Method and apparatus for calibration of an antenna array

Abstract

A method (1000) for calibration of an antenna array is provided. The method comprises deriving (1100) at least one Wiener filter coefficient for the antenna array based on at least one power delay profile characteristic related to the antenna array and at least one signal-to-noise ratio related to the antenna array. The method further comprises estimating (1200) at least one transmitter parameter and / or at least one receiver parameter using the at least one Wiener filter coefficient for the antenna array, and calibrating (1300) the antenna array based on the at least one transmitter parameter and / or the at least one receiver parameter.

Description

Technical Field

[0001] This disclosure generally relates to the calibration of antenna arrays. This disclosure discloses a method, apparatus, computer program product, non-transitory computer-readable medium, and computer program. Background Technology

[0002] A key attribute of fifth-generation (5G) radio systems is the increased capacity in the radio network. Beamforming is a technique used by 5G radio systems to efficiently deliver the desired increased capacity. Specifically, 5G radio base stations can utilize large antenna arrays comprising dozens or even hundreds of antennas (also referred to herein as antenna elements). Each antenna element is connected to a radio transceiver path. Applying appropriate scaling within the transceiver path enables beamforming by effectively controlling the spatial coherent superposition of the desired signal and the coherent subtraction of the unwanted signal. This beamforming enables high antenna gain for the desired user equipment (UE) and allows parallel communication with multiple UEs using the same time / frequency resources through the use of orthogonal spatial communication paths (i.e., through the use of orthogonal beams).

[0003] Periodic calibration of antenna gain and phase for all transmitter and receiver antenna paths in an antenna array is useful for achieving desired beamforming performance in a base station (BS). A commonly used periodic calibration method is the mutual coupling antenna calibration (MCAC) method.

[0004] The purpose of antenna calibration (AC) based on mutual coupling (MC) is to utilize existing mutual coupling effects to estimate and equalize the gain and phase difference between the transmitter and receiver paths. Patent US 2022149517 A1, entitled "Efficient Antenna Calibration For Large Antenna Arrays," details the MCAC function.

[0005] When the system is subjected to frequency-selective hardware (HW) damage (e.g., HW leakage), near-field environmental damage (e.g., reflections from nearby metallic objects), HW aging-related degradation along with low signal-to-noise ratio, and interference-dominated operating conditions, the MC-based AC methods available in the literature produce very high phase and gain alignment errors. The poor parameter estimation capabilities of existing algorithms lead to degraded antenna branch alignment, thereby reducing the performance of the beamforming function supported by the BS system.

[0006] Furthermore, existing MC-based AC algorithms are highly complex because they require multiple iterations to estimate transmitter and receiver phase gain errors to achieve acceptable performance targets. Moreover, the complexity increases linearly with the supported system bandwidth. Summary of the Invention

[0007] The purpose of this invention is to facilitate the calibration of antenna arrays.

[0008] According to one aspect of the present invention, a method for calibrating an antenna array is provided. The method includes: deriving at least one Wiener filter coefficient for the antenna array based on at least one power delay domain characteristic associated with the antenna array and at least one signal-to-noise ratio associated with the antenna array. The method further includes: estimating at least one transmitter parameter and / or at least one receiver parameter using the at least one Wiener filter coefficient for the antenna array. The method further includes: calibrating the antenna array based on the at least one transmitter parameter and / or at least one receiver parameter.

[0009] In the field of signal processing, the Wiener filter is a filter that can be used to generate estimates of random processes. The Wiener filter attempts to minimize the mean square error between the estimate of the random process and the random process itself.

[0010] Wiener filter coefficients can be coefficients representing the equations of the Wiener filter or coefficients representing the matrix of the Wiener filter.

[0011] The power delay domain profile can be a characteristic of the power delay domain distribution (profile) of the propagation medium. The power delay domain distribution of the propagation medium can represent the average power of the received signal with respect to the delay of the first arrival path in a multipath transmission environment.

[0012] Signal-to-noise ratio (SNR) is a measure that compares the level of a signal to the level of noise; specifically, it is defined as the ratio of signal power to noise power. Further, signal-to-noise-to-interference ratio (SNR) is a measure that compares the level of a signal to the level of noise plus interference; specifically, it is defined as the ratio of signal power to the sum of noise power and interference power.

[0013] Transmitter / receiver parameters can be transmitter / receiver amplitude parameters, configured as gain control elements applied to the transmitter / receiver path of the antenna array. The gain control element can be an analog or digital element capable of changing the amplitude of the input signal based on input and / or target amplitude values. Transmitter / receiver parameters can also be transmitter / receiver phase parameters, configured as phase control elements applied to the transmitter / receiver path of the antenna array. The phase control element can be an analog or digital element capable of changing the phase of the input signal based on input and / or target phase values. Transmitter / receiver parameters can also be transmitter / receiver frequency parameters, configured as frequency control elements applied to the transmitter / receiver path of the antenna array. The frequency control element can be an analog or digital element capable of changing the frequency of the input signal based on input and / or target frequency values.

[0014] According to the example, deriving at least one Wiener filter coefficient for an antenna array may include: deriving at least one Wiener filter coefficient for the transmitter path of the antenna array based on at least one power delay domain characteristic associated with the transmitter path of the antenna array and the signal-to-noise ratio associated with the transmitter path of the antenna array.

[0015] The antenna array may include separate transmitter paths. Each transmitter path of the antenna array may include gain control elements, phase control elements, and / or frequency control elements to provide gain, phase, and frequency calibrations between the transmitter paths of the antenna array, respectively.

[0016] According to the example, the estimation may include estimating at least one transmitter parameter using at least one Wiener filter coefficient for the transmitter path of the antenna array.

[0017] According to the example, deriving at least one Wiener filter coefficient for an antenna array may include: deriving at least one Wiener filter coefficient for the receiver path of the antenna array based on at least one power delay domain feature associated with the receiver path of the antenna array and the signal-to-noise ratio associated with the receiver path of the antenna array.

[0018] The antenna array may include separate receiver paths for the antenna array. Each receiver path of the antenna array may include gain control elements, phase control elements, and / or frequency control elements to provide gain, phase, and / or frequency calibration between the receiver paths of the antenna array, respectively.

[0019] According to the example, the estimation may include estimating at least one receiver parameter using at least one Wiener filter coefficient for the receiver path of the antenna array.

[0020] According to the example, at least one power delay domain feature may be based on at least one transmit and / or receive calibration signal, and / or at least one coupling matrix of the antenna array, at least one end-to-end coupling response measurement of the antenna array, and / or at least one antenna coupling parameter.

[0021] The transmit or receive calibration signal can be a known or predetermined signal between the transmitter and receiver. Alternatively, the transmit or receive calibration signal can be a silent period between the transmitter and receiver, i.e., when no signal is being transmitted.

[0022] The coupling matrix of an antenna array can be defined as a square matrix describing the coupling between different elements in the system. The elements of the coupling matrix are typically coupling coefficients, which quantify the strength of the coupling between two elements in the system. The coupling matrix is ​​used to characterize the mutual coupling between antenna elements at the port level. Mutual coupling is the electromagnetic interaction between antenna elements in an array. The coupling matrix helps in understanding how each antenna element interacts with other antenna elements and can be beneficial for the design and optimization of antenna arrays.

[0023] The end-to-end coupling response of an antenna array describes the energy absorbed by one antenna element when another nearby antenna element in the array is operating. End-to-end coupling in an antenna array can include the effects of hardware defects in the transmitter path (i.e., leakage or hardware degradation due to aging), hardware defects in the receiver path, and / or mutual coupling effects. Mutual coupling can be defined as the electromagnetic interaction between antenna elements in an antenna array. The current formed in each antenna element of the antenna array depends on its own excitation and also on contributions from adjacent antenna elements. Mutual coupling is inversely proportional to the spacing between different antenna elements in the antenna array. Mutual coupling is generally undesirable because energy that should or can be radiated is absorbed by nearby antenna array elements. Therefore, mutual coupling degrades the efficiency and performance of antenna array elements when transmitting or receiving signals.

[0024] Antenna coupling parameters can be parameters of the coupling matrix of the antenna array.

[0025] The effects of mutual coupling can be observed or simulated by changing the spacing between antenna elements in the array. Any change in the spacing between elements alters the mutual impedance between the antenna elements.

[0026] According to the example, at least one power delay domain feature may include at least one of hardware power delay domain features, at least one of near-field power delay domain features, and / or at least one of far-field power delay domain features.

[0027] Hardware power delay domain characteristics can be the average power of the received signal in terms of the delay relative to the first arrival path in multipath transmission, created by transceiver hardware impairments present in the radio transceiver system.

[0028] The near-field power delay domain characteristic can be the average power of the received signal in terms of the delay relative to the first path of arrival in multipath transmission, created by the coupling behavior of the near-field environment created by the transceiver antenna and the field-installed radio transceiver system.

[0029] The far-field power delay domain characteristic can be the average power of the received signal in terms of the delay relative to the first arrival path in multipath transmission, created by an external calibration device (i.e., a remote antenna array in a distributed multi-antenna system) or by electromagnetic emission.

[0030] According to the example, at least one hardware power delay domain feature and / or at least one near-field power delay domain feature may be based on at least one coupling matrix of the antenna array.

[0031] According to the example, at least one hardware power delay domain feature and / or at least one near-field power delay domain feature and / or at least one far-field power delay domain feature may be based on at least one transmit and / or receive calibration signal.

[0032] According to the example, at least one basic function can be assigned to at least one power delay domain feature.

[0033] Fundamental functions can be elements of a specific basis for a function space. Each function in a function space can be represented as a linear combination of fundamental functions, just as each vector in a vector space can be represented as a linear combination of basis vectors. In the realm of approximation theory, fundamental functions can be used in interpolation, and mixtures of fundamental functions can provide interpolation functions. In this application, exponential and sinc fundamental functions have been discussed, but any other fundamental function, i.e., uniformly distributed fundamental functions, can also be used.

[0034] According to the example, at least one exponential fundamental function can be assigned to at least one hardware domain feature and / or at least one near-field power delay domain feature.

[0035] According to the example, at least one sinc fundamental function can be assigned to at least one far-field power delay domain feature and / or at least one near-field power delay domain feature.

[0036] According to the example, at least one Wiener filter coefficient for the antenna array, the transmitter path of the antenna array, and / or the receiver path of the antenna array can be scaled such that the gain of at least one Wiener filter coefficient for the antenna array, the transmitter path of the antenna array, and / or the receiver path of the antenna array is unitary.

[0037] According to the example, at least one transmitter and / or receiver parameter may be at least one of transmitter and / or receiver phase parameter, transmitter and / or receiver amplitude parameter, and / or transmitter and / or receiver frequency parameter.

[0038] Based on the example, the estimation can be based on the LS estimation method, ML estimation method, EM estimation method, MMSE estimation method, or LMMSE estimation method.

[0039] In the fields of statistics and signal processing, the least squares (LS) estimation method is an estimation method that minimizes the sum of squares of errors.

[0040] In the fields of statistics and signal processing, the maximum likelihood (ML) estimation method is an estimation method that maximizes the likelihood function of the parameter to be estimated.

[0041] In the fields of statistics and signal processing, the expectation-maximization (EM) estimation method is an iterative estimation method that finds the ML or maximum a posteriori (MAP) estimates of the parameter to be estimated.

[0042] In statistics and signal processing, the minimum mean square error (MMSE) estimation method is an estimation method that minimizes the mean square error (MSE).

[0043] In statistics and signal processing, the linear MMSELMMSE estimation method is a linear approximation of the MMSE estimation method.

[0044] Based on the example, the estimation can be based on at least one iterative estimation algorithm.

[0045] An iterative estimation algorithm can be a mathematical process that uses initial values ​​to generate a sequence of improved approximate solutions to a class of problems, where the nth approximation is derived from previous approximations. A specific implementation of an iterative method (e.g., gradient descent, coordinate descent, hill climbing, Newton's method, or a quasi-Newton method like Broyden–Fletcher–Goldfarb–Shanno (BFGS)) with a termination criterion is an algorithm for that iterative method. An iterative method is said to be convergent if the corresponding sequence converges for a given initial approximation.

[0046] According to one aspect of the invention, an apparatus for calibrating an antenna array is provided. The apparatus includes a processor and a memory. The memory contains instructions executable by the processor, thereby enabling the apparatus to derive at least one Wiener filter coefficient for the antenna array based on at least one power delay domain characteristic associated with the antenna array and at least one signal-to-noise ratio associated with the antenna array. The apparatus is also operable to estimate at least one transmitter parameter and / or at least one receiver parameter using the at least one Wiener filter coefficient for the antenna array. The apparatus is further operable to calibrate the antenna array based on at least one transmitter parameter and / or at least one receiver parameter.

[0047] Based on the example, the device can be operated to perform any of the proposed examples.

[0048] According to the example, the device may include an antenna array.

[0049] According to one aspect of the invention, an antenna array that has been calibrated according to any of the proposed aspects or examples is proposed.

[0050] According to one aspect of the invention, a computer program product is provided. The computer program product includes instructions that, when executed on at least one processor, cause the at least one processor to perform the method according to any of the proposed aspects or examples.

[0051] According to one aspect of the invention, a computer program is provided. The computer program includes instructions that, when executed on at least one processor, cause the at least one processor to perform the method described according to any of the proposed aspects or examples.

[0052] According to one aspect of the invention, a tangible, non-transitory computer-readable medium comprising instructions is provided. The tangible, non-transitory computer-readable medium comprises instructions that, when executed on at least one processor, cause at least one processor to perform the method described according to any of the proposed aspects or examples. Attached Figure Description

[0053] The inventive concept will now be described by way of example with reference to the accompanying drawings, wherein:

[0054] Figure 1 This is a flowchart of an example of the MCAC algorithm for calibrating mutually coupled antennas using Wiener filters.

[0055] Figure 2 This is a block diagram illustrating a simplified example of delay-domain signal analysis and feature extraction.

[0056] Figure 3 This is a block diagram illustrating an example of feature set extraction and classification based on simplified delay-domain signal analysis (DSA).

[0057] Figure 4 This is a block diagram illustrating an example of calculating Wiener filter coefficients.

[0058] Figure 5 This is a flowchart illustrating an example of a method for calibrating an antenna array, performed by a device for calibrating an antenna array.

[0059] Figure 6 This is a flowchart illustrating an example of a method for calibrating an antenna array, performed by a device for calibrating an antenna array.

[0060] Figure 7 This is a block diagram of an apparatus for calibrating an antenna array, which performs a method for calibrating an antenna array.

[0061] Figure 8 This is a block diagram of an embodiment of an apparatus for calibrating an antenna array, which performs a method for calibrating an antenna array.

[0062] Figure 9 It is a block diagram of examples of computer program products, non-transitory computer-readable media, and computer programs. Detailed Implementation

[0063] The inventive concept will now be described more fully below with reference to the accompanying drawings, which illustrate certain embodiments of the inventive concept. However, the inventive concept can be implemented in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided by way of example so that this disclosure will be thorough and complete, and will fully convey the scope of the inventive concept to those skilled in the art. The same reference numerals refer to the same elements throughout the specification. Any step or feature indicated by dashed lines should be considered optional.

[0064] Note that the descriptions given herein may refer to 3GPP cellular communication systems, and therefore 3GPP terminology or similar terms are used. However, the concepts disclosed herein are not limited to 3GPP systems.

[0065] Note that for the purposes of the description given herein, the terms (one or more) Wiener filters, (one or more) Wiener filter coefficients, (one or more) sets of Wiener filters, and (one or more) sets of Wiener filter coefficients may be used interchangeably and should not be used to unduly limit the scope of this application.

[0066] Note that, for the purposes of the description herein, an antenna array may include one or more antenna elements AE. An antenna array may also be referred to as a transceiver antenna array to highlight its ability to both receive and transmit signals. An antenna array may include a separate transmitter branch (also referred to herein as a transmitter path) and a separate receiver branch (also referred to herein as a receiver path) for each of the one or more antenna elements. Each of the transmitter and receiver branches may include gain control elements, phase control elements, and / or frequency control elements to provide gain, phase, and / or frequency calibration, respectively, between the transmitter and receiver branches.

[0067] The MCAC method utilizes the natural mutual coupling between antenna elements to perform antenna array calibration. In the MCAC method, the calibration signal is transmitted from the transmitter path of the transmitter antenna and received at the receiver path of the receiver antenna. The received calibration signal can be represented by the end-to-end transfer function as shown below. .

[0068]

[0069] Where T and R are the transfer functions of the transmitter and receiver paths of the antenna array, respectively. S is the mutual coupling between the transmitter and receiver antenna paths, which is assumed to be known in advance and can be measured, for example, in a laboratory environment or tested after the antenna array is installed at a physical site. N is additive white Gaussian noise.

[0070] In other words, the end-to-end transfer function Y is obtained by sending a calibration signal from the transmitter path of the antenna array and receiving a calibration signal at the receiver path of the antenna array. The end-to-end transfer function is affected by the hardware characteristics of the antenna array and the channel conditions between the transmitter path and the receiver path of the antenna array.

[0071] Based on the end-to-end transfer function Y measurement, the transmitter path T and receiver path R transfer functions can be estimated from the above equation. For example, iterative maximum likelihood (ML) estimation techniques, iterative expectation-maximization (EM) estimation techniques, or iterative least squares (LS) estimation techniques can be used for this estimation.

[0072] This application proposes a calibration method and apparatus for designing Wiener filter coefficients for transmitter and receiver antenna paths, and then using the designed Wiener filter coefficients along with existing estimation methods to improve the performance of antenna array calibration. Thus, the following improvements can be achieved:

[0073] • Reduce the performance impact of rapid changes in far-field interference levels and near-field environmental conditions, which affect the quality of the received MCAC calibration signal and thus the antenna calibration performance.

[0074] • Reduce the performance impact of slow degradation over time from field hardware characteristics (hardware leakage, oscillator characteristic changes, etc.).

[0075] • Compared to existing calibration methods, it reduces complexity and converges faster compared to existing iterative calibration methods. Therefore, the disclosed calibration method can be executed by an efficient device with minimal storage requirements for low-cost execution.

[0076] Figure 1 This is a flowchart of an example of the MCAC algorithm for calibrating mutually coupled antennas using Wiener filters.

[0077] An example of a mutually coupled antenna calibration algorithm based on an iterative parameter estimation method can begin with the initial stage of the iterative parameter estimation loop. In the initial stage of the iterative parameter estimation loop, a new instance of the periodic antenna calibration process begins.

[0078] During the MCAC process initialization phase, the parameters to be estimated can be identified. For example, these parameters could be the optimal amplitude, phase, and / or frequency of each antenna element in the antenna array. Furthermore, during the MCAC process initialization phase, other input parameters can be defined and initialized with values, such as the initial range of each parameter to be estimated and the number K of signal delay domain features to be selected during the Tx / Rx Wiener filter design phase.

[0079] In the MCAC end-to-end transfer function sequence extraction in the Rx stage, the received end-to-end transfer function estimate is sent to the Tx / Rx Wiener filter design stage.

[0080] During the Tx / Rx Wiener filter design phase, Wiener filters are used to obtain one or more transmitter paths and one or more receiver paths for the antenna elements of the antenna array. Figure 2 , Figure 3 and Figure 4 Examples of this stage are detailed in the relevant descriptions. The Tx / Rx Wiener filter design stage takes the number of selected signal delay domain features K, the end-to-end transfer function, and the scattering matrix (S matrix) of the receiving antenna array as input. The Tx / Rx Wiener filter design stage outputs a transmitter Wiener filter and a receiver Wiener filter.

[0081] In the Tx and Rx parameter estimation stages, the transmitter and receiver Wiener filters are applied for estimation, which can be accomplished, for example, using existing LS, EM, or ML methods. Applying the Tx / Rx Wiener filter to the Tx / Rx estimation method results in MMSE or LMMSE estimators for the transmitter and receiver parameters.

[0082] During the Tx and Rx convergence error calculation phase, the estimated errors of the transmitter and receiver parameters are obtained. If the estimated error is below a threshold, the transmitter and receiver parameters are used to calibrate the antenna array during the antenna array calibration phase. If the estimated error is not below the threshold, new estimates of the transmitter and receiver parameters are performed based on the current estimates during the Wiener filter application phases for Tx and Rx parameter estimation. The threshold can be predetermined or determined during the loop.

[0083] Figure 2 This is a block diagram illustrating a simplified example of delay-domain signal analysis and feature extraction.

[0084] Signals in the frequency domain The signal is transformed into a time-domain signal using the inverse discrete Fourier transform (DFT). .

[0085] Then, based on the signal in the time domain Calculate the signal power in the delay domain .

[0086] For delay domain signal power The Teager-Keser (TK) energy operator is applied to reveal the signal power in the delay domain. Any signal delay domain characteristics present in the signal.

[0087] The Teager-Kaiser energy operator estimates the energy of a signal. The energy estimate is derived from the signal's instantaneous amplitude and instantaneous frequency. Therefore, the TK operator can be used to detect instantaneous changes in amplitude or frequency in a given signal.

[0088] Signal delay domain features are defined as identifiable artifacts present in the power delay domain signal and are represented by tuples (relative power, delay value).

[0089] A predefined power threshold is applied to the signal delay domain features to select K signal delay domain features, which can be based on the relative power of each power delay domain feature. The choice of the number of signal delay domain features K takes into account the reduction in complexity of the antenna array calibration algorithm. This reduction in complexity can also manifest as faster convergence of the iterative calibration algorithm. The number of signal delay domain features K is a parameter that can be tuned to optimize the trade-off between the complexity and accuracy of the signal representation in the power delay domain.

[0090] K signal delay domain features constitute the signal delay domain feature set .

[0091] Signal delay domain feature set The kth entry Through tuples To express.

[0092] The use of a simplified delay-domain signal analysis method is illustrated in the figures and related descriptions below.

[0093] Figure 3 This is a block diagram illustrating an example of feature set extraction and classification based on simplified delay-domain signal analysis (DSA).

[0094] The following example illustrates the computation of feature set extraction and classification based on simplified delay-domain signal analysis (DSA).

[0095] The scattering matrix (S matrix) is processed by the first DSA box.

[0096] The output of the first DSA frame is a feature set with k signal delay domain features. ,in, These characteristics represent hardware delay domain features, which are part of the hardware of the antenna array and the hardware of the device that includes the antenna array. The S-matrix (also known as the mutual coupling matrix) is considered to be known through measurements, estimations, or previously obtained knowledge of the coupling parameters performed on the hardware of the antenna array or the hardware of the device that includes the antenna array.

[0097] End-to-end transfer function Processed by the second DSA frame. The output of the second DSA frame is a feature set with K signal delay domain features. These characteristics represent hardware latency domain characteristics and over-the-air (OTA) latency domain characteristics. OTA latency domain characteristics can be further divided into near-field latency domain characteristics and / or far-field latency domain characteristics.

[0098] End-to-end transfer function It is considered known through measurement, estimation, or prior acquisition as part of the antenna calibration process.

[0099] Feature set and It is used as input to the feature classification process. The feature classification process will take the feature set as input. and Each feature is categorized into hardware delay domain features, near-field delay domain features, or far-field delay domain features. (This is not a feature set.) Any feature of a portion is identified as an OTA delay domain feature and can be classified as a near-field delay domain feature or a far-field delay domain feature.

[0100] As an example, classification can be achieved by comparing two feature sets. and Each signal delay domain feature is used for execution. Only the feature set... All signal delay domain features can be classified as hardware delay domain features. These belong only to the feature set. All signal delay domain features can be classified as near-field delay domain features or far-field delay domain features. A signal delay domain feature can be classified as a near-field delay domain feature if its relative power is higher than a predetermined power threshold and its relative delay value is lower than a predetermined air delay threshold. Similarly, a delay domain feature can be classified as a far-field delay domain feature if its relative power is higher than a predetermined power threshold and its relative delay value is higher than a predetermined air delay threshold.

[0101] In feature set and When signal delay domain features overlap, a threshold can be used for classification.

[0102] As an example, if the relative delay value of a delay domain feature is lower than a predetermined hardware delay threshold and if the relative power value of a delay domain feature is higher than a predetermined power threshold, then the signal delay domain feature can be classified as a hardware delay domain feature. The hardware delay threshold and / or power threshold may be related to a set of S-parameters. If the relative power of a delay domain feature is higher than a predetermined power threshold and if the relative delay value of a delay domain feature is lower than a predetermined air delay threshold and higher than a predetermined hardware delay threshold, then the delay domain feature can be classified as a near-field delay domain feature. If the relative power of a delay domain feature is higher than a predetermined power threshold and if the relative delay value of a delay domain feature is higher than a predetermined air delay threshold and higher than a predetermined hardware delay threshold, then the delay domain feature can be classified as a far-field delay domain feature.

[0103] If the relative power of the delay domain feature is below a predetermined power threshold, the delay domain feature can be classified as noise and cannot be used to estimate the Wiener filter coefficients.

[0104] After feature classification, each signal delay domain feature will be a hardware delay domain feature, a near-field delay domain feature, or a far-field delay domain feature, and will be referred to as a classified signal delay domain feature.

[0105] In the basic function association, each classified signal delay domain feature is associated with the corresponding basic function.

[0106] As an example, each hardware delay domain feature is associated with an exponential function in the frequency domain. Each near-field delay domain feature is associated with a sinc function in the frequency domain. Each far-field delay domain feature is associated with a sinc function in the frequency domain. Other fundamental functions can be used to leverage the different properties and characteristics of the various fundamental functions.

[0107] After performing feature classification and basic function association, the feature set is obtained. Feature set It is a set consisting of delay-domain features of each classified signal, each with a corresponding associated fundamental function. Feature set It was used in Wiener filter design.

[0108] As discussed above, it is applied to the S-matrix and the end-to-end transfer function. Both methods of DSA can reduce the computational complexity in feature extraction and classification processes. Furthermore, they are applied to the S-matrix and end-to-end transfer function. Both methods' DSA approaches can facilitate feature set construction and appropriate association with basic functions, and can also promote the convergence of iterative parameter estimation methods.

[0109] In the S-matrix and end-to-end transfer function After applying the DSA method, K signal delay domain features were identified. K1 signal delay domain features were identified as hardware delay domain features, and K2 signal delay domain features were identified as air delay domain features. In other words, the K signal delay domain features are the total number of selected signal delay domain features. K1 signal delay domain features (where K1 signal delay domain features are identified as hardware delay domain features) are a subset of the K signal delay domain features. K2 signal delay domain features (where K2 signal delay domain features are identified as air delay domain features) are a subset of the K signal delay domain features. The requirement is... .

[0110] Thus, the feature set after classification Each entry will have the following three associated values:

[0111]

[0112] In this case, based on the previous example, When dealing with near-field and far-field delay domain characteristics, it can be a sinc function, or when dealing with hardware delay domain characteristics, it can be an exponential function, and it is used to calculate the frequency autocorrelation function.

[0113] Figure 4 This is a block diagram illustrating an example of calculating Wiener filter coefficients.

[0114] The following example illustrates the calculation of Wiener filter coefficients.

[0115] Classified feature set Instantaneous SNR value of the transmitter path of the antenna array Instantaneous SNR value of the receiver path of the antenna array This can be used for the design of Wiener filter coefficients. Furthermore, the Wiener filter length... It can be based on the classified feature set Defined by the maximum latency value.

[0116] The frequency autocorrelation matrix of the transmitter path of the antenna array is represented as: .

[0117] The frequency autocorrelation matrix of the receiver path of the antenna array is represented as follows: .

[0118] and Known methods can be used to classify feature sets. Instantaneous SNR value of the transmitter path of the antenna array Instantaneous SNR value of the receiver path of the antenna array and Wiener filter length We use our knowledge to calculate.

[0119] The Wiener filter coefficients can be calculated using the following equation:

[0120]

[0121]

[0122] in, It is the Wiener filter for the transmitter path of the antenna array, and It is the Wiener filter for the receiver path of the antenna array.

[0123] and It can be used within iterative multi-parameter estimation methods (e.g., iterative maximum likelihood or iterative least squares) or any other multi-parameter estimation method.

[0124] Parameter convergence can be achieved through scaling. and One or more coefficients of the sum are used to make the total filter gain single and thus avoid non-convergence in iterative multi-parameter estimation. Furthermore, convergence can be facilitated by scaling the sum under the assumption that another parameter is known. and The filter is applied independently to facilitate the corresponding parameter estimation.

[0125] Compared with existing per-frequency antenna parameter estimation methods, it can achieve The number of iterations is reduced, thereby reducing the complexity of the calibration process.

[0126] As mentioned above, in Figure 1An example of an MCAC estimation calibration event is shown. The Wiener filter coefficients for the transmitter path and receiver path of the antenna array remain unchanged during the multi-parameter estimation iterations. For each new instance of MCAC, the Wiener filter is redesigned based on the received MCAC training sequence.

[0127] Figure 5 It is a device used for calibrating the antenna array (not shown; see, for example) Figure 7 and Figure 8 (and related descriptions) A flowchart of an example of a calibration method 1000 for an antenna array performed.

[0128] According to the example, the method 1000 for calibrating an antenna array includes: deriving 1100 at least one Wiener filter coefficient for the antenna array based on at least one power delay domain characteristic associated with the antenna array and at least one signal-to-noise ratio associated with the antenna array.

[0129] According to the example, the method 1000 for calibrating an antenna array includes: estimating 1200 at least one transmit parameter and / or at least one receiver parameter using at least one Wiener filter coefficient for the antenna array.

[0130] According to the example, the method 1000 for calibrating an antenna array includes calibrating the antenna array 1300 based on at least one transmitter parameter and / or at least one receiver parameter.

[0131] Figure 6 It is a device used for calibrating the antenna array (not shown; see, for example) Figure 7 and Figure 8 (and related descriptions) A flowchart illustrating an example embodiment of a method 1000 for calibrating an antenna array, performed.

[0132] According to an embodiment, deriving at least one Wiener filter coefficient for the antenna array 1100 based on at least one power delay domain feature associated with the antenna array and at least one signal-to-noise ratio associated with the antenna array may alternatively or additionally include deriving at least one Wiener filter coefficient for the transmitter path of the antenna array 1110 based on at least one power delay domain feature associated with the transmitter path of the antenna array and the signal-to-noise ratio associated with the transmitter path of the antenna array.

[0133] According to an embodiment, deriving at least one Wiener filter coefficient for the antenna array 1100 based on at least one power delay domain feature associated with the antenna array and at least one signal-to-noise ratio associated with the antenna array may alternatively or additionally include deriving at least one Wiener filter coefficient for the receiver path of the antenna array 1120 based on at least one power delay domain feature associated with the receiver path of the antenna array and the signal-to-noise ratio associated with the receiver path of the antenna array.

[0134] According to an embodiment, deriving at least one Wiener filter coefficient for an antenna array based on at least one power delay domain feature associated with the antenna array and at least one signal-to-noise ratio associated with the antenna array may additionally include: at least one power delay domain feature being based on: at least one transmit and / or receive calibration signal, at least one coupling matrix of the antenna array, at least one end-to-end coupling response measurement of the antenna array, and / or at least one antenna coupling parameter.

[0135] According to an embodiment, deriving at least one Wiener filter coefficient for the antenna array based on at least one power delay domain feature associated with the antenna array and at least one signal-to-noise ratio associated with the antenna array may additionally include: at least one power delay domain feature including at least one of hardware power delay domain features, at least one of near-field power delay domain features, and / or at least one of far-field power delay domain features.

[0136] According to an embodiment, at least one power delay domain feature includes at least one hardware power delay domain feature, at least one near-field power delay domain feature, and / or at least one far-field power delay domain feature, which may additionally include: at least one hardware domain feature and / or at least one near-field power delay domain feature is based on at least one coupling matrix of the antenna array.

[0137] According to an embodiment, at least one power delay domain feature includes at least one hardware power delay domain feature, at least one near-field power delay domain feature, and / or at least one far-field power delay domain feature, which may additionally include: at least one hardware domain feature and / or at least one near-field power delay domain feature and / or at least one far-field power delay domain feature based on at least one transmitter path and / or a receiver path of an antenna array calibration signal.

[0138] According to an embodiment, deriving at least one Wiener filter coefficient for the antenna array based on at least one power delay domain feature associated with the antenna array and at least one signal-to-noise ratio associated with the antenna array may additionally include: at least one fundamental function being assigned to at least one power delay domain feature.

[0139] According to an embodiment, assigning at least one fundamental function to at least one power delay domain feature may alternatively or additionally include assigning at least one exponential fundamental function to at least one hardware domain feature and / or at least one near-field power delay domain feature.

[0140] According to an embodiment, assigning at least one fundamental function to at least one power delay domain feature may alternatively or additionally include: assigning at least one sinc fundamental function to at least one far-field power delay domain feature and / or at least one near-field power delay domain feature.

[0141] According to an embodiment, deriving at least one Wiener filter coefficient for the antenna array based on at least one power delay domain characteristic associated with the antenna array and at least one signal-to-noise ratio associated with the antenna array may additionally include: scaling at least one Wiener filter coefficient for any one of the antenna array, and / or the transmitter path of the antenna array, and / or the receiver path of the antenna array, such that the gain of at least one Wiener filter coefficient for any one of the antenna array, and / or the transmitter path of the antenna array, and / or the receiver path of the antenna array is single.

[0142] According to an embodiment, estimating at least one transmit parameter and / or at least one receiver parameter 1200 using at least one Wiener filter coefficient for the antenna array may alternatively or additionally include estimating at least one transmitter parameter 1210 using at least one Wiener filter coefficient for the transmitter path of the antenna array.

[0143] According to an embodiment, estimating at least one transmit parameter and / or at least one receiver parameter 1200 using at least one Wiener filter coefficient for the antenna array may alternatively or additionally include estimating at least one receiver parameter 1220 using at least one Wiener filter coefficient for the receiver path of the antenna array.

[0144] According to an embodiment, using at least one Wiener filter coefficient for the antenna array to estimate at least one transmit parameter and / or at least one receiver parameter may additionally include: LS-based estimation method, ML-based estimation method, EM-based estimation method, MMSE-based estimation method, or LMMSE-based estimation method.

[0145] According to an embodiment, estimating at least one transmit parameter and / or at least one receiver parameter using at least one Wiener filter coefficient for the antenna array may additionally include: based on at least one iterative estimation algorithm.

[0146] According to an embodiment, the method 1000 for calibrating an antenna array includes calibrating the 1300 antenna array based on at least one transmitter parameter and / or at least one receiver parameter.

[0147] Figure 7 This is a block diagram of an apparatus 2000 for calibrating an antenna array, which is a method 1000 for calibrating an antenna array.

[0148] According to the example, device 2000 includes processor 2100 and memory 2200.

[0149] According to the example, memory 2200 contains instructions executable by processor 2100, thereby enabling device 2000 to derive Wiener filter coefficients for antenna arrays based on at least one power delay domain characteristic associated with the antenna array and at least one signal-to-noise ratio associated with the antenna array.

[0150] According to the example, memory 2200 contains instructions executable by processor 2100, thereby enabling device 2000 to estimate at least one transmitter parameter and / or at least one receiver parameter using at least one Wiener filter coefficient for the antenna array.

[0151] According to the example, memory 2200 contains instructions executable by processor 2100, thereby enabling device 2000 to operate to calibrate an antenna array based on at least one transmitter parameter and / or at least one receiver parameter.

[0152] According to an embodiment, the device 2000 is operable to perform any embodiment of method 1000.

[0153] Figure 8 This is a block diagram of an embodiment of an apparatus 2000 for calibrating an antenna array, which performs a method 1000 for calibrating an antenna array.

[0154] The apparatus 2000 for calibrating the antenna array may alternatively or additionally include a derivation unit 2300 for deriving at least one Wiener filter coefficient for the antenna array based on at least one power delay domain characteristic associated with the antenna array and at least one signal-to-noise ratio associated with the antenna array.

[0155] The device 2000 for calibrating the antenna array may alternatively or additionally include an estimation unit 2400 for estimating at least one transmit parameter and / or at least one receiver parameter using at least one Wiener filter coefficient for the antenna array.

[0156] The apparatus 2000 for calibrating the antenna array may alternatively or additionally include a calibration unit 2500 for calibrating the 1300 antenna array based on at least one transmitter parameter and / or at least one receiver parameter.

[0157] Preferably, device 2000 is a radio access node in a cellular communication network (e.g., a base station in a 3GPP 5G NR network). However, device 2000 may alternatively be, for example, an access point in a local area network (e.g., an access point in a WiFi network), a wireless communication device (e.g., a UE in a 3GPP 5G NR network), a beamforming transceiver, etc. Device 2000 may perform beamforming via antenna array 2600. This beamforming may be, for example, analog beamforming, which is performed by controlling the gain and phase for each antenna branch via corresponding gain and phase control elements. However, it should be understood that in some other embodiments, device 2000 may perform, for example, hybrid beamforming, i.e., beamforming performed partly in the digital domain and partly in the analog domain, or may perform digital beamforming (i.e., beamforming performed entirely in the digital domain).

[0158] The apparatus 2000 for calibrating the antenna array 2600 may alternatively or additionally include a derivation unit 2310 for deriving at least one Wiener filter coefficient for the transmitter path of the antenna array based on at least one power delay domain characteristic associated with the transmitter path of the antenna array and the signal-to-noise ratio associated with the transmitter path of the antenna array. Further, the derivation unit 2300 may include the derivation unit 2310.

[0159] The apparatus 2000 for calibrating the antenna array may alternatively or additionally include a derivation unit 2320 for deriving at least one Wiener filter coefficient for the receiver path of the antenna array based on at least one power delay domain characteristic associated with the receiver path of the antenna array and the signal-to-noise ratio associated with the receiver path of the antenna array. Further, the derivation unit 2300 may include the derivation unit 2320.

[0160] The apparatus 2000 for calibrating an antenna array may alternatively or additionally include an estimation unit 2410 for estimating at least one transmitter parameter using at least one Wiener filter coefficient for the transmitter path of the antenna array. Further, the estimation unit 2400 may include the estimation unit 2410.

[0161] The apparatus 2000 for calibrating an antenna array may alternatively or additionally include an estimation unit 2420 for estimating at least one receiver parameter using at least one Wiener filter coefficient for the receiver path of the antenna array. Further, the estimation unit 2400 may include the estimation unit 2420.

[0162] According to an embodiment, the apparatus 2000 for calibrating the antenna array 2600 may additionally include the antenna array 2600.

[0163] The antenna array 2600 can be a phased antenna array module (PAAM), an advanced antenna system (AAS), or an antenna system.

[0164] Antenna array 2600 can be implemented as one or more radio ASICs, and processor 2100 is a baseband processor, which is implemented as, for example, one or more processors, such as, for example, one or more CPUs, one or more baseband ASICs, one or more field-programmable gate arrays (FPGAs), or any combination thereof.

[0165] Antenna array 2600 may include a number of antenna elements (AEs).

[0166] Antenna array 2600 may include a separate transmit branch (also referred to herein as a transmit path) and a separate receive branch (also referred to herein as a receive path) for each AE.

[0167] Each transmit branch may include gain control elements, phase control elements, and / or frequency control elements controlled by processor 2100 to provide gain and phase calibration between transmit branches.

[0168] The gain control element, phase control element, and / or frequency control element of the transmit branch controlled by the processor 2100 can provide simulated beamforming for the signal transmitted by the device 2000 for calibration of the antenna array 2600.

[0169] Note that the simulation calibration and simulation beamforming are shown as examples in this document; however, this disclosure is not limited thereto.

[0170] Each receiver branch may include gain control elements, phase control elements, and / or frequency control elements controlled by processor 2100 to provide gain and phase calibration between receiver branches.

[0171] The gain control element, phase control element and / or frequency control element of the receiving branch controlled by the processor 2100 can provide analog beamforming for the signal received by the device 2000.

[0172] The device 2000 may additionally include a self-calibration unit 2700.

[0173] The self-calibration unit 2700 can be implemented in hardware or a combination of hardware and software. At least some of the functions of the self-calibration unit 2700 described herein can be implemented in software executed by one or more processors (e.g., one or more CPUs, one or more ASICs, one or more FPGAs, etc., or any combination thereof).

[0174] The self-calibration unit 2700 may include a controller unit 2710. The controller unit 2710 typically operates to control the self-calibration subsystem 2700 and the antenna array 2600 to perform the calibration process as described herein.

[0175] Figure 9 This is a block diagram illustrating examples of a computer program product 3100, a non-transitory computer-readable medium 3200, and a computer program 3300.

[0176] According to the example, computer program product 3100 includes instructions that, when executed on at least one processor, cause at least one processor to execute computer program 3300.

[0177] Computer program product 3100 may include non-transitory computer-readable medium 3200, such as, for example, a Universal Serial Bus (USB) memory, a plug-in card, an embedded driver, or a read-only memory.

[0178] According to the example, the non-transitory computer-readable medium 3200 may include instructions that, when executed on at least one processor, cause at least one processor to execute a computer program 3300. The non-transitory computer-readable medium 3200 may include the computer program 3300. In other words, the computer program 3300 may be stored on the non-transitory computer-readable medium 3200.

[0179] According to the example, computer program 3300 includes instructions that, when executed on at least one processor 3100, cause at least one processor to perform method 1000.

Claims

1. A method (1000) for calibrating an antenna array, the method comprising: Based on the following, derive at least one Wiener filter coefficient (1100) for the antenna array: At least one power delay domain feature associated with the antenna array, and At least one signal-to-noise ratio associated with the antenna array; The at least one Wiener filter coefficient used for the antenna array is used to estimate (1200) at least one transmitter parameter and / or at least one receiver parameter; The antenna array is calibrated (1300) based on the at least one transmitter parameter and / or the at least one receiver parameter.

2. The method according to claim 1, wherein, Derivation of at least one Wiener filter coefficient (1100) for the antenna array includes deriving at least one Wiener filter coefficient (1110) for the transmitter path of the antenna array based on the following: At least one power delay domain feature associated with the transmitter path of the antenna array, and The signal-to-noise ratio associated with the transmitter path of the antenna array.

3. The method according to claim 2, wherein, The estimation (1200) includes estimating (1210) at least one transmitter parameter using at least one Wiener filter coefficient for the transmitter path of the antenna array.

4. The method according to any one of the preceding claims, wherein, Derivation (1100) of at least one Wiener filter coefficient for the antenna array includes: deriving (1120) at least one Wiener filter coefficient for the receiver path of the antenna array based on the following: At least one power delay domain feature associated with the receiver path of the antenna array, and The signal-to-noise ratio associated with the receiver path of the antenna array.

5. The method according to claim 4, wherein, The estimation (1200) includes estimating (1220) at least one receiver parameter using at least one Wiener filter coefficient for the receiver path of the antenna array.

6. The method according to any one of the preceding claims, wherein, The at least one power delay domain feature is based on: At least one transmit and / or receive calibration signal, At least one coupling matrix of the antenna array, At least one antenna array end-to-end coupling response measurement, and / or At least one antenna coupling parameter.

7. The method according to any one of the preceding claims, wherein, The at least one power delay domain feature includes: At least one of the hardware power delay domain characteristics, At least one of the near-field power delay domain characteristics, and / or At least one of the far-field power delay domain characteristics.

8. The method according to claim 7, wherein, The at least one hardware domain feature and / or the at least one near-field power delay domain feature is based on at least one coupling matrix of the antenna array.

9. The method according to any one of claims 7 to 8, wherein, The at least one hardware domain feature and / or the at least one near-field power delay domain feature and / or the at least one far-field power delay domain feature are based on at least one transmitter path of the antenna array and / or the receiver path of the antenna array calibration signal.

10. The method according to any one of the preceding claims, wherein, At least one basic function is assigned to the at least one power delay domain feature.

11. The method according to claim 10, wherein, At least one exponential fundamental function is assigned to the at least one hardware domain feature and / or the at least one near-field power delay domain feature.

12. The method according to any one of claims 10 to 11, wherein, At least one sinc fundamental function is assigned to the at least one far-field power delay domain feature and / or the at least one near-field power delay domain feature.

13. The method according to any one of the preceding claims, wherein, The at least one Wiener filter coefficient for either the antenna array and / or the transmitter path and / or the receiver path of the antenna array is scaled such that the gain of at least one Wiener filter coefficient for either the antenna array and / or the transmitter path and / or the receiver path of the antenna array is single.

14. The method according to any one of the preceding claims, wherein, The at least one transmitter and / or receiver parameter is at least one of the following: Transmitter and / or receiver phase parameters, Transmitter and / or receiver amplitude parameters, and / or Transmitter and / or receiver frequency parameters.

15. The method according to any one of the preceding claims, wherein, The estimation is based on the LS estimation method, ML estimation method, EM estimation method, MMSE estimation method, or LMMSE estimation method.

16. The method according to any one of the preceding claims, wherein, The estimation is based on at least one iterative estimation algorithm.

17. An apparatus (2000) for calibrating an antenna array, comprising a processor (2100) and a memory (2200), the memory containing instructions executable by the processor, thereby enabling the apparatus to: Based on the following, derive at least one Wiener filter coefficient for the antenna array: At least one power delay domain feature associated with the antenna array, and The signal-to-noise ratio associated with the antenna array; At least one transmitter parameter and / or at least one receiver parameter are estimated using the at least one Wiener filter coefficient for the antenna array; The antenna array is calibrated based on the at least one transmitter parameter and / or the at least one receiver parameter.

18. The apparatus according to claim 17, wherein, The device (2000) is operable to perform the method according to any one of claims 2 to 16.

19. The apparatus according to claim 17, wherein, The device includes the antenna array (2600).

20. An antenna array (2600) calibrated by the method according to any one of claims 1 to 16, or calibrated by the apparatus according to claim 17 or 19.

21. A computer program (3300) comprising instructions that, when executed on at least one processor, cause the at least one processor to perform the method according to any one of claims 1 to 16.

22. A computer program product (3100) comprising instructions that, when executed on at least one processor, cause the at least one processor to perform the computer program (3300) according to claim 21.

23. A tangible, non-transitory computer-readable medium (3200) comprising instructions that, when executed on at least one processor, cause the at least one processor to execute the computer program (3300) according to claim 21.

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