Interference elimination method and device, medium, product and equipment

Abstract

The invention discloses an interference elimination method and device, a medium, a product and equipment. The method comprises the following steps: determining an average energy value of to-be-detected time domain data; determining module value cumulative distribution information corresponding to the time domain data; determining the number of interested sampling points in the time domain data based on the average energy value and the module value cumulative distribution information; performing interference detection on the time domain data based on the number of the interested sampling points; and carrying out interference elimination processing on the time domain data based on an interference detection result, thereby accurately eliminating the interference suffered by the equipment, and facilitating the equipment to carry out efficient and high-performance post-stage decisions, such as efficient and high-performance cell search, grid scanning and other processes.

Description

Technical Field

[0001] The present application relates to the field of wireless technology, and in particular to an interference elimination method, device, medium, product and equipment. Background Art

[0002] In some devices with wireless functions, such as RedCap (Reduced Capability, lightweight terminals), one of its typical characteristics is that the data received at the antenna port of the device will be bursty. For example, in the TDD (Time Division Duplexing) frequency band, there is a situation where the uplink and downlink share the same frequency band. In some network deployment scenarios with large amounts of uplink data, such as video surveillance, strong uplink signal interference will seriously affect the quality of the downlink signal. For example, during the synchronization process or raster scanning process of the device, such as Figure 1 As shown, when the device receives the SSB (Synchronization Signal / PBCH) signal of the local cell and there is a strong uplink interference signal in the adjacent time, the following can be obtained by correlating with the local sequence: Figure 2 As shown in the results, the SSB signal of this cell causes an obvious correlation peak of this cell. However, since the power of the uplink interference signal is very large, the absolute power of the uplink interference correlation result often exceeds the correlation peak of this cell, which makes it easy to cause misjudgment in the subsequent decision-making. Summary of the invention

[0003] In order to solve the above-mentioned technical problems, the embodiments of the present application propose an interference elimination method, device, medium, product and equipment, which can accurately eliminate the interference to the device, so that the device can perform efficient and high-performance post-stage decisions, such as efficient and high-performance cell search and raster scanning processes.

[0004] In a first aspect, an embodiment of the present application provides an interference elimination method, including:

[0005] Determine the average energy value of the time domain data to be detected;

[0006] Determine the module value cumulative distribution information corresponding to the time domain data;

[0007] Determining the number of sampling points of interest in the time domain data based on the average energy value and the modulus value cumulative distribution information;

[0008] Based on the number of sampling points of interest, performing interference detection on the time domain data;

[0009] Based on the interference detection result, interference elimination processing is performed on the time domain data.

[0010] Further, the determining the average energy value of the time domain data to be detected includes:

[0011] Determining the modulus values ​​of all sampling points in the time domain data;

[0012] An average value of the modulus values ​​of all the sampling points is calculated to serve as the average energy value.

[0013] Further, the determining of the module value cumulative distribution information corresponding to the time domain data includes:

[0014] Determining the modulus values ​​of all sampling points in the time domain data;

[0015] Based on the module values ​​of all the sampling points, the module value cumulative distribution information is determined using a cumulative distribution function (CDF).

[0016] Further, determining the number of sampling points of interest in the time domain data based on the average energy value and the modulus cumulative distribution information includes:

[0017] Determining a distribution interval of interest based on the average energy value and preset upper and lower limits of the interval ratio;

[0018] The number of sampling points of interest is determined based on the distribution interval of interest and the module value cumulative distribution information.

[0019] Further, the performing interference detection on the time domain data based on the number of sampling points of interest includes:

[0020] Based on the comparison result between the preset sampling threshold and the number of sampling points of interest, interference detection is performed on the time domain data.

[0021] Further, the performing interference elimination processing on the time domain data based on the interference detection result includes:

[0022] Determining a reference amplitude based on the interference detection result and the time domain data;

[0023] Determining a gain multiple based on the reference amplitude and a preset target amplitude;

[0024] The gain multiple is used to perform interference elimination processing on the time domain data.

[0025] Further, determining a reference amplitude based on the interference detection result and the time domain data includes:

[0026] Determining the modulus values ​​of all sampling points in the time domain data;

[0027] When the interference detection result satisfies a first preset condition, taking the maximum value of the modulus values ​​as the reference amplitude;

[0028] When the interference detection result meets the second preset condition, the average energy value is used as the reference amplitude.

[0029] Furthermore, determining the gain multiple based on the reference amplitude and the preset target amplitude includes:

[0030] Determining whether the reference amplitude is less than or equal to the target amplitude;

[0031] In the case where the reference amplitude is less than or equal to the target amplitude, the reference amplitude is multiplied by a preset multiple as a new reference amplitude, so as to cyclically execute the steps of determining whether the reference amplitude is less than or equal to the target amplitude and the subsequent steps until the new reference amplitude is greater than the target amplitude to obtain the number of cycles, wherein the preset multiple is greater than 1;

[0032] Based on the number of cycles, the gain multiple is determined.

[0033] Further, the time domain data corresponds to a word length, and the use of the gain multiple to perform interference elimination processing on the time domain data includes:

[0034] Based on the gain multiple, shifting each sampling point in the time domain data;

[0035] Based on a preset data maximum value, saturation peak clipping processing is performed on each shifted sampling point in the time domain data to obtain target time domain data;

[0036] Based on the word length and the preset expected number of output bits, the target time domain data is compressed to obtain time domain data after interference elimination processing.

[0037] Further, the determining the modulus values ​​of all sampling points in the time domain data includes:

[0038] For each sampling point, an approximate algorithm is used to perform calculation based on the data corresponding to the sampling point in the time domain data to obtain a modulus value of the sampling point.

[0039] In a second aspect, an embodiment of the present application provides an interference elimination device, including:

[0040] An average energy value determination module, used to determine the average energy value of the time domain data to be detected;

[0041] a module for determining the cumulative distribution of modulus values, used to determine the cumulative distribution information of modulus values ​​corresponding to the time domain data; a module for determining the number of sampling points of interest in the time domain data based on the average energy value and the cumulative distribution information of modulus values;

[0042] A detection module, configured to perform interference detection on the time domain data based on the number of sampling points of interest;

[0043] The elimination module is used to perform interference elimination processing on the time domain data based on the interference detection result.

[0044] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned interference elimination methods are implemented.

[0045] In a fourth aspect, an embodiment of the present application provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of any of the above-mentioned interference elimination methods.

[0046] In a fifth aspect, an embodiment of the present application provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the steps of any of the above-mentioned interference elimination methods when executing the computer program.

[0047] In summary, the embodiments of the present application have at least the following beneficial effects:

[0048] By adopting the embodiment of the present application, the average energy value of the time domain data to be detected is determined; the module value cumulative distribution information corresponding to the time domain data is determined; based on the average energy value and the module value cumulative distribution information, the number of sampling points of interest in the time domain data is determined; based on the number of sampling points of interest, the time domain data is subjected to interference detection; based on the interference detection result, the time domain data is subjected to interference elimination processing, so that the interference to the device can be accurately eliminated, so that the device can perform efficient and high-performance post-stage decision-making, for example, efficient and high-performance cell search and raster scanning processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of signals during the synchronization process or the raster scanning process of the device provided in the embodiment of the present application;

[0050] Figure 2 It is a schematic diagram of the results of the correlation between the cell and uplink interference provided by the embodiment of the present application;

[0051] Figure 3 is a flowchart of an interference elimination method provided in an embodiment of the present application;

[0052] Figure 4 is a schematic diagram of interference elimination provided by an embodiment of the present application;

[0053] Figure 5is a schematic diagram of interference elimination provided by an embodiment of the present application;

[0054] Figure 6 is a schematic diagram of calculating the modulus value provided in an embodiment of the present application;

[0055] Figure 7 is a schematic diagram of a simulation example of interference elimination provided in an embodiment of the present application;

[0056] Figure 8 is a schematic diagram of the structure of an interference elimination device provided in an embodiment of the present application;

[0057] Fig. 9 It is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. 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 this application.

[0059] In the description of the present application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, features defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "multiple" is two or more. In the description of the present application, the term "including" and its variations are open inclusions, i.e., "including but not limited to". The term "based on" means "at least partially based on". The term "according to" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments".

[0060] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0061] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0062] The following is an explanation of some terminology concepts involved in the embodiments of the present application:

[0063] SSB (Synchronization Signal / PBCH), SSB is a key technology in 5G NR (5th Generation New Radio, fifth generation mobile communication technology), used to support initial access and synchronization between UE (User Equipment, terminal equipment) and base station.

[0064] PSS (Primary Synchronization Signal), PSS is used for symbol time and frequency synchronization, and to determine the ID within the physical layer ID (Identity document) group of the cell. PSS consists of a specific sequence with good autocorrelation and cross-correlation, which facilitates UE detection and synchronization.

[0065] SSS (Secondary Synchronization Signal), SSS provides frame boundary detection and identification of cell ID groups, and can be used in conjunction with PSS to determine the complete physical layer cell ID. SSS also has good autocorrelation, but compared to PSS, SSS has a longer sequence length to carry more information.

[0066] SCS (Subcarrier Spacing) is a key parameter in OFDM (Orthogonal Frequency Division Multiplexing) and OFDMA (Orthogonal Frequency Division Multiple Access) systems, especially in modern wireless communication systems such as 4G LTE (Fourth Generation Long-Term Evolution) and 5GNR. SCS determines the frequency width of each subcarrier, thus affecting many aspects of the system, including bandwidth efficiency, spectrum utilization, multipath fading resistance, signal processing complexity, and battery life.

[0067] CDF (Cumulative Distribution Function) can be used to describe the probability that a random variable takes a value less than or equal to a specific value.

[0068] First, see Figure 3 , shows a schematic flow chart of an interference elimination method provided in an embodiment of the present application, the method includes steps S301-S305, which are specifically as follows:

[0069] S301, determining the average energy value of the time domain data to be detected;

[0070] It should be noted that a segment of time domain data in this embodiment may generally include a number of sampling points, each of which is used to represent the amplitude of a signal at a certain moment. Exemplarily, the average energy value of a segment of time domain data may be represented by calculating the mean of the instantaneous energy of each sampling point in the segment of time domain data, wherein the instantaneous energy of each sampling point may be represented by using the square value or modulus value of the sampling point.

[0071] For example, see Figure 4 In this embodiment, the time domain data to be detected may be a section of time domain data obtained by caching the input data Xin(1..N).

[0072] S302, determining the module value cumulative distribution information corresponding to the time domain data;

[0073] It should be noted that the module value cumulative distribution information in this embodiment can be used to characterize the module value cumulative distribution corresponding to the time domain data. In one example, the module value cumulative distribution information can be generated based on the sorted module value, and the sorted module value can be obtained by sorting the module values ​​corresponding to each sampling point in the time domain data. In another example, the module value cumulative distribution information corresponding to the time domain data can also be generated using a pre-trained model.

[0074] S303, determining the number of sampling points of interest in the time domain data based on the average energy value and the modulus cumulative distribution information;

[0075] It should be noted that the number of sampling points of interest in this embodiment may refer to the number of points that meet preset conditions among all sampling points in the time domain data, wherein the preset condition may be determined based on the average energy value and the modulus cumulative distribution information. For example, the modulus cumulative distribution information can determine the modulus cumulative distribution of all sampling points in the time domain data, and then combined with the corresponding threshold determined by the average energy value, the number of sampling points of interest can be determined.

[0076] S304, performing interference detection on the time domain data based on the number of sampling points of interest;

[0077] In one example, whether there is interference in the time domain data can be detected by determining whether the number of sampling points of interest falls within a preset range of sampling points.

[0078] S305: Perform interference elimination processing on the time domain data based on the interference detection result.

[0079] In one example, when the interference detection result indicates the presence of interference, a preset interference elimination technology can be used to eliminate interference on the time domain data, wherein the interference elimination technology may include but is not limited to at least one of the following: data interpolation processing, moving average smoothing processing, median filtering processing, frequency domain filtering processing, and elimination processing based on a pre-trained interference elimination model.

[0080] In specific implementation, the embodiments of the present application can be applied to the synchronization process of the RedCap system to eliminate time domain interference.

[0081] For example, in some prior art technologies for eliminating time domain interference, the following methods are generally used to achieve the elimination:

[0082] 1) The input signal is forcibly aligned according to the power of the input signal, that is, the power over a period of time is counted, and the amplitude of the signal is adjusted according to the current power so that the output power is on the same level.

[0083] 2) Identify and eliminate interference based on the distribution of the correlation results of the PSS signal, that is, identify interference by comparing the relative power of each correlation result sampling point with other nearby sampling points in the time domain, and eliminate interference by reducing the power of the interfering part.

[0084] However, the prior art generally has the following problems:

[0085] 1) The method of forcibly pulling the power to the same level is easily affected by the glitch signal when encountering a very strong glitch signal, causing the amplitude of the useful signal to be pulled very low.

[0086] 2) The method of identifying and eliminating interference based on the distribution of correlation results of the PSS signal requires a complex algorithm, which is heavily dependent on the quality and strength of the input signal, is highly complex, and is prone to misjudging useful correlation peaks.

[0087] 3) In order to ensure the validity of the useful signal in the above input signal / PSS signal, a large amount of time domain data to be stored requires a large bit width, which occupies a large amount of storage space and bus access bandwidth.

[0088] Accordingly, in the embodiment of the present application, cumulative distribution can be used to distinguish the nature of time domain burst interference (such as time domain burst interference in the RedCap system), especially continuous strong interference and sudden burr interference. And only the data needs to be pre-processed at the front end, and no complex recognition algorithm is required in the later stage, that is, there is no need to perform complex algorithm processing on the PSS signal, which can improve efficiency and reduce the memory and bandwidth required.

[0089] In an optional implementation, determining the average energy value of the time domain data to be detected includes:

[0090] Determining the modulus values ​​of all sampling points in the time domain data;

[0091] An average value of the modulus values ​​of all the sampling points is calculated to serve as the average energy value.

[0092] In one example, the average energy value P can be calculated by the following formula in a specific embodiment: avg :

[0093]

[0094] Where N is the length of the time domain data, that is, the number of sampling points in the time domain data, x i Used to indicate the i

[0095] sampling points, abs(x c ) represents the modulus value corresponding to the i-th sampling point.

[0096] In an optional implementation, the determining the modulus cumulative distribution information corresponding to the time domain data includes:

[0097] Determining the modulus values ​​of all sampling points in the time domain data;

[0098] Based on the module values ​​of all the sampling points, the module value cumulative distribution information is determined using a cumulative distribution function (CDF).

[0099] In one example, after arranging the modulus values ​​of all sampling points in ascending order, the cumulative frequency of the modulus value of each arranged sampling point (the proportion of all modulus values ​​less than or equal to the modulus value of the sampling point) can be calculated, so as to plot the corresponding CDF to obtain the cumulative distribution information of the modulus value.

[0100] In an optional implementation, determining the number of sampling points of interest in the time domain data based on the average energy value and the modulus cumulative distribution information includes:

[0101] Determining a distribution interval of interest based on the average energy value and preset upper and lower limits of the interval ratio;

[0102] Based on the distribution interval of interest and the cumulative distribution information of the modulus value, the number of sampling points of interest N is determined cdf .

[0103] In one example, the lower limit of the distribution interval of interest can be determined by multiplying the average energy value by the lower limit of the interval ratio, and the upper limit of the distribution interval of interest can be determined by multiplying the average energy value by the upper limit of the interval ratio. low *P avg ,th high *P avg ], where th low Indicates the lower limit of the interval ratio, th high represents the upper limit of the interval ratio, P avg Represents the average energy value.

[0104] In an optional implementation, the performing interference detection on the time domain data based on the number of sampling points of interest includes:

[0105] Based on the comparison result between the preset sampling threshold and the number of sampling points of interest, interference detection is performed on the time domain data.

[0106] In this embodiment, the sampling threshold N can be preset. cdf_max , when the number of sampling points of interest N cdf >N cdf_maxWhen the number of sampling points of interest is less than or equal to the sampling threshold, it is determined that there is no continuous strong interference in the time domain data. It should be understood that the continuous strong interference in the time domain data here usually refers to relatively obvious interference. When the number of sampling points of interest is less than or equal to the sampling threshold, it is determined that there is no continuous strong interference in the time domain data.

[0107] In an optional implementation, performing interference elimination processing on the time domain data based on the interference detection result includes:

[0108] Determining a reference amplitude based on the interference detection result and the time domain data;

[0109] It should be noted that the reference amplitude in this embodiment may refer to an ideal or normal amplitude of a signal when it is not affected by interference. In one example, when the interference detection result indicates the presence of interference, the reference amplitude may be determined by analyzing the uninterrupted signal portion in the time domain data, or the reference amplitude may be estimated from the signal of the entire time domain data by a statistical method (such as mean, median).

[0110] Determining a gain multiple based on the reference amplitude and a preset target amplitude;

[0111] It should be noted that the target amplitude in this embodiment may refer to the amplitude that the signal is expected to reach after the interference is eliminated, and the gain multiple may be a coefficient used to adjust the current signal amplitude to the target amplitude, wherein the target amplitude may be the same as the reference amplitude, but may also be adjusted according to specific application requirements. For example, if a higher signal-to-noise ratio is required in the system, a slightly higher target amplitude may be selected to achieve the system's requirement for the signal-to-noise ratio.

[0112] The gain multiple is used to perform interference elimination processing on the time domain data.

[0113] In one example, each sampling point of the time domain data may be multiplied by a gain factor to achieve interference cancellation.

[0114] In an optional implementation manner, determining a reference amplitude based on the interference detection result and the time domain data includes:

[0115] Determining the modulus values ​​of all sampling points in the time domain data;

[0116] When the interference detection result satisfies a first preset condition, taking the maximum value of the modulus values ​​as the reference amplitude;

[0117] When the interference detection result meets the second preset condition, the average energy value is used as the reference amplitude.

[0118] Exemplarily, the first preset condition may include the existence of continuous strong interference, and the second preset condition may include the existence of burst glitch interference.

[0119] In one example, see Figure 5 , when the interference detection result indicates that there is continuous strong interference in the time domain data, the reference amplitude X ref Determined as the maximum value P among the modulo values max , that is, X ref =P max , where P max =max{abs(x i )}. If the interference detection result indicates that there is no continuous strong interference in the time domain data (there is burst burr interference), then X ref =P avg .

[0120] In this embodiment, continuous strong interference does not affect the use of average energy value to level out, and has no effect on PSS correlation, and the sudden glitch interference is forced to be saturated, which does not affect the bit width of the overall data and saves data storage. In addition, different interference elimination schemes can be used for different interferences: for continuous strong interference, it is aligned with the maximum sampling point; for sudden glitch interference, it is aligned with the average energy, and the sudden glitch interference is saturated.

[0121] In an optional implementation manner, determining the gain multiple based on the reference amplitude and a preset target amplitude includes:

[0122] Determining whether the reference amplitude is less than or equal to the target amplitude;

[0123] In the case where the reference amplitude is less than or equal to the target amplitude, the reference amplitude is multiplied by a preset multiple as a new reference amplitude, so as to cyclically execute the steps of determining whether the reference amplitude is less than or equal to the target amplitude and the subsequent steps until the new reference amplitude is greater than the target amplitude to obtain the number of cycles, wherein the preset multiple is greater than 1;

[0124] Based on the number of cycles, the gain multiple is determined.

[0125] For example, see Figure 5 , the preset multiple can be equal to 2, so that the new reference amplitude X′ obtained in each cycle ref That is the last reference amplitude X ref twice (X′ ref =2*X ref ), until X′ ref Greater than target amplitude X target Then use the number of cycles at this time to determine the gain multiple Nshift For example, the number of cycles can be directly used as the gain multiplier N shift .

[0126] In an optional implementation manner, the time domain data corresponds to a word length, and the using the gain multiple to perform interference elimination processing on the time domain data includes:

[0127] Based on the gain multiple, shifting each sampling point in the time domain data;

[0128] In one example, the shift can be performed by the following formula:

[0129]

[0130] Among them, x i Used to indicate the sampling point before the i-th shift, x i ′ Used to indicate the i-th shifted sampling point.

[0131] It is not difficult to understand that the time domain data in this embodiment is binary, so the above-mentioned preset multiple is 2 accordingly. For ease of understanding, the following uses the case where the time domain data is binary to explain, but if other bases are used to represent the time domain data, the above-mentioned shift formula, preset multiples, etc. can also take other values, which will not be repeated here.

[0132] Based on a preset data maximum value, saturation peak clipping processing is performed on each shifted sampling point in the time domain data to obtain target time domain data;

[0133] It should be noted that saturation peak clipping can be used to prevent electrical signals from being distorted during the amplification process due to exceeding the maximum output capacity of the device. When the signal level exceeds the range that the device can linearly process, the signal will be flattened or clipped.

[0134] In one example, based on a preset data maximum value, performing saturation peak clipping processing on each shifted sampling point in the time domain data to obtain target time domain data may include:

[0135] Determine the overflowed sampling points among the shifted sampling points;

[0136] For each overflow sampling point, the larger one between the overflow sampling point and the preset data minimum value is determined, and the smaller one between the larger one and the preset data maximum value is determined as the sampling point after saturation peak clipping. For example, it can be expressed as: i ′ =min{max(x i ,x min ),x max}, where x iUsed to indicate the i-th overflow sampling point, x i ′ Used to indicate the sampling point after the i-th saturation clipping, x min Indicates the minimum value of the data, x max Indicates the maximum value of data;

[0137] The corresponding overflowed sampling points in the time domain data are replaced by the sampling points after saturation peak clipping, so as to obtain the target time domain data.

[0138] Based on the word length and the preset expected number of output bits, the target time domain data is compressed to obtain time domain data after interference elimination processing.

[0139] In one example, compressing the target time domain data based on the word length and the preset expected number of output bits to obtain the time domain data after interference cancellation processing may include:

[0140] The word length N bit_in Subtract the expected number of output bits N bit_out Get the number of compressed bits N comp , that is, N comp =N bit_in -N bit_out ; Among them, the expected number of output bits can be defined according to the application scenario and performance requirements.

[0141] The number of bits used for compression is N comp To compress the target time domain data through the following formula;

[0142]

[0143] Among them, x i Used to indicate the i-th sampling point to be compressed in the target time domain data, x i ′ Used to indicate the i-th sample point after compression.

[0144] In this embodiment, data may be compressed according to application scenarios and performance requirements during the interference elimination process, thereby saving data storage space.

[0145] In addition, in another example, a pre-trained compression model may be used to compress the target time domain data according to the word length and a preset expected number of output bits.

[0146] In an optional implementation, determining the modulus values ​​of all sampling points in the time domain data includes:

[0147] For each sampling point, an approximate algorithm is used to perform calculation based on the data corresponding to the sampling point in the time domain data to obtain a modulus value of the sampling point.

[0148] In one example, see Figure 6 , the data corresponding to the sampling point can refer to the complex number corresponding to the sampling point. In this way, the approximate algorithm can be expressed by the following formula:

[0149]

[0150] Among them, abs(x i ) represents the complex number x corresponding to the i-th sampling point i The modulus value, real(x i ) represents x i The real part, imag(x i ) represents x i The imaginary part of min{} means taking the smallest element, m means the approximate order, n j Represents the jth preset variable.

[0151] See also Figure 7 , an experimental simulation example is given below.

[0152] In a complex scenario, first, there are three continuous strong interferences in the original sampled signal, on this basis, a stronger burr interference is introduced. Through the solution of the embodiment of the present application, two kinds of interference can be processed at the same time. For the three continuous interferences, it is determined to be a normal state, and the reference amplitude takes the maximum energy of a single sampling point, that is, X ref =P max After the interference is eliminated, this part is displayed as a state of balance with the signal amplitude of the cell, and the performance of the subsequent PSS related process is not affected.

[0153] Similarly, for stronger glitch interference, the reference amplitude is the average energy of the data segment: X ref =P avg In this case, N shift It will be much larger than the range that the burr part can withstand, so that the burr part is saturated and the interference here will not affect the subsequent PSS-related processes, as shown in the signal with interference removed in the figure.

[0154] On the second aspect, accordingly, an embodiment of the present application also provides an interference elimination device, which can implement all the processes of the interference elimination method provided in the above embodiment.

[0155] See also Figure 8 , shows a schematic diagram of the structure of the interference elimination device provided in an embodiment of the present application, including:

[0156] The average energy value determination module 801 is used to determine the average energy value of the time domain data to be detected;

[0157] A module value cumulative distribution determination module 802 is used to determine the module value cumulative distribution information corresponding to the time domain data;

[0158] An interesting sampling point quantity determination module 803 is used to determine the interesting sampling point quantity in the time domain data based on the average energy value and the modulus value cumulative distribution information;

[0159] A detection module 804, configured to perform interference detection on the time domain data based on the number of sampling points of interest;

[0160] The elimination module 805 is used to perform interference elimination processing on the time domain data based on the interference detection result.

[0161] In an optional implementation, determining the average energy value of the time domain data to be detected includes:

[0162] Determining the modulus values ​​of all sampling points in the time domain data;

[0163] An average value of the modulus values ​​of all the sampling points is calculated to serve as the average energy value.

[0164] In an optional implementation, the determining the modulus cumulative distribution information corresponding to the time domain data includes:

[0165] Determining the modulus values ​​of all sampling points in the time domain data;

[0166] Based on the module values ​​of all the sampling points, the module value cumulative distribution information is determined using a cumulative distribution function (CDF).

[0167] In an optional implementation, determining the number of sampling points of interest in the time domain data based on the average energy value and the modulus cumulative distribution information includes:

[0168] Determining a distribution interval of interest based on the average energy value and preset upper and lower limits of the interval ratio;

[0169] The number of sampling points of interest is determined based on the distribution interval of interest and the module value cumulative distribution information.

[0170] In an optional implementation, the performing interference detection on the time domain data based on the number of sampling points of interest includes:

[0171] Based on the comparison result between the preset sampling threshold and the number of sampling points of interest, interference detection is performed on the time domain data.

[0172] In an optional implementation, performing interference elimination processing on the time domain data based on the interference detection result includes:

[0173] Determining a reference amplitude based on the interference detection result and the time domain data;

[0174] Determining a gain multiple based on the reference amplitude and a preset target amplitude;

[0175] The gain multiple is used to perform interference elimination processing on the time domain data.

[0176] In an optional implementation manner, determining a reference amplitude based on the interference detection result and the time domain data includes:

[0177] Determining the modulus values ​​of all sampling points in the time domain data;

[0178] When the interference detection result satisfies a first preset condition, taking the maximum value of the modulus values ​​as the reference amplitude;

[0179] When the interference detection result meets the second preset condition, the average energy value is used as the reference amplitude.

[0180] In an optional implementation manner, determining the gain multiple based on the reference amplitude and a preset target amplitude includes:

[0181] Determining whether the reference amplitude is less than or equal to the target amplitude;

[0182] In the case where the reference amplitude is less than or equal to the target amplitude, the reference amplitude is multiplied by a preset multiple as a new reference amplitude, so as to cyclically execute the steps of determining whether the reference amplitude is less than or equal to the target amplitude and the subsequent steps until the new reference amplitude is greater than the target amplitude to obtain the number of cycles, wherein the preset multiple is greater than 1;

[0183] Based on the number of cycles, the gain multiple is determined.

[0184] In an optional implementation manner, the time domain data corresponds to a word length, and the using the gain multiple to perform interference elimination processing on the time domain data includes:

[0185] Based on the gain multiple, shifting each sampling point in the time domain data;

[0186] Based on a preset data maximum value, saturation peak clipping processing is performed on each shifted sampling point in the time domain data to obtain target time domain data;

[0187] Based on the word length and the preset expected number of output bits, the target time domain data is compressed to obtain time domain data after interference elimination processing.

[0188] In an optional implementation, determining the modulus values ​​of all sampling points in the time domain data includes:

[0189] For each sampling point, an approximate algorithm is used to perform calculation based on the data corresponding to the sampling point in the time domain data to obtain a modulus value of the sampling point.

[0190] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned interference elimination methods are implemented.

[0191] In a fourth aspect, an embodiment of the present application provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of any of the above-mentioned interference elimination methods.

[0192] In a fifth aspect, an embodiment of the present application provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the steps of any of the above-mentioned interference elimination methods when executing the computer program.

[0193] See also Fig. 9 The computer device of this embodiment includes: a processor 901, a memory 902, and a computer program stored in the memory 902 and executable on the processor 901, such as an interference elimination program. When the processor 901 executes the computer program, the steps in the above-mentioned various interference elimination method embodiments are implemented, such as Figure 3 Steps S301-S305 are shown.

[0194] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 902 and executed by the processor 901 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the computer device.

[0195] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may include, but not limited to, a processor 901 and a memory 902. Those skilled in the art may understand that the schematic diagram is only an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than shown in the figure, or may combine certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0196] The processor 901 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 the processor 901 may also be any conventional processor, etc. The processor 901 is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0197] The memory 902 can be used to store the computer program and / or module, and the processor 901 implements various functions of the computer device by running or executing the computer program and / or module stored in the memory 902, and calling the data stored in the memory 902. The memory 902 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 902 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0198] Wherein, if the module / unit integrated in the computer device 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 this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The 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 embodiments when executed by the processor 901. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0199] In summary, the embodiments of the present application have at least the following beneficial effects:

[0200] By adopting the embodiment of the present application, the average energy value of the time domain data to be detected is determined; the module value cumulative distribution information corresponding to the time domain data is determined; based on the average energy value and the module value cumulative distribution information, the number of sampling points of interest in the time domain data is determined; based on the number of sampling points of interest, the time domain data is subjected to interference detection; based on the interference detection result, the time domain data is subjected to interference elimination processing, so that the interference to the device can be accurately eliminated, so that the device can perform efficient and high-performance post-stage decision-making, for example, efficient and high-performance cell search and raster scanning processes.

[0201] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus the necessary hardware platform, and of course, it can also be implemented entirely by hardware. Based on such an understanding, all or part of the contribution of the technical solution of the present application to the background technology can be embodied in the form of a software product, and the computer software product can be stored in a storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0202] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications are also considered to be within the scope of protection of the present application.

Claims

1. An interference elimination method, characterized in that: include: Determine the average energy value of the time domain data to be detected; Determine the module value cumulative distribution information corresponding to the time domain data; Determining the number of sampling points of interest in the time domain data based on the average energy value and the modulus value cumulative distribution information; Based on the number of sampling points of interest, performing interference detection on the time domain data; Based on the interference detection result, interference elimination processing is performed on the time domain data.

2. The interference elimination method according to claim 1, characterized in that: The step of determining the average energy value of the time domain data to be detected includes: Determining the modulus values ​​of all sampling points in the time domain data; An average value of the modulus values ​​of all the sampling points is calculated to serve as the average energy value.

3. The interference elimination method according to claim 1, characterized in that: The determining the module value cumulative distribution information corresponding to the time domain data includes: Determining the modulus values ​​of all sampling points in the time domain data; Based on the module values ​​of all the sampling points, the module value cumulative distribution information is determined using a cumulative distribution function (CDF).

4. The interference elimination method according to claim 1, characterized in that: The determining the number of sampling points of interest in the time domain data based on the average energy value and the modulus cumulative distribution information includes: Determining a distribution interval of interest based on the average energy value and preset upper and lower limits of the interval ratio; The number of sampling points of interest is determined based on the distribution interval of interest and the module value cumulative distribution information.

5. The interference elimination method according to claim 1, characterized in that: The performing interference detection on the time domain data based on the number of sampling points of interest includes: Based on the comparison result between the preset sampling threshold and the number of sampling points of interest, interference detection is performed on the time domain data.

6. The interference elimination method according to claim 1, characterized in that: The performing interference elimination processing on the time domain data based on the interference detection result includes: Determining a reference amplitude based on the interference detection result and the time domain data; Determining a gain multiple based on the reference amplitude and a preset target amplitude; The gain multiple is used to perform interference elimination processing on the time domain data.

7. The interference elimination method according to claim 6, characterized in that: The determining a reference amplitude based on the interference detection result and the time domain data includes: Determining the modulus values ​​of all sampling points in the time domain data; When the interference detection result satisfies a first preset condition, taking the maximum value of the modulus values ​​as the reference amplitude; When the interference detection result meets the second preset condition, the average energy value is used as the reference amplitude.

8. The interference elimination method according to claim 6, characterized in that: The step of determining the gain multiple based on the reference amplitude and the preset target amplitude includes: Determining whether the reference amplitude is less than or equal to the target amplitude; In the case where the reference amplitude is less than or equal to the target amplitude, the reference amplitude is multiplied by a preset multiple as a new reference amplitude, so as to cyclically execute the steps of determining whether the reference amplitude is less than or equal to the target amplitude and the subsequent steps until the new reference amplitude is greater than the target amplitude to obtain the number of cycles, wherein the preset multiple is greater than 1; Based on the number of cycles, the gain multiple is determined.

9. The interference elimination method according to claim 6, characterized in that: The time domain data corresponds to a word length, and the use of the gain multiple to perform interference elimination processing on the time domain data includes: Based on the gain multiple, shifting each sampling point in the time domain data; Based on a preset data maximum value, saturation peak clipping processing is performed on each shifted sampling point in the time domain data to obtain target time domain data; Based on the word length and the preset expected number of output bits, the target time domain data is compressed to obtain time domain data after interference elimination processing.

10. The interference elimination method according to any one of claims 2, 3 and 7, characterized in that: The determining of the modulus values ​​of all sampling points in the time domain data includes: For each sampling point, an approximate algorithm is used to perform calculation based on the data corresponding to the sampling point in the time domain data to obtain a modulus value of the sampling point.

11. An interference elimination device, characterized in that: include: An average energy value determination module, used to determine the average energy value of the time domain data to be detected; A module for determining the cumulative distribution of modulus values, used to determine the cumulative distribution information of modulus values ​​corresponding to the time domain data; interested A sampling point quantity determination module, used to determine the number of sampling points of interest in the time domain data based on the average energy value and the module value cumulative distribution information; A detection module, configured to perform interference detection on the time domain data based on the number of sampling points of interest; The elimination module is used to perform interference elimination processing on the time domain data based on the interference detection result.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the interference elimination method according to any one of claims 1 to 10 is implemented.

13. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by a processor, the interference elimination method according to any one of claims 1 to 10 is implemented.

14. A computer device, characterized in that: The invention comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the interference elimination method according to any one of claims 1 to 10 when executing the computer program.

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