Signal-to-noise ratio estimation method and device, electronic equipment, storage medium and program product

Abstract

The invention relates to the technical field of communication measurement, in particular to a signal-to-noise ratio estimation method and device, electronic equipment, a storage medium and a program product, and the signal-to-noise ratio estimation method comprises the steps that dual-receiving-antenna terminal equipment receives a wireless signal sent by a base station; acquiring channel characteristics and noise characteristics based on the wireless signal; performing noise cancellation processing on the noise feature to obtain a first noise feature; acquiring signal transmission capability information of a channel between the dual-receiving antenna terminal equipment and the base station based on the channel feature and the first noise feature; and based on the signal transmission capability information and the unit matrix, obtaining first signal-to-noise ratio estimation corresponding to linear multiple-input multiple-output detection and second signal-to-noise ratio estimation corresponding to nonlinear multiple-input multiple-output detection. According to the method and the device, the Cholesky decomposition and matrix inversion operation required by a traditional scheme are avoided, the calculation complexity is greatly reduced, the implementation requirements of a hardware circuit on the area and the power consumption are relieved, and the link state after nonlinear MIMO detection can be accurately represented.

Description

Signal-to-noise ratio estimation methods, devices, electronic devices, storage media, and software products Technical Field

[0001] This disclosure relates to the field of communication measurement technology, and in particular to a signal-to-noise ratio estimation method, apparatus, electronic device, storage medium, and program product. Background Technology

[0002] In the Channel State Information (CSI) feedback mechanism of 4G and 5G cellular systems, terminal devices need to estimate the signal-to-noise ratio (SNR) based on real-time link measurements. This allows them to select appropriate Precoding Matrix Indicator (PMI), Rank Indicator (RI), and Channel Quality Indicator (CQI) indicators to report to the base station. These indicators serve as a reference for the base station to adaptively adjust link transmission parameters. Traditional SNR estimation schemes are generally based on the closed-form solution of the Minimum Mean Square Error (MMSE).

[0003] However, traditional SNR estimation schemes suffer from two main drawbacks. First, they are computationally complex, requiring Cholesky decomposition (Chol) and matrix inversion, which imposes requirements on the area and power consumption of the hardware circuitry. Second, they lack robustness, failing to accurately characterize the link state after nonlinear multiple-input multiple-output (MIMO) detection, potentially leading to an overestimation of the reported RI, necessitating manual correction. Summary of the Invention

[0004] To address the aforementioned technical issues, this disclosure provides a signal-to-noise ratio estimation method, apparatus, electronic device, storage medium, and program product.

[0005] In a first aspect, this disclosure provides a signal-to-noise ratio (SNR) estimation method, comprising: a dual-receiving antenna terminal device receiving a wireless signal transmitted by a base station; acquiring channel features and noise features based on the wireless signal; performing noise cancellation processing on the noise features to acquire a first noise feature; acquiring signal transmission capability information of the channel between the dual-receiving antenna terminal device and the base station based on the channel features and the first noise feature; and acquiring a first SNR estimate corresponding to linear multiple-input multiple-output (MIMO) detection and a second SNR estimate corresponding to nonlinear multiple-input multiple-output (MIMO) detection based on the signal transmission capability information and an identity matrix.

[0006] Furthermore, according to the method of the first aspect of this disclosure, based on a wireless signal, channel features and noise features are obtained, including: performing noise estimation on the wireless signal to obtain a noise covariance matrix, the noise covariance matrix being used to describe the noise features; and performing channel estimation on the wireless signal to obtain a channel estimation matrix, the channel estimation matrix being used to characterize the channel features.

[0007] Furthermore, according to the method of the first aspect of this disclosure, noise features are subjected to noise cancellation processing to obtain a first noise feature, including: performing noise cancellation processing on the noise covariance matrix using matrix inversion calculation to obtain the inverse noise covariance matrix, wherein the inverse noise covariance matrix is ​​used to characterize the first noise feature.

[0008] Furthermore, according to the method of the first aspect of this disclosure, signal transmission capability information of the channel between the dual-receiving antenna terminal device and the base station is obtained based on channel characteristics and a first noise characteristic, including: obtaining the conjugate transpose matrix of the channel estimation matrix based on the channel estimation matrix; obtaining the channel covariance estimation matrix based on the channel estimation matrix, its conjugate transpose matrix, and the inverse noise covariance matrix, wherein the channel covariance estimation matrix is ​​used to characterize the signal transmission capability information of the channel between the dual-receiving antenna terminal device and the base station.

[0009] Furthermore, according to the method of the first aspect of this disclosure, based on signal transmission capability information and an identity matrix, a first signal-to-noise ratio (SNR) estimate corresponding to linear multiple-input multiple-output (MIMO) detection and a second SNR estimate corresponding to nonlinear multiple-input multiple-output (MIMO) detection are obtained, including: performing identity matrix processing on the channel covariance estimation matrix to obtain a Q matrix; extracting real-valued complex elements of the Q matrix to obtain multiple real-valued parameters; and obtaining the first SNR estimate corresponding to linear multiple-input multiple-output (MIMO) detection and the second SNR estimate corresponding to nonlinear multiple-input multiple-output (MIMO) detection based on the multiple real-valued parameters.

[0010] Secondly, this disclosure provides a signal-to-noise ratio (SNR) estimation apparatus, comprising: a signal receiving unit for receiving a wireless signal transmitted by a base station through a dual-receiving antenna terminal device; a first acquisition unit for acquiring channel features and noise features based on the wireless signal; a second acquisition unit for performing noise cancellation processing on the noise features to acquire a first noise feature; a third acquisition unit for acquiring signal transmission capability information of the channel between the dual-receiving antenna terminal device and the base station based on the channel features and the first noise feature; and a SNR estimation acquisition unit for acquiring a first SNR estimate corresponding to linear multiple-input multiple-output (MIMO) detection and a second SNR estimate corresponding to nonlinear multiple-input multiple-output (MIMO) detection based on the signal transmission capability information and an identity matrix.

[0011] Furthermore, according to the apparatus of the second aspect of this disclosure, the first acquisition unit is further configured to: perform noise estimation on the wireless signal to acquire a noise covariance matrix, the noise covariance matrix being used to describe noise characteristics; and perform channel estimation on the wireless signal to acquire a channel estimation matrix, the channel estimation matrix being used to characterize channel characteristics.

[0012] Thirdly, this disclosure provides an electronic device, including: a memory for storing computer-readable instructions; and a processor for executing the computer-readable instructions, causing the electronic device to perform the method as described in any embodiment of the first aspect.

[0013] Fourthly, this disclosure provides a non-transitory computer-readable storage medium for storing computer-readable instructions that, when executed by a processor, cause the processor to perform the method as described in any embodiment of the first aspect.

[0014] Fifthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the method as described in any embodiment of the first aspect.

[0015] This disclosure provides a signal-to-noise ratio (SNR) estimation method, apparatus, electronic device, storage medium, and program product. The method extracts channel and noise features from the received wireless signal, performs noise cancellation on the noise features, and then combines the channel features and the processed noise features to obtain channel signal transmission capability information. Based on this information and the identity matrix, SNR estimates for linear and nonlinear MIMO detection are obtained. This avoids the Cholsky decomposition and matrix inversion operations required by traditional schemes, significantly reducing computational complexity and alleviating hardware implementation requirements in terms of area and power consumption. It also accurately characterizes the link state after nonlinear MIMO detection, improving the robustness of SNR estimation. It can accurately output SNR results under both linear and nonlinear MIMO detection, avoiding the problem of overreported RI (Indicator Resonance). No manual correction is required, enabling terminal devices to more accurately estimate the real-time link SNR and rationally select PMI / RI / CQI reporting, providing a more reliable reference for base stations to adaptively adjust link transmission parameters.

[0016] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0017] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 is a schematic diagram of a communication system architecture provided in an embodiment of this disclosure; Figure 2 is a schematic diagram of a traditional signal-to-noise ratio (SNR) estimation example provided in an embodiment of this disclosure; Figure 3 is a flowchart of a SNR estimation method provided in an embodiment of this disclosure; Figure 4 is a schematic diagram of a SNR estimation example provided in an embodiment of this disclosure; Figure 5 is a structural block diagram of a SNR estimation device provided in an embodiment of this disclosure; Figure 6 is a hardware block diagram of an electronic device provided in an embodiment of this disclosure; Figure 7 is a schematic diagram of a computer-readable storage medium provided in an embodiment of this disclosure. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this disclosure more apparent, exemplary embodiments according to this disclosure will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments of this disclosure. It should be understood that this disclosure is not limited to the exemplary embodiments described herein.

[0020] First, let’s take a look at the application scenarios according to the embodiments of this disclosure with reference to FIG1.

[0021] Figure 1 is a schematic diagram of the architecture of a communication system provided in an embodiment of this disclosure. As shown in Figure 1, the communication system includes at least a base station 101 and a terminal 102.

[0022] It is understandable that the terminal 102 receives the signal sent by the base station 101, determines the signal-to-noise ratio based on the received signal, and then feeds back the information carrying the signal-to-noise ratio to the base station 101 so that the base station 101 can select appropriate PMI / RI / CQI or other instructions to process the signal to be sent to the terminal 102 based on the signal-to-noise ratio.

[0023] Figure 2 is a schematic diagram of a traditional signal-to-noise ratio (SNR) estimation example provided in this embodiment. In the receiver scenario of a 4G / 5G cellular system, the inputs to the noise estimation module 201 and the channel estimation module 202 of the terminal are the wireless signals actually received by the terminal from the base station. As shown in Figure 2, the traditional SNR estimation method specifically includes the following steps: (1) The noise estimation module 201 performs noise estimation processing on the wireless signal and outputs a noise covariance estimation matrix. For the noise covariance estimation matrix The lower triangular matrix is ​​obtained by performing the Choleski decomposition. The channel estimation module 202 performs channel estimation processing on the wireless signal and outputs a channel estimation matrix. .

[0024] (2) For the lower triangular matrix Inverting the matrix yields... .

[0025] (3) Channel estimation matrix Noise whitening is performed to obtain the channel estimation whitening matrix. .

[0026] (4) Whitening matrix of channel estimation Obtain by performing conjugate transpose Calculate the channel covariance estimation matrix .

[0027] (5) Estimation of the channel covariance matrix Adding the identity matrix I, we obtain the matrix .

[0028] (6) For matrices Inverting the matrix yields... .

[0029] (7) Calculate based on the closed-form solution of MMSE .

[0030] Extract Q 1 diagonal element (Q) 1 ) u diagonal element (Q) 1 ) u Taking the reciprocal gives ((Q) 1 ) u ) 1 Divide by the number of receiving antennas x (1 / x), subtract 1, and then perform a logarithmic operation to finally obtain the signal-to-noise ratio estimation result based on MMSE. .

[0031] Figure 3 is a flowchart illustrating a signal-to-noise ratio estimation method provided in an embodiment of this disclosure. As shown in Figure 3, this method can be executed by a dual-receiving antenna terminal device (such as a 2RX terminal), and specifically includes the following steps: Step 301: The dual-receiving antenna terminal device receives the wireless signal sent by the base station.

[0032] In one embodiment of this disclosure, a dual-receiving-antenna terminal device refers to a terminal such as a mobile phone or tablet equipped with two independent receiving antennas (a basic configuration for 4G / 5G multi-antenna reception). When a base station transmits a wireless signal, the signal propagates through space and can be simultaneously captured by the receiving antennas of the dual-receiving-antenna terminal device.

[0033] Step 302: Based on the wireless signal, obtain channel characteristics and noise characteristics.

[0034] In one embodiment of this disclosure, a known reference signal pre-transmitted by the base station in the wireless signal is used to perform a matching operation between the wireless signal and the known reference signal, thereby deducing the attenuation, delay, and other changes of the wireless signal on the link, and finally obtaining the channel characteristics. The signal is extracted from the idle region of the wireless signal where there is no valid data, and the distribution characteristics of the noise are statistically analyzed to obtain the noise characteristics.

[0035] For example, based on a wireless signal, obtaining channel features and noise features includes: performing noise estimation on the wireless signal to obtain a noise covariance matrix, the noise covariance matrix being used to represent the noise features; and performing channel estimation on the wireless signal to obtain a channel estimation matrix, the channel estimation matrix being used to characterize the channel features.

[0036] Specifically, channel estimation refers to using a known reference signal pre-transmitted by the base station in the wireless signal, performing a matching operation between the wireless signal and the known reference signal, and then inferring the channel characteristics. Noise estimation refers to extracting the signal from idle regions of the wireless signal without valid data, and then statistically analyzing the noise distribution characteristics to obtain noise characteristics. Channel characteristics are represented by a channel estimation matrix, and noise characteristics are represented by a noise covariance matrix. Using a known reference signal pre-transmitted by the base station in the wireless signal, a matching operation is performed between the wireless signal and the known reference signal to infer the attenuation, delay, and other changes of the wireless signal on the link, ultimately obtaining the channel estimation matrix. Extracting the signal from idle regions of the wireless signal without valid data, and then statistically analyzing the noise distribution characteristics, yields the noise covariance matrix.

[0037] Step 303: Perform noise cancellation processing on the noise features to obtain the first noise feature.

[0038] In one embodiment of this disclosure, non-pure noise components in the noise features are identified and removed to obtain purer noise features, namely, the first noise feature.

[0039] For example, performing noise cancellation processing on noise features to obtain a first noise feature includes: performing noise cancellation processing on the noise covariance matrix using matrix inversion calculation to obtain the inverse noise covariance matrix, which is used to characterize the first noise feature.

[0040] Specifically, the noise covariance matrix is ​​inverted to identify and remove non-pure noise components from the noise features, resulting in the inverse noise covariance matrix, which is used to characterize the first noise feature.

[0041] Step 304: Based on channel characteristics and first noise characteristics, obtain the signal transmission capability information of the channel between the dual-receiving antenna terminal equipment and the base station.

[0042] In one embodiment of this disclosure, signal transmission capability information refers to a precise and concrete description of the actual ability of the communication channel between the base station and the dual-receiving antenna terminal to effectively transmit wireless signals in a real noise environment after removing non-pure noise components from the noise features. The channel features characterizing the physical transmission properties of the link and the purified, de-noiseed first noise features are fused using a low-complexity operation to ultimately obtain information that accurately and quantitatively reflects the channel signal transmission capability between the dual-receiving antenna terminal and the base station.

[0043] For example, based on channel characteristics and a first noise characteristic, the signal transmission capability information of the channel between the dual-receiving antenna terminal device and the base station is obtained, including: obtaining the conjugate transpose matrix of the channel estimation matrix based on the channel estimation matrix; obtaining the channel covariance estimation matrix based on the channel estimation matrix, its conjugate transpose matrix, and the inverse noise covariance matrix, wherein the channel covariance estimation matrix is ​​used to characterize the signal transmission capability information of the channel between the dual-receiving antenna terminal device and the base station.

[0044] Specifically, the channel estimation matrix is ​​transposed to obtain the transpose of the channel estimation matrix. The channel estimation matrix and its transpose are multiplied by the inverse noise covariance matrix to achieve the fusion of channel features and first noise features, thereby obtaining the channel covariance estimation matrix. The channel covariance estimation matrix is ​​used to characterize the signal transmission capability information of the channel between the dual-receiver antenna terminal equipment and the base station.

[0045] Step 305: Based on the signal transmission capability information and the identity matrix, obtain the first signal-to-noise ratio estimate corresponding to linear multiple-input multiple-output detection and the second signal-to-noise ratio estimate corresponding to nonlinear multiple-input multiple-output detection.

[0046] In one embodiment of this disclosure, obtaining a first signal-to-noise ratio (SNR) estimate corresponding to linear multiple-input multiple-output (MIMO) detection and a second SNR estimate corresponding to nonlinear multiple-input multiple-output (MIMO) detection based on signal transmission capability information and an identity matrix includes: performing identity matrix processing on the channel covariance estimation matrix to obtain a Q matrix; extracting real-valued complex elements of the Q matrix to obtain multiple real-valued parameters; and obtaining the first SNR estimate corresponding to linear multiple-input multiple-output (MIMO) detection and the second SNR estimate corresponding to nonlinear multiple-input multiple-output (MIMO) detection based on the multiple real-valued parameters.

[0047] Specifically, the channel covariance estimation matrix and the identity matrix are added to obtain the Q matrix. All complex elements in the Q matrix are then converted into single real values ​​using low-complexity realization operations, ultimately yielding multiple real parameters. Using these multiple real parameters as unique inputs, two simple real-valued operation models—one for linear MIMO detection and the other for nonlinear MIMO detection—are employed to calculate the first and second signal-to-noise ratio (SNR) estimates, respectively.

[0048] In summary, according to the technical solution provided in the embodiments of this disclosure, this disclosure extracts channel features and noise features from the received wireless signal, performs noise cancellation processing on the noise features, and then combines the channel features and the processed noise features to obtain channel signal transmission capability information. Based on the channel signal transmission capability information and the identity matrix, the signal-to-noise ratio (SNR) estimates corresponding to linear and nonlinear MIMO detection are obtained respectively. This avoids the Cholsky decomposition and matrix inversion operations required by traditional schemes, significantly reducing computational complexity and alleviating the implementation requirements of hardware circuits in terms of area and power consumption. It can also accurately characterize the link state after nonlinear MIMO detection, improve the robustness of SNR estimation, and accurately output the SNR results under linear and nonlinear MIMO detection. It avoids the problem of overreported RI, eliminates the need for manual correction, and enables terminal devices to more accurately estimate the real-time link SNR and reasonably select PMI / RI / CQI reporting, providing a more reliable reference for base stations to adaptively adjust link transmission parameters.

[0049] To describe in detail the signal-to-noise ratio (SNR) estimation method provided in this disclosure embodiment, please refer to Figure 4. Figure 4 is a schematic diagram of an SNR estimation example provided in this disclosure embodiment. As shown in Figure 4, the SNR estimation method specifically includes the following steps: (1) The noise estimation module (NE) and channel estimation module (CE) of the 2RX terminal perform noise estimation processing and channel estimation processing on the wireless signal received from the base station respectively to obtain the channel estimation matrix. Estimating the covariance matrix of noise , and These represent the number of antennas configured at the receiving and transmitting ends of a 2RX terminal, respectively.

[0050] Specifically, let the wireless signal be... and known reference signals pre-transmitted by the base station in wireless signals Then the CSI measurement model is written as:

[0051] in, Represents the channel matrix. This represents the noise matrix. In this embodiment, the CE outputs the channel estimation matrix. NE output noise estimation covariance matrix .

[0052] (2) Perform matrix inversion on the noise estimation covariance matrix to obtain the inverse noise covariance matrix. .

[0053] (3) Obtain the channel estimation matrix The conjugate transpose of the channel estimation matrix will be used to determine the channel estimation matrix. The conjugate transpose matrix and the inverse of the noise covariance matrix and channel estimation matrix Multiply to obtain the channel covariance estimation matrix. .

[0054] (4) Estimating the channel covariance matrix Adding it to the identity matrix I yields the matrix .

[0055] (5) Extract the complex elements of the Q matrix into real numbers to obtain multiple real parameters. .

[0056] (6) According to calculate and .

[0057] Specifically, the first signal-to-noise ratio estimate is calculated as follows: ,in

[0058] The second signal-to-noise ratio estimate is calculated as follows: ,in

[0059] This represents the SNR based on MMSE detection, corresponding to the first signal-to-noise ratio estimate, and characterizing the post-processing signal-to-noise ratio measurement of linear MIMO detection. This represents the SNR based on MMSE detection, corresponding to the second signal-to-noise ratio estimate, and characterizes the upper bound of the post-processing signal-to-noise ratio measurement for nonlinear MIMO detection.

[0060] The specific steps described above are as follows:

[0061] An example of signal-to-noise ratio estimation in this embodiment is shown in Figure 4. CE output NE output , Matrix inversion directly yields Then multiply by the right. and left multiplication get , Add unit array Calculated By performing low-complexity realization operations on all complex elements in the Q matrix, each complex element is transformed into a single real value, ultimately yielding multiple real parameters a, b, and c. Using these multiple real parameters a, b, and c as unique inputs, two simple real-valued operation models—one adapted for linear MIMO detection and the other for nonlinear MIMO detection—are employed to calculate the first signal-to-noise ratio estimate. Second signal-to-noise ratio estimation As can be seen, the signal-to-noise ratio (SNR) estimation method provided in this disclosure does not require Cho decomposition and Inv processing. A very simple calculation can yield two SNR estimates, which can be used for CSI link measurements during linear and nonlinear MIMO detection.

[0062] First signal-to-noise ratio estimation Second signal-to-noise ratio estimation They can be used together or independently.

[0063] This disclosure also provides a signal-to-noise ratio (SNR) estimation device. Figure 5 is a structural block diagram of an SNR estimation device provided in an embodiment of this disclosure. As shown in Figure 5, the SNR estimation device 500 includes: In a second aspect, this disclosure provides an SNR estimation device, including: a signal receiving unit 501, a first acquisition unit 502, a second acquisition unit 503, a third acquisition unit 504, and an SNR estimation acquisition unit 505.

[0064] In one exemplary embodiment, the signal receiving unit 501 is configured to receive a wireless signal transmitted by a base station through a dual-receiving antenna terminal device; in one exemplary embodiment, the first acquisition unit 502 is configured to acquire channel features and noise features based on the wireless signal; in one exemplary embodiment, the second acquisition unit 503 is configured to perform noise cancellation processing on the noise features to acquire a first noise feature; in one exemplary embodiment, the third acquisition unit 504 is configured to acquire signal transmission capability information of the channel between the dual-receiving antenna terminal device and the base station based on the channel features and the first noise feature; in one exemplary embodiment, the signal-to-noise ratio (SNR) estimation acquisition unit 505 is configured to acquire a first SNR estimate corresponding to linear multiple-input multiple-output (MIMO) detection and a second SNR estimate corresponding to nonlinear multiple-input multiple-output (MIMO) detection based on the signal transmission capability information and the identity matrix.

[0065] Furthermore, the first acquisition unit 502 is also used to: perform noise estimation on the wireless signal and acquire a noise covariance matrix, the noise covariance matrix being used to describe the noise characteristics; perform channel estimation on the wireless signal and acquire a channel estimation matrix, the channel estimation matrix being used to characterize the channel characteristics.

[0066] Figure 6 is a hardware block diagram of an electronic device provided in an embodiment of this disclosure. The electronic device 600 according to an embodiment of this disclosure includes at least a processor and a memory for storing computer-readable instructions. When the computer-readable instructions are loaded and executed by the processor, the processor performs the signal-to-noise ratio estimation method described in any of the preceding embodiments of this disclosure.

[0067] The electronic device 600 shown in Figure 6 specifically includes a central processing unit (CPU) 601, a graphics processing unit (GPU) 602, and a memory 603. These units are interconnected via a bus 604. The CPU 601 and / or GPU 602 can function as the aforementioned processors, and the memory 603 can function as the aforementioned memory for storing computer-readable instructions. Furthermore, the electronic device 600 may also include a communication unit 605, a storage unit 606, an output unit 607, an input unit 608, and an external device 609, all of which are also connected to the bus 604.

[0068] Figure 7 is a schematic diagram of a computer-readable storage medium provided in an embodiment of this disclosure. As shown in Figure 7, the computer-readable storage medium 700 according to an embodiment of this disclosure stores computer-readable instructions 701. When the computer-readable instructions 701 are executed by a processor, the signal-to-noise ratio estimation method described in any of the foregoing embodiments of this disclosure with reference to the above figures is performed. The computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0069] This disclosure further provides a computer program product, including a computer program that, when executed by a processor, implements the signal-to-noise ratio estimation method described in any of the preceding embodiments of this disclosure.

[0070] The above description, with reference to Figures 1 to 7, details a signal-to-noise ratio (SNR) estimation method, apparatus, electronic device, storage medium, and program product. Compared to existing technologies, the SNR estimation method of this disclosure has the following advantages: 1) Low hardware resource overhead, as it does not require Chol decomposition and matrix inversion operations compared to traditional methods; 2) Low power consumption overhead, as it does not require complex matrix decomposition processing; 3) Strong robustness, capable of simultaneously handling CSI link measurements with both nonlinear and linear MIMO detection; 4) High accuracy, allowing for a more precise selection of CSI results from the two SNR estimation results. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0071] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0072] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0073] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0074] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0075] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0076] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0077] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A signal-to-noise ratio estimation method, characterized in that, include: Dual-antenna terminal equipment receives wireless signals sent by the base station; Based on the wireless signal, channel characteristics and noise characteristics are obtained; The noise feature is subjected to noise cancellation processing to obtain the first noise feature; Based on the channel characteristics and the first noise characteristics, obtain the signal transmission capability information of the channel between the dual receiving antenna terminal device and the base station; Based on the signal transmission capability information and the identity matrix, a first signal-to-noise ratio estimate corresponding to linear multiple-input multiple-output detection and a second signal-to-noise ratio estimate corresponding to nonlinear multiple-input multiple-output detection are obtained.

2. The signal-to-noise ratio estimation method according to claim 1, characterized in that, The step of obtaining channel features and noise features based on the wireless signal includes: performing noise estimation on the wireless signal to obtain a noise covariance matrix, wherein the noise covariance matrix is ​​used to represent the noise features; and performing channel estimation on the wireless signal to obtain a channel estimation matrix, wherein the channel estimation matrix is ​​used to characterize the channel features.

3. The signal-to-noise ratio estimation method according to claim 2, characterized in that, The step of performing noise cancellation processing on the noise feature to obtain the first noise feature includes: performing noise cancellation processing on the noise covariance matrix using matrix inversion calculation to obtain the inverse noise covariance matrix, wherein the inverse noise covariance matrix is ​​used to characterize the first noise feature.

4. The signal-to-noise ratio estimation method according to claim 3, characterized in that, The step of obtaining the signal transmission capability information of the channel between the dual-receiving antenna terminal device and the base station based on the channel characteristics and the first noise characteristics includes: obtaining the conjugate transpose matrix of the channel estimation matrix based on the channel estimation matrix; obtaining the channel covariance estimation matrix based on the channel estimation matrix, its conjugate transpose matrix, and the inverse noise covariance matrix, wherein the channel covariance estimation matrix is ​​used to characterize the signal transmission capability information of the channel between the dual-receiving antenna terminal device and the base station.

5. The signal-to-noise ratio estimation method according to claim 4, characterized in that, The step of obtaining a first signal-to-noise ratio (SNR) estimate corresponding to linear multiple-input multiple-output (MIMO) detection and a second SNR estimate corresponding to nonlinear multiple-input multiple-output (MIMO) detection based on the signal transmission capability information and the identity matrix includes: performing identity matrix processing on the channel covariance estimation matrix to obtain a Q matrix; extracting real-valued complex elements of the Q matrix to obtain multiple real-valued parameters; and obtaining the first SNR estimate corresponding to linear multiple-input multiple-output (MIMO) detection and the second SNR estimate corresponding to nonlinear multiple-input multiple-output (MIMO) detection based on the multiple real-valued parameters.

6. A signal-to-noise ratio estimation device, characterized in that, include: The signal receiving unit is used to receive wireless signals sent by the base station through a dual-receiving antenna terminal device; The first acquisition unit is used to acquire channel characteristics and noise characteristics based on the wireless signal; The second acquisition unit is used to perform noise cancellation processing on the noise feature to acquire the first noise feature; The third acquisition unit is used to acquire signal transmission capability information of the channel between the dual receiving antenna terminal device and the base station based on the channel characteristics and the first noise characteristics. The signal-to-noise ratio (SNR) estimation acquisition unit is used to acquire a first SNR estimate corresponding to linear multiple-input multiple-output (MIMO) detection and a second SNR estimate corresponding to nonlinear multiple-input multiple-output (MIMO) detection based on the signal transmission capability information and the identity matrix.

7. The signal-to-noise ratio estimation device according to claim 6, characterized in that, The first acquisition unit is further configured to: perform noise estimation on the wireless signal to obtain a noise covariance matrix, wherein the noise covariance matrix is ​​used to describe the noise characteristics; and perform channel estimation on the wireless signal to obtain a channel estimation matrix, wherein the channel estimation matrix is ​​used to characterize the channel characteristics.

8. An electronic device, characterized in that, include: A memory for storing computer-readable instructions; and a processor for executing the computer-readable instructions, causing the electronic device to perform the signal-to-noise ratio estimation method as described in any one of claims 1-5.

9. A non-transitory computer-readable storage medium for storing computer-readable instructions, characterized in that, When the computer-readable instructions are executed by a processor, the processor performs the signal-to-noise ratio estimation method as described in any one of claims 1-5.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the signal-to-noise ratio estimation method as described in any one of claims 1-5.

Images