Probe selection method and device for dynamic channel reconstruction, equipment, medium and product

Abstract

The invention relates to a probe selection method and device for dynamic channel reconstruction, equipment, a medium and a product, and is applied to the technical field of wireless communication. The method comprises the following steps: acquiring standard channel parameters of a plurality of road points in a dynamic channel, the number of snapshots and a probe set of each road point; and according to the standard channel parameters of every two adjacent road points and the number of the snapshots, calculating the channel parameter variation of each snapshot at the moment. And calculating a to-be-selected probe set of each snapshot according to the channel parameter variable quantity and the probe sets of every two adjacent road points. And based on a probe selection model, screening the to-be-selected probe set of each snapshot to obtain an optimal probe combination of each snapshot. The change of the probe position of the dynamic channel is reflected through the channel parameter variable quantity corresponding to each snapshot, and the probe selection of the snapshots is associated with the probe of the adjacent road point. And the problem of complicated calculation caused by selecting the probe corresponding to the snapshot from all the probes is avoided. Therefore, the complexity of dynamic channel reconstruction is reduced.

Description

Technical Field

[0001] The present application belongs to the field of wireless communication technology, and specifically relates to a probe selection method, device, equipment, medium and product for dynamic channel reconstruction. Background Art

[0002] Dynamic channel reconstruction can adjust the allocation and use of channel resources in real time according to network status and business needs. Dynamic channel reconstruction can more accurately simulate the channel characteristics in the actual communication environment, including spatial characteristics, temporal characteristics and their joint characteristics, which can improve the effective use of network resources and the continuity of services.

[0003] Generally, when performing dynamic channel reconstruction, the optimal target probe combination in the current channel can be screened based on all probes in the current channel and the parameters of the dynamic channel. However, this selection method will ignore the correlation between the target probes at each moment, reducing the efficiency of probe selection. Therefore, it will increase the complexity of dynamic channel reconstruction. Summary of the invention

[0004] The present disclosure is proposed in view of the above problems. The present disclosure provides a probe selection method, device, equipment, medium and product for dynamic channel reconstruction, which reduces the complexity of dynamic channel reconstruction.

[0005] According to one aspect of the present disclosure, a probe selection method for dynamic channel reconstruction is provided, comprising:

[0006] Acquire standard channel parameters of a plurality of waypoints in a dynamic channel, the number of snapshots, and a probe set for each of the waypoints, wherein the snapshots are pointers inserted at a time between every two waypoints;

[0007] Calculating a channel parameter change at a time instant of each snapshot according to standard channel parameters of every two adjacent waypoints and the number of snapshots;

[0008] Calculating a candidate probe set for each snapshot according to the channel parameter variation and the probe sets of every two adjacent waypoints;

[0009] Based on the probe selection model, the candidate probe set of each snapshot is screened to obtain the optimal probe combination of each snapshot.

[0010] Optionally, calculating the candidate probe set of each snapshot according to the channel parameter variation and the probe set of every two waypoints includes:

[0011] Determine a first time interval between each of the snapshots and an adjacent first waypoint, and determine a second time interval between each of the snapshots and an adjacent second waypoint; the first waypoint is located earlier than the second waypoint;

[0012] Calculating a first channel parameter offset of each snapshot relative to the first waypoint according to the channel parameter change and the first time interval; and calculating a second channel parameter offset of each snapshot relative to the second waypoint according to the channel parameter change and the second time interval;

[0013] Calculate a first sub-probe set of each snapshot according to the first channel parameter offset and the probe set of the first waypoint; and calculate a second sub-probe set of each snapshot according to the second channel parameter offset and the probe set of the second waypoint;

[0014] The first sub-probe set and the second sub-probe set are used as candidate probe sets for each of the snapshots.

[0015] Optionally, the calculating, according to the standard channel parameters of every two adjacent waypoints and the number of snapshots, the channel parameter variation at the moment of each snapshot includes:

[0016] Taking a difference between the standard channel parameters of every two adjacent waypoints, and calculating the sum of the channel parameter changes at the moments of all the snapshots between every two adjacent waypoints;

[0017] The channel parameter variation at the time of each snapshot is calculated according to the sum of the channel parameter variation divided by the number of snapshots.

[0018] Optionally, before screening the candidate probe set of each snapshot based on the probe selection model to obtain the optimal probe combination of each snapshot, the method further includes:

[0019] When it is determined that there is no preset probe at the position of the target probe in the candidate probe set of each snapshot, grouping the preset probes in the dynamic channel to obtain a plurality of probe groups;

[0020] Determine a target probe group that is closest to the position of the target probe;

[0021] All probes in the target probe group are replaced with the target probes that are not preset in the candidate probe set.

[0022] Optionally, after screening the candidate probe set of each snapshot based on the probe selection model to obtain the optimal probe combination of each snapshot, the method further includes:

[0023] Obtaining the relative power of each probe in the optimal probe combination;

[0024] Performing convex optimization processing on the relative power of each probe to calculate the weight of each probe;

[0025] The dynamic channel is reconstructed based on the optimal probe combination of the snapshot, the weight of each probe of the snapshot, the probe set of each waypoint and the probe weight of each waypoint.

[0026] Optionally, the screening of the candidate probe set of each snapshot based on the probe selection model to obtain the optimal probe combination of each snapshot includes:

[0027] Generate a candidate probe area of ​​the probe set to be selected for each snapshot based on the probe selection algorithm, the number of probe activations preset for the dynamic channel, and the probe position in the probe set to be selected for each snapshot;

[0028] The candidate probe regions of each of the snapshots are classified and corrected to obtain an optimal probe combination of each of the snapshots.

[0029] According to another aspect of the present disclosure, a probe selection device for dynamic channel reconstruction is provided, comprising:

[0030] An acquisition module, used to acquire standard channel parameters of a plurality of waypoints in a dynamic channel, the number of snapshots and a probe set of each of the waypoints, wherein the snapshot is a pointer inserted at a time between every two waypoints;

[0031] A first calculation module, configured to calculate a channel parameter variation at a time instant of each snapshot according to standard channel parameters of every two adjacent waypoints and the number of snapshots;

[0032] A second calculation module, configured to calculate a candidate probe set of each snapshot according to the channel parameter variation and the probe sets of every two adjacent waypoints;

[0033] The screening module is used to screen the candidate probe set of each snapshot based on the probe selection model to obtain the optimal probe combination of each snapshot.

[0034] According to another aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned probe selection method for dynamic channel reconstruction.

[0035] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and the program is executed by a processor to implement the above-mentioned probe selection method for dynamic channel reconstruction.

[0036] According to another aspect of the present disclosure, a computer program product is provided, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the probe selection method for implementing the above-mentioned dynamic channel reconstruction.

[0037] In the present disclosure, standard channel parameters of multiple waypoints in a dynamic channel, the number of snapshots and the probe set of each waypoint are obtained, and the snapshot is a pointer inserted at the moment between every two waypoints. According to the standard channel parameters of every two adjacent waypoints and the number of snapshots, the channel parameter change at the moment of each snapshot is calculated. According to the channel parameter change and the probe set of every two adjacent waypoints, the candidate probe set of each snapshot is calculated. Based on the probe selection model, the candidate probe set of each snapshot is screened to obtain the optimal probe combination of each snapshot. The change in the probe position of the dynamic channel is reflected by the channel parameter change corresponding to each snapshot. When calculating the candidate probe set of each snapshot, the probe selection of the snapshot is associated with the probe of the adjacent waypoint. The problem of complex calculation caused by selecting the probe corresponding to each snapshot from all probes is avoided. Therefore, the complexity of dynamic channel reconstruction can be reduced.

[0038] It is to be understood that both the foregoing general description and the following detailed description are exemplary, and are intended to provide further explanation of the technology as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and other purposes, features and advantages of the present disclosure will become more apparent by describing the embodiments of the present disclosure in more detail in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0040] Figure 1 A flow chart of a probe selection method for dynamic channel reconstruction provided by the present disclosure.

[0041] Figure 2 Schematic diagram of the time-averaged spatial correlation error and the number of activated probes provided for the present disclosure.

[0042] Figure 3 A schematic diagram of the time required for probe selection and the number of activated probes provided by the present invention.

[0043] Figure 4 A schematic structural diagram of a probe selection device for dynamic channel reconstruction provided by the present invention.

[0044] Figure 5 A hardware block diagram of an electronic device provided by the present disclosure.

[0045] Figure 6 A schematic diagram of a computer program product provided by the present disclosure. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the application scenario of the solution of the present application is first described below.

[0047] In order to meet the growing data needs of mankind, the sixth generation (6G) communication system will undergo a new paradigm shift and apply new enabling technologies. The new paradigm shift can be summarized as an integrated communication network of air, land, and sea, including satellites, drones, ultra-dense ground networks, underground communications, maritime communications, and underwater acoustic communications. In order to provide higher data rates, technologies such as massive multiple input multiple output (Massive MIMO) and carrier aggregation (CA) will be used. In order to realize the new paradigm and enabling technologies of 6G, performance testing is required in a darkroom during the research and development stage to evaluate the feasibility of the technology. However, the new trend of 6G will lead to the characteristics of massive and time-varying wireless channels, and traditional channel reconstruction methods are difficult to cope with these new changing trends. Dynamic channel reconstruction can adjust the allocation and use of channel resources in real time according to network status and business needs. Dynamic channel reconstruction can more accurately simulate the channel characteristics in the actual communication environment, including spatial characteristics, temporal characteristics, and their joint characteristics, which can improve the effective utilization of network resources and the continuity of services.

[0048] At present, when performing dynamic channel reconstruction, the optimal target probe combination in the channel at the current moment can be screened based on all the probes in the channel at the current moment and the parameters of the dynamic channel. For example, the orthogonal matching pursuit algorithm in the sparse representation is used to select a small number of activated probes for target channel reconstruction. In each round of verification, the reconstruction error after replacing the verified probe with the unselected probe is calculated and compared until all probes are verified and the optimal target probe combination is obtained. However, this selection method selects probes based on all probes at each moment, and the probe selections are independent of each other, which will ignore the correlation between the target probes at each moment and reduce the efficiency of probe selection. Therefore, it will increase the complexity of dynamic channel reconstruction.

[0049] In the present disclosure, standard channel parameters of multiple waypoints in a dynamic channel, the number of snapshots and the probe set of each waypoint are obtained, and the snapshot is a pointer inserted at the moment between every two waypoints. According to the standard channel parameters of every two adjacent waypoints and the number of snapshots, the channel parameter change at the moment of each snapshot is calculated. According to the channel parameter change and the probe set of every two adjacent waypoints, the candidate probe set of each snapshot is calculated. Based on the probe selection model, the candidate probe set of each snapshot is screened to obtain the optimal probe combination of each snapshot. The change in the probe position of the dynamic channel is reflected by the channel parameter change corresponding to each snapshot. When calculating the candidate probe set of each snapshot, the probe selection of the snapshot is associated with the probe of the adjacent waypoint. The problem of complex calculation caused by selecting the probe corresponding to each snapshot from all probes is avoided. Therefore, the complexity of dynamic channel reconstruction can be reduced.

[0050] In order to make the purpose, technical solution and advantages of the present disclosure more obvious, the exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure, and it should be understood that the present disclosure is not limited to the exemplary embodiments described here.

[0051] Figure 1 The present invention provides a flow chart of a probe selection method for dynamic channel reconstruction. Figure 1 As shown, the method includes:

[0052] S101: Obtain standard channel parameters of multiple waypoints in a dynamic channel, the number of snapshots, and a probe set for each waypoint.

[0053] The dynamic channel needs to be reconstructed according to the probe antenna in the channel. Therefore, the selection of the probe at each moment in the channel is particularly important for dynamic channel reconstruction.

[0054] Specifically, a waypoint is a marking point of a signal data track marked in a dynamic channel, and the position of the waypoint in the reconstructed dynamic channel can be preset. According to the dynamic channel type, the standard channel parameters of each waypoint under the dynamic channel type are obtained. Among them, the standard channel parameters include the azimuth, elevation, angle spread and cluster power corresponding to the waypoint.

[0055] The standard channel parameters at the waypoints are the data of some moments in the dynamic channel. In order to ensure the accuracy of dynamic channel reconstruction, it is necessary to perform linear interpolation between the waypoints to generate snapshots. The snapshot is a pointer inserted at the moment between every two waypoints. In this embodiment, uniform interpolation is performed between every two waypoints, and the number of inserted snapshots is the same. The number of snapshots inserted between every two waypoints is obtained to facilitate the calculation of the subsequent channel parameter change.

[0056] According to the probe selection algorithm, the probes required for dynamic channel reconstruction at each waypoint are selected to obtain the probe set of each waypoint. In this embodiment, the probe set of each waypoint can be used to determine the optimal probe combination at the moment of the snapshot. By associating the probe selection of the snapshot with the probes of the adjacent waypoints, the problem of complex calculation caused by selecting the probe corresponding to each snapshot from all the probes can be avoided.

[0057] S102: Calculate the channel parameter variation at the time of each snapshot according to the standard channel parameters of every two adjacent waypoints and the number of snapshots.

[0058] Specifically, according to the channel angle in the standard channel parameters of the waypoints, the channel parameter change at the corresponding moment of the snapshot inserted between every two waypoints is calculated, that is, the first-order change of the channel angle. Among them, the channel angle includes the channel azimuth angle and the channel elevation angle. For a dynamic channel, the channel angle change can characterize the trend of the entire dynamic channel changing over time, and thus accurately simulate the movement trend of the optimal probe combination required for the dynamic channel.

[0059] S103: Calculate a candidate probe set for each snapshot according to the channel parameter variation and the probe set of every two adjacent waypoints.

[0060] Specifically, the probe set of each waypoint is obtained as mentioned above. In this embodiment, the candidate probe set corresponding to each snapshot is calculated based on the probe set of each two adjacent waypoints and the channel parameter change of each snapshot inserted between each two adjacent waypoints. In this way, the determination of the candidate probe set at the moment of each snapshot is related to the probe sets of the waypoints before and after. The probe sets of the waypoints can accurately simulate the movement trend of the optimal probe combination required for the snapshot inserted in the dynamic channel, while reducing the number of elements of the probe set used to determine the optimal probe combination of each snapshot.

[0061] S104: Based on the probe selection model, the candidate probe set of each snapshot is screened to obtain the optimal probe combination of each snapshot.

[0062] Specifically, in this embodiment, the probe selection model can be constructed by the MultiShot algorithm or the BER algorithm, and the set of candidate probes for each snapshot is input into the trained probe selection model. According to the number of activated probes preset in the probe selection model, the optimal probe combination for each snapshot can be obtained.

[0063] Figure 2 Schematic diagram of the time-averaged spatial correlation error and the number of activated probes.

[0064] Among them, method 1 is based on the MultiShot algorithm, and all probes are used for probe selection at each moment; method 2 is based on the BER algorithm, and all probes are used for probe selection at each moment; method 3 is based on the MultiShot algorithm, and probe selection is performed based on the change in channel parameters and the probe set of every two adjacent waypoints at each snapshot; method 4 is based on the BER algorithm, and probe selection is performed based on the change in channel parameters and the probe set of every two adjacent waypoints at each snapshot.

[0065] Figure 3 Schematic diagram of the time required for probe selection and the number of active probes.

[0066] Among them, method 1 is based on the MultiShot algorithm, and all probes are used for probe selection at each moment; method 2 is based on the BER algorithm, and all probes are used for probe selection at each moment; method 3 is based on the MultiShot algorithm, and probe selection is performed based on the change in channel parameters and the probe set of every two adjacent waypoints at each snapshot; method 4 is based on the BER algorithm, and probe selection is performed based on the change in channel parameters and the probe set of every two adjacent waypoints at each snapshot.

[0067] In the present disclosure, standard channel parameters of multiple waypoints in a dynamic channel, the number of snapshots and the probe set of each waypoint are obtained, and the snapshot is a pointer inserted at the moment between every two waypoints. According to the standard channel parameters of every two adjacent waypoints and the number of snapshots, the channel parameter change at the moment of each snapshot is calculated. According to the channel parameter change and the probe set of every two adjacent waypoints, the candidate probe set of each snapshot is calculated. Based on the probe selection model, the candidate probe set of each snapshot is screened to obtain the optimal probe combination of each snapshot. The change in the probe position of the dynamic channel is reflected by the channel parameter change corresponding to each snapshot. When calculating the candidate probe set of each snapshot, the probe selection of the snapshot is associated with the probe of the adjacent waypoint. The problem of complex calculation caused by selecting the probe corresponding to each snapshot from all probes is avoided. Therefore, the complexity of dynamic channel reconstruction can be reduced.

[0068] In a possible implementation, an exemplary method for calculating a candidate probe set for each snapshot according to a channel parameter variation and a probe set of every two waypoints includes:

[0069] A first time interval between each snapshot and an adjacent first waypoint is determined, and a second time interval between each snapshot and an adjacent second waypoint is determined.

[0070] Specifically, for every two adjacent waypoints, the waypoint at the smaller time can be used as the first waypoint, and the waypoint at the later time can be used as the second waypoint. Multiple snapshots are inserted between the first waypoint and the second waypoint, and the time interval between each snapshot and the first waypoint is used as the first time interval, and the time interval between each snapshot and the second waypoint is used as the second time interval.

[0071] For example, the number of snapshots inserted between the first waypoint and the second waypoint is N s -1, for the ssth snapshot inserted between the first waypoint and the second waypoint, the first time interval Δt1 between the snapshot and the adjacent first waypoint is ss, and the second time interval Δt2 between the snapshot and the adjacent second waypoint is N s -Δt1.

[0072] Afterwards, a first channel parameter offset of each snapshot relative to the first waypoint is calculated according to the channel parameter change and the first time interval; and a second channel parameter offset of each snapshot relative to the second waypoint is calculated according to the channel parameter change and the second time interval.

[0073] Specifically, the calculated values ​​of the channel parameter changes corresponding to different dynamic channel types may be different. For example, when the types of dynamic channels between two waypoints are the same, the dynamic laws of the clusters in the channel are similar, and the channel parameter changes can be averaged to describe the trend of the entire channel over time; when the types of dynamic channels between two waypoints are different, the dynamic laws of the clusters in the channel are relatively complex, and the mean of the channel parameter changes is not enough to describe the trend of the entire channel over time, and the channel parameter changes of all clusters need to be used.

[0074] As mentioned above, the channel parameter variation includes the channel azimuth angle variation and the channel elevation angle variation.

[0075] When the types of dynamic channels between two waypoints are the same, the first channel parameter offset can be calculated according to the following formula:

[0076]

[0077] in, is the first channel azimuth offset in the first channel parameter offset, ΔAOD(wp) is the channel azimuth variation in the channel parameter variation, and Δt1 is the first time interval.

[0078] Δθ1=mean(ΔEOD(wp))·Δt1

[0079] Wherein, Δθ1 is the first channel pitch angle offset in the first channel parameter offset, ΔEOD(wp) is the channel pitch angle variation in the channel parameter variation, and Δt1 is the first time interval.

[0080] When the types of dynamic channels between two waypoints are the same, the second channel parameter offset can be calculated according to the following formula:

[0081]

[0082] in, is the second channel azimuth offset in the second channel parameter offset, ΔAOD(wp) is the channel azimuth change in the channel parameter change, N s -Δt1 is the second time interval.

[0083] Δθ2=mean(ΔEOD(wp))·(N s -Δt1)

[0084] Wherein, Δθ2 is the second channel pitch angle offset in the second channel parameter offset, ΔEOD(wp) is the channel pitch angle change in the channel parameter change, N s -Δt1 is the second time interval.

[0085] When the types of dynamic channels between two waypoints are different, the first channel parameter offset can be calculated according to the following formula:

[0086]

[0087] in, is the first channel azimuth offset in the first channel parameter offset, ΔAOD(wp) is the channel azimuth variation in the channel parameter variation, and Δt1 is the first time interval.

[0088] Δθ1=ΔEOD(wp)·Δt1

[0089] Wherein, Δθ1 is the first channel pitch angle offset in the first channel parameter offset, ΔEOD(wp) is the channel pitch angle variation in the channel parameter variation, and Δt1 is the first time interval.

[0090] When the types of dynamic channels between two waypoints are different, the second channel parameter offset can be calculated according to the following formula:

[0091]

[0092] in, is the second channel azimuth offset in the second channel parameter offset, ΔAOD(wp) is the channel azimuth change in the channel parameter change, N s -Δt1 is the second time interval.

[0093] Δθ2=ΔEOD(wp)·(Ns -Δt1)

[0094] Wherein, Δθ2 is the second channel pitch angle offset in the second channel parameter offset, ΔEOD(wp) is the channel pitch angle change in the channel parameter change, N s -Δt1 is the second time interval.

[0095] Afterwards, the first sub-probe set of each snapshot is calculated according to the first channel parameter offset and the probe set of the first waypoint; and the second sub-probe set of each snapshot is calculated according to the second channel parameter offset and the probe set of the second waypoint. The first sub-probe set and the second sub-probe set are used as the candidate probe sets for each snapshot.

[0096] Specifically, the channel parameter offset in the dynamic channel can be mapped to the offset of the activated probe space angle. According to the calculated probe position at each snapshot, combined with the probe sets of the first waypoint and the second waypoint, the first sub-probe set relative to the first waypoint and the second sub-probe set relative to the second waypoint of the snapshot are determined, and the selected probe set for each snapshot is obtained.

[0097] When the type of dynamic channel between two waypoints is the same, the probe position at each snapshot can be calculated according to the following formula:

[0098]

[0099] in, is the azimuth of the first channel at the moment of the snapshot, is the probe set of the first waypoint, is the first channel azimuth offset in the first channel parameter offset, θ1(t) is the first channel elevation angle at the moment of the snapshot, Δθ1 is the first channel elevation angle offset in the first channel parameter offset, is the azimuth of the second channel at the moment of the snapshot, is the probe set of the second waypoint, is the second channel azimuth offset in the second channel parameter offset, θ2(t) is the second channel elevation angle at the moment of the snapshot, and Δθ2 is the second channel elevation angle offset in the second channel parameter offset.

[0100] When the types of dynamic channels between two waypoints are different, the first sub-probe set and the second sub-probe set of each snapshot can be calculated according to the following formula:

[0101]

[0102] in, is the azimuth of the first channel at the moment of the snapshot, is the probe set of the first waypoint, is the first channel azimuth offset in the first channel parameter offset, θ1(t) is the first channel elevation angle at the moment of the snapshot, Δθ1 is the first channel elevation angle offset in the first channel parameter offset, is the azimuth of the second channel at the moment of the snapshot, is the probe set of the second waypoint, is the second channel azimuth offset in the second channel parameter offset, θ2(t) is the second channel elevation angle at the moment of the snapshot, and Δθ2 is the second channel elevation angle offset in the second channel parameter offset.

[0103] In a possible implementation, an exemplary method for calculating the channel parameter change amount at the time of each snapshot according to the standard channel parameters of every two adjacent waypoints and the number of snapshots includes:

[0104] The standard channel parameters of every two adjacent waypoints are subtracted, and the sum of the channel parameter changes at the time of all snapshots between every two adjacent waypoints is calculated; the channel parameter change at the time of each snapshot is calculated by dividing the sum of the channel parameter changes by the number of snapshots.

[0105] Specifically, the channel parameter change at each snapshot moment can be calculated according to the following formula:

[0106]

[0107] Wherein, ΔAOD(wp) is the channel azimuth change in the channel parameter change, AOD(wp+1) is the channel azimuth of the second waypoint among two adjacent waypoints, AOD(wp) is the channel azimuth of the first waypoint among two adjacent waypoints, EOD(wp+1) is the channel elevation angle of the second waypoint among two adjacent waypoints, EOD(wp) is the channel elevation angle of the first waypoint among two adjacent waypoints, N s Add 1 to the number of snapshots inserted between two adjacent waypoints.

[0108] And, AOD(wp)∈R C×1 and EOD∈R C×1 are the azimuth and elevation angles at the first waypoint wp, respectively. wp ,C wp+1 ), C is the number of channel clusters at the first waypoint wp wp and the number of channel clusters C at the second waypoint wp+1 wp+1 The larger of .

[0109] In a possible implementation, before screening the candidate probe set of each snapshot based on the probe selection model to obtain the optimal probe combination of each snapshot, the method further includes:

[0110] When it is determined that there is no preset probe at the position of the target probe in the candidate probe set of each snapshot, the preset probes in the dynamic channel are grouped to obtain multiple probe groups; the target probe group closest to the position of the target probe is determined; and all probes in the target probe group replace the target probes that are not preset in the candidate probe set.

[0111] Specifically, before screening the optimal probe combination of each snapshot, the probes in the candidate probe set of each snapshot may not be exactly distributed at the preset probe positions in the darkroom. All probes with preset distribution in the darkroom can be grouped by the rectangular grouping method to obtain multiple probe groups, and the nearest target probe group can be found by the positions of the probes in the candidate probe set, and the probe positions of the candidate probe set can be updated according to the actual positions of the probes in the target probe group.

[0112] In a possible implementation, after screening the candidate probe set of each snapshot based on the probe selection model to obtain the optimal probe combination of each snapshot, the method further includes:

[0113] The relative power of each probe in the optimal probe combination is obtained; the relative power of each probe is subjected to convex optimization processing to calculate the weight of each probe; and the dynamic channel is reconstructed based on the optimal probe combination of the snapshot, the weight of each probe of the snapshot, the probe set of each waypoint and the probe weight of each waypoint.

[0114] Specifically, after determining the optimal probe combination for each snapshot, the weight of each probe can be determined through a convex optimization method according to the position of each probe in the optimal probe combination and the relative power of each probe, and the dynamic channel can be reconstructed according to the probe weight at each snapshot and the probe weight at each waypoint.

[0115] In a possible implementation, based on the probe selection model, the candidate probe set of each snapshot is screened to obtain the optimal probe combination of each snapshot. An exemplary method includes:

[0116] Based on the probe selection model, the number of probe activations preset by the dynamic channel and the probe positions in the candidate probe set of each snapshot, the candidate probe area of ​​the candidate probe set of each snapshot is generated; the candidate probe area of ​​each snapshot is classified and corrected to obtain the optimal probe combination of each snapshot.

[0117] Specifically, in this embodiment, the probe selection model can be constructed by the MultiShot algorithm or the BER algorithm, and the candidate probe set of each snapshot is input into the trained probe selection model. According to the preset number of activated probes in the probe selection model, after generating the candidate probe area, the candidate probe area is classified and corrected to obtain the optimal probe combination for each snapshot.

[0118] Figure 4 The present invention provides a schematic diagram of the structure of a probe selection device for dynamic channel reconstruction. Figure 4 As shown, the device 400 includes: an acquisition module 410, a first calculation module 420, a second calculation module 430 and a screening module 440.

[0119] An acquisition module 410 is used to acquire standard channel parameters of a plurality of waypoints in a dynamic channel, the number of snapshots, and a probe set for each of the waypoints, wherein the snapshot is a pointer inserted at a time between every two waypoints;

[0120] A first calculation module 420, configured to calculate a channel parameter variation at a time instant of each snapshot according to standard channel parameters of every two adjacent waypoints and the number of snapshots;

[0121] A second calculation module 430 is used to calculate a candidate probe set of each snapshot according to the channel parameter variation and the probe sets of every two adjacent waypoints;

[0122] The screening module 440 is used to screen the candidate probe set of each snapshot based on the probe selection model to obtain the optimal probe combination of each snapshot.

[0123] Optionally, the second calculation module is used for:

[0124] Determine a first time interval between each of the snapshots and an adjacent first waypoint, and determine a second time interval between each of the snapshots and an adjacent second waypoint; the first waypoint is located earlier than the second waypoint;

[0125] Calculating a first channel parameter offset of each snapshot relative to the first waypoint according to the channel parameter change and the first time interval; and calculating a second channel parameter offset of each snapshot relative to the second waypoint according to the channel parameter change and the second time interval;

[0126] Calculate a first sub-probe set of each snapshot according to the first channel parameter offset and the probe set of the first waypoint; and calculate a second sub-probe set of each snapshot according to the second channel parameter offset and the probe set of the second waypoint;

[0127] The first sub-probe set and the second sub-probe set are used as candidate probe sets for each of the snapshots.

[0128] Optionally, the first calculation module is used to:

[0129] Taking a difference between the standard channel parameters of every two adjacent waypoints, and calculating the sum of the channel parameter changes at the moments of all the snapshots between every two adjacent waypoints;

[0130] The channel parameter variation at the time of each snapshot is calculated according to the sum of the channel parameter variation divided by the number of snapshots.

[0131] Optionally, the device is also used for:

[0132] Before screening the candidate probe set of each snapshot based on the probe selection model to obtain the optimal probe combination of each snapshot, when it is determined that there is no preset probe at the position of the target probe in the candidate probe set of each snapshot, the preset probes in the dynamic channel are grouped to obtain multiple probe groups;

[0133] Determine a target probe group that is closest to the position of the target probe;

[0134] All probes in the target probe group are replaced with the target probes that are not preset in the candidate probe set.

[0135] Optionally, the device is also used for:

[0136] The probe selection model is based on which the candidate probe set of each snapshot is screened to obtain the optimal probe combination of each snapshot, and then the relative power of each probe in the optimal probe combination is obtained;

[0137] Performing convex optimization processing on the relative power of each probe to calculate the weight of each probe;

[0138] The dynamic channel is reconstructed based on the optimal probe combination of the snapshot, the weight of each probe of the snapshot, the probe set of each waypoint and the probe weight of each waypoint.

[0139] Optionally, the screening module is used to:

[0140] Generate a candidate probe area of ​​the probe set to be selected for each snapshot based on the probe selection algorithm, the number of probe activations preset for the dynamic channel, and the probe position in the probe set to be selected for each snapshot;

[0141] The candidate probe regions of each of the snapshots are classified and corrected to obtain an optimal probe combination of each of the snapshots.

[0142] The present application also provides an electronic device to perform the above-mentioned probe selection method for dynamic channel reconstruction. Figure 5 It shows a schematic diagram of an electronic device provided by some embodiments of the present application. Figure 5 As shown, the electronic device 50 includes: a processor 500, a memory 501, a bus 502 and a communication interface 503, wherein the processor 500, the communication interface 503 and the memory 501 are connected via the bus 502; the memory 501 stores a computer program that can be run on the processor 500, and when the processor 500 runs the computer program, the probe selection method for dynamic channel reconstruction provided in any of the aforementioned embodiments of the present application is executed.

[0143] The memory 501 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the device network element and at least one other network element is realized through at least one communication interface 503 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0144] The bus 502 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 501 is used to store a program, and the processor 500 executes the program after receiving an execution instruction. The probe selection method for dynamic channel reconstruction disclosed in any implementation of the embodiment of the present application may be applied to the processor 500, or implemented by the processor 500.

[0145] The processor 500 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 500. The above processor 500 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 501, and the processor 500 reads the information in the memory 501 and completes the steps of the above method in combination with its hardware.

[0146] The electronic device provided in the embodiment of the present application and the probe selection method for dynamic channel reconstruction provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0147] An embodiment of the present application also provides a computer-readable storage medium corresponding to the probe selection method for dynamic channel reconstruction provided in the aforementioned embodiment. The computer-readable storage medium shown may be a CD on which a computer program is stored. When the computer program is run by a processor, it executes the probe selection method for dynamic channel reconstruction provided in any of the aforementioned embodiments.

[0148] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0149] The computer-readable storage medium provided in the above-mentioned embodiment of the present application and the probe selection method for dynamic channel reconstruction provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0150] The present application embodiment also provides a computer program product 600, such as Figure 6 The computer program product carries a computer program 601, and the instructions included in the program code can be used to execute the steps of the probe selection method for dynamic channel reconstruction described in the above method embodiment. For details, please refer to the above method embodiment, which will not be repeated here.

[0151] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium. In another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0152] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limiting. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0153] The block diagrams of the devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including," "comprising," "having," and the like are open words, referring to "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or," and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0154] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0155] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0156] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0157] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0158] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A probe selection method for dynamic channel reconstruction, characterized in that: include: Acquire standard channel parameters of a plurality of waypoints in a dynamic channel, the number of snapshots, and a probe set for each of the waypoints, wherein the snapshots are pointers inserted at a time between every two waypoints; Calculating a channel parameter change at a time instant of each snapshot according to standard channel parameters of every two adjacent waypoints and the number of snapshots; Calculating a candidate probe set for each snapshot according to the channel parameter variation and the probe sets of every two adjacent waypoints; Based on the probe selection model, the candidate probe set of each snapshot is screened to obtain the optimal probe combination of each snapshot.

2. The method according to claim 1, characterized in that The step of calculating the candidate probe set of each snapshot according to the channel parameter variation and the probe set of every two waypoints comprises: Determine a first time interval between each of the snapshots and an adjacent first waypoint, and determine a second time interval between each of the snapshots and an adjacent second waypoint; the first waypoint is located earlier than the second waypoint; Calculating a first channel parameter offset of each snapshot relative to the first waypoint according to the channel parameter change and the first time interval; and calculating a second channel parameter offset of each snapshot relative to the second waypoint according to the channel parameter change and the second time interval; Calculating a first sub-probe set of each snapshot according to the first channel parameter offset and the probe set of the first waypoint; and calculating a second sub-probe set of each snapshot according to the second channel parameter offset and the probe set of the second waypoint; The first sub-probe set and the second sub-probe set are used as candidate probe sets for each of the snapshots.

3. The method according to claim 1, characterized in that: The calculating, according to the standard channel parameters of every two adjacent waypoints and the number of snapshots, the channel parameter variation at the moment of each snapshot comprises: Taking a difference between the standard channel parameters of every two adjacent waypoints, and calculating the sum of the channel parameter changes at the moments of all the snapshots between every two adjacent waypoints; The channel parameter variation at the time of each snapshot is calculated according to the sum of the channel parameter variation divided by the number of snapshots.

4. The method according to claim 1, characterized in that Before screening the candidate probe set of each snapshot based on the probe selection model to obtain the optimal probe combination of each snapshot, the method further includes: When it is determined that there is no preset probe at the position of the target probe in the candidate probe set of each snapshot, grouping the preset probes in the dynamic channel to obtain a plurality of probe groups; Determine a target probe group that is closest to the position of the target probe; All probes in the target probe group are replaced with the target probes that are not preset in the candidate probe set.

5. The method according to claim 1, characterized in that After screening the candidate probe set of each snapshot based on the probe selection model to obtain the optimal probe combination of each snapshot, the method further includes: Obtaining the relative power of each probe in the optimal probe combination; Performing convex optimization processing on the relative power of each probe to calculate the weight of each probe; The dynamic channel is reconstructed based on the optimal probe combination of the snapshot, the weight of each probe of the snapshot, the probe set of each waypoint and the probe weight of each waypoint.

6. The method according to claim 1, characterized in that The method of screening the candidate probe set of each snapshot based on the probe selection model to obtain the optimal probe combination of each snapshot includes: Generate a candidate probe area of ​​the probe set to be selected for each snapshot based on the probe selection algorithm, the number of probe activations preset for the dynamic channel, and the probe position in the probe set to be selected for each snapshot; The candidate probe regions of each of the snapshots are classified and corrected to obtain an optimal probe combination of each of the snapshots.

7. A probe selection device for dynamic channel reconstruction, characterized in that: include: An acquisition module, used to acquire standard channel parameters of a plurality of waypoints in a dynamic channel, the number of snapshots and a probe set of each of the waypoints, wherein the snapshot is a pointer inserted at a time between every two waypoints; A first calculation module, configured to calculate a channel parameter variation at a time instant of each snapshot according to standard channel parameters of every two adjacent waypoints and the number of snapshots; A second calculation module, configured to calculate a candidate probe set of each snapshot according to the channel parameter variation and the probe sets of every two adjacent waypoints; The screening module is used to screen the candidate probe set of each snapshot based on the probe selection model to obtain the optimal probe combination of each snapshot.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: The processor runs the computer program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code, and when the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes any one of claims 1 to 6.

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