Method and system for self-interference cancellation in full-duplex communication

Abstract

A method and system for self-interference cancellation in full-duplex communication is provided. The method includes generating a kernel matrix comprising basis functions representing self-interference produced by a transmit signal of a full-duplex communication system; performing principal component regression (PCR) based on the kernel matrix; selecting a subset of principal components based on a measure related to reconstruction error of a receive signal; estimating a coefficient vector using the subset of principal components to generate an estimate of the self-interference; and cancelling the self-interference from the receive signal based on the estimate of the self-interference.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Provisional Application No. 63 / 789,586, filed April 16, 2025, the disclosure of which is incorporated herein by reference in its entirety as if fully described. Technical Field

[0003] This disclosure generally relates to full-duplex wireless communication systems. More specifically, the subject matter disclosed herein relates to improvements in self-interference cancellation using principal component regression (PCR) techniques. Background Technology

[0004] Full-duplex wireless communication systems can transmit and receive simultaneously on the same frequency band, which improves spectral efficiency. An obstacle to deploying such systems is self-interference, which can occur when a strong transmitted signal leaks into the receiver chain and distorts a weaker, desired signal.

[0005] To address this issue, existing techniques have used large sets of basis functions to model self-interference and applied adaptive filtering or PCR to estimate the coefficients used for reconstruction and subtraction of interference from the received signal.

[0006] One problem with the above method is that using a large set of basis functions leads to high computational complexity and numerical instability when many functions in the function are related. These effects can reduce elimination accuracy and limit system performance. Summary of the Invention

[0007] To overcome the problems mentioned above, this paper describes methods and systems for enhanced PCR.

[0008] In one approach, principal components can be selected based on the reconstruction error of the received signal.

[0009] In another approach, components can be selected using a metric derived from the eigenvalues ​​that reflects the error in the coefficient estimation.

[0010] In another approach, the nuclear matrix can be scaled based on the correlation between the nuclear matrix and the received signal before PCR.

[0011] The method described above can improve upon previous methods by reducing computational complexity, minimizing multicollinearity, and stabilizing coefficient estimation. As a result, self-interference estimation can be more accurate, cancellation performance can be improved, and full-duplex communication can become more reliable and efficient.

[0012] In an embodiment, a method for self-interference cancellation in a full-duplex communication system includes: generating a kernel matrix comprising basis functions representing self-interference generated by a transmitted signal of the full-duplex communication system; performing PCR based on the kernel matrix; selecting a subset of principal components based on a metric related to the reconstruction error of the received signal; using the subset of principal components to estimate a coefficient vector to generate an estimate of the self-interference; and eliminating the self-interference from the received signal based on the estimate of the self-interference.

[0013] In an embodiment, a method for self-interference cancellation in a full-duplex communication system includes: generating a kernel matrix comprising basis functions representing self-interference generated by a transmitted signal of the full-duplex communication system; performing PCR on the kernel matrix; selecting a subset of principal components based on a metric calculated from eigenvalues, singular vectors, and the correlation between the kernel matrix and the received signal, the metric reflecting the error when estimating a coefficient vector from the subset of principal components; using the subset of principal components to estimate the coefficient vector to generate an estimate of the self-interference; and canceling the self-interference from the received signal based on the estimate of the self-interference.

[0014] In an embodiment, a method for self-interference cancellation in a full-duplex communication system includes: generating a kernel matrix comprising basis functions representing self-interference generated by a transmitted signal of the full-duplex communication system; scaling the kernel matrix based on the correlation between the basis functions and the received signal; performing PCR on the scaled kernel matrix; selecting a subset of principal components based on the eigenvalues ​​of the PCR; using the subset of principal components to estimate a coefficient vector to generate an estimate of the self-interference; and canceling the self-interference from a signal received at a receiver of the full-duplex communication system based on the estimate of the self-interference. Attached Figure Description

[0015] In the following sections, aspects of the subject matter disclosed herein will be described with reference to exemplary embodiments illustrated in the figures, wherein:

[0016] Figure 1 This is a block diagram of a system for self-interference cancellation in a full-duplex communication system according to an embodiment;

[0017] Figure 2 This is a diagram illustrating the components that contribute to self-interference in a full-duplex communication system.

[0018] Figure 3 This is a flowchart of a method for self-interference cancellation in a full-duplex communication system using PCR with reconstruction error metric, according to an embodiment.

[0019] Figure 4 This is a flowchart of a method for self-interference cancellation in a full-duplex communication system using PCR with an eigenvalue-based error metric, according to an embodiment.

[0020] Figure 5This is a flowchart of a method for self-interference cancellation in a full-duplex communication system using PCR with correlation-based scaling utilizing basis functions, according to an embodiment.

[0021] Figure 6 This is a graph illustrating the eigenvalue distribution of the R matrix according to an embodiment, showing all basis functions used for self-interference cancellation and a subset of selected basis functions;

[0022] Figure 7 This is a block diagram of an electronic device in a network environment according to an embodiment; and

[0023] Figure 8 A system including UEs and gNBs communicating with each other is shown. Detailed Implementation

[0024] In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of this disclosure. However, those skilled in the art will understand that the disclosed aspects can be practiced without these specific details. In other instances, well-known methods, processes, components, and circuits have not been described in detail so as not to obscure the subject matter of this disclosure.

[0025] Throughout this specification, references to “an embodiment” or “an embodiment” mean that a particular feature, structure, or characteristic described in connection with that embodiment may be included in at least one embodiment disclosed herein. Therefore, the phrases “in an embodiment,” “in an embodiment,” or “according to an embodiment” (or other phrases with similar meanings) appearing in various places throughout this specification may not necessarily refer to the same embodiment. Furthermore, particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In this regard, as used herein, the word “exemplary” means “serving as an example, instance, or illustration.” Any embodiment described herein as “exemplary” should not be construed as necessarily preferred or advantageous over other embodiments. Additionally, particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Additionally, depending on the context of the discussion herein, singular terms may include corresponding plural forms, and plural terms may include corresponding singular forms. Similarly, hyphenated terms (e.g., "two-dimensional", "pre-determined", "pixel-specific", etc.) may occasionally be used interchangeably with their corresponding unhyphenated versions (e.g., "two dimensional", "predetermined", "pixel specific", etc.), and uppercase entries (e.g., "Counter Clock", "Row Select", "PIXOUT", etc.) may be used interchangeably with their corresponding non-uppercase versions (e.g., "counter clock", "row select", "pixout", etc.). This occasional interchangeability should not be considered as inconsistency between them.

[0026] Furthermore, depending on the context of this discussion, singular terms may include their corresponding plural forms, and plural terms may include their corresponding singular forms. It should also be noted that the various figures shown and discussed herein (including component illustrations) are for illustrative purposes only and are not drawn to scale. For example, the dimensions of some elements may be magnified relative to others for clarity. Additionally, reference numerals are repeated in the figures where appropriate to indicate corresponding and / or similar elements.

[0027] The technical terms used herein are for the purpose of describing some exemplary embodiments only and are not intended to limit the claimed subject matter. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that, when used in this specification, the terms “comprising” and / or “including” specify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0028] It will be understood that when an element or layer is referred to as being on another element or layer, "connected to another element or layer," or "coupled to another element or layer," the element or layer may be directly on, directly connected to, or directly coupled to the other element or layer, or there may be intermediate elements or layers. Conversely, when an element is referred to as being "directly on another element or layer," "directly connected to another element or layer," or "directly coupled to another element or layer," there are no intermediate elements or layers. The same reference numerals always refer to the same elements. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0029] As used herein, the terms “first,” “second,” etc., serve as labels for the nouns preceding them and do not imply any kind of ordering (e.g., spatial, temporal, logical, etc.) unless explicitly defined as such. Furthermore, the same reference numerals may be used across two or more figures to refer to parts, components, blocks, circuits, units, or modules having the same or similar functions. However, this usage is merely for the purpose of reducing description and facilitating discussion; it does not imply that the construction or architectural details of such components or units are identical across all embodiments, or that such commonly referred-to parts / modules are the only way to implement some of the exemplary embodiments disclosed herein.

[0030] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this subject pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the context of the relevant field, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0031] As used herein, the term "module" means any combination of software, firmware, and / or hardware configured to provide the functionality described herein in conjunction with modules. For example, software may be embodied as a software package, code, and / or instruction set or instructions, and the term "hardware" as used in any implementation described herein may, for example, individually or in any combination, include assemblies, hardwired circuitry, programmable circuitry, state machine circuitry, and / or firmware storing instructions executed by programmable circuitry. Modules may be embodied collectively or individually as circuitry forming part of a larger system, such as, but not limited to, integrated circuits (ICs), system-on-a-chip (SoCs), assemblies, etc.

[0032] As used in this paper, "self-interference cancellation" refers to techniques used to estimate and subtract interference caused by the transmitter's own signal leakage into the receiver's path in a full-duplex communication system. Some examples of "self-interference cancellation" involve reconstructing the interference using kernel basis functions with coefficients estimated by least squares or recursive least squares, and applying PCR to improve estimation accuracy.

[0033] As used in this article, a "full-duplex communication system" refers to a wireless system in which transmission and reception occur simultaneously on the same frequency band. Some examples of "full-duplex communication systems" are base stations, mobile handheld terminals, and modem chips configured to support concurrent uplink and downlink communication.

[0034] As used in this paper, a “kernel matrix” refers to a matrix whose columns include basis functions that model the self-interference generated by the transmitted signal. Some examples of “kernel matrices” are matrices formed using polynomial basis functions, IQ imbalance terms, or parallel Hammerstein structures representing different distortion components of the transmitted signal.

[0035] As used in this paper, "basis function" refers to a mathematical function representing the self-interference component generated by the transmitted signal in a full-duplex communication system. Some examples of "basis functions" are polynomial terms of the transmitted signal, memory terms that capture a delayed version of the transmitted signal, and combinations thereof. As used in this paper, "PCA" refers to Principal Component Analysis, a statistical technique used to transform relevant basis functions into a set of orthogonal components ranked according to their contribution to variance. Some examples of "PCA" are selecting a reduced subset of components based on the reconstruction error of the received signal, or selecting components based on an error metric derived from eigenvalues.

[0036] As used in this paper, "principal component" refers to an orthogonal vector obtained by applying PCA to the kernel matrix, where each orthogonal vector represents a combination of basis functions of the transformation ranked by significance. Some examples of "principal components" are components reserved to minimize the reconstruction error of the received signal, or components selected based on a coefficient error metric.

[0037] PCR is a regression method that uses PCA to reduce input data to principal components, and then performs linear regression on these components.

[0038] As used in this paper, "measures related to the reconstruction error of the received signal" refers to a value that measures how accurately the self-interference estimate reproduces the portion of the received signal attributable to the interference. Some examples of "measures related to the reconstruction error of the received signal" are scores calculated from the eigenvalues ​​and projection coefficients of the kernel matrix, or rankings of principal components based on their contribution to minimizing the mean square error between the received signal and the interference estimate.

[0039] As used in this paper, “coefficient vector” refers to the set of estimated weights applied to the basis functions of the kernel matrix to generate a self-perturbed estimate. Some examples of “coefficient vectors” are vectors estimated using other adaptive algorithms that minimize the error, such as least squares, recursive least squares, or least squares.

[0040] As used in this article, “mean squared error” refers to a statistical measure of the average squared difference between an estimated value and the true or observed value. Some examples of “mean squared error” are the error between the received signal and the self-interference estimate, or the error between the estimated coefficient vector and the true coefficient vector.

[0041] As used in this article, "eigenvalue decomposition" refers to a matrix factorization technique that represents a matrix as the product of three matrices: an eigenvector matrix, a diagonal matrix of eigenvalues, and the conjugate transpose of another eigenvector matrix. Some examples of "eigenvalue decomposition" are decomposing the covariance matrix of a kernel matrix into eigenvalues ​​and eigenvectors, or using eigenvalues ​​to determine the relative importance of principal components.

[0042] As used in this article, the "covariance matrix" refers to a matrix that captures the statistical correlation between basis functions or features in a dataset. Some examples of "covariance matrices" are matrices formed from kernel matrices. Alternatively, it can be used as input to PCA to identify the covariance matrix of the principal components.

[0043] As used in this article, a “receiver chain in a full-duplex communication system” refers to a sequence of components that process the received signal after it enters the receiver. Some examples of a “receiver chain” are analog-to-digital converters (ADCs), digital baseband processing modules, adaptive filters, and cancellation blocks that subtract interference estimates from the received signal.

[0044] As used in this paper, the “cumulative threshold of measures associated with principal components” refers to a criterion for selecting a subset of principal components by accumulating the measures of principal components until a specified proportion of the total number is reached. Some examples of “cumulative threshold of measures associated with principal components” are retaining enough principal components to capture 90% of the total eigenvalue energy, or selecting components until the cumulative sum of reconstruction error measures exceeds a defined percentage.

[0045] As used in this paper, "least squares estimation" refers to a mathematical technique used to estimate coefficients by minimizing the sum of squared differences between observed values ​​and values ​​predicted by the model. Some examples of "least squares estimation" involve solving for a coefficient vector that minimizes the error between the received signal and the interference estimate.

[0046] As used in this paper, "multicollinearity" refers to a condition where two or more basis functions or features are highly correlated, leading to redundancy and instability in coefficient estimation. Some examples of "multicollinearity" are the correlation between polynomial basis functions in the kernel matrix, or eigenvectors associated with small eigenvalues ​​that amplify noise in the estimation.

[0047] As used in this paper, "a measure of error in estimating the coefficient vector, calculated from the eigenvalues ​​of the PCA" refers to a measurement derived from the eigenvalues ​​of the covariance matrix that quantifies how the omission or retention of certain principal components affects the accuracy of the coefficient estimation. Some examples of "a measure of error in estimating the coefficient vector, calculated from the eigenvalues ​​of the PCA" are bias terms associated with discarded components, noise terms weighted by the inverse of the eigenvalues, and combined error scores used to rank and select principal components for self-interference cancellation.

[0048] As used in this paper, “eigenvalue” refers to a scalar value that indicates the magnitude of the variance captured by the corresponding eigenvector in matrix factorization. Some examples of “eigenvalues” are values ​​obtained from the EVD of the covariance matrix of the kernel matrix, or eigenvalues ​​used to determine the relative importance of principal components in PCA.

[0049] As used in this article, “scaling” refers to adjusting the size of basis function or kernel matrix entries, for example, by correlation-weighted or variance-based adjustments.

[0050] Embodiments of this disclosure provide methods and systems for self-interference cancellation in full-duplex communication. In some embodiments, a kernel matrix of basis functions can be generated to represent self-interference generated by the transmitted signal, and PCA can be applied to identify a reduced subset of components that capture the main interference characteristics. Different techniques can be used to select the subset, including reconstruction error metrics, eigenvalue-based error metrics, or correlation-based scaling of basis functions.

[0051] In various embodiments, coefficients are estimated from selected components and used to generate an interference estimate subtracted from the received signal within the receiver chain. By reducing the number of basis functions while maintaining accuracy, the disclosed embodiments provide efficient elimination with improved numerical stability and lower computational complexity, thereby enabling reliable operation of full-duplex communication systems. The methods described herein can be implemented in hardware such as modem or baseband processor chips, or in programmable logic, digital signal processors, or general-purpose processors that execute software instructions.

[0052] Figure 1 The illustration depicts an architecture for implementing self-interference cancellation in a full-duplex communication system according to an embodiment. More specifically, Figure 1Functional blocks are shown that work together to model, estimate, and subtract self-interference generated by the transmitted signal, so that the desired received signal can be recovered.

[0053] refer to Figure 1 At each time instance i, the kernel generator 105 can form an input vector x(i), which includes a nonlinear kernel component representing the self-interference generated by the transmitted signal. The input vector x(i) can be processed in the transverse filter 110 by the coefficient vector... Weighting to generate the output signal This represents the estimate of self-interference. The estimate can be... The received signal y(i) is compared with the signal obtained at the output of the analog-to-digital converter (ADC) 125. The summer 120 can subtract the received signal from y(i). To generate the residual signal e(i) = y(i) – The residual signal e(i) can be provided to the adaptive weight control module 115, which can use an adaptive algorithm such as least squares or recursive least squares to update the coefficient vector. The updated coefficient vector can then be fed back to the transverse filter 110, enabling the disturbance estimation to be refined over time.

[0054] In some embodiments, PCA can be applied to the kernel matrix formed by the kernel generator 105 prior to coefficient estimation. PCA can reduce the dimensionality of the basis functions and alleviate multicollinearity, thereby improving the numerical stability of the adaptive weight control module 115 and reducing computational complexity. A reduced set of principal components can be used in the transverse filter 110 to produce more accurate estimates of self-interference with less resource required. Figure 1 The functional blocks can be implemented as dedicated electronic circuits, programmable logic, or as software modules executed by one or more processors.

[0055] Figure 2 The illustration shows an example component of self-interference in a full-duplex communication system. More specifically, Figure 2 This demonstrates how different distortion mechanisms contribute to the overall interference observed in the receive band.

[0056] exist Figure 2In the example, linear component 205 can represent direct leakage in the transmit signal to the receive path. IQ imbalance component 210 can represent distortion caused by mismatch between in-phase and quadrature signal paths. Nonlinear component 215 can represent higher-order distortion, such as polynomial nonlinearity generated by defects in the power amplifier or mixer. Arrow 220 can indicate the combined contribution of these components to the receive band along the horizontal frequency axis and the vertical power axis. The notation “Tx Interference BW” can represent the transmit interference bandwidth containing the distortion component, while “Rx BW” can represent the receive bandwidth occupied by the desired signal, illustrating how interference overlaps with the receive band.

[0057] As shown in the figure, self-interference includes not only linear leakage, but also IQ imbalance and nonlinear distortion. These components correspond to... Figure 1 The nonlinear kernel function is generated by the kernel generator 105 that forms the input vector x(i). In various embodiments, the PCA-based selection described herein can be used to capture... Figure 2 The kernel function is operated on to describe one or more of the linear, IQ imbalance and nonlinear contributions, thereby producing a reduced and more efficient basis set for self-interference cancellation.

[0058] In some embodiments, the kernel component x(i) for modeling self-interference can be generated based on a parallel Hammerstein structure. For example... Figure 2 As shown, self-interference can include linear leakage, IQ imbalance and nonlinear distortion, and the kernel generator 105 can form basis functions that capture these effects.

[0059] In the following, when modeling self-interference in a full-duplex system, according to embodiments of this disclosure, the interference estimate at time i can be expressed as shown in equation (1):

[0060] (1)

[0061] In equation (1), It can be a form The estimated coefficients of the basis functions.

[0062] The coefficient to be estimated The total number can be equal to .

[0063] This formula highlights that the number of kernel functions grows rapidly with the nonlinear order and memory depth, leading to high-dimensional modeling of self-interference.

[0064] For clarity of notation, in this disclosure, It can correspond to the kernel matrix. It can be from The covariance matrix obtained by EVD decomposition (Right now ) eigenvector matrix, This can correspond to the estimated weighting coefficients, while It can correspond to the true coefficient, and This can correspond to noise power.

[0065] The following flowchart ( Figures 3-5 The illustration shows three alternative embodiments for selecting a subset of basis functions. Each embodiment applies PCR in a different way to emphasize reconstruction accuracy. Figure 3 ), coefficient stability ( Figure 4 ) or correlation with the received signal ( Figure 5 ).

[0066] Figure 3 This is a flowchart of a method for selecting a subset of basis functions for self-interference cancellation in a full-duplex communication system, according to an embodiment. The method determines which basis functions most significantly contribute to accurately estimating self-interference, with the aim of minimizing the mean square error (MSE) between the received signal and the interference estimate. The interference estimate is related to the received signal y. The MSE can be represented as shown in equation (2):

[0067] (2)

[0068] In equation (2), It can be an estimated coefficient vector. It can represent the covariance matrix The k-th eigenvalue, and It can be given by equation (3):

[0069] (3)

[0070] In equation (3), It can represent inclusion The matrix of eigenvectors Hermitian transpose of the k-th column. It can represent a noise vector, and This could be noise power. (Item) This can correspond to the total number of samples, while It can represent the total The number of selected principal components (or basis functions) among the available components.

[0071] It can reflect the squared error between the received signal and the interference estimate, which is averaged across multiple signal samples. Figure 3The subsequent steps in the flowchart describe how to select basis functions and estimate coefficients to minimize the error.

[0072] In step 310, at each time index i, a transmitted signal sample can be obtained from the transmitter path of the full-duplex system, and a received signal sample can be obtained at the output of the ADC 125. The transmitted samples can be later used to generate kernel components that capture nonlinearity and memory effects, while the received samples contain both the desired signal and self-interference, as well as noise. In some embodiments, a set of N transmitted and received samples can be collected for processing. Preprocessing operations such as de-meaning or normalization of the transmitted and received signal samples can be performed to facilitate subsequent covariance calculation and PCR.

[0073] In step 320, the transmitted signal samples can be processed by kernel generator 105 to construct a kernel matrix X, whose columns represent nonlinear basis functions modeling self-interference generated by the transmitted signal. These basis functions may include polynomial terms, memory terms, or combinations thereof, capturing effects such as amplifier nonlinearity and IQ imbalance. The received signal y can be associated with this kernel matrix such that the interference estimate can be expressed as... ,in This can be the coefficient vector to be estimated. The covariance matrix can be calculated based on the kernel matrix. (Also expressed as) ), which is used as the basis for subsequent PCA.

[0074] In step 330, the covariance matrix can be decomposed using eigenvalue decomposition (EVD) or eigenvalue decomposition. This is used to obtain the eigenvector matrix A and the diagonal matrix Λ of eigenvalues. Each eigenvalue... The energy contribution of the corresponding principal component can be represented, while the eigenvector defines the direction in the feature space of the transformation. This decomposition enables subsequent ranking and selection of the components that best capture self-disturbing structures, while reducing redundancy among relevant basis functions.

[0075] In step 340, a metric can be calculated for each principal component to quantify its contribution to accurately reconstructing self-interference. In one approach, an alignment factor can be determined to assess the correlation between the measured received signal y and the k-th principal component obtained from the decomposition of the covariance matrix. The measure of the k-th component can then be expressed as in equation (4):

[0076] = (4)

[0077] In equation (4), It can be the k-th eigenvalue of the covariance matrix R, and This metric can represent the normalized projection of the received signal onto the corresponding eigenvector. It reflects the reconstruction error introduced by discarding the k-th component. Components with higher metric values ​​are more aligned with the self-interference structure and therefore have greater significance for accurate coefficient estimation. In contrast, components with lower metric values ​​contribute little to the reconstruction and can be omitted with minimal impact.

[0078] In step 350, the principal components can be ranked based on the selection metric calculated in step 340. Specifically, the set of metric values ​​{S} k The components can be sorted in descending order, so that the components with the largest values ​​appear first. Ranking in this way ensures that the basis functions most strongly aligned with the self-interference structure are prioritized. By arranging the components in order of decreasing contribution, the method identifies which basis functions provide the greatest reduction in reconstruction error when retained. This sorting also enables an efficient selection process in subsequent steps, as multiple components can be selected as the top M entries in the sorted list, while the remainder can be discarded with minimal loss of accuracy.

[0079] In step 360, a subset of principal components can be selected from the ranking list to form a reduced basis for coefficient estimation. In one embodiment, the top M components with the highest metric values ​​are retained, while the remaining components are discarded. The retained eigenvectors define the transformation matrix. It is applied to the kernel matrix X to produce a reduced kernel matrix. .

[0080] This reduction eliminates redundant or low-contribution basis functions, thereby reducing computational complexity and mitigating numerical instability caused by multicollinearity. The number M of retained components can be determined by a fixed threshold, a cumulative percentage of total metrics, or other adaptive criteria. By selecting only the most significant components, this method achieves accurate self-interference modeling while reducing resource usage in subsequent coefficient estimation.

[0081] In step 370, the kernel matrix X can be projected onto a reduced basis formed by the selected principal components to obtain the reduced kernel matrix. Using this reduced kernel matrix, the coefficient vector can be estimated. To model self-interference. In one embodiment, minimizing the squared error can be used. The least squares (LS) algorithm is used to perform the estimation. In other embodiments, recursive least squares (RLS) or other adaptive estimation algorithms can be used to update in real time as new signal samples are acquired. By using a reduced set dependent on basis functions, the estimation process requires less computation, converges faster, and provides enhanced numerical stability compared to using a full kernel matrix. The resulting coefficient vector captures the contribution of the selected principal components to the disturbance structure, preparing the system to generate disturbance estimates for elimination.

[0082] In step 380, the reduced kernel matrix and estimated coefficient vector obtained in step 370 can be used to generate the disturbance estimate. Specifically, the estimate is formed as ,in Includes the selected principal components and These represent their corresponding coefficient values. This operational reconstruction addresses the portion of the received signal attributable to self-interference, including distortion caused by linear leakage, IQ imbalance, and nonlinear effects. Because the estimation is based only on the most significant components, it more accurately models the major interference contributions while suppressing noise and low-impact terms. The interference estimate is then made available for subtraction from the received signal to improve overall cancellation performance.

[0083] In step 390, the interference estimate generated in step 380 can be subtracted from the received signal y. The residual signal is obtained. This subtraction can be performed in the digital domain within the receiver chain after the ADC 125. The residual signal contains the desired communication signal with significantly reduced distortion from self-interference and additive noise. By removing the main interference components, this method improves the effective signal-to-interference-plus-noise ratio (SINR) and enables more reliable demodulation of the desired signal. In some embodiments, the residual signal can also be fed back to update the coefficient estimation process, thereby further refining the interference cancellation performance over time.

[0084] Figure 4 This is a flowchart of a method for selecting a subset of basis functions for self-interference cancellation in a full-duplex communication system, according to an embodiment. In this embodiment, the method involves minimizing the estimated coefficient vector that models the self-interference. The MSE is used to select basis functions. The MSE can be expressed as shown in equation (5):

[0085] (5)

[0086] In equation (5), It can represent the projection coefficient of the k-th eigenvector. It can be the k-th eigenvalue of the covariance matrix, and This can be noise power. The first term represents the estimation bias introduced by discarding higher-order eigenvectors, while the second term reflects the estimation noise from the retained eigenvectors. To balance these effects, S is quantified.k It can be defined as shown in equation (6):

[0087] = (6)

[0088] With higher S k The basis functions of the value can be ranked as more important for accurate self-interference estimation. Figure 4 The method sorts and selects subsets of principal components with the highest scores, thereby reducing multicollinearity and improving stability, while minimizing... The overall MSE.

[0089] Similar to Figure 3 In steps 310 and 410, transmitted samples from the transmitter path and received samples from the ADC can be collected. A block of N samples can be used, and simple preprocessing such as mean removal or normalization can be applied to prepare data for analysis of covariance and PCR.

[0090] As in Figure 3 In step 320, and in step 420, the transmitted samples can be processed to form a kernel matrix X, whose columns represent nonlinear basis functions modeling self-interference. Based on X, the covariance matrix can be calculated. The covariance matrix is ​​then used in a later step to perform PCR and evaluate the metric used to select the most relevant basis functions.

[0091] As in Figure 3 In step 330, and in step 430, the EVD of the covariance matrix R can be performed to obtain eigenvalues ​​and eigenvectors, which are then used in the next step to calculate the coefficient-error metric.

[0092] In step 440, a metric can be computed to evaluate each basis function relative to the minimized coefficient vector. The relative importance of the MSE. The MSE of the coefficient vector can be expressed as shown in equation (5). In equation (5), the variables , and It can be defined as above. The first term of equation (5) represents the estimation bias introduced by discarding higher-order eigenvectors, while the second term reflects the estimation noise from the retained eigenvectors.

[0093] To balance these effects, a metric (or fraction) S can be computed for each basis function. k As given in equation (6), this score reflects the degree to which the k-th basis function contributes useful signal information while taking noise sensitivity into account. It has a larger S k The basis functions of the values ​​can be considered more relevant for accurate coefficient estimation. This can be done by...k The principal component sets are ranked in descending order to guide the selection in subsequent steps.

[0094] With minimizing the reconstruction error of the received signal Figure 3 Unlike step 340, step 440 focuses on minimizing the error in the coefficient vector itself, thereby addressing both bias and noise in parameter estimation.

[0095] In step 450, the corresponding fraction S of the basis function calculated in step 440 can be used as a basis. k The basis functions are ranked. Ranking can be performed in descending order, prioritizing basis functions with higher scores that indicate a stronger contribution to accurate coefficient estimation. A subset of M principal components can then be selected from the total set of P available components by retaining those with the highest scores. This selected subset forms a reduced transformation matrix, which is subsequently applied to the kernel matrix to obtain the reduced representation used in coefficient estimation. Discarding lower-ranked components in this way reduces computational complexity and mitigates noise amplification associated with weak eigenvalues.

[0096] In step 460, the kernel matrix X can be projected onto a reduced transformation matrix formed by the selected M principal components from step 450. This operation produces a reduced kernel matrix, denoted as X. final It contains only the most salient components for self-interference estimation. By discarding less relevant basis functions, the reduced kernel matrix reduces computational complexity while preserving the key features needed for accurate coefficient estimation.

[0097] In step 470, similar to Figure 3 Step 370 can use the reduced kernel matrix X final To estimate the coefficient vector The estimation can be performed using algorithms such as least squares or recursive least squares, thus providing weights for modeling self-interference.

[0098] In step 480, corresponding to Figure 3 In step 380, the estimated coefficient vector can be applied to the reduced kernel matrix to generate an estimate of self-interference.

[0099] In step 490, similar to Figure 3 Step 390 involves subtracting the self-interference estimate from the received signal within the receiver chain. This subtraction produces a residual signal, in which the desired received signal is recovered more accurately with reduced distortion.

[0100] Compared to emphasizing the accuracy of reconstructed received signals Figure 3 compared to, Figure 4An example is provided that directly minimizes the error of the coefficient vector. This example achieves improved stability in coefficient estimation, especially in the presence of noise or multicollinearity.

[0101] Figure 5 This is a flowchart of a method for selecting a subset of basis functions for self-interference cancellation in a full-duplex communication system, according to an embodiment. In this embodiment, PCA can be applied to a kernel matrix in which the basis functions are scaled according to their correlation with the received signal. By weighting the kernel functions proportionally to their alignment with the received signal before performing PCR, the method finds the components most relevant to the interference estimation. Correlation-based scaling improves the robustness of principal component selection and reduces the influence of irrelevant or weakly correlated basis functions.

[0102] In step 510, in a manner similar to Figure 3 Step 310 and Figure 4 In step 410, transmitted signal samples can be obtained from the transmitter path, and received signal samples can be obtained at the output of the ADC. A block of N samples can be collected for processing. In some embodiments, preprocessing such as mean reduction or normalization of the samples can be performed to facilitate the construction of the kernel matrix and subsequent principal component analysis.

[0103] In step 520, similar to Figure 3 Step 320 and Figure 4 Step 420 may involve processing the transmitted signal samples to construct a kernel matrix X, representing columnar representations of nonlinear basis functions modeling the self-interference generated by the transmitted signal. These basis functions may include polynomial terms, memory terms, or combinations thereof. The kernel matrix may be used as the basis for correlation-based scaling in subsequent steps.

[0104] In step 530, a correlation value can be determined between each basis function of the kernel matrix X and the received signal y. These correlation values ​​indicate the relative alignment of each basis function with the received signal, reflecting how strongly each basis function contributes to modeling self-interference. Based on the calculated correlation values, scaling weights can be assigned to the columns of the kernel matrix. Basis functions with stronger correlations can be emphasized, while basis functions with weaker correlations can be deweighted (or not emphasized). The scaling weights can then be applied to form a weighted kernel matrix, as described in step 540.

[0105] In step 540, the kernel matrix X can be rescaled using the correlation-based weights determined in step 530. Each column of X, representing the basis functions, can be multiplied by its corresponding weight, such that the resulting weighted kernel matrix emphasizes the basis functions more strongly correlated with the received signal. The scaled kernel matrix, denoted as X, can then be used in subsequent covariance analysis and principal component selection. scaledBy reweighting the basis functions in proportion to their correlation with the basis functions, this method guides the PCR process to focus on the most relevant components for accurate self-interference estimation.

[0106] In step 550, the scaled kernel matrix X can be used. scaled Calculate the covariance matrix. PCA can then be performed on the covariance matrix, for example, by applying EVD to obtain eigenvalues ​​and eigenvectors. Eigenvalues ​​indicate the relative significance of the corresponding eigenvectors, while eigenvectors represent orthogonalized versions of the weighted basis functions. Because the kernel matrix is ​​scaled in steps 530 and 540, the PCR process emphasizes basis functions that are not only orthogonal but also strongly correlated with the received signal.

[0107] In step 560, the principal components obtained from the PCA of the scaled kernel matrix can be ranked, and a subset can be selected. Because the kernel matrix is ​​weighted according to its correlation with the received signal, the PCA process prioritizes orthogonalized basis functions that are strongly correlated with the signal of interest. A subset of M principal components can be retained from a total of P available components, where the component with the largest eigenvalue after scaling is selected. The selected components form the transformation matrix applied to the reduced kernel matrix in the next step. This is based on the reconstruction error. Figure 3 and based on coefficient error Figure 4 different, Figure 5 The component selection is derived by aligning with the correlation of the received signal, thereby improving the robustness in identifying the most relevant basis functions.

[0108] In step 570, the kernel matrix X can be... scaled The projection is onto a subset of the M principal components selected in step 560. This projection produces a reduced kernel matrix, denoted as X. final It contains only the most significant basis functions emphasized by correlation-based scaling and PCR. By discarding less relevant or weakly relevant components, the reduced kernel matrix reduces computational complexity while preserving the key features needed for accurate coefficient estimation in subsequent steps.

[0109] In step 580, similar to Figure 3 Step 370 and Figure 4 Step 470 can use the reduced kernel matrix X final The coefficient vector used to model self-interference is estimated using the received signal y. In one embodiment, least squares can be used to perform the estimation to minimize the relationship between y and X. final The error between the reconstructions; in other embodiments, recursive least squares or another adaptive estimator is used to update the estimation when new samples are acquired. Using X with relevance emphasis finalIt improves numerical stability and focuses the estimation on the basis functions most relevant to the received signal.

[0110] In step 590, similar to Figure 3 Step 390 and Figure 4 Step 490 can estimate the coefficient vector. Kernel matrix X applied to reduction final This generates an estimate of self-interference. This estimate can then be subtracted from the received signal within the receiver chain, resulting in a residual signal from which the desired received signal is recovered more accurately with reduced distortion.

[0111] Figure 5 Flowcharts and Figure 3 and Figure 4 The difference in the flowchart lies in applying correlation-based scaling to the kernel matrix before PCA. This emphasizes the basis functions best aligned with the received signal, making the selection of principal components more robust and reducing the influence of weak or uncorrelated functions. Therefore, this method complements... Figure 3 Reconstruction error examples and Figure 4 Example of coefficient error.

[0112] Figure 6 The diagram shows 600, which is an example distribution of the eigenvalues ​​of the covariance matrix R used in the PCA-based basis selection process. The horizontal axis can represent the cardinality, corresponding to the ordering of the eigenvectors obtained from the EVD of the kernel matrix. The vertical axis can represent the magnitude of the eigenvalues ​​associated with each basis function. Larger eigenvalues ​​can indicate that more variance is captured and therefore contributes more significantly to the components modeling self-interference.

[0113] In the example shown, the set of all available basis functions is represented in one format (e.g., shaded), while the subset of principal components selected according to the method described above is shown in another format. The cumulative power threshold, indicated by the boundary line, separates the retained basis functions from the discarded ones. Retaining only the subset of basis functions associated with the largest eigenvalues ​​allows for a reduction in the dimension of the kernel matrix while preserving the principal features of self-perturbations.

[0114] Figure 6 The distribution of eigenvalues ​​shown demonstrates how PCA concentrates most of the signal energy in a smaller number of basis functions. By selecting a subset of basis functions above a threshold, the system improves computational efficiency and stability while maintaining accurate interference cancellation performance. Therefore, Figure 6 It shows Figure 3 (Based on reconstruction error) Figure 4 (Based on coefficient error) and Figure 5 The effect of the basis selection steps described in (correlation-based scaling) is that each step can use the eigenvalue size to determine which components to retain.

[0115] Figure 7 This is a block diagram of an electronic device in a network environment 700 according to an embodiment.

[0116] refer to Figure 7 In network environment 700, electronic device 701 can communicate with electronic device 702 via a first network 798 (e.g., a short-range wireless communication network), or with electronic device 704 or server 708 via a second network 799 (e.g., a long-range wireless communication network). Electronic device 701 can communicate with electronic device 704 via server 708. Electronic device 701 may include processor 720, memory 730, input device 750, sound output device 755, display device 760, audio module 770, sensor module 776, interface 777, haptic module 779, camera module 780, power management module 788, battery 789, communication module 790, subscriber identification module (SIM) card 796, or antenna module 797. In one embodiment, at least one component (e.g., display device 760 or camera module 780) may be omitted from electronic device 701, or one or more other components may be added to electronic device 701. Some components may be implemented as a single integrated circuit (IC). For example, a sensor module 776 (e.g., a fingerprint sensor, an iris sensor, an illuminance sensor, etc.) may be embedded in a display device 760 (e.g., a display).

[0117] The processor 720 can execute software (e.g., program 740) to control at least one other component (e.g., hardware or software component) of the electronic device 701 coupled to the processor 720, and can perform various data processing or calculations.

[0118] As at least part of data processing or computation, processor 720 can load commands or data received from another component (e.g., sensor module 776 or communication module 790) into volatile memory 732, process the commands or data stored in volatile memory 732, and store the resulting data in non-volatile memory 734. Processor 720 may include a main processor 721 (e.g., a central processing unit (CPU) or application processor (AP)) and an auxiliary processor 723 (e.g., a graphics processing unit (GPU), image signal processor (ISP), sensor hub processor, or communication processor (CP)) that may operate independently of or in conjunction with the main processor 721. Optionally or additionally, auxiliary processor 723 may be adapted to consume less power than the main processor 721 or to perform specific functions. Auxiliary processor 723 may be implemented separately from or as part of the main processor 721.

[0119] When the main processor 721 is inactive (e.g., in sleep mode), the auxiliary processor 723 can control at least some of the functions or states associated with at least one component of the electronic device 701 (other than the main processor 721), such as display device 760, sensor module 776, or communication module 790. Alternatively, when the main processor 721 is active (e.g., executing an application), the auxiliary processor 723 can, together with the main processor 721, control at least some of the functions or states associated with at least one component of the electronic device 701, such as display device 760, sensor module 776, or communication module 790. The auxiliary processor 723 (e.g., an image signal processor or a communication processor) can be implemented as part of another component (e.g., camera module 780 or communication module 790) functionally associated with the auxiliary processor 723.

[0120] Memory 730 may store various data used by at least one component of electronic device 701 (e.g., processor 720 or sensor module 776). The various data may include, for example, input or output data of software (e.g., program 740) and associated commands. Memory 730 may include volatile memory 732 or non-volatile memory 734. Non-volatile memory 734 may include internal memory 736 and / or external memory 738.

[0121] The program 740 can be stored as software in the memory 730 and may include, for example, an operating system (OS) 742, middleware 744, or application 746.

[0122] Input device 750 can receive commands or data from outside electronic device 701 (e.g., a user) that will be used by another component of electronic device 701 (e.g., processor 720). Input device 750 may include, for example, a microphone, mouse, or keyboard.

[0123] The sound output device 755 can output sound signals to the outside of the electronic device 701. The sound output device 755 may include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as playing multimedia or recording, and the receiver can be used to receive incoming calls. The receiver can be implemented separately from the speaker or as part of the speaker.

[0124] Display device 760 can visually provide information to the outside of electronic device 701 (e.g., to a user). Display device 760 may include, for example, a display, a holographic device, or a projector, and control circuitry for controlling a corresponding one of the display, holographic device, and projector. Display device 760 may include touch circuitry adapted to detect touch, or sensor circuitry (e.g., a pressure sensor) adapted to measure the intensity of the force caused by touch.

[0125] The audio module 770 can convert sound into electrical signals and vice versa. The audio module 770 can acquire sound via the input device 750, or output sound via the sound output device 755 or headphones of the external electronic device 702 that are directly (e.g., wired) or wirelessly connected to the electronic device 701.

[0126] Sensor module 776 can detect the operating state of electronic device 701 (e.g., power or temperature) or the environmental state outside electronic device 701 (e.g., user state), and then generate an electrical signal or data value corresponding to the detected state. Sensor module 776 may include, for example, a gesture sensor, gyroscope sensor, atmospheric pressure sensor, magnetic sensor, accelerometer, grip sensor, proximity sensor, color sensor, infrared (IR) sensor, biometric sensor, temperature sensor, humidity sensor, or illuminance sensor.

[0127] Interface 777 may support one or more specific protocols for direct (e.g., wired) or wireless connection between electronic device 701 and external electronic device 702. Interface 777 may include, for example, a High Definition Multimedia Interface (HDMI), a Universal Serial Bus (USB) interface, a Secure Digital (SD) card interface, or an audio interface.

[0128] Connection terminal 778 may include a connector, through which electronic device 701 can be physically connected to external electronic device 702. Connection terminal 778 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0129] The haptic module 779 can convert electrical signals into mechanical stimulation (e.g., vibration or movement) or electrical stimulation that can be recognized by a user via touch or kinesthesia. The haptic module 779 may include, for example, a motor, a piezoelectric element, or an electrical stimulator.

[0130] Camera module 780 can capture still or moving images. Camera module 780 may include one or more lenses, an image sensor, an image signal processor, or a flash. Power management module 788 can manage the power supply to electronic device 701. Power management module 788 may be implemented as at least a part of, for example, a power management integrated circuit (PMIC).

[0131] Battery 789 can power at least one component of electronic device 701. Battery 789 may include, for example, a non-rechargeable primary battery, a rechargeable rechargeable battery, or a fuel cell.

[0132] Communication module 790 can support the establishment of a direct (e.g., wired) or wireless communication channel between electronic device 701 and external electronic devices (e.g., electronic device 702, electronic device 704, or server 708), and perform communication via the established communication channel. Communication module 790 may include one or more communication processors that can operate independently of processor 720 (e.g., AP), and support direct (e.g., wired) or wireless communication. Communication module 790 may include wireless communication module 792 (e.g., cellular communication module, short-range wireless communication module, or Global Navigation Satellite System (GNSS) communication module) or wired communication module 794 (e.g., local area network (LAN) communication module or power line communication (PLC) module). A corresponding one of these communication modules can communicate via a first network 798 (e.g., a short-range communication network, such as BLUETOOTH). TM The communication module 792 communicates with external electronic devices via a wireless communication module 793 (e.g., a Wi-Fi Direct or Infrared Data Association (IrDA) standard) or a second network 799 (e.g., a long-range communication network such as a cellular network, the Internet, or a computer network (e.g., a LAN or a wide area network (WAN))). These various types of communication modules can be implemented as a single component (e.g., a single IC) or as multiple components separate from each other (e.g., multiple ICs). The wireless communication module 792 can use user information (e.g., an International Mobile Subscriber Identity (IMSI)) stored in the user identification module 796 to identify and authenticate electronic devices 701 within the communication network (e.g., a first network 798 or a second network 799).

[0133] Antenna module 797 can transmit or receive signals or power from the outside of electronic device 701 (e.g., external electronic device). Antenna module 797 may include one or more antennas, and at least one antenna suitable for a communication scheme used in a communication network (such as a first network 798 or a second network 799) can be selected from the one or more antennas, for example, via communication module 790 (e.g., wireless communication module 792). Signals or power can then be transmitted or received between communication module 790 and external electronic device via the selected at least one antenna.

[0134] Commands or data can be sent or received between electronic device 701 and external electronic device 704 via server 708 connected to the second network 799. Each of electronic devices 702 and 704 can be a device of the same or different type as electronic device 701. All or some operations to be performed at electronic device 701 can be performed at one or more of the external electronic devices 702, 704, or 708. For example, if electronic device 701 is required to automatically or in response to a request from a user or another device to perform a function or service, instead of performing that function or service, or in addition to performing that function or service, electronic device 701 can request one or more external electronic devices to perform at least a portion of the function or service. Upon receiving the request, one or more external electronic devices can perform at least a portion of the requested function or service, or additional functions or services related to the request, and transmit the result of the execution to electronic device 701. Electronic device 701 can provide the result as at least a part of its response to the request, with or without further processing. For this purpose, cloud computing, distributed computing, or client-server computing technologies can be used, for example.

[0135] In some embodiments, it is possible to Figure 7 The self-interference cancellation method disclosed herein is executed on one or more components of the electronic device 701. For example, a processor 720, which may include a main processor 721 or an auxiliary processor 723, may execute instructions for generating a kernel matrix, performing PCA, and estimating coefficients for self-interference cancellation. Alternatively or additionally, a communication module 790, including processing elements associated with transceiver operation, may implement at least a portion of the interference cancellation process in hardware or firmware. In some embodiments, adaptive filtering and PCA operations may be distributed between the processor 720 and the communication module 790 to balance the computational load. Thus, the disclosed method can be executed by the processor 720 or by a combination of these components within the receiver chain of the communication module 790.

[0136] Figure 8 A system including a UE 805 and a gNB 810 communicating with each other is shown. The UE may include a radio 815 and processing circuitry (or means for processing) 820, which can perform various methods disclosed herein, such as Figure 1 The method is illustrated. For example, the processing circuit 820 can receive transmissions from the network node (gNB) 810 via radio 815, and the processing circuit 820 can send signals to the gNB 810 via radio 815.

[0137] Embodiments of the subject matter and operations described in this specification can be implemented in digital electronic circuits, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or combinations thereof. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs (i.e., one or more modules of computer program instructions) encoded on a computer storage medium for execution by a data processing device or for controlling the operation of a data processing device. Alternatively or additionally, the program instructions may be encoded on artificially generated propagating signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information for transmission to a suitable receiver device for execution by the data processing device. The computer storage medium may be or is included in a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination thereof. Furthermore, although the computer storage medium is not a propagating signal, it may be a source or destination of computer program instructions encoded in artificially generated propagating signals. The computer storage medium may also be or be included in one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices). Furthermore, the operations described in this specification can be implemented as operations performed by a data processing apparatus on data stored on one or more computer-readable storage devices or received from other sources.

[0138] While this specification may contain numerous specific implementation details, these details should not be construed as limiting the scope of any claimed subject matter, but rather as descriptions of features specific to particular embodiments. Certain features described in the context of individual embodiments in this specification may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments. Furthermore, although features may be described above as functioning in certain combinations and even initially claimed in this way, in some cases one or more features from the claimed combination may be removed from the combination, and the claimed combination may be for sub-combinations or variations thereof.

[0139] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or to perform all the shown operations to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of the various system components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0140] Therefore, specific embodiments of the subject matter have been described herein. Other embodiments are within the scope of the claims. In some cases, the actions set forth in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing may be advantageous.

[0141] As those skilled in the art will recognize, the innovative concepts described herein can be modified and varied across a wide range of applications. Therefore, the scope of the claimed subject matter should not be limited to any of the specific exemplary teachings discussed above, but is defined by the claims.

Claims

1. A method for self-interference cancellation in a full-duplex communication system, the method comprising: Generate a kernel matrix comprising basis functions representing self-interference generated by the transmitted signals of the full-duplex communication system; Principal component regression PCR is performed based on the kernel matrix; A subset of principal components is selected based on a metric related to the reconstruction error of the received signal. A subset of the principal components is used to estimate the coefficient vector to generate an estimate of the self-interference; as well as The self-interference is eliminated from the received signal based on the estimation of the self-interference.

2. The method according to claim 1, wherein, The metric is based on the mean square error between the received signal and the estimate of the self-interference.

3. The method according to claim 1 further includes performing eigenvalue decomposition of the covariance matrix of the kernel matrix.

4. The method according to claim 1, wherein, The selection process includes ranking the principal components based on the metric and retaining the principal component with the highest metric value.

5. The method according to claim 1, wherein, The self-interference estimate is subtracted from the received signal within the receiver chain of the full-duplex communication system.

6. The method of claim 1, further comprising determining the number of principal components to be retained based on a cumulative threshold of a metric associated with the principal components.

7. The method according to claim 1, wherein, Estimating the coefficient vector includes applying at least one of least squares estimation or recursive least squares estimation.

8. The method according to claim 1, wherein, Performing the PCR reduces multicollinearity among the basis functions by transforming the nuclear matrix into a set of orthogonal principal components and discarding components associated with lower metric values.

9. A method for self-interference cancellation in a full-duplex communication system, the method comprising: Generate a kernel matrix comprising basis functions representing self-interference generated by the transmitted signals of the full-duplex communication system; Principal component regression PCR was performed on the kernel matrix; A subset of principal components is selected based on a metric calculated from eigenvalues, singular vectors, and the correlation between the kernel matrix and the received signal, the metric reflecting the error in estimating the coefficient vector from the subset of principal components; The coefficient vector is estimated using a subset of the principal components to generate an estimate of the self-interference. as well as The self-interference is eliminated from the received signal based on the estimation of the self-interference.

10. The method according to claim 9, wherein, The metric takes into account the error contributed by the discarded principal components and the error contributed by the retained principal components.

11. The method according to claim 9, wherein, The selection includes discarding principal components based on the aforementioned metric.

12. The method according to claim 9, wherein, Estimating the coefficient vector includes applying at least one of least squares estimation or recursive least squares estimation.

13. The method of claim 9, further comprising determining the number of principal components to be retained based on a cumulative threshold of the metric.

14. The method according to claim 9, wherein, Performing the PCR reduces multicollinearity among the basis functions by transforming the nuclear matrix into a set of orthogonal principal components and discarding components associated with lower metric values.

15. The method according to claim 9, wherein, The self-interference estimate is subtracted from the received signal within the receiver chain of the full-duplex communication system.

16. A method for self-interference cancellation in a full-duplex communication system, the method comprising: Generate a kernel matrix comprising basis functions representing self-interference generated by the transmitted signals of the full-duplex communication system; The kernel matrix is ​​scaled based on the correlation between the basis functions and the received signal; Perform principal component regression PCR on the scaled kernel matrix; A subset of principal components is selected based on the characteristic values ​​of the PCR. A subset of the principal components is used to estimate the coefficient vector to generate an estimate of the self-interference; as well as Based on the estimation of the self-interference, the self-interference is eliminated from the signal received at the receiver of the full-duplex communication system.

17. The method according to claim 16, wherein, Scaling the kernel matrix involves weighting each basis function in proportion to its correlation with the received signal.

18. The method according to claim 16, wherein, The selection process involves ranking the principal components based on their eigenvalues ​​and retaining the principal components with the highest eigenvalues.

19. The method of claim 16, wherein, Estimating the coefficient vector includes applying at least one of least squares estimation or recursive least squares estimation.

20. The method of claim 16, wherein, The self-interference estimate is subtracted from the received signal within the receiver chain of the full-duplex communication system.

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