Noise estimation method and device, computer equipment and chip

Abstract

The invention relates to a noise estimation method and device, computer equipment and a chip. The method comprises the following steps: acquiring a synchronization signal block SSB, and extracting at least one target signal from the SSB; for each target signal, determining at least two noise data corresponding to the target signal; determining target noise data corresponding to the target signal according to the at least two kinds of noise data; and determining comprehensive noise data of the SSB according to the target noise data corresponding to the various target signals. The method provided by the invention can improve the accuracy of noise estimation.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a noise estimation method, apparatus, computer equipment, and chip. Background Technology

[0002] In NR (New Radio) systems, the noise estimation results of the SSB (Synchronization Signal Block) directly affect the performance of the downlink PBCH (Physical Broadcast Channel) link, PDCCH (Physical Downlink Control Channel) link, and UE (User Equipment) mobility management. However, the SSB exhibits sparsity, and existing noise estimation methods can lead to the loss of some noise data corresponding to the reference signal in the SSB, thereby increasing the noise estimation error. Summary of the Invention

[0003] Therefore, it is necessary to provide a noise estimation method, apparatus, computer equipment, and chip that can improve the accuracy of noise estimation in response to the above-mentioned technical problems.

[0004] In a first aspect, this application provides a noise estimation method, comprising: acquiring a synchronization signal block (SSB) and extracting at least one target signal from the SSB; for each target signal, determining at least two noise data corresponding to the target signal, wherein the at least two noise data are determined based on each part of the target signal; determining target noise data corresponding to the target signal based on the at least two noise data; and determining comprehensive noise data of the SSB based on the target noise data corresponding to the various target signals.

[0005] In one embodiment, determining at least two types of noise data corresponding to the target signal includes: determining the unit type corresponding to each resource unit in the target signal; and determining the noise data corresponding to the same unit type based on each resource unit of the same unit type in the target signal.

[0006] In one embodiment, determining the unit type corresponding to each resource unit in the target signal includes: determining the unit type corresponding to each resource unit in the target signal based on the positional distribution of each resource unit in the target signal.

[0007] In one embodiment, determining the noise data corresponding to the same unit type based on each resource unit of the same unit type in the target signal includes: performing channel estimation on each resource unit in the target signal to obtain noise estimation data for the corresponding resource unit; and determining the noise data corresponding to the same unit type based on the noise estimation data corresponding to each resource unit of the same unit type in the target signal.

[0008] In one embodiment, determining the target noise data corresponding to the target signal based on at least two types of noise data includes: determining the target noise data corresponding to the target signal based on at least two types of noise data and the weights corresponding to each type of noise data.

[0009] In one embodiment, the cell type includes edge type and non-edge type; the weight of the noise data corresponding to the non-edge type is greater than the weight of the noise data corresponding to the edge type, and the weight of the noise data corresponding to the edge type is greater than 0.

[0010] In one embodiment, the method further includes: determining the weight corresponding to the non-edge type based on the proportion of the number of non-edge type resource units in the target signal; and / or determining the weight corresponding to the edge type based on the proportion of the number of edge type resource units in the target signal.

[0011] Secondly, this application provides a noise estimation apparatus, comprising: a signal acquisition module for acquiring a synchronization signal block (SSB) and extracting at least one target signal from the SSB; a first determination module for determining at least two noise data corresponding to each target signal, wherein the at least two noise data are determined based on each part of the target signal; a second determination module for determining target noise data corresponding to the target signal based on the at least two noise data; and a third determination module for determining comprehensive noise data of the SSB based on the target noise data corresponding to various target signals.

[0012] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method provided in the first aspect.

[0013] Fourthly, this application also provides a chip, including a processor and a communication interface, wherein the processor is configured to cause the chip to perform the steps of the method provided in the first aspect.

[0014] Fifthly, this application also provides a chip module, including a communication module, a power module, a storage module, and a chip, wherein: the power module is used to provide electrical energy to the chip module; the storage module is used to store data and instructions; the communication module is used for internal communication within the chip module, or for communication between the chip module and external devices; and the chip is used to perform the steps of the method provided in the first aspect above.

[0015] In a sixth aspect, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method provided in the first aspect.

[0016] In a seventh aspect, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method provided in the first aspect.

[0017] The aforementioned noise estimation method, apparatus, computer equipment, and chip determine at least two types of noise data corresponding to each target signal in the SSB; and determine target noise data corresponding to the target signal based on the at least two types of noise data. Since the at least two types of noise data are determined based on each part of the target signal, this can improve the problem of partial noise data loss corresponding to the target signal during the noise estimation process, reduce noise estimation error, improve the accuracy of target noise data, and thus improve the accuracy of the overall noise data of the SSB. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a noise estimation method in one embodiment;

[0020] Figure 2A This is a schematic diagram of the SSB structure in one embodiment;

[0021] Figure 2B This is a comparative diagram of noise loss corresponding to AWGN in one embodiment;

[0022] Figure 2C This is a comparative diagram of noise loss corresponding to TDLA in one embodiment;

[0023] Figure 2D This is a comparative diagram of noise loss corresponding to TDLB in one embodiment;

[0024] Figure 2E This is a comparative diagram of noise loss corresponding to TDLC in one embodiment;

[0025] Figure 3 This is a flowchart illustrating the steps for determining at least two types of noise data in one embodiment.

[0026] Figure 4 This is a flowchart illustrating the steps for determining the unit type in one embodiment;

[0027] Figure 5 This is a flowchart illustrating the noise data determination steps in one embodiment;

[0028] Figure 6 This is a flowchart illustrating the target noise data determination steps in one embodiment;

[0029] Figure 7 This is a flowchart illustrating the weight setting steps in one embodiment;

[0030] Figure 8 This is a schematic diagram of the noise estimation device in one embodiment;

[0031] Figure 9 This is a diagram of the internal structure of a computer device in one embodiment;

[0032] Figure 10 This is an internal structure diagram of a chip module in one embodiment. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0034] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0035] In one exemplary embodiment, a noise estimation method is provided, see [link to relevant documentation]. Figure 1 The method includes:

[0036] S110, acquire SSB, and extract at least one target signal from SSB.

[0037] The synchronization signal block is a key signal in the NR system used for cell search and initial access. Its main function is to help user equipment quickly identify cells, complete downlink synchronization, and obtain basic system information.

[0038] Among them, see Figure 2A The SSB consists of three signals: the PSS (Primary Synchronization Signal), the SSS (Secondary Synchronization Signal), and the PBCH (Physical Broadcast Channel). In the time domain, the SSB occupies four OFDM (Orthogonal Frequency Division Multiplexing) symbols, with the PSS and SSS each occupying one symbol, and the PBCH occupying two symbols. In the frequency domain, the SSB occupies 240 resource elements (REs), with the PSS and SSS each occupying 127 REs, the PBCH on the second and fourth symbols each occupying 240 REs, and the PBCH on the third symbol occupying a total of 96 REs. SetTo0 is zero-padded and not used for transmitting valid data, occupying a total of 130 REs.

[0039] The target signal can be any two of the following: PBCH, SSS, and SetTo0 in the SSB. Of course, other types of signals in the SSB can also be selected as the target signal, which are not limited here.

[0040] S120, for each target signal, determine at least two noise data corresponding to the target signal.

[0041] Among them, at least two types of noise data are determined based on the signals of each part of the target signal. For example, the target signal includes three types: PBCH, SSS, and SetTo0, and at least two types of noise data corresponding to PBCH, at least two types of noise data corresponding to SSS, and at least two types of noise data corresponding to SetTo0 are determined.

[0042] The noise data can be the estimated noise covariance, or it can be the estimated noise parameters, which is not limited here.

[0043] S130, determine the target noise data corresponding to the target signal based on at least two types of noise data.

[0044] For example, the target noise data corresponding to PBCH is determined based on at least two types of noise data corresponding to PBCH. The target noise data corresponding to SSS is determined based on at least two types of noise data corresponding to SSS. The target noise data corresponding to SetTo0 is determined based on at least two types of noise data corresponding to SetTo0.

[0045] S140, determine the comprehensive noise data of SSB based on the target noise data corresponding to various target signals.

[0046] For example, the target noise data corresponding to PBCH, the target noise data corresponding to SSS, and the target noise data corresponding to SetTo0 are used to determine the comprehensive noise data of SSB.

[0047] This can be achieved by weighting the target noise data corresponding to each target signal to obtain the overall noise data of the SSB. For example, the overall noise data can be calculated using the following formula:

[0048]

[0049] In the formula, To synthesize noise data, This is the target noise data corresponding to PBCH. This refers to the target noise data corresponding to SSS. This represents the target noise data corresponding to SetTo0. u is the type weight corresponding to PBCH, v is the type weight corresponding to SSS, and 1-uv is the type weight of SetTo0.

[0050] See Figure 2B This is a schematic diagram of the noise estimation loss curve under the AWGN (Additive White Gaussian Noise Channel) condition, with a subcarrier spacing of 15kHz and a receiving antenna port number of 2.

[0051] See Figure 2C This is a schematic diagram of the noise estimation loss curve under the TDLA (Tap Delay Line-A) model, with a subcarrier spacing of 15kHz and a receiving antenna port of 2.

[0052] See Figure 2D This is a schematic diagram of the noise estimation loss curve under the TDLB (Tap Delay Line-B) model, with a subcarrier spacing of 15kHz and a receiving antenna port of 2.

[0053] See Figure 2EThe figure shows the curve of noise estimation loss under the TDLC (Tap Delay Line-C) model, with a subcarrier spacing of 15kHz and 2 receiving antenna ports.

[0054] In the above Figures 2B-2E In the diagram, "Original" represents the noise loss (NE Loss) as a function of signal-to-noise ratio (SNR) for noise data obtained using traditional noise estimation methods, while "Proposed" represents the noise loss (NE Loss) as a function of SNR for comprehensive noise data obtained using this embodiment. Comparison reveals that in the low to medium SNR region (-10 to 15 dB), "Proposed" can reduce noise loss by 0.1 dB compared to "Original"; and in the high SNR region (15 to 30 dB), "Proposed" can reduce noise loss by up to 6 dB compared to "Original". Therefore, this embodiment can reduce noise loss, thereby improving the accuracy of noise estimation.

[0055] The noise estimation method described above determines at least two types of noise data corresponding to each target signal in the SSB; based on these at least two types of noise data, it determines the target noise data corresponding to the target signal. Since the at least two types of noise data are determined based on each part of the target signal, this method can improve the problem of partial noise data loss corresponding to the target signal during the noise estimation process, reduce noise estimation errors, improve the accuracy of the target noise data, and thus improve the accuracy of the overall noise data of the SSB.

[0056] Based on the technical solution provided in the above embodiments, an optional embodiment is provided, in which the determination steps of at least two noise data in S120 are refined.

[0057] See Figure 3 The detailed steps for determining at least two types of noise data include:

[0058] S310, determine the unit type corresponding to each resource unit in the target signal.

[0059] The method of classifying unit types can be determined based on the position of the resource unit in the target signal. Of course, other methods can also be used, which are not limited here.

[0060] S320: Determine the noise data corresponding to the same unit type based on the resource units of the same unit type in the target signal.

[0061] For example, the cell types in the target signal A include cell type a1 and cell type a2. For each resource cell of cell type a1, a noise data b1 is determined; for each resource cell of cell type a2, a noise data b2 is determined. Thus, for the target signal A, noise data b1 and noise data b2 are obtained.

[0062] In this embodiment, noise data corresponding to the same unit type is determined based on each resource unit of the same unit type in the target signal, thereby obtaining noise data corresponding to each unit type to improve the problem of noise data loss during noise estimation.

[0063] Based on the technical solutions provided in the above embodiments, an optional embodiment is provided, in which the unit type determination step in S310 is refined.

[0064] See Figure 4 The steps for determining the unit type include:

[0065] S410, Based on the positional distribution of each resource unit in the target signal, determine the unit type corresponding to each resource unit in the target signal.

[0066] It is understandable that the positional distribution of each resource unit in the target signal can be understood as the position of the resource unit in the target signal. For example, if the target signal is SSS, and SSS includes 127 resource units, some resource units are located at the beginning of SSS, some resource units are located at the end of SSS, and some resource units are located in the middle part of SSS.

[0067] Since noise data of edge resource units is more easily lost in noise estimation techniques, unit types can be divided into edge types (i.e., the unit type corresponding to resource units located at the beginning and end) and non-edge types (i.e., the unit type corresponding to resource units located in the middle). In other words, unit types include edge types and non-edge types.

[0068] In practical scenarios, to facilitate the application of noise estimation methods to digital circuits, the following formula can be used to calculate the number of non-edge type resource units in the target signal:

[0069]

[0070] in, The number of resource units in the target signal. q represents the number of non-edge type resource units in the target signal, where q is a user-defined positive integer.

[0071] Understandably, upon obtaining Afterwards, and The difference is used to obtain the number of resource units of the edge type. .based on and It identifies non-edge type resource units and edge type resource units from the target signal.

[0072] In this embodiment, based on the positional distribution of each resource unit in the target signal, the unit type corresponding to each resource unit in the target signal is determined. It is evident that the unit type reflects the position of the resource unit in the target signal, thus enabling the subsequent calculation of corresponding noise data for resource units at different positions, avoiding noise data loss. For example, for resource units located at the edge, the corresponding noise data can be determined to avoid the loss of noise data for resource units at the edge, thereby improving the accuracy of noise estimation.

[0073] Based on the technical solutions provided in the above embodiments, an optional embodiment is provided, in which the noise data determination step in S320 is refined.

[0074] See Figure 5 The detailed steps for determining noise data include:

[0075] S510 performs channel estimation on each resource element in the target signal to obtain noise estimation data for the corresponding resource element.

[0076] For example, for each resource unit in the target signal, the corresponding noise estimation data is calculated using the following formula:

[0077]

[0078] In the formula, It is the noise estimation data of the k-th resource element in the target signal under its l-th OFDM symbol and p-th receive antenna port; This refers to the channel estimation data determined for the k-th resource element under its l-th OFDM symbol and p-th receive antenna port based on a coarse channel estimation method. This refers to the channel estimation data determined for the k-th resource element under its l-th OFDM symbol and p-th receive antenna port based on the fine channel estimation method.

[0079] Among these methods, fine-grained channel estimation can employ statistical channel estimation techniques such as MMSE (Minimum Mean Square Error) filtering or DFT (Discrete Fourier Transform) smoothing. Other methods can also be used, but are not limited here.

[0080] S520, based on the noise estimation data corresponding to each resource unit of the same type in the target signal, determine the noise data corresponding to the same unit type.

[0081] For example, for edge types, the corresponding noise data is calculated using the following formula:

[0082]

[0083] In the formula, Z1 represents the noise data corresponding to the edge type. This represents the number of receiving antenna ports corresponding to the k-th resource unit. The number of OFDM symbols belonging to the k-th resource unit. The number of edge-type resource units, A collection of edge-type resource units.

[0084] For example, for non-edge types, the corresponding noise data is calculated using the following formula:

[0085]

[0086] In the formula, Z2 represents the noise data corresponding to the non-edge type. A collection of non-edge type resource units. This represents the number of non-edge type resource units.

[0087] In this embodiment, for each resource unit, the noise estimation data corresponding to the resource unit is determined based on the channel estimation method. Then, based on the noise estimation data corresponding to each resource unit of the same unit type, the noise data corresponding to the same unit type is determined to ensure that accurate noise data is obtained.

[0088] Based on the technical solutions provided in the above embodiments, an optional embodiment is provided, in which the target noise data determination step in S130 is refined.

[0089] See Figure 6 The detailed steps for determining target noise data include:

[0090] S610, determine the target noise data corresponding to the target signal based on at least two types of noise data and the weights corresponding to each type of noise data.

[0091] For example, the weight of the noise data corresponding to the non-edge type is 'a', and the weight of the noise data corresponding to the edge type is 1-a. Therefore, the target noise data is:

[0092]

[0093] In the formula, For target noise data.

[0094] In order to preserve the performance gain of edge resource units and reduce the noise estimation error caused by the loss of noise data of edge resource units, the weight of noise data corresponding to non-edge types needs to be greater than the weight of noise data corresponding to edge types, and the weight of noise data corresponding to edge types is greater than 0.

[0095] In this embodiment, by weighting at least two types of noise data, both different noise data are taken into account, and the different impacts of different noise data on the target noise data are reflected, thereby obtaining accurate and reasonable target noise data.

[0096] Based on the technical solutions provided in the above embodiments, an optional embodiment is provided, in which the above noise estimation method is further refined to include a weight setting step.

[0097] See Figure 7 The detailed weight setting steps include:

[0098] S710, determine the weight corresponding to the non-edge type based on the proportion of non-edge type resource units in the target signal; and / or, determine the weight corresponding to the edge type based on the proportion of edge type resource units in the target signal.

[0099] For example, the weights of noise data corresponding to non-edge types are calculated using the following formula:

[0100]

[0101] Next, based on the above calculation formula, the weight of the noise data corresponding to the edge type is 1-a.

[0102] For example, the weights of noise data corresponding to edge types are calculated using the following formula:

[0103]

[0104] Next, based on the above calculation formula, the weight of the noise data corresponding to the non-edge type is =a.

[0105] In this embodiment, the weight of the noise data corresponding to the corresponding unit type is determined based on the proportion of resource units of different unit types in the target signal, which can improve the rationality of the weight setting and thus obtain accurate target noise data.

[0106] Of course, besides the methods provided in the above embodiments for calculating weights, other methods can also be used. For example, to facilitate the application of the above noise estimation method to digital circuits, weight 'a' can be calculated using the following formula:

[0107]

[0108] In the formula, r is a preset positive integer.

[0109] In real-world scenarios, SSBs are easily interfered with by signals on the same frequency, especially edge-type resource units. Furthermore, considering the sparsity of SSBs, it's crucial to utilize as many non-edge-type resource units as possible for noise estimation. To achieve this, a higher proportion of non-edge-type resource units is required, dictating that r must be greater than or equal to q.

[0110] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0111] Based on the same inventive concept, this application also provides a noise estimation apparatus for implementing the noise estimation method described above. This apparatus can be applied to or integrated into a chip or chip module, for example. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more noise estimation apparatus embodiments provided below can be found in the limitations of the noise estimation method described above, and will not be repeated here.

[0112] In one exemplary embodiment, such as Figure 8 As shown, a noise estimation device is provided, comprising: a signal acquisition module 810, a first determination module 820, a second determination module 830, and a third determination module 840; wherein:

[0113] The signal acquisition module 810 is used to acquire the synchronization signal block SSB and extract at least one target signal from the SSB.

[0114] The first determining module 820 is used to determine at least two types of noise data corresponding to each target signal; wherein the at least two types of noise data are determined based on each part of the target signal.

[0115] The second determining module 830 is used to determine the target noise data corresponding to the target signal based on at least two types of noise data;

[0116] The third determination module 840 is used to determine the comprehensive noise data of SSB based on the target noise data corresponding to various target signals.

[0117] In one embodiment, the first determining module includes: a first determining submodule, configured to determine the unit type corresponding to each resource unit in the target signal; and a second determining submodule, configured to determine the noise data corresponding to the same unit type based on the resource units of the same unit type in the target signal.

[0118] In one embodiment, the first determining submodule is specifically used to: determine the unit type corresponding to each resource unit in the target signal based on the positional distribution of each resource unit in the target signal.

[0119] In one embodiment, the second determining submodule is specifically used to: perform channel estimation on each resource unit in the target signal to obtain noise estimation data of the corresponding resource unit; and determine the noise data corresponding to the same unit type based on the noise estimation data corresponding to each resource unit of the same unit type in the target signal.

[0120] In one embodiment, the second determining module is specifically used to: determine the target noise data corresponding to the target signal based on at least two types of noise data and the weights corresponding to each type of noise data.

[0121] In one embodiment, the cell type includes edge type and non-edge type; the weight of the noise data corresponding to the non-edge type is greater than the weight of the noise data corresponding to the edge type, and the weight of the noise data corresponding to the edge type is greater than 0.

[0122] In one embodiment, the apparatus further includes: a weight determination module, configured to determine the weight corresponding to a non-edge type based on the proportion of non-edge type resource units in the target signal; and / or, to determine the weight corresponding to an edge type based on the proportion of edge type resource units in the target signal.

[0123] Regarding the modules / units included in the various devices and products described in the above embodiments, they can be software modules / units, hardware modules / units, or a combination of both. For example, for various devices and products applied to or integrated into a chip, all of their modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits; for various devices and products applied to or integrated into a chip module, all of their modules / units can be implemented using hardware methods such as circuits, and different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The components can be implemented using software programs that run on the processor integrated within the chip module. The remaining (if any) modules / units can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into the terminal, each of its components / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or in different components within the terminal. Alternatively, at least some modules / units can be implemented using software programs that run on the processor integrated within the terminal, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits.

[0124] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements the steps of the aforementioned noise estimation method.

[0125] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0126] In one exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps: acquiring a synchronization signal block (SSB) and extracting at least one target signal from the SSB; for each target signal, determining at least two types of noise data corresponding to the target signal, wherein the at least two types of noise data are determined based on each part of the target signal; determining target noise data corresponding to the target signal based on the at least two types of noise data; and determining comprehensive noise data of the SSB based on the target noise data corresponding to the various target signals.

[0127] In one embodiment, the step of "determining at least two types of noise data corresponding to the target signal" implemented by the processor when executing the computer program includes: determining the unit type corresponding to each resource unit in the target signal; and determining the noise data corresponding to the same unit type based on each resource unit of the same unit type in the target signal.

[0128] In one embodiment, the step of "determining the unit type corresponding to each resource unit in the target signal" implemented by the processor when executing the computer program includes: determining the unit type corresponding to each resource unit in the target signal based on the positional distribution of each resource unit in the target signal.

[0129] In one embodiment, the step of "determining noise data corresponding to the same unit type based on each resource unit of the same unit type in the target signal" implemented by the processor when executing the computer program includes: performing channel estimation on each resource unit in the target signal to obtain noise estimation data of the corresponding resource unit; and determining the noise data corresponding to the same unit type based on the noise estimation data corresponding to each resource unit of the same unit type in the target signal.

[0130] In one embodiment, the step of "determining target noise data corresponding to the target signal based on at least two types of noise data" implemented by the processor when executing a computer program includes: determining the target noise data corresponding to the target signal based on at least two types of noise data and the weights corresponding to each type of noise data.

[0131] In one embodiment, the cell type includes edge type and non-edge type; the weight of the noise data corresponding to the non-edge type is greater than the weight of the noise data corresponding to the edge type, and the weight of the noise data corresponding to the edge type is greater than 0.

[0132] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining the weight corresponding to the non-edge type based on the proportion of the number of non-edge type resource units in the target signal; and / or determining the weight corresponding to the edge type based on the proportion of the number of edge type resource units in the target signal.

[0133] Based on the same inventive concept, embodiments of this application also provide a chip, including a processor and a communication interface; the communication interface is used to receive or transmit data; the processor is configured to cause the chip to perform the following steps: acquiring a synchronization signal block (SSB) and extracting at least one target signal from the SSB; for each target signal, determining at least two noise data corresponding to the target signal, wherein the at least two noise data are determined based on each part of the target signal; determining target noise data corresponding to the target signal based on the at least two noise data; and determining comprehensive noise data of the SSB based on the target noise data corresponding to the various target signals.

[0134] In one embodiment, the processor is configured to cause the chip to perform the step of "determining at least two types of noise data corresponding to the target signal", which includes: determining the cell type corresponding to each resource cell in the target signal; and determining the noise data corresponding to the same cell type based on each resource cell of the same cell type in the target signal.

[0135] In one embodiment, the processor is configured to cause the chip to perform the step of "determining the cell type corresponding to each resource unit in the target signal", which includes: determining the cell type corresponding to each resource unit in the target signal based on the positional distribution of each resource unit in the target signal.

[0136] In one embodiment, the processor is configured to cause the chip to perform the step of "determining noise data corresponding to the same unit type based on each resource unit of the same unit type in the target signal", which includes: performing channel estimation on each resource unit in the target signal to obtain noise estimation data of the corresponding resource unit; and determining the noise data corresponding to the same unit type based on the noise estimation data corresponding to each resource unit of the same unit type in the target signal.

[0137] In one embodiment, the processor is configured to cause the chip to perform the step of "determining target noise data corresponding to a target signal based on at least two types of noise data", which includes: determining the target noise data corresponding to the target signal based on at least two types of noise data and the weights corresponding to each type of noise data.

[0138] In one embodiment, the cell type includes edge type and non-edge type; the weight of the noise data corresponding to the non-edge type is greater than the weight of the noise data corresponding to the edge type, and the weight of the noise data corresponding to the edge type is greater than 0.

[0139] In one embodiment, the processor is further configured to cause the chip to perform the following steps: determining the weight corresponding to the non-edge type based on the proportion of the number of non-edge type resource units in the target signal; and / or determining the weight corresponding to the edge type based on the proportion of the number of edge type resource units in the target signal.

[0140] It is understood that the chip involved in the embodiments of this application may be a field-programmable gate array (FPGA), may be an application-specific integrated circuit (ASIC), may be a system on chip (SoC), may be a central processor unit (CPU), may be a network processor (NP), may be a digital signal processor (DSP), may be a microcontroller unit (MCU), may be a programmable logic device (PLD), or other integrated chips, etc.

[0141] Based on the same inventive concept, this application also provides a chip module, such as... Figure 10 As shown, the chip module includes a communication module, a power module, a storage module, and a chip. Specifically: the power module provides power to the chip module; the storage module stores data and instructions; the communication module enables internal communication within the chip module or communication between the chip module and external devices; and the chip corresponds to the chip in the aforementioned chip embodiment. The implementation of this chip module can be found in the relevant content of the aforementioned chip embodiment, and will not be repeated here.

[0142] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the noise estimation method described above.

[0143] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the noise estimation method described above.

[0144] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0145] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0146] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A noise estimation method, characterized in that, include: Acquire the synchronization signal block SSB and extract at least one target signal from the SSB; For each target signal, at least two types of noise data are determined; wherein, the at least two types of noise data are determined based on each part of the target signal. Based on the at least two types of noise data, determine the target noise data corresponding to the target signal; Based on the target noise data corresponding to the various target signals, the comprehensive noise data of the SSB is determined.

2. The method according to claim 1, characterized in that, The determination of at least two types of noise data corresponding to the target signal includes: Determine the unit type corresponding to each resource unit in the target signal; Based on each resource unit of the same type in the target signal, determine the noise data corresponding to the same unit type.

3. The method according to claim 2, characterized in that, Determining the unit type corresponding to each resource unit in the target signal includes: Based on the positional distribution of each resource unit in the target signal, the unit type corresponding to each resource unit in the target signal is determined.

4. The method according to claim 2, characterized in that, The step of determining the noise data corresponding to the same unit type based on each resource unit of the same unit type in the target signal includes: Channel estimation is performed on each resource unit in the target signal to obtain noise estimation data for the corresponding resource unit; Based on the noise estimation data corresponding to each resource unit of the same unit type in the target signal, the noise data corresponding to the same unit type is determined.

5. The method according to any one of claims 1 to 4, characterized in that, Determining the target noise data corresponding to the target signal based on the at least two types of noise data includes: The target noise data corresponding to the target signal is determined based on the at least two types of noise data and the weights corresponding to each type of noise data.

6. The method according to claim 5, characterized in that, The cell types include edge types and non-edge types; the weight of the noise data corresponding to the non-edge type is greater than the weight of the noise data corresponding to the edge type, and the weight of the noise data corresponding to the edge type is greater than 0.

7. The method according to claim 6, characterized in that, The method further includes: The weight corresponding to the non-edge type is determined based on the proportion of non-edge type resource units in the target signal; and / or The weight corresponding to the edge type is determined based on the proportion of resource units of the edge type in the target signal.

8. A noise estimation device, characterized in that, The device includes: The signal acquisition module is used to acquire the synchronization signal block (SSB) and extract at least one target signal from the SSB. The first determining module is used to determine at least two types of noise data corresponding to each target signal; wherein the at least two types of noise data are determined based on each part of the target signal; The second determining module is used to determine the target noise data corresponding to the target signal based on the at least two types of noise data; The third determining module is used to determine the comprehensive noise data of the SSB based on the target noise data corresponding to the various target signals.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A chip, characterized in that, The device includes a processor and a communication interface, the processor being configured to cause the chip to perform the steps of the method described in any one of claims 1 to 7.

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