Wave beam tracking method and system based on particle filtering for millimeter wave scene

Abstract

The invention relates to the technical field of millimeter wave communication, in particular to a wave beam tracking method and system based on particle filtering for a millimeter wave scene, and dynamically judges whether a tracking step needs to be executed by comparing signal-to-noise ratio fading conditions after beam forming of front and back time slots. And if tracking is needed, two disturbance angles are generated near a previous time slot data beam pointing angle, a measurement beam is formed, and a received signal strength ratio of the measurement beam is used as an observation value of the adaptive particle filter algorithm. An angle particle set is generated in a half-wave width interval with a current beam directional angle as a center, particle weights are calculated in combination with dual non-central F distribution, and then an optimal particle is selected to update a data beam direction. According to the invention, millimeter wave beams in different mobility level scenes can be efficiently tracked, the communication performance is guaranteed, the tracking overhead is reduced, the overhead is adaptively adjusted according to the user movement rate, the beam tracking precision can be remarkably improved, and the method is particularly suitable for millimeter wave communication in a high-speed scene.

Description

Technical Field

[0001] The present invention relates to the field of millimeter wave communication technology, and in particular to an Adaptive Beam Tracking Method based on Particle Filter (ABTPF) and a system for millimeter wave scenarios. Background Art

[0002] Millimeter wave communication, with its high bandwidth characteristics, has become a key technology to support ultra-high-speed data communication and an important direction to promote the evolution of mobile communication systems. However, due to the inherent high path loss of millimeter wave signals, communication equipment uses large-scale multiple-input multiple-output and beamforming technology to obtain the directional gain of the signal to ensure the coverage of communication. When using beamforming technology, in order to achieve high directional gain and optimize communication performance, the main path angles of the millimeter wave channel, namely the angle of arrival (AoA) and the angle of departure (AoD), must be accurately estimated to ensure that the beam direction is aligned with the main channel path direction. The initial establishment of a millimeter wave communication link requires beam alignment, which usually requires searching in the complete angle space. Existing beam initial alignment methods mainly include compressed sensing and spatial scanning. However, due to the large angle search space, initial alignment usually brings a large beam training overhead.

[0003] After completing the initial alignment of the beam, the millimeter wave communication transceiver terminal needs to maintain the main path direction of the channel through dynamic tracking, and update the beam direction in real time to ensure high-quality connection of the communication link. For the beam tracking problem, the existing technology has proposed a variety of solutions, but there are still limitations. For example, the codebook design based on hierarchical search uses codebooks of different resolutions to be arranged hierarchically in space to cover the search requirements of wide beams and narrow beams. Although this method effectively reduces the search overhead, its tracking accuracy is limited by the codebook resolution, and it is difficult to maintain efficient performance in complex dynamic scenes. For another example, beam tracking based on reinforcement learning uses the Q learning method to evaluate the simulated beamforming vector in the wireless channel to find the best beam pair that maximizes the signal interference plus noise ratio. This method requires a large-scale search of the discrete action space. When the Q table is too large, it may lead to overfitting, thereby affecting the actual application effect. Beam tracking based on supervised learning uses a long short-term memory network for offline training and realizes beam prediction through a large amount of pre-labeled sample data, but it has a strong dependence on data, and the offline training process brings high time and cost overhead.

[0004] Machine learning-based beam tracking methods usually require a large amount of training data to ensure model validity. However, in scenarios where data is scarce, traditional filtering methods are introduced to meet beam tracking needs. For example, the Kalman filter (KF) method estimates through a linear Gaussian model, but due to the error propagation problem when dealing with nonlinear estimation problems, the tracking error will gradually accumulate over time. To reduce such errors, the extended Kalman filter (EKF) linearizes the nonlinear problem to improve the estimation error, but its performance is still limited by the error introduced by linearization. On the other hand, the standard particle filter (SPF) and its variants are widely used in beam tracking, representing the state probability distribution through a particle set and using particle weights to approximate the posterior distribution of the signal. Compared with EKF, PF shows stronger robustness in dealing with nonlinear and non-Gaussian problems. However, the above schemes usually assume that the statistical characteristics of angle changes and path gain changes (such as variance) are known, while these parameters are often difficult to obtain accurately in actual scenarios. In addition, current particle filtering schemes generally ignore the problem of dynamically adjusting the tracking overhead according to the mobility in the scene, which will lead to a waste of computing resources. Summary of the invention

[0005] To this end, the present invention provides a particle filtering-based beam tracking method and system for millimeter wave scenarios. Measurements are performed through a pair of auxiliary beams, and the received signal strength ratio is calculated based on the measurement information as the observation value of the adaptive particle filtering algorithm. The main path angle of the channel is estimated according to the observation value to establish a good communication link. The signal-to-noise ratio fading after adjacent time slot beamforming is used to determine whether the tracking step needs to be performed, thereby achieving efficient tracking of millimeter wave beams in scenarios with different mobility levels and ensuring communication performance.

[0006] According to the design scheme provided by the present invention, on the one hand, a beam tracking method based on particle filtering for millimeter wave scenarios is provided, comprising:

[0007] Determine whether the difference in signal-to-noise ratio after beamforming between the current time slot and the previous time slot exceeds a set tracking threshold. If the difference exceeds the tracking threshold, beam tracking is triggered.

[0008] Add two specified offset angles to the data beam direction to generate a pair of auxiliary measurement waves, and start the particle filter algorithm based on the auxiliary measurement beam to dynamically estimate the target's motion state and adjust the beam direction;

[0009] Among them, the particle filter algorithm calculates the received signal strength ratio of the auxiliary measurement beam and inputs it into the particle filter as the observation value. It generates the angle particle set of the current time slot according to the half-wave width of the beam pointing angle of the previous time slot data, and then calculates the angle particle weight of the current time slot based on the probability density function of the double non-central F distribution. The angle particle with the highest weight is selected as the optimal angle particle of the path angle estimation value, and the data beam direction of the current time slot is updated with the optimal angle particle. The particle weight is used to measure the matching degree between the particle and the observation value.

[0010] As a beam tracking method based on particle filtering for millimeter wave scenarios of the present invention, further, for the measurement auxiliary beam, the receiving end in the data beam direction is set as the fixed end, and the transmitting end is set as the mobile end, then the data beam pointing angle in the t-1th time slot is Adding and subtracting the disturbance angle δ, two auxiliary measurement beams are generated, and their received signals are expressed as:

[0011]

[0012] in, represents the conjugate transpose; w and f represent the beamforming vectors at the receiving end and the transmitting end, respectively, and H t is the channel matrix of the tth time slot, and s is the pilot signal, and k Indicates the pilot length; is the channel path departure angle; M R and M T Respectively represent the number of antennas at the receiving end R and the transmitting end T; z - and z + The mean is 0 and the variance is The received signal contains the angle information of the channel path, which is the key parameter for estimating the angle of the main channel path.

[0013] As a beam tracking method based on particle filtering for millimeter wave scenarios of the present invention, further, a process of obtaining a received signal strength ratio of an auxiliary measurement beam includes:

[0014] Based on the received signal, the received signal strengths of the two auxiliary measurement beams are obtained, which are respectively expressed as: in, Respectively represent the received signals of two auxiliary measurement beams;

[0015] The ratio metric is calculated based on the received signal strength, where the calculation process of the ratio metric is expressed as:

[0016]

[0017] The received signal strength ratio is obtained based on the ratio measurement. The received signal strength ratio calculation formula is expressed as:

[0018] As a beam tracking method based on particle filtering for millimeter wave scenarios of the present invention, further, an angle particle set of the current time slot is generated according to the half-wave width of the beam pointing angle of the previous time slot data, including:

[0019] Set the angle particle set x of the particle filter algorithm in the tth time slot t Contains N particles, and its generation strategy is expressed as:

[0020]

[0021] Where i represents the particle index, The uniform distribution range of particles is

[0022] As a beam tracking method based on particle filtering for millimeter wave scenarios of the present invention, further, the particle weight of the current time slot angle is calculated based on the probability density function of the double non-central F distribution, including: setting the particles to obey the double non-central F distribution with degrees of freedom υ1 and υ2, and determining the weight of each particle using the double non-central F distribution probability density, wherein the weight calculation process of each particle is: for the i-th particle, the relevant double non-central parameter is expressed as:

[0023]

[0024] P represents the transmission power, Indicates that the channel estimation value is calculated using the i-th particle in time slot t; the weight of the i-th particle is given by the following formula Calculated, f F (·) represents the probability density function of the doubly noncentral F distribution.

[0025] As a beam tracking method based on particle filtering for millimeter wave scenarios of the present invention, further, the optimal angle particle as the path angle estimation value is selected according to the angle particle weight, and the current time slot data beam direction is updated based on the optimal angle particle, including:

[0026] use Normalize the particle weights, resample the particles according to the normalized particle weights, and retain particles with weights greater than the specified value during resampling. and its weight And obtain a new particle set by copying the retained particles and weight set Among them, the new particle set and weight set are expressed as: Represents the resampling operation strategy function to update the particle set through resampling operation and weight set

[0027] The estimated value of the target data beam pointing angle AoA is calculated using the resampled particle set. The estimated value calculation formula is expressed as: N is the total number of particles.

[0028] As a beam tracking method based on particle filtering for millimeter wave scenarios of the present invention, further, a process of comparing the signal-to-noise ratio after beamforming of the current time slot with that of the previous time slot includes:

[0029] Get the signal-to-noise ratio γ after beamforming in the tth time slot t and the signal-to-noise ratio after beamforming in the t-1th time slot γ t-1 , if the signal-to-noise ratio after beamforming of the two time slots meets the condition:

[0030] |γ t -γ t-1 |>ι

[0031] Then, a beam tracking is triggered in the tth time slot, where the time slot subscript is ignored and the calculation formula of the signal-to-noise ratio after beamforming is expressed as: P represents the transmission power and ι is the tracking threshold.

[0032] On the other hand, the present invention also provides a beam tracking system based on particle filtering for millimeter wave scenarios, comprising: a judgment module and a tracking module, wherein:

[0033] A judgment module, used to judge whether the difference of the signal-to-noise ratio after beamforming between the current time slot and the previous time slot exceeds a set tracking threshold, and if the difference exceeds the tracking threshold, it is determined that beam tracking is required in the current time slot;

[0034] The tracking module is used to add two specified offset angles to the data beam direction to generate a pair of auxiliary measurement beams, and start the particle filter algorithm based on the auxiliary measurement beams to dynamically estimate the motion state of the target and adjust the beam direction;

[0035] Among them, in the particle filtering algorithm, by obtaining the received signal strength ratio of the auxiliary measurement beam, the received signal strength ratio is used as the observation value of the particle filtering algorithm, and the angle particle set of the current time slot is generated according to the half-wave width of the pointing angle of the data beam of the previous time slot. The angle particle weight of the current time slot is calculated based on the probability density function of the double non-central F distribution, and the optimal angle particle as the path angle estimation value is selected according to the angle particle weight, and the data beam direction of the current time slot is updated based on the optimal angle particle. The particle weight is used to measure the matching degree between the particle and the observation value.

[0036] Beneficial effects of the present invention:

[0037] The present invention adds two disturbance angles to form a measurement beam based on the data beam pointing angle of the previous time slot, and uses its received signal strength ratio as the observation value of the adaptive particle filtering algorithm. Generate an angle particle set within the half-wave width interval, calculate the particle weight in combination with the dual non-central F distribution, and select the optimal particle to update the data beam direction. By comparing the signal-to-noise ratio after beamforming of the previous and next time slots, dynamically determine whether to perform particle filtering, thereby reducing the tracking overhead. And by comparing the difference in the signal-to-noise ratio after beamforming between consecutive time slots, the tracking mechanism is triggered only when the difference exceeds a preset threshold. This scheme significantly reduces unnecessary tracking overhead, enables the tracking strategy to be adaptively adjusted according to the user's mobile speed, and maintains high-precision angle tracking and low loss rate in high dynamic scenarios. It is further shown through Experiments 1 and 2 in the embodiment that the ABTPF scheme of this case can adapt to a variety of mobility communication scenarios and meet the high quality requirements of millimeter wave communication links under different dynamic conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The figure is a schematic diagram of a beam tracking process based on particle filtering for a millimeter wave scenario in an embodiment;

[0039] Figure 2 It is a schematic diagram of the motion model and the measurement model in the embodiment;

[0040] Figure 3 The angle tracking result and each tracking error in the embodiment are shown;

[0041] Figure 4 It is a schematic diagram showing the influence of different tracking thresholds on the tracking error and tracking times of the scheme in the embodiment;

[0042] Figure 5 Schematic diagram of signal-to-noise ratio and tracking error after beamforming under different parameters for different algorithms in the embodiment;

[0043] Figure 6 It is a schematic diagram of the comparison of each tracking error of different algorithms at different motion speeds in the embodiment;

[0044] Figure 7 It is a comparison diagram of the tracking times of different algorithms at different motion speeds in the embodiments;

[0045] Figure 8 This is a schematic diagram of the scene route in the embodiment;

[0046] Fig. 9 It is a schematic diagram of the signal-to-noise ratio after scene beamforming in the embodiment;

[0047] Fig.10Schematic diagram of the analysis of beam tracking results in the scene route in the embodiment. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the present invention clearer and more understandable, the present invention is further described in detail below in conjunction with the accompanying drawings and technical solutions.

[0049] In millimeter wave mobile communication scenarios, due to the mobility of communication terminals and the dynamic changes of the communication environment, the main path direction of the communication channel may change rapidly. In order to effectively cope with the rapid changes in the channel path direction and reduce the beam training overhead, the communication transceiver terminal usually adopts the beam tracking method. However, how to design a low-overhead, high-tracking-precision beam tracking algorithm has become a technical problem that needs to be solved urgently. Figure 1 As shown, a particle filtering-based beam tracking method for a millimeter wave scenario is provided, comprising:

[0050] S101, judging whether the difference of the signal-to-noise ratio after beamforming between the current time slot and the previous time slot exceeds a set tracking threshold, if the difference exceeds the tracking threshold, judging that beam tracking is required for the current time slot;

[0051] S102, adding two specified offset angles in the data beam direction to generate a pair of auxiliary measurement beams, and starting a particle filter algorithm based on the auxiliary measurement beams to dynamically estimate the motion state of the target and adjust the beam direction;

[0052] Among them, in the particle filtering algorithm, by obtaining the received signal strength ratio of the auxiliary measurement beam, the received signal strength ratio is used as the observation value of the particle filtering algorithm, and the angle particle set of the current time slot is generated according to the half-wave width of the pointing angle of the data beam of the previous time slot. The angle particle weight of the current time slot is calculated based on the probability density function of the double non-central F distribution, and the optimal angle particle as the path angle estimation value is selected according to the angle particle weight, and the data beam direction of the current time slot is updated based on the optimal angle particle. The particle weight is used to measure the matching degree between the particle and the observation value.

[0053] By comparing the difference in signal-to-noise ratio after beamforming between the current time slot and the previous time slot, if the difference exceeds the tracking threshold, the particle filter algorithm is started to estimate the target motion state. In the particle filter algorithm, a pair of auxiliary beams are introduced based on the data beam direction for beam measurement. The auxiliary beam is formed by adding a specific offset angle to the data beam pointing angle at the transceiver end, and contains two beams in different directions; the received signal strength ratio measured by the auxiliary beam pair is used as the observation value of the particle filter algorithm to estimate the main path angle of the channel; the particle filter algorithm generates a particle set within the half-wave width interval of the data beam pointing angle of the previous time slot; the particle weight is calculated based on the probability density function of the double non-central F distribution; the optimal particle is calculated according to the particle weight as the estimated value of the main path angle of the channel, and the data beam direction is updated.

[0054] like Figure 2 As shown, two auxiliary beams in different directions are formed by adding a predefined offset angle to the direction of the data beam.

[0055] Definition M R and M T Denote the number of antennas of the base station (receiver) and the mobile user (transmitter) respectively. The channel matrix between the receiver and the transmitter in time slot t is expressed as The main path angles AoA and AoD of the channel are defined as θ and Considering the use of uniform linear antenna arrays at the receiver and transmitter, the data beam steering vectors at the receiver and transmitter are expressed as:

[0056]

[0057] in and θ and For ease of understanding, only the uplink scenario is analyzed, that is, AoA is estimated only at the receiving end, and the AoD at the transmitting end is known (i.e. ). The downlink scenario analysis is similar. The data beam direction of the receiving end in the t-1th time slot A pair of auxiliary beams is formed by adding a disturbance angle, which is used for beam measurement in the tth time slot. The auxiliary beam can be expressed as:

[0058]

[0059] δ is the disturbance angle, is the half beam width. Then the received signals corresponding to this pair of auxiliary beams in time slot t are expressed as:

[0060]

[0061] in, represents the conjugate transpose, represents the pilot signal, z - and z + The mean is 0 and the variance is The received signal contains the angle information of the channel path and is a key parameter for estimating the angle of the main channel path. The corresponding received signal strength can be expressed as:

[0062]

[0063] Calculate the ratio metric:

[0064]

[0065] make have:

[0066]

[0067] Received signal strength ratio ε t As the observed value of ABTPF. Particle set x of ABTPF algorithm in time slot t t Contains N particles, and the generation strategy is:

[0068]

[0069] i represents the particle index, represents uniform distribution. The weight of the particle is used to measure the particle and the observation value ε t The particle obeys the doubly non-central F distribution with degrees of freedom υ1 and υ2. The particle weight is determined by calculating the probability density of the distribution. For the i-th particle, the relevant doubly non-central parameters are:

[0070]

[0071] Where P represents the received power, It means that the channel estimation value is calculated using the i-th particle in the t time slot, which is expressed as:

[0072]

[0073] in represents the estimated path gain of the tth time slot, estimated using the received signal obtained from the data beam of the t-1th time slot

[0074]

[0075] is the channel steering vector estimated using the ith particle and is expressed as:

[0076]

[0077] The weight of the i-th particle in the t-th time slot is calculated as follows:

[0078]

[0079] Among them, f F (·) represents the probability density function of the doubly noncentral F distribution. The particle weights are normalized:

[0080]

[0081] Resample the particles according to the normalized weights and retain the particles with larger weights and its weight And copy it as a new particle collection and weight set It is expressed as:

[0082]

[0083] in Represents the resampling operation strategy function, which is used to update the particle set and weight set Using the resampled particle set, the estimated value of the target AoA is calculated according to the following formula:

[0084]

[0085] In order to adapt to the motion scenes with different speed change frequencies and reduce unnecessary tracking overhead, the tracking threshold ι is introduced. If the signal-to-noise ratio γ after beamforming in the tth time slot is t Compared with the signal-to-noise ratio after beamforming in the t-1th time slot γ t-1 satisfy:

[0086] |γ t -γ t-1 |>ι

[0087] A tracking will be performed. Ignoring the time slot index, the signal-to-noise ratio expression after beamforming is:

[0088]

[0089] Further, based on the above method, an embodiment of the present invention also provides a beam tracking system based on particle filtering for millimeter wave scenarios, comprising: a judgment module and a tracking module, wherein:

[0090] A judgment module, used to judge whether the difference of the signal-to-noise ratio after beamforming between the current time slot and the previous time slot exceeds a set tracking threshold, and if the difference exceeds the tracking threshold, it is determined that beam tracking is required in the current time slot;

[0091] The tracking module is used to add two specified offset angles to the data beam direction to generate a pair of auxiliary measurement beams, and start the particle filter algorithm based on the auxiliary measurement beams to dynamically estimate the motion state of the target and adjust the beam direction;

[0092] Among them, in the particle filtering algorithm, by obtaining the received signal strength ratio of the auxiliary measurement beam, the received signal strength ratio is used as the observation value of the particle filtering algorithm, and the angle particle set of the current time slot is generated according to the half-wave width of the pointing angle of the data beam of the previous time slot. The angle particle weight of the current time slot is calculated based on the probability density function of the double non-central F distribution, and the optimal angle particle as the path angle estimation value is selected according to the angle particle weight, and the data beam direction of the current time slot is updated based on the optimal angle particle. The particle weight is used to measure the matching degree between the particle and the observation value.

[0093] To verify the effectiveness of this solution, the following is a further explanation based on the data from Experiments 1 and 2:

[0094] Experiment 1: Study the beam tracking problem between a fixed millimeter wave communication base station (receiver) and a millimeter wave communication mobile terminal (transmitter). The transmitter is an omnidirectional antenna, and only the beam tracking of the receiver is considered. Assume that the communication channel follows the block fading model, which is specifically defined as follows:

[0095]

[0096] where g l,t is the path complex coefficient, t represents the influence of the scattering path in the tth time slot. The mobile terminal motion model is established as:

[0097] θ t =θ t-1 +Ω+n t

[0098] Where Ω is the fixed value of the channel angle change between adjacent time slots, which is a constant; n t is the random perturbation angle, with mean 0 and variance The Gaussian distribution of The simulation parameter settings are shown in Table 1.

[0099] Table 1 Simulation parameter settings

[0100]

[0101]

[0102] The experiment demonstrates the tracking error, signal-to-noise ratio fading, tracking times, realignment times and measurement overhead of the proposed ABTPF and existing SPF and EKF under different conditions.

[0103] Figure 3 It shows that the ABTPF solution in this case works in the line-of-sight (LoS) scenario when σ θ =0.05δ,Ω=0.1δ,ι=1dB, signal-to-noise ratio before beamforming Angular tracking results for 1000 time slots. Figure 3 (a) The changes in the ABTPF tracking angle and the channel angle are compared, where the ABTPF keeps the angle unchanged in some time slots. This is because the signal-to-noise ratio decay does not reach the preset tracking threshold ι, so tracking is not triggered. Figure 3 (b) shows the normalized tracking error of ABTPF, which is obtained by calculating the ratio of the error in each tracking time slot to the half beamwidth δ: In the 1000 time slot simulation, ABTPF triggers a total of 165 trackings, which only accounts for 16.5% of the total time slots, while the SPF and EKF schemes need to track in each time slot, that is, they need to perform 1000 trackings.

[0104] Figure 4 The tracking error and tracking times of ABTPF under different tracking thresholds ι (ι = 0.5dB, 1.0dB, 1.5dB) in LoS communication scenarios are shown. Among them, the motion model parameters are: σ θ =0.05δ, Ω=0.1δ. The tracking error is the average value of 1000 repeated simulations. As can be seen from the figure, the larger the tracking threshold, the smaller the average number of required tracking times. Under the tracking thresholds of ι=1.0dB and ι=1.5dB, the ABTPF tracking error curves are close to overlap, which may be because the signal-to-noise ratio drop caused by the ABTPF tracking error is mostly concentrated at 1dB or below.

[0105] Figure 5 It shows that in the LoS communication scenario, when ε = 10dB, σ θ =0.15δ,Ω=0.2δ,ι=0, the signal-to-noise ratio and tracking error after beamforming. Since SPF and EKF are model-driven tracking methods, it is known in the simulation that σ θ and Ω. Figure 5 It can be seen that when ABTPF tracks in each time slot, the SNR after beamforming almost coincides with the upper bound of no tracking error, and the tracking error is basically stable below 0.1. The SPF tracking error is stable at 0.25, and for the EKF algorithm, due to the error accumulation problem, its maximum tracking error reaches 0.9.

[0106] Figure 6 and Figure 7The performance of different algorithms (ABTPF, SPF and EKF) under different conditions is shown, aiming to evaluate the performance of these algorithms in the face of different degrees of "speed" changes (through the angle increment Ω and the angle perturbation variance σ θ It reflects the robustness to noise interference. Figure 6 It shows that when σ θ =0.05δ, ι = 1dB, the impact of the angle increment Ω on the tracking error and tracking times of ABTPF, SPF and EKF. It should be noted that the SPF and EKF algorithms assume that the parameter σ θ and Ω are known. When Ω=0.3δ,ι=1dB, Figure 7 Shows different σ θ The tracking errors and tracking times of ABTPF, SPF and EKF under . Figure 6 and Figure 7 It can be seen that even with a rapid change in angle (Ω = 0.3δ or σ θ =0.25δ), the maximum tracking error of ABTPF does not exceed 0.18; in contrast, although SPF shows high stability under different signal-to-noise ratios and "moving speeds", its tracking error is twice or even more than that of ABTPF in this case. The tracking error of EKF does not exceed 0.4, but this is achieved under the condition of multiple realignment. From the perspective of the number of tracking times, ABTPF can dynamically adjust the number of tracking times according to the tracking threshold, and its maximum number of tracking times is only about half of that of the other two methods.

[0107] Tables 2 and 3 compare the performance of the three algorithms of this case, ABTPF, SPF and EKF, under LoS and NLoS conditions, facing different degrees of "speed" changes, including realignment ratio, average tracking error and average measurement overhead. The number of simulations is 1000, among which SPF and EKF are both known parameters σ θ and Ω. The realignment criterion is: the signal-to-noise ratio γ after the current beamforming t Compared to the SNR after beamforming after realignment P(t RE The realignment ratio indicates the ratio of the number of realignments to the total tracking time slots, and the average tracking error is the average tracking error of each time slot in the simulation. The average measurement overhead is measured by the average number of beam measurements per time slot. SPF and EKF use one measurement beam per time slot, with a beam measurement of 1, while ABTPF uses two measurement beams, with a beam measurement of 2. For the time slot t where realignment is performed, RE, the number of beam measurements is 64, since an exhaustive search is required on the codebook of 64 discrete Fourier transform (DFT) beams. θ =0.15δ,Ω=0.2δ, the channel angle changes slowly at this time, and the three methods do not need to be realigned, and can stably track the main path angle of the channel. In contrast, the average tracking error of ABTPF is about 25% of the other two schemes, and the average measurement overhead is only 34% of the other two schemes. Table 3 is σ θ =0.35δ,Ω=0.4δ, when the channel angle changes rapidly. In the LoS and NLoS scenarios, SPF and EKF have 2.9% and 17.5% realignment rates, respectively, while ABTPF has a realignment rate of less than 1% (the highest is only 0.8%). In terms of tracking error, the average error of ABTPF does not exceed 0.159, while the minimum tracking errors of SPF and EKF are 0.441 and 0.573, respectively, which are about 2.7 times and 3.6 times that of ABTPF. At the same time, the average measurement overhead of ABTBF is lower than that of EKF and SPF. It can be seen that even under the condition of drastic channel changes, ABTPF still maintains high tracking accuracy and low overhead.

[0108] Table 2 Performance comparison of different algorithms: σ θ =0.15δ, Ω=0.2δ, ι=1dB, ε=10dB

[0109]

[0110]

[0111] Table 3 Performance comparison of different algorithms: σ θ =0.35δ, Ω=0.4δ, ι=1dB, ε=10dB

[0112]

[0113] From the above experimental data, it can be seen that the ABTPF solution in this case does not need to rely on a known motion model, and can adaptively adjust the tracking overhead according to the set tracking threshold while maintaining high tracking accuracy. Under a typical statistical channel model, ABTPF shows superior performance compared to SPF and EKF.

[0114] Experiment 2: In order to further verify the performance of the ABTPF solution in this case, ray tracing software was used to model the real scene. The scene data contains both LoS and NLoS channels, making the changes in channel gain and path angle more complicated. In the simulation scenario, the base station (BS) is the receiving end and the mobile end (UE) is the transmitting end. Both ends use auxiliary beam pairs for beam tracking to estimate AoA and AoD respectively. Figure 8 The environment and UE moving route map of the scenario of Example 2 are shown. The specific scenario and the parameters related to the ABTPF algorithm are shown in Table 4.

[0115] Table 4 Scenarios and ABTPF related parameters

[0116]

[0117] Fig. 9 The diagram shows the change in signal-to-noise ratio after beamforming in the scenario. The effective channel gain at both the BS and UE sides has a sudden change near 40m. The reason for this change is that there is no building obstruction between the BS and the UE, the AoA and AoD send sudden changes, and the channel changes from the NLoS channel to the LoS channel. From 380m, the path gain drops sharply and is rapidly decaying. This is because the distance increases, there are buildings obstructing, the AoD changes greatly, and the channel changes from LoS to NLoS, causing the path gain to drop sharply and is rapidly decaying.

[0118] Fig.10 The successful application of the ABTPF solution in a high-precision ray tracing scenario (UE moving speed is 72km / h) was demonstrated. Fig.10 Figure a shows the comparison between the signal-to-noise ratio after ABTPF beamforming (red curve) and the maximum signal-to-noise ratio obtained without tracking error (black curve). Fig.10 (b) in Figure 2 shows the indicator of ABTPF realignment during tracking, where "1" indicates realignment and "0" indicates no realignment. From the start of tracking to about time slot 3700, the ABTPF algorithm successfully tracked the main path of the channel in the NLoS scenario (the signal-to-noise ratio after beamforming was close to the optimal value) and no realignment occurred. Starting from about time slot 3700, since the NLoS path and the LoS path exist in the channel at the same time, and the NLoS path still has a higher path strength, the effective channel gain of ABTPF at this time drops significantly due to the signal-to-noise ratio of about 300 time slots. At about time slot 4100, the NLoS path weakens, the channel path switches to the LoS path, and ABTPF needs to be realigned, see Fig.10 (b) in the figure. At about time slot 35000, the LoS path weakens and the channel switches to the NLoS path. ABTPF also successfully tracks the main path angle of the channel. As the distance of the UE becomes farther and the angle changes rapidly, the channel gain becomes very weak. After time slot 37500 at the end of the trajectory, the communication link is often lost and frequent beam alignment is required. In fact, at Fig.10 During the whole tracking process shown, ABTPF only tracked 215 times, accounting for about 0.53% of the total time slots, and the measurement overhead was 1.06×10 -2 .

[0119] Table 5 shows the tracking performance of the ABTPF algorithm when the UE moving speed is 20km / h, 43.2km / h and 72km / h. The tracking accuracy κ is defined as the number of times the signal-to-noise ratio drops by more than 3dB after beamforming during the tracking process, which accounts for the proportion of the total tracking time slots. As can be seen from Table 5, the tracking accuracy of ABTPF under the three moving speeds is almost the same. When the moving speed is slow, ABTPF reduces the measurement overhead by reducing the number of tracking times; when the moving speed is fast, ABTPF ensures the stability of the tracking accuracy by increasing the number of tracking times.

[0120] Table 5 Performance display at different moving speeds

[0121] Moving speed (km / h) 20 43.2 72 Tracking time slot ratio 0.039％ 0.32％ 0.53％ Measuring overhead <![CDATA[0.078×10 -2 ]]> <![CDATA[0.65×10 -2 ]]> <![CDATA[1.06×10 -2 ]]> κ 2.00％ 2.35％ 2.17％

[0122] The above two experimental data show that this solution can adapt to a variety of mobility communication scenarios and meet the high-quality requirements of millimeter wave communication links under different dynamic conditions.

[0123] Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0124] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0125] The units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person of ordinary skill in the art may use different methods to implement the described functions for each specific application, but such implementation is not considered to be beyond the scope of the present invention.

[0126] Those skilled in the art will appreciate that all or part of the steps in the above method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits, and accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or in the form of software function modules. The present invention is not limited to any specific form of combination of hardware and software.

[0127] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A particle filtering-based beam tracking method for millimeter wave scenarios, characterized in that: Include: Determine whether the difference in signal-to-noise ratio after beamforming between the current time slot and the previous time slot exceeds a set tracking threshold. If the difference exceeds the tracking threshold, it is determined that beam tracking is required in the current time slot. On the basis of the data beam direction, two specified offset angles are added and a pair of auxiliary measurement beams are generated, so as to start the particle filter algorithm in the current time slot based on the auxiliary measurement beam to dynamically estimate the target motion state and adjust the beam direction; Among them, in the particle filtering algorithm, by obtaining the received signal strength ratio of the auxiliary measurement beam, the received signal strength ratio is used as the observation value of the particle filtering algorithm, and the angle particle set of the current time slot is generated according to the half-wave width of the pointing angle of the data beam of the previous time slot. The angle particle weight of the current time slot is calculated based on the probability density function of the double non-central F distribution, and the optimal angle particle as the path angle estimation value is selected according to the angle particle weight, and the data beam direction of the current time slot is updated based on the optimal angle particle. The particle weight is used to measure the matching degree between the particle and the observation value.

2. The particle filtering-based beam tracking method for millimeter wave scenarios according to claim 1, characterized in that: For the measurement auxiliary beam, the receiving end in the data beam direction is set as the fixed end, the transmitting end is set as the mobile end, and the data beam pointing angle is set to Under this condition, adding and subtracting two disturbance angles δ, the received signals of the two auxiliary measurement beams are respectively expressed as: in, represents the conjugate transpose; w and f represent the beamforming vectors at the receiving end and the transmitting end, respectively, and H t is the channel matrix of the tth time slot, and s is the pilot signal, and k Indicates the pilot length; is the channel path departure angle; M R and M T Respectively represent the number of antennas at the receiving end R and the transmitting end T; z - and z + The mean is 0 and the variance is The two are independent of each other.

3. The particle filtering-based beam tracking method for millimeter wave scenarios according to claim 1, characterized in that: The process of obtaining the received signal strength ratio of the auxiliary measurement beam includes: The received signal strength of the auxiliary measurement beam is obtained based on the received signal. The received signal strengths of the two auxiliary measurement beams are respectively expressed as: in, Respectively represent the received signals of two auxiliary measurement beams; The ratio metric is calculated based on the received signal strength, where the calculation process of the ratio metric is expressed as: The received signal strength ratio is calculated based on the ratio metric, and the calculation formula is:

4. The particle filtering-based beam tracking method for millimeter wave scenarios according to claim 2, characterized in that: The angle particle set of the current time slot is generated according to the half-wave width of the beam pointing angle of the previous time slot data, including: Set the angle particle set x of the particle filter algorithm in the tth time slot t Contains N particles, and its generation strategy is expressed as: Where i represents the particle index, The uniform distribution range of particles is 5. The particle filtering-based beam tracking method for millimeter wave scenarios according to claim 2, characterized in that: The angle particle weight of the current time slot is calculated based on the probability density function of the double non-central F distribution, including: setting the particles to obey the double non-central F distribution with degrees of freedom υ1 and υ2, and determining the weight of each particle using the double non-central F distribution probability density, wherein the particle weight calculation process is: for the i-th particle, the relevant double non-central parameter is expressed as: P represents the transmission power, Indicates that the channel estimation value is calculated using the i-th particle in time slot t; the weight of the i-th particle is given by the following formula Calculated, f F (·) represents the probability density function of the doubly noncentral F distribution.

6. The particle filtering-based beam tracking method for millimeter wave scenarios according to claim 5, characterized in that: The optimal angle particle is selected as the path angle estimation value according to the angle particle weight, and the data beam direction of the current time slot is updated based on the optimal angle particle, including: use Normalize the particle weights, resample the particles according to the normalized particle weights, and retain particles with weights greater than the specified value during resampling. and its weight And obtain a new particle set by copying the retained particles and weight set Among them, the new particle set and weight set are expressed as: Represents the resampling operation strategy function, which updates the particle set through resampling operation and weight set The estimated value of the target data beam pointing angle AoA is calculated using the resampled particle set. The estimated value calculation formula is expressed as: N is the total number of particles.

7. The particle filtering-based beam tracking method for millimeter wave scenarios according to claim 2, characterized in that: The process of comparing the beamforming signal-to-noise ratio of the current time slot with that of the previous time slot includes: Calculate the signal-to-noise ratio γ after beamforming in the tth time slot t and the signal-to-noise ratio after beamforming in the t-1th time slot γ t-1 , if the signal-to-noise ratio after beamforming of the two time slots meets the condition: c t -c t-1 |>i Then, a beam tracking is triggered in the tth time slot, where the time slot subscript is ignored and the calculation formula of the signal-to-noise ratio after beamforming is expressed as: P represents the transmission power and ι is the tracking threshold.

8. A particle filter-based beam tracking system for millimeter wave scenarios, characterized in that: Contains: judgment module and tracking module, among which, A judgment module, used to judge whether beam tracking is required in the current time slot by comparing the signal-to-noise ratio after beamforming between the current time slot and the previous time slot; The tracking module is used to add two specified offset angles to the data beam direction to generate a pair of auxiliary measurement beams, and start the particle filter algorithm based on the auxiliary measurement beams to dynamically estimate the motion state of the target and adjust the beam direction; Among them, the particle filter algorithm obtains the received signal strength ratio of the auxiliary measurement beam and uses the received signal strength ratio as the observation value of the particle filter algorithm. The angle particle set of the current time slot is generated according to the half-wave width of the pointing angle of the data beam of the previous time slot. The angle particle weight of the current time slot is calculated based on the probability density function of the double non-central F distribution. The optimal angle particle is selected as the path angle estimation value according to the angle particle weight, and the data beam direction of the current time slot is updated based on the optimal angle particle. The particle weight is used to measure the matching degree between the particle and the observation value.

9. An electronic device, characterized in that: include: at least one processor, and a memory coupled to the at least one processor; The memory stores a computer program, and the computer program can be executed by the at least one processor to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 7 can be implemented.

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