Same-frequency interference confrontation method and system in 5G ultra-dense networking scene

Abstract

The invention belongs to the technical field of same-frequency interference pairs, and particularly relates to a same-frequency interference confrontation method and system in a 5G ultra-dense networking scene. Identifying the ID of the cell with the strongest signal; estimating a channel by adopting a least square method; subtracting the interference component of the cell with the strongest signal from the original signal to obtain an interference-removed signal; performing parameter initialization; searching the base station with the maximum correlation with the residual error in each iteration, and updating the selected base station set; updating the reconstruction matrix and estimating a new channel response vector; and finally, updating the residual error, and carrying out next iteration. Checking iteration conditions, and if the number of iterations does not reach a predefined upper limit or the L2 norm of the residual does not exceed a threshold value, continuing iteration; and otherwise, terminating the algorithm, and using the finally estimated B set as a result to realize same-frequency interference confrontation in a 5G ultra-dense networking scene. The method and the device are used for solving the problem that positioning observed quantity needs to be extracted as much as possible in application of 5G NAVSOP.

Description

Technical Field

[0001] The present invention belongs to the technical field of co-channel interference, and specifically relates to a method and system for countering co-channel interference in a 5G ultra-dense networking scenario. Background Art

[0002] In urban environments, 5G networks usually build networks by deploying low-power nodes under macro base stations. This deployment strategy is called HetNets, which consists of macro base stations and various low-power nodes such as micro base stations and pico base stations. The main advantages of heterogeneous networks include high flexibility in network networking, improved spectrum utilization, faster transmission speeds and lower deployment costs. However, this network structure also brings new challenges, especially because macro base stations and low-power nodes share the same frequency in the same cell, which may lead to the emergence of co-frequency interference problems. This interference will have a huge impact on cell search. When using 5G signals for communication, it is often only necessary to connect to the node with the strongest power. However, in the application of 5G NAVSOP, it is necessary to extract as many positioning observations as possible. The present invention will study the problems in this scenario and propose interference countermeasures strategies.

[0003] like Figure 7 As shown in Figure 1, in heterogeneous networks, the antenna of the receiving device is often surrounded by multiple macro base stations. When the receiver captures the signal of the target cell, it is also affected by the signals of the surrounding non-target cells. The strength of these interference signals depends on the transmission strength of the interference signal and the distance between the receiver and the interfering base station. In 5G networks, the cell search process usually uses a method based on correlation calculation. Interference signals will cause the calculation results to produce multiple correlation peaks, which may cause erroneous cell detection. This phenomenon seriously affects the efficiency and accuracy of cell search. Summary of the invention

[0004] The present invention provides a method and system for countering co-channel interference in a 5G ultra-dense networking scenario, so as to solve the problem of extracting as many positioning observation quantities as possible in the application of 5GNAVSOP.

[0005] The present invention is achieved through the following technical solutions:

[0006] A method for countering co-channel interference in a 5G ultra-dense networking scenario, the method comprising the following steps:

[0007] Step 1: Identify the cell ID with the strongest signal;

[0008] Step 2: Use the cell with the strongest signal identified in step 1 to estimate the channel using the least squares method;

[0009] Step 3: Subtract the interference component of the cell with the strongest signal from the original signal to obtain the signal after interference removal;

[0010] Step 4: Initialize parameters based on the anti-interference signal of step 3, wherein the parameters include setting the number of iterations, the selected base station set, the residual, and the reconstruction matrix;

[0011] Step 5: Find the base station with the largest correlation with the residual in each iteration and update the selected base station set;

[0012] Step 6: Based on the base station set updated in step 5, the reconstruction matrix is ​​updated and a new channel response vector is estimated;

[0013] Step 7: Finally update the residual and proceed to the next iteration;

[0014] Step 8: Check the iteration condition. If the number of iterations does not reach the predefined upper limit or the L2 norm of the residual does not exceed the threshold, continue to iterate; otherwise, the algorithm terminates and uses the final estimate As a result, the same-frequency interference countermeasure is achieved in 5G ultra-dense networking scenarios.

[0015] Furthermore, the step 1 is specifically to identify the cell ID with the strongest signal from the received signal by performing correlation operation with known PSS and SSS sequences, which is expressed by the correlation operation formula in the following formula:

[0016]

[0017] Among them, R corr,PSS (n) represents the cross-correlation result of all possible time shifts n of the PSS sequence, R corr,SSS (n) represents the cross-correlation result of all possible time shifts n of the SSS sequence, and y(m) is the received signal sampled in the discrete time domain after removing the carrier frequency. , represents the complex conjugate of the IFFT transformed local SSS replica of the i-th type, represents the complex conjugate of the ith locally generated PSS replica after IFFT transformation, and M is the length of the discrete signal y(m).

[0018] Furthermore, the step 2 uses the least square method to estimate the channel specifically as follows:

[0019]

[0020] in, is the estimated channel response, X is the pilot signal, and y is the received signal.

[0021] Furthermore, the step 3 is specifically as follows:

[0022]

[0023] in, is the estimated channel response, X is the pilot signal, y is the received signal, and y ic Interference at the receiving end.

[0024] Furthermore, the parameter initialization in step 4 is specifically to set the number of iterations i=1, the selected base station set Residual r0 = y ic , Reconstruct the matrix

[0025]

[0026] in, is the base station with the largest correlation with the residual, x p represents the complex conjugate of the training matrix of the pth base station after IFFT transformation, r i-1 is the residual calculated in the previous step.

[0027] Furthermore, the step 5 is specifically as follows:

[0028]

[0029] in, is the selected base station set. is the base station with the largest correlation with the residual.

[0030] Furthermore, the step 6 is specifically as follows:

[0031] The updated reconstruction matrix formula is:

[0032]

[0033] The new channel response vector formula is:

[0034]

[0035] in, is the new channel response vector, and z is the channel response vector.

[0036] Furthermore, the step 7 is specifically as follows:

[0037]

[0038] in, is the new channel response vector.

[0039] A system for countering co-channel interference in a 5G ultra-dense networking scenario, the system using the above-mentioned method for countering co-channel interference in a 5G ultra-dense networking scenario, the system comprising:

[0040] Identification unit: identifies the cell ID with the strongest signal;

[0041] Channel estimation unit: for the cell with the strongest signal identified by the identification unit, the least squares (LS) method is used to estimate the channel;

[0042] Interference signal removal unit: subtracts the interference component of the cell with the strongest signal from the original signal to obtain the signal after interference removal;

[0043] Initialization unit: initializing parameters based on the anti-interference signal of the interference signal removal unit, wherein the parameters include setting the number of iterations, the selected base station set, the residual and the reconstruction matrix;

[0044] Update unit: finds the base station with the greatest correlation with the residual in each iteration and updates the selected base station set;

[0045] Based on the updated base station set, the reconstruction matrix is ​​updated and a new channel response vector is estimated;

[0046] Finally, update the residual and proceed to the next iteration;

[0047] Iteration unit: Check the iteration condition. If the number of iterations does not reach the predefined upper limit or the L2 norm of the residual does not exceed the threshold, continue to iterate; otherwise, the algorithm terminates and uses the final estimate As a result, the same-frequency interference countermeasure is achieved in 5G ultra-dense networking scenarios.

[0048] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0049] The beneficial effects of the present invention are:

[0050] The present invention not only inherits the robustness of the SIC algorithm in a strong interference environment, but also avoids the residual effects left after multiple interference eliminations, and demonstrates excellent resolution that exceeds the SCP algorithm in the face of multiple interference sources or scenarios with comparable signal quality.

[0051] The present invention can eliminate interference more effectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of the method of the present invention.

[0053] Figure 2It is a schematic diagram of algorithm evaluation for the base station centralized distribution scenario of the base station layout in Table 3 of the present invention, including (a) a schematic diagram of cross-correlation algorithm evaluation, (b) a schematic diagram of SIC algorithm evaluation, (c) a schematic diagram of SCP algorithm evaluation, and (d) a schematic diagram of ICSP algorithm evaluation.

[0054] Figure 3 It is a schematic diagram of algorithm evaluation for the base station centralized distribution scenario in Table 4 of the base station layout of the present invention, including (a) a schematic diagram of cross-correlation algorithm evaluation, (b) a schematic diagram of SIC algorithm evaluation, (c) a schematic diagram of SCP algorithm evaluation, and (d) a schematic diagram of ICSP algorithm evaluation.

[0055] Figure 4 It is the present invention, wherein (a) is a schematic diagram of cross-correlation algorithm evaluation, (b) is a schematic diagram of SIC algorithm evaluation, (c) is a schematic diagram of SCP algorithm evaluation, and (d) is a schematic diagram of ICSP algorithm evaluation.

[0056] Figure 5 Schematic diagrams of algorithm evaluation for NLOS scenarios of the present invention, including (a) a schematic diagram of cross-correlation algorithm evaluation, (b) a schematic diagram of SIC algorithm evaluation, (c) a schematic diagram of SCP algorithm evaluation, and (d) a schematic diagram of ICSP algorithm evaluation.

[0057] Figure 6 It is the anti-noise test diagram of the present invention.

[0058] Figure 7 It is a schematic diagram of the HetNets networking model in the background technology.

[0059] Figure 8 This is a flow chart of the cell search based on SIC of the present invention. DETAILED DESCRIPTION

[0060] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0061] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0062] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0063] The following is attached to this application specification Figure 1-8 , the technical solutions in the embodiments of the present application are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0064] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0065] Different from the single-band / base station signal reception mode commonly established in the prior art, the present invention adopts a model in which a receiver UE receives overlapping signals from multiple same-frequency base stations under HetNets network conditions.

[0066] Implementation Method 1

[0067] This embodiment provides a method for countering co-channel interference in a 5G ultra-dense networking scenario.

[0068] Co-channel interference signal model: In a 5G NR system with multiple base stations and multipath propagation characteristics, the qth base station uses OFDM modulation, and its transmission signal can be expressed as follows:

[0069]

[0070] In the formula, k represents the time domain sample, c q,n Indicates the modulated data or symbol on the nth subcarrier of base station q, which may be a composite symbol of multiple modulation formats such as QAM or PSK. N represents the total number of subcarriers, N u Represents the number of subcarriers used to transmit the center spectrum, and the remaining subcarriers are located at both ends to form a guard band.

[0071] At the receiving end, considering multiple base stations and multipath effects, the superimposed signal y(k) can be expressed as follows:

[0072]

[0073] Where B includes all received base station identifiers and L represents the total number of multipaths in the assumed environment. For each base station q and each path l, Δf q,l and φ q,l denote the carrier frequency offset and arbitrary carrier phase of path l from base station q, respectively, and h q,l and τ q,l describes the channel gain and time delay of path q from base station q. Finally, w(k) represents complex Gaussian noise with zero mean and fixed variance.

[0074] With the widespread application of UDNs and HetNets technologies, it has become particularly difficult to extract positioning observations from these complex network distributions. Although these problems are not so prominent in communication use - users tend to connect to the base station with the strongest signal to maintain stable communication, when performing opportunity signal navigation, at least four base stations are usually required to ensure sufficient positioning data. At this time, the base station with the strongest signal in the same frequency band may cause significant interference to the positioning data obtained from other base stations with lower power.

[0075] Cell search algorithm

[0076] In OFDM systems, the problem of co-channel interference has attracted in-depth research by many researchers. The strategies to solve this type of interference mainly include: interference elimination at the receiving end, interference coordination at the base station end, interference suppression, and beamforming using millimeter wave bands. Interference coordination mainly involves the optimal configuration of time-frequency resources, such as allocating time resources in the time domain through time division multiplexing, and shifting OFDM symbols. In the frequency domain, interference is reduced by distributing control channels and data channels in different frequency bands, and the impact on other base stations is reduced by adjusting the transmit power. Interference suppression randomizes the interference signal and converts it into a noise-like form. Although this increases the system noise level, it effectively reduces interference. Beamforming reduces interference by focusing the beam energy in a specific direction.

[0077] Although all the above-mentioned methods based on the base station side are effective, in the passive positioning scenario of 5GNAVSOP, the terminal has no right to interact with the transmitter. The only way to solve this problem is to use the idea of ​​interference elimination or develop a more effective cell search algorithm.

[0078] Cross-correlation time domain cell search algorithm:

[0079] The time domain cross-correlation algorithm is a traditional cell search algorithm, which extracts the unique identifier of the cell, namely the cell ID, from the received signal.

[0080] After removing the carrier frequency from the received analog signal, it is sampled into a discrete time domain signal y(m). Next, the algorithm performs a cross-correlation operation on y(m) to determine the starting position of the PSS in the signal. This is achieved by performing a cross-correlation calculation on the signal and a known copy of the PSS. The formula for the correlation calculation is:

[0081]

[0082] in, represents the complex conjugate of the IFFT transformed local PSS replica, and R corr,PSS (n) represents the cross-correlation results for all possible time shifts n. Different values ​​of i represent three different PSS copies.

[0083] Next, calculate the energy spectrum P of each cross-correlation function i (n) is calculated as follows,

[0084] P i (n)=|R corr,PSS (n)| 2

[0085] By comparing the cross-correlation function values ​​of all PSS replicas and the received signal, a rough synchronization position estimate can be obtained. and Within the group, as shown below:

[0086]

[0087] and It is obtained through the relevant calculation of SSS.

[0088] SIC-based cell search algorithm:

[0089] The SIC-based interference cancellation technology first completes the channel estimation of the interference signal, then implements demodulation and decoding, reconstructs the interference signal, and finally eliminates it. Interference cancellation technology can improve the user's anti-interference ability at the receiving end. This section mainly studies how to solve the cell search problem under the co-channel interference of the physical broadcast channel in 5G NAVSOP based on interference cancellation.

[0090] In order to achieve the purpose of interference elimination, it can usually be divided into serial interference elimination and parallel interference elimination. The process of serial interference elimination is first introduced below.

[0091] After the signal passes through the channel, it reaches the receiving end. After being processed by the filter, the 5G cell search algorithm based on PSS and SSS is used to detect the ID of the first strongest interfering cell, and time synchronization and channel estimation are performed to prepare for the reconstruction of the interference signal. Then, the signal is reconstructed based on the detected 5GPSS and SSS signals and the estimated channel, and the reconstructed signal is subtracted to complete the elimination of the first interfering cell. Repeat the above steps until the target cell is detected. When the first interfering cell is detected, there is no reconstructed signal. Starting from the second interfering cell, the reconstructed signal of the first interfering cell is subtracted before performing time domain correlation detection. Since the interference is removed to a certain extent, the search performance of the next cell is improved.

[0092] like Figure 2 The following is the process of the cell search algorithm based on SIC:

[0093] Select the cell with the largest correlation value: by correlating the received signal with the local synchronization signal,

[0094] The cell with the largest correlation value is selected as the cell to be processed. Since there are only three possible PSS signals, different cells may correspond to the same PSS signal. Therefore, the correlation value of the SSS signal can be considered as the selection metric, because different SSS signals correspond to different cell IDs;

[0095] Search for the strongest cell: Perform cell search for the strongest cell and parse out the PSS, SSS, PBCH and DMRS information required for local reconstruction;

[0096] Obtain local PSS, SSS and DM-RS signals: Use the PSS and SSS signals detected in the previous step and the cell ID to obtain the local PSS, SSS and DM-RS signals. Through LS estimation, the channel estimates of PSS, SSS and DM-RS are obtained, and the noise level matrix is ​​obtained at the same time, as shown in the following formula:

[0097]

[0098] Complete PBCH reconstruction: According to the channel estimation results and the noise level matrix, the MMSE method is used to detect the PBCH. Finally, the PBCH reconstruction is completed by using the detection results, as shown in the following formula:

[0099]

[0100] Eliminate interference at the receiving end: Perform channel estimation through the final reconstructed SS / PBCH block, combined with the reference grid, to generate the frequency domain form of the receiving end. By modulating back to the time domain through OFDM, the signal can be eliminated at the receiving end, as shown in the following formula:

[0101]

[0102] Check whether all cells are detected. If not, return the cell with the largest correlation value. If detected, output the current set.

[0103] SCP-based cell search algorithm:

[0104] The second type of anti-interference strategy is to use compressed sensing technology instead of the traditional cross-correlation algorithm for cell search. This involves studying how to use MMV to jointly recover grouped sparse signals with a common support set. Here, the present invention introduces the GMMV model, which allows a variety of measurement vectors to be obtained through different sensing matrices. It is established that the cell search problem in HetNets can be solved by using the GMMV joint block sparse signal recovery framework. In this framework, the subspace correlation tracking algorithm can be used to handle the cell search problem, which outperforms the interference elimination algorithm introduced in the previous chapter in performance. In addition, the compressed sensing framework can recover the required parameters of all base stations in the set at the same time, which avoids the traditional method used to determine the parameters. and Calculate the correlation.

[0105] In order to apply the compressed sensing framework, the present invention simplifies and reformulates the formula of the superposition signal y(k). First, extract its main components and obtain the following formula:

[0106]

[0107] Among them, X q represents the training matrix of each base station, z q Indicates the corresponding channel response.

[0108] In order to develop an extended model, the present invention further connects the training matrices of all base stations in series to construct a larger matrix, such as the following formula:

[0109] X=[X1,X2,...,X C ]

[0110] Each X q are all one-dimensional matrices N×L, where N represents the number of subcarriers in an OFDM symbol and L is defined as the uniform length of the channel impulse response used to capture the path delay in a multipath environment. Ignoring the frequency offset of z, integrating the channel responses of all base stations yields the following formula:

[0111] z=[z1,z2,...,z C ] T

[0112] Combining the mentioned elements and applying them to the formula for extracting the main components, it can be rewritten as the following formula:

[0113] y=Xz+w

[0114] So far, the present invention has successfully established a block sparse reconstruction framework. Based on this framework, algorithms such as SCP can be used for sparse reconstruction.

[0115] The following is the process of the cell search algorithm based on SCP:

[0116] 1. Initialization parameters, initial index i, candidate set Residual r0, estimation matrix

[0117] 2. When i≤S max or || r i-1 ||2≥T set , perform the following steps:

[0118] 3. Find the index with the highest correlation with the residual:

[0119] 4. Update candidate set and the estimation matrix

[0120] 5. Estimate the signal using the updated matrix:

[0121] 6. Update the residual for the next iteration:

[0122] 7. Check whether the iteration conditions are met.

[0123] Finally, the candidate set B is output, and the elements contained in it are the cell IDs.

[0124] The method comprises the following steps:

[0125] Step 1: Identify the cell ID with the strongest signal;

[0126] Step 2: Use the cell with the strongest signal identified in step 1 to estimate the channel using the least squares (LS) method;

[0127] Step 3: Subtract the interference component of the cell with the strongest signal from the original signal to obtain the signal after interference removal;

[0128] Step 4: Initialize parameters based on the anti-interference signal of step 3, wherein the parameters include setting the number of iterations, the selected base station set, the residual, and the reconstruction matrix;

[0129] Step 5: Find the base station with the largest correlation with the residual in each iteration and update the selected base station set;

[0130] Step 6: Based on the base station set updated in step 5, the reconstruction matrix is ​​updated and a new channel response vector is estimated;

[0131] Step 7: Finally update the residual and proceed to the next iteration;

[0132] Step 8: Check the iteration condition. If the number of iterations does not reach the predefined upper limit or the L2 norm of the residual does not exceed the threshold, continue to iterate; otherwise, the algorithm terminates and uses the final estimate As a result, the same-frequency interference countermeasure is achieved in 5G ultra-dense networking scenarios.

[0133] Furthermore, the step 1 is specifically to identify the cell ID with the strongest signal from the received signal by performing correlation operation with known PSS and SSS sequences, which is expressed by the correlation operation formula in the following formula:

[0134]

[0135] Among them, R corr,PSS (n) represents the cross-correlation result of all possible time shifts n of the PSS sequence, R corr,SSS (n) represents the cross-correlation result of all possible time shifts n of the SSS sequence, and y(m) is the received signal sampled in the discrete time domain after removing the carrier frequency. , represents the complex conjugate of the IFFT transformed local SSS replica of the i-th type, represents the complex conjugate of the ith locally generated PSS replica after IFFT transformation, and M is the length of the discrete signal y(m).

[0136] Furthermore, the step 2 uses the least squares (LS) method to estimate the channel specifically as follows:

[0137]

[0138] in, is the estimated channel response, X is the pilot signal, and y is the received signal.

[0139] Furthermore, the step 3 is specifically as follows:

[0140]

[0141] in, is the estimated channel response, X is the pilot signal, y is the received signal, and y ic Interference at the receiving end.

[0142] Furthermore, the parameter initialization in step 4 is specifically to set the number of iterations i=1, the selected base station set Residual r0 = y ic , Reconstruct the matrix

[0143]

[0144] in, is the base station with the largest correlation with the residual, x p represents the complex conjugate of the training matrix of the pth base station after IFFT transformation, r i-1 is the residual calculated in the previous step.

[0145] Furthermore, the step 5 is specifically as follows:

[0146]

[0147] in, is the selected base station set. is the base station with the largest correlation with the residual.

[0148] Furthermore, the step 6 is specifically as follows:

[0149] The updated reconstruction matrix formula is:

[0150]

[0151] The new channel response vector formula is:

[0152]

[0153] in, is the new channel response vector, and z is the channel response vector.

[0154] Furthermore, the step 7 is specifically as follows:

[0155]

[0156] in, is the new channel response vector.

[0157] The present invention not only inherits the robustness of the SIC algorithm in a strong interference environment, but also avoids the residual effects left after multiple interference eliminations, and demonstrates excellent resolution that exceeds the SCP algorithm in the face of multiple interference sources or scenarios with comparable signal quality.

[0158] The complexity of the three signal preprocessing algorithms SIC, SCP, and ICSP is analyzed. In the present invention, the complexity of the algorithm is measured by the number of multiplications required. Assuming that the algorithm needs to iterate K times, the complexity of these three algorithms is shown in Table 1.

[0159] Table 1 Algorithm complexity

[0160]

[0161] Among them, K represents the number of algorithm iterations, C×L represents the number of columns of matrix X, and N represents the number of rows of matrix X. According to the data in Table 1, the complexity of the ICSP algorithm is slightly increased compared with the SCP algorithm.

[0162] In order to evaluate the ability of the ICSP algorithm to handle co-channel interference in a heterogeneous network environment, and to explore its ability to enhance positioning accuracy, the present invention constructs a sophisticated two-dimensional simulation scenario to simulate the signal distribution of a 5G base station network in a city. The selected transmission frequency is f = 3509.76 MHz, which complies with the specifications of the communication frequency band in reality. In view of the usual transmission power range of cellular base stations, the transmission power of the base station is set to Pt = 10 dBm. Using the Free Space Path Loss (FSPL) model, the signal strength can be calculated according to the following formula

[0163]

[0164] Where d represents the propagation distance, f represents the signal frequency, and L fsdB represents the free space path loss, and c represents the speed of light. The signal strength formula can then be expanded and simplified to the following formula:

[0165]

[0166] When d is in meters and f is in Hertz, substituting the speed of light c in meters per second into the above formula yields the following:

[0167] L fsdB =20·log 10 (d)+20·log 10 (f)-147.55

[0168] The present invention calculates the signal strength of the base station received by each grid point and uses the TDL channel model to simulate the multipath effect in the signal propagation process. In the simulation environment, the receiver moves along the preset path and records the signal data in real time. The data is processed by four preprocessing algorithms: cross-correlation, SIC, SCP and ICSP.

[0169] Scenario 1: Base stations are distributed discretely

[0170] First, in order to be closer to the actual application scenario, the simulation scenario is set in a rectangular area of ​​130m×120m, and 5 simulated co-frequency base stations are deployed in it, where the average inter-station distance (InterSite Distance, ISD) is less than 100 meters. The specific location coordinates of the base stations are shown in Table 2. According to the free space path loss model, the signal strength of the 5 base stations received by each grid point is calculated, and the multipath effect caused by its propagation is simulated according to the TDL channel. In this environment, the simulated receiver collects signal data in real time at each small black grid point, and processes these data using four different processing algorithms. The processing results obtained at each grid point are mapped in the figure in the form of color depth. The quantitative results are given above the figure. The experimental results obtained are shown in Figure 2 middle.

[0171] according to Figure 2 The experimental results show that the performance of each algorithm is analyzed as follows:

[0172] Cross-correlation algorithm: This traditional method has poor positioning effect when there is a strong dominant signal near the base station.

[0173] In the experimental area, its effective positioning coverage is only 60.26%, and the positioning parameters of 2.78 base stations can be recovered on average. Figure 2 As shown in (a), the dark area is mainly located at the junction of multiple base station coverage areas. This is because the receiver in the edge area receives similar signal strengths from multiple base stations, while at locations close to a single base station, it is difficult to recover the signals of other base stations with the same frequency due to interference from the dominant signal.

[0174] Table 2 Base station location information

[0175]

[0176] SIC algorithm: This algorithm improves the traditional method and increases the effective positioning coverage rate in the experimental area to 83.33%. On average, it can extract the positioning information of 3.21 base stations. Although the algorithm can continuously eliminate the interference of the dominant signal and enhance the availability of the remaining weaker signals, Figure 2 (b) shows that the distribution of dark areas is discontinuous, which is due to the residual noise introduced during the elimination process, which reduces the stability of the algorithm when the base station signal strength is similar.

[0177] SCP algorithm: This method establishes a GMMV recovery framework and uses block sparse reconstruction to search for cells, which increases the effective positioning coverage to 85.90% in the experimental area and can recover the positioning parameters of 3.79 base stations on average. Figure 2The distribution of dark areas in (c) is similar to that of the cross-correlation method, but it performs better in environments with multi-base station interference, thanks to the high accuracy of the compressed sensing framework. However, the performance of this method in strong interference environments still does not meet the positioning expectations.

[0178] ICSP algorithm: The ICSP algorithm proposed in this invention combines the interference elimination capability of SIC with the cell search advantage of SCP, achieving 100% effective positioning coverage in this experimental scenario, and further improving the base station parameter recovery by 20.3% compared with the SCP algorithm. Figure 2 As shown in (d), the dark area widely covers the entire test range and shows excellent algorithm performance under various test environments.

[0179] Scenario 2: Centralized distribution of base stations

[0180] Table 3 Base station location information

[0181]

[0182] In order to further verify the robustness of the algorithm and explore the correlation between the base station parameter recovery capability and the base station distribution, the present invention designs two representative experimental scenarios. The base station layout in the first scenario is shown in Table 3. At each point in this area, the four algorithms mentioned above are applied by the test receiver to recover the base station information. The experimental results are shown in Figure 3 The experimental group then randomly selected one of the four base stations in the cluster and moved it close to another isolated base station, which changed the concentration ratio of the base stations. The location information of the base stations is shown in Table 4. The test receiver was used at each location in the area to recover the base station information using four different algorithms. The experimental results are shown in Figure 4 shown.

[0183] Table 4 Base station location information

[0184]

[0185] Figure 3 and Figure 4 The experimental results show that when the base station locations are concentrated, even in this extreme scenario, the ICSP algorithm proposed in the present invention still shows significant improvements in positioning coverage and the average number of effectively restored base stations.

[0186] Scenario 3: NLOS scenario

[0187] In order to further verify the applicability of the algorithm's base station parameter recovery capability in NLOS scenarios, we adjust the channel and combine it with the base station distribution shown in Table 5 to obtain Figure 5 The experimental results shown

[0188] Table 4 Base station location information

[0189]

[0190] Figure 5 The experimental results show that when the experimental scenario is in NLOS, the effects of all algorithms are lower than those in the LOS scenario, but the ICSP algorithm proposed in the present invention still shows significant improvements in positioning coverage and the average number of effectively restored base stations and is effective.

[0191] Signal-to-noise-interference ratio algorithm performance test

[0192] This experiment aims to evaluate the adaptability of the ICSP algorithm proposed in this invention in a co-channel interference environment. The experiment is based on a specific signal strength configuration and includes an extremely high-intensity interference signal.

[0193] At the beginning of the experiment, the present invention sets a benchmark signal strength, in which the strength of one signal is significantly higher than other signals, thus causing obvious co-channel interference. During the experiment, by gradually adjusting the strength of this dominant signal and monitoring its impact on the signal-to-noise interference ratio (SINR) of the weakest signal, the change of SINR is positioned on the horizontal axis, and the effective base station recovery rate of various algorithms is used as the corresponding value of the vertical axis. In this way, the adjustment effect of the ICSP algorithm in the change of SINR is evaluated.

[0194] The calculation formula of signal-to-noise-interference ratio is:

[0195]

[0196] like Figure 6 As shown in the figure, due to the algorithmic characteristics of the SIC algorithm, it only eliminates one strongest interference signal each time, and when the transmission power difference between the interfering cells is not large, it is difficult to find the maximum correlation value, which reduces the performance of the cell search. This shows that when the signal-to-noise interference ratio decreases, the performance decreases instead. The algorithm based on subspace correlation tracking works very well when the cell power difference is small, but its performance degrades seriously when strong interference exists. The ICSP algorithm performs well in dealing with interference changes. In the entire test range (SINR from -18dB to -13dB), the algorithm stably achieves an effective base station recovery rate of more than 75% (where the effective base station recovery rate refers to the percentage of base stations that can extract effective positioning observations to the total number of base stations, and 75% means that three out of four are recovered, and effective positioning can be completed in a two-dimensional scenario), which is significantly better than other comparison algorithms.

[0197] This paper explores in depth the possibility of potential improvements to 5G positioning receivers, especially the application of signal processing algorithms to mitigate the impact of co-channel interference on the extraction of positioning observations in ultra-dense network environments.

[0198] In view of the impact of co-channel interference on the extraction of positioning observation data in 5G UDNs deployment, the algorithm of the present invention can significantly improve the accuracy and effectiveness of positioning data in the scenario of co-channel deployment of multiple base stations. It integrates the interference elimination function of SIC and the cell search enhancement advantage of SCP, achieves full positioning coverage in a standard simulation environment, and significantly outperforms other comparative algorithms in the recovery of the number of effective base stations. Experimental results show that the algorithm is suitable for urban 5G UDNs environment to effectively alleviate the impact of co-channel interference on the extraction of positioning observations.

[0199] Implementation Method 2

[0200] This embodiment provides a system for countering co-channel interference in a 5G ultra-dense networking scenario. The system uses the method for countering co-channel interference in a 5G ultra-dense networking scenario in Embodiment 1. The system includes:

[0201] Identification unit: identifies the cell ID with the strongest signal;

[0202] Channel estimation unit: for the cell with the strongest signal identified by the identification unit, the least squares (LS) method is used to estimate the channel;

[0203] Interference signal removal unit: subtracts the interference component of the cell with the strongest signal from the original signal to obtain the signal after interference removal;

[0204] Initialization unit: initializing parameters based on the anti-interference signal of the interference signal removal unit, wherein the parameters include setting the number of iterations, the selected base station set, the residual and the reconstruction matrix;

[0205] Update unit: finds the base station with the greatest correlation with the residual in each iteration and updates the selected base station set;

[0206] Based on the updated base station set, the reconstruction matrix is ​​updated and a new channel response vector is estimated;

[0207] Finally, update the residual and proceed to the next iteration;

[0208] Iteration unit: Check the iteration condition. If the number of iterations does not reach the predefined upper limit or the L2 norm of the residual does not exceed the threshold, continue to iterate; otherwise, the algorithm terminates and uses the final estimate As a result, the same-frequency interference countermeasure is achieved in 5G ultra-dense networking scenarios.

[0209] Implementation Method 3

[0210] An embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the memory is used to store software programs and modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory and the processor are connected via a bus. Specifically, the processor implements any step in the first embodiment above by running the computer program stored in the memory.

[0211] It should be understood that in the embodiments of the present invention, the processor referred to may be a central processing unit (CPU), and the processor may also be 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 the processor may also be any conventional processor, etc.

[0212] The memory may include a read-only memory, a flash memory, and a random access memory, and provides instructions and data to the processor. A part or all of the memory may also include a nonvolatile random access memory.

[0213] It should be understood that if the above-mentioned integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing the relevant hardware through a computer program. The above-mentioned computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method implementations when executed by the processor. Among them, the above-mentioned computer program includes computer program code, and the above-mentioned computer program code can be in source code form, object code form, executable file or some intermediate form. The above-mentioned computer-readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the above-mentioned computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0214] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined in the present invention may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown in the present invention, but rather to the widest range consistent with the principles and novel features disclosed in the present invention.

[0215] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the above-mentioned device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the implementation method 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 units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, which will not be repeated here.

[0216] It should be noted that the methods and detailed examples provided in the above embodiments can be combined with the devices and equipment provided in the embodiments, and references can be made to each other, and no further details will be given.

[0217] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0218] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal equipment and method can be implemented in other ways. For example, the device / equipment implementation described above is only illustrative, for example, the division of the above modules or units is only a logical function division, and in actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0219] The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above-mentioned embodiments, or replace some of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for countering co-channel interference in a 5G ultra-dense networking scenario, characterized in that: The method comprises the following steps: Step 1: Identify the cell ID with the strongest signal; Step 2: Use the cell with the strongest signal identified in step 1 to estimate the channel using the least squares method; Step 3: Subtract the interference component of the cell with the strongest signal from the original signal to obtain the signal after interference removal; Step 4: Initialize parameters based on the anti-interference signal of step 3, wherein the parameters include setting the number of iterations, the selected base station set, the residual, and the reconstruction matrix; Step 5: Find the base station with the largest correlation with the residual in each iteration and update the selected base station set; Step 6: Based on the base station set updated in step 5, the reconstruction matrix is ​​updated and a new channel response vector is estimated; Step 7: Finally update the residual and proceed to the next iteration; Step 8: Check the iteration condition. If the number of iterations does not reach the predefined upper limit or the L2 norm of the residual does not exceed the threshold, continue to iterate; otherwise, the algorithm terminates and uses the final estimate As a result, the same-frequency interference countermeasure is achieved in 5G ultra-dense networking scenarios.

2. The method according to claim 1, characterized in that Specifically, step 1 is to identify the cell ID with the strongest signal from the received signal by performing correlation operation with known PSS and SSS sequences, which is expressed by the correlation operation formula in the following formula: Among them, R corr,PSS (n) represents the cross-correlation result of all possible time shifts n of the PSS sequence, R corr,SSS (n) represents the cross-correlation result of all possible time shifts n of the SSS sequence, and y(m) is the received signal sampled in the discrete time domain after removing the carrier frequency. , represents the complex conjugate of the IFFT transformed local SSS replica of the i-th type, represents the complex conjugate of the ith locally generated PSS replica after IFFT transformation, and M is the length of the discrete signal y(m).

3. The method according to claim 1, characterized in that The step 2 uses the least square method to estimate the channel specifically as follows: in, is the estimated channel response, X is the pilot signal, and y is the received signal.

4. The method according to claim 1, characterized in that: The step 3 is specifically as follows: Among them, y ic Interference at the receiving end.

5. The method according to claim 1, characterized in that The parameter initialization in step 4 is specifically to set the number of iterations i=1, the selected base station set Residual r0 = y ic , Reconstruct the matrix in, is the base station with the largest correlation with the residual, x p represents the complex conjugate of the training matrix of the pth base station after IFFT transformation, r i-1 is the residual calculated in the previous step.

6. The method according to claim 1, characterized in that The step 5 is specifically as follows: in, is the selected base station set. is the base station with the largest correlation with the residual.

7. The method according to claim 1, characterized in that The step 6 is specifically as follows: The updated reconstruction matrix formula is: The new channel response vector formula is: in, is the new channel response vector, and z is the channel response vector.

8. The method according to claim 1, characterized in that The step 7 is specifically as follows: in, is the new channel response vector.

9. A co-channel interference countermeasure system in a 5G ultra-dense networking scenario, characterized in that: The system uses the co-channel interference countermeasure method in the 5G ultra-dense networking scenario according to claims 1-8, and the system includes: Identification unit: identifies the cell ID with the strongest signal; Channel estimation unit: for the cell with the strongest signal identified by the identification unit, the least square method is used to estimate the channel; Interference signal removal unit: subtracts the interference component of the cell with the strongest signal from the original signal to obtain the signal after interference removal; Initialization unit: initializing parameters based on the anti-interference signal of the interference signal removal unit, wherein the parameters include setting the number of iterations, the selected base station set, the residual and the reconstruction matrix; Update unit: finds the base station with the greatest correlation with the residual in each iteration and updates the selected base station set; Based on the updated base station set, the reconstruction matrix is ​​updated and a new channel response vector is estimated; Finally, update the residual and proceed to the next iteration; Iteration unit: Check the iteration condition. If the number of iterations does not reach the predefined upper limit or the L2 norm of the residual does not exceed the threshold, continue to iterate; otherwise, the algorithm terminates and uses the final estimate As a result, the same-frequency interference countermeasure is achieved in 5G ultra-dense networking scenarios.

10. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

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