Interference cancellation method, apparatus, medium, product and device

Abstract

The application discloses an interference cancellation method, device, medium, product and equipment. The method comprises the following steps: determining the average energy value of time domain data to be detected; determining the modulus 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 modulus cumulative distribution information; performing interference detection on the time domain data based on the number of interested sampling points; and performing interference cancellation processing on the time domain data based on the interference detection result, so that the interference received by the equipment can be accurately cancelled, and the equipment can make efficient and high-performance subsequent decisions, such as efficient and high-performance cell search and raster scanning processes.

Description

TECHNICAL FIELD

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

[0002] In some wireless devices, such as RedCap (Reduced Capability, lightweight terminal), one of the typical characteristics is that the data received at the antenna port of the device will exhibit burstiness. For example, in the case of TDD (Time Division Duplexing) frequency bands, there are uplink and downlink shared frequency bands. In some uplink data-intensive deployment scenarios, such as video monitoring, strong uplink signal interference can seriously affect the quality of the downlink signal. For example, as shown in FIG. 1, during the synchronization process or grid scanning process of the device, the device receives the SSB (Synchronization Signal / PBCH) signal of the current cell, and there is a strong uplink interference signal in the adjacent time. By correlating with the local sequence, the result shown in FIG. 2 can be obtained. At this time, the SSB signal of the current cell causes a significant correlation peak of the current cell, but due to the large power of the uplink interference signal, the absolute power of the uplink interference correlation result often exceeds the correlation peak of the current cell, thereby causing misjudgment during the later decision-making. Figure 1 Figure 2 SUMMARY

[0003] To solve the above technical problems, the embodiments of the present application provide an interference cancellation method, device, medium, product and equipment, which can accurately eliminate the interference received by the device, so as to facilitate the device to make efficient and high-performance later decision-making, such as efficient and high-performance cell search and grid scanning processes.

[0004] In a first aspect, the embodiments of the present application provide an interference cancellation method, comprising:

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

[0006] determining the modulus 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 cumulative distribution information;

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

[0009] performing interference cancellation processing on the time domain data based on the interference detection result.

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

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

[0012] calculating the average value of the modulus values of all sampling points as the average energy value.

[0013] Further, the determining the modulus value cumulative distribution information corresponding to the time domain data comprises:

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

[0015] determining the modulus value cumulative distribution information by using a cumulative distribution function (CDF) based on the modulus values of all sampling points.

[0016] Further, the 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 comprises:

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

[0018] determining the number of sampling points of interest based on the distribution interval of interest and the modulus value cumulative distribution information.

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

[0020] performing interference detection on the time domain data based on a comparison result between a preset sampling threshold and the number of sampling points of interest.

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

[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] performing interference cancellation processing on the time domain data by using the gain multiple.

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

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

[0027] in a case where the interference detection result satisfies a first preset condition, taking the maximum value in the modulus values as the reference amplitude.

[0028] In a case where the interference detection result satisfies a second preset condition, the average energy value is taken as the reference amplitude.

[0029] Further, the determining the gain multiple based on the reference amplitude and a preset target amplitude comprises:

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

[0031] In a case where the reference amplitude is less than or equal to the target amplitude, multiplying the reference amplitude by a preset multiple to obtain a new reference amplitude, and performing the determining whether the reference amplitude is less than or equal to the target amplitude and the subsequent steps in a loop until a loop number is obtained after the new reference amplitude is greater than the target amplitude, wherein the preset multiple is greater than 1.

[0032] determining the gain multiple based on the loop number.

[0033] Further, the time domain data corresponds to a word length, and the performing the interference cancellation processing on the time domain data based on the gain multiple comprises:

[0034] performing a shift on each sample point in the time domain data based on the gain multiple;

[0035] performing a saturation peak clipping processing on each shifted sample point in the time domain data based on a preset data extreme value to obtain target time domain data;

[0036] performing compression on the target time domain data based on the word length and a preset expected output bit number to obtain time domain data after the interference cancellation processing.

[0037] Further, the determining the modulus value of each sample point in the time domain data comprises:

[0038] for each sample point, performing calculation based on data corresponding to the sample point in the time domain data by using an approximation algorithm to obtain the modulus value of the sample point.

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

[0040] an average energy value determination module configured to determine an average energy value of time domain data to be detected;

[0041] a modulus value cumulative distribution determination module configured to determine modulus value cumulative distribution information corresponding to the time domain data; and

[0042] detecting, by a detection module, interference based on the number of sampling points of interest in the time domain data;

[0043] canceling, by a canceling module, interference based on the interference detection result.

[0044] In a third aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps of the interference canceling method in any of the above aspects.

[0045] In a fourth aspect, an embodiment of the present application provides a computer program product, which includes computer instructions. The computer instructions are executed by a processor to implement the steps of the interference canceling method in any of the above aspects.

[0046] In a fifth aspect, an embodiment of the present application provides a computer device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. The processor executes the computer program to implement the steps of the interference canceling method in any of the above aspects.

[0047] To sum up, the embodiments of the present application have at least the following beneficial effects:

[0048] By determining the average energy value of the time domain data to be detected, determining the modulus 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 cumulative distribution information, detecting interference based on the number of sampling points of interest in the time domain data, and canceling interference based on the interference detection result, the interference received by the device can be accurately canceled, so that the device can make efficient and high-performance subsequent decisions, such as efficient and high-performance cell search and raster scanning processes. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is a signal schematic diagram in a synchronization process or a raster scanning process of a device provided by an embodiment of the present application;

[0050] Figure 2 is a schematic diagram of the correlation result of the current cell and uplink interference provided by an embodiment of the present application;

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

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

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

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

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

[0056] Figure 8 is a schematic diagram of a structure of an interference cancellation apparatus provided by an embodiment of the present application;

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

[0058] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0059] In the description of the present application, the terms "first", "second", "third", etc. are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", "third", etc. can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specified. In the description of the present application, the term "comprising" and its variants are open-ended, that is, "including but not limited to". The term "based on" is "at least partially based on". The term "according to" is "at least partially according to". 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 the present application, it should be noted that, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connection" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0061] In the description of the present application, it is necessary to explain that, unless otherwise defined, all technical and scientific terms used in the present application are the same as the meanings understood by the persons skilled in the art. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments, and are not intended to limit the present application. For the ordinary skilled person in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0062] Some terms and concepts related to the embodiments of the present application are explained as follows:

[0063] SSB (Synchronization Signal / PBCH, Synchronization Broadcast Block), SSB is a key technology in 5G NR (5th Generation New Radio, 5th Generation Mobile Communication Technology), which is used to support the initial access and synchronization between UE (User Equipment, terminal device) and base station.

[0064] PSS (Primary Synchronization Signal, 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 is composed of a specific sequence, which has good autocorrelation and cross-correlation, facilitating UE detection and synchronization.

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

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

[0067] CDF (Cumulative Distribution Function), which can be used to describe the probability of a random variable taking a value less than or equal to a certain specific value.

[0068] In a first aspect, see Figure 3 , a flowchart of a method for interference cancellation provided by an embodiment of the present application is shown, which includes steps S301-S305, 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 the present embodiment can generally include a plurality of sampling points, each sampling point being used to represent the amplitude of the signal at a certain time. For example, the average energy value of a segment of time domain data can be represented by calculating the average of the instantaneous energy of each sampling point in the segment of time domain data, wherein the instantaneous energy of each sampling point can be represented by using the square value or modulus value of the sampling point.

[0071] For example, see Figure 4 , the time domain data to be detected in the present embodiment can be a segment of time domain data obtained by buffering the input data Xin (1..N).

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

[0073] It should be noted that the modulus cumulative distribution information in the embodiment can be used to represent the modulus cumulative distribution of the time domain data. In an example, the modulus cumulative distribution information can be generated based on the sorted modulus values, which can be obtained by sorting the modulus values corresponding to each sampling point in the time domain data. In another example, a pre-trained model can also be used to generate the modulus cumulative distribution information corresponding to the time domain data.

[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 the embodiment can refer to the number of points in all sampling points in the time domain data that meet a preset condition, wherein the preset condition can 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 in combination with the corresponding threshold value 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 an example, whether there is interference in the time domain data can be detected by judging whether the number of sampling points of interest falls within a preset range of the number of sampling points.

[0078] S305, performing interference cancellation processing on the time domain data based on the interference detection result.

[0079] In an example, in the case where the interference detection result indicates that there is interference, a preset interference cancellation technique can be used to cancel the interference in the time domain data, wherein the interference cancellation technique can 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 cancellation processing based on a pre-trained interference cancellation model.

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

[0081] For example, in some existing technologies for eliminating time domain interference, the following methods are usually used:

[0082] 1) According to the power of the input signal, forcibly power align the input signal, that is, statistics the power of a period of time, and adjust the amplitude of the signal according to the current power, so that the output power is on a horizontal line.

[0083] 2) Identify and eliminate interference based on the distribution of the correlation results of the PSS signal, that is, identify interference by sampling the relative power of each correlation result and other sampling points near it in the time domain, and eliminate interference by power pulling down the interference part.

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

[0085] 1) The method of forcibly pulling the power in line is easily affected by the spur signal when encountering a very strong spur signal, resulting in a low amplitude of the useful signal.

[0086] 2) The method of identifying and eliminating interference based on the distribution of the correlation results of the PSS signal requires complex algorithms, is heavily dependent on the quality and strength of the input signal, has high complexity, and is prone to misjudgment of useful correlation peaks.

[0087] 3) In order to ensure the effectiveness of the useful signal in the above input signal / PSS signal, a large amount of time domain data needs a large bit width, occupying a large amount of storage space and bus access bandwidth.

[0088] Correspondingly, in the embodiments of the present application, the cumulative distribution can be used to distinguish the nature of the time domain burst interference (such as the time domain burst interference in the RedCap system), especially the continuous strong interference and the burst spur interference. And only the data needs to be preprocessed in the front end, without the need for complex recognition algorithms in the later stage, that is, without the need for complex algorithm processing of the PSS signal, which can improve the efficiency and reduce the required occupied memory and bandwidth.

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

[0090] Determine the modulus value of all sampling points in the time domain data.

[0091] Calculate the average value of the modulus values of all sampling points as the average energy value.

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

[0093]

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

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

[0096] In an alternative implementation, the determining of the modulus cumulative distribution information corresponding to the time domain data comprises:

[0097] determining the modulus of all sampling points in the time domain data;

[0098] determining the modulus cumulative distribution information by using a cumulative distribution function (CDF) based on the modulus of all sampling points.

[0099] In one example, the modulus cumulative distribution information can be obtained by calculating the cumulative frequency (the proportion of all modulus values less than or equal to the modulus of the sampling point) of the modulus of each arranged sampling point after arranging the modulus of all sampling points in ascending order, and then drawing the corresponding CDF.

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

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

[0102] determining the number of sampling points of interest N based on the distribution interval of interest and the modulus cumulative distribution information 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 proportion, 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 proportion. In this way, the distribution interval of interest can be represented as [th low *P avg ,th high *P avg ], where th low represents the lower limit of the interval proportion, th high represents the upper limit of the interval proportion, and P avg represents the average energy value.

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

[0105] performing interference detection on the time domain data based on a comparison result between a preset sampling threshold and the number of sampling points of interest.

[0106] In this embodiment, a sampling threshold N cdf_max may be preset, and the number of sampling points of interest N cdf may be compared with the sampling threshold N cdf_maxIf the number of the 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, the interference elimination processing on the time domain data based on the interference detection result comprises:

[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 the embodiment can refer to an ideal or normal amplitude of the signal without interference. In an example, in the case that the interference detection result indicates that there is interference, the reference amplitude can be determined by analyzing the signal part in the time domain data without interference, or estimated from the signal in 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 the embodiment can refer to an amplitude that the signal is expected to reach after the interference is eliminated, and the gain multiple can be a coefficient for adjusting the current signal amplitude to the target amplitude. The target amplitude can be the same as the reference amplitude, but can 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 can be selected to meet the system requirement for signal-to-noise ratio.

[0112] performing interference elimination processing on the time domain data by using the gain multiple.

[0113] In an example, each sampling point of the time domain data can be multiplied by the gain multiple to complete the interference elimination.

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

[0115] determining a modulus value of all sampling points in the time domain data;

[0116] in a case that the interference detection result meets a first preset condition, taking a maximum value in the modulus value as the reference amplitude;

[0117] in a case that the interference detection result meets a second preset condition, taking the average energy value as the reference amplitude.

[0118] For example, the first preset condition can include existence of continuous strong interference, and the second preset condition can include existence of burst interference.

[0119] In one example, referring to Figure 5 , in a case where the interference detection result indicates existence of continuous strong interference in the time domain data, the reference amplitude X ref is determined as the maximum value P max in the modulus. ref That is, X max = P max . max = max{abs(x i )}. In a case where the interference detection result indicates non-existence of continuous strong interference (existence of burst interference) in the time domain data, X ref = P avg .

[0120] In the embodiment, the continuous strong interference does not affect the use of the average energy value for flattening, and thus has no effect on PSS correlation, and the burst interference is forced to be saturated, and thus has no effect on the bit width of the overall data, saving data storage. In addition, different interference elimination schemes can be used for different interference: for continuous strong interference, the maximum sampling point is aligned; and for burst interference, the average energy is aligned, and the burst interference is saturated.

[0121] In an optional embodiment, the determining of the gain multiple based on the reference amplitude and a preset target amplitude comprises:

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

[0123] in a case where the reference amplitude is less than or equal to the target amplitude, multiplying the reference amplitude by a preset multiple to obtain a new reference amplitude, and performing the determining of whether the reference amplitude is less than or equal to the target amplitude and the subsequent steps in a loop until the new reference amplitude is greater than the target amplitude, to obtain a loop number, wherein the preset multiple is greater than 1.

[0124] determining the gain multiple based on the loop number.

[0125] For example, referring to Figure 5 , the preset multiple can be equal to 2, so that the new reference amplitude X′ ref obtained each time is twice the last reference amplitude X ref (X′ ref = 2*X ref ), until X′ ref is greater than the target amplitude X target . The loop number at this time is used to determine the gain multiple Nshift For example, the number of cycles can be directly taken as the gain multiple N shift .

[0126] In an alternative embodiment, the time domain data corresponds to a word length, and the interference cancellation processing on the time domain data based on the gain multiple comprises:

[0127] Shifting each sample point in the time domain data based on the gain multiple;

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

[0129]

[0130] wherein x i is used to indicate the sample point before the i-th shifting, x i ′ is used to indicate the sample point after the i-th shifting.

[0131] It is not difficult to understand that the time domain data in this embodiment is binary at this time, and the above-mentioned preset multiple is 2 accordingly. For the convenience of understanding, the case where the time domain data is binary is used to explain and describe below, but if other numerals are used to represent the time domain data, the above-mentioned shifting formula, preset multiple, etc. can also take other values, which will not be described here.

[0132] Performing saturation clipping processing on each sample point after the shifting based on the preset data maximum value to obtain target time domain data;

[0133] It should be noted that the saturation clipping processing can be used to prevent distortion of the electrical signal in the amplification process due to exceeding the maximum output capacity of the device. When the signal level exceeds the range that can be linearly processed by the device, the signal will be flattened or cut.

[0134] In one example, performing saturation clipping processing on each sample point after the shifting based on the preset data maximum value to obtain target time domain data can comprise:

[0135] Determining the overflow sample point among each sample point after the shifting;

[0136] For each overflow sample point, determining the greater one between it and the preset data minimum value, and determining the smaller one between the greater one and the preset data maximum value as the sample point after the saturation clipping, which can be represented as: x i ′ = min{max(x i ,x min ),x max}, wherein x ix i ′ x min x max x

[0137] x

[0138] x

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

[0140] The word length N bit_in The expected output bit number N bit_out The compressed bit number N comp , that is, N comp = N bit_in -N bit_out ; wherein the expected output bit number can be defined according to application scenarios and performance requirements.

[0141] The compressed bit number N comp is used to compress the target time-domain data by the following formula:

[0142]

[0143] x i x i ′ x

[0144] In this embodiment, the data can be compressed according to application scenarios and performance requirements in the process of interference cancellation, thereby saving data storage space.

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

[0146] In an optional implementation, the determination of the modulus value of all sampling points in the time-domain data includes:

[0147] For each sampling point, the modulus value of the sampling point is obtained by using an approximation algorithm based on the data corresponding to the sampling point in the time-domain data.

[0148] In one example, referring to Figure 6 , the data corresponding to the sampling point can refer to a complex number corresponding to the sampling point, and thus the approximation algorithm can be expressed by the following formula:

[0149]

[0150] Wherein, abs(x i ) represents the modulus of the complex number x i corresponding to the i-th sampling point, real(x i ) represents the real part of x i , imag(x i ) represents the imaginary part of x i , min{} represents the minimum element, m represents the order of approximation, and n j represents the j-th preset variable.

[0151] Referring to Figure 7 , an experimental simulation example is given below.

[0152] In a complex scenario, first, there are three continuous strong interferences in the original sampling signal, and stronger spur interference is introduced. By the scheme of the embodiments of the present application, both kinds of interference can be processed. For the three continuous interferences, it is determined to be a normal state, and the reference amplitude is the maximum energy of a single sampling point, that is, X ref =P max This part shows a balanced state with the amplitude of the cell signal after interference elimination, and the performance of the later PSS related process is not affected.

[0153] Similarly, for stronger spur interference, the reference amplitude is the average energy of the data segment: X ref =P avg In this case, N shift will be much larger than the range of the spur part, so that the spur part is saturated and processed, and the interference at this place will not affect the later PSS correlation process, as shown in the removed interference signal in the figure.

[0154] In a second aspect, correspondingly, the embodiments of the present application also provide an interference elimination device capable of implementing all the processes of the interference elimination method provided by the above embodiments.

[0155] Referring to Figure 8 , a structural schematic diagram of an interference elimination device provided by the embodiments of the present application is shown, which comprises:

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

[0157] The module 802 is configured to determine the modulus cumulative distribution information corresponding to the time domain data.

[0158] The module 803 is configured to determine the number of sampling points of interest in the time domain data based on the average energy value and the modulus cumulative distribution information.

[0159] The module 804 is configured to perform interference detection on the time domain data based on the number of sampling points of interest.

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

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

[0162] The modulus values of all sampling points in the time domain data are determined.

[0163] The average value of the modulus values of all sampling points is calculated as the average energy value.

[0164] In an optional implementation, the determination of the modulus cumulative distribution information corresponding to the time domain data comprises:

[0165] The modulus values of all sampling points in the time domain data are determined.

[0166] The modulus cumulative distribution information is determined by using a cumulative distribution function (CDF) based on the modulus values of all sampling points.

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

[0168] A distribution interval of interest is determined based on the average energy value and preset upper and lower limits of an interval ratio.

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

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

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

[0172] In an optional implementation, the interference cancellation processing on the time domain data based on the interference detection result comprises:

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

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

[0175] perform interference cancellation processing on the time domain data by using the gain multiple.

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

[0177] determining a modulus value of all sampling points in the time domain data;

[0178] in a case where the interference detection result satisfies a first preset condition, taking a maximum value in the modulus value as the reference amplitude;

[0179] in a case where the interference detection result satisfies a second preset condition, taking the average energy value as the reference amplitude.

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

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

[0182] in a case where the reference amplitude is less than or equal to the target amplitude, multiplying the reference amplitude by a preset multiple to obtain a new reference amplitude, and performing the judging of whether the reference amplitude is less than or equal to the target amplitude and the subsequent steps in a loop until a loop number is obtained after the new reference amplitude is greater than the target amplitude, wherein the preset multiple is greater than 1;

[0183] determining the gain multiple based on the loop number.

[0184] In an optional implementation, the time domain data corresponds to a word length, and the performing of the interference cancellation processing on the time domain data by using the gain multiple comprises:

[0185] performing a shift on each sampling point in the time domain data based on the gain multiple;

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

[0187] performing compression on the target time domain data based on the word length and a preset expected output bit number to obtain time domain data after the interference cancellation processing.

[0188] In an alternative implementation, the determining the modulus value of each sampling point in the time domain data comprises:

[0189] For each sampling point, the modulus value of the sampling point is obtained by using an approximation algorithm based on the data corresponding to the sampling point in the time domain data.

[0190] In a third aspect, an embodiment of the present application provides a computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the steps of the interference cancellation method of any of the preceding embodiments.

[0191] In a fourth aspect, an embodiment of the present application provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the interference cancellation method of any of the preceding embodiments.

[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 the interference cancellation method of any of the preceding embodiments when executing the computer program.

[0193] Referring to Figure 9 The computer device of the embodiment comprises 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 cancellation program. The processor 901 implements the steps of the various interference cancellation method embodiments described above when executing the computer program, such as the steps S301-S305 shown in the figure. Figure 3

[0194] For example, the computer program can 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 can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the computer device.

[0195] The computer device can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The computer device can include, but is not limited to, the processor 901 and the memory 902. Those skilled in the art can understand that the schematic diagram is only an example of the computer device, and does not limit the computer device, which can include more or fewer components than the diagram, or combine certain components, or different components, for example, the computer device can also include an input / output device, a network access device, a bus, and the like.

[0196] ​The processor 901 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor 901 can also be any conventional processor. The processor 901 is the control center of the computer device, and is connected with various parts of the computer device through various interfaces and lines.

[0197] The memory 902 can be used to store computer programs and / or modules, and the processor 901 realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 902, and calling 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, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created according to 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 nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0198] The modules / units integrated in the computer device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), 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 embodiments of the present application, the average energy value of the time domain data to be detected is determined, the modulus cumulative distribution information corresponding to the time domain data is determined, the number of sampling points of interest in the time domain data is determined based on the average energy value and the modulus cumulative distribution information, the time domain data is subjected to interference detection based on the number of sampling points of interest, and the time domain data is subjected to interference cancellation processing based on the interference detection result, so that the interference received by the device can be accurately eliminated, so as to facilitate the device to make efficient and high-performance subsequent decisions, such as efficient and high-performance cell search and raster scanning processes.

[0201] Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary hardware platforms, and of course, it can also be implemented entirely by hardware. Based on such understanding, all or part of the technical solutions of the present application that contribute to the background art can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a plurality of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0202] The above is the preferred embodiment of the present application. It should be noted that, for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which are also considered within the scope of protection of the present application.

Claims

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

2. The interference cancellation method as described in claim 1, characterized in that, Determining the average energy value of the time-domain data to be detected includes: Determine the modulus of all sampling points in the time-domain data; Calculate the average value of the modulus values ​​of all the sampling points to obtain the average energy value.

3. The interference cancellation method as described in claim 1, characterized in that, Determining the cumulative distribution information of the modulus corresponding to the time-domain data includes: Determine the modulus of all sampling points in the time-domain data; Based on the magnitude values ​​of all the sampling points, the cumulative distribution information of the magnitude values ​​is determined using the cumulative distribution function (CDF).

4. The interference cancellation method as described in claim 1, characterized in that, 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 the modulus includes: Based on the average energy value and the preset upper and lower limits of the interval ratio, the distribution interval of interest is determined; The number of sampling points of interest is determined based on the distribution interval of interest and the cumulative distribution information of the modulus.

5. The interference cancellation method as described in claim 1, characterized in that, The interference detection of the time-domain data based on the number of sampling points of interest includes: Interference detection is performed on the time-domain data based on the comparison between the preset sampling threshold and the number of sampling points of interest.

6. The interference cancellation method as described in claim 1, characterized in that, The interference cancellation processing of the time-domain data based on the interference detection results includes: Based on the interference detection results and the time-domain data, a reference amplitude is determined; The gain factor is determined based on the reference amplitude and the preset target amplitude; The time-domain data is subjected to interference cancellation processing using the aforementioned gain factor.

7. The interference cancellation method as described in claim 6, characterized in that, The step of determining the reference amplitude based on the interference detection result and the time-domain data includes: Determine the modulus of all sampling points in the time-domain data; If the interference detection result meets the first preset condition, the maximum value among the modulus values ​​is taken as the reference amplitude; If the interference detection result meets the second preset condition, the average energy value is used as the reference amplitude.

8. The interference cancellation method as described in claim 6, characterized in that, Determining the gain factor based on the reference amplitude and the preset target amplitude includes: Determine whether the reference amplitude is less than or equal to the target amplitude; If the reference amplitude is less than or equal to the target amplitude, the reference amplitude is multiplied by a preset multiple to obtain a new reference amplitude. The steps of determining whether the reference amplitude is less than or equal to the target amplitude and subsequent steps are executed repeatedly until the new reference amplitude is greater than the target amplitude to obtain the number of iterations. The preset multiple is greater than 1. The gain factor is determined based on the number of cycles.

9. The interference cancellation method as described in claim 6, characterized in that, The time-domain data corresponds to the word length, and the interference cancellation processing of the time-domain data using the gain factor includes: Based on the gain factor, each sampling point in the time domain data is shifted; Based on preset data maximum and minimum values, saturation peak clipping is performed on each shifted sampling point in the time domain data to obtain the 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 cancellation processing.

10. The interference cancellation method according to any one of claims 2, 3, and 7, characterized in that, Determining the modulus of all sampling points in the time-domain data includes: For each sampling point, the modulus value of the sampling point is calculated using an approximation algorithm based on the data corresponding to the sampling point in the time domain data.

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

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

13. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the interference cancellation method according to any one of claims 1-10.

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

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