A residual carrier frequency offset estimation method, system, device and storage medium for channel state information

Abstract

The present application relates to the technical field of communication signal processing, and relates to a residual carrier frequency offset estimation method, system, device and storage medium for channel state information; the residual carrier frequency offset estimation method comprises the following steps: obtaining channel state information of a plurality of data packets; performing sparse optimization on the channel state information based on an overcomplete delay dictionary, eliminating the influence of packet detection delay, and obtaining sparse observation values; for each data packet, taking a maximum modulus element from the sparse observation values as carrier frequency offset information of the data packet; constructing an autocorrelation matrix based on the carrier frequency offset information of all data packets, performing eigenvalue decomposition on the autocorrelation matrix, and obtaining a noise subspace; performing super-resolution spectrum estimation on the residual carrier frequency offset based on the noise subspace by using a MUSIC algorithm, and obtaining an estimated value of the residual carrier frequency offset. The present application can eliminate the interference of packet detection delay and realize high-precision residual carrier frequency offset estimation.

Description

Technical Field

[0001] This invention relates to the field of communication signal processing technology, and in particular to a method, system, device, and storage medium for estimating residual carrier frequency offset of channel state information. Background Technology

[0002] Channel State Information (CSI) is information about the current channel transmission conditions obtained by estimating the channel using pilot information known to both the transmitter and receiver. In Orthogonal Frequency Division Multiplexing (OFDM) systems, the acquired CSI typically includes Packet Detection Delay (PDD), Phase Locked Loop (PLL) phase offset, and Carrier Frequency Offset (CFO). Therefore, further processing of the acquired CSI is required to obtain more accurate channel state information for applications such as localization and sensing.

[0003] Traditional carrier frequency offset estimation methods have poor performance when there is phase offset in PDD and PLL. Therefore, after compensation, CSI still has a large residual CFO, making it difficult to obtain high-precision CSI for more complex sensing scenarios and high-precision positioning. Summary of the Invention

[0004] This application aims to at least solve the technical problems existing in the prior art, and to provide a method, system, device and storage medium for estimating residual carrier frequency offset of channel state information.

[0005] In a first aspect, the present invention provides a method for estimating the residual carrier frequency offset of channel state information, the method comprising: Obtain channel state information for multiple data packets; Based on the overcomplete delay dictionary, the channel state information is sparsely optimized to eliminate the influence of packet detection delay and obtain sparse observations. For each data packet, the maximum modulus element is taken from the sparse observations and used as the carrier frequency offset information of the data packet; An autocorrelation matrix is ​​constructed based on the carrier frequency offset information of all data packets, and the autocorrelation matrix is ​​subjected to eigenvalue decomposition to obtain the noise subspace. Based on the noise subspace, the MUSIC algorithm is used to perform super-resolution spectral estimation of the residual carrier frequency offset, and the estimated value of the residual carrier frequency offset is obtained.

[0006] Optionally, the number of data packets is denoted as The channel state information for each data packet includes Subcarriers, Indicates the packet index. Indicates the subcarrier index of the data packet. , , will data packet subcarrier The channel state information is denoted as ; The expression is: in, For data packets The number of paths, For data packets Path number, For data packets path Path gain, For data packets path The time delay; For the center frequency, For subcarriers The baseband frequency, For data packets Packet detection delay, This represents the fixed phase deviation of the phase-locked loop. For data packets Residual carrier frequency offset of channel state information, For data packets timestamp, For data packets subcarrier Observational noise in channel state information.

[0007] Optionally, the step of performing sparse optimization on the channel state information based on an overcomplete delay dictionary to eliminate the impact of packet detection delay and obtain sparse observations includes: For each data packet Complete delay sequences have been designed A complete delay dictionary matrix has been created. ,in To provide a complete latency dictionary dimension; According to the data packet Channel state information, overcomplete delay dictionary matrix and data packets Observation noise in channel state information determines sparse observations: Data packets Channel state information The matrix form is represented as ; For sparse observations, Indicates data packet Observational noise of channel state information; Construct a norm 1 relaxed convex optimization problem: ; in, For the first normal form, It is the second normal form; "Subject to" specifies the constraints of the optimization problem. These are preset parameters; Solve the norm 1 relaxed convex optimization problem, based on Channel state information of each data packet We obtain sparse observations that eliminate the effects of packet detection delay. .

[0008] Optionally, for each data packet The formula for calculating carrier frequency offset information is: ; ; in, For data packets Carrier frequency offset information index, For data packets Carrier frequency offset information.

[0009] Optionally, the step of constructing an autocorrelation matrix based on the carrier frequency offset information of all data packets, and performing eigenvalue decomposition on the autocorrelation matrix to obtain a noise subspace includes: Will Carrier frequency offset information of each data packet Represented in matrix form: ; Calculate matrix autocorrelation matrix : ; in This indicates the conjugate transpose. Represents the mathematical expectation; For autocorrelation matrix Perform eigenvalue decomposition: ; Wherein, the dimension of the signal subspace is , The columns constitute the signal subspace. For the signal subspace A diagonal matrix composed of eigenvalues. The columns constitute the noise subspace. For the noise subspace A diagonal matrix composed of eigenvalues.

[0010] Optionally, the step of using the MUSIC algorithm to perform super-resolution spectral estimation of the residual carrier frequency offset based on the noise subspace to obtain an estimated value of the residual carrier frequency offset includes: A complete carrier frequency offset sequence has been designed. ,in, To achieve a complete carrier frequency offset dictionary dimension; The MUSIC pseudospectral values ​​of all candidate carrier frequency offsets are calculated based on the noise subspace and the overcomplete carrier frequency offset sequence; the number of candidate carrier frequency offsets is... , For candidate carrier frequency offset index; from Select the maximum value from the MUSIC pseudospectral values, and use the candidate carrier frequency offset index corresponding to the maximum MUSIC pseudospectral value. Candidate carrier frequency offset index Corresponding overcomplete carrier frequency offset This is the estimated value of the residual carrier frequency offset.

[0011] Optionally, the formula for calculating the MUSIC pseudospectral value of the candidate carrier frequency offset is: ; in, It should be a very small constant value to prevent the denominator from being 0. The guiding vector is defined as follows: .

[0012] In a second aspect, the present invention provides a residual carrier frequency offset estimation system for channel state information, the system comprising: The acquisition module is used to acquire channel state information for multiple data packets; The sparse processing module is used to perform sparse optimization on the channel state information based on the overcomplete delay dictionary, eliminate the influence of packet detection delay, and obtain sparse observations. The processing module is used to take the maximum modulus element from the sparse observations for each data packet as the carrier frequency offset information of the data packet; The feature decomposition module is used to construct an autocorrelation matrix based on the carrier frequency offset information of all data packets, and to perform feature decomposition on the autocorrelation matrix to obtain the noise subspace. The output module is used to perform super-resolution spectral estimation of the residual carrier frequency offset based on the noise subspace using the MUSIC algorithm, so as to obtain an estimated value of the residual carrier frequency offset.

[0013] Thirdly, the present invention provides an electronic device, the electronic device comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the residual carrier frequency offset estimation method for channel state information described above.

[0014] Fourthly, the present invention also provides a computer-readable storage medium storing at least one computer program, which is executed by a processor in an electronic device to implement the residual carrier frequency offset estimation method for channel state information described above.

[0015] In summary, this application includes the following beneficial technical effects: This application constructs an overcomplete delay dictionary and combines it with sparsity optimization to remove the interference factor of packet detection delay from the channel state information, thus avoiding the problem of reduced estimation accuracy caused by the coupling between packet detection delay and residual carrier frequency offset in traditional methods. On this basis, the MUSIC super-resolution spectrum estimation algorithm is further adopted, which can distinguish very close frequency components even under the condition of a limited number of data packets, thereby greatly improving the estimation accuracy of carrier frequency offset. Furthermore, this invention extracts the strongest path as carrier frequency offset information through sparse optimization, effectively filtering out noise interference and complex phase effects from weaker paths in multipath environments. By decomposing the observation data space into signal and noise subspaces through eigenvalue decomposition, and using the MUSIC algorithm to perform spectral peak search in a noisy background, this method can still maintain good estimation performance in low signal-to-noise ratio environments. At the same time, by jointly processing the channel state information of multiple data packets, this invention can accurately track the slow changes of residual carrier frequency offset among multiple data packets, adapting to dynamic channel environments. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a method for estimating the residual carrier frequency offset of channel state information according to an embodiment of the present invention. Figure 2 This is a MUSIC pseudospectral value of the candidate carrier frequency offset provided in an embodiment of the present invention; Figure 3 This is a residual carrier frequency offset estimation error diagram for channel state information provided in an embodiment of the present invention; Figure 4This is a schematic diagram of the structure of an electronic device that implements the residual carrier frequency offset estimation method for the channel state information according to an embodiment of the present invention.

[0017] Reference numerals: 10, processor; 11, memory; 12, communication bus; 13, communication interface.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown 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 are only used to explain the present invention, and should not be construed as limiting the present invention.

[0020] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0021] In the description of this invention, unless otherwise specified and limited, it should be noted that the terms "installation", "connection" and "linking" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two components. They can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0022] Reference Figure 1 The diagram shown is a flowchart illustrating a method for estimating the residual carrier frequency offset of channel state information according to an embodiment of the present invention. In this embodiment, the method for estimating the residual carrier frequency offset of channel state information includes: S1. Obtain channel state information for multiple data packets.

[0023] The receiver acquires Channel State Information (CSI) for multiple data packets. CSI is information about the current channel transmission conditions obtained by estimating the channel using pilot information known to both the transmitter and receiver. CSI reflects characteristics such as signal attenuation, delay, and phase changes during propagation between the transmitter and receiver. In Orthogonal Frequency Division Multiplexing (OFDM) systems, this information is typically represented in complex form, including amplitude and phase information.

[0024] Specifically, the number of data packets is denoted as The channel state information for each data packet includes Subcarriers, using Indicates the packet index. This represents the subcarrier index of the data packet; where, For data packet numbering, Number the subcarrier for each data packet, and then assign the data packet to the subcarrier. subcarrier The channel state information is denoted as ; The expression is: ; in, For data packets The number of paths, For data packets Path number, For data packets path Path gain, For data packets path The time delay; For the center frequency, For subcarriers The baseband frequency; For data packets Packet detection delay is the error between the time when the receiver detects the start time of the data packet and the actual time of arrival. This error causes the phase to change linearly with the subcarrier frequency. The fixed phase deviation of the phase-locked loop is expressed in radians. The fixed phase deviation of the phase-locked loop is a constant phase introduced by the receiver hardware. For data packets The residual carrier frequency offset (CFO) in channel state information is a small frequency difference that remains due to the incomplete synchronization of the local oscillator frequencies at the transmitting and receiving ends. For data packets timestamp, For data packets subcarrier Observational noise in channel state information.

[0025] S2. Based on the overcomplete delay dictionary, the channel state information is sparsely optimized to eliminate the influence of packet detection delay and obtain sparse observations.

[0026] To eliminate the interference of packet detection delay on residual carrier frequency offset estimation, this step utilizes the sparsity of the channel in the time delay domain. Sparsity in the time delay domain means that in a typical multipath environment, energy is concentrated only on a few discrete path delays, while the energy is zero or close to zero at most delay points.

[0027] Specifically, based on an overcomplete delay dictionary, sparse optimization is performed on the channel state information to eliminate the impact of packet detection delay, resulting in sparse observations, including: S21. First, for each data packet... To address this, an overcomplete delay dictionary is designed. This dictionary is a set containing a large number of hypothetical delay values, with a dimension (the number of hypothetical delay values) much larger than the actual number of multipath paths, thus enabling high-resolution coverage of all possible delay values. Specifically, an overcomplete delay sequence is designed. A complete delay dictionary matrix has been created. ,in To achieve a complete latency dictionary dimension, satisfying ; S22, According to the data packet Channel state information, overcomplete delay dictionary matrix and data packets Observation noise in channel state information determines sparse observations: Data packets Channel state information The matrix form is represented as ; For sparse observations, Indicates data packet Observational noise of channel state information; in: , , , For data packets path latency ,like It is exactly equal to the complete time delay sequence One of the items, assuming ,but Typically, the design of an overcomplete delay sequence cannot guarantee a certain term. and They are exactly equal, but because , It still exhibits sparsity in the time-delay domain.

[0028] In order to extract data packets To eliminate the impact of packet detection delay on carrier frequency offset estimation from channel state information, an overcomplete delay dictionary is constructed by utilizing the sparsity of channel state information in the time delay domain. A norm 1 relaxed convex optimization problem is then solved to obtain the channel state information. Recover sparse observations that eliminate the effects of packet detection delay. .

[0029] Construct a norm 1 relaxed convex optimization problem: ; in, For the first normal form, It is the second normal form. These are preset parameters used to reflect noise levels and resolution errors of the overcomplete delay dictionary.

[0030] Solving the norm 1 relaxed convex optimization problem can be achieved by... Channel state information of each data packet Determine sparse observations to eliminate the effects of packet detection delay. .

[0031] In actual deployment, if it cannot be determined The approximate range can be determined using a two-stage overcomplete delay dictionary. The first stage involves designing a large-range, coarse-precision overcomplete delay sequence. The sequence difference is large, which can cover a wider range but has lower precision. Then, the norm 1 relaxation convex optimization problem is solved to obtain... , The position of the non-zero element corresponds to The approximate range. Then, in the second stage, a small-range, fine-precision, overcomplete time delay sequence is designed. The sequence difference is small, which can cover a smaller range but achieve higher accuracy. Then, the norm 1 relaxation convex optimization problem is solved to obtain... . That is, sparse observations to eliminate packet detection delay. .

[0032] Because the overcomplete delay dictionary has already modeled and compensated for packet detection delay as an unknown delay offset, the sparse observations obtained by solving the norm 1 relaxed convex optimization problem eliminate the influence of packet detection delay. It can be seen that by constructing an overcomplete delay dictionary and using sparse optimization, the interference factor of packet detection delay can be separated from the channel state information, avoiding the problem of reduced estimation accuracy caused by the mutual coupling of packet detection delay and residual carrier frequency offset in traditional methods, and improving the prediction accuracy of the estimated value of residual carrier frequency offset.

[0033] S3. For each data packet, take the largest modulus element from the sparse observations as the carrier frequency offset information of the data packet.

[0034] For each data packet From sparse observations The maximum modulus element is taken, which corresponds to the time-delay domain response of the strongest energy path in the data packet. Since the packet detection delay has been eliminated, the phase change of the strongest path is mainly affected by the residual carrier frequency offset (CFO). Therefore, the maximum modulus element is defined as the carrier frequency offset information of the data packet.

[0035] Data packets carrier frequency offset information Represented as ; .

[0036] In indoor low-speed scenarios (such as WiFi sensing), for Each data packet typically lasts for milliseconds; therefore, it is assumed that the path gain and delay of the strongest energy path, as well as the residual carrier frequency offset of the channel state information, are the same. The mathematical expression is: in for The path gain of the strongest energy path for each data packet. for The latency of the strongest energy path for each data packet. for The residual carrier frequency offset of the channel state information of each data packet. The noise is a mixture of observation noise of channel state information, resolution error of overcomplete delay dictionary, and norm 1 relaxation convex optimization error. Assume... Noise in each data packet Independent and identically distributed, noise power is .

[0037] By extracting the strongest energy path, noise interference and complex phase effects of weaker paths in a multipath environment are further filtered out. The problem of residual carrier frequency offset estimation is simplified to the problem of frequency estimation of a single-frequency signal (i.e., the strongest path), which greatly simplifies subsequent processing.

[0038] S4. Construct an autocorrelation matrix based on the carrier frequency offset information of all data packets, and perform eigenvalue decomposition on the autocorrelation matrix to obtain the noise subspace.

[0039] Specifically, an autocorrelation matrix is ​​constructed based on the carrier frequency offset information of all data packets, and eigenvalue decomposition is performed on the autocorrelation matrix to obtain a noise subspace, including: Will Carrier frequency offset information of each data packet Represented in matrix form: ; Calculate matrix autocorrelation matrix : ; in, This indicates the conjugate transpose. The expected value represents the mathematical expectation. In practice, since it is impossible to obtain the ideal mathematical expectation, the autocorrelation matrix is ​​usually approximated by smoothing the subsequences, for example, by using forward and backward smoothing techniques.

[0040] For autocorrelation matrix Perform eigenvalue decomposition: ; Wherein, the dimension of the signal subspace is , The columns constitute the signal subspace. For the signal subspace A diagonal matrix composed of eigenvalues. The columns constitute the noise subspace. For the noise subspace A diagonal matrix composed of eigenvalues.

[0041] By using eigenvalue decomposition, the observed data space is divided into a signal subspace and a noise subspace; this method can effectively distinguish between signals and noise and maintain good performance even in low signal-to-noise ratio environments.

[0042] S5. Based on the noise subspace, the MUSIC algorithm is used to perform super-resolution spectral estimation of the residual carrier frequency offset to obtain the estimated value of the residual carrier frequency offset.

[0043] The MUSIC (Multiple Signal Classification) algorithm is a classic super-resolution spectral estimation method. Its core idea is to use the orthogonality between the noise subspace and the signal steering vector to estimate signal parameters by searching for peaks in the pseudo-spectrum.

[0044] Specifically, based on the noise subspace, the MUSIC algorithm is used to perform super-resolution spectral estimation of the residual carrier frequency offset to obtain an estimated value of the residual carrier frequency offset, including: S51. A complete carrier frequency offset sequence has been designed.

[0045] Design an overcomplete carrier frequency offset dictionary, that is, an overcomplete carrier frequency offset sequence containing all possible candidate frequency offsets. ,in, The size of the overcomplete carrier frequency offset dictionary determines the estimation resolution.

[0046] S52. Calculate the MUSIC pseudospectral values ​​of all candidate carrier frequency offsets based on the noise subspace and the overcomplete carrier frequency offset sequence; the number of candidate carrier frequency offsets is... , This is the frequency offset index for candidate carriers.

[0047] Calculate the multiple signal classification (MUSIC) pseudospectral values ​​for all candidate carrier frequency offsets. The formula for calculating the MUSIC pseudospectral values ​​for candidate carrier frequency offsets is as follows: ; in, It should be a very small constant value to prevent the denominator from being 0. The steering vector for the candidate carrier frequency offset is defined as: .

[0048] When the candidate carrier frequency offset is equal to the true residual carrier frequency offset (CFO), its steering vector will fall into the signal subspace, thus becoming orthogonal to the noise subspace. At this point, the denominator is close to zero, and the pseudo-spectral value will form a sharp peak.

[0049] S53, from Select the maximum value from the MUSIC pseudospectral values, and use the candidate carrier frequency offset index corresponding to the maximum MUSIC pseudospectral value. Candidate carrier frequency offset index Corresponding overcomplete carrier frequency offset This is the estimated value of the residual carrier frequency offset.

[0050] This step utilizes the MUSIC algorithm to achieve super-resolution estimation of residual carrier frequency offset. Compared to traditional methods, the MUSIC algorithm can distinguish very close frequency components even with a small number of data packets (i.e., short observation time), thus greatly improving the estimation accuracy of carrier frequency offset and enabling it to meet the needs of complex scenarios such as high-precision positioning and sensing.

[0051] The performance of the residual carrier frequency offset estimation method for channel state information in this application is verified below. Figure 2 The MUSIC pseudospectral value of the candidate carrier frequency offset provided in an embodiment of the present invention is from... Figure 2 It can be seen that there are obvious peaks in the pseudo-spectral values, and the candidate carrier frequency offset values ​​corresponding to the peaks are the residual carrier frequency offset estimates of the channel state information. Figure 3 This is a residual carrier frequency offset estimation error map of channel state information provided in an embodiment of the present invention, from... Figure 3 As can be seen from the data, the residual carrier frequency offset estimation method for channel state information proposed in this application has a small estimation error and can track the slow changes in residual carrier frequency offset between multiple data packets. The estimated value (dashed line) is in high agreement with the true value (solid line), and the estimation error is extremely small, proving that the method has extremely high estimation accuracy and good tracking performance.

[0052] Based on the same inventive concept, an embodiment of the present invention provides a residual carrier frequency offset estimation system for channel state information.

[0053] The residual carrier frequency offset estimation system for channel state information described in this invention can be installed in an electronic device. Depending on the functions implemented, the residual carrier frequency offset estimation system for channel state information includes: The acquisition module is used to acquire channel state information for multiple data packets; The sparse processing module is used to perform sparse optimization on the channel state information based on the overcomplete delay dictionary, eliminate the influence of packet detection delay, and obtain sparse observations. The processing module is used to take the maximum modulus element from the sparse observations for each data packet as the carrier frequency offset information of the data packet; The feature decomposition module is used to construct an autocorrelation matrix based on the carrier frequency offset information of all data packets, and to perform feature decomposition on the autocorrelation matrix to obtain the noise subspace. The output module is used to perform super-resolution spectral estimation of the residual carrier frequency offset based on the noise subspace using the MUSIC algorithm, so as to obtain an estimated value of the residual carrier frequency offset.

[0054] The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0055] The various variations and specific examples of the residual carrier frequency offset estimation method for channel state information provided in the above embodiments are also applicable to the residual carrier frequency offset estimation system for channel state information in this embodiment. Through the foregoing detailed description of the residual carrier frequency offset estimation method for channel state information, those skilled in the art can clearly understand the implementation method of the residual carrier frequency offset estimation system for channel state information in this embodiment. For the sake of brevity, it will not be described in detail here.

[0056] This application also discloses an electronic device, such as Figure 4 The diagram shown is a schematic representation of an electronic device for estimating the residual carrier frequency offset of channel state information according to an embodiment of the present invention. The electronic device may include at least one processor 10, a memory 11 communicatively connected to the at least one processor, a communication bus 12, and a communication interface 13. It may also include a computer program stored in the memory 11 and executable on the processor 10, such as a program for estimating the residual carrier frequency offset of channel state information.

[0057] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., methods for estimating residual carrier frequency offset of channel state information), and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0058] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed on the electronic device, such as the code for methods for estimating residual carrier frequency offset of channel state information, but also to temporarily store data that has been output or will be output.

[0059] The communication bus 12 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0060] Communication interface 13 is used for communication between the aforementioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), and optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0061] Figure 4 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 4The structure shown does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0062] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to at least one processor 10 via a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be elaborated further here.

[0063] It should be understood that the embodiments are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0064] Furthermore, if the modules / units integrated into the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile.

[0065] This application provides a computer-readable storage medium, including, for example, any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM). The computer-readable storage medium stores a computer program capable of being loaded by a processor and executing the residual carrier frequency offset estimation method for channel state information described in the above embodiments.

[0066] In the description of this specification, the references to terms such as "an embodiment," "some embodiments," "example," "specific example," "a implementation," "a preferred implementation," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. 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.

[0067] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for estimating the residual carrier frequency offset of channel state information, characterized in that, The method includes: Obtain channel state information for multiple data packets; Based on an overcomplete delay dictionary, sparse optimization is performed on the channel state information to eliminate the impact of packet detection delay, resulting in sparse observations, including: For each data packet Complete delay sequences have been designed A complete delay dictionary matrix has been created. ,in To provide a complete latency dictionary dimension; According to the data packet Channel state information, overcomplete delay dictionary matrix and data packets Observation noise in channel state information determines sparse observations: Data packets Channel state information The matrix form is represented as ; For sparse observations, Indicates data packet Observational noise of channel state information; Construct a norm 1 relaxed convex optimization problem: ； in, For the first normal form, It is the second normal form. These are preset parameters used to reflect noise levels and resolution errors of the overcomplete delay dictionary; Solve the norm 1 relaxed convex optimization problem, based on Channel state information of each data packet We obtain sparse observations that eliminate the effects of packet detection delay. ; For each data packet, the maximum modulus element is taken from the sparse observations and used as the carrier frequency offset information of the data packet; An autocorrelation matrix is ​​constructed based on the carrier frequency offset information of all data packets, and the autocorrelation matrix is ​​subjected to eigenvalue decomposition to obtain the noise subspace. Based on the aforementioned noise subspace, the MUSIC algorithm is used to perform super-resolution spectral estimation of the residual carrier frequency offset, yielding an estimated value of the residual carrier frequency offset, including: A complete carrier frequency offset sequence has been designed. ,in, To achieve a complete carrier frequency offset dictionary dimension; The MUSIC pseudospectral values ​​of all candidate carrier frequency offsets are calculated based on the noise subspace and the overcomplete carrier frequency offset sequence; the number of candidate carrier frequency offsets is... , For candidate carrier frequency offset index; from Select the maximum value from the MUSIC pseudospectral values, and use the candidate carrier frequency offset index corresponding to the maximum MUSIC pseudospectral value. Candidate carrier frequency offset index Corresponding overcomplete carrier frequency offset This is the estimated value of the residual carrier frequency offset.

2. The residual carrier frequency offset estimation method for channel state information as described in claim 1, characterized in that, The number of data packets is denoted as The channel state information for each data packet includes Subcarriers, Indicates the packet index. Indicates the subcarrier index of the data packet. , , will data packet subcarrier The channel state information is denoted as ; The expression is: in, For data packets The number of paths, For data packets Path number, For data packets path Path gain, For data packets path The time delay; The center frequency; For subcarriers The baseband frequency; For data packets Packet detection delay; This represents the fixed phase deviation of the phase-locked loop, expressed in radians. For data packets Residual carrier frequency offset in channel state information; For data packets timestamp; For data packets subcarrier Observational noise in channel state information.

3. The residual carrier frequency offset estimation method for channel state information as described in claim 1, characterized in that, For each data packet The carrier frequency offset information is represented as ； ； in, For data packets Carrier frequency offset information index, For data packets Carrier frequency offset information.

4. The residual carrier frequency offset estimation method for channel state information as described in claim 1, characterized in that, The process of constructing an autocorrelation matrix based on carrier frequency offset information from all data packets, and performing eigenvalue decomposition on the autocorrelation matrix to obtain a noise subspace, includes: Will Carrier frequency offset information of each data packet Represented in matrix form: ； Calculate matrix autocorrelation matrix : ； in This indicates the conjugate transpose. Represents the mathematical expectation; For autocorrelation matrix Perform eigenvalue decomposition: ； Wherein, the dimension of the signal subspace is , The columns constitute the signal subspace. For the signal subspace A diagonal matrix composed of eigenvalues. The columns constitute the noise subspace. For the noise subspace A diagonal matrix composed of eigenvalues.

5. The residual carrier frequency offset estimation method for channel state information as described in claim 1, characterized in that, The formula for calculating the MUSIC pseudospectral value of the candidate carrier frequency offset is: ； in, It should be a very small constant value to prevent the denominator from being 0. The guiding vector is defined as follows: 。 6. A residual carrier frequency offset estimation system for channel state information, used to implement the residual carrier frequency offset estimation method for channel state information according to any one of claims 1 to 5, characterized in that, include: The acquisition module is used to acquire channel state information for multiple data packets; The sparse processing module is used to perform sparse optimization on the channel state information based on the overcomplete delay dictionary, eliminate the influence of packet detection delay, and obtain sparse observations. The processing module is used to take the maximum modulus element from the sparse observations for each data packet as the carrier frequency offset information of the data packet; The feature decomposition module is used to construct an autocorrelation matrix based on the carrier frequency offset information of all data packets, and to perform feature decomposition on the autocorrelation matrix to obtain the noise subspace. The output module is used to perform super-resolution spectral estimation of the residual carrier frequency offset based on the noise subspace using the MUSIC algorithm, so as to obtain an estimated value of the residual carrier frequency offset.

7. An electronic device, characterized in that, The electronic device includes: At least one processor (10); and, A memory (11) communicatively connected to the at least one processor (10); The memory (11) stores a computer program that can be executed by the at least one processor (10) to enable the at least one processor (10) to perform the residual carrier frequency offset estimation method for channel state information as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program; when the computer program is executed by a processor, it implements the residual carrier frequency offset estimation method for channel state information as described in any one of claims 1 to 5.

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