A time-varying multipath clustering method for dynamic channels

Abstract

The present invention provides a time-varying multipath clustering method for a dynamic channel. The method comprises: initializing the parameters of the MPC to obtain the multipath components of multiple M frames; tracking the MPC according to the multipath components to obtain the evolution trajectory of the MPC between consecutive frames; calculating the fluctuation trend of the MPC trajectory according to the evolution trajectory of the MPC between consecutive frames; dividing two MPC trajectories into three position situations according to the fluctuation trend of the MPC trajectory: complete overlap, partial overlap and complete separation, calculating the distance between two MPC trajectories in combination with the fluctuation trend of each trajectory, and performing MPC clustering according to the distance between two MPC trajectories. The clustering method proposed in the present invention uses the fluctuation trend of the MPC trajectory and the trajectory spacing as the clustering basis, and can accurately identify and cluster overlapping trajectories with similar fluctuation trends and similar trajectory spacing.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a time-varying multipath clustering method for a dynamic channel. Background Art

[0002] With the development of intelligent transportation systems, the research on vehicle communications has received widespread attention. Due to the mobility of communication terminals and the dynamic changes of the surrounding environment, wireless channels in mobile communications generally have time-varying characteristics. Therefore, the description and modeling of dynamic channel characteristics are very important. At present, a large number of channel measurement results have shown that the parameters of MPC (multipath component) (such as delay, angle, power, etc.) are clustered in time-varying channels. Therefore, a suitable MPC clustering method is needed to accurately and comprehensively understand the dynamic channel characteristics of MPC.

[0003] An automatic clustering method for MIMO channel parameters based on multipath component distance in the prior art uses a K-Means algorithm and utilizes the MPC distance (MCD) to quantify the similarity between MPCs.

[0004] An automatic clustering framework for MIMO channel data with path power parameters in the prior art further optimizes the K-Means algorithm and uses MPC power as a weighting factor to obtain cluster centers.

[0005] The disadvantages of the above-mentioned methods in the prior art are: although these works have conducted clustering research on MPC, they are mainly designed for static channels, that is, in static channels, the clustering process is performed on each frame of the channel separately, which is obviously not applicable to time-varying dynamic channels. Modeling of dynamic channels requires not only clustering within a single frame, but also exploring the time-varying characteristics of MPC between consecutive frames, which is lacking in the current static channel clustering algorithm. On the other hand, the currently proposed trajectory clustering algorithm is too one-sided, only considering some sections of the MPC trajectory and discarding non-overlapping sections, which will lead to inaccurate channel clustering. For this reason, it is necessary to develop an accurate recognition method based on the evolution of MPC over time. Summary of the invention

[0006] The embodiments of the present invention provide a time-varying multipath clustering method for a dynamic channel to overcome the problems of the prior art.

[0007] In order to achieve the above object, the present invention adopts the following technical scheme.

[0008] A time-varying multipath clustering method for a dynamic channel, comprising:

[0009] Initialize the parameters of MPC and obtain the multipath components of multiple M frames:

[0010] The MPC is tracked according to the multipath components to obtain an evolution trajectory of the MPC between consecutive frames.

[0011] Calculating the fluctuation trend of the MPC trajectory according to the evolution trajectory of the MPC between consecutive frames;

[0012] According to the fluctuation trend of MPC trajectories, two MPC trajectories are divided into three position situations: complete overlap, partial overlap and complete separation. The distance between two MPC trajectories is calculated based on the fluctuation trend of each trajectory, and MPC clustering is performed based on the distance between two MPC trajectories.

[0013] Preferably, the initialization of the MPC parameters to obtain the multipath components of multiple M frames includes:

[0014] Initialize the MPC parameters, including power α, delay τ, arrival angle (φ R ,θ R ) and departure angle (φ T ,θ T ) parameters, and obtain the multipath components of N M frames;

[0015]

[0016] in, is the power of the nth MPC in the mth frame, is the delay of the nth MPC in the mth frame, and are the departure azimuth and departure pitch angle of the nth MPC in the mth frame, and are the arrival azimuth and arrival elevation of the nth MPC in the mth frame.

[0017] Preferably, the tracking of the MPC according to the multipath component to obtain the evolution trajectory of the MPC between consecutive frames includes:

[0018] The Kalman filter is used to track the MPC. According to the power, angle and delay of the MPC of the mth frame, the estimated value of the MPC of the m+1th frame is estimated by the Kalman filter.

[0019] Use the Kuhn–Munkres algorithm to convert the true MPC value of the mth frame into and the MPC estimate of the m+1th frame Matching is performed so that the weight sum of the global matching is the minimum value, and the global best matching is obtained. The matching threshold is calculated as follows:

[0020]

[0021] in, for and of matches, U is the set of all matches, yes and multipath distance.

[0022] At two MPC intervals As the basis for judgment, whether there is disappearance and rebirth of MPC in the tracking process, the judgment threshold is set as η. It is believed that is a new MPC, and the specific threshold formula is:

[0023]

[0024] Preferably, the calculation of the fluctuation trend of the MPC trajectory according to the evolution trajectory of the MPC between consecutive frames includes:

[0025] The states of the j MPCs on each trajectory are divided into five states: 0, 1, -1, 2, and -2. The decision formula is calculated as follows:

[0026]

[0027] Among them, δ 1 and δ 2 is the state judgment threshold, state 0 means that the trajectory state of the nth MPC trajectory remains unchanged from the mth frame to the (m+1)th frame, 1 / (-1) means that the trajectory shows an upward / downward trend from the mth frame to the (m+1)th frame, and 2 / (-2) means that the trajectory shows a rapid upward / downward trend from the mth frame to the (m+1)th frame;

[0028] If MPC is from the mth frame to the (m+lth c ) frame, then the (l c +1) The frame MPC is divided into a segment and the fluctuation of the trajectory of this segment is calculated using the following formula:

[0029]

[0030] Among them, l c Indicates the length of the trajectory segment with the same continuous state, represents the fluctuation of the trajectory segment, express and distance.

[0031] Preferably, the two MPC trajectories are divided into three position situations according to the fluctuation trend of the MPC trajectories: complete overlap, partial overlap and complete separation, the distance between the two MPC trajectories is calculated in combination with the fluctuation trend of each trajectory, and MPC clustering is performed according to the distance between the two MPC trajectories, including:

[0032] If there is a situation where trajectory A and trajectory B completely overlap and the length of trajectory A is greater than that of trajectory B, the length of trajectory A is extended to obtain A′, which is calculated as follows:

[0033]

[0034] Among them, m A =1,...,M A is the number of frames of trajectory A, m B =1,...,M B is the number of frames of trajectory B, L A′ represents the frame length of trajectory A′, D c (m A′ ,m B ) represents the fluctuation difference between trajectory A and trajectory B, express and distance;

[0035] If track A and track B partially overlap, the calculation is as follows:

[0036]

[0037] Among them, l o represents the length of the overlapping part of trajectory A and trajectory B, l n Indicates the length of the non-overlapping portion of track A and track B. It's time. is the time domain function of trajectory A;

[0038] If track A and track B are completely separated, the calculation is as follows:

[0039]

[0040] Among them, l s is the shortest length between trajectory A and trajectory B, is the time domain function of trajectory B;

[0041] After obtaining the distance between two MPC trajectories, the KPD algorithm is used to cluster each trajectory. Trajectories with closer distances are clustered into one category, and trajectories with farther distances are clustered into another category.

[0042] It can be seen from the technical solutions provided by the above-mentioned embodiments of the present invention that the clustering method proposed in the embodiments of the present invention uses the fluctuation trend and trajectory spacing of the MPC trajectory as the clustering basis, and can accurately identify and cluster overlapping trajectories with similar fluctuation trends and similar trajectory spacing.

[0043] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0045] Figure 1 A processing flow chart of a time-varying multipath clustering method for a dynamic channel provided by an embodiment of the present invention.

[0046] Figure 2 A graph of tracking results of a time-varying MPC provided by an embodiment of the present invention.

[0047] Figure 3 A schematic diagram of an MPC trajectory position provided in an embodiment of the present invention.

[0048] Figure 4 A clustering result diagram based on simulation data provided by an embodiment of the present invention.

[0049] Figure 5 A clustering result diagram based on vehicle channel measured data provided by an embodiment of the present invention.

[0050] Figure 6 A KPD clustering result diagram based on vehicle channel measured data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0052] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0053] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0054] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0055] In order to accurately track and cluster MPC in a dynamic channel, the processing flow of a time-varying multipath clustering method based on a dynamic channel proposed in an embodiment of the present invention is as follows: Figure 1 As shown, the processing steps include the following:

[0056] Step S10: Initialize the MPC power α, delay τ, and arrival angle (φ R ,θ R ), departure angle (φ T ,θ T ) and other parameters, the multipath component MPC of N M frames can be expressed as follows:

[0057]

[0058] in, is the power of the nth MPC in the mth frame, is the delay of the nth MPC in the mth frame, and are the departure azimuth and departure pitch angle of the nth MPC in the mth frame, and are the arrival azimuth and arrival elevation of the nth MPC in the mth frame.

[0059] Due to the complexity of the mobile communication environment, there are a large number of unevenly distributed scatterers, which makes the received signal a synthesis of multiple multipaths from different directions and phases. Therefore, the study of wireless channel characteristics depends on the study of multipath components.

[0060] Step S20: Tracking the MPC according to the multipath components to obtain an evolution trajectory of the MPC between consecutive frames.

[0061] Figure 2 A tracking result diagram of a time-varying MPC is provided in an embodiment of the present invention. The specific processing steps are as follows:

[0062] Step 1: Use Kalman filtering to track the MPC, that is, based on the power, angle, delay and other dimensions of the MPC of the mth frame, use Kalman filtering to estimate the estimated value of the MPC of the m+1th frame

[0063] Step 2: Use the Kuhn–Munkres algorithm to convert the true MPC value of the mth frame and the MPC estimate of the m+1th frame Matching is performed so that the weight sum of the global matching is the minimum, that is, the global best matching is obtained. The matching threshold is calculated as follows:

[0064]

[0065] in, for and of matches, U is the set of all matches, yes and multipath distance.

[0066] Step 3: Due to the movement of the communication terminal and the changes in the scatterers in the environment, each MPC suddenly appears or disappears during the propagation process. Therefore, it is necessary to determine whether there is "disappearance" or "rebirth" of MPC during the tracking process. As the basis for judgment, let the judgment threshold be η. If It is believed that is a new MPC, and the specific threshold formula is:

[0067]

[0068] Step S30: Since the channel is time-varying, different MPCs will form different evolution trajectories in consecutive frames as time changes. The fluctuation trend of the MPC trajectory is calculated based on the evolution trajectory of the MPC between consecutive frames.

[0069] Figure 3 A schematic diagram of an MPC trajectory position provided by an embodiment of the present invention. The specific processing steps include:

[0070] Step 1: Divide the states of the j MPCs on each trajectory into five states: 0, 1, -1, 2, and -2. The decision formula is calculated as follows:

[0071]

[0072] Among them, δ 1 and δ 2 is the state judgment threshold. State 0 means that the trajectory state of the nth MPC trajectory remains unchanged from the mth frame to the (m+1)th frame. 1 / (-1) means that the trajectory shows an upward / downward trend from the mth frame to the (m+1)th frame. 2 / (-2) means that the trajectory shows a rapid upward / downward trend from the mth frame to the (m+1)th frame.

[0073] Step 2: If MPC is from the mth frame to the (m+lth c ) frame, then the (l c +1) The frame MPC is divided into a segment and the fluctuation of the trajectory of this segment is calculated using the following formula:

[0074]

[0075] Among them, l c Indicates the length of the trajectory segment with the same continuous state, represents the fluctuation of the trajectory segment, express and distance.

[0076] Step S40: according to the fluctuation trend of the MPC trajectories, the two MPC trajectories are divided into three position situations: complete overlap, partial overlap and complete separation, the distance between the two MPC trajectories is calculated in combination with the fluctuation trend of each trajectory, and MPC clustering is performed according to the distance between the two MPC trajectories.

[0077] Figure 4 A clustering result diagram based on simulation data is provided in an embodiment of the present invention. The specific processing process includes:

[0078] If there is a situation where trajectory A and trajectory B completely overlap and the length of trajectory A is greater than that of trajectory B, the length of trajectory A is extended to obtain A′, which is calculated as follows:

[0079]

[0080] Among them, m A =1,...,M A is the number of frames of trajectory A, m B =1,...,M B is the number of frames of trajectory B, L A′ represents the frame length of trajectory A′, D c (m A′ ,m B ) represents the fluctuation difference between trajectory A and trajectory B, express and distance.

[0081] If track A and track B partially overlap, the calculation is as follows:

[0082]

[0083] Among them, l o Indicates the length of the overlapping part of trajectory A and trajectory B, l n Indicates the length of the non-overlapping portion of track A and track B. It's time. is the time domain function of trajectory A.

[0084] If track A and track B are completely separated, the calculation is as follows:

[0085]

[0086] Among them, l s is the shortest length between trajectory A and trajectory B, is the time domain function of trajectory B.

[0087] After obtaining the distance between two MPC trajectories, it is used as the basis for clustering and the KPD (Kernel-power-density-based clustering algorithm) algorithm is used to cluster each trajectory. Trajectories with closer distances are clustered into one category, and trajectories with farther distances are clustered into another category.

[0088] The following specifically describes a time-varying multipath clustering method based on dynamic channels of the present invention through accompanying drawings and embodiments. In this case, measured data is collected from vehicle channel measurements, and each frame time is about 20 ms.

[0089] In order to verify the superiority of the time-varying channel clustering designed by the present invention, this method is compared with the KPD clustering method. Figure 5 A clustering result diagram based on vehicle channel measured data provided by an embodiment of the present invention. Figure 6 A KPD clustering result diagram based on vehicle channel measured data provided by an embodiment of the present invention. Figure 5 and Figure 6 It can be seen that the algorithm proposed in the present invention can identify the dynamic trajectory of MPC between consecutive frames and accurately cluster the overlapping trajectories. It can be seen that the present invention can indeed accurately cluster the time-varying multipath components in the dynamic channel, verifying the effectiveness and reliability of the proposed algorithm.

[0090] In summary, the dynamic channel clustering method proposed in the embodiment of the present invention is different from the static channel clustering method and is suitable for high-speed mobile communication terminals and complex communication scenarios.

[0091] The present invention proposes a clustering method for time-varying MPCs in dynamic channels, which obtains the time-varying trajectory of each MPC by tracking and matching it, and uses the fluctuation trend of the MPC trajectory and the trajectory spacing as the clustering basis. The present invention comprehensively considers the overlapping and separation positions of the MPC trajectories, and can accurately identify the evolution process of the MPC cluster when the trajectories overlap, meeting the requirements of time-varying channel characteristic description and modeling.

[0092] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0093] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0094] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A time-varying multipath clustering method for dynamic channels, It is characterized in that include: Initialize the parameters of MPC and obtain the multipath components of multiple M frames: Tracking the MPC according to the multipath components to obtain an evolution trajectory of the MPC between consecutive frames; Calculating the fluctuation trend of the MPC trajectory according to the evolution trajectory of the MPC between consecutive frames; According to the fluctuation trend of MPC trajectories, two MPC trajectories are divided into three positions: complete overlap, partial overlap and complete separation. The distance between two MPC trajectories is calculated based on the fluctuation trend of each trajectory, and MPC clustering is performed based on the distance between two MPC trajectories. The method of calculating the fluctuation trend of the MPC trajectory according to the evolution trajectory of the MPC between consecutive frames includes: The states of the j MPCs on each trajectory are divided into five states: 0, 1, -1, 2, and -2. The decision formula is calculated as follows: Among them, δ 1 and δ 2 is the state judgment threshold, state 0 means that the trajectory state of the nth MPC trajectory remains unchanged from the mth frame to the (m+1)th frame, 1 / (-1) means that the trajectory shows an upward / downward trend from the mth frame to the (m+1)th frame, and 2 / (-2) means that the trajectory shows a rapid upward / downward trend from the mth frame to the (m+1)th frame; If MPC is from the mth frame to the (m+lth c ) frame, then the (l c +1) The frame MPC is divided into a segment and the fluctuation of the trajectory of this segment is calculated using the following formula: Among them, l c Indicates the length of the trajectory segment with the same continuous state, represents the fluctuation of the trajectory segment, Represents MPC With MPC distance; The two MPC trajectories are divided into three position situations according to the fluctuation trend of the MPC trajectories: complete overlap, partial overlap and complete separation, the distance between the two MPC trajectories is calculated in combination with the fluctuation trend of each trajectory, and MPC clustering is performed according to the distance between the two MPC trajectories, including: If there is a situation where trajectory A and trajectory B completely overlap and the length of trajectory A is greater than that of trajectory B, the length of trajectory A is extended to obtain A′, which is calculated as follows: where m A = 1, ..., M A is the number of frames of trajectory A, and m B = 1, ..., M B is the number of frames of trajectory B, L A′ represents the frame length of trajectory A', D c (m A′ , m B ) represents the fluctuation gap between trajectory A and trajectory B, represents MPC and MPC distance; If track A and track B partially overlap, the calculation is as follows: Among them, l o Indicates the length of the overlapping part of trajectory A and trajectory B, l n represents the length of the non-overlapping part of track A and track B, It's time. is the time domain function of trajectory A; If track A and track B are completely separated, the calculation is as follows: Among them, l s is the shortest length between trajectory A and trajectory B, is the time domain function of trajectory B; After obtaining the distance between two MPC trajectories, the KPD algorithm is used to cluster each trajectory. Trajectories with closer distances are clustered into one category, and trajectories with farther distances are clustered into another category.

2. The method according to claim 1, It is characterized in that The parameters of the initialization MPC are used to obtain the multipath components of multiple M frames, including: Initialize the MPC parameters, including power α, delay τ, arrival angle (φ R ,θ R ) and departure angle (φ T ,θ T ) parameters, and obtain the multipath components of N M frames; in, is the power of the nth MPC in the mth frame, is the delay of the nth MPC in the mth frame, and are the departure azimuth and departure pitch angle of the nth MPC in the mth frame, and are the arrival azimuth and arrival elevation of the nth MPC in the mth frame.

3. The method according to claim 2, It is characterized in that The tracking of the MPC according to the multipath component to obtain the evolution trajectory of the MPC between consecutive frames includes: The Kalman filter is used to track the MPC. According to the power, angle and delay of the MPC of the mth frame, the estimated value of the MPC of the m+1th frame is estimated by the Kalman filter. Use the Kuhn–Munkres algorithm to convert the true MPC value of the mth frame into and the MPC estimate of the m+1th frame Matching is performed so that the weight sum of the global matching is the minimum value, and the global best matching is obtained. The matching threshold is calculated as follows: in, for and of matches, U is the set of all matches, yes and Multipath distance; With two MPC spacing As the basis for judgment, whether there is disappearance and rebirth of MPC in the tracking process, the judgment threshold is set as η. It is believed that is a new MPC, and the specific threshold formula is:

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