of-sight (LOS) scenarios of the three links result in different channel power gains, which in turn affect the received SNR and transmission success probability. Therefore, it is necessary to solve for each of the eight scenarios separately and then weighted sum them according to the line-of-sight / non-line-of-sight probability to determine the overall transmission success probability.

[0047] Furthermore, the number of antennas, N0, affects both the mainlobe width and mainlobe gain. A larger N0 results in a narrower mainlobe, leading to longer communication distances and more severe path loss. Simultaneously, as the number of antennas increases, the mainlobe gain increases, resulting in greater signal gain. Therefore, to optimize a uniform linear array, balancing these two trade-offs and determining the number of antennas that optimizes the probability of successful transmission, we need to take the derivative of the probability of successful transmission with respect to the number of antennas, N0. This allows us to determine the value of N0 that maximizes the probability of successful transmission, thus optimizing the uniform linear array.

[0048] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0049] In summary, the present invention adopts the RIS-assisted millimeter-wave communication system model to transmit under the Nakagami-m channel. By weighing the change in the transmission success probability caused by the change in the number of antennas, a method for optimizing the number of antennas is provided to maximize the transmission success probability.

[0050] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a model diagram of the RIS-assisted downlink millimeter wave communication system of the present invention;

[0052] Figure 2 This is a graph showing the effect of the number of reflective elements on the transmission success probability of the present invention;

[0053] Figure 3 This is a diagram showing the impact of the number of antennas on the probability of successful transmission in the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0055] In the description of the present invention, it is to be understood that the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0056] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0057] It should be further understood that the term "and / or" as used in the present specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. For example, A and / or B may represent: A alone, A and B simultaneously, or B alone. In addition, the character " / " herein generally indicates that the associated items are in an "or" relationship.

[0058] It should be understood that although the terms "first," "second," and "third" may be used to describe preset ranges in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are merely used to distinguish one preset range from another. For example, without departing from the scope of embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0059] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0060] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0061] The present invention provides a method for optimizing the number of antennas configured in a RIS-assisted millimeter wave communication system, comprising the following steps:

[0062] S1. Establish a RIS-assisted millimeter-wave downlink communication system model and its channel model, and discuss the signal-to-noise ratio at the receiver.

[0063] S101. Establish a RIS-assisted millimeter wave downlink communication system model. The system model is shown in the figure below. Figure 1 As shown, the base station is equipped with a uniform linear array with N0 antennas, and the user is equipped with a single receiving antenna. Consider a pair of base station-user points, represented by Ψ = (X, Y), where is the location of the base station, is the user's location, and the distance between them is a fixed value r>0. RIS is used for auxiliary communication, and its distribution obeys independent PPP, which is expressed as The density is λ RIS , there are K reflective elements on each RIS. Assume that there are linear obstacles in the system, and their center distribution obeys independent PPP;

[0064] S102. Without loss of generality, simplify the antenna gain. Assume that the main lobe is pointed toward the user and adopt a flat-top antenna model. The antenna gain is expressed as:

[0065]

[0066] where G is the mainlobe gain, which is equal to N0, g is the sidelobe gain, θ is the angle away from the mainlobe alignment, and θ0 is the mainlobe width, which is given by:

[0067]

[0068] in, It represents the ratio of antenna spacing to carrier wavelength. The more antennas there are, the greater the main lobe gain will be, but the main lobe width will also decrease.

[0069] S103, the association rule is that the base station communicates with the RIS closest to it in the main lobe;

[0070] represents the RIS located in the main lobe, where Z l is the position of the lth RIS, then the position of the associated RIS is expressed as:

[0071]

[0072] Here, ||·|| represents the Euclidean distance.

[0073] From this, we can get the distance from the associated RIS to the base station And the distance between RIS and the user

[0074] S104. Since there are obstacles in the system, whether the link is blocked determines whether the link is a line-of-sight link (LOS) or a non-line-of-sight link (NLOS);

[0075] The probability that the link is a line-of-sight link is:

[0076] P LOS (d) = e -βd

[0077] The probability that the link is a non-line-of-sight link is

[0078] P NLOS (d) = 1-P LOS (d) = 1 - e -βd

[0079] Where d represents the length of the link, Represents the average line-of-sight range. Due to the attenuation effect of blocking on the signal, the fading parameters of the line-of-sight link and the non-line-of-sight link are different. For path loss, the path loss coefficients of the line-of-sight link and the non-line-of-sight link are respectively represented by α L and α N Indicates that, and there is α L <α N ; For small-scale fading, the fading parameters of the line-of-sight link and the non-line-of-sight link are respectively represented by m L and m N Indicates that, and there is m L >m N .

[0080] S105. Establish a channel model. Assume that the small-scale fading of the channel is Nakagami-m fading. The baseband equivalent channels of base station-user, base station-RIS, and RIS-user are expressed as:

[0081]

[0082]

[0083] in,[·] T is the transpose operation. Consider that the wireless channel is subject to large-scale fading and small-scale fading, where large-scale fading is represented by path loss with a loss exponent α ≥ 2; small-scale fading is Nakagami-m fading. Then the base station-user channel power gain is:

[0084]

[0085] in, is the average channel power gain when the reference distance is 1m, f c represents the carrier frequency, c represents the speed of light. The path loss of the base station-user channel is r -α , g d represents small-scale fading, g d ~Γ(m,1 / m), where m is its small-scale fading parameter.

[0086] Similarly, the channel power gains between the base station and the kth RIS reflector element and the kth RIS reflector element and the user are expressed as:

[0087]

[0088]

[0089] Among them, g1~Γ(m1,1 / m1), m1 is its small-scale fading parameter, g2~Γ(m2,1 / m2), m2 is its small-scale fading parameter; α1 and α2 represent the path loss coefficients from the base station to the kth RIS reflector element and the kth RIS reflector element to the user, respectively.

[0090] In addition, when the signal is reflected by RIS, RIS can use its own phase shift controller to adjust the phase shift of the signal. represents the RIS phase shift matrix, where φ k ∈[0,2π) represents the phase shift adjustment of the signal by the kth reflective element, and it is assumed that after the RIS adjusts the signal phase, the signal can be superimposed in phase at the user.

[0091] In summary, the channel between the base station and the kth RIS reflector, the phase shift adjustment of the kth RIS reflector, and the channel between the kth RIS reflector and the user constitute the reflection channel:

[0092] h ir,k =h i,k T Φh r,k

[0093] The amplitude gain of the reflection channel is h ir,k |=|h i,k | T |h r,k |, then the amplitude gain of the composite channel is:

[0094]

[0095] Among them, h d , h i,k and h r,k , k=1,…,K are independent of each other.

[0096] S106. The received signal-to-noise ratio at the user is:

[0097]

[0098] Among them, P0 represents the transmission power of a single antenna, δ 2 Represents the noise power at the user.

[0099] In step S1, a system model is established based on random geometry theory, the channel amplitude gain is analyzed, and the received signal-to-noise ratio is obtained.

[0100] Furthermore, since the distribution of RIS can be viewed as random points in space, it can be modeled using a spatial point process. At the same time, considering the actual situation, obstacles are taken into account in this method, and their distribution is also modeled as PPP.

[0101] Furthermore, this method uses a flat-top antenna model, in which the mainlobe gain and sidelobe gain of the antenna are fixed, and the mainlobe width narrows as the number of antennas increases. This step not only reflects the constraints imposed by the number of antennas on the mainlobe width, but also demonstrates its impact on antenna gain, while simplifying the analysis of a complex problem without loss of generality.

[0102] Furthermore, the association rule is set to allow the base station to communicate with the closest RIS within its main lobe. Since the antenna's energy is primarily concentrated in the main lobe, the RIS within the main lobe is identified as a possible reflection surface for communication. Based on this, the closest RIS is selected to minimize the impact of path loss. The shorter the path, the greater the probability that the link is line-of-sight.

[0103] Furthermore, due to their high frequency, millimeter waves are more susceptible to obstruction than lower-frequency signals. For example, certain materials, such as concrete walls on the exterior of buildings, can cause millimeter wave attenuation as high as 60–109 dB. Depending on whether the link is obstructed, millimeter wave links can be categorized as line-of-sight (LOS) or non-line-of-sight (NLOS). These two types of links exhibit significant differences in fading parameters, impacting the signal significantly. Therefore, the presence of obstacles is considered in the system model to more accurately simulate real-world conditions.

[0104] Furthermore, the composite channel amplitude gain consists of three parts: base station-user, base station-RIS and RIS-user. Its channel model is analyzed based on the Nakagami-m channel.

[0105] Furthermore, the received signal-to-noise ratio is defined as the ratio of the received signal power to the received noise power.

[0106] S2. Based on the obtained received signal-to-noise ratio, ensure that it is greater than a set threshold and obtain the probability of successful transmission;

[0107] S201. Discuss the probability density function of distance;

[0108] From the hole probability of the Poisson process, we can see that the probability density function of r1 is:

[0109]

[0110] Since changes in r1 will cause changes in r2, the two are related, and their joint probability density function needs to be analyzed, specifically:

[0111] The angle Θ between r and r1 follows a uniform distribution, Given r1, the conditional probability that r2 is less than a certain value R2 has the following equivalent relationship:

[0112]

[0113] in, Let Ω=cosΘ, its distribution function is

[0114]

[0115] According to the cosine theorem, we have

[0116]

[0117] Substituting into

[0118]

[0119] Taking its derivative, we can get the conditional probability density function given r1 and r2.

[0120]

[0121] Finally, the joint probability density function of r1 and r2 is

[0122]

[0123]

[0124] S202, discussing the statistical characteristics of the composite channel;

[0125] The composite channel amplitude gain is Because it is too complicated, it is approximated as a gamma distribution, that is:

[0126] |H|~Γ(a,b)

[0127] Among them, a is the shape parameter and b is the inverse scale parameter. These two parameters are obtained through parameter matching, specifically:

[0128] For the base station-user channel, the first-order moment and second-order moment of the channel amplitude gain are:

[0129]

[0130]

[0131] The variance is

[0132] The channel amplitude gain of the base station-kth RIS-user channel is a double Nakagami random variable, and its first-order moment and second-order moment are:

[0133]

[0134]

[0135] The variance is h d , h ir,k , k=1,…,K are independent of each other, so the mean and variance of the composite channel amplitude gain are:

[0136]

[0137]

[0138] From the above, the parameters of the gamma distribution are:

[0139]

[0140]

[0141] S203: The probability that the received signal-to-noise ratio is greater than the set threshold is the transmission success probability, which is expressed as:

[0142]

[0143] Among them, Γ th For a given threshold, solving for the transmission success probability is essentially solving the distribution function of the composite channel amplitude gain. Due to the presence of obstacles, the three independent links—base station-user, base station-RIS, and RIS-user—may all become line-of-sight or non-line-of-sight links, resulting in different |H|.

[0144] Therefore, the probability of successful transmission needs to be discussed case by case. L represents a line-of-sight link, N represents a non-line-of-sight link, and the joint event {ABC} represents the three-link situation, where events A, B, and C represent the base station-user link, base station-RIS link, and RIS-user link, respectively. The eight possible situations are:

[0145] {LLL,LLN,LNL,LNN,NLL,NLN,NNL,NNN}

[0146] Represented as a set

[0147] For example, case 2 (LLN) means that the base station-user link and the base station-RIS link are line-of-sight links, and the user-RIS link is non-line-of-sight. The probability is:

[0148]

[0149] Under the conditions of case 2, the probability of successful transmission is:

[0150]

[0151] Where |H2| is the composite channel amplitude gain in case 2, α=α1=α L , α2=α N , m=m1=m L and m2=m N .

[0152] In summary, substituting G=N0, the probability of successful transmission is expressed as:

[0153]

[0154] Among them, |H i | represents the composite channel amplitude gain for case i, a i represents the shape parameter of case i, b i represents the inverse scale parameter of case i, represents the probability of the line-of-sight situation in situation i, represents the transmission success probability of case i.

[0155] In step S2, the probability that the received signal-to-noise ratio is greater than the set threshold is the transmission success probability, which can be obtained by analyzing the distribution of the composite channel amplitude gain and the distribution of the distance.

[0156] Furthermore, since distance is a random variable, deriving the probability of successful transmission requires obtaining the probability density function of distance. Based on the theory of random geometry, the probability density function of r1 is derived. Since r1 and r2 are related, it is necessary to derive the joint probability density expression of the two.

[0157] Furthermore, deriving the transmission success probability essentially derives the distribution function of the composite channel amplitude gain, thus requiring its probability density function. Because its distribution is complex, we apply the gamma distribution, which fits positive random variables well, to approximate the composite channel amplitude gain. The statistical characteristics of the composite channel amplitude gain are then derived through parameter matching.

[0158] Furthermore, based on the previous analysis of blocking, eight scenarios of line-of-sight / non-line-of-sight composite channels need to be discussed, and finally the expression for the final transmission success probability is derived from the total probability formula.

[0159] S3. Express the transmission success probability as a function of the number of antennas, discuss the impact of the number of antennas on the transmission success probability, provide an expression for the number of antennas that maximizes the transmission success probability, and use a numerical solution to achieve optimization.

[0160] When the number of antennas is small, the main lobe is wider, making it easier to find a RIS close to the base station for communication, but the antenna gain is low. When the number of antennas increases, the antenna gain increases, but the main lobe narrows, making it difficult for the base station to find a nearby RIS. To balance this, the number of antennas needs to be optimized.

[0161] The derivative of the transmission success probability with respect to the number of antennas N0 is derived as:

[0162]

[0163] By setting the derivative to 0, we can obtain the optimal number of antennas that maximizes the probability of successful transmission.

[0164] Since a closed-form expression cannot be obtained, a numerical solution can be used to solve it in order to optimize the configuration of the number of antennas.

[0165] In step S3, the key to optimizing the configuration of the number of antennas is to find a value of the number of antennas that maximizes the probability of successful transmission, which is essentially to find the extreme value of the probability of successful transmission.

[0166] Furthermore, the derivative of the transmission success probability with respect to the number of antennas is derived and set to 0, thereby obtaining the optimal number of antennas.

[0167] In another embodiment of the present invention, a system for optimizing the configuration of the number of antennas in a RIS-assisted millimeter-wave communication system is provided. The system can be used to implement the above-mentioned method for optimizing the configuration of the number of antennas in a RIS-assisted millimeter-wave communication system. Specifically, the system for optimizing the configuration of the number of antennas in a RIS-assisted millimeter-wave communication system includes a receiving module, a probability module, and an optimization module.

[0168] The receiving module establishes a RIS-assisted millimeter-wave downlink communication system model and a corresponding channel model to determine the received signal-to-noise ratio at the receiving end.

[0169] The probability module makes the received signal-to-noise ratio greater than a set threshold according to the received signal-to-noise ratio obtained by the receiving module, and obtains the probability of successful transmission;

[0170] The optimization module expresses the transmission success probability obtained by the probability module as a function of the number of antennas. Based on the impact of the number of antennas on the transmission success probability, the number of antennas that maximizes the transmission success probability is determined, and a numerical solution is used to optimize the configuration of the number of antennas.

[0171] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the RIS-assisted millimeter wave communication system antenna quantity configuration optimization method, including:

[0172] A RIS-assisted millimeter-wave downlink communication system model and its corresponding channel model are established to determine the received signal-to-noise ratio (SNR) at the receiving end. The transmission success probability is calculated by ensuring that the SNR is greater than a set threshold. The transmission success probability is expressed as a function of the number of antennas. Based on the impact of the number of antennas on the transmission success probability, the number of antennas required to maximize the transmission success probability is determined, and a numerical solution is used to optimize the configuration of the number of antennas.

[0173] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (Non-Volatile Memory), such as at least one disk memory.

[0174] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the method for optimizing the number of antennas configured in the RIS-assisted millimeter-wave communication system in the above embodiment. The processor may load and execute the following steps:

[0175] A RIS-assisted millimeter-wave downlink communication system model and its corresponding channel model are established to determine the received signal-to-noise ratio (SNR) at the receiving end. The transmission success probability is calculated by ensuring that the SNR is greater than a set threshold. The transmission success probability is expressed as a function of the number of antennas. Based on the impact of the number of antennas on the transmission success probability, the number of antennas required to maximize the transmission success probability is determined, and a numerical solution is used to optimize the configuration of the number of antennas.

[0176] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0177] The simulation parameters are set as follows:

[0178]

[0179] See also Figure 2 , is the effect of the number of RIS reflective elements K on the transmission success probability. N0=9. Figure 2 It can be seen that when the number of RIS reflective elements K increases, the probability of successful transmission increases significantly. This is because each reflective element can reflect the signal. The more elements there are, the more signals are superimposed in phase at the receiving end, which is equivalent to enhancing the signal.

[0180] See also Figure 3 , is the effect of the number of antennas N0 on the probability of successful transmission. Γ th =-5dB. Figure 3 It can be seen that there is a value of N0 that maximizes the probability of successful transmission. Under the condition of K = 4000, when the number of antennas is 10, the probability of successful transmission is maximized, which is a 12% improvement compared to the overall performance. At the same time, after optimizing the number of antennas, using fewer antennas can achieve a higher probability of successful transmission than using more antennas, thus achieving better results at a lower cost.

[0181] In summary, the present invention provides a method and system for optimizing the configuration of the number of antennas in a RIS-assisted millimeter-wave communication system. This method addresses the impact of the number of antennas on the mainlobe width and mainlobe gain (the greater the number of antennas, the narrower the mainlobe and the greater the mainlobe gain). By establishing a RIS-assisted millimeter-wave communication system model, and considering the actual situation of determining whether the link is line-of-sight or non-line-of-sight when the small-scale fading is Nakagami-m fading, the probability of successful transmission at the user is derived. By weighing the change in the probability of successful transmission caused by changes in the number of antennas, we provide a method for optimizing the configuration of the number of antennas to maximize the probability of successful transmission, thereby achieving the best effect at a lower cost, and verifying the results through simulation. The simulation results show that we can find an optimal method for configuring the number of antennas to achieve the best probability of successful transmission.

[0182] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0183] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0184] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0185] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, and can be electrical, mechanical, or other forms.

[0186] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0187] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0188] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the 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, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable 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 disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0189] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0190] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0191] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0192] The above content is only for explaining the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for optimizing the number of antennas configured in a RIS-assisted millimeter wave communication system, characterized in that: The following steps are involved: S1. Establish a RIS-assisted millimeter wave downlink communication system model and a corresponding channel model to determine the received signal-to-noise ratio at the receiving end; S2. According to the received signal-to-noise ratio obtained in step S1, ensure that the received signal-to-noise ratio is greater than a set threshold, and obtain the transmission success probability, which is specifically: S201, determine the distance between the associated RIS and the base station and the distance from the associated RIS to the user The joint probability density function, the joint probability density function Specifically: in, For a given Under the conditions, The conditional probability density function of for The probability density function of is the density, is the main lobe width, is the fixed value of the distance between the base station and the user; S202: Obtaining shape parameters of channel power gain distribution through parameter matching and inverse scale parameter ; S203: The probability that the received signal-to-noise ratio is greater than the set threshold is used as the transmission success probability. , determine the transmission success probability based on the results obtained in step S201 and step S202 , the probability of successful transmission for: in, For the situation The composite channel amplitude gain, For the situation The shape parameters, For the situation The inverse scale parameter of For the situation Probability of line-of-sight situation, For the situation The probability of successful transmission is is the fixed value of the distance between the base station and the user, is the receiving signal-to-noise ratio threshold, is the transmission power of a single antenna, is the main lobe gain, is an item in the probability density function of the Gamma distribution, is the noise power at the user; S3. Express the transmission success probability obtained in step S2 as a function of the number of antennas. Based on the influence of the number of antennas on the transmission success probability, find the number of antennas that maximizes the transmission success probability. Use a numerical solution to optimize the configuration of the number of antennas. The derivative of is expressed as follows: in, is the transmission success probability, For the situation Probability of line-of-sight situation, for and The joint probability density function of is an item in the probability density function of the Gamma distribution, is the Gamma function, is the angle away from the main lobe alignment direction, is the transmission power of a single antenna, is the number of antennas, is the receiving signal-to-noise ratio threshold, is the noise power at the user, For the situation The shape parameters, For the situation The inverse scale parameter of .

2. The method for optimizing the number of antennas in a RIS-assisted millimeter wave communication system according to claim 1, wherein: Step S1 is specifically as follows: S101. Establish a RIS-assisted millimeter wave downlink communication system model. The base station is equipped with a uniform linear array and the number of antennas is , the user is equipped with a single receiving antenna and uses RIS for auxiliary communication. Each RIS has A reflective element; S102: Based on the millimeter wave downlink communication system model obtained in step S101, the main lobe direction of the uniform linear array is set to align with the user, and a flat-top antenna model is adopted to simplify the antenna gain. ; S103, according to the flat-top antenna model obtained in step S102, setting an association rule such that the base station communicates with the RIS closest to it in the main lobe; S104. According to the association rule obtained in step S103, for a communication link, determine whether the link is a line-of-sight link or a non-line-of-sight link according to whether the link is blocked; S105: Establish a channel model based on the line-of-sight link or non-line-of-sight link determined in step S104, assuming that the channel small-scale fading is Nakagami- Fading, the baseband equivalent channels between base station and user, base station and RIS, and RIS and user are 、 and , determine the received signal-to-noise ratio at the user based on the received signal power at the user .

3. The method for optimizing the number of antennas in a RIS-assisted millimeter wave communication system according to claim 2, wherein: In step S105, the received signal-to-noise ratio at the user for: in, Indicates the transmit power of a single antenna, represents the noise power at the user, is the amplitude gain of the composite channel, is the main lobe gain.

4. A system for optimizing the number of antennas configured in a RIS-assisted millimeter wave communication system, characterized in that: include: The receiving module establishes the RIS-assisted millimeter wave downlink communication system model and the corresponding channel model to determine the received signal-to-noise ratio at the receiving end; The probability module makes the received signal-to-noise ratio greater than the set threshold according to the received signal-to-noise ratio obtained by the receiving module, and obtains the transmission success probability, which is specifically: Determine the distance between the associated RIS and the base station and the distance from the associated RIS to the user The joint probability density function, the joint probability density function Specifically: in, For a given Under the conditions, The conditional probability density function of for The probability density function of is the density, is the main lobe width, is the fixed value of the distance between the base station and the user; The shape parameters of the channel power gain distribution are obtained by parameter matching and inverse scale parameter ; The probability that the received signal-to-noise ratio is greater than the set threshold is used as the probability of successful transmission , determine the transmission success probability based on the obtained results , specifically: in, For the situation The composite channel amplitude gain, For the situation The shape parameters, For the situation The inverse scale parameter of For the situation Probability of line-of-sight situation, For the situation The probability of successful transmission is is the fixed value of the distance between the base station and the user, is the receiving signal-to-noise ratio threshold, is the transmission power of a single antenna, is the main lobe gain, is an item in the probability density function of the Gamma distribution, is the noise power at the user; The optimization module expresses the transmission success probability obtained by the probability module as a function of the number of antennas. Based on the influence of the number of antennas on the transmission success probability, the number of antennas that maximizes the transmission success probability is solved. The numerical solution is used to optimize the configuration of the number of antennas. The transmission success probability is related to the number of antennas. The derivative of is expressed as follows: in, is the transmission success probability, For the situation Probability of line-of-sight situation, for and The joint probability density function of is an item in the probability density function of the Gamma distribution, is the Gamma function, is the angle away from the main lobe alignment direction, is the transmission power of a single antenna, is the number of antennas, is the receiving signal-to-noise ratio threshold, is the noise power at the user, For the situation The shape parameters, For the situation The inverse scale parameter of .

5. The system for optimizing the number of antennas configured for a RIS-assisted millimeter wave communication system according to claim 4, wherein: The receiving module is specifically: Establish a RIS-assisted millimeter wave downlink communication system model, where the base station is equipped with a uniform linear array and the number of antennas is , the user is equipped with a single receiving antenna and uses RIS for auxiliary communication. Each RIS has A reflective element; According to the obtained millimeter wave downlink communication system model, the main lobe direction of the uniform linear array is set to align with the user, and the flat-top antenna model is adopted to simplify the antenna gain. ; According to the obtained flat-top antenna model, the association rule is set as the base station communicates with the RIS closest to it in the main lobe; According to the obtained association rules, for a communication link, whether the link is blocked or not is determined to be a line-of-sight link or a non-line-of-sight link; According to the determined line-of-sight link or non-line-of-sight link, a channel model is established, and the small-scale fading of the channel is assumed to be Nakagami- Fading, the baseband equivalent channels between base station and user, base station and RIS, and RIS and user are 、 and , determine the received signal-to-noise ratio at the user based on the received signal power at the user .

6. The system for optimizing the number of antennas configured in a RIS-assisted millimeter wave communication system according to claim 5, wherein: Receiver signal-to-noise ratio at the user for: in, Indicates the transmit power of a single antenna, represents the noise power at the user, is the amplitude gain of the composite channel, is the main lobe gain.

7. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the method of claim 1 , 2 , or 3 .

8. A computing device, characterized in that include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for executing the method according to claim 1, 2, or 3.

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