Modeling and fading analysis method and device based on environment perception geometric random channel model

Abstract

This application provides a method and apparatus for modeling and fading analysis of a geometric random channel model based on environmental awareness. The method includes the following steps: acquiring the location information of obstacles in the environment and constructing an environmental awareness map using an occupancy grid algorithm; establishing a random channel model based on the environmental awareness map and the signal propagation path; wherein the signal propagation path includes a line-of-sight path and a single-hop path. In this application embodiment, by establishing a geometric random channel model based on environmental awareness, the accuracy of channel modeling is significantly improved. Compared with geometric random channel models in related technologies, it can obtain a channel impulse response closer to the real scene and more realistically reflect the distribution of obstacles and signal propagation characteristics in the environment.

Description

Technical Field

[0001] This application relates to the field of channel modeling, and more particularly to a method and apparatus for modeling and fading analysis of an environment-aware geometric random channel model. Background Technology

[0002] With the rapid development of mobile communication technology, users' demands for the accuracy and adaptability of channel models are constantly increasing, and the high requirements for digital environment awareness are driving the research of sixth-generation mobile communication technology. Compared with 5G networks, 6G networks have improved upon existing 5G capabilities, with peak data transmission rates reaching 50-200 Gbit / s, 2.5-10 times the upper limit of 5G, and mobility supporting speeds of 500km / h-1000km / h. New capabilities for 6G include enhanced perception-related capabilities, with continuously improving positioning accuracy to 1-10cm. One typical scenario of 6G is the integration of sensing and communication, where modeling and fading analysis of environment-aware channel models is expected to improve the performance of 6G mobile communication technology, enabling it to achieve the corresponding capability indicators. As national research on 6G mobile communication technology continues to advance, research on channel modeling for environment awareness is becoming increasingly urgent.

[0003] Geometric random channel (GRS) models are commonly used channel models in wireless communication. They typically represent the number of paths by a probability distribution over parameters such as amplitude, delay, and angle. Generally, they are divided into multiple clusters in the angular or time domain. By considering the geometric characteristics of the surrounding environment, such as the geometry and distribution of scattering objects like buildings, GRS models can characterize the multipath effects and signal fading characteristics of wireless signals during propagation. Environment-aware GRS models further improve the accuracy of channel models, enabling them to reflect channel characteristics more closely to real-world environments.

[0004] However, the modeling of geometric channel models for environmental perception is still in its early stages of research both domestically and internationally, and its accuracy is not high. Summary of the Invention

[0005] In view of this, embodiments of this application provide a method and apparatus for modeling and fading analysis of an environment-aware geometric random channel model, so as to at least partially solve the above problems.

[0006] According to a first aspect of the embodiments of this application, a modeling and fading analysis method for a geometric random channel model based on environmental awareness is provided. The method includes the following steps: acquiring the location information of obstacles in the environment and constructing an environmental awareness map using an occupancy grid algorithm; establishing a geometric random channel model based on the environmental awareness map and a signal propagation path; wherein the signal propagation path includes a line-of-sight path and a single-hop path.

[0007] Furthermore, the above method also includes: determining the probability density distribution of the incident angle when the signal arrives at the receiving end based on the transmitting and receiving positions and the point cloud position of the obstacle in the environmental perception map; inputting the probability density distribution of the incident angle as the prior information of the angle distribution of the random channel model into the geometric random channel model, and thereby obtaining the channel time-frequency-space correlation function that can analyze signal fading.

[0008] Furthermore, in the above method, the step of acquiring the location information of obstacles in the environment and constructing an environmental perception map using an occupancy grid algorithm further includes: dividing the environment into grids; when the detection instrument detects an obstacle in a grid, determining that the grid is in an occupied state; when the detection instrument detects no obstacle in a grid, determining that the grid is in an idle state, and simultaneously determining that all grids between the detection instrument and the obstacle are in an idle state; adjusting the position and sampling azimuth of the detection instrument, performing detection according to the above method, determining the occupancy probability of the grid based on the updated state data of the detected grids, and finally obtaining an environmental perception map covered by the occupancy probability of the grids.

[0009] Furthermore, in the above method, adjusting the position and sampling azimuth of the detection instrument, and determining the occupancy probability of the grid based on the updated detected grid occupancy status data, ultimately obtaining an environmental perception map covered by the occupancy probability of the occupant grids, specifically involves:

[0010] Use m i Z represents the i-th cell. 1:t If m is the set of all observations up to time t, then i The occupancy probability at time t is P(m) i |Z 1:t );

[0011] According to Bayes' theorem, the probability of occupancy of the grid at time t+1 is:

[0012]

[0013] Among them, P(Z) t+1 |m i ) indicates that in m i Z was observed t+1 The probability of;

[0014] Using logarithmic odds l(m) i ) represents the m of the raster. i Occupancy probability, i.e.

[0015]

[0016] Wherein, P(m) i ) indicates that the detection instrument is at the grid m i The probability of observing an obstacle;

[0017] In the initial state without any measured values, P0(m) i Since ) = 0.5, the initial occupancy probability of the grid is...

[0018]

[0019] Update the occupancy probability of the raster using the following formula.

[0020]

[0021] In the above formula, Represents grid m i This is the case when the space is idle;

[0022] A log-probability greater than 0 indicates that the grid cell contains an obstacle, while a log-probability not greater than 0 indicates that the grid cell is unobstructed.

[0023] Furthermore, in the above method, a random channel model is established based on the environmental perception map and the signal propagation path. The signal propagation path includes a line-of-sight path and a single-hop path. Specifically, the h of the channel impulse response of the geometric random channel model is... pq (t) is

[0024]

[0025] in: This represents the channel impulse response for a line-of-sight path. This represents the channel impulse response of a single-hop path.

[0026]

[0027] Where: p(p=1,...,M) T ) represents the selected p-th transmitter, q(q=1,...,M) R ) represents the q-th receiver, M T M represents the total number of transmitters. R Indicates the total number of receivers;

[0028] K is the Ricean K-factor, Ω pqf is the total power between the p-th transmitter and the q-th receiver. c For the carrier frequency, τ pq Let f be the propagation time from the p-th transmitter to the q-th receiver. Rm and f Tm The maximum Doppler frequency on the transceiver side is caused by the relative motion of scatterers between the transceiver and the environment. The departure angle of the line-of-sight path. γ is the angle of arrival for the line-of-sight path. T γ represents the direction of motion of the transmitting end. R η represents the direction of motion of the receiving end. SB To represent the proportion of a single reflection component to the total scattered power, N i Number of scattering points, phase Let be a random variable distributed on the interval [-π, π). Let the departure angle be the angle at which the emitter strikes the i-th scatterer. Let be the angle of arrival of the ray after it strikes the i-th scatterer at the receiver. and They are discrete random variables, and the departure angle and arrival angle satisfy a certain relationship that allows them to be converted to each other through geometric relationships.

[0029]

[0030] Furthermore, in the above method, the step of inputting the incident angle as prior information of the angle distribution of the random channel model into the random channel model, and thereby deriving the model channel time-frequency-space correlation function that can analyze signal fading, specifically involves:

[0031] For any different carrier frequency f in the channel c and f c ', carrier frequency f c The p-th transmitter to the q-th receiver and the carrier frequency f c There are two paths from the p'-th transmitter to the q'-th receiver, with the antenna element spacing between the transmitter and receiver being δ. T and δ R The channel impulse responses for the two paths are h, respectively. pq (t) and h' p'q' (t), and thus the channel time-frequency-space correlation function is defined as:

[0032]

[0033] Where: E[·] and (·) * Let Ω represent the statistical expectation and the complex conjugate, respectively. pqΩ represents the total power in the link between the p-th transmitter and the q-th receiver. p'q' Let τ represent the total power in the link from the p-th transmitter to the q'-th receiver, τ represent the propagation time from the p-th transmitter to the q-th receiver, and χ represent the frequency difference between the two carriers. It can be observed that this time-frequency-space correlation function is time-separated by τ and space-separated by δ. T and δ R And a function of frequency separation χ.

[0034] Furthermore, in the above method, the channel time-frequency-space correlation function for analyzing signal fading, which is derived from this method, includes the channel time-frequency-space correlation function for the line-of-sight path, specifically:

[0035]

[0036] Where: G1=Pcosβ T -Qcosβ R ;

[0037] H1 = f Tm cosγ T -f Rm cosγ R ;

[0038] L1 = Dk p′ δ T cosβ T +k q′ δ R cosβ R ;

[0039] P=(p′-p)δ T / λ, Q=(q′-q)δ R / λ, where D represents the distance from the transmitter to the receiver, β T and β R k represents the tilt angle of the multi-element antenna at the transmitting and receiving ends, respectively. q =(M R -2q+1) / 2、k p =(M T -2p+1) / 2.

[0040] Furthermore, in the above method, the channel time-frequency-space correlation function for analyzing signal fading, which is derived from this method, includes the channel time-frequency-space correlation function for a single-hop path, specifically:

[0041]

[0042] in:

[0043]

[0044]

[0045] I0(·) is a zeroth-order modified Bessel function of the first kind. This represents the departure angle of the transmitter and the arrival angle of the receiver.

[0046] It is a control of angle

[0047] The real parameters of the angular distribution, For angle The average value.

[0048] According to a second aspect of the embodiments of this application, an electronic device is provided, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; the memory is used to store at least one executable instruction, wherein the executable instruction causes the processor to perform an operation corresponding to any of the above-described environmentally aware geometric random channel modeling and fading analysis methods.

[0049] According to a third aspect of the embodiments of this application, a computer storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the modeling and fading analysis method of any of the above-described environment-aware geometric random channel models.

[0050] In this embodiment, by establishing an environment-aware geometric random channel model, the accuracy of channel modeling is significantly improved. Compared with geometric random channel models in related technologies, it can obtain a channel impulse response that is closer to the real scene, and can more realistically reflect the distribution of obstacles and signal propagation characteristics in the environment, thus bringing the following advantages: First, it can provide more accurate signal fading and multipath effect analysis, which is crucial for the performance evaluation and optimization of wireless communication systems; second, the environment-aware channel model can adapt to different environmental changes, improving the robustness of the system; third, through accurate channel modeling, resource allocation and signal processing algorithm design can be carried out more effectively, thereby improving communication efficiency and data transmission rate; finally, this model helps to reduce signal interference and noise impact, improve communication quality, and provide more refined and reliable performance predictions for wireless communication systems. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings.

[0052] Figure 1 This is a flowchart illustrating the steps of a modeling and fading analysis method for an environment-aware geometric random channel model proposed in an embodiment of this application.

[0053] Figure 2 This is a flowchart illustrating the steps involved in constructing an environment-aware map using the occupancy grid algorithm in an embodiment of this application.

[0054] Figure 3 This is a flowchart of another step in a modeling and fading analysis method for an environment-aware geometric random channel model proposed in an embodiment of this application.

[0055] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0056] To enable those skilled in the art to better understand the technical solutions in the embodiments of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art should fall within the protection scope of the embodiments of this application.

[0057] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0058] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0059] The specific implementation of the embodiments of this application will be further described below with reference to the accompanying drawings.

[0060] See Figure 1 This application provides a modeling and fading analysis method for an environment-aware geometric random channel model, which includes the following steps:

[0061] Step S110: Obtain the location information of obstacles in the environment and construct an environment-aware map using the occupancy grid algorithm.

[0062] In practice, detection instruments such as millimeter-wave radar sensors can be used to perceive the environment and collect the location information of obstacles around the radar sensors and other detection instruments. This location information includes the distance and azimuth of the obstacle from the measurement location of the millimeter-wave radar sensor.

[0063] In this embodiment, a map for environmental perception is obtained by transmitting millimeter-wave signals and acquiring reflected echo signals after encountering obstacles, and then using an occupancy grid algorithm. Specifically, when the radar sensor detects an obstacle, the grid with the obstacle is designated as occupied, increasing the occupancy probability of the occupied grid; and the grid between the radar sensor position and the obstacle is designated as idle, increasing the idle probability of the idle grid. The radar sensor position and sampling azimuth are adjusted, and environmental perception is repeatedly performed using the occupancy grid algorithm, updating the grid occupancy probability continuously. Finally, the entire map covered by the occupancy probability of the occupied grid is obtained.

[0064] Step S120: Establish a geometric random channel model based on the environmental perception map and the signal propagation path. The signal propagation path includes a line-of-sight (LoS) path and a single-bounce (SB) path.

[0065] This embodiment only considers line-of-sight paths and single-hop paths when establishing the geometric random channel model. Therefore, the h-value of the channel impulse response (CIR) of the geometric random channel model can be obtained. pq (t) is represented as

[0066]

[0067] in: For the CIR of the Loss path, The CIR for the SB path.

[0068] In this embodiment, by establishing an environment-aware geometric random channel model, the accuracy of geometric channel modeling is significantly improved. Compared with geometric random channel models in related technologies, this embodiment can obtain a channel impulse response that is closer to the real scene, and can more realistically reflect the distribution of obstacles and signal propagation characteristics in the environment, thus bringing the following advantages: First, it can provide more accurate signal fading and multipath effect analysis, which is crucial for the performance evaluation and optimization of wireless communication systems; second, the environment-aware geometric random channel model can adapt to different environmental changes, improving the robustness of the system; third, through accurate channel modeling, resource allocation and signal processing algorithm design can be carried out more effectively, thereby improving communication efficiency and data transmission rate; finally, this model helps to reduce signal interference and noise impact, improve communication quality, and provide more refined and reliable performance predictions for wireless communication systems.

[0069] See Figure 2 In some implementation methods, Figure 1 The process of obtaining the location information of obstacles in the environment and constructing an environment-aware map using an occupancy grid algorithm, as shown, can further include the following steps:

[0070] Step S210: Divide the environment into grids.

[0071] Specifically, the size of each grid cell can be determined based on the resolution of the environment. The resolution of the environment refers to the actual physical space represented by each grid cell when the environment is divided into grids. Resolution is a parameter that measures the level of detail in a grid map; it determines the area or volume covered by a single grid cell on the map. Generally speaking, the more grid cells there are, the closer the resulting environment perception map is to the real environment. That is, higher resolution means a smaller actual area or volume represented by each grid cell, resulting in a more detailed map; conversely, lower resolution means a larger area or volume represented by each grid cell, resulting in a coarser map.

[0072] Step S220: Deploy detection instruments in the environment. When the detection instruments detect an obstacle in the grid, set the grid to an occupied state. When the detection instruments detect no obstacle in the grid, determine that the grid is in an idle state, and at the same time determine that all grids between the detection instruments and the obstacle are in an idle state.

[0073] In this embodiment, based on the occupancy grid algorithm, detection instruments (such as radar sensors) are deployed in the detection environment. When the radar sensor detects an obstacle within a grid, the grid with the obstacle is set to an occupied state, increasing the occupancy probability of the occupied grid. Conversely, the grid between the radar sensor position and the obstacle is set to an idle state, increasing the idle probability of the idle grid.

[0074] Step S230: Adjust the position and sampling azimuth of the detection instrument, repeat the above steps to perform detection, and determine the occupancy probability of the grid based on the updated state data of the detected grid, and finally obtain the environmental perception map covered by the occupancy probability of the grid.

[0075] In this embodiment, the position of the radar sensor, the sampling azimuth angle and other parameters are continuously adjusted, and the above detection method is repeated to obtain the state of the grid, so as to continuously update the occupancy probability and idle probability of the grid.

[0076] It should be noted that the placement of the radar sensor and the selection of its sampling azimuth angle are crucial for sensing obstacle information in the environment. The more locations and azimuth angles selected, the more accurate the environmental obstacle information obtained. In practice, the installation location of the radar sensor and the number of sampling azimuth angles can be determined according to the actual environment; this embodiment does not impose any limitations on them.

[0077] In one specific implementation, the occupancy probability of a raster can be updated using Bayes' theorem.

[0078] Specifically, using m i Z represents the i-th cell. 1:t If m is the set of all observations up to time t (i.e., all detection data regarding whether a grid is occupied), then m i The occupancy probability at time t can be expressed as P(m i |Z 1:t Therefore, the occupancy probability of the grid at time t+1 can be expressed as:

[0079]

[0080] Where: P(Z) t+1 |m i ) indicates that in m i Z was observed t+1The probability of observing the data at time t+1, given that the grid is occupied, is P(Z). t+1 |Z 1:t ) represents the situation where, given all previous observation data Z 1:t Under these conditions, new data Z was observed. t+1 The probability of this is often referred to as the "prediction probability" or "evidence probability" in Bayesian filtering.

[0081] Furthermore, the probability of a radar sensor detecting an obstacle is denoted by P(m). i For ease of calculation, the logarithmic probability l(m) is usually used. i ) represents the occupancy probability, i.e.

[0082]

[0083] In the initial state without any measured values, P0(m) i =0.5, therefore the grid m i The initial occupancy probability is

[0084]

[0085] When updating the occupancy probability of a raster, only the log odds value is added or subtracted. The log odds of the raster are updated after each observation. Represents grid m i In the idle state, the grid m can be adjusted using the following formula. i Update the logarithmic odds:

[0086]

[0087] Z t For the observation at time t;

[0088] In this embodiment, when the radar sensor detects an obstacle, the grid with the obstacle is selected as occupied and the occupancy probability of the grid is increased. For the grid between the obstacle and the radar sensor, the Bresanham algorithm is used to obtain the positions of other grids between the radar sensor and the grid occupied by the obstacle, and the occupancy probability of these grids is reduced.

[0089] By changing the position and sampling azimuth of the radar sensor, and repeatedly applying the aforementioned occupancy grid algorithm during these changes, environmental perception is performed, and the grid occupancy probability is updated. Ultimately, the entire map covered by the occupancy probability of the occupant grids is obtained. A higher occupancy probability indicates that the grid is more likely to be occupied, and vice versa. For ease of obstacle location determination and calculation, logarithmic probability is used to represent the occupancy probability. In practice, the logarithmic probability threshold for determining the presence of obstacles can be set to 0. Grids with a logarithmic probability greater than 0 represent obstacle locations in the entire environment; a logarithmic probability greater than 0 indicates the presence of obstacles in the grid, while a logarithmic probability not greater than 0 indicates the absence of obstacles in the grid.

[0090] In some embodiments, Figure 1 The step S120 shown, which establishes a random channel model based on the environmental perception map and the signal propagation path, can be specifically as follows: based on the environmental perception map obtained from the occupancy probability of the grid, obtain the location information of the scatterers in the environmental perception map, determine their distribution characteristics, calculate parameters such as channel delay, and establish a geometric random channel model using the signal propagation path, which includes line-of-sight path and single-hop path.

[0091] Establish a geometric random channel model, considering p(p=1,...,M) T ) represents the selected p-th transmitter, q(q=1,...,M) R ) represents the q-th receiver, M T M represents the total number of transmitters. R This indicates the total number of receivers.

[0092] In this embodiment, the geometric random channel model only considers the Loss-Side (LoS) path and the Single-Side (SB) path, thereby allowing the h-value of the Channel Impulse Response (CIR) to be expressed. pq (t) is represented as

[0093]

[0094] in: The CIR of the Loss path, The CIR representing the SB path can be expressed as follows:

[0095]

[0096]

[0097] Where: K represents the Ricean K-factor, Ω pq f represents the total power between the p-th transmitter and the q-th receiver. c τ represents the carrier frequency.pq f represents the propagation time from the p-th transmitter to the q-th receiver. Rm and f Tm This represents the maximum Doppler frequency on the transceiver side caused by the relative motion between the transceiver and scatterers in the environment. This represents the angle of departure (AoD) of the Loss of Path (LoS) path. γ represents the angle of arrival (AoA) of the Loss of Arrival (LoS) path. T Indicates the direction of motion of the transmitting end, γ R Indicates the direction of motion of the receiving end, η SB N represents the proportion of the total scattered power to the single reflection component. i The number of scattering points and the phase ψ are indicated. ni It represents a random variable distributed on the interval [-π, π). It is the AoD of the emitter striking the i-th scatterer. It is the AoA of the ray that reaches the receiver after hitting the i-th scatterer. and AoD and AoA are discrete random variables, and they satisfy a certain relationship that allows them to be converted into each other through geometric relationships.

[0098]

[0099] See Figure 3 The figure shows another flowchart of the modeling and fading analysis method for an environment-aware geometric random channel model provided in this application. As shown in the figure, the method includes the following steps:

[0100] Step S110: Obtain the location information of obstacles in the environment and construct an environment-aware map using the occupancy grid algorithm.

[0101] Step S120: Establish a random channel model based on the environmental perception map and the signal propagation path. The signal propagation path includes the Loss-of-Speed ​​(LoS) path and the Sub-Speed ​​(SB) path.

[0102] The specific implementation process of steps S110 and S120 can be found in the above description, and will not be repeated here in this embodiment.

[0103] Step S130: Determine the probability density distribution of the angle of arrival (AOA) when the signal reaches the receiver based on the transmit / receive location and the point cloud location of the obstacle in the environmental perception map.

[0104] For the geometric random channel model established in this embodiment, based on the grid coordinates of the transceiver and sampling points in the environmental information perceived by the radar sensor, the grid position is represented by the grid center, and the probability density distribution of AoA corresponding to all points is obtained in this way.

[0105] Based on point cloud maps, and assuming that only line-of-sight links and single-hop links exist, the two-dimensional signal angle of arrival corresponding to each point cloud data is determined according to the transmit and receive positions and the positions of all obstacle point clouds.

[0106] It should be noted that, in this embodiment, the order of steps S120 and S130 is not important and can be changed.

[0107] Step S140: The probability density distribution of the incident angle is used as the prior information of the angle distribution of the geometric random channel model and input into the geometric random channel model, and the channel time-frequency-space correlation function that can analyze signal fading is derived from it.

[0108] In this embodiment, the probability density distribution of AOA is calculated based on the perceived point cloud data, and it is used as prior information of the angle distribution to be input into the geometry-based random channel model, and the channel time-frequency-space correlation function under this environment is further derived.

[0109] In one example, the probability density distribution of AoA for all points is used as prior information and input into the established geometric random channel model to obtain the correlation function. The correlation function can be determined by the following formula:

[0110] For any different carrier frequency f in the channel c and f c There are two paths from the p-th transmitter to the q-th receiver and from the p'-th transmitter to the q'-th receiver (i.e., for carrier frequency f). c The path from the p-th transmitter to the q-th receiver is defined for the carrier frequency f. c (The path from the p'-th transmitter to the q'-th receiver is another path), and the spacing between the antenna elements at the transmitter and receiver is δ. T and δ R The CIR values ​​for the two paths are represented as h. pq (t) and h' p'q' (t) Thus, the channel time-frequency-space correlation function is defined as:

[0111]

[0112] Where: E[·] and (·) * Let Ω represent the statistical expectation and the complex conjugate, respectively. pq Ω represents the total power in the link between the p-th transmitter and the q-th receiver.p'q' Let τ represent the total power in the link from the p'-th transmitter to the q'-th receiver, τ represent the propagation time from the p-th transmitter to the q-th receiver, and χ represent the frequency difference between the two carriers. It can be observed that this time-frequency-space correlation function is time-separated by τ and space-separated by δ. T and δ R And a function of frequency separation χ.

[0113] In some embodiments, the channel time-frequency-space correlation function of the LoS of the above geometric random channel model can be written as:

[0114]

[0115] Where: K pq Let K represent the Ricean K-factor from the p-th transmitter to the q-th receiver. p'q' Let G1 represent the Ricean K-factor from the p'-th transmitter to the q'-th receiver, where G1 = Pcosβ. T -Qcosβ R H1 = f Tm cosγ T -f Rm cosγ R L1 = Dk p′ δ T cosβ T +k q′ δ R cosβ R In these formulas, P = (p′ - p)δ T / λ, Q=(q′-q)δ R / λ, where D represents the distance from the transmitter to the receiver, β T and β R These represent the tilt angles of the multi-element antennas at the transmitting and receiving ends, respectively, where λ represents the wavelength, and k represents the wavelength. q =(M R -2q+1) / 2、k p =(M T -2p+1) / 2.

[0116] In some embodiments, the channel time-frequency-space correlation function of the SB link in the above geometric random channel model can be written as:

[0117]

[0118] in: I0(·) is a zeroth-order modified Bessel function of the first kind. Representing the AoD at the transmitting end and the AoA at the receiving end, the von Mise distribution is applied to the two-ring model. It is a control of angle The real parameters of the angular distribution, For angle The average value.

[0119] By combining the channel time-frequency-space correlation functions of the LoS link and SB link mentioned above, the channel time-frequency-space correlation function of the entire environment obtained from the sensed AoA is obtained.

[0120] In summary, the embodiments of this application use multiple sensors to collect environmental information data from the actual environment, use the collected environmental information to model a geometric random channel model, analyze the fading characteristics of the established model, and thereby obtain a channel model that is closer to the real environment.

[0121] The modeling and fading analysis method of the environment-aware geometric random channel model in this embodiment can be executed by any suitable electronic device with data processing capabilities, including but not limited to: servers, mobile terminals (such as mobile phones, PADs, etc.) and PCs.

[0122] Reference Figure 4 This document illustrates a schematic diagram of an electronic device according to an embodiment of this application. The specific embodiments of this application do not limit the specific implementation of the electronic device.

[0123] like Figure 4 As shown, the electronic device may include: a processor 402, a communications interface 404, a memory 406, and a communication bus 408. The processor 402, communications interface 404, and memory 406 communicate with each other via the communication bus 408.

[0124] Communication interface 404 is used to communicate with other electronic devices or servers.

[0125] The processor 402 is used to execute program 410, specifically to perform the relevant steps in the above-described embodiment of the modeling and fading analysis method based on the environment-aware geometric random channel model.

[0126] Specifically, program 410 may include program code that includes computer operation instructions.

[0127] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The smart device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0128] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0129] The specific implementation of each step in procedure 410 can be found in the corresponding steps and units described in the above embodiments of the modeling and fading analysis method based on the environment-aware geometric random channel model, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the aforementioned method embodiments, and will not be repeated here.

[0130] This application also provides a computer storage medium storing a computer program that, when executed by a processor, implements the above-described modeling and fading analysis method based on an environment-aware geometric random channel model.

[0131] It should be noted that, depending on the implementation needs, the various components / steps described in the embodiments of this application can be broken down into more components / steps, or two or more components / steps or parts of the operation of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of this application.

[0132] The methods described in the embodiments of this application can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as CD ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded over a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, it implements the computer storage medium described herein, on which a computer program is stored, which, when executed by a processor, implements a method for modeling and fading analysis based on an environment-aware geometric random channel model. Furthermore, when a general-purpose computer accesses the code used to implement the modeling and fading analysis method of the environment-aware geometric random channel model shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the modeling and fading analysis method of the environment-aware geometric random channel model shown herein.

[0133] Those skilled in the art will recognize that the units and method steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art 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 embodiments of this application.

[0134] The above embodiments are only used to illustrate the embodiments of this application, and are not intended to limit the embodiments of this application. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the embodiments of this application. Therefore, all equivalent technical solutions also fall within the scope of the embodiments of this application, and the patent protection scope of the embodiments of this application should be defined by the claims.

Claims

1. A modeling and fading analysis method for an environment-aware geometric random channel model, characterized in that, Includes the following steps: Acquire the location information of obstacles in the environment and construct an environment-aware map using an occupancy grid algorithm; A geometric random channel model is established based on the environmental perception map and the signal propagation path; wherein the signal propagation path includes a line-of-sight path and a single-hop path.

2. The method according to claim 1, characterized in that, Also includes: The incident angle when the signal reaches the receiving end is determined based on the transmitting and receiving locations and the point cloud positions of the obstacles in the environmental perception map. The incident angle is used as the prior information of the angle distribution of the random channel model and input into the geometric random channel model to obtain the channel time-frequency-space correlation function that can analyze signal fading.

3. The method according to any one of claims 1 or 2, characterized in that, The step of acquiring the location information of obstacles in the environment and constructing an environment-aware map using an occupancy grid algorithm further includes: The environment is divided into grids; A detection instrument is deployed in the environment. When the detection instrument detects an obstacle in the grid, the grid is determined to be occupied. When the detection instrument detects no obstacle in the grid, the grid is determined to be idle. At the same time, it is determined that all grids between the detection instrument and the obstacle are idle. Adjust the position and sampling azimuth of the detection instrument, perform detection according to the above method, and determine the occupancy probability of the grid based on the updated detected grid state data, and finally obtain the environmental perception map covered by the grid occupancy probability.

4. The method according to claim 3, characterized in that, The process involves adjusting the position and sampling azimuth of the detection instrument, determining the occupancy probability of the grid based on the updated detected grid occupancy status data, and ultimately obtaining an environmental perception map covered by the occupancy probability of the occupant grids. Specifically: Use m i Z represents the i-th cell. 1:t If m is the set of all observations up to time t, then i The occupancy probability at time t is P(m) i |Z 1:t ); According to Bayes' theorem, the probability of occupancy of the grid at time t+1 is: Among them, P(Z) t+1 |m i ) indicates that in m i Z was observed t+1 The probability of; Using logarithmic odds l(m) i ) represents the m of the raster. i Occupancy probability, i.e. Wherein, P(m) i ) indicates that the detection instrument is at the grid m i The probability of observing an obstacle; In the initial state without any measured values, P0(m) i Since ) = 0.5, the initial occupancy probability of the grid is... Update the occupancy probability of the raster using the following formula. In the above formula, Represents grid m i This is the case when the space is idle; A log-probability greater than 0 indicates that the grid cell contains an obstacle, while a log-probability not greater than 0 indicates that the grid cell is unobstructed.

5. The method according to claim 4, characterized in that, A geometrical random channel model is established based on the environmental perception map and the signal propagation path. The signal propagation path includes a line-of-sight path and a single-hop path, specifically: The channel impulse response h of the geometric random channel model pq (t) is: in: This represents the channel impulse response for a line-of-sight path. This represents the channel impulse response of a single-hop path. Where: p(p=1,...,M) T ) represents the selected p-th transmitter, q(q=1,...,M) R ) represents the q-th receiver, M T M represents the total number of transmitters. R Indicates the total number of receivers; K is the Ricean K-factor, Ω pq f is the total power between the p-th transmitter and the q-th receiver. c For the carrier frequency, τ pq Let f be the propagation time from the p-th transmitter to the q-th receiver. Rm and f Tm The maximum Doppler frequency on the transceiver side is caused by the relative motion of scatterers between the transceiver and the environment. The departure angle of the line-of-sight path. γ is the angle of arrival for the line-of-sight path. T γ represents the direction of motion of the transmitting end. R η represents the direction of motion of the receiving end. SB To represent the proportion of a single reflection component to the total scattered power, N i Number of scattering points, phase Let be a random variable distributed on the interval [-π, π). Let the departure angle be the angle at which the emitter strikes the i-th scatterer. Let be the angle of arrival of the ray after it strikes the i-th scatterer at the receiver. and They are discrete random variables, and the departure angle and arrival angle satisfy a certain relationship that can be converted to each other by geometric relations:

6. The method according to claim 5, characterized in that, The step of inputting the incident angle as prior information of the angle distribution of the random channel model into the random channel model, and deriving the model channel time-frequency-space correlation function that can analyze signal fading, specifically involves: For any different carrier frequency f in the channel c and f c ', carrier frequency f c The p-th transmitter to the q-th receiver and the carrier frequency f c There are two paths from the p'-th transmitter to the q'-th receiver, with the antenna element spacing between the transmitter and receiver being δ. T and δ R The channel impulse responses for the two paths are h, respectively. pq (t) and h' p'q' (t), and thus the channel time-frequency-space correlation function is defined as: Where: E[·] and (·) * Let Ω represent the statistical expectation and the complex conjugate, respectively. pq Ω represents the total power in the link between the p-th transmitter and the q-th receiver. p'q' Let τ represent the total power in the link from the p'-th transmitter to the q'-th receiver, τ represent the propagation time from the p-th transmitter to the q-th receiver, and χ represent the frequency difference between the two carriers. It can be observed that this time-frequency-space correlation function is time-separated by τ and space-separated by δ. T and δ R And a function of frequency separation χ.

7. The method according to claim 6, characterized in that, The aforementioned model, which derives the channel time-frequency-space correlation function for analyzing signal fading, includes the channel time-frequency-space correlation function for the line-of-sight path, specifically: Where: G1=Pcosβ T -Qcosβ R ; H1=f Tm cosγ T -f Rm cosγ R ; L1=Dk p′ d T cosβ T +k q′ d R cosβ R ; P=(p′-p)δ T / λ, Q=(q′-q)δ R / λ, where D represents the distance from the transmitter to the receiver, β T and β R k represents the tilt angle of the multi-element antenna at the transmitting and receiving ends, respectively. q =(M R -2q+1) / 2、k p =(M T -2p+1) / 2.

8. The method according to claim 6, characterized in that, The aforementioned method, which derives the channel time-frequency-space correlation function for analyzing signal fading, includes the channel time-frequency-space correlation function for a single-hop path, specifically: in: I0(·) is a zeroth-order modified Bessel function of the first kind. This represents the departure angle of the transmitter and the arrival angle of the receiver. It is a control of angle The real parameters of the angular distribution, For angle The average value.

9. An electronic device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the modeling and fading analysis method of the environment-aware geometric random channel model as described in any one of claims 1-8.

10. A computer storage medium storing a computer program thereon, which, when executed by a processor, implements the modeling and fading analysis method of an environment-aware geometric random channel model as described in any one of claims 1-8.

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