Wireless communication transmission performance analysis method based on intelligent reflecting surface

Abstract

The invention discloses a wireless communication transmission performance analysis method based on an intelligent reflecting surface. The processing method comprises the following steps: creating a wireless communication model in simulation software or a real environment; extracting multipath components such as path gain through a ray tracing technology; performing channel modeling and completing channel reconstruction according to the multipath component; respectively carrying out accuracy analysis on the time delay and the azimuth angle from time and space angles; and carrying out transmission performance analysis on the wireless communication system in combination with the intelligent reflecting surface. According to the method, the resolution (namely accuracy) of the multipath component (MPC) can be effectively improved, the gain of the intelligent reflecting surface (RIS) on the signal transmission efficiency is proved, the transmission performance of the wireless communication signal can be comprehensively evaluated, and a scientific basis is provided for the optimization design and transmission performance analysis of a wireless communication system.

Description

Technical Field

[0001] This invention relates to the fields of wireless communication and channel characteristic analysis, and in particular to a method for analyzing the transmission performance of wireless communication based on a smart reflector. Background Technology

[0002] To meet the ever-growing demands of networks and communications, 6G has become a focus of academic research due to its advantages such as high speed, wide coverage, and low latency. Numerous research findings have emerged to further advance 6G development. Among these, Smart Reflectors (RIS) have become a core technology of 6G due to their ease of deployment and high gain. To expand the application of RIS in real-world scenarios, we need to model and analyze its channels in different environments, enabling us to simulate various indoor and outdoor conditions and extract multipath components (MPC) for channel characteristic analysis.

[0003] Recently, many articles have used simulation platforms and measurement instruments to model and analyze problems such as RIS deployment, signal coverage, and path loss. However, due to the unavoidable accuracy limitations of real-world measurement instruments, certain errors arise compared to actual conditions. Therefore, designing a general analysis method as a reference for new measurement data is crucial. This method serves as a benchmark to verify new measurement results. Through reconstruction, potential errors can be identified and corrected, similar to calibrating a new radar system using a radar target signal simulator. Furthermore, there is an interrelationship between reconstruction and measurement, allowing for mutual evaluation. This interaction improves the accuracy and reliability of channel measurements, ultimately enhancing the overall quality of the research. Summary of the Invention

[0004] Purpose of the invention: The main purpose of this invention is to provide a method for analyzing the transmission performance of wireless communication based on intelligent reflective surfaces. By performing channel modeling and reconstruction on the measurement data, the accuracy of the data in space and time is evaluated, providing a scientific basis for the optimized design and transmission performance analysis of wireless communication systems.

[0005] Technical Solution: To achieve the above-mentioned objectives, the present invention adopts the following technical solution: a method for analyzing the transmission performance of wireless communication based on a smart reflector, comprising the following steps:

[0006] (1) Create a wireless communication model: In a real environment or simulation software, set up a communication system that includes a transmitter (TX) and multiple receivers (RX), and add a smart reflector (RIS) to enhance signal transmission performance.

[0007] (2) Extracting multipath components: Based on the wireless communication model described in step (1), the multipath components (MPC) of signal transmission are obtained by ray tracing, which mainly include path gain (PG), delay, angle of incidence (AOA) and angle of emission (AOD).

[0008] (3) Channel Modeling and Reconstruction: Based on the signal transmission multipath components of the communication model described in step (2), the transmission channel can be modeled as follows:

[0009] h(τ,φ T φ R ) = h d (τ,φ T φ R )+h r (τ,φ T φ R )

[0010] in:

[0011] h d Represents a deterministic component;

[0012] h r Represents random components;

[0013] τ represents time delay;

[0014] φ T Represents the launch angle;

[0015] φ R Represents the angle of incidence.

[0016] The angle delay power spectrum of the reconstructed transmission channel can be expressed as:

[0017]

[0018] in:

[0019] P represents path gain:

[0020] Represents the expectation operator.

[0021] (4) Accuracy Analysis: Based on the channel reconstruction described in step (3), spatial and temporal accuracy analysis is performed on the results to verify the effectiveness and necessity of the reconstruction. Spatially, the accuracy is mainly analyzed for AOA and AOD; temporally, the accuracy is analyzed for Delay.

[0022] (5) Transmission performance analysis: As described in step (4), after verifying the accuracy of channel reconstruction, the transmission performance of wireless communication is analyzed, mainly focusing on path loss (PL), which can be expressed as:

[0023]

[0024] Here, we introduce a floating intercept (FI) path loss model to fit the data, which can be expressed as:

[0025] PL FI (d)=α+β10log 10 (d)

[0026] in:

[0027] d represents the distance between TX and RX;

[0028] α is the floating intercept in dB;

[0029] β is the linear slope.

[0030] As the distance d increases, PL gradually increases, and it is related to PL FI The fit is good and conforms to the free space path loss trend. Comparing the PL before and after adding RIS, it can be concluded that RIS can significantly enhance the signal transmission capability and coverage of the wireless communication system, and greatly improve the system's operating efficiency. Attached Figure Description

[0031] Figure 1 This is the overall flowchart of the present invention;

[0032] Figure 2 It is a wireless communication model;

[0033] Figure 3 This is a performance analysis graph showing how path loss changes with distance d before and after adding RIS. Detailed Implementation

[0034] The technical solution of the present invention will be further described below with reference to the embodiments.

[0035] like Figure 1 As shown, the wireless communication transmission performance analysis method based on a smart reflector of the present invention includes the following steps:

[0036] (1) Creating a wireless communication model: In a real-world environment or simulation software, set up a communication system containing a transmitter (TX) and multiple receivers (RX), and add a smart reflector (RIS) to enhance signal transmission performance. This embodiment uses Wireless Insite simulation software, and the simulation scenario is as follows. Figure 2 As shown, this is a simplified "L"-shaped corridor containing one TX and 30 RX in a non-line-of-sight state. RIS are placed on the wall at the corner. The software allows configuration of the transmitted signal characteristics, the types of TX and RX, and parameters such as the reflection coefficient of the RIS.

[0037] (2) Extracting multipath components: Based on the wireless communication model described in step (1), the multipath components (MPC) of signal transmission are obtained through the ray tracing technology built into the simulation software. These mainly include parameters such as path gain (PG), delay (Delay), incident angle (AOA), and emission angle (AOD).

[0038] (3) Channel Modeling and Reconstruction: Based on the signal transmission multipath components of the communication model described in step (2), the transmission channel can be modeled as follows:

[0039] h(τ,φ T φ R ) = h d (τ,φ T φ R )+h r (τ,φ T φ R )

[0040] in:

[0041] h d Represents a deterministic component;

[0042] h r Represents random components;

[0043] τ represents time delay;

[0044] φ T Represents the launch angle;

[0045] φ R Represents the angle of incidence.

[0046] The extracted MPC amplitude γ is used as a weight on the Kronecker product of time delay and azimuth angle to perform multipath channel impulse response (CIR) reconstruction and generate three-dimensional data.

[0047]

[0048] in

[0049]

[0050] in:

[0051] L is the number of MPCs;

[0052] l is the index of MPC;

[0053] a T This is the antenna pattern of TX;

[0054] a R This is the antenna pattern of the RX.

[0055] a u The autocorrelation function of the detected signal;

[0056] N, n, and Δ represent the total number of samples in the specified domain, the index of a single sample, and the sampling interval, respectively.

[0057] By substituting the signal autocorrelation function extracted from the measured CIR into the above formula, and simultaneously compensating for the antenna patterns of TX and RX, the parameters of MPC can be obtained, thus completing channel reconstruction.

[0058] The angle delay power spectrum of the reconstructed transmission channel can be expressed as:

[0059]

[0060] in:

[0061] P represents path gain;

[0062] Represents the expectation operator.

[0063] (4) Accuracy Analysis: Based on the channel reconstruction described in step (3), spatial and temporal accuracy analysis is performed on the results to verify the effectiveness and necessity of the reconstruction. Spatially, the accuracy is mainly verified for AOA and AOD when the half-power beamwidth (HPBW) is 9° (the standard for common transmitters on the market). Temporally, the accuracy is verified for Delay based on the 8GHz carrier bandwidth (which can be customized) to see if the accuracy can reach 0.125ns.

[0064] (5) Transmission performance analysis: As described in step (4), after verifying the accuracy of channel reconstruction, the transmission performance of wireless communication is analyzed, mainly focusing on path loss (PL), which can be expressed as:

[0065]

[0066] Here, we introduce a floating intercept (FI) path loss model to fit the data, which can be expressed as:

[0067] PL FI (d)=α+β10log 10 (d)

[0068] in:

[0069] d represents the distance between TX and RX;

[0070] α is the floating intercept in dB;

[0071] β is the linear slope.

[0072] The simulation results of this embodiment are as follows: Figure 3 As shown, PL gradually increases with increasing distance d, and is related to PL FI The fit is good and conforms to the free-space path loss trend, verifying that the proposed method can accurately describe the signal transmission path loss. Comparing the power loss (PL) before and after adding RIS, the overall difference is 18 dB, indicating that RIS can significantly enhance the signal transmission capability and coverage of the wireless communication system, greatly improving the system's efficiency.

Claims

1. A method for analyzing the transmission performance of wireless communication based on a smart reflector, the method comprising the following steps: (1) Create a wireless communication model; (2) Extracting multipath components; (3) Channel modeling and reconstruction; (4) Accuracy analysis; (5) Transmission performance analysis.

2. According to the wireless communication transmission performance analysis method based on intelligent reflector as described in claim 1, in step (1), the wireless communication model construction method is as follows: in the actual environment or simulation software, a communication system containing a transmitter (TX) and multiple receivers (RX) is set up, and an intelligent reflector (RIS) that can enhance the signal transmission performance is added to it.

3. According to the wireless communication model described in claim 2, the multipath components (MPC) of signal transmission are obtained through ray tracing, mainly including path gain (PG), delay, angle of incidence (AOA), and angle of emission (AOD).

4. According to claim 3, the signal transmission multipath components of the communication model can be modeled as follows: h(t,f T ,f R )=h d (t,f T ,f R )+h r (t,f T ,f R ) in: h d Represents a deterministic component; h r Represents random components; τ represents time delay; φ T Represents the launch angle; φ R Represents the angle of incidence. The angle delay power spectrum of the reconstructed transmission channel can be expressed as: in: P represents path gain; Represents the expectation operator.

5. According to claim 4, the channel reconstruction results are subjected to spatial and temporal accuracy analysis to verify the effectiveness and necessity of the reconstruction. Spatially, the analysis focuses on AOA and AOD; temporally, it focuses on Delay.

6. According to claim 5, after the accuracy of channel reconstruction is verified, the transmission performance of wireless communication is analyzed, mainly focusing on path loss (PL), which can be expressed as: Here, we introduce a floating intercept (FI) path loss model to fit the data, which can be expressed as: PL FI (d) Gα+β10log 10 (d) in: d represents the distance between TX and RX; α is the floating intercept in dB; β is the linear slope. As the distance d increases, PL gradually increases, and it is related to PL FI The fit is good and conforms to the free space path loss trend. Comparing the PL before and after adding RIS, it can be concluded that RIS can significantly enhance the signal transmission capability and coverage of the wireless communication system, and greatly improve the system's operating efficiency.

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