A channel estimation method for a wireless communication system assisted by an intelligent reflector

Abstract

This invention belongs to the field of wireless communication and specifically designs a channel estimation method. For a location-assisted intelligent reflector (RIS) communication system, this invention analyzes the channel estimation error based on channel statistical characteristics. This invention constructs the statistical characteristics of the channel based on the user's location with errors, and then uses the minimum mean square error method for channel estimation. Results show that the channel estimation error decreases significantly with increasing signal-to-noise ratio; the larger the size of the intelligent reflector, the smaller the channel estimation error and the better the estimation effect; the larger the actual distance, the smaller the mean square error, i.e., the smaller the relative position error, and the better the channel estimation effect. The advantage of this invention is that it constructs the prior statistical distribution of the channel based on user location information, improving the accuracy of channel estimation.

Description

Technical Field

[0001] This invention relates to a channel estimation method for an intelligent reflector-assisted wireless communication system based on inaccurate user GPS location information. Background Technology

[0002] Given the passive structure and numerous reflective elements of reconfigurable intelligent surfaces (RIS), obtaining channel state information (CSI) is crucial. Due to the passive nature of RIS, unlike traditional repeaters, they cannot actively transmit; they can only reflect electromagnetic signals that strike them. Therefore, traditional CSI estimation techniques are unsuitable for RIS scenarios. A typical approach in RIS scenarios is to estimate cascaded channels rather than individual channels. Because of the large number of reflective elements, channel training overhead becomes very high, and the complexity of channel estimation algorithms is correspondingly high. Summary of the Invention

[0003] The purpose of this invention is to solve the problem of high complexity in cascaded channel estimation of intelligent reflector-assisted wireless communication systems.

[0004] To address the difficulties in channel estimation for smart reflector-assisted wireless communication, introducing location information-assisted channel estimation can effectively solve these problems. Utilizing user location information provided by GPS to obtain the LOS path angle and channel gain information can provide prior information for channel estimation.

[0005] Although the locations of the base station, smart reflector, and user are known, allowing for the construction of a channel, the user's location is subject to error due to user mobility. Therefore, the user-smart reflector channel requires accurate estimation. This invention aims to derive the statistical characteristics of the user channel based on the distribution of location errors, thereby estimating the channel.

[0006] Technical solution of the present invention:

[0007] A channel estimation method for an intelligent reflector-assisted wireless communication system, characterized by comprising the following steps:

[0008] Step 1: Establish a model of an intelligent reflective surface-assisted wireless communication system.

[0009] This model utilizes user location information provided by GPS to obtain the angle and channel gain information of the line-of-sight (LOS) path. Furthermore, since the location of the RIS is fixed, this invention assumes that the base station (BS) has complete knowledge of this information.

[0010] Without loss of generality, this invention assumes that the base station is located at the origin (0, 0, 0) of the three-dimensional coordinate system, and the RIS is located at (x... S y S , z S Then the effective emission angle (AOD) from BS to RIS is:

[0011]

[0012] in Let be the distance between BS and RIS. Similarly, the effective angle of arrival (AOA) of RIS can be calculated as follows:

[0013]

[0014] Due to user mobility or other unfavorable conditions, the user location information provided by GPS is not perfect. Specifically, the actual accurate location k of user k, i.e. (x U，k y U，k , z U，k ) evenly distributed over a radius of Y and a center point of Inside the sphere, among which It is the approximate location of user k provided by GPS, with some errors.

[0015] The effective emission angle from RIS to user k is

[0016]

[0017] in This represents the distance from RIS to user k.

[0018] The channel from the smart reflector to the base station is represented as follows:

[0019]

[0020] in, ρ represents the line-of-sight link portion of the channel. B2I The path propagation gain parameter is a random variable that follows a Gaussian distribution with zero mean and variance related to distance. Since the positions of the base station and the smart reflector are fixed, the transmit and receive angles of the line-of-sight link are also known. This is known. Similarly, the channel from the smart reflector to the k-th user is represented as...

[0021]

[0022] in, This vector represents the line-of-sight link portion of the channel; it is location-dependent and contains the path propagation gain parameter ρ.I2U，k Set it as a random variable that follows a Gaussian distribution with zero mean and variance related to distance.

[0023] The key to location-assisted channel estimation lies in the availability of user location information provided by GPS to the base station. The estimated value and path propagation gain parameter ρ I2U，k The statistical characteristics, i.e., ρ I2U，k The variance of the data allows the present invention to utilize these estimates and statistical characteristics for channel estimation.

[0024] Step 2: Calculate location-based channel features based on the probability distribution of location information.

[0025] In order to perform channel estimation, this invention utilizes a location-based probability distribution to construct the channel probability distribution and calculates the channel features based on location information, thereby providing prior information for subsequent channel estimation.

[0026] To facilitate channel estimation, this invention requires channel state information analysis for a single channel. The effective transmission angle from RIS to user k can be decomposed into...

[0027]

[0028] in

[0029]

[0030] in These represent the position errors along the x, y, and z axes, respectively. The estimation error ε y-S2U，k It satisfies the following distribution:

[0031]

[0032] The domain of the independent variable is in

[0033]

[0034] ε y-S2U，k The mean and variance are given by the following formulas.

[0035]

[0036]

[0037] Step 3: Based on the statistical characteristics calculated in Step 2, channel estimation is performed using the minimum mean square error method.

[0038] The key to location-assisted channel estimation lies in the availability of user location information provided by GPS to the base station. The estimated value and path propagation gain parameter ρ I2U，k The statistical characteristics, i.e., ρ I2U，k The variance of the values ​​is used to estimate the channel, allowing the present invention to utilize these estimates and statistical characteristics for channel estimation. The proposed minimum mean square error estimation method improves accuracy while reducing channel estimation overhead and system estimation complexity. The statistically based estimation also ensures the robustness and stability of the channel estimation.

[0039] This invention describes the single-user scenario; the multi-user scenario is similar. The signal received by the base station can be represented as follows:

[0040]

[0041] The objective of this invention is to estimate the cascaded channel H based on the received signal. k Specifically, it is expressed as

[0042]

[0043] because It is known and can be obtained based on the user location information provided by GPS. The estimated value Therefore, the estimated cascaded channel H k The task can be transformed into estimating the product ρ of two Gaussian variables. B2I ρ I2U，k This is the objective. The present invention writes the estimated value of the cascaded channel as...

[0044]

[0045] The present invention will now describe in detail how to obtain the linear minimum mean square error estimation method. Furthermore, based on the derived results, this invention will further analyze the impact of location information on channel estimation performance.

[0046] The k-th user sends pilot signal a k The signal received by the base station is represented as follows:

[0047]

[0048] This invention denotes the linear minimum mean square error estimation vector as v, and then estimates the product ρ of two Gaussian variables. k The mean square error ε can be expressed as

[0049]

[0050] The linear minimum mean square error estimation vector v can be derived by taking the partial derivative of ε with respect to v and setting it to zero. The specific process is as follows:

[0051]

[0052] Furthermore, the present invention can obtain the estimation result obtained using the linear minimum mean square error estimation method. as follows:

[0053]

[0054] Next, this invention needs to derive as well as They are respectively

[0055]

[0056]

[0057] The following is a key derivation. Because ρ k =ρ B2I ρ I2U，k And these two variables ρ B2I and ρ I2U，k They are independent and have a mean of zero, so Furthermore, the present invention has

[0058]

[0059] And because of the variable ρ B2I and ρ I2U，k They are respectively

[0060]

[0061]

[0062] Observation reveals that the magnitude of the estimated mean square error ε is related to the location information provided by GPS, specifically the estimated distance. related.

[0063] Beneficial effects:

[0064] (1) This invention transforms the estimation of the complete channel into a channel estimation based on the statistical characteristics of the path propagation gain parameter, making full use of the user location information provided by the GPS system and reducing system complexity.

[0065] (2) This invention fully considers the existence of GPS positioning error and gives a quantitative relationship between positioning accuracy and channel estimation accuracy, thus making the scheme universally applicable. Attached Figure Description

[0066] Figure 1 This is a graph showing the relationship between channel estimation error and signal-to-noise ratio.

[0067] Figure 2 This is a graph showing the relationship between channel estimation error and distance for smart reflective surfaces of different sizes.

[0068] Figure 3 This is a graph showing the relationship between channel estimation error and the number of base station antennas. Detailed Implementation

[0069] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification, but the features of this invention are not limited to these embodiments. To provide a deep understanding of the present invention, many specific details will be included in the following description. The present invention may also be implemented without using these details. Furthermore, to avoid confusion or obscuring the focus of the present invention, some specific details will be omitted in the description. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0070] In the description of this embodiment, it should be noted that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. Unless otherwise explicitly specified and limited, the term "intelligent reflective surface" should be interpreted broadly; for example, it can be an intelligent reflective surface of any shape and with any number of reflective elements. Those skilled in the art can understand the specific meaning of the above terms in this embodiment based on the specific circumstances.

[0071] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0072] A channel estimation method for an intelligent reflector-assisted wireless communication system, characterized by comprising the following steps:

[0073] Step 1,

[0074] A model of an intelligent reflector-assisted wireless communication system is established. This model utilizes user location information provided by GPS to obtain the line-of-sight (LOS) path angle and channel gain information. Furthermore, since the location of the RIS is fixed, this invention assumes that the base station (BS) has complete knowledge of this information.

[0075] Without loss of generality, this invention assumes that the base station is located at the origin (0, 0, 0) of the three-dimensional coordinate system, and the RIS is located at (x... S y S , z SThen the effective emission angle (AOD) from BS to RIS is:

[0076]

[0077] in Let be the distance between BS and RIS. Similarly, the effective angle of arrival (AOA) of RIS can be calculated as follows:

[0078]

[0079] Due to user mobility or other unfavorable conditions, the user location information provided by GPS is not perfect. Specifically, the actual accurate location k of user k, i.e. (x U，k y U，k , z U，k ) evenly distributed over a radius of Y and a center point of Inside the sphere, among which It is the approximate location of user k provided by GPS, with some errors.

[0080] The effective emission angle from RIS to user k is

[0081]

[0082] in This represents the distance from RIS to user k.

[0083] The channel from the smart reflector to the base station is represented as follows:

[0084]

[0085] in, ρ represents the line-of-sight link portion of the channel. B2I The path propagation gain parameter is a random variable that follows a Gaussian distribution with zero mean and variance related to distance. Since the positions of the base station and the smart reflector are fixed, the transmit and receive angles of the line-of-sight link are also known. This is known. Similarly, the channel from the smart reflector to the k-th user is represented as...

[0086]

[0087] in, This vector represents the line-of-sight link portion of the channel; it is location-dependent and contains the path propagation gain parameter ρ. I2U，k Set it as a random variable that follows a Gaussian distribution with zero mean and variance related to distance.

[0088] The key to location-assisted channel estimation lies in the availability of user location information provided by GPS to the base station. The estimated value and path propagation gain parameter ρ I2U，k The statistical characteristics, i.e., ρ I2U，k The variance of the data allows the present invention to utilize these estimates and statistical characteristics for channel estimation.

[0089] Step 2,

[0090] In order to perform channel estimation, this invention utilizes a location-based probability distribution to construct the channel probability distribution and calculates the channel features based on location information, thereby providing prior information for subsequent channel estimation.

[0091] To facilitate channel estimation, this invention requires channel state information analysis for a single channel. The effective transmission angle from RIS to user k can be decomposed into...

[0092]

[0093] in

[0094]

[0095] in These represent the position errors along the x, y, and z axes, respectively. The estimation error ε y-S2U，k It satisfies the following distribution:

[0096]

[0097] The domain of the independent variable is in

[0098]

[0099] ε y-S2U，k The mean and variance are given by the following formulas.

[0100]

[0101]

[0102] Step 3,

[0103] This invention uses the least mean square error method for channel estimation based on the statistical characteristics calculated in step 2. The key to location-assisted channel estimation lies in the availability of user location information provided to the base station by GPS. The estimated value and path propagation gain parameter ρ I2U，k The statistical characteristics, i.e., ρ I2U，kThe variance of the values ​​is used to estimate the channel, allowing the present invention to utilize these estimates and statistical characteristics for channel estimation. The proposed minimum mean square error estimation method improves accuracy while reducing channel estimation overhead and system estimation complexity. The statistically based estimation also ensures the robustness and stability of the channel estimation.

[0104] This invention describes the single-user scenario; the multi-user scenario is similar. The signal received by the base station can be represented as follows:

[0105]

[0106] The objective of this invention is to estimate the cascaded channel H based on the received signal. k Specifically, it is expressed as

[0107]

[0108] because It is known and can be obtained based on the user location information provided by GPS. The estimated value Therefore, the estimated cascaded channel H k The task can be transformed into estimating the product ρ of two Gaussian variables. B2I ρ I2U，k This is the objective. The present invention writes the estimated value of the cascaded channel as...

[0109]

[0110] The present invention will now describe in detail how to obtain the linear minimum mean square error estimation method. Furthermore, based on the derived results, this invention will further analyze the impact of location information on channel estimation performance.

[0111] The k-th user sends pilot signal a k The signal received by the base station is represented as follows:

[0112]

[0113] This invention denotes the linear minimum mean square error estimation vector as v, and then estimates the product ρ of two Gaussian variables. k The mean square error ε can be expressed as

[0114]

[0115] The linear minimum mean square error estimation vector v can be derived by taking the partial derivative of ε with respect to v and setting it to zero. The specific process is as follows:

[0116]

[0117] Furthermore, the present invention can obtain the estimation result obtained using the linear minimum mean square error estimation method. as follows:

[0118]

[0119] Next, this invention needs to derive as well as They are respectively

[0120]

[0121]

[0122] The following is a key derivation. Because ρ k =ρ B2I ρ I2U，k And these two variables ρ B2I and ρ I2U，k They are independent and have a mean of zero, so Furthermore, the present invention has

[0123]

[0124] And because of the variable ρ B2I and ρ I2U，k They are respectively

[0125]

[0126]

[0127] Observation reveals that the magnitude of the estimated mean square error ε is related to the location information provided by GPS, specifically the estimated distance. related.

[0128] Specifically, step 1 is to establish a wireless communication system model.

[0129] This invention assumes that the system has only one user and is equipped with a single antenna, while the base station is equipped with multiple antennas. The channel from the smart reflector to the base station is then represented as follows:

[0130]

[0131] in, ρ represents the line-of-sight link portion of the channel. B2I Let represent the path propagation gain parameter, which is a random variable following a Gaussian distribution with zero mean and variance related to distance. Similarly, the channel from the smart reflector to the k-th user is represented as...

[0132]

[0133] in, This vector represents the line-of-sight link portion of the channel; it is location-dependent and contains the path propagation gain parameter ρ. I2U，k Set it as a random variable that follows a Gaussian distribution with zero mean and variance related to distance.

[0134] Step 2: Calculate channel features based on location information

[0135] To facilitate channel estimation, this invention requires calculating channel characteristics based on location information.

[0136] Due to estimation error ε y-S2U，k It satisfies the following distribution:

[0137]

[0138] The domain of the independent variable is in

[0139]

[0140] Therefore, ε y-S2U，k The mean and variance can be expressed as

[0141]

[0142]

[0143] Step 3, channel estimation using the minimum mean square error method

[0144] User location information provided to base stations based on GPS can be obtained. The estimated value and path propagation gain parameter ρ I2U，k The statistical characteristics, i.e., ρ I2U，k The variance of the data allows the present invention to utilize these estimates and statistical characteristics for channel estimation.

[0145] This invention presents the case of a single user. The signal received by the base station can be represented as follows:

[0146]

[0147] The objective of this invention is to estimate the cascaded channel H based on the received signal. k Specifically, it is expressed as

[0148]

[0149] because It is known and can be obtained based on the user location information provided by GPS. The estimated value Therefore, the estimated cascaded channel H k The task can be transformed into estimating the product ρ of two Gaussian variables. B2I ρ I2U，k This is the objective. The present invention writes the estimated value of the cascaded channel as...

[0150]

[0151] Next, this invention will utilize the linear minimum mean square error estimation method to obtain... The k-th user sends pilot signal a k The signal received by the base station is represented as follows:

[0152]

[0153] This invention denotes the linear minimum mean square error estimation vector as v, and then estimates the product ρ of two Gaussian variables. k The mean square error ε can be expressed as

[0154]

[0155] The linear minimum mean square error estimation vector v can be derived by taking the partial derivative of ε with respect to v and setting it to zero. The specific process is as follows:

[0156]

[0157] Furthermore, the present invention can obtain the estimation result obtained using the linear minimum mean square error estimation method. as follows:

[0158]

[0159] Next, this invention needs to derive as well as They are respectively

[0160]

[0161]

[0162] The following is a key derivation. Because ρ k =v B2I ρ I2U，k And these two variables ρ B2I and ρ I2U，k They are independent and have a mean of zero, therefore Furthermore, the present invention has

[0163]

[0164] And because of the variable ρ B2I and ρ I2U，k They are respectively

[0165]

[0166]

[0167] The magnitude of the estimated mean square error ε is related to the location information provided by GPS, i.e., to the estimated distance. related.

[0168] The performance of channel estimation in this invention was verified through simulation.

[0169] Figure 1 This diagram illustrates the relationship between channel estimation error and signal-to-noise ratio (SNR). Five lines represent the changes in the mean square error of channel estimation as the SNR increases when the number of smart reflector elements (N) is 4, 8, 16, 32, and 64, respectively. Here, the location error distribution radius is set to 1 meter, and the relationship between the user and the smart reflector is set to a fixed value of 10 meters. The diagram shows that as the SNR increases, the estimation error gradually decreases, and the error is less than 10 dBm after the SNR reaches 15 dBm. -3 Meanwhile, as the size of the intelligent reflective surface increases, the estimation error at the same signal-to-noise ratio also decreases significantly. At low signal-to-noise ratios, the error reduction resulting from increasing the number of components is more significant, while at high signal-to-noise ratios, this error reduction relatively saturates.

[0170] Figure 2 The relationship between channel estimation error and distance is depicted for smart reflectors of different sizes. Five lines represent the changes in the mean square error of channel estimation as the distance between the smart reflector and the user increases when the number of smart reflector elements N is 4, 8, 16, 32, and 64, respectively. Here, the position error distribution radius is set to 1 meter. The following conclusions can be drawn from the figures: First, the estimation error gradually decreases with increasing distance. This is because, when the signal-to-noise ratio remains constant, the estimation error mainly comes from the position error. Since this invention sets the position error radius constant, the position error gradually decreases relative to the distance as the distance between the smart reflector and the user increases, thus reducing the channel estimation error. Second, as the size of the smart reflector increases, the estimation error at the same distance also decreases significantly. This indicates that the smart reflector can effectively improve the gain of channel estimation, and using a larger-sized smart reflector can significantly compensate for the reduced channel estimation performance due to the smaller distance. Simultaneously, it can be noted that as the number of reflector elements increases to a certain extent, the influence of distance on the estimation error decreases rapidly; that is, a large-sized smart reflector can almost negate the influence of the user's position on channel estimation.

[0171] Figure 3The relationship between channel estimation error and the number of base station antennas was depicted. The three sets of graphs—circular, star-shaped, and pentagonal—represent 4, 16, and 64 intelligent reflector elements, respectively. Solid lines represent a transmit SNR of 10 dBm, and dashed lines represent a transmit SNR of 20 dBm. The number of base station antennas was set from 10 to 80. The following conclusions can be drawn from the graphs: First, as the number of base station transmitter antennas increases, the mean square error of channel estimation gradually decreases. When the number of antennas reaches a certain level, the gain in reducing error gradually decreases until saturation. Second, under the same intelligent reflector size, increasing the SNR from 10 dBm to 20 dBm significantly reduces the mean square error. Third, the more intelligent reflector elements there are, the greater the gain in reducing error for the same increase in SNR, indicating that intelligent reflectors have a significant effect on improving system performance.

[0172] While the present invention has been illustrated and described with reference to certain preferred embodiments, those skilled in the art should understand that the above description is a further detailed explanation of the invention in conjunction with specific embodiments, and should not be construed as limiting the specific implementation of the invention to these descriptions. Various changes in form and detail can be made by those skilled in the art, including several simple deductions or substitutions, without departing from the spirit and scope of the invention.

Claims

1. A channel estimation method for an intelligent reflector-assisted wireless communication system, characterized in that, Includes the following steps: Step 1: Establish a model of an intelligent reflective surface-assisted wireless communication system; Step 2: Calculate the channel features based on location information based on the probability distribution of location; Step 3: Based on the statistical characteristics calculated in Step 2, channel estimation is performed using the minimum mean square error method; Step 1 is as follows: The model utilizes user location information provided by GPS to obtain the angle and channel gain information of the line-of-sight path; Assume the base station is located at the origin of the three-dimensional coordinate system. RIS is located The effective emission angle from BS to RIS is then... in The distance between BS and RIS; Similarly, the effective angle of arrival of RIS is user actual accurate location ,Right now Evenly distributed in a radius of The center point is Inside the sphere, among which GPS users The approximate position with errors; From RIS to Users The effective launch angle is in Indicates RIS to users distance; The channel from the smart reflector to the base station is represented as follows: in, This represents the line-of-sight link portion of the channel. The path propagation gain parameter is a random variable that follows a Gaussian distribution with zero mean and variance related to distance. The distance from the intelligent reflector to the... The channel representation for each user is as follows: in, This vector represents the line-of-sight link portion of the channel; it is location-dependent and represents the path propagation gain parameter. Set as a random variable that follows a Gaussian distribution with zero mean and variance related to distance; Location-assisted channel estimation (LAS) utilizes user location information provided to the base station by GPS to obtain... The estimated value and path propagation gain parameters The statistical characteristics, namely The variance of the channel is used to estimate the channel using these estimates and statistical characteristics. Step 2 is as follows: By utilizing location-based probability distributions, a channel probability distribution is constructed, and location-based channel characteristics are calculated, providing prior information for subsequent channel estimation. Channel state information analysis for individual channels; from RIS to user The effective launch angle is decomposed into in in , , , , They are along Position error in the axial direction; estimation error It satisfies the following distribution: The domain of the independent variable is ,in The mean and variance are given by the following formulas. ； Step 3 is as follows: Location-assisted channel estimation utilizes user location information provided to the base station by GPS to obtain... The estimated value and path propagation gain parameters The statistical characteristics, namely The variance of the channel is used to estimate the channel using these estimates and statistical characteristics. The signal received by the base station is represented as The goal is to estimate the cascaded channel based on the received signal. Specifically, it is expressed as It is known, and based on the user location information provided by GPS, that... The estimated value Estimate cascaded channels The task is transformed into estimating the product of two Gaussian variables. This objective; the estimated value of the cascaded channel is written as Obtained using the linear minimum mean square error estimation method : No. A user sends a pilot signal. The signal received by the base station is represented as follows: The linear minimum mean square error estimation vector used is denoted as Then estimate the product of the two Gaussian variables. Mean square error Represented as Linear minimum mean square error estimation vector By seeking right The partial derivatives are obtained by setting them to zero; the specific process is as follows: Furthermore, the estimation results obtained using the linear minimum mean square error estimation method are obtained. as follows: Next, we deduce as well as , respectively Derived : Two variables and They are mutually independent and have a mean of zero. Furthermore, there are Another variable and They are respectively Estimated mean square error The estimated size and distance related.

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