6G wireless channel modeling method based on random antenna array

Abstract

The invention discloses a 6G wireless channel modeling method based on a random antenna array, relates to the technical field of wireless communication, and realizes accurate modeling and optimization of a channel space correlation function, a time correlation function and a channel capacity by modeling the position of a transmitting antenna into a one-dimensional Wiener process and combining a 6G universal channel model. Comprising the steps of random antenna array modeling based on the Wiener process, integration of a 6G universal channel model, derivation of a space-time correlation function, and calculation and visualization of channel capacity. The method can effectively enhance spatial diversity, reduce channel correlation and significantly improve channel capacity, and especially has significant advantages in a dynamic environment. Through simulation verification, the stronger the antenna motion is, the faster the space and time correlation attenuation is, and the more obvious the channel capacity improvement is. The invention provides a new solution for channel modeling and optimization of the 6G communication system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, in particular to a 6G wireless channel modeling method based on random antenna array. BACKGROUND

[0002] With the development of 6G (Sixth Generation) communication systems, the limitations of traditional fixed-position antenna systems in dynamic environments are increasingly evident. Fixed antenna systems cannot adapt to the rapid changes in channel environments, leading to insufficient spatial diversity, decreased beamforming accuracy, and increased interference. To address these challenges, position reconfigurable antennas have gradually become a research hotspot. Such antennas can dynamically adjust their positions, directions, or configurations, thereby enhancing signal alignment, reducing interference, and lowering energy consumption.

[0003] However, existing channel models are mostly based on simplified assumptions and fail to fully consider the impact of antenna motion on channel statistical properties, especially the changes in spatial-time-frequency correlation functions and channel capacity. Therefore, there is an urgent need for a channel modeling method that combines random antenna arrays with 6G universal channel models to accurately reflect channel characteristics in dynamic environments. SUMMARY

[0004] The technical problem to be solved by the present application is to overcome the shortcomings of the prior art and provide a 6G wireless channel modeling method based on random antenna array, which models the motion of random antenna array as a one-dimensional Wiener process and combines with 6G universal channel model to realize accurate modeling and optimization of channel spatial-time correlation function and channel capacity.

[0005] The technical problem to be solved by the present application is to overcome the shortcomings of the prior art and provide a 6G wireless channel modeling method based on random antenna array, which models the motion of random antenna array as a one-dimensional Wiener process and combines with 6G universal channel model to realize accurate modeling and optimization of channel spatial-time correlation function and channel capacity.

[0006] According to the 6G wireless channel modeling method based on random antenna array proposed by the present application, the following steps are included:

[0007] Step S1, model the position of the transmitting antenna as a one-dimensional Wiener process, and keep the receiving antenna stationary;

[0008] Step S2, model the channel impulse response using the 6G universal channel model, and decompose the channel impulse response into line-of-sight and non-line-of-sight components;

[0009] Step S3, calculate the dynamic distance change between antennas through the random displacement of the one-dimensional Wiener process, and map it to the 6G universal channel model;

[0010] Step S4, based on the dynamic distance change between antennas and the line-of-sight and non-line-of-sight components in the 6G universal channel model, derive the spatial correlation function and the time correlation function, and analyze the influence of antenna motion on the spatial correlation function and the time correlation function.

[0011] Step S5, based on the line of sight and non-line of sight components, and the MIMO channel capacity formula, the channel capacity is calculated, and the influence of antenna motion on the channel capacity is analyzed;

[0012] Step S6, based on the influence of antenna motion on the spatial correlation function and the time correlation function, the influence of antenna motion on the channel capacity, the spatial correlation function, the time correlation function and the channel capacity are numerically simulated, and the change of channel characteristics is shown.

[0013] As a further optimization scheme of the 6G wireless channel modeling method based on a random antenna array according to the application, in step S1, the position of the transmitting antenna is modeled as a one-dimensional Wiener process, and the displacement of the transmitting antenna with respect to time satisfies the following condition:

[0014] The expected value of the displacement is zero, and the variance increases linearly with time, that is Wherein is the mathematical expectation, t represents the time length of the antenna motion, W p (t) represents the displacement of the antenna, Var[·] represents the variance, and σ p represents the intensity of the one-dimensional Wiener process.

[0015] As a further optimization scheme of the 6G wireless channel modeling method based on a random antenna array according to the application, in step S2, the channel parameter matrix of the 6G universal channel model is represented as:

[0016] H=[PL·SH·BL·WE·AL] 1 / 2 ·H s

[0017] Wherein, H is the channel parameter matrix, PL is the path loss, SH is the shadow fading, BL is the blocking effect, AL is the atmospheric absorption loss, WE is the weather influence loss, and H s is the small scale fading.

[0018] As a further optimization scheme of the 6G wireless channel modeling method based on a random antenna array according to the application, the small scale fading H s is represented as:

[0019]

[0020] Wherein, M T is the number of antenna elements in the transmitting antenna array, M R is the number of antenna elements in the receiving antenna array, f c is the carrier frequency, R is the receiving end, T is the transmitting end, τ is the time delay, is the pth transmitting antenna element and the qth receiving antenna element The channel impulse response between is the line-of-sight LoS component and the non-line-of-sight NLoS component superposition;

[0021]

[0022]

[0023] Among them, K R (t) is the Rice factor at time t; is the delay of the LoS path at time t, For the tth moment and The vector distance between are the pth transmitting antenna element and the qth receiving antenna element respectively, c is the speed of light; N qp (t) is the total number of paths at time t; M n (t) is the total number of sub-paths at time t; Under NLoS conditions arrive The power of the mth subpath in the nth path; For the tth moment and is the time delay of the mth subpath in the nth path between them, e is the natural base, j is the imaginary unit, and δ(·) is the Dirac function.

[0024] As a further optimization scheme of the 6G wireless channel modeling method based on random antenna arrays described in the present invention, in step S3, the dynamic distance change between antennas is calculated by the following formula:

[0025]

[0026] Where D is the fixed distance between the transmitting antenna and the receiving antenna, is the radial distance of the pth transmitting antenna, is the radial distance of the qth receiving antenna, and are the elevation and azimuth angles of the transmitting antenna, respectively, d pq (t) represents the distance between the pth transmitting antenna and the qth receiving antenna.

[0027] As a further optimization scheme of the 6G wireless channel modeling method based on random antenna array described in the present invention, in step S4, the spatial correlation function for:

[0028]

[0029] in, and They represent the correlation functions of the line-of-sight and non-line-of-sight components, t represents the duration of antenna movement, f represents the frequency of antenna operation, K represents the Rice factor, Δr T represents the transpose of the difference between the antenna position vectors;

[0030]

[0031] Among them, the intermediate variable δ Tx is the distance between two adjacent transmitting antennas in the initial state, the intermediate variable is the carrier frequency f c The corresponding wavelength, σ p is the intensity of the Wiener process of the pth transmitting antenna, For the The strength of the Wiener process of each transmitting antenna;

[0032]

[0033] Among them, P surv (Δr T ) is the spatial survival probability of the cluster, f represents the frequency of antenna operation, is the NLoS condition at time t arrive The power of the mth subpath in the nth path, is the NLoS condition at time t arrive The power of the mth subpath in the nth path, is the distance between the pth transmitting antenna and the qth receiving antenna at time t, For the transmitting antenna elements, is the first The distance between the qth transmitting antenna and the qth receiving antenna.

[0034] As a further optimization scheme of the 6G wireless channel modeling method based on random antenna array described in the present invention, in step S4, the time correlation function R qp (t, f; Δt) is:

[0035]

[0036] in, and denote the time correlation functions of the line-of-sight and non-line-of-sight components, respectively, and Δt is the time difference;

[0037]

[0038] wherein, is the variance of the Wiener process;

[0039]

[0040] wherein, P surv (Δt) is the time survival probability of the cluster, is the power of the mth sub-path in the nth path from t to t+Δt, to the power of the mth sub-path in the nth path from t to t+Δt, is the distance between the pth transmitting antenna and the qth receiving antenna at t+Δt.

[0041] As a further optimization scheme of the 6G wireless channel modeling method based on a random antenna array according to the present application, in step S5, the calculation formula of the channel capacity C is:

[0042]

[0043] wherein, H is the channel matrix, SNR is the signal-to-noise ratio, M Tx is the number of transmitting antennas, det(·) is the determinant operation, (·) H denotes the conjugate transpose operation.

[0044] As a further optimization scheme of the 6G wireless channel modeling method based on a random antenna array according to the present application, in step S6, the spatial correlation function, the time correlation function and the channel capacity are numerically simulated by using a simulation platform; the simulation platform constructed can show the changes of the channel characteristics, including the dynamic updating of the spatial correlation function, the time correlation function and the channel capacity.

[0045] Compared with the prior art, the above technical scheme of the present application has the following technical effects:

[0046] (1) Enhancing spatial diversity: by modeling the position of the transmitting antenna as a one-dimensional Wiener process, the present application can effectively enhance the spatial diversity of the antenna array, reduce the channel correlation, and thus improve the anti-interference ability and spectral efficiency of the system.

[0047] (2) Real-time channel characteristic extraction and prediction: the present application combines the 6G universal channel model and the Wiener process to extract and predict the channel characteristics in real time, including the spatial correlation function and the time correlation function, to ensure that the system maintains high performance and stability in a dynamic environment.

[0048] (3) Improving channel capacity: By analyzing the impact of antenna motion on channel capacity, the present application shows that antenna motion can significantly improve channel capacity, especially under high signal-to-noise ratio conditions. The stronger the antenna motion, the more significant the improvement in channel capacity.

[0049] (4) Optimizing system performance: The present application provides a channel modeling method based on random antenna arrays, which can simulate dynamic scenarios in a virtual environment, optimize the deployment and performance of real communication networks, and achieve rapid iteration and improvement, thereby improving the overall performance and efficiency of 6G networks. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 is the overall implementation flowchart of the present application;

[0051] Figure 2 is the antenna schematic diagram of the present application;

[0052] Figure 3 is the spatial correlation function diagram of the present application;

[0053] Figure 4 is the time correlation function diagram of the present application;

[0054] Figure 5 is the channel capacity diagram of the present application. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be described in detail below with reference to the drawings and specific embodiments.

[0056] To achieve the above-mentioned purpose, the present application proposes a 6G wireless channel modeling method based on random antenna arrays, as shown in Figure 1 The specific steps are as follows:

[0057] S1, the position of the transmitting antenna is modeled as a one-dimensional Wiener process, as shown in Figure 2 The displacement changes with time and satisfies the following conditions: the expected value of the displacement is zero, and the variance increases linearly with time, i.e.

[0058] S2, the channel parameter matrix of the 6G universal channel model is represented as:

[0059]

[0060] where H is the channel matrix, PL is the path loss, SH is the shadow fading, BL is the blocking effect, AL is the atmospheric absorption loss, WE is the weather influence loss, and H s is the small-scale fading.

[0061] Small-scale fading H sExpressed as:

[0062]

[0063] Among them, M T is the number of antenna elements in the transmitting antenna array, M R is the number of antenna elements in the receiving antenna array, f c is the carrier frequency, R is the receiving end, T is the transmitting end, τ is the delay, is the pth transmitting antenna array element With the qth receiving antenna array element The channel impulse response between is the line-of-sight LoS component and the non-line-of-sight NLoS component The superposition of, the specific formula is;

[0064]

[0065] Among them, K R (t) is the Rice factor at time t; is the delay of the LoS path at time t, For the tth moment and The vector distance between are the pth transmitting antenna element and the qth receiving antenna element respectively, c is the speed of light; N qp (t) is the total number of paths at time t; M n (t) is the total number of sub-paths at time t; Under NLoS conditions arrive The power of the mth subpath in the nth path; For the tth moment and is the time delay of the mth subpath in the nth path between them, e is the natural base, j is the imaginary unit, and δ(·) is the Dirac function.

[0066] S3. The dynamic distance change between antennas is calculated using the following formula:

[0067]

[0068] Where D is the fixed distance between the transmitting antenna and the receiving antenna, is the radial distance of the pth transmitting antenna, is the radial distance of the qth receiving antenna, and are the elevation and azimuth angles of the transmitting antenna, respectively, d qp(t) represents the distance between the pth transmitting antenna and the qth receiving antenna.

[0069] S4. Spatial correlation function for:

[0070]

[0071] in, and They represent the correlation functions of the line-of-sight and non-line-of-sight components, t represents the duration of antenna movement, f represents the frequency of antenna operation, K represents the Rice factor, Δr T Represents the transpose of the difference between the antenna position vectors.

[0072]

[0073] in, is the intermediate variable, δ Tx is the distance between two adjacent transmitting antennas in the initial state, is an intermediate variable, is the carrier frequency f c The corresponding wavelength, σ p is the intensity of the Wiener process of the pth transmitting antenna, For the The strength of the Wiener process of each transmitting antenna;

[0074]

[0075] Among them, P surv (Δr T ) is the spatial survival probability of the cluster, f represents the frequency of antenna operation, is the NLoS condition at time t arrive The power of the mth subpath in the nth path, For the transmitting antenna elements, is the NLoS condition at time t arrive The power of the mth subpath in the nth path, is the distance between the pth transmitting antenna and the qth receiving antenna at time t, is the first The distance between the qth transmitting antenna and the qth receiving antenna.

[0076] S5, time correlation function R qp (t, fp; Δt) is:

[0077]

[0078] in, and denote the time correlation function of the line-of-sight and non-line-of-sight components, respectively, and Δt is the time difference;

[0079]

[0080] wherein, is the variance of the Wiener process;

[0081]

[0082] wherein, P surv (Δt) is the time survival probability of the cluster, is the power of the mth sub-path in the nth path from t to t+Δt under the NLoS condition; to is the distance between the pth transmitting antenna and the qth receiving antenna at t+Δt.

[0083] S6, the calculation formula of the channel capacity C is:

[0084]

[0085] wherein, H is the channel matrix, SNR is the signal-to-noise ratio, M Tx is the number of transmitting antennas, det(·) is the determinant operation, (·) H denotes the conjugate transpose operation.

[0086] S7, the simulation platform constructed shows the changes of channel characteristics, including the spatial correlation function, the time correlation function and the dynamic update of the channel capacity. Figure 3 shows the spatial correlation function of the antenna under different motion intensities, Figure 4 shows the time correlation function of the antenna under different motion intensities, Figure 5 shows the channel capacity of the antenna under different motion intensities.

[0087] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which shall be covered within the protection scope of the present application.​

Claims

1. A 6G wireless channel modeling method based on random antenna array, characterized in that: include: Step S1: Model the position of the transmitting antenna as a one-dimensional Wiener process, and the receiving antenna remains stationary; Step S2: Model the channel impulse response using the 6G universal channel model and decompose the channel impulse response into line-of-sight and non-line-of-sight components; Step S3: Calculate the dynamic distance change between antennas through random displacement of the one-dimensional Wiener process and map it to the 6G universal channel model; Step S4: derive spatial correlation function and temporal correlation function based on the dynamic distance change between antennas and the line-of-sight and non-line-of-sight components in the 6G universal channel model, and analyze the impact of antenna motion on the spatial correlation function and the temporal correlation function; Step S5: Calculate the channel capacity based on the line-of-sight and non-line-of-sight components and the MIMO channel capacity formula, and analyze the impact of antenna motion on the channel capacity; Step S6: Based on the influence of antenna motion on the spatial correlation function and the time correlation function, and the influence of antenna motion on the channel capacity, numerical simulation is performed on the spatial correlation function, the time correlation function and the channel capacity to show the change of channel characteristics.

2. A 6G wireless channel modeling method based on a random antenna array according to claim 1, characterized in that: In step S1, the position of the transmitting antenna is modeled as a one-dimensional Wiener process, and the displacement of the transmitting antenna changes with time and satisfies the following conditions: The expected value of the displacement is zero, and the variance grows linearly with time, i.e. in is the mathematical expectation, t represents the duration of antenna movement, W p (t) represents the displacement of the antenna, Var[·] represents the variance, σ p Represents the intensity of the one-dimensional Wiener process.

3. A 6G wireless channel modeling method based on random antenna array according to claim 1, characterized in that: In step S2, the channel parameter matrix of the 6G universal channel model is expressed as: H=[PL·SH·BL·WE·AL] 1 / 2 ·H s Among them, H is the channel parameter matrix, PL is the path loss, SH is the shadow fading, BL is the blocking effect, AL is the atmospheric absorption loss, WE is the weather effect loss, H s Small-scale fading.

4. A 6G wireless channel modeling method based on random antenna array according to claim 3, characterized in that: Small scale fading H s Expressed as: Among them, M T is the number of antenna elements in the transmitting antenna array, M R is the number of antenna elements in the receiving antenna array, f c is the carrier frequency, R is the receiving end, T is the transmitting end, τ is the delay, is the pth transmitting antenna array element With the qth receiving antenna array element The channel impulse response between is the line-of-sight LoS component and the non-line-of-sight NLoS component superposition; Among them, K R (t) is the Rice factor at time t; is the delay of the LoS path at time t, For the tth moment and The vector distance between are the pth transmitting antenna element and the qth receiving antenna element respectively, c is the speed of light; N qp (t) is the total number of paths at time t; M n (t) is the total number of sub-paths at time t; Under NLoS conditions arrive The power of the mth subpath in the nth path; For the tth moment and is the time delay of the mth subpath in the nth path between them, e is the natural base, j is the imaginary unit, and δ(·) is the Dirac function.

5. The 6G wireless channel modeling method based on random antenna array according to claim 1, characterized in that: In step S3, the dynamic distance change between antennas is calculated using the following formula: Where D is the fixed distance between the transmitting antenna and the receiving antenna, is the radial distance of the pth transmitting antenna, is the radial distance of the qth receiving antenna, and are the elevation and azimuth angles of the transmitting antenna, respectively, d qp (t) represents the distance between the pth transmitting antenna and the qth receiving antenna.

6. A 6G wireless channel modeling method based on random antenna array according to claim 1, characterized in that: In step S4, the spatial correlation function for: in, and They represent the correlation functions of the line-of-sight and non-line-of-sight components, t represents the duration of antenna movement, f represents the frequency of antenna operation, K represents the Rice factor, Δr T represents the transpose of the difference between the antenna position vectors; Among them, the intermediate variable δ Tx is the distance between two adjacent transmitting antennas in the initial state, the intermediate variable is the carrier frequency f c The corresponding wavelength, σ p is the intensity of the Wiener process of the pth transmitting antenna, For the The strength of the Wiener process of each transmitting antenna; Among them, P surv (Δr T ) is the spatial survival probability of the cluster, f represents the frequency of antenna operation, is the NLoS condition at time t arrive The power of the mth subpath in the nth path, is the NLoS condition at time t arrive The power of the mth subpath in the nth path, is the distance between the pth transmitting antenna and the qth receiving antenna at time t, For the transmitting antenna elements, is the first The distance between the qth transmitting antenna and the qth receiving antenna.

7. A 6G wireless channel modeling method based on random antenna array according to claim 6, characterized in that: In step S4, the time correlation function R qp (t, f; Δt) is: in, and denote the time correlation functions of the line-of-sight and non-line-of-sight components, respectively, and Δt is the time difference; in, is the variance of the Wiener process; Among them, P surv (Δt) is the time survival probability of the cluster, Under NLoS conditions at time t+Δt arrive The power of the mth subpath in the nth path, is the distance between the pth transmitting antenna and the qth receiving antenna at time t+Δt.

8. The 6G wireless channel modeling method based on random antenna array according to claim 1, characterized in that: In step S5, the calculation formula of the channel capacity C is: Among them, H is the channel matrix, SNR is the signal-to-noise ratio, M Tx is the number of transmitting antennas, det(·) is the determinant operation, (·) H Represents the conjugate transpose operation.

9. The 6G wireless channel modeling method based on random antenna array according to claim 1, characterized in that: In step S6, a simulation platform is used to perform numerical simulation on the spatial correlation function, the time correlation function and the channel capacity; the constructed simulation platform can display the changes in channel characteristics, including the dynamic update of the spatial correlation function, the time correlation function and the channel capacity.

Images