CN114679356B - A method, device and storage medium for extracting channel full-dimensional parameters independent of likelihood function - Google Patents

Abstract

The invention discloses a channel full-dimensional parameter extraction method, device and storage medium independent of the likelihood function, which receives signal data through multiple channels; preprocesses the signal data to obtain the time delay peak value of the signal data; The peak value is the input information, and the azimuth and elevation angles corresponding to each sub-path in the delay peak are calculated by using the forward-backward spatial smoothing MUSIC algorithm; the complex amplitude of the sub-path is calculated according to the azimuth and elevation angles corresponding to each sub-path; the aggregate delay peak , the azimuth and elevation angles corresponding to each subpath, and the complex amplitude, to obtain the channel full-dimensional parameter set; the present invention can reduce the influence of Doppler frequency shift on the subpath complex amplitude estimation by preprocessing the signal, so that the estimated The complex amplitude is more accurate. Compared with the parameter estimation algorithm based on the likelihood function that introduces EM iteration such as SAGE, it does not require iteration, takes less time, and does not converge to a local optimal solution caused by improper initial value setting. Thus estimating the problem of false paths.

Description

A method, device and storage medium for extracting full-dimensional channel parameters independent of likelihood function

Technical Field

The invention belongs to the technical field of wireless channel parameter extraction, and in particular relates to a method for extracting full-dimensional channel parameters that is independent of likelihood functions.

Background Art

In order to meet the requirements of future wireless communication networks (increasing data rates, reducing latency, energy and cost), designing and evaluating various advanced wireless communication technologies in different communication systems requires the ability to capture the characteristics exhibited by the above technologies on the corresponding channels.

The Quasi Deterministic Radio Channel Generator (QuaDRiGa), a geometry-based statistical ray tracing channel modeling method, has been widely recognized by the industry. However, to adapt to specific application scenarios, it is also necessary to input channel parameters of various scales of the scene into the model. These channel parameters can only be obtained from a large amount of actual channel measurement data.

In terms of parameter extraction, in order to fully extract the parameters of each dimension of the channel, the most widely used algorithm is the Space Alternating Generalized Expectation-maximization (SAGE) algorithm. However, since SAGE is a parameter estimation algorithm based on likelihood function that introduces the iteration of the expectation-maximization algorithm (EM) to reduce the complexity of the maximum likelihood (ML) algorithm, each iteration must search for the parameters of each dimension of the subpath to satisfy the maximum likelihood condition, which is very time-consuming when there are many subpaths. And due to the characteristics of the EM algorithm, when there are several subpaths with similar parameters or the initial parameter value is not set appropriately, it is easy for the result to converge to the local optimal solution and estimate the false path.

Summary of the invention

The purpose of the present invention is to provide a channel full-dimensional parameter extraction method that does not rely on the likelihood function, so as to improve the accuracy of channel parameter extraction.

The present invention adopts the following technical solution: a method for extracting full-dimensional channel parameters that does not rely on likelihood function, comprising the following steps:

receiving signal data through multiple channels; wherein the signal data is a PN sequence or CFR data;

Preprocess the signal data to obtain the delay peak value of the signal data;

Taking the delay peak as input information, the forward and backward spatial domain smoothing MUSIC algorithm is used to calculate the azimuth and elevation angles corresponding to each subpath in the delay peak.

Calculate the complex amplitude of each subpath according to the azimuth and elevation angle corresponding to the subpath;

The delay peak, the azimuth and elevation angles corresponding to each subpath, and the complex amplitude are collected to obtain the full-dimensional parameter set of the channel.

Preferably, when the signal data is a PN sequence, the preprocessing is:

Perform sliding correlation on the PN sequence.

Preferably, before preprocessing the CFR data, the following steps are further included:

Perform IFFT on the CFR data to obtain the CIR data corresponding to the CFR data.

Preferably, when the signal data is CFR data, the preprocessing is:

The CIR data is divided into a noise segment and a valid signal segment using the cyclic prefix length;

Determine a first noise threshold according to the noise segment, and select a first time delay position of the valid signal segment according to the first noise threshold;

Determine a union of first delay positions of CIR data of the same time slot received by different receiving channels to obtain a first delay position set;

Taking the intersection of multiple first delay position sets of different time slots to obtain a second delay position set;

Calculate the covariance matrix corresponding to each element in the second time delay position set, and perform eigenvalue decomposition on the covariance matrix to obtain the maximum eigenvalue and the minimum eigenvalue;

The ratio of the maximum eigenvalue to the minimum eigenvalue is calculated, and elements whose ratio is greater than the second noise threshold are selected from the second delay position set to form a third delay position set, and the third delay position set is used as the delay peak value of the signal data.

Preferably, when the signal data is a PN sequence, after calculating the complex amplitude of each subpath according to the azimuth and elevation angle corresponding to the subpath, the method further includes:

Calculate the phase difference of different time slots of each subpath;

determining the Doppler shift of the subpath according to the phase difference;

Constructing a local PN sequence based on Doppler frequency shift;

The local PN sequence is used to perform sliding correlation on the PN sequence, and the process is continued until the complex amplitude of each subpath is obtained again.

Preferably, when the Doppler frequency shift difference between the sub-paths is less than the difference threshold:

The weighted average method is used to calculate the average Doppler shift of each time delay cluster;

A local PN sequence is generated based on the average Doppler shift.

Preferably, when the signal data is a PN sequence, the channel full-dimensional parameter set further includes:

Doppler shift for each subpath.

Another technical solution of the present invention: a channel full-dimensional parameter extraction device that does not rely on likelihood function, comprising:

A receiving module, used for receiving signal data through multiple channels; wherein the signal data is a PN sequence or CFR data;

A preprocessing module, used to preprocess the signal data to obtain the delay peak value of the signal data;

The first calculation module is used to use the delay peak as input information and use the forward and backward spatial domain smoothing MUSIC algorithm to calculate the azimuth and elevation angle corresponding to each subpath in the delay peak;

A second calculation module, used for calculating the complex amplitude of each subpath according to the azimuth and elevation angle corresponding to the subpath;

The collection module is used to collect the delay peak value, the azimuth and elevation angle corresponding to each sub-path, and the complex amplitude to obtain the full-dimensional parameter set of the channel.

Another technical solution of the present invention: a device for extracting full-dimensional channel parameters that is independent of likelihood function, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for extracting full-dimensional channel parameters that is independent of likelihood function is implemented.

Another technical solution of the present invention: a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned channel full-dimensional parameter extraction method that does not depend on the likelihood function.

The beneficial effects of the present invention are as follows: the present invention can reduce the influence of Doppler frequency shift on sub-path complex amplitude estimation by preprocessing the signal, so that the estimated complex amplitude is more accurate. Compared with the parameter estimation algorithm based on likelihood function which introduces EM iteration such as SAGE, it does not require iteration, requires a shorter time, and does not have the problem of converging to the local optimal solution and estimating the false path due to improper initial value setting.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a method for extracting full-dimensional channel parameters that does not rely on a likelihood function according to an embodiment of the present invention;

FIG2 is a schematic diagram of a frame format of a transmitting end of a channel measurement system according to an embodiment of the present invention;

FIG3 is a schematic diagram of a frame format of a transmitting end of an NR system according to an embodiment of the present invention;

4 is a flow chart of processing input data by a channel parameter extraction method according to an embodiment of the present invention;

FIG5 is a schematic diagram of subarray selection for forward and backward spatial domain smoothing in an embodiment of the present invention;

FIG6 is a schematic diagram of delay position selection based on eigenvalue assistance provided in an embodiment of the present invention;

FIG7 is a comparison diagram of the estimation results of the method according to an embodiment of the present invention and the SAGE running time;

FIG8 is an angular power spectrum diagram of parameters obtained by processing actual data collected by the base station using the method and the forward and backward spatial domain smoothing MUSIC algorithm in an embodiment of the present invention;

FIG9 is an angle power spectrum diagram reconstructed after obtaining angle domain parameters using the proposed method using data collected by the base station in a single-user line-of-sight scenario in an embodiment of the present invention;

10 is a comparison diagram of the beam receiving power reconstructed by parameters obtained by processing the actual data collected by the base station using the method and the forward and backward spatial domain smoothing MUSIC algorithm in an embodiment of the present invention and the original beam receiving power;

FIG11 is a schematic diagram of the structure of a device for extracting full-dimensional channel parameters that does not rely on a likelihood function according to an embodiment of the present invention.

DETAILED DESCRIPTION

The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

The current 5G New Radio (NR) system often uses beamforming to improve system capacity, so the acquisition of angle domain parameters is particularly important. The NR system uses a sounding reference signal (SRS) to detect the channel in the uplink, but it is currently mainly used to obtain the channel frequency response (CFR). Obtaining effective angle information from the existing CFR is of practical significance for base station beam resource configuration.

In practical applications, delay estimation often uses sliding correlation based on pseudo-noise (PN) or constant envelope zero autocorrelation (CAZAC) sequence. Its resolution depends on the chip width. As the bandwidth of signals that can be transmitted by current wireless transmission devices continues to increase, the chip width can be reduced accordingly. Therefore, the delay resolution based on this method continues to improve.

In terms of angle estimation, the characteristic structure algorithm Multiple Signal Classification (MUSIC) not only has a shorter estimation time but also has a higher resolution. However, it generally requires the number of incoming waves to be known and the incoming waves to be uncorrelated, while the number of paths in the channel is often unknown and since each path has the same detection signal, the received signals on different paths are coherent.

In terms of base station beam deployment, most NR systems currently only perform eigenvalue decomposition on the obtained multi-channel CFR data, and use the eigenvalue information to roughly deploy beams and allocate resources, or directly bring CFR into the angle estimation algorithm to distinguish subpaths only from the spatial domain and obtain their angles.

In fact, by performing an inverse fast Fourier transform (IFFT) on the frequency domain CFR to obtain the time domain channel impulse response (CIR), the system's time domain resolution can be used to initially separate the paths to obtain delay clusters, and further separate them in the spatial domain to extract more precise angle information. However, due to the existence of the leakage mechanism, the angle domain information of different paths will leak to different delay positions, so some preprocessing operations are required to ensure the quality and quantity of the extracted angle information.

The present invention discloses a method for extracting full-dimensional parameters of a channel that is independent of a likelihood function, as shown in FIG1 , and comprises the following steps: step S110, receiving signal data through multiple channels; wherein the signal data is a PN sequence or CFR data (i.e., channel frequency response data); step S120, preprocessing the signal data to obtain a delay peak value of the signal data; step S130, taking the delay peak value as input information, and using a forward and backward spatial domain smoothing MUSIC algorithm to calculate the azimuth and elevation angles corresponding to each subpath in the delay peak value; step S140, calculating the complex amplitude of each subpath according to the azimuth and elevation angle corresponding to the subpath; step S150, collecting the delay peak value, the azimuth and elevation angle corresponding to each subpath, and the complex amplitude to obtain a set of full-dimensional parameters of the channel.

The present invention can reduce the influence of Doppler frequency shift on sub-path complex amplitude estimation by preprocessing the signal, so that the estimated complex amplitude is more accurate. Compared with the parameter estimation algorithm based on likelihood function which introduces EM iteration such as SAGE, it does not require iteration, takes less time, and does not have the problem of converging to the local optimal solution and estimating the false path due to improper initial value setting.

Specifically, in the channel measurement system, the transmitter sends a PN sequence modulated by BPSK according to a certain frame format, and the receiver completes multi-channel reception by an array with a known arrangement and antenna pattern; the 5G-NR system is similar, the transmitter is the UE and modulates the SRS (i.e., sounding reference signal) according to the protocol frame format, and the receiver is the BS and processes the received SRS to obtain the channel CFR data of multiple receiving channels.

In the embodiment of the present invention, the channel mathematical model of the obtained complete parameters is expressed as:

和f_l,p分别对应第l簇内第p条子路径的复幅度和多普勒频移。

为第l簇内第p条子路径对应的方向向量，其中θ_l,p和

分别为其仰角和方位角。Among them, h(τ,t) is the time domain response, L and P _l are the number of delay clusters and the number of subpaths contained in the delay cluster l respectively. The delay resolution of the system can actually only distinguish each delay cluster but not the subpaths within the cluster. The delay of each delay cluster is expressed as τ _l . τ and t are different descriptions of different time scales. τ is in units of PN code chip width or NR system sampling interval, while t is in units of PN frame interval or NR system slot interval. δ(τ) is the unit impulse function.

and _fl,p correspond to the complex amplitude and Doppler shift of the p-th subpath in the l-th cluster, respectively.

is the direction vector corresponding to the p-th subpath in the l-th cluster, where θ _l,p and

are its elevation and azimuth respectively.

The receiving end processes the baseband data after sampling and down-conversion of multiple RF channels, and contains the array arrangement and antenna pattern information in the specified reference system. Taking the uniform planar array placed in the XOZ plane as an example, the signal received by each receiving channel (i.e., the received PN sequence) is expressed as:

Where _Nm (τ,t) is the additive Gaussian noise on the mth receiving channel, M and N are the number of array elements of the uniform array in the X-axis and Y-axis directions respectively, a(τ) is the baseband signal expression of the transmitted signal, where τ is in units of the system sampling interval size, which is a single chip width for the channel measurement system.

为从

方向入射来波的阵列导向矢量的第m元素，其包含阵列排布信息以及天线方向图信息，可展开为：For the NR system, it is the inverse of the system bandwidth.

For

The mth element of the array steering vector of the incident wave contains the array arrangement information and antenna pattern information, which can be expanded as:

是第m阵元方向图在

上的复幅度，a_x(m)、a_z(m)是第m阵元在XOZ平面的坐标，

是载波在第m阵元相对参考原点的距离所引起的相移。in,

is the directional pattern of the mth array element

a _x (m) and a _z (m) are the coordinates of the mth element in the XOZ plane.

It is the phase shift caused by the distance of the carrier at the mth array element relative to the reference origin.

More specifically, as shown in FIG2, in the channel measurement system, the transmitter uses the full bandwidth to send the BPSK modulated PN sequence in a certain frame format with a period _Ts . The number of PN sequence chips is K, and the duration of a single chip is _Tp . The receiving end completes multi-channel reception by an array with a known arrangement and antenna pattern. As shown in FIG3, the transmitting end of the NR system is the UE. While continuously sending wireless frames, the UE modulates the SRS according to the frame format specified by the protocol. The location of the time domain resources occupied is shown in FIG2. The frequency domain is sent in a 2-comb manner as shown in FIG3 in the protocol. The total bandwidth of the NR system includes 272 RBs, each RB contains 12 REs, and each RE corresponds to a subcarrier with a frequency interval of 30kHz. The receiving end is the BS and processes the received SRS. After processing, the channel CFR data of multiple receiving channels are obtained. Due to the limited processing capability of the base station, each collection task can only collect SRS of more than 40 slots in the time domain. The SRS of each slot can only obtain 68 consecutive RBs in the frequency domain. In addition, the base station also downsamples the CFR data by one quarter when storing data. Therefore, each collection task can only obtain channel CFR data of more than 40 slots on 68*12÷2÷4=102 REs.

Preferably, when the signal data is a PN sequence, the preprocessing is: sliding correlation of the PN sequence. Specifically, according to the description of the channel space-time characteristics in the QuaDRiGa channel model theory, the spatial scattering environment of the wireless channel between the transmitter moving in a small range and the stationary receiver hardly changes. Therefore, multiple snapshots (different snapshots) are used to receive PN sequence data or multiple slots (time slots) of SRS data to complete channel exploration or angle domain information collection. The exploration signal received in the channel measurement system is sliding correlated with the local PN sequence in each snapshot to obtain the delay peak value of each PN sequence. The channel CFR of each slot obtained by BS processing in the NR system is converted into the time domain channel impulse response CIR using the inverse fast Fourier transform IFFT.

In the channel measurement system, the received signal on the NM receiving channel is correlated with the local PN sequence sliding:

T_p为单个码片的时间宽度，K为PN码的码长，N'(τ,t)表示接收阵列上的噪声与PN信号滑动相关的结果，由于KP_b很大，因此在后面可忽略噪声N'(τ,t)的影响。时延簇l对应的时延峰值表示为：Among them, <y(τ,t),a(τ)> means using a(τ) to do sliding correlation on each receiving channel data y(τ,t) of the tth slot, ⊙ represents the convolution operation,

_Tp is the time width of a single chip, K is the code length of the PN code, and N'(τ, t) represents the result of the sliding correlation between the noise on the receiving array and the PN signal. Since _KPb is very large, the influence of the noise N'(τ, t) can be ignored later. The delay peak corresponding to the delay cluster l is expressed as:

In the NR system, the base station receives and processes the SRS signals received by all channels according to the OFDM architecture. The operation corresponding to the sliding correlation is to perform IFFT on the acquired CFR data to obtain the baseband CIR data of each slot, which is expressed as follows:

T_s为系统采样周期，当

不是整数时，对应时延为τ_l的时延簇的功率成分会泄露到所有时延位置上。N为系统中的子载波数目，也是单个slot内的采样点数。

为每条子路表达式中不依赖角度的成分。Φ_l,p(t)为时延簇l内子路径p在第t个slot由多普勒频移造成的额外相位差，由于NR系统的应用场景多为城市宏小区这类低速移动场景，因此推导中假设系统是准静态的，即Φ_l,p(t)其不随n变化。Among them, Y(k,t) is the frequency domain received data of the tth slot,

_Ts is the system sampling period.

When it is not an integer, the power component of the delay cluster corresponding to the delay of τ _l will leak to all delay positions. N is the number of subcarriers in the system, which is also the number of sampling points in a single slot.

is the angle-independent component in the expression of each subpath. _{Φ l,p} (t) is the additional phase difference caused by Doppler frequency shift in the tth slot of subpath p in delay cluster l. Since the application scenarios of NR systems are mostly low-speed mobile scenarios such as urban macro cells, the derivation assumes that the system is quasi-static, that is, Φ _l,p (t) does not change with n.

For the NR system, since the channel CIR obtained by IFFT of the base station's channel CFR data has obvious peak leakage, the cyclic prefix (CP) length is first used to divide it into a noise segment and a valid signal segment, and the maximum amplitude of the noise segment is used as the noise threshold η. The threshold η is used to select the delay position Ω of the valid signal segment, and the union of the delay position sets on multiple receiving channels is taken to resist the spatial fading caused by multipath on the array:

再取交集，以此来降低噪声干扰。得到新的时延位置集合：Different slots

Then take the intersection to reduce noise interference. Get the new delay position set:

处所有通道的CIR峰值计算所有slot下的协方差矩阵：Because some delay positions may have less leaked delay power or there may be power leaked from multiple paths, the angle information of a large number of sub-paths is mixed and difficult to distinguish using MUSIC. In order to obtain correct and reliable angle domain information, discarding the CIR peaks at these delay positions will only lose the power of a small part of the angle information of the sub-paths, but reduce the adverse effect of noise on the ZF algorithm, which can provide guarantee for subsequent amplitude estimation. The effective delay position selection method is based on the eigenvalue auxiliary. For any delay position

The CIR peak of all channels at Calculate the covariance matrix under all slots:

接下对

进行特征值分解，得到其最大和最小特征值：in,

Next pair

Perform eigenvalue decomposition to obtain its maximum and minimum eigenvalues:

到集合

否则抛弃该时延位置，在后续提取角度和幅值信息时，只使用

内时延位置对应的CIR峰值。When λ _max /λ _min ＞η ₂ , keep the delay position

To Collection

Otherwise, the delay position is discarded and only the angle and amplitude information is used in the subsequent extraction.

CIR peak value corresponding to the inner delay position.

In one embodiment, the forward and backward spatial domain smoothing MUSIC algorithm is used to process the peak values of multiple snapshots/slots, distinguish the subpaths within each delay cluster and obtain their azimuth and elevation angles. First, the peak values of multiple channels are vectorized:

为：Then, we select the subarrays of the forward and backward spatial domain smoothing algorithm, where the array elements of M columns contain p _x mutually interlaced homogeneous subarrays, and the array elements of N rows contain p _z mutually interlaced homogeneous subarrays. The two-dimensional plane array is divided into p _x × p _z subarrays, whose size is _Ms = M+1-p _x columns and _Ns = N+1-p _z rows. Using _Vl (t) of T snapshots/slots, we get the covariance matrix after forward and backward spatial domain smoothing.

for:

表示Kronecker积，(·)^H表示取共轭转置，

为M_s×M_s的单位矩阵；

为M_s×M_s的置换矩阵，它的反对角线上元素为1，其余元素均为0。in,

represents the Kronecker product, (·) ^H represents the conjugate transpose,

is the identity matrix of _Ms × _Ms ;

is a permutation matrix of _Ms × _Ms , whose anti-diagonal elements are 1 and the rest are 0.

进行特征值分解，根据最小特征值的模和接收通道的信噪比设置门限ξ_l，绝对值大于门限的P_l个特征值对应时延簇l内的P_l条子路径，用小于门限的特征值对应的特征向量v_i,i＝P_l+1,...NM构造正交空间

时延簇l内的P_l条子路径各自对应的阵列导向向量

均与B_⊥正交。为时延簇l构造的MUSIC伪谱

并对其进行二维搜索寻找峰值：Next,

Perform eigenvalue decomposition, set the threshold ξ _l according to the modulus of the minimum eigenvalue and the signal-to-noise ratio of the receiving channel, P _l eigenvalues with absolute values greater than the threshold correspond to P _l subpaths in the delay cluster l, and use the eigenvectors v _i , i = P _l +1, ... NM corresponding to the eigenvalues less than the threshold to construct an orthogonal space

The array steering vectors corresponding to the P _l sub-paths in the delay cluster l are

are orthogonal to B _⊥ . The MUSIC pseudo-spectrum constructed for the time-delay cluster l

And perform a two-dimensional search on it to find the peak:

并将

该位置周围半径Δ置为零，再搜索下一峰值直至得到P_l条子路径的仰角和方位角。Δ的取值决定了将多大角度范围内子路径的合成一条径，其值得选取决于子阵列尺寸、snapshot/slot数目以及噪声，在对角度估计精度要求不高的一般应用情况下可取1～5°。After each peak position is obtained, record its azimuth and elevation position

and will

The radius Δ around the position is set to zero, and the next peak is searched until the elevation and azimuth of P _l sub-paths are obtained. The value of Δ determines the angle range of the sub-paths to be synthesized into one path. The value depends on the sub-array size, the number of snapshots/slots, and the noise. In general applications where the angle estimation accuracy is not required, 1 to 5° can be selected.

In another embodiment, when the signal data is CFR data, the preprocessing is as follows: using the cyclic prefix length to divide the CIR data into a noise segment and a valid signal segment; determining a first noise threshold according to the noise segment, and selecting a first delay position of the valid signal segment according to the first noise threshold; determining the union of the first delay positions of the CIR data of the same time slot received by different receiving channels to obtain a first delay position set; taking the intersection of multiple first delay position sets of different time slots to obtain a second delay position set; calculating the covariance matrix corresponding to each element in the second delay position set, and performing eigenvalue decomposition on the covariance matrix to obtain a maximum eigenvalue and a minimum eigenvalue; calculating the ratio of the maximum eigenvalue to the minimum eigenvalue, selecting elements in the second delay position set whose ratio is greater than the second noise threshold to form a third delay position set, and using the third delay position set as the delay peak of the signal data.

Channel exploration or angle domain information acquisition is completed using PN sequence receiving data of multiple snapshots or SRS data of multiple slots. The exploration signal received in the channel measurement system is sliding correlated in each snapshot using the local PN sequence. The channel CFR of each slot obtained by BS processing in the NR system is converted into time domain CIR using IFFT.

The system delay resolution is used to distinguish the delay clusters. The distinction of subpaths within the cluster and the estimation of their azimuth and elevation angles are completed by the forward and backward spatial domain smoothing MUSIC algorithm for the uniform plane array. The subarray selection method of the uniform plane array is shown in Figure 5. The original array of size N×M is divided into _px × _pz subarrays of size _Ns × _MS . Finally, the ZF algorithm is used to calculate the complex amplitude of each subpath, and the phase difference between the complex amplitudes of the subpaths obtained by different snapshots or slots is used to estimate the Doppler shift of the subpath.

Using the known subpath angle and ZF algorithm to analyze the complex amplitude and Doppler shift of P _l subpaths in the delay cluster l, first convert the noise-free part of V _l (t) into matrix product form:

然后利用ZF算法得到

的最小二乘估计：The angle estimation result of the subpath in cluster l can be used to obtain

Then use the ZF algorithm to get

The least squares estimate of :

计算簇l内子路径的多普勒频移估计：Using T snapshots/slots

Calculate the Doppler shift estimate for the subpath within cluster l:

Among them, κ(t) is a factor used to offset the 2π periodicity of the phase, and its calculation expression is:

和

后，在收发已有定时同步的基础上可以很容易的计算

进而得到复幅度估计

此外，无论是信道测量或是NR系统，要估计子路多普勒频移时，slot之间的间隔或PN不同帧之间的帧间隔T_f均需要满足：Got it

and

After that, it can be easily calculated based on the existing timing synchronization of sending and receiving

Then we get the complex amplitude estimate

In addition, whether it is channel measurement or NR system, when estimating the sub-path Doppler shift, the interval between slots or the frame interval _Tf between different PN frames must satisfy:

Wherein, _fd is the maximum Doppler frequency shift of the channel in the application scenario.

The system delay resolution can only distinguish delay clusters. The subpaths in the cluster carry the same signal, so the received signals of each subpath are coherent. It is necessary to use the forward and backward spatial domain smoothing MUSIC algorithm for the uniform planar array to complete the distinction and calculate its azimuth and elevation. Then use the forward and backward spatial domain smoothing MUSIC algorithm for the uniform planar array to complete the distinction and calculate its azimuth and elevation. The phase difference between the complex amplitudes of the subpaths obtained from different snapshots or slots is used to estimate the Doppler frequency shift of the subpath.

More specifically, in the case of channel measurement applications in high-speed mobile scenarios, since the Doppler frequency shift on the subpath cannot be ignored, the time of only a single PN code chip will cause a significant phase change, which will reduce the autocorrelation peak of the PN sliding correlation. Using this correlation peak to calculate the complex amplitude will result in a large error.

Therefore, first make the following adjustments to the channel model of each snapshot:

并用它与接收信号做滑动相关，从而抵消多普勒频移的影响。After obtaining the subpath Doppler shift estimate, the result can be used to construct the local PN sequence containing the Doppler shift

And use it to do sliding correlation with the received signal to offset the impact of Doppler frequency shift.

This can be divided into two specific cases. When the Doppler shifts between sub-paths in a cluster differ greatly (special scenarios), the Doppler shift of each sub-path can be used to construct a corresponding local sequence to extract the complex amplitude of each sub-path without the influence of Doppler shift. For example, to extract the p-th sub-path in the l-th cluster, first perform sliding correlation to obtain the peak value of the corresponding delay cluster l:

的相关结果，A为时延簇l内其他子路径对p子路径滑动相关峰值的干扰，当子路径之间的多普勒频移较大时，该干扰随着PN序列的长度增大而减小。接下来由已估计出的子路径角度和时延l的峰值向量计算消除多普勒频移后的复幅度：Where, N”(τ,t) is the noise and

The correlation result is , A is the interference of other subpaths in the delay cluster l on the sliding correlation peak of the p subpath. When the Doppler frequency shift between subpaths is large, the interference decreases as the length of the PN sequence increases. Next, the complex amplitude after eliminating the Doppler frequency shift is calculated by the estimated subpath angle and the peak vector of the delay l:

为第l簇内第p子路径对应的导向矢量估计。in,

is the steering vector estimate corresponding to the p-th subpath in the l-th cluster.

When the Doppler shift difference between sub-paths is small, such as in general scenarios such as urban macro cells, the influence of factor A cannot be ignored, so it is impossible to eliminate the influence of Doppler shift for each path separately. Instead, the influence of Doppler shift on sub-paths in each delay cluster is offset as much as possible. In order to ensure the elimination effect of Doppler shift on paths with large amplitudes, the weighted average method is used to determine the average Doppler shift on delay cluster l.

对接收序列做滑动相关得到

按照之前的复幅度估计处理流程，用已经求得的子路径角度和峰值向量完成多普勒频移部分消除的子路径复幅度估计。Next, adopt

Perform sliding correlation on the received sequence to obtain

According to the previous complex amplitude estimation process, the subpath complex amplitude estimation with the Doppler shift partially eliminated is completed using the obtained subpath angle and peak vector.

As shown in Figure 4, for the channel depth sounding application based on PN sequence, the Doppler shift estimation result of the subpath is used to eliminate the influence of Doppler shift on the subpath complex amplitude estimation. For the NR system, since the channel data estimated by LS has power leakage in the time domain as shown in Figure 6, the delay position selection method based on eigenvalue assistance is used for preprocessing to determine the delay position with high signal-to-noise ratio and easy extraction of angle information, thereby reducing the adverse effects of CIR leakage.

Specifically, when the signal data is a PN sequence, after calculating the complex amplitude of each subpath according to the azimuth and elevation angle corresponding to the subpath, the method further includes: calculating the phase difference of different time slots of each subpath; determining the Doppler frequency shift of the subpath according to the phase difference; constructing a local PN sequence based on the Doppler frequency shift; and performing sliding correlation on the PN sequence using the local PN sequence, and continuing the execution until the complex amplitude of each subpath is obtained again.

In one embodiment, when the Doppler frequency shift difference between each sub-path is less than a difference threshold: a weighted average method is used to calculate the average Doppler frequency shift of each delay cluster; and a local PN sequence is generated based on the average Doppler frequency shift.

Finally, based on the channel parameter estimation results obtained from multiple snapshots/slots, the large and small scale channel parameters and their probability statistical distribution are calculated, and spectrograms such as the Power Azimuth Spectrum (PAS) are drawn as shown in Figures 8 and 9. When the signal data is a PN sequence, the full-dimensional channel parameter set also includes: the Doppler frequency shift of each subpath.

In summary, the method proposed in the present invention utilizes the spatial and temporal separability of the channel to distinguish each subpath in the channel and further extract parameters. In the delay domain, PN sliding correlation or IFFT is used to distinguish the path and obtain the delay information of the path. In the spatial domain, the MUSIC algorithm based on forward and backward spatial smoothing is introduced to distinguish the subpaths in the path and obtain their azimuth and elevation angles. Then, the angle information and the correlation peaks or CIR peaks of multiple receiving channels are used to parse the subpath complex amplitude, and finally the corresponding Doppler shift is estimated according to the phase difference of the subpath complex amplitude in different snapshots/slots. In addition, the introduction of Doppler shift elimination in the complex amplitude estimation module can obtain a more accurate complex amplitude in high-speed mobile scenarios. In the delay estimation module, a delay position selection preprocessing method based on eigenvalue assistance is introduced, so that the proposed method can more effectively extract the angle domain information in the NR system channel data. Moreover, the simulation verifies the efficiency of the method, and the processing results of the measured data illustrate the effectiveness of the angle domain information extracted by the method.

The following content is a simulation verification of the method of the present invention. Set the channel parameter table shown in Table 1 to simulate the parameters of each sub-path in the wireless multipath channel, and use SAGE and the proposed method to extract the channel parameters respectively. Both methods use a PN sequence of length K=511, and each code chip width dt=3.69* ^10-6 s. The azimuth and elevation angles of the sub-path are randomly selected between 1° and 180°. According to the description of QuaDRiGa, the Doppler shift and angle of the sub-paths in the same cluster are mostly close, so this is taken into account when setting the paths with simultaneous delays. In addition, the number of known paths in SAGE is assumed to be known by default, and the various threshold settings of the proposed method are assumed to be appropriate. The rest of the relevant configurations are shown in Table 2.

Table 1

Table 2

Add paths in turn and record the running results of the proposed algorithm and SAGE each time. The running time of the two algorithms and the number of iterations of SAGE for different numbers of paths are shown in Figure 7. The specific estimation results are recorded in Table 3. It is considered that the estimated delay deviation of each path is less than dt, the angle deviation is less than 1°, the Doppler frequency shift is less than 1Hz, and the amplitude estimation error is less than 0.01 when the estimation is correct.

As can be seen from Figure 7, when the number of paths is small, the proposed algorithm and SAGE have equivalent estimation performance but shorter running time. The convergence of SAGE depends on its sub-path composition, and the number of iterations required for convergence cannot be determined in advance. When there are simultaneous arrival paths, the estimation effect is not good, and it is easy to converge to the local optimal solution, resulting in estimation failure that does not conform to the actual situation. The method proposed in the present invention handles this problem more in line with the actual situation. The running time of the SAGE algorithm initialization increases with the increase of the number of paths, while the running time of the method proposed in the present invention mainly increases with the increase of the number of clusters, and the iteration time of the SAGE algorithm is mainly related to the number of iterations and the number of paths.

Table 3

As shown in Figure 9, the angle power spectrum reconstructed after the angle domain parameters are obtained by the proposed method using the data collected by the base station in the single-user line of sight (LOS) scenario, the circle position with the label represents the direction of the 32 beams formed by the 32 receiving units on the base station side using the discrete Fourier transform (DFT) codebook. As shown in Figure 8, the angle spectrum reconstructed after the CFR data of each channel of the base station is directly brought into the forward and backward spatial domain smoothing MUSIC algorithm to extract the angle domain parameters. It can be seen that the main power area of the angle spectrum constructed by the method of the present invention is the same as that of the latter, but the angle power spectrum constructed by the method of the present invention is more refined. To verify its effectiveness, the power proportion of the received CFR on the 32 beams was reconstructed using the angle domain parameters extracted by each of them and compared with the power proportion on the original 32 beams. The results are shown in Figure 10. It can be seen that the restored power proportion of the method of the present invention is closer to the original beam power proportion. In addition, it can be seen that the beam power reconstruction performance is better in the main power area of the angle spectrum with strong receiving power, which verifies its effectiveness.

Compared with the parameter estimation algorithm based on likelihood function that introduces EM iteration, such as SAGE, the method of the present invention does not require iteration, requires shorter time, and does not have the problem of converging to the local optimal solution and estimating the false path due to improper initial value setting. Compared with various estimation algorithms for channel parameters, it can effectively distinguish the sub-paths within the delay cluster and obtain its azimuth, elevation, complex amplitude, Doppler frequency shift and other parameters. The present invention not only utilizes the characteristics of 5G large bandwidth, uses the high resolution capability of the delay domain to distinguish the delay clusters within the channel, but also utilizes the characteristics of 5G large-scale antennas, and uses the forward and backward spatial domain smoothing MUSIC algorithm with high angular resolution for coherent sources to distinguish the sub-paths within the cluster, so a large number of sub-paths can be extracted from the time domain and spatial domain and their parameters can be used to accurately characterize the channel. In addition, for the 5G channel depth measurement application based on PN sequence, a Doppler frequency elimination method is provided to reduce its influence on the complex amplitude estimation of the sub-path. For the NR system, a corresponding preprocessing method is provided to extract angle domain parameters from channel CIR data using this algorithm. Its effect is better than that of processing CFR with forward and backward spatial domain smoothing MUSIC, and the angle information obtained is more complete and reliable.

The present invention also discloses a channel full-dimensional parameter extraction device that does not rely on the likelihood function, as shown in Figure 11, including: a receiving module 210, used to receive signal data through multiple channels; wherein the signal data is a PN sequence or CFR data; a preprocessing module 220, used to preprocess the signal data to obtain the delay peak value of the signal data; a first calculation module 230, used to use the delay peak value as input information, and adopt the forward and backward spatial domain smoothing MUSIC algorithm to calculate the azimuth and elevation angle corresponding to each subpath in the delay peak value; a second calculation module 240, used to calculate the complex amplitude of each subpath according to the azimuth and elevation angle corresponding to each subpath; a collection module 250, used to collect the delay peak value, the azimuth and elevation angle corresponding to each subpath, and the complex amplitude to obtain a channel full-dimensional parameter set.

It should be noted that the information interaction, execution process and other contents between the modules of the above-mentioned device are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The functional modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

The present invention also discloses a device for extracting full-dimensional parameters of a channel that is independent of a likelihood function, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for extracting full-dimensional parameters of a channel that is independent of a likelihood function is implemented.

The device may be a computing device such as a desktop minicomputer, a notebook, a PDA, or a cloud server. The device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the device may include more or fewer components, or a combination of certain components, or different components, such as an input/output device, a network access device, etc.

The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

In some embodiments, the memory may be an internal storage unit of the device, such as a hard disk or memory of the device. In other embodiments, the memory may also be an external storage device of the device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the device. Further, the memory may also include both an internal storage unit of the device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

The present invention also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method for extracting full-dimensional parameters of a channel that is independent of a likelihood function is implemented.

Computer-readable media may include at least: any entity or device that can carry computer program codes to a camera/terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disk. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

Claims (7)

1. A method for extracting full-dimensional channel parameters that does not rely on likelihood function, characterized in that it comprises the following steps: Receiving signal data through multiple channels; wherein the signal data is a PN sequence or CFR data; Preprocessing the signal data to obtain a delay peak value of the signal data; When the signal data is a PN sequence, the preprocessing is: performing sliding correlation on the PN sequence; When the signal data is CFR data, IFFT is performed on the CFR data to obtain CIR data corresponding to the CFR data; the preprocessing is: Dividing the CIR data into a noise segment and a valid signal segment using a cyclic prefix length; Determine a first noise threshold according to the noise segment, and select a first delay position of the valid signal segment according to the first noise threshold; Determine a union of first delay positions of the CIR data in the same time slot received by different receiving channels to obtain a first delay position set; Taking the intersection of multiple first delay position sets of different time slots to obtain a second delay position set; Calculating a covariance matrix corresponding to each element in the second time delay position set, and performing eigenvalue decomposition on the covariance matrix to obtain a maximum eigenvalue and a minimum eigenvalue; Calculate the ratio of the maximum eigenvalue to the minimum eigenvalue, select elements whose ratio is greater than the second noise threshold in the second delay position set to form a third delay position set, and use the third delay position set as the delay peak value of the signal data; Taking the delay peak as input information, a forward and backward spatial domain smoothing MUSIC algorithm is used to calculate the azimuth and elevation angles corresponding to each subpath in the delay peak; Calculating the complex amplitude of each subpath according to the azimuth and elevation angle corresponding to the subpath; The delay peak, the azimuth and elevation angles corresponding to each subpath, and the complex amplitude are collected to obtain a full-dimensional channel parameter set. 2. A method for extracting full-dimensional channel parameters independent of likelihood function according to claim 1, characterized in that when the signal data is a PN sequence, after calculating the complex amplitude of each subpath according to the azimuth and elevation angle corresponding to the subpath, the method further comprises: Calculating the phase difference of different time slots of each subpath; determining a Doppler frequency shift of the subpath according to the phase difference; constructing a local PN sequence based on the Doppler frequency shift; The local PN sequence is used to perform sliding correlation on the PN sequence, and the process is continued until the complex amplitude of each sub-path is obtained again. 3. A method for extracting full-dimensional channel parameters independent of likelihood function as claimed in claim 2, characterized in that when the Doppler frequency shift difference between the sub-paths is less than a difference threshold: The weighted average method is used to calculate the average Doppler shift of each time delay cluster; A local PN sequence is generated based on the average Doppler shift. 4. The method for extracting full-dimensional channel parameters independent of likelihood function according to claim 1, wherein when the signal data is a PN sequence, the full-dimensional channel parameter set further comprises: The Doppler shift of each of the subpaths. 5. A channel full-dimensional parameter extraction device that does not rely on likelihood function, characterized by comprising: A receiving module, configured to receive signal data through multiple channels; wherein the signal data is a PN sequence or CFR data; A preprocessing module, used for preprocessing the signal data to obtain a delay peak value of the signal data; When the signal data is a PN sequence, the preprocessing is: performing sliding correlation on the PN sequence; When the signal data is CFR data, IFFT is performed on the CFR data to obtain CIR data corresponding to the CFR data; the preprocessing is: Dividing the CIR data into a noise segment and a valid signal segment using a cyclic prefix length; Determine a first noise threshold according to the noise segment, and select a first delay position of the valid signal segment according to the first noise threshold; Determine a union of first delay positions of the CIR data in the same time slot received by different receiving channels to obtain a first delay position set; Taking the intersection of multiple first delay position sets of different time slots to obtain a second delay position set; Calculating a covariance matrix corresponding to each element in the second time delay position set, and performing eigenvalue decomposition on the covariance matrix to obtain a maximum eigenvalue and a minimum eigenvalue; Calculate the ratio of the maximum eigenvalue to the minimum eigenvalue, select elements whose ratio is greater than the second noise threshold in the second delay position set to form a third delay position set, and use the third delay position set as the delay peak value of the signal data; A first calculation module is used to use the delay peak as input information and use a forward and backward spatial domain smoothing MUSIC algorithm to calculate the azimuth and elevation angle corresponding to each subpath in the delay peak; A second calculation module, used for calculating the complex amplitude of each subpath according to the azimuth and elevation angle corresponding to the subpath; The collection module is used to collect the delay peak value, the azimuth and elevation angle corresponding to each sub-path, and the complex amplitude to obtain a full-dimensional channel parameter set. 6. A device for extracting full-dimensional channel parameters that is independent of likelihood function, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a method for extracting full-dimensional channel parameters that is independent of likelihood function as described in any one of claims 1 to 4 when executing the computer program. 7. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements a channel full-dimensional parameter extraction method that is independent of a likelihood function as described in any one of claims 1 to 4.

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