Fast channel prediction method, device and equipment based on dynamic ray tracing

Abstract

The present invention provides a fast channel prediction method, apparatus, and device based on dynamic ray tracing. The method comprises: obtaining antenna information of a transceiver, motion parameters of a mobile object, and propagation environment information in a wireless propagation environment; obtaining first geometric information and first field strength information of multipath components (MPCs) at the start time of a predicted time period based on the propagation environment information and antenna information, and obtaining second geometric information and second field strength information of the MPCs at the end time of the predicted time period; obtaining motion trajectory equations and birth and death phenomena of the MPCs within the predicted time period based on the first geometric information, second geometric information, and motion parameters; and extrapolating the motion trajectory equations, first field strength information, and second field strength information based on the birth and death phenomena to obtain geometric prediction information and field strength prediction information of the MPCs at the predicted time within the predicted time period. The method of the present invention achieves high accuracy and efficiency in channel prediction.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a fast channel prediction method, device and equipment based on dynamic ray tracing. Background Art

[0002] To meet the high-quality, wide-coverage demands of future communications services, sixth-generation (6G) mobile communications will employ a variety of enabling technologies, including digital twins, integrated sensing and communications, and ultra-large-scale antenna arrays. Networks will face the enormous challenge of operating efficiently and flexibly in increasingly complex and dynamic propagation environments.

[0003] As the communication medium between the transmitter and receiver in a communication system, the propagation characteristics of the wireless channel determine the ultimate performance ceiling of the communication system. To unlock further potential, it is crucial to accurately acquire the wireless channel in advance to adapt to the ever-changing environment. Currently, the mainstream channel prediction methods include AI-based channel prediction and ray tracing (RT)-based channel prediction. AI-based channel prediction often requires a large amount of data training, which poses challenges in interpretability and model generalization. Furthermore, due to the lack of physical mechanisms, the accuracy of multipath components (MPCs) prediction cannot be guaranteed. RT-based channel prediction, based on optical principles, offers interpretability and high accuracy, but requires a complex process of finding propagation paths and calculating path field strengths. This high prediction complexity leads to low computational efficiency. The shortcomings of these existing methods have made them unable to meet the requirements for high-precision and fast channel prediction in future 6G dynamic scenarios. Summary of the Invention

[0004] The purpose of the present invention is to provide a fast channel prediction method, device and equipment based on dynamic ray tracing, which is used to solve the problems of poor interpretability, insufficient generalization, poor prediction accuracy and high complexity of current channel prediction methods.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a fast channel prediction method based on dynamic ray tracing, comprising:

[0006] Obtain antenna information of transceivers, motion parameters of mobile objects and propagation environment information in wireless propagation environments;

[0007] Obtaining, based on the propagation environment information and the antenna information, first geometric information and first field strength information of the multipath components MPCs at a start time of a time period to be predicted, and second geometric information and second field strength information of the MPCs at an end time of the time period to be predicted;

[0008] Obtaining, based on the first geometric information, the second geometric information, and the motion parameters, motion trajectory equations and birth and death phenomena of the MPCs within the time period to be predicted;

[0009] Based on the birth and death phenomenon, extrapolation is performed according to the motion trajectory equation, the first field strength information and the second field strength information to obtain geometric prediction information and field strength prediction information of the MPCs at the predicted moment within the predicted time period.

[0010] Optionally, the first geometric information includes position information of the action point of the MPCs at the starting time, and the second geometric information includes position information of the action point of the MPCs at the ending time;

[0011] The step of obtaining the motion trajectory equation and the life and death phenomenon of the MPCs within the time period to be predicted based on the first geometric information, the second geometric information, and the motion parameters includes:

[0012] Based on the position information of the action point of the MPCs at the starting moment, forward calculation is performed using the principles of geometric optics and consistent diffraction to obtain a first motion trajectory equation of the action point of the MPCs within the time period to be predicted;

[0013] Based on the position information of the action point of the MPCs at the end time, reverse calculation is performed using the principles of geometric optics and consistent diffraction to obtain a second motion trajectory equation of the action point of the MPCs within the time period to be predicted;

[0014] According to the first motion trajectory equation, the second motion trajectory equation and the motion parameters, the life and death phenomena of the MPCs in the time period to be predicted are obtained.

[0015] Optionally, obtaining the life and death phenomena of the MPCs within the time period to be predicted according to the first motion trajectory equation, the second motion trajectory equation, and the motion parameters includes:

[0016] Determining, based on the first motion trajectory equation and the motion parameter, the earliest time at which the action point of the MPCs at the starting moment moves out of the building boundary in the wireless propagation environment as the extinction time of the MPCs at the starting moment;

[0017] Determining, based on the second motion trajectory equation and the motion parameters, a latest time at which the action point of the MPCs at the end moment moves to a building boundary in the wireless propagation environment as a birth time of the MPCs at the end moment;

[0018] Extrapolation is performed based on the extinction time of the MPCs at the starting moment and the birth time of the MPCs at the ending moment to obtain the birth and death phenomena of the MPCs in the time period to be predicted.

[0019] Optionally, based on the birth and death phenomenon, extrapolation is performed according to the motion trajectory equation, the first field strength information, and the second field strength information to obtain geometric prediction information and field strength prediction information of the MPCs in the time period to be predicted, including:

[0020] Based on the life and death phenomenon, obtaining MPCs existing at the time to be predicted within the time period to be predicted;

[0021] Extrapolating the motion trajectory equation to obtain geometric prediction information of the MPCs at the time to be predicted;

[0022] Based on electromagnetic principles and the geometric prediction information, extrapolation is performed according to the first field strength information and the second field strength information to obtain the field strength prediction information of the MPCs at the time to be predicted.

[0023] Optionally, the method further includes:

[0024] Obtain the actual predicted time of the channel and the preset channel correlation time in the wireless propagation environment;

[0025] Dividing the actual prediction time into N prediction time segments according to the actual prediction time and the channel correlation time, wherein N is an integer greater than 1, and the time period to be predicted is any one of the N prediction time segments;

[0026] The geometric prediction information and field strength prediction information of the MPCs at any time to be predicted in each prediction time segment of the N prediction time segments are obtained in sequence.

[0027] An embodiment of the present invention further provides a fast channel prediction device based on dynamic ray tracing, comprising:

[0028] A first acquisition module is used to obtain antenna information of a transceiver, motion parameters of a mobile object and propagation environment information in a wireless propagation environment;

[0029] a first calculation module, configured to obtain, based on the propagation environment information and the antenna information, first geometric information and first field strength information of the multipath components MPCs at a start time of a time period to be predicted, and second geometric information and second field strength information of the MPCs at an end time of the time period to be predicted;

[0030] a second calculation module, configured to obtain, based on the first geometric information, the second geometric information, and the motion parameters, motion trajectory equations and birth and death phenomena of the MPCs within the time period to be predicted;

[0031] The third calculation module is used to perform extrapolation based on the life and death phenomenon, the motion trajectory equation, the first field strength information and the second field strength information to obtain the geometric prediction information and field strength prediction information of the MPCs at the predicted moment in the predicted time period.

[0032] An embodiment of the present invention also provides a network device, comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the fast channel prediction method based on dynamic ray tracing as described in any one of the above items.

[0033] An embodiment of the present invention further provides a readable storage medium, comprising: a program stored on the readable storage medium, and when the program is executed by a processor, the steps of the fast channel prediction method based on dynamic ray tracing as described in any one of the above items are implemented.

[0034] An embodiment of the present invention further provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the fast channel prediction method based on dynamic ray tracing as described in any one of the above items.

[0035] At least one of the above technical solutions of the present invention has the following beneficial effects:

[0036] In the above scheme, for any prediction time period, prediction is performed based on the principles of optical propagation. Compared to existing schemes, the present invention takes into account the birth and death of a large number of MPCs that exist in reality. Therefore, based on the propagation environment information at the start and end times of the prediction time period, bidirectional extrapolation is performed based on the antenna information of the transceiver and the motion parameters of the mobile object in the wireless propagation environment to maximize the prediction of the birth and death of MPCs at any time within the prediction time period, thereby improving the accuracy of channel prediction. In addition, during the prediction process, based on the birth and death of MPCs, the present invention uses the changing patterns of geometric information and field strength information of the MPCs' points of action at different times to perform geometric extrapolation and field strength extrapolation, respectively. This replaces the traditional ray tracing method of emitting a large number of ray tubes (usually thousands or even more) in a spherical form in three-dimensional space point by point and time, and performing the single-point single-shot geometric calculation process of this scheme on each ray tube. The geometric prediction information and field strength prediction information of the MPCs at the predicted time within the prediction time period are obtained, thereby improving the computational efficiency of channel prediction. The method provided by the present invention has certain interpretability and generalization. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of a flow chart of a fast channel prediction method based on dynamic ray tracing according to an embodiment of the present invention;

[0038] Figure 2 is a schematic diagram of a wireless propagation environment according to an embodiment of the present invention;

[0039] Figure 3 A schematic diagram of extrapolating geometry and field strength information based on reflection paths in an embodiment of the present invention;

[0040] Figure 4 Schematic diagram of a flow chart of a fast channel prediction method based on dynamic ray tracing according to an embodiment of the present invention;

[0041] Figure 5 Schematic diagram of the structure of a fast channel prediction device based on dynamic ray tracing according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention and not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0043] like Figure 1 As shown, an embodiment of the present invention provides a fast channel prediction method based on dynamic ray tracing, comprising:

[0044] Step S101, obtaining antenna information of a transceiver, motion parameters of a mobile object, and propagation environment information in a wireless propagation environment;

[0045] In step S101, a three-dimensional wireless propagation environment model (such as Figure 2 As shown), antenna information of the transceiver, motion parameters of the mobile object and propagation environment information are obtained in the three-dimensional wireless propagation environment model, wherein the transceiver includes a transmitter and a receiver, antenna information includes but is not limited to the number of antennas, polarization, gain and transmission power; motion parameters of the mobile object include but are not limited to velocity vector Acceleration vector Wherein, i=1,2,…,m, where m is the number of mobile objects; the propagation environment information includes but is not limited to information such as the position, shape, size, and material of scatterers in the wireless propagation environment. The propagation environment information corresponding to different moments is different. Generally speaking, the predicted propagation environment information at the initial moment is the same as the initial environment information set in the propagation environment model. The propagation environment information corresponding to the remaining moments is calculated using the initial environment information and the motion parameters of the mobile objects.

[0046] Step S102: obtaining first geometric information and first field strength information of multipath components MPCs at the start time of a time period to be predicted, and obtaining second geometric information and second field strength information of MPCs at the end time of the time period to be predicted, based on the propagation environment information and the antenna information.

[0047] In step S102, the propagation environment information and antenna information at the start time nTc of the time period to be predicted are input into the ray tracing algorithm for simulation to obtain the first geometric information and first field strength information of the MPCs at the time nTc, wherein the first geometric information includes the position of the action point of each multipath component (MPC) at the time nTc. j=1,2,…,J, J is the total number of MPCs at that moment, k=1,2,…,K, k is the number of action points of the mth MPC, and the first field strength information includes the receiving field strength of each MPC at the receiver The meaning of j is the same as above. Similarly, the propagation environment information and antenna information at the end time (n+1)Tc of the time period to be predicted are input into the ray tracing algorithm for simulation to obtain the second geometric information and second field strength information of the MPCs at the time (n+1)Tc, where the second geometric information includes the position of the action point of each multipath component (MPC) at the time (n+1)Tc. j=1,2,…,J, J is the total number of MPCs at that moment, k=1,2,…,K, k is the number of action points of the mth MPC, and the second field strength information includes the receiving field strength E of each MPC at the receiver j (n+1)Tc , j has the same meaning as above.

[0048] Step S103, obtaining the motion trajectory equation and birth and death phenomena of the MPCs in the time period to be predicted based on the first geometric information, the second geometric information and the motion parameters;

[0049] In step S103, based on the obtained geometric information of the MPCs at time nTc and time (n+1)Tc, extrapolation is performed according to the principles of geometric optics and consistent diffraction to obtain the motion trajectory equations and life and death phenomena of the MPCs in the entire time period to be predicted. This is different from the traditional RT-based channel prediction method that requires moment-by-moment calculation.

[0050] Step S104 , based on the birth and death phenomenon, extrapolate according to the motion trajectory equation, the first field strength information, and the second field strength information to obtain geometric prediction information and field strength prediction information of the MPCs at any predicted moment within the predicted time period.

[0051] In step S104, based on the birth and death phenomenon, the geometric prediction information and field strength prediction information of the MPCs at any predicted moment within the predicted time period can be obtained by direct extrapolation according to the motion trajectory equation, the first field strength information and the second field strength information, thereby eliminating the complex calculation process in the traditional RT-based channel prediction method, making it simpler and more efficient.

[0052] In an embodiment of the present invention, for any prediction time period, prediction is performed based on the principles of optical propagation. Compared to existing solutions, the present invention takes into account the birth and death of a large number of MPCs that exist in reality. Therefore, based on the propagation environment information at the start and end times of the prediction time period, bidirectional extrapolation is performed based on the antenna information of the transceiver and the motion parameters of the mobile object in the wireless propagation environment to maximize the prediction of the birth and death of MPCs at any time within the prediction time period, thereby improving the accuracy of channel prediction. Furthermore, during the prediction process, based on the birth and death of MPCs, the present invention utilizes the changing patterns of geometric information and field strength information of the MPCs' points of action at different times to perform geometric extrapolation and field strength extrapolation, respectively. This replaces the traditional ray tracing method of emitting a large number of ray tubes (typically thousands or even more) in a spherical manner in three-dimensional space point by point and moment by moment, and performing the single-point, single-shot geometric calculation process of this solution on each ray tube. The geometric prediction information and field strength prediction information of the MPCs at the predicted time within the prediction time period are obtained, thereby improving the computational efficiency of channel prediction. Furthermore, the method provided by the present invention has certain interpretability and generalizability.

[0053] Optionally, the first geometric information includes position information of the action point of the MPCs at the starting time, and the second geometric information includes position information of the action point of the MPCs at the ending time;

[0054] The step of obtaining the motion trajectory equation and the life and death phenomenon of the MPCs within the time period to be predicted based on the first geometric information, the second geometric information, and the motion parameters includes:

[0055] Based on the position information of the action point of the MPCs at the starting moment, forward calculation is performed using the principles of geometric optics and consistent diffraction to obtain a first motion trajectory equation of the action point of the MPCs within the time period to be predicted;

[0056] Based on the position information of the action point of the MPCs at the end time, reverse calculation is performed using the principles of geometric optics and consistent diffraction to obtain a second motion trajectory equation of the action point of the MPCs within the time period to be predicted;

[0057] According to the first motion trajectory equation, the second motion trajectory equation and the motion parameters, the life and death phenomena of the MPCs in the time period to be predicted are obtained.

[0058] In the embodiment of the present invention, the position information of the action points of the MPCs at the start time nTc and the end time (n+1)Tc of the time period to be predicted are calculated respectively, thereby obtaining the first motion trajectory equation and the second motion trajectory equation. The process is described in detail below:

[0059] The action point position of MPCs at nTc Using the principles of geometric optics and consistent diffraction, the first motion trajectory equation of the action points of these MPCs in the time period from nTc to (n+1)Tc is forward calculated. For example, the point P of a primary reflection path acting on plane y=0 R , according to the principle of mirror reflection in geometric optics, P R The position at time t satisfies the following equation:

[0060]

[0061] in P R The three-dimensional coordinates, (x TX ,y TX ,z TX ) and (x RX ,y RX ,z RX ) are the three-dimensional coordinates of the transmitter and receiver respectively. Using the chain rule, the velocity equation of the reflection point can be obtained by taking the derivative of the above equation with respect to time t. For example, for the velocity component in the x direction

[0062]

[0063] where v TX and v RX The velocity equations of the x, y, and z directions are combined to obtain the trajectory equation of the action point on the path. Similarly, according to the reflection theorem, transmission theorem and consistent diffraction theory in geometric optics, we can find all

[0064] Similarly, based on the position of the action point of MPCs at time (n+1)Tc Using the principles of geometric optics and consistent diffraction, the second motion trajectory equation of the action points of these MPCs in the time period from nTc to (n+1)Tc is reversely calculated.

[0065] Finally, according to the first motion trajectory equation the second motion trajectory equation and the motion parameters, the birth and death phenomena of the MPCs within the to-be-predicted time period are obtained.

[0066] Optionally, obtaining the birth and death phenomena of the MPCs within the to-be-predicted time period according to the first motion trajectory equation, the second motion trajectory equation and the motion parameters includes:

[0067] According to the first motion trajectory equation and the motion parameters, the earliest time when the action point of the MPCs at the starting moment moves out of the building boundary in the wireless propagation environment is determined as the extinction time of the MPCs at the starting moment;

[0068] According to the second motion trajectory equation and the motion parameters, the latest time when the action point of the MPCs at the ending moment moves to the building boundary in the wireless propagation environment is determined as the birth time of the MPCs at the ending moment;

[0069] Based on the extinction time of the MPCs at the starting moment and the birth time of the MPCs at the ending moment, extrapolation is performed to obtain the birth and death phenomena of the MPCs within the to-be-predicted time period.

[0070] In an embodiment of the present invention, the method for obtaining the birth and death phenomena of the MPCs is specifically described. According to the first motion trajectory equation and the motion parameters, the earliest time among the times when one or more action points of the MPCs existing at the nTc moment move out of the building boundary is calculated, that is, the extinction time corresponding to the MPCs at the starting moment nTc According to the second motion trajectory equation and the motion parameters, the latest time among the times when one or more action points of the MPCs existing at the (n + 1)Tc moment move to the building boundary is calculated That is, the birth time corresponding to the MPCs at the ending moment (n + 1)Tc.

[0071] For any to-be-predicted moment t in the nth prediction time period, where t ∈ {t|nTc < t < (n + 1)Tc}, judge the relationship between t and and and then extrapolate the birth and death phenomena of the MPCs in the nth prediction time period.

[0072] Optionally, based on the birth and death phenomena, according to the motion trajectory equation, the first field strength information and the second field strength information, extrapolation is performed to obtain the geometric prediction information and field strength prediction information of the MPCs at the to-be-predicted moment within the to-be-predicted time period, including:

[0073] Based on the life and death phenomenon, obtaining MPCs existing at the time to be predicted within the time period to be predicted;

[0074] Extrapolating the motion trajectory equation to obtain geometric prediction information of the MPCs at the time to be predicted;

[0075] Based on electromagnetic principles and the geometric prediction information, extrapolation is performed according to the first field strength information and the second field strength information to obtain the field strength prediction information of the MPCs at the time to be predicted.

[0076] In the embodiment of the present invention, after obtaining the birth and death phenomenon of MPCs, obtain the MPCs existing at the time t to be predicted within the time period to be predicted, and record the number as O, and then according to and Extrapolate the positions of the action points of these MPCs at time t That is, the geometric prediction information of MPCs This application is based on the phenomenon of life and death and directly extrapolates according to the motion trajectory equation. Unlike the traditional RT calculation method, it replaces the traditional ray tracing method of emitting massive ray tubes (usually thousands or even more) in a spherical form in three-dimensional space point by point and moment by moment and performing the single-point single-time geometric calculation process in this scheme on each ray tube.

[0077] like Figure 3 As shown, taking the reflection path as an example, the transmitter, receiver and transmitter mirror point all move according to the rules, then the reflection point r n The MPCs also move according to the rules, and thus the field intensity changes of the MPCs are regular. Based on the calculation equations of reflection, transmission, and diffraction field intensity in electromagnetics, the relationship between the electric field intensity of the same MPC at different positions is obtained, and the electric field intensity of the MPCs at different times can be extrapolated. The specific steps are as follows:

[0078] 1) Based on the calculation equations of reflection, transmission, and diffraction field strength in electromagnetics, the relationship between the electric field strength of the same MPC at different times is obtained. For example, if the reflection path satisfies:

[0079]

[0080] Among them, t0 is the reference time, t i is the extrapolated time, is the reflection coefficient, which can be obtained by Fresnel reflection theorem. and are the incident vector and the reflection vector of the reflection, which can be obtained from the geometric information of the action point. Similarly, the expressions of the transmission path and the diffraction path are the same as above, but in the expression of the transmission path, is the transmission coefficient. In the expression of the diffraction path, is the diffraction coefficient.

[0081] 2) For any prediction time t ∈ {t|nTc < t < (n + 1)Tc}, based on the Use And Extrapolate to obtain the field strength prediction information of O MPCs at time t Different from the traditional RT method.

[0082] Finally, integrate the geometric prediction information of MPCs within nTc to (n + 1)Tc And the field strength prediction information Complete the channel prediction for the nth prediction time.

[0083] Optionally, the method further includes:

[0084] Obtain the actual prediction time of the channel in the wireless propagation environment and the preset channel correlation time;

[0085] According to the actual prediction time and the channel correlation time, divide the actual prediction time into N prediction times, where N is an integer greater than 1, and the prediction time period to be predicted is any one of the N prediction times; <9>

[0086] Sequentially obtain the geometric prediction information and field strength prediction information of MPCs at any prediction time within each of the N prediction times.

[0087] In the embodiment of the present invention, the actual prediction time is directly obtained, and the channel correlation time is obtained by looking up the statistical channel coherence time of the current scenario category or set according to an empirical value. Among them, the category of the current scenario is determined according to the International Telecommunication Union (ITU) 38.901 standard, including but not limited to urban macrocell, urban microcell, rural area outside the city, etc. Divide the actual prediction time into N prediction times, N = T all / Tc, where T all Represents the actual prediction time, and Tc represents the channel correlation time. When performing channel prediction in the embodiment of the present invention, predict each prediction time in sequence, that is, sequentially obtain the geometric prediction information and field strength prediction information of MPCs at any prediction time within each of the N prediction times. As Figure 4 Shown, after completing the channel prediction within the nth prediction time, let n = n + 1, and judge whether is greater than N at this time. If it is greater, end the prediction. If it is less than or equal to, perform channel prediction on the nth prediction time at this time. Generally, the initial time n = 1.

[0088] The channel prediction method provided by the present invention is compared and evaluated with the AI-based channel prediction method in the prior art through a specific embodiment. The specific steps are as follows:

[0089] 1) Obtain a real-world street scene, such as the one from the open source OpenStreetMap website. The scene dimensions are 800 meters long, 400 meters wide, and 40 meters high. Define the transmitter and receiver as mobile objects, each moving at a speed of 36 km / h along a 300-meter-long lane. Set Tc to 1 second, and predict MPCs every 0.1 seconds.

[0090] 2) Using the RT full simulation as the true value benchmark, the proposed method is compared with the existing methods in terms of MPCs geometry and field strength. Define ε G and ε E are the average relative errors of the predicted value and the true value at all prediction moments, and the information of MPCs is extracted as the power delay profile (PDP) of the wireless channel, and the error of the spectrum is ε PDP The comparison results are shown in Table 1.

[0091]

[0092] Table 1: Comparison of prediction errors

[0093] The results show that the proposed method has a significant difference in MPCs geometric mean error ε G Compared with the existing method, the average error of field strength is reduced by 21%. E Taking into account the geometric and field strength errors of MPCs, the power delay spectrum of the wireless channel is extracted based on the information of MPCs, and the average error of the spectrum is ε PDP A decrease of 26.4%.

[0094] 3) On the same 12th Gen Intel(R) Core(MT) i7-12700H@4.7GHz hardware platform, the runtime complexity of the proposed method is compared with that of the existing RT algorithm. The results are shown in Table 2.

[0095] Running phase (time unit: s) Existing methods Method of the present invention Predicting MPCs geometry information 3608 341 Predict MPCs field strength information 37 7 total 3645 348

[0096] Table 2: Run time comparison results

[0097] The results show that the method of the present invention uses geometric extrapolation instead of geometric path finding in geometric information prediction, which reduces the calculation time by 90.5%. In addition, the method of the present invention uses field strength extrapolation instead of field strength calculation in field strength acquisition, which significantly reduces the calculation time by 81.1%, and the total running time is reduced by 90.5%.

[0098] In summary, compared with the prior art, the method described in the embodiment of the present invention takes into account the birth and death of a large number of MPCs in reality, thereby significantly improving the accuracy of channel prediction. In addition, during the prediction process, based on the birth and death of MPCs, the present invention uses the changing patterns of the geometric information and field strength information of the points of action of MPCs at different times to perform geometric extrapolation and field strength extrapolation, respectively, to obtain the geometric prediction information and field strength prediction information of the MPCs at the time to be predicted within the prediction time period. This replaces the traditional ray tracing method of emitting a large number of ray tubes (usually thousands or even more) in a spherical form in three-dimensional space point by point and moment by moment, and performing the single-point single-time geometric calculation process of this solution on each ray tube. This improves the computational efficiency of channel prediction and reduces the running time. In addition, the method provided by the present invention has certain interpretability and generalization.

[0099] like Figure 5 As shown, an embodiment of the present invention further provides a fast channel prediction device based on dynamic ray tracing, comprising:

[0100] The first acquisition module 501 is used to obtain antenna information of a transceiver, motion parameters of a mobile object, and propagation environment information in a wireless propagation environment;

[0101] A first calculation module 502 is configured to obtain, based on the propagation environment information and the antenna information, first geometric information and first field strength information of the multipath components MPCs at the start time of the time period to be predicted, and second geometric information and second field strength information of the MPCs at the end time of the time period to be predicted;

[0102] A second calculation module 503 is configured to obtain the motion trajectory equation and the birth and death phenomenon of the MPCs within the time period to be predicted based on the first geometric information, the second geometric information and the motion parameters;

[0103] The third calculation module 504 is used to perform extrapolation based on the life and death phenomenon, the motion trajectory equation, the first field strength information and the second field strength information to obtain geometric prediction information and field strength prediction information of the MPCs at the predicted moment in the predicted time period.

[0104] Optionally, the first geometric information in the first calculation module 502 includes position information of the action point of the MPCs at the starting time; the second geometric information in the first calculation module 502 includes position information of the action point of the MPCs at the ending time;

[0105] The second calculation module 503 includes:

[0106] a first calculation unit, configured to perform forward calculation based on the position information of the action point of the MPCs at the starting moment, using the principle of geometric optics and the principle of consistent diffraction, to obtain a first motion trajectory equation of the action point of the MPCs within the time period to be predicted;

[0107] a second calculation unit, configured to perform reverse calculation based on the position information of the action point of the MPCs at the end moment, using the principle of geometric optics and the principle of consistent diffraction, to obtain a second motion trajectory equation of the action point of the MPCs within the time period to be predicted;

[0108] The third calculation unit is used to obtain the life and death phenomenon of the MPCs in the time period to be predicted according to the first motion trajectory equation, the second motion trajectory equation and the motion parameters.

[0109] Optionally, the third computing unit includes:

[0110] a first determining unit, configured to determine, based on the first motion trajectory equation and the motion parameter, an earliest time at which the action point of the MPCs at the starting moment moves out of a building boundary in the wireless propagation environment as an extinction time of the MPCs at the starting moment;

[0111] a second determining unit, configured to determine, based on the second motion trajectory equation and the motion parameter, a latest time at which the action point of the MPCs at the end moment moves to a building boundary in the wireless propagation environment as a birth time of the MPCs at the end moment;

[0112] The first extrapolation unit is configured to perform extrapolation based on the extinction time of the MPCs at the starting moment and the birth time of the MPCs at the ending moment, to obtain the birth and death phenomena of the MPCs within the time period to be predicted.

[0113] Optionally, the third calculation module 504 includes:

[0114] A first acquisition unit is configured to acquire, based on the birth and death phenomenon, MPCs existing at the time to be predicted within the time period to be predicted;

[0115] A second extrapolation unit is configured to perform extrapolation based on the motion trajectory equation to obtain geometric prediction information of the MPCs at the time to be predicted;

[0116] The third extrapolation unit is configured to perform extrapolation based on the electromagnetic principle and the geometric prediction information according to the first field strength information and the second field strength information to obtain the field strength prediction information of the MPCs at the time to be predicted.

[0117] Optionally, the device further comprises:

[0118] The second acquisition module acquires the actual predicted time and preset channel correlation time of the channel in the wireless propagation environment;

[0119] a first division module, which divides the actual prediction time into N prediction time segments according to the actual prediction time and the channel correlation time, wherein N is an integer greater than 1, and the time period to be predicted is any one of the N prediction time segments;

[0120] The third acquisition module is used to sequentially obtain the geometric prediction information and field strength prediction information of the MPCs at any time to be predicted in each prediction time segment of the N prediction time segments.

[0121] It should be noted that the embodiment of the device is a device corresponding to the embodiment of the above method, and all implementation methods in the embodiment of the above method are applicable to the embodiment of the device and can achieve the same technical effect.

[0122] An embodiment of the present invention also provides a network device, comprising: a processor, a memory, and a program stored in the memory and runnable on the processor. When the program is executed by the processor, the program implements the fast channel prediction method based on dynamic ray tracing as described in any of the above items, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0123] An embodiment of the present invention further provides a computer-readable storage medium, comprising: a program stored on the computer-readable storage medium, wherein when the program is executed by a processor, the program implements the steps of the fast channel prediction method based on dynamic ray tracing as described in any of the above items, and can achieve the same technical effect. To avoid repetition, the details are not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0124] An embodiment of the present invention also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the steps of the fast channel prediction method based on dynamic ray tracing as described in any of the above items are implemented, and the same technical effects can be achieved. To avoid repetition, they will not be described here.

[0125] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0126] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A fast channel prediction method based on dynamic ray tracing, characterized in that: include: Obtain antenna information of transceivers, motion parameters of mobile objects and propagation environment information in wireless propagation environments; Obtaining, based on the propagation environment information and the antenna information, first geometric information and first field strength information of the multipath components MPCs at a start time of a time period to be predicted, and second geometric information and second field strength information of the MPCs at an end time of the time period to be predicted; Obtaining, based on the first geometric information, the second geometric information, and the motion parameters, motion trajectory equations and birth and death phenomena of the MPCs within the time period to be predicted; Based on the birth and death phenomenon, extrapolation is performed according to the motion trajectory equation, the first field strength information and the second field strength information to obtain geometric prediction information and field strength prediction information of the MPCs at the predicted moment within the predicted time period.

2. The fast channel prediction method based on dynamic ray tracing according to claim 1, characterized in that: The first geometric information includes position information of the action point of the MPCs at the starting time, and the second geometric information includes position information of the action point of the MPCs at the ending time; The step of obtaining the motion trajectory equation and the life and death phenomenon of the MPCs within the time period to be predicted based on the first geometric information, the second geometric information, and the motion parameters includes: Based on the position information of the action point of the MPCs at the starting moment, forward calculation is performed using the principles of geometric optics and consistent diffraction to obtain a first motion trajectory equation of the action point of the MPCs within the time period to be predicted; Based on the position information of the action point of the MPCs at the end time, reverse calculation is performed using the principles of geometric optics and consistent diffraction to obtain a second motion trajectory equation of the action point of the MPCs within the time period to be predicted; According to the first motion trajectory equation, the second motion trajectory equation and the motion parameters, the life and death phenomena of the MPCs in the time period to be predicted are obtained.

3. The fast channel prediction method based on dynamic ray tracing according to claim 2, characterized in that: Obtaining the life and death phenomena of the MPCs within the time period to be predicted according to the first motion trajectory equation, the second motion trajectory equation, and the motion parameters, including: Determining, based on the first motion trajectory equation and the motion parameter, the earliest time at which the action point of the MPCs at the starting moment moves out of the building boundary in the wireless propagation environment as the extinction time of the MPCs at the starting moment; Determining, based on the second motion trajectory equation and the motion parameters, a latest time at which the action point of the MPCs at the end moment moves to a building boundary in the wireless propagation environment as a birth time of the MPCs at the end moment; Extrapolation is performed based on the extinction time of the MPCs at the starting moment and the birth time of the MPCs at the ending moment to obtain the birth and death phenomena of the MPCs in the time period to be predicted.

4. The fast channel prediction method based on dynamic ray tracing according to claim 1, characterized in that: Based on the birth and death phenomenon, extrapolation is performed according to the motion trajectory equation, the first field strength information, and the second field strength information to obtain geometric prediction information and field strength prediction information of the MPCs at the time to be predicted within the time period to be predicted, including: Based on the life and death phenomenon, obtaining MPCs existing at the time to be predicted within the time period to be predicted; Extrapolating the motion trajectory equation to obtain geometric prediction information of the MPCs at the time to be predicted; Based on electromagnetic principles and the geometric prediction information, extrapolation is performed according to the first field strength information and the second field strength information to obtain the field strength prediction information of the MPCs at the time to be predicted.

5. The fast channel prediction method based on dynamic ray tracing according to claim 1, characterized in that: The method further comprises: Obtain the actual predicted time of the channel and the preset channel correlation time in the wireless propagation environment; Dividing the actual prediction time into N prediction time segments according to the actual prediction time and the channel correlation time, wherein N is an integer greater than 1, and the time period to be predicted is any one of the N prediction time segments; The geometric prediction information and field strength prediction information of the MPCs at any time to be predicted in each prediction time segment of the N prediction time segments are obtained in sequence.

6. A fast channel prediction device based on dynamic ray tracing, characterized in that: include: A first acquisition module is used to obtain antenna information of a transceiver, motion parameters of a mobile object and propagation environment information in a wireless propagation environment; a first calculation module, configured to obtain, based on the propagation environment information and the antenna information, first geometric information and first field strength information of the multipath components MPCs at a start time of a time period to be predicted, and second geometric information and second field strength information of the MPCs at an end time of the time period to be predicted; a second calculation module, configured to obtain, based on the first geometric information, the second geometric information, and the motion parameters, motion trajectory equations and birth and death phenomena of the MPCs within the time period to be predicted; The third calculation module is used to perform extrapolation based on the life and death phenomenon, the motion trajectory equation, the first field strength information and the second field strength information to obtain the geometric prediction information and field strength prediction information of the MPCs at the predicted moment in the predicted time period.

7. A network device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the fast channel prediction method based on dynamic ray tracing according to any one of claims 1 to 5.

8. A readable storage medium, characterized in that: include: The readable storage medium stores a program, and when the program is executed by the processor, the steps of the fast channel prediction method based on dynamic ray tracing according to any one of claims 1 to 5 are implemented.

9. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the fast channel prediction method based on dynamic ray tracing as claimed in any one of claims 1 to 5.

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