Signal processing method and apparatus, device, chip and medium

Abstract

The present disclosure provides a signal processing method, device, equipment, chip and medium, wherein the signal processing method comprises: obtaining first time domain data of a target signal; determining total power and discrete information of the target signal according to the first time domain data, wherein the discrete information is used to describe the discrete degree of the target signal; and determining the target signal-to-noise ratio according to the total power and the discrete information of the target signal. The technical problem of low signal processing efficiency and high system resource consumption in the prior art is solved.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a signal processing method, apparatus, device, chip, and medium. Background Technology

[0002] In some versions of communication protocols (e.g., Bluetooth), during signal processing, a metric characterizing the quality of the estimated signal is required, such as the signal-to-noise ratio (SNR). SNR reflects signal quality; a higher SNR indicates a more reliable signal, ensuring the accuracy of other parameter estimations (e.g., frequency or phase). Conversely, a lower SNR indicates a less reliable signal. Related technologies typically require simultaneous estimation of the signal's frequency, phase, and amplitude, and then use these estimated values ​​to obtain the SNR.

[0003] This approach results in low signal processing efficiency and consumes significant system resources. Summary of the Invention

[0004] This disclosure aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, this disclosure proposes a signal processing method, apparatus, communication device, chip, and storage medium to improve signal processing efficiency and save system resources.

[0006] A first aspect of this disclosure provides a signal processing method, comprising: acquiring first time-domain data of a target signal; determining the total power and discrete information of the target signal based on the first time-domain data, wherein the discrete information is used to describe the degree of discreteness of the target signal; and determining the target signal-to-noise ratio based on the total power and discrete information of the target signal.

[0007] A second aspect of this disclosure provides a signal processing apparatus, comprising: an acquisition module for acquiring first time-domain data of a target signal; a first determination module for determining the total power and discrete information of the target signal based on the first time-domain data, wherein the discrete information is used to describe the degree of discreteness of the target signal; and a second determination module for determining a target signal-to-noise ratio based on the total power and discrete information of the target signal.

[0008] A third aspect of this disclosure provides a communication device, including: a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the signal processing method as proposed in the first aspect of this disclosure.

[0009] A fourth aspect of this disclosure provides a chip including a processing circuit and an interface circuit; wherein the interface circuit is used to read instructions and send instructions to the processing circuit so that the processing circuit executes the signal processing method as proposed in the first aspect of this disclosure.

[0010] A fifth aspect of this disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the signal processing method described above.

[0011] The signal processing method, apparatus, communication device, chip, and storage medium disclosed herein acquire first time-domain data of a target signal and determine the total power and discrete information of the target signal based on the first time-domain data. The discrete information describes the degree of dispersion of the target signal. The target signal-to-noise ratio (SNR) is determined based on the total power and discrete information of the target signal. Since the analysis and processing of the signal's time-domain data determines the total power and discrete information of the target signal, and the target SNR is estimated by combining the total power and discrete information, there is no need to estimate the phase and amplitude of the target signal during signal processing. This simplifies the signal processing process and significantly reduces the system resources required for processing, thereby improving signal processing efficiency and saving system resources.

[0012] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0013] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0014] Figure 1 This is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure;

[0015] Figure 2 This is a schematic flowchart of a signal processing method provided in an embodiment of the present disclosure;

[0016] Figure 3 This is a schematic flowchart of another signal processing method provided in an embodiment of the present disclosure;

[0017] Figure 4 This is a schematic flowchart of another signal processing method provided in an embodiment of the present disclosure;

[0018] Figure 5 This is a schematic diagram of the structure of a signal processing device provided in an embodiment of the present disclosure;

[0019] Figure 6 A block diagram of an exemplary communication device suitable for implementing embodiments of the present disclosure is shown;

[0020] Figure 7 This is a schematic diagram of the structure of a chip according to an embodiment of this disclosure;

[0021] Figure 8 This is a schematic diagram of another chip structure proposed in an embodiment of this disclosure. Detailed Implementation

[0022] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0023] In embodiments of this disclosure, the communication device may be, for example, a terminal or a network device, and there is no limitation thereto.

[0024] Figure 1 This is a schematic diagram of the architecture of a communication system according to embodiments of this disclosure. Figure 1 As shown, the communication system 100 may include a terminal 101 and a network device 102. The network device 102 may include at least one of an access network device and a core network device.

[0025] In some embodiments, terminal 101 includes, but is not limited to, at least one of the following: mobile phone, wearable device, Internet of Things device, car with communication function, smart car, tablet computer, computer with wireless transceiver function, virtual reality (VR) terminal, augmented reality (AR) terminal, wireless terminal in industrial control, wireless terminal in self-driving, wireless terminal in remote medical surgery, wireless terminal in smart grid, wireless terminal in transportation safety, wireless terminal in smart city, and wireless terminal in smart home.

[0026] In some embodiments, the access network device is, for example, a node or device that connects a terminal to a wireless network. The access network device may include, but is not limited to, at least one of the following in a 5G communication system: evolved Node B (eNB), next-generation eNB (ng-eNB), next-generation Node B (gNB), node B (NB), home node B (HNB), home evolved node B (HeNB), wireless backhaul device, radio network controller (RNC), base station controller (BSC), base transceiver station (BTS), base band unit (BBU), mobile switching center, base station in a 6G communication system, open RAN, cloud RAN, base station in other communication systems, and access node in a WiFi system.

[0027] In some embodiments, the technical solutions of this disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within access network devices involved in the embodiments of this disclosure can be transformed into internal interfaces of Open RAN. The processes and information interactions between these internal interfaces can be implemented by software or programs.

[0028] In some embodiments, the access network device may be composed of a central unit (CU) and a distributed unit (DU). The CU may also be called a control unit. The CU-DU structure can separate the protocol layer of the access network device. Some of the protocol layer functions are centrally controlled by the CU, while the remaining part or all of the protocol layer functions are distributed in the DU and centrally controlled by the CU. However, this is not the only possibility.

[0029] In some embodiments, a core network device may be a single device comprising one or more network elements, or it may be multiple devices or a group of devices, each comprising all or part of one or more network elements. Network elements may be virtual or physical. The core network includes, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

[0030] It is understood that the communication system described in this disclosure is for the purpose of more clearly illustrating the technical solutions of this disclosure, and does not constitute a limitation on the technical solutions proposed in this disclosure. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions proposed in this disclosure are also applicable to similar technical problems.

[0031] The following embodiments of this disclosure can be applied to Figure 1 The communication system 100 shown, or a part thereof, but not limited to it. Figure 1 The entities shown are illustrative; a communication system may include... Figure 1 All or part of the main body, or may include Figure 1 Other entities besides the main body, the number and form of each entity are arbitrary, the connection relationship between the entities is illustrative, the entities may not be connected or may be connected, and the connection can be in any way, it can be a direct connection or an indirect connection, it can be a wired connection or a wireless connection.

[0032] The embodiments disclosed herein can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), 6th generation mobile communication system (6G), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New Radio Access (NX), Future Generation Radio Access (FX), Global System for Mobile Communications (GSM), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X) systems, systems utilizing other communication methods, and next-generation systems built upon them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0033] In some communication protocols (such as Bluetooth), during signal processing, a metric characterizing the quality of the estimated signal is required, such as the signal-to-noise ratio (SNR). SNR reflects signal quality; a higher SNR indicates a more reliable signal, ensuring accuracy when using the signal to estimate other parameters (such as frequency or phase). Conversely, a lower SNR indicates a less reliable signal. Related technologies typically require simultaneous estimation of the signal's frequency, phase, and amplitude, and then use these estimates to obtain the SNR. This approach is inefficient and resource-intensive.

[0034] This disclosure provides a signal processing method to address the aforementioned technical problems. The method acquires first time-domain data of a target signal and determines the total power and discrete information of the target signal based on this data. The discrete information describes the degree of dispersion of the target signal. The target signal-to-noise ratio (SNR) is then determined based on the total power and discrete information of the target signal. Since the total power and discrete information of the signal are analyzed and processed to determine the total power and discrete information of the target signal, and the target SNR is estimated by combining these two factors, the phase and amplitude of the target signal do not need to be estimated during the signal processing. This simplifies the signal processing procedure and significantly reduces the system resources required for processing, thereby improving signal processing efficiency and saving system resources.

[0035] In this disclosure, the aforementioned communication system is, for example, Global System for Mobile Communications (GSM) or Bluetooth (BT) system.

[0036] In this embodiment of the disclosure, the target signal refers to the signal to be processed. The target signal can be, for example, a complex single-tone signal. For example, the frequency correction burst (FB) signal in a GSM system, the channel sounding (CS) tone in a BT system, and so on.

[0037] The signal processing method in this embodiment can be applied to the baseband receiver of a communication device, such as the aforementioned terminal or network device, without limitation.

[0038] Figure 2 This is a schematic flowchart of a signal processing method provided in an embodiment of the present disclosure.

[0039] like Figure 2As shown, the signal processing method includes:

[0040] Step S201: Obtain the first time-domain data of the target signal.

[0041] The first time-domain data refers to the time-domain data sequence of the received target signal. The first time-domain data is used to characterize the functional expression of the received target signal in the time domain.

[0042] Optionally, in some embodiments, after receiving the target signal, the communication device may perform signal-to-noise ratio estimation based on the first time-domain data of the target signal.

[0043] Optionally, in some embodiments, the target signal may be, for example, a discrete signal. The first time-domain data can be represented as r(n), where n represents the index of the sampling point. The expression formula for the first time-domain data can be as follows:

[0044]

[0045] Where, the real number A represents the target amplitude, j is the imaginary unit, f is the target frequency, and T... s The sampling interval is... Let w(n) be the initial phase of the target signal, w(n) be Gaussian noise, N be the number of sampling points actually used (also known as the actual number of sampling points), and exp() represent the natural exponential function.

[0046] Step S202: Based on the first time-domain data, determine the total power and discrete information of the target signal, wherein the discrete information is used to describe the degree of discreteness of the target signal.

[0047] Discrete information is used to describe the degree of discreteness of the target signal. The degree of discreteness refers to the discreteness of the target signal's values ​​in time or space.

[0048] The total power of the target signal may include the power of the actual signal carried by the target signal, as well as the power of the noise in the target signal.

[0049] After determining the first time-domain data of the target signal, the first time-domain data can be analyzed and transformed to determine the total power and discrete information of the target signal. For example, artificial intelligence can be used to process the first time-domain data to analyze and obtain the total power and discrete information of the target signal; or at least some parameters in the first time-domain data can be statistically analyzed to determine the total power and discrete information of the target signal; or any other possible method can be combined to determine the total power and discrete information of the target signal based on the first time-domain data, without limitation.

[0050] Optionally, in some embodiments, in the process of determining the total power and discrete information of the target signal based on the first time-domain data, the actual number of sampling points of the target signal can be determined, and the first time-domain data can be processed according to the actual number of sampling points to obtain the total power, and the first time-domain data can be processed according to the first number of sampling points to obtain the discrete information, wherein the first number of sampling points is less than or equal to the actual number of sampling points. This achieves accurate and rapid processing to obtain the total power and discrete information of the target signal without consuming excessive computing resources. When applied to communication devices with relatively low hardware resource configurations, it can effectively ensure the accuracy of the total power and discrete information, thus achieving a wider range and better signal processing performance.

[0051] The actual number of sampling points can be N as mentioned above.

[0052] Optionally, in some embodiments, in the process of processing the first time-domain data according to the actual number of sampling points to obtain the total power, the absolute value of the first time-domain data at each sampling point can be determined, and the absolute value at each sampling point can be squared to obtain a processed value. Then, the processed values ​​at multiple sampling points can be averaged, and the averaged value can be used as the total power. For example, the formula for the total power can be expressed as follows:

[0053]

[0054] The total power can be expressed as P total , where n represents the index of the sampling point.

[0055] Optionally, in some embodiments, any other possible methods can be used to calculate the total power of the target signal based on the first time-domain data, such as power statistics, artificial intelligence, etc., without limitation.

[0056] The first number of sampling points can be customized based on the needs of the analysis. It can be represented as N1 and can be less than the actual number of sampling points N. For example, an algorithm parameter m can be defined, where m can be a positive integer (m > 0). For instance, m = 1. The first number of sampling points N1 can then be N1 = Nm.

[0057] Optionally, in some embodiments, the first number of sampling points can be used to perform discreteness analysis on the first time-domain data to obtain discrete information, and there is no limitation on this.

[0058] Optionally, in some embodiments, in the process of processing the first time-domain data according to the first number of sampling points to obtain discrete information, multiple sample data can be extracted from the first time-domain data according to the first number of sampling points, and the sample mean of the multiple sample data can be determined. Then, the sample variance can be determined based on the multiple sample data and the sample mean, and the sample variance can be used as discrete information. Therefore, by sampling the first time-domain data based on the first number of sampling points to obtain multiple sample data, analyzing the distribution of the multiple sample data to obtain the sample variance, and then determining the sample variance as discrete information, the amount of data processed can be effectively reduced, thereby supporting a reduction in the complexity of signal processing. At the same time, the accuracy of discrete information analysis can be ensured, further supporting the accuracy of signal processing.

[0059] For example, determining discrete information S 2 The formula is as follows:

[0060]

[0061] in, Let r1(n) be the sample mean, where r1(n) represents the sample data. Let r1(n) represent the sample mean, where the formula for the sample data is as follows:

[0062] r1(n)=r * (n)r(n+m),0≤n≤N1-1;

[0063] Wherein, the first number of sampling points N1 can be: N1 = Nm, and the algorithm parameter m can be a positive integer, that is: m > 0. For example, m = 1 can be taken, r * (n) is the conjugate of r(n). For the definitions of other parameters in the above formula, please refer to the above description for details, which will not be repeated here.

[0064] Step S203: Determine the target signal-to-noise ratio based on the total power and discrete information of the target signal.

[0065] After determining the total power and discrete information of the target signal based on the first time-domain data to obtain the total power and dispersion of the target signal, the target signal-to-noise ratio (SNR) can be determined by combining the total power and discrete information of the target signal. For example, the noise power of the target signal can be analyzed using discrete information, and then the target SNR can be estimated based on the total power and noise power; or the total power and discrete information can be processed using artificial intelligence to obtain the target SNR; or any other possible method can be used to determine the target SNR based on the total power and discrete information of the target signal, without limitation.

[0066] Optionally, in some embodiments, in the process of determining the target signal-to-noise ratio (SNR) based on the total power and discrete information of the target signal, the noise power of the target signal can be determined based on the discrete information, and the target SNR can be determined based on the total power and noise power. This allows for accurate and rapid processing to obtain the noise power of the target signal, thereby supporting the rapid estimation of the target SNR using the total power and noise power. It comprehensively ensures the accuracy of the estimation and is independent of the phase and amplitude of the target signal, significantly simplifying the signal processing process.

[0067] For example, the discrete information mentioned above can be represented as S 2 Discrete information can be obtained by solving the sample variance of the first time-domain data based on the first number of sampling points. This sample variance can then be used as discrete information, and the noise power can be determined based on this discrete information using the following formula. An example is as follows:

[0068]

[0069] Among them, P noise Indicates noise power.

[0070] Optionally, in some embodiments, in the process of determining the target signal-to-noise ratio based on the total power and noise power, the total power and noise power can be subtracted, and the result of the subtraction is the signal power. Then, the ratio of the signal power to the noise power is determined, and this ratio is used as the target signal-to-noise ratio. There are no restrictions on this.

[0071] In this embodiment, first time-domain data of the target signal is acquired, and the total power and discrete information of the target signal are determined based on the first time-domain data. The discrete information describes the degree of dispersion of the target signal. The target signal-to-noise ratio (SNR) is determined based on the total power and discrete information of the target signal. Since the total power and discrete information of the target signal are analyzed and processed to determine the total power and discrete information, and the target SNR is estimated by combining the total power and discrete information, there is no need to estimate the phase and amplitude of the target signal during signal processing. This simplifies the signal processing process and significantly reduces the system resources required for processing, thereby improving signal processing efficiency and saving system resources.

[0072] Figure 3 This is a schematic flowchart of another signal processing method provided in an embodiment of the present disclosure.

[0073] like Figure 3 As shown, the signal processing method includes:

[0074] Step S301: Acquire the first time-domain data of the target signal.

[0075] Step S302: Determine the actual number of sampling points for the target signal.

[0076] Step S303: Process the first time-domain data according to the actual number of sampling points to obtain the total power.

[0077] Step S304: Process the first time-domain data according to the first number of sampling points to obtain discrete information, wherein the first number of sampling points is less than or equal to the actual number of sampling points.

[0078] Step S305: Determine the noise power of the target signal based on the discrete information.

[0079] For a detailed description of steps S301-S305, please refer to the above embodiments, which will not be repeated here.

[0080] Step S306: Determine whether the total power is less than or equal to the noise power, and obtain a reference result.

[0081] Step S307: Determine the signal power based on the reference results, total power, and noise power.

[0082] In this embodiment of the disclosure, to further improve the accuracy of the target signal-to-noise ratio (SNR), the total power and noise power can be compared, and the comparison result can be referred to as the reference result. If the reference result shows that the total power is less than or equal to the noise power, it indicates that the true SNR of the target signal is relatively low. In this case, the signal power calculation method can be further optimized to obtain a more accurate signal power. Conversely, if the reference result shows that the total power is greater than the noise power, it indicates that the true SNR of the target signal is relatively high. In this case, the signal power can be directly determined based on the total power and noise power. This improves the flexibility of signal processing and is effectively applicable to personalized application scenarios.

[0083] Optionally, in some embodiments, when the reference result is that the total power is greater than the noise power, the difference between the total power and the noise power is calculated, and the result of the difference is determined as the signal power. In this case, the reference result is that the total power is greater than the noise power, which means that the true SNR of the target signal is relatively high. The target signal-to-noise ratio can be determined directly based on the total power and the noise power, which can also ensure the accuracy of the target signal-to-noise ratio. For example, the difference between the total power and the noise power can be calculated, and the result of the difference is determined as the signal power.

[0084] For example, if P total >P noise So, the signal power is:

[0085] P signal =P total -P noise ;

[0086] Where the actual SNR of the target signal is relatively high, the calculated signal power is expressed as P. signal .

[0087] Optionally, in some embodiments, if the reference result shows that the total power is less than or equal to the noise power, the signal power is determined based on the noise power and the first time-domain data. In this case, if the reference result shows that the total power is less than or equal to the noise power, it indicates that the true SNR of the target signal is relatively low. In this case, the signal power calculation method can be further optimized to obtain a more accurate signal power.

[0088] Optionally, in some embodiments, in the process of determining the signal power based on the noise power and the first time-domain data, the first time-domain data may be zero-padding based on the second number of sampling points to obtain the second time-domain data, wherein the second number of sampling points is greater than or equal to the actual number of sampling points of the target signal. The second time-domain data is then frequency-domain transformed based on the second number of sampling points to obtain the first frequency-domain data. The first frequency-domain data is then cyclically shifted based on the second number of sampling points to obtain the second frequency-domain data. Finally, the second frequency-domain data is processed based on the actual number of sampling points, the second number of sampling points, a first boundary value, and a second boundary value to obtain the signal power, wherein the first boundary value is less than the second boundary value. This significantly improves the effectiveness of the signal power optimization process, ensuring the accuracy of the estimated signal power even when the actual SNR of the target signal is relatively low, thereby improving the accuracy of subsequent target signal-to-noise ratio estimation.

[0089] Examples are given below:

[0090] Let r(n) be the sequence after zero padding

[0091]

[0092] Where, N FFT N represents the number of the second sampling points. FFT ≥N, N FFT An empirical value that can be chosen as a power of 2, such as N, can be selected. FFT =4096, or we can let This indicates rounding up to the nearest integer. zp (n) represents the second time-domain data.

[0093] Optionally, in some embodiments, the second time-domain data is frequency-domain transformed according to the second number of sampling points to obtain the first frequency-domain data, as shown in the following formula:

[0094]

[0095] Where R(k) represents r zp N of (n)FFT Fast Fourier transform (FFT) of points, 0≤k≤N FFT -1, R(k) is an optional example of the first frequency domain data mentioned above.

[0096] Optionally, in some embodiments, the first frequency domain data is cyclically shifted according to the second number of sampling points to obtain the second frequency domain data, as shown in the following formula:

[0097] By performing a cyclic shift on R(k), we obtain:

[0098]

[0099] Among them, (p) q This means taking p modulo q, with the result ranging from 0 to q-1, where p takes the value of "". q takes N FFT R shift (k) is an optional example of the second frequency domain data described above.

[0100] Furthermore, the signal power is obtained according to the following formula:

[0101]

[0102] Wherein, the summation boundaries are k1 and k2, k1 is an optional example of the first boundary value mentioned above, k2 is an optional example of the second boundary value mentioned above, and P′ signal This represents the optimized signal power when the actual SNR of the target signal is relatively low.

[0103] Optionally, in some embodiments, the first and second boundary values ​​described above can be determined based on certain methods. For example, the estimated frequency and sampling interval of the target signal can be obtained, and a reference boundary value can be determined based on the estimated frequency, sampling interval, and the number of second sampling points. The first and second boundary values ​​can then be determined based on the reference boundary value. This supports improving the estimation accuracy of the first and second boundary values, while also reducing the amount of data required for estimation and ensuring the optimization effect of the signal power.

[0104] For example, the first boundary value k1 and the second boundary value k2 can be assigned according to the following formula:

[0105]

[0106] in, To estimate the frequency, Among them, T s The sampling interval Δf can be represented by the sampling interval T. s and the number of second sampling points N FFT Confirmed, kmax Indicates the reference boundary value.

[0107] If k max If the integer is , then:

[0108] k1=k2=k max ;

[0109] If k max If it is not an integer, then:

[0110]

[0111] in, This indicates rounding down. This indicates rounding up to the nearest integer.

[0112] Step S308: Determine the target signal-to-noise ratio based on the total power, signal power, and noise power.

[0113] After obtaining the signal power, the target signal-to-noise ratio (SNR) can be determined based on the total power, signal power, and noise power. For example, the ratio of signal power to noise power can be used to determine the SNR to be processed, and then the total power can be used to optimize the SNR to be processed to obtain the target SNR.

[0114] In this embodiment, first time-domain data of the target signal is acquired, and the total power and discrete information of the target signal are determined based on the first time-domain data. The discrete information describes the degree of dispersion of the target signal, and the target signal-to-noise ratio (SNR) is determined based on the total power and discrete information of the target signal. Since the total power and discrete information of the signal are analyzed and processed to determine the total power and discrete information of the target signal, and the target SNR is estimated by combining the total power and discrete information, there is no need to estimate the phase and amplitude of the target signal during signal processing. This simplifies the signal processing process and significantly reduces the system resources required for processing, thereby improving signal processing efficiency and saving system resources. To further improve the accuracy of the target SNR, the total power and noise power can be compared. The comparison result can be called the reference result. If the reference result is that the total power is less than or equal to the noise power, it indicates that the true SNR of the target signal is relatively low. In this case, the signal power calculation method can be further optimized to obtain a more accurate signal power. If the reference result is that the total power is greater than the noise power, it indicates that the true SNR of the target signal is relatively not low, and the signal power can be directly determined based on the total power and noise power. This improves the flexibility of signal processing and makes it suitable for personalized application scenarios.

[0115] Figure 4 This is a schematic flowchart of another signal processing method provided in an embodiment of the present disclosure.

[0116] like Figure 4 As shown, the signal processing method includes:

[0117] Step S401: Acquire the first time-domain data of the target signal.

[0118] Step S402: Based on the first time-domain data, determine the total power and discrete information of the target signal, wherein the discrete information is used to describe the degree of discreteness of the target signal.

[0119] Step S403: Determine the noise power of the target signal based on the discrete information.

[0120] Step S404: Determine whether the total power is less than or equal to the noise power, and obtain a reference result.

[0121] Step S405: Determine the signal power based on the reference results, total power, and noise power.

[0122] For a detailed description of steps S401-S405, please refer to the above embodiments, which will not be repeated here.

[0123] Step S406: Determine the first signal-to-noise ratio based on the signal power and noise power.

[0124] Optionally, in some embodiments, the ratio of signal power to noise power can be determined as the first signal-to-noise ratio.

[0125] For example, the first signal-to-noise ratio can be determined based on the following formula:

[0126]

[0127] Where SNR represents the first signal-to-noise ratio, and the calculated signal power is denoted as P, assuming the actual SNR of the target signal is not low. signal .

[0128] Step S407: Determine the second signal-to-noise ratio based on the total power and the signal power.

[0129] Optionally, in some embodiments, the second signal-to-noise ratio can be determined based on the total power and the signal power.

[0130] For example, the second signal-to-noise ratio can be determined based on the following formula:

[0131]

[0132] Where SNR′ represents the second signal-to-noise ratio, P′ signal This represents the optimized signal power when the actual SNR of the target signal is relatively low.

[0133] Step S408: Determine a target signal-to-noise ratio based on the first signal-to-noise ratio and the second signal-to-noise ratio.

[0134] In the embodiments of the present disclosure, it is also possible to support determining a target signal-to-noise ratio with higher accuracy based on the above-obtained first signal-to-noise ratio and second signal-to-noise ratio. Thus, the accuracy of the target signal-to-noise ratio can be comprehensively improved, supporting the improvement of signal processing accuracy.

[0135] Optionally, in some embodiments, in the process of implementing the determination of the target signal-to-noise ratio based on the first signal-to-noise ratio and the second signal-to-noise ratio, it may be that when the first signal-to-noise ratio is less than a first threshold and the second signal-to-noise ratio is less than a second threshold, the second signal-to-noise ratio is determined as the target signal-to-noise ratio, where the first threshold is less than the second threshold; when the first signal-to-noise ratio is greater than or equal to the first threshold, or the second signal-to-noise ratio is greater than or equal to the second threshold, the first signal-to-noise ratio is determined as the target signal-to-noise ratio. Support determining a target signal-to-noise ratio with higher precision based on the above-obtained first signal-to-noise ratio and second signal-to-noise ratio. Thus, the precision of the target signal-to-noise ratio can be comprehensively improved, supporting the improvement of signal processing effects.

[0136] For example, let:

[0137]

[0138] where SNR′ represents the second signal-to-noise ratio.

[0139] If SNR′ < Th′ (an optional example of the second threshold, which can be taken as 2.5 dB (when comparing, SNR′ can be converted to dB value)), and SNR < Th (an optional example of the first threshold, which can be taken as 2 dB (when comparing, SNR′ can be converted to dB value)), that is: the first threshold is less than the second threshold, then the corrected SNR can be set, that is, take SNR′ as the target signal-to-noise ratio:

[0140] SNR″ = SNR′ (that is, take the second signal-to-noise ratio as the target signal-to-noise ratio SNR″);

[0141] Otherwise, take SNR as the target signal-to-noise ratio:

[0142] SNR″ = SNR (that is, take the first signal-to-noise ratio as the target signal-to-noise ratio SNR″).

[0143] In this embodiment, first time-domain data of the target signal is acquired, and the total power and discrete information of the target signal are determined based on the first time-domain data. The discrete information describes the degree of dispersion of the target signal. The target signal-to-noise ratio (SNR) is determined based on the total power and discrete information of the target signal. Since the total power and discrete information of the target signal are analyzed and processed to determine the total power and discrete information, and the target SNR is estimated by combining the total power and discrete information, there is no need to estimate the phase and amplitude of the target signal during signal processing. This simplifies the signal processing process and significantly reduces the system resources required for processing, thereby improving signal processing efficiency and saving system resources. A first SNR is determined based on the signal power and noise power, and a second SNR is determined based on the total power and signal power. Finally, the target SNR is determined based on the first and second SNRs. This supports determining a more accurate target SNR based on the first and second SNRs obtained above, thereby comprehensively improving the accuracy of the target SNR and supporting improved signal processing performance.

[0144] Figure 5 This is a schematic diagram of the structure of a signal processing device provided in an embodiment of the present disclosure.

[0145] like Figure 5 As shown, the signal processing device 50 includes:

[0146] The acquisition module 501 is used to acquire the first time-domain data of the target signal.

[0147] The first determining module 502 is used to determine the total power and discrete information of the target signal based on the first time-domain data, wherein the discrete information is used to describe the degree of discreteness of the target signal.

[0148] The second determining module 503 is used to determine the target signal-to-noise ratio based on the total power and discrete information of the target signal.

[0149] Optionally, in some embodiments, the first determining module 502 is configured to:

[0150] Determine the actual number of sampling points for the target signal;

[0151] The first time-domain data is processed based on the actual number of sampling points to obtain the total power;

[0152] The first time-domain data is processed based on the first number of sampling points to obtain discrete information, wherein the first number of sampling points is less than or equal to the actual number of sampling points.

[0153] Optionally, in some embodiments, the first determining module 502 is configured to:

[0154] Based on the first number of sampling points, extract multiple sample data from the first time domain data;

[0155] Determine the sample mean of multiple sample data;

[0156] Based on multiple sample data and the sample mean, the sample variance is determined and then defined as discrete information.

[0157] Optionally, in some embodiments, the second determining module 503 is configured to:

[0158] Determine the noise power of the target signal based on discrete information;

[0159] The target signal-to-noise ratio is determined based on the total power and noise power.

[0160] Optionally, in some embodiments, the second determining module 503 is configured to:

[0161] Determine whether the total power is less than or equal to the noise power to obtain a reference result;

[0162] The signal power is determined based on the reference results, total power, and noise power.

[0163] The target signal-to-noise ratio is determined based on the total power, signal power, and noise power.

[0164] Optionally, in some embodiments, the second determining module 503 is further configured to:

[0165] If the reference result indicates that the total power is greater than the noise power, the difference between the total power and the noise power is calculated, and the result of this difference is determined as the signal power; and / or

[0166] If the reference result is that the total power is less than or equal to the noise power, the signal power is determined based on the noise power and the first time-domain data.

[0167] Optionally, in some embodiments, the second determining module 503 is further configured to:

[0168] The first time-domain data is padded with zeros according to the second number of sampling points to obtain the second time-domain data, wherein the second number of sampling points is greater than or equal to the actual number of sampling points of the target signal;

[0169] The second time-domain data is transformed into the first frequency-domain data based on the second number of sampling points.

[0170] The first frequency domain data is cyclically shifted according to the second number of sampling points to obtain the second frequency domain data;

[0171] The second frequency domain data is processed based on the actual number of sampling points, the second number of sampling points, the first boundary value, and the second boundary value to obtain the signal power, wherein the first boundary value is less than the second boundary value.

[0172] Optionally, in some embodiments, the second determining module 503 is further configured to:

[0173] Obtain the estimated frequency and sampling interval of the target signal;

[0174] The reference boundary value is determined based on the estimated frequency, sampling interval, and number of second sampling points;

[0175] Determine the first boundary value and the second boundary value based on the reference boundary value.

[0176] Optionally, in some embodiments, the second determining module 503 is further configured to:

[0177] The first signal-to-noise ratio is determined based on the signal power and noise power;

[0178] The second signal-to-noise ratio is determined based on the total power and the signal power;

[0179] The target signal-to-noise ratio is determined based on the first signal-to-noise ratio and the second signal-to-noise ratio.

[0180] Optionally, in some embodiments, the second determining module 503 is further configured to:

[0181] If a first signal-to-noise ratio (SNR) is less than a first threshold and a second SNR is less than a second threshold, the second SNR is determined as the target SNR, wherein the first threshold is less than the second threshold; and / or

[0182] If the first signal-to-noise ratio is greater than or equal to the first threshold, or if the second signal-to-noise ratio is greater than or equal to the second threshold, the first signal-to-noise ratio is determined as the target signal-to-noise ratio.

[0183] It should be noted that the foregoing explanation of the signal processing method embodiments also applies to the signal processing apparatus of this embodiment, and will not be repeated here.

[0184] In this embodiment, first time-domain data of the target signal is acquired, and the total power and discrete information of the target signal are determined based on the first time-domain data. The discrete information describes the degree of dispersion of the target signal. The target signal-to-noise ratio (SNR) is determined based on the total power and discrete information of the target signal. Since the total power and discrete information of the target signal are analyzed and processed to determine the total power and discrete information, and the target SNR is estimated by combining the total power and discrete information, there is no need to estimate the phase and amplitude of the target signal during signal processing. This simplifies the signal processing process and significantly reduces the system resources required for processing, thereby improving signal processing efficiency and saving system resources.

[0185] To implement the above embodiments, this disclosure also proposes a communication device, including: a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0186] Figure 6 A block diagram of an exemplary communication device suitable for implementing embodiments of the present disclosure is shown. Figure 6 The communication device 12 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein. The communication device may be, for example, a terminal, and there is no limitation thereto.

[0187] like Figure 6 As shown, the communication device 12 is presented in the form of a general-purpose computing device. The components of the communication device 12 may include, but are not limited to: one or more processors or processing units 16, memory 28, and bus 18 connecting different system components (including memory 28 and processing unit 16).

[0188] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0189] The communication device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the communication device 12, including volatile and non-volatile media, and removable and non-removable media.

[0190] Memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and / or cache 32. Communication device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 6 Not shown; usually referred to as a "hard drive".

[0191] although Figure 6 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0192] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0193] The communication device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable human interaction with the communication device 12, and / or with any device that enables the communication device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, the communication device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of the communication device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the communication device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0194] The processing unit 16 executes various functional applications and data processing by running programs stored in the memory 28, such as implementing the methods mentioned in the foregoing embodiments.

[0195] To implement the above embodiments, this disclosure also proposes a chip, including: the chip includes processing circuitry configured to perform the methods provided in the foregoing embodiments.

[0196] Figure 7 This is a schematic diagram of the structure of a chip according to an embodiment of this disclosure. See also... Figure 7 The diagram shown is a schematic representation of the structure of chip 700, but is not limited thereto.

[0197] Chip 700 includes processing circuit 701 and interface circuit 702. Interface circuit 702 is used to read instructions and send instructions to processing circuit 701 so that processing circuit 701 executes the above-described method.

[0198] Optionally, such as Figure 8 As shown, Figure 8 This is a schematic diagram of another chip structure proposed in an embodiment of this disclosure. Chip 700 may further include: a memory 703 for storing instructions, and an interface circuit 702 for reading the instructions stored in the memory 703.

[0199] Optionally, the interface circuit 702 is connected to the memory 703. The interface circuit 702 can be used to receive signals from the memory 703 or other devices, and can also be used to send signals to the memory 703 or other devices. For example, the interface circuit 702 can read instructions stored in the memory 703 and send those instructions to the processing circuit 701.

[0200] Optionally, the number of memories 703 can be one or more. The number of interface circuits 702 can also be one or more.

[0201] In some embodiments, the interface circuit 702 performs at least one of the communication steps such as sending and / or receiving in the above method, and the processing circuit 701 performs other steps.

[0202] In some embodiments, the terms interface circuit, interface, transceiver pin, transceiver, etc., can be used interchangeably.

[0203] Alternatively, all or part of the memory 703 may be located outside of the chip 700.

[0204] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the methods proposed in the foregoing embodiments of this disclosure.

[0205] To implement the above embodiments, this disclosure also proposes a computer program product that, when instructions in the computer program product are executed by a processor, performs the method proposed in the foregoing embodiments of this disclosure.

[0206] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this disclosure all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0207] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0208] This disclosure is intended to provide implementation schemes for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0209] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0210] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0211] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0212] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0213] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0214] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0215] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0216] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A signal processing method, characterized in that, include: Acquire the first time-domain data of the target signal; Based on the first time-domain data, the total power and discrete information of the target signal are determined, wherein the discrete information describes the degree of discreteness of the target signal, and the total power includes the power of the actual signal carried by the target signal and the power of noise in the target signal; and Based on the discrete information, determine the noise power of the target signal; Determine whether the total power is less than or equal to the noise power to obtain a reference result; If the reference result indicates that the total power is less than or equal to the noise power, the signal power is determined based on the noise power and the first time-domain data. The target signal-to-noise ratio is determined based on the total power, the signal power, and the noise power.

2. The method according to claim 1, characterized in that, The step of determining the total power and discrete information of the target signal based on the first time-domain data includes: Determine the actual number of sampling points for the target signal; The first time-domain data is processed based on the actual number of sampling points to obtain the total power; The first time-domain data is processed according to the first number of sampling points to obtain the discrete information, wherein the first number of sampling points is less than or equal to the actual number of sampling points.

3. The method according to claim 2, characterized in that, The step of processing the first time-domain data according to the first number of sampling points to obtain the discrete information includes: Based on the first number of sampling points, extract multiple sample data from the first time domain data; Determine the sample mean of the multiple sample data; Based on the multiple sample data and the sample mean, the sample variance is determined, and the sample variance is defined as the discrete information.

4. The method according to claim 1, characterized in that, The step of determining the signal power based on the reference result, the total power, and the noise power further includes: If the reference result indicates that the total power is greater than the noise power, the difference between the total power and the noise power is calculated, and the result of the difference is determined as the signal power.

5. The method according to claim 4, characterized in that, Determining the signal power based on the noise power and the first time-domain data includes: The first time-domain data is padded with zeros according to the second number of sampling points to obtain the second time-domain data, wherein the second number of sampling points is greater than or equal to the actual number of sampling points of the target signal; The second time-domain data is transformed into the first frequency-domain data based on the second number of sampling points. The first frequency domain data is cyclically shifted according to the second number of sampling points to obtain the second frequency domain data; The second frequency domain data is processed based on the actual number of sampling points, the second number of sampling points, the first boundary value, and the second boundary value to obtain the signal power, wherein the first boundary value is less than the second boundary value.

6. The method according to claim 5, characterized in that, The method further includes: Obtain the estimated frequency and sampling interval of the target signal; The reference boundary value is determined based on the estimated frequency, the sampling interval, and the number of the second sampling points; The first boundary value and the second boundary value are determined based on the reference boundary value.

7. The method according to any one of claims 1-6, characterized in that, Determining the target signal-to-noise ratio based on the total power, the signal power, and the noise power includes: A first signal-to-noise ratio is determined based on the signal power and the noise power; The second signal-to-noise ratio is determined based on the total power and the signal power; The target signal-to-noise ratio is determined based on the first signal-to-noise ratio and the second signal-to-noise ratio.

8. The method according to claim 7, characterized in that, Determining the target signal-to-noise ratio based on the first signal-to-noise ratio and the second signal-to-noise ratio includes: If the first signal-to-noise ratio is less than a first threshold and the second signal-to-noise ratio is less than a second threshold, the second signal-to-noise ratio is determined as the target signal-to-noise ratio, wherein the first threshold is less than the second threshold; and / or If the first signal-to-noise ratio is greater than or equal to the first threshold, or if the second signal-to-noise ratio is greater than or equal to the second threshold, the first signal-to-noise ratio is determined as the target signal-to-noise ratio.

9. A signal processing apparatus, characterized in that, include: The acquisition module is used to acquire the first time-domain data of the target signal; The first determining module is configured to determine the total power and discrete information of the target signal based on the first time-domain data, wherein the discrete information is used to describe the degree of discreteness of the target signal, and the total power includes the power of the actual signal carried by the target signal and the power of the noise in the target signal; as well as The second determining module is configured to: determine the noise power of the target signal based on the discrete information; determine whether the total power is less than or equal to the noise power to obtain a reference result; if the reference result indicates that the total power is less than or equal to the noise power, determine the signal power based on the noise power and the first time-domain data; and determine the target signal-to-noise ratio based on the total power, the signal power, and the noise power.

10. A communication device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-8.

12. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-8.

13. A chip, characterized in that, The chip includes a processing circuit and an interface circuit; wherein the interface circuit is used to read instructions and send the instructions to the processing circuit so that the processing circuit executes the method as described in any one of claims 1-8.

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