Method executed by electronic equipment and electronic equipment

Abstract

The invention discloses a method executed by electronic equipment and the electronic equipment. A method performed by an electronic device includes: receiving a narrowband reference signal; estimating a frequency domain correlation matrix using the narrowband reference signal; and performing narrowband channel estimation on the narrowband reference signal using the estimated frequency domain correlation matrix.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Provisional Application No. 63 / 699,895, filed September 27, 2024, the disclosure of which is incorporated herein by reference in its entirety as if fully set forth herein. Technical Field

[0003] This disclosure generally relates to channel estimation (CE). More specifically, the subject matter disclosed herein relates to improvements to CE in orthogonal frequency division multiplexing (OFDM) communication systems, and methods and apparatus for estimating frequency domain channel correlations based on narrowband reference signals. Background Technology

[0004] In communication systems such as fifth-generation (5G) and beyond, narrowband channel estimation (NBCE) primarily refers to channel estimation techniques designed for narrowband scenarios within narrowband systems or wider bandwidth systems. NBCE can be used in various 5G use cases, such as massive machine-type communications (mMTC) and / or Internet of Things (IoT) applications, where devices operate with limited power and bandwidth resources. NBCE can be used in wireless communications to estimate channels over relatively narrow portions of the system bandwidth (e.g., 1, 2, or 4 resource blocks (RBs)), typically focusing on identifying the characteristics of communication channels where the signal bandwidth is significantly smaller than the carrier frequency.

[0005] In Long Term Evolution (LTE) or New Radio (NR) communications, NBCE can be achieved due to the presence of pilot symbols (e.g., demodulation reference signals (DMRS)). Typically, it is assumed that some type of narrowband reference signal is available for the purpose of channel estimation.

[0006] To perform NBCE, frequency domain (FD) channel correlation coefficients can be used, for example, matrix and Typically, the correlation coefficient can be estimated from a broadband reference signal (e.g., a tracking reference signal (TRS)) that is quasi-co-located (QCL) with a narrowband reference signal (e.g., a DMRS) used for NBCE. The two reference signals are referred to as QCL when they share the same channel properties (or more specifically, two antenna ports) such that the channel statistics of one antenna port can be inferred from the other antenna port.

[0007] However, the presence of a wideband reference signal for QCL is not always guaranteed, e.g., depending on base station (gNB) configuration. Moreover, in some practical scenarios, the QCL relationship between a wideband signal (e.g., TRS) and a narrowband signal (e.g., DMRS) can only hold approximately or even be violated. More specifically, a QCL violation (or QCL mismatch) occurs when the actual channel conditions between the two signals significantly differ from the QCL assumption made by the receiver.

[0008] Thus, if the wideband reference signal for QCL is not available, only holds approximately, or a QCL violation occurs, the FD correlation coefficients can not be estimated correctly, making the NBCE performance unreliable or even unavailable. SUMMARY

[0009] To address these types of issues, the present disclosure provides various methods for estimating FD channel correlation coefficients based on available narrowband observations without the need for a wideband reference signal for QCL. That is, according to aspects of the present disclosure, the present disclosure provides various methods for estimating FD channel correlation coefficients that do not rely on a wideband reference signal for QCL, but rather rely on available narrowband observations.

[0010] More specifically, the present disclosure provides various methods for power delay profile (PDP) estimation based on least squares (LS), sparse Bayesian learning (SBL), or block SBL (BSBL). For these methods, the estimated PDP can be converted into FD channel correlation coefficients and then used to perform NBCE. A PDP can refer to a measurement in wireless communications that shows the average received signal power as a function of the propagation time delay. For example, a PDP can be used to visualize how a signal, after being transmitted, arrives at a receiver via multiple paths (multipath propagation), each path having a different delay and attenuation power.

[0011] The above-described methods improve upon previous methods in that the above-described methods enhance robustness to QCL mismatch, the above-described methods can utilize available narrowband signals (e.g., DMRS) to perform NBCE without the need for additional wideband signals (e.g., TRS).

[0012] The above-described methods are also practical due to low algorithmic complexity.

[0013] The above-described methods are also robust to noisy delay spread parameter values.

[0014] In an embodiment, a method performed by an electronic device includes receiving a narrowband reference signal, estimating a frequency domain correlation matrix using the narrowband reference signal, and performing narrowband channel estimation for the narrowband reference signal using the estimated frequency domain correlation matrix.

[0015] In an embodiment, an electronic device includes a transceiver; and a processor configured to: receive, via the transceiver, a narrowband reference signal; estimate a frequency domain correlation matrix using the narrowband reference signal; and perform narrowband channel estimation on the narrowband reference signal using the estimated frequency domain correlation matrix. BRIEF DESCRIPTION OF DRAWINGS

[0016] In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the disclosed subject matter. However, it will be appreciated that the disclosed aspects can be practiced without these specific details. In other instances, well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the disclosed subject matter.

[0017] Figure 1 A method of performing NBCE using a wideband reference signal QCLed with a narrowband reference signal is shown;

[0018] Figure 2 A method of performing NBCE using a narrowband reference signal according to an embodiment is shown;

[0019] Figure 3 A method of performing NBCE using a narrowband reference signal including an adaptive delay spread (DS) procedure according to an embodiment is shown;

[0020] Figure 4 A method of performing NBCE using a narrowband reference signal including an adaptive DS procedure according to an embodiment is shown;

[0021] Figure 5 is a flowchart showing a method performed by an electronic device according to an embodiment;

[0022] Figure 6 is a block diagram of an electronic device in a network environment according to an embodiment; and

[0023] Figure 7 A system including a UE and a gNB in communication with each other according to an embodiment is shown. DETAILED DESCRIPTION

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

[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 one embodiment," "in an embodiment," or "according to an embodiment" (or other phrases with similar meanings) appearing throughout this specification do not necessarily refer to the same embodiment. Furthermore, in one or more embodiments, particular features, structures, or characteristics may be combined in any suitable manner. 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 being more preferred or advantageous than other embodiments. Furthermore, in one or more embodiments, particular features, structures, or characteristics may be combined in any suitable manner. Additionally, depending on the context of the discussion herein, singular terms may include their corresponding plural forms, and plural terms may include their corresponding singular forms. Similarly, hyphenated terms (e.g., "two-dimensional", "pre-determined", "pixel-specific") are occasionally used interchangeably with their non-hyphenated counterparts (e.g., "two-dimensional", "pre-determined", "pixel-specific"), while uppercase entries (e.g., "Counter Clock", "Row Select", "PIXOUT") are interchangeable with their non-uppercase counterparts (e.g., "counter clock", "row select", "pixout"). This occasional interchangeability should not be considered inconsistent with each other.

[0026] Furthermore, depending on the context of the discussion herein, 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 diagrams) are for illustrative purposes only and are not drawn to scale. For example, the dimensions of some elements may be exaggerated relative to others for clarity. Additionally, reference numerals have been repeated in the figures where deemed appropriate to indicate corresponding and / or similar elements.

[0027] The terminology used herein is for the purpose of describing some exemplary embodiments only and is 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 also be understood that the terms “comprising” and / or “including…” as used in this specification 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 a component or layer is referred to as being "on," "connected to," or "coupled to" another component or layer, that component or layer may be directly on, connected to, or coupled to that component or layer, or there may be intermediate components or layers. Conversely, when a component is referred to as being "directly on," "directly connected to," or "directly coupled to" another component or layer, there are no intermediate components or layers. The same numbers always refer to the same component. As used herein, the term "and / or" includes any and all combinations of one or more of the items listed in connection with the preceding item.

[0029] As used herein, the terms “first,” “second,” etc., serve as labels for the nouns that follow 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 sake of simplicity and ease of discussion; it does not imply that the construction or architectural details of these components or units are identical across all embodiments, or that these commonly referenced 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 also be 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" refers to 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 include, for example, single or arbitrary combinations of 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] Although embodiments of this disclosure are described below with reference to the downlink of an NR system, the embodiments are also applicable to uplink and / or sidelink transmissions and other types of communication systems.

[0033] As described above, in order to perform NBCE, the FD correlation coefficient can be estimated based on a wideband reference signal (e.g., TRS) with respect to the QCL of the narrowband reference signal (e.g., DMRS).

[0034] However, the existence of a wideband reference signal for the QCL is not always guaranteed, and the QCL relationship between the two signals may only be approximately true, or even violated when the actual channel conditions between the wideband signal (e.g., TRS) and the narrowband signal (e.g., DMRS) differ significantly from the QCL assumptions made by the receiver. If the wideband reference signal for the QCL is unavailable, only approximately true, or a QCL violation occurs, the FD correlation coefficient may not be accurately estimated, making NBCE performance unreliable or unusable.

[0035] This disclosure provides various methods for estimating FD channel correlation coefficients based on available narrowband observations and a wideband reference signal that does not require a QCL. In other words, this disclosure provides various methods for estimating FD channel correlation coefficients that do not depend on a wideband reference signal for the QCL, but rather on available narrowband observations.

[0036] Figure 1 A method for performing NBCE using a wideband reference signal with a narrowband reference signal QCL is shown.

[0037] refer to Figure 1 Electronic devices can receive narrowband reference signals. (For example, the Physical Downlink Shared Channel (PDSCH) DMRS signal) 120 QCL wideband reference signal (For example, TRS) 110. TRS can be a type of Channel State Information Reference Signal (CSI-RS) used by the UE for fine-grained time and frequency tracking to compensate for time and frequency deviations during downlink data transmission.

[0038] As mentioned above, in order to perform NBCE at 102, FD channel correlation coefficients can be used, for example, a matrix. and At position 101, it can be determined based on the narrowband reference signal. 120 QCL wideband reference signal 110. Estimating the correlation coefficient. Various methods can be used to estimate the correlation coefficient from the broadband reference signal. For example, the broadband reference signal can be transformed to the delay domain (DD) using the inverse discrete Fourier transform (IDFT). The resulting DD signal can be denoised, and the delay point (PDP) can be estimated from the DD signal. Then, the FD channel correlation coefficient can be calculated from the estimated PDP.

[0039] Receive vector for each OFDM symbol ( The number of DMRS subcarriers within a precoded resource block group (PRG) can be modeled as shown in equation (1).

[0040] (1)

[0041] In equation (1), and This indicates the use of frequency domain orthogonal coverage code (FD-OCC), and It is a port. The channel vector at the DMRS subcarrier location, and It is a complex Gaussian random vector, where, Although a specific structure is provided here. and Examples are provided, but the embodiments disclosed herein are not limited to these and can be extended to others. and Other values. For example, It can be 0.

[0042] For the sake of simplicity, in the following text, It may not have been explicitly mentioned because it was set as the identity matrix.

[0043] By representing the number of resource blocks (RBs) within a PRG as ,for and DMRS type 1, And for DMRS type 2, .

[0044] Using the FD channel correlation coefficients obtained at position 101, i.e., the matrix and And the combined FD-Minimum Mean Square Error (MMSE) algorithm for NBCE at point 102, linear MMSE (LMMSE) estimation. 130 can be given by equation (2).

[0045] (2)

[0046] In equation (2), H represents the Hermitian transpose or conjugate transpose of the matrix.

[0047] As shown in equation (2), the broadband reference signal at 101 can be used at 102. 110 Estimated Matrix and To the narrowband reference signal 120 executes NBCE.

[0048] However, as mentioned above, the wideband reference signal of QCL The existence of 110 is not always guaranteed; for example, it depends on the gNB configuration. Furthermore, in some practical scenarios, the broadband reference signal... 110 and narrowband reference signal The QCL relationship between 1 and 20 may only be approximately true, or may even be violated.

[0049] To avoid these types of problems, various methods are provided according to embodiments of this disclosure based on narrowband reference signals. 120 Estimation Matrix and Without using a broadband reference signal 110.

[0050] Power delay distribution (PDP) is a tool used to characterize the time spread of a multipath channel. It essentially depicts the average received signal strength as a function of the time delay of each multipath component.

[0051] As shown in equation (3), the Discrete Fourier Transform (DFT) can be used to transform the PDP length. The number of DD taps is reduced to represent the channel vector.

[0052]

[0053] (3)

[0054] In equation (3), L represents the expected length of the PDP, and T represents the transpose operation. and This represents the DD channel impulse response (CIR) vector, and Equation (4) can be used to determine this.

[0055] (4)

[0056] In equation (4), RE represents the location of the DMRS in the FD, and This represents a relatively large DFT matrix, for example, .

[0057] For DMRS type I case And for DMRS type II, .

[0058] Here, we assume the PDP length is... It is known.

[0059] For depends With sufficiently high DD resolution, we can assume The components of} are uncorrelated. We can further assume... It is a zero mean with covariance, as shown in equation (5).

[0060] (5)

[0061] Equation (5) represents the result of ordinary second-order statistics without cross-correlation assumptions across the two levels. Here, the goal is to estimate the diagonal matrix. Subsequently, the matrix used to perform NBCE can be calculated. and .

[0062] For example, Equation (6) can be used to calculate it.

[0063] (6)

[0064] The estimated PDP can be used The calculation is shown in equation (7).

[0065] (7)

[0066] Figure 2 A method for performing NBCE using a narrowband reference signal according to an embodiment is illustrated. Specifically, Figure 2 This demonstrates the use of a narrowband reference signal. 220 without requiring a broadband reference signal (For example, such as) Figure 1 The method shown in 110) is used to execute NBCE.

[0067] refer to Figure 2 At point 201, the electronic device can calculate the received narrowband reference signal. 220 (e.g., DMRS) sample covariance matrix For example, the sample covariance matrix can be calculated as follows: The measurements are collected in terms of frequency, space, and time.

[0068] At position 202, the electronic device can use the sample covariance matrix. To perform DMRS-based PDP estimation, thereby obtaining the diagonal matrix. According to various embodiments of this disclosure, DMRS-based PDP estimation at 202 can be performed using the LS method, the SBL-based method, or the BSBL-based method, which will be described in more detail below.

[0069] At point 203, electronic devices can be used. To perform FD correlation matrix estimation in order to obtain the matrix and .

[0070] At position 204, the narrowband reference signal at position 203 is used. 220 Estimated Matrix and Electronic devices can handle narrowband reference signals 220 Perform NBCE to obtain LMMSE estimate 230, for example, as given in equation (2).

[0071] As mentioned above, electronic devices can use the sample covariance matrix. To perform DMRS-based PDP estimation (e.g., in Figure 2 (at position 202 in the text), so that the LS method can be used to obtain .

[0072] Received narrowband reference signal Covariance matrix of 220 It can be expressed as shown in equation (8).

[0073]

[0074] (8)

[0075] right Vectorization yields equation (9).

[0076]

[0077]

[0078]

[0079] (9)

[0080] In equation (9), This indicates the element-wise conjugation operation. This represents the Khatri-Rao product, which is a column-wise Kronecker product, and is symbolized by... For example, consider two matrices D and E with the same number of columns. Then, ,in, and Let represent the j-th column of D and E respectively.

[0081] By representation ,in, Represents the sample covariance matrix, PDP estimation (e.g., in...) Figure 2 The LS solution at position 202 is given by equation (10).

[0082] (10)

[0083] In equation (10), + denotes Moore-Penrose or pseudo-inverse. Let... For a column-rank matrix B, the pseudoinverse can be computed as follows: You can add minor regularization terms. To avoid ill-conditioned matrix inversion, as shown in equation (11).

[0084] (11)

[0085] Received It may not be a non-negative vector, and therefore, additional steps can be performed to set the negative components in the estimate to zero.

[0086] To reduce the complexity of LS PDP estimation, the number of taps to be estimated can be limited by assuming “piecewise uniform PDP”, as shown in equation (12).

[0087] (12)

[0088] In equation (12), Using this model, the LS equation can be given by equation (13).

[0089] (13)

[0090] In equation (13), . It can be estimated as shown in equation (14).

[0091] (14)

[0092] Using equation (14), the size of the matrix inverse is from Reduce to .

[0093] As mentioned above, electronic devices can use the sample covariance matrix. To perform DMRS-based PDP estimation (e.g., at 202) so that an SBL-based approach can be used to obtain .

[0094] SBL is a Bayesian technique for sparse decomposition of a signal. More specifically, it is a general framework that, by combining a unique FD-OCC structure, can be dedicated to PDP estimation based on the received signal in equation (3).

[0095] The SBL-based method has some advantages over the LS method because it utilizes the sparsity of the PDP and finds the maximum likelihood estimation method. .

[0096] Applying a Gaussian prior: Although Gaussian priors can be utilized, the Sparse Signal Recovery (SSR) problem does not require modeling unknown sparse vectors that obey Gaussian priors. Even so, the SBL process begins by imposing (empirical) Gaussian priors, and it learns the prior parameters, i.e., the PDP in this case, based on measurements.

[0097] The following prior distributions in equation (15) can be applied.

[0098] (15)

[0099] In equation (15), Due to the FD-OCC structure, in calculation Next, post-processing steps are performed to estimate the expected value. matrix.

[0100] Maximum likelihood estimation (MLE) optimization problem: In equation (1) Distribution ,in, .

[0101] The diagonal elements are unique, as defined under SBL in equation (15). Therefore, the marginal likelihood maximization problem can be equivalently written as shown in equation (16).

[0102] (16)

[0103] In equation (16), it is assumed that multiple independent and identically distributed measurements across the FD or time domain (TD) are available, and .

[0104] Iterative algorithm: Under the SBL-based method, the optimization in equation (16) can be solved iteratively. Let... and use Initialize, and set one component to a positive value in each iteration. This method is also greedy because it selects... The components are used for updates, which leads to the maximum increase in the log-likelihood function.

[0105] set up This indicates that it has been added to the model to form. of The set of column indexes. That is, let's say... This indicates that it is set to a positive value. The components. New column. It can be added to a collection The model is represented.

[0106] First, we perform a separation of the terms in the cost function of equation (16), for some These items depend on Using the matrix inversion lemma, we obtain equation (17).

[0107] (17)

[0108] In equation (17), ,in, and , and among them, .

[0109] The goal here is to select the optimal greedy column index in this iteration by solving equation (18). .

[0110] (18)

[0111] superior The minimization of can be calculated in closed form, as shown in equation (19).

[0112] (19)

[0113] For each column index k, in At the above optimal value, the problem in equation (18) is simplified as shown in equation (20).

[0114] (20)

[0115] The algorithm will... Add to the model to perform the update. And update accordingly , and This uses the new calculation The subsequent values ​​can be updated recursively.

[0116] and The calculation can include calculation The inverse is updated in each iteration. This is because each iteration only adds... Since it is a single column, it can be updated using the matrix inversion lemma. Iteration The value at a location can be represented by a superscript. Highlight. More specifically, set Let represent the inverse matrix used in equation (17), and let represent the inverse matrix used in equation (17). Indicates to iteration The column index is added at that point. Then, for the next iteration, i.e. Update the inverse matrix as shown in equation (21).

[0117] (twenty one)

[0118] In equation (21), and .

[0119] Then, as shown in equations (22) and (23), update In order to obtain .

[0120] (twenty two)

[0121] (twenty three)

[0122] The SBL method (and the BSBL-based method described below) can be achieved solely through... And depends on the measurement Therefore, it can be done by... calculate To reduce the complexity of the algorithm, making Such a computation can be performed using truncated singular value decomposition (SVD). .

[0123] Algorithm steps

[0124] Step 1: Initialization

[0125] Step 2: Calculate according to equation (17) and (Effective recursive implementations are provided in equations (22) and (23))

[0126] Step 3: Select the best column index according to equation (20)

[0127] Step 4: Calculate the value corresponding to the index according to equation (19). Optimal PDP tap values ​​for columns

[0128] Step 5: Add to the model, i.e., update and Proceed to step 2 and repeat until predetermined conditions are met, for example, until the desired number of PDP taps have been updated or a process has been performed. The next iteration.

[0129] The SBL-based method does not mandate a specific structure when estimating the PDP. For the received signal in equation (1), equation (24) holds.

[0130] (twenty four)

[0131] Furthermore, the identical terms following the maximum likelihood estimate can be forced by averaging the two estimates. Therefore, the estimate... It can be replaced by equation (25).

[0132] (25)

[0133] The length of the PDP estimated above is This is the expected length.

[0134] As mentioned above, electronic devices can use the sample covariance matrix. To perform DMRS-based PDP estimation so that a BSBL-based approach can be used to obtain... .

[0135] Applying a Gaussian prior: Specifically, the following priors can be applied using equation (26).

[0136] (26)

[0137] In equation (26), .

[0138] MLE optimization problem: Therefore, in equation (1) Distribution ,in, It can be determined according to equation (27).

[0139] (27)

[0140] The modified marginal likelihood maximization problem can be equivalently written as shown in equation (28).

[0141] (28)

[0142] Iterative algorithm: In order to apply the prior in equation (26), in each iteration, the index is... (in, Add both columns simultaneously to the column by The model is represented because these two columns share the same Next, according to equation (29), we can... and The contributions of the related columns are written into the cost function in equation (28).

[0143] (29)

[0144] In equation (29), ,in, and .

[0145] The goal is to select the optimal greedy column index pair in this iteration by solving equation (30). .

[0146] (30)

[0147] Unlike SBL-based methods, regarding The closed-form expression for the internal optimization is unknown. Therefore, a heuristic approach can be provided, as shown in equation (31) below.

[0148] (31)

[0149] The inverse matrix can be updated at each iteration by following steps similar to those of SBL-based methods. Instead of calculating the inverse, we can use the matrix inverse lemma as shown in equation (32).

[0150] (32)

[0151] In equation (32), and In contrast to SBL-based methods, this approach adds [something] at each iteration. Since there are two columns, the reverse update uses rank 2 for modification. Update In order to obtain As shown in equations (33) and (34).

[0152] (33)

[0153]

[0154] (34)

[0155] Algorithm steps

[0156] Step 1: Initialization

[0157] Step 2: Calculate according to equation (29) and (Effective recursive implementations are provided in equations (33) and (34))

[0158] Step 3: Select the best column index according to equation (30)

[0159] Step 4: Calculate the value corresponding to the index according to equation (31). and Optimal PDP tap values ​​for columns

[0160] Step 5: Add to the model, i.e., update and Proceed to step 2 and repeat until predetermined conditions are met, for example, until the desired number of PDP taps have been updated or a process has been performed. The next iteration.

[0161] The above method assumes a maximum number of PDP taps (i.e., DS information regarding [the specific data] is available through other sources. However, in cases where such information is unavailable or partially available, the algorithm may rely on estimates that can be modified or adapted over time.

[0162] Figure 3A method for performing NBCE using a narrowband reference signal, including an adaptive DS process, according to an embodiment is illustrated. More specifically, Figure 3 A method for performing NBCE is shown, which includes an adaptive DS process, wherein an initial DS value is set and increased over time.

[0163] refer to Figure 3 The operations at positions 301 to 304 correspond to the operations at positions 201 to 204 as described above.

[0164] At point 301, the electronic device can calculate the received narrowband reference signal. 320 (e.g., DMRS) sample covariance matrix .

[0165] At position 302, the electronic device can use the sample covariance matrix. To perform DMRS-based PDP estimation, thereby obtaining the diagonal matrix. .

[0166] At point 303, electronic devices can be used. To perform FD correlation matrix estimation in order to obtain the matrix and .

[0167] At position 304, the narrowband reference signal at position 303 is used. 320 estimated matrix and Electronic devices can handle narrowband reference signals 320 Perform NBCE to obtain LMMSE estimate 330, for example, as given in equation (2).

[0168] Electronic devices can, for example, set an initial DS value based on TRS. This value can be lower than the actual length of the PDP. Here, This represents the PDP estimate obtained using any of the methods described above. At 305, the electronic device can determine whether the initial DS value should be increased. For example, at 305, the electronic device can determine whether the following condition in equation (35) holds.

[0169] (35)

[0170] In equation (35), a trial-and-error method can be used to select the threshold. ,For example, dB.

[0171] If the condition holds at 305, then at 306, the DS value (i.e., the PDP length) can be increased by a fixed amount, for example, 10 taps, to improve NBCE performance. More specifically, the increased DS value can be used for subsequent DMRS-based PDP estimation at 302.

[0172] Figure 4 A method for performing NBCE using a narrowband reference signal, including an adaptive DS process, according to an embodiment is illustrated. More specifically, Figure 4 A method for performing NBCE is shown, which includes an adaptive DS process, wherein an initial DS value is set and decreased over time.

[0173] refer to Figure 4 The operations at 401 to 404 correspond to the operations at 201 to 204 as described above.

[0174] At position 401, the electronic device can calculate the received narrowband reference signal. 420 (e.g., DMRS) sample covariance matrix .

[0175] At position 402, the electronic device can use the sample covariance matrix. To perform DMRS-based PDP estimation, thereby obtaining the diagonal matrix. .

[0176] At point 403, electronic devices can be used. To perform FD correlation matrix estimation in order to obtain the matrix and .

[0177] At 404, use the narrowband reference signal at 403. 420 Estimated Matrix and Electronic devices can handle narrowband reference signals 420 Perform NBCE to obtain LMMSE estimate 430, for example, as given in equation (2).

[0178] Electronic devices can, for example, set an initial DS value based on TRS. This value can be higher than the actual PDP length.

[0179] At position 405, the electronic device can determine whether the initial DS value should be reduced. For example, at position 405, the electronic device can base its decision on the estimated PDP. The number of zero taps at the end of the middle determines the reduced DS value, i.e., the PDP length. In other words, at 405, if the PDP estimate is expressed as shown in equation (36):

[0180] (36)

[0181] At position 406, the PDP length can be reduced to a certain value. More specifically, the reduced DS value can be used for subsequent DMRS-based PDP estimation at 402.

[0182] By storing the recommendations on multiple time slots And then reducing it to the maximum value within that recommendation can add additional reliability to this decision. For example, the method can wait for two time slots before reducing the PDP length, during which time the estimated PDP has trailing zeros.

[0183] According to another embodiment, it is also possible to utilize Figure 3 and Figure 4 A combination of methods.

[0184] Figure 5 This is a flowchart illustrating a method performed by an electronic device according to an embodiment.

[0185] refer to Figure 5 In step 501, the electronic device can receive a narrowband reference signal. For example, such as... Figure 2 As shown at point 201, the electronic device can calculate the received narrowband reference signal. 220 (e.g., DMRS) sample covariance matrix .

[0186] At step 502, the electronic device can use a narrowband reference signal to estimate the frequency domain correlation matrix. For example, as Figure 2 As shown, at position 202, the electronic device can use the sample covariance matrix. To perform DMRS-based PDP estimation, thereby obtaining the diagonal matrix. And at point 203, electronic devices can use To perform FD correlation matrix estimation in order to obtain the matrix and .

[0187] At step 503, the electronic device can use the estimated frequency domain correlation matrix to perform NBCE on the narrowband reference signal. For example, as Figure 2 As shown at position 204, the narrowband reference signal is used at position 203. 220 Estimated Matrix and Electronic devices can handle narrowband reference signals 220 Perform NBCE to obtain LMMSE estimate 230. In other words, electronic devices can perform NBCE on a narrowband reference signal without requiring an additional wideband signal (e.g., TRS).

[0188] Figure 6 This is a block diagram of an electronic device in a network environment 600 according to an embodiment. For example, such as... Figure 6 The illustrated electronic device 601 can perform NBCE using a narrowband reference signal, such as Figure 2 , Figure 3 and Figure 4 The method is shown below.

[0189] refer to Figure 6 In network environment 600, electronic device 601 can communicate with electronic device 602 via a first network 698 (e.g., a short-range wireless communication network), or with electronic device 604 or server 608 via a second network 699 (e.g., a long-range wireless communication network). Electronic device 601 can communicate with electronic device 604 via server 608. Electronic device 601 may include processor 620, memory 630, input device 650, sound output device 655, display device 660, audio module 670, sensor module 676, interface 677, connection terminal 678, haptic module 679, camera module 680, power management module 688, battery 689, communication module 690, subscriber identification module (SIM) 696, or antenna module 697. In one embodiment, at least one of the components (e.g., display device 660 or camera module 680) may be omitted from electronic device 601, or one or more other components may be added to electronic device 601. Some of the components may be implemented as a single integrated circuit (IC). For example, sensor module 676 (e.g., fingerprint sensor, iris sensor, or illuminance sensor) may be embedded in display device 660 (e.g., display).

[0190] Processor 620 can execute software (e.g., program 640) to control at least one other component (e.g., hardware or software component) of electronic device 601 coupled to processor 620, and can, for example, according to Figure 2 , Figure 3 and Figure 4 The methods shown are used to perform various data processing or calculations.

[0191] As at least part of data processing or computation, processor 620 can load commands or data received from another component (e.g., sensor module 676 or communication module 690) into volatile memory 632, process the commands or data stored in volatile memory 632, and store the resulting data in non-volatile memory 634. Processor 620 may include a main processor 621 (e.g., a central processing unit (CPU) or application processor (AP)) and an auxiliary processor 623 (e.g., a graphics processing unit (GPU), image signal processor (ISP), sensor hub processor, or communication processor (CP)), which may operate independently of or in conjunction with the main processor 621. Additionally or alternatively, auxiliary processor 623 may be adapted to consume less power than the main processor 621 or to perform specific functions. Auxiliary processor 623 may be implemented separately from or as part of the main processor 621.

[0192] When the main processor 621 is inactive (e.g., in sleep mode), the auxiliary processor 623 may take over from the main processor 621. Alternatively, when the main processor 621 is active (e.g., executing an application), the auxiliary processor 623 may work with the main processor 621 to control at least some functions or states associated with at least one component of the electronic device 601 (e.g., display device 660, sensor module 676, or communication module 690). The auxiliary processor 623 (e.g., an image signal processor or a communication processor) may be implemented as part of another component (e.g., a camera module 680 or communication module 690) that is functionally associated with the auxiliary processor 623.

[0193] Memory 630 may store various data used by at least one component of electronic device 601 (e.g., processor 620 or sensor module 676). The various data may include, for example, software (e.g., program 640) and input or output data for commands associated with it. Memory 630 may include volatile memory 632 or non-volatile memory 634. Non-volatile memory 634 may include internal memory 636 and / or external memory 638.

[0194] The program 640 can be stored as software in the memory 630 and may include, for example, an operating system (OS) 642, middleware 644, or application 646.

[0195] Input device 650 can receive commands or data from outside electronic device 601 (e.g., a user) that will be used by another component of electronic device 601 (e.g., processor 620). Input device 650 may include, for example, a microphone, mouse, or keyboard.

[0196] The sound output device 655 can output sound signals to the outside of the electronic device 601. The sound output device 655 may include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as playing multimedia or recording, while the receiver can be used to receive incoming calls. The receiver can be implemented separately from the speaker or as part of the speaker.

[0197] Display device 660 can visually provide information to the outside of electronic device 601 (e.g., to a user). Display device 660 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 660 may include touch circuitry adapted to detect touch, or sensor circuitry adapted to measure the intensity of the force caused by a touch (e.g., a pressure sensor).

[0198] The audio module 670 can convert sound into electrical signals and vice versa. The audio module 670 can acquire sound via the input device 650 or output sound via headphones or sound output device 655 of an external electronic device 602 that is directly (e.g., wired) or wirelessly coupled to the electronic device 601.

[0199] Sensor module 676 can detect the operating state of electronic device 601 (e.g., power or temperature) or the environmental state outside electronic device 601 (e.g., user state), and then generate an electrical signal or data value corresponding to the detected state. Sensor module 676 may include, for example, a gesture sensor, gyroscope sensor, barometric pressure sensor, magnetic sensor, accelerometer, grip sensor, proximity sensor, color sensor, infrared (IR) sensor, biometric sensor, temperature sensor, humidity sensor, or illuminance sensor.

[0200] Interface 677 may support one or more specified protocols for coupling electronic device 601 directly (e.g., wired) or wirelessly to external electronic device 602. Interface 677 may include, for example, a High Definition Multimedia Interface (HDMI), a Universal Serial Bus (USB) interface, a Secure Digital Card (SD) interface, or an audio interface.

[0201] Connection terminal 678 may include a connector via which electronic device 601 can be physically connected to external electronic device 602. Connection terminal 678 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0202] The tactile module 679 can convert electrical signals into mechanical stimuli (e.g., vibration or motion) or electrical stimuli, which can be recognized by a user via touch or kinesthesia. The tactile module 679 may include, for example, a motor, a piezoelectric element, or an electrical stimulator.

[0203] Camera module 680 can capture still or moving images. Camera module 680 may include one or more lenses, an image sensor, an image signal processor, or a flash. Power management module 688 can manage the power supplied to electronic device 601. Power management module 688 may be implemented as at least a part of, for example, a power management integrated circuit (PMIC).

[0204] Battery 689 can supply power to at least one component of electronic device 601. Battery 689 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0205] Communication module 690 can support the establishment of a direct (e.g., wired) or wireless communication channel between electronic device 601 and external electronic devices (e.g., electronic device 602, electronic device 604, or server 608), and perform communication via the established communication channel. Communication module 690 may include one or more communication processors that can operate independently of processor 620 (e.g., AP), and communication module 690 supports direct (e.g., wired) or wireless communication. Communication module 690 may include a wireless communication module 692 (e.g., a cellular communication module, a short-range wireless communication module, or a Global Navigation Satellite System (GNSS) communication module) or a wired communication module 694 (e.g., a local area network (LAN) communication module or a power line communication (PLC) module). A corresponding one of these communication modules can communicate via a first network 698 (e.g., a short-range communication network, such as BLUETOOTH). TM The communication module 692 communicates with external electronic devices via a wireless communication module 699 (e.g., a Wi-Fi Direct or Infrared Data Association (IrDA) standard) or a second network 699 (e.g., a remote 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 that are separate from each other (e.g., multiple ICs). The wireless communication module 692 can use subscriber information (e.g., International Mobile Subscriber Identity (IMSI)) stored in the subscriber identification module 696 to identify and authenticate electronic devices 601 in the communication network (e.g., a first network 698 or a second network 699).

[0206] Antenna module 697 can transmit signals or power to or from the outside of electronic device 601 (e.g., external electronic device). Antenna module 697 may include one or more antennas, from which, for example, at least one antenna suitable for a communication scheme used in a communication network (such as a first network 698 or a second network 699) can be selected by communication module 690 (e.g., wireless communication module 692). Signals or power can then be transmitted or received between communication module 690 and external electronic device via the selected at least one antenna.

[0207] Commands or data can be sent or received between electronic device 601 and external electronic device 604 via server 608 coupled to a second network 699. Each of electronic devices 602 and 604 can be a device of the same or different type as electronic device 601. All or some operations to be performed at electronic device 601 can be performed at one or more of the external electronic devices 602, 604, or server 608. For example, if electronic device 601 is required to perform a function or service automatically or in response to a request from a user or another device, instead of performing the function or service, or in addition to performing the function or service, electronic device 601 can request one or more external electronic devices to perform at least a portion of the function or service. The one or more external electronic devices receiving the request 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 performance to electronic device 601. Electronic device 601 can provide the result, with or without further processing, as at least part of a response to the request. For this purpose, cloud computing, distributed computing, or client-server computing technologies can be used, for example.

[0208] Figure 7 A system comprising a UE 705 and a gNB 710 communicating with each other, according to an embodiment, is shown.

[0209] refer to Figure 7 The UE may include various methods that can perform the methods disclosed herein (e.g., Figure 2 , Figure 3 and Figure 4 The method shown includes a processing circuit (or means for processing) 720 and a radio 715. For example, the processing circuit 720 can receive transmissions, such as narrowband reference signals, from a network node (gNB) 710 via the radio 715, and the processing circuit 720 can transmit signals to the gNB 710 via the radio 715. The processing circuit 720 can also perform various data processing or calculations, for example, according to... Figure 2 , Figure 3 and Figure 4The method shown.

[0210] The embodiments and operations of the subject matter 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 in a combination of one or more of these. 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 or control of the operation of a data processing apparatus. Alternatively or additionally, the program instructions can be encoded on an artificially generated propagating signal (e.g., a machine-generated electrical, optical, or electromagnetic signal) to encode information for transmission to a suitable receiver device for execution by the data processing apparatus. The computer storage medium can be a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination thereof, or included therein. Furthermore, while the computer storage medium is not a propagating signal, it can be a source or destination of computer program instructions encoded in an artificially generated propagating signal. The computer storage medium can also be one or more separate physical components or media (e.g., multiple CDs, discs, or other storage devices) or included therein. 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.

[0211] While this specification may contain many details of specific implementations, such 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 this specification within the context of separate embodiments 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 separately 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, one or more features from a claimed combination may be removed from that combination in some cases, and the claimed combination may be for sub-combinations or variations thereof.

[0212] Similarly, although 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 or sequence shown, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of 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.

[0213] Therefore, specific embodiments of the subject matter have been described herein. Other embodiments are within the scope of the following claims. In some cases, the actions set forth in the claims may 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 sequence shown to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0214] 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 specific exemplary teachings discussed above, but rather is defined instead by the following claims.

Claims

1. A method performed by an electronic device, the method comprising: Receive narrowband reference signals; The narrowband reference signal is used to estimate the frequency domain correlation matrix; as well as The estimated frequency domain correlation matrix is ​​used to perform narrowband channel estimation on the narrowband reference signal.

2. The method according to claim 1, wherein, Estimating the frequency domain correlation matrix using the narrowband reference signal includes: The narrowband reference signal is used to calculate the sample covariance matrix; The power delay distribution (PDP) of the narrowband reference signal is estimated using the sample covariance matrix; and The frequency domain correlation matrix is ​​estimated using the estimated PDP of the narrowband reference signal.

3. The method according to claim 2, wherein, Estimating the PDP of the narrowband reference signal includes using the least squares method to estimate the PDP of the narrowband reference signal.

4. The method according to claim 2, wherein, Estimating the PDP of the narrowband reference signal includes using a sparse Bayesian learning-based method to estimate the PDP of the narrowband reference signal.

5. The method according to claim 4, wherein, Estimating the PDP of the narrowband reference signal using the sparse Bayesian learning-based method includes: Initialize variables, including the estimated PDP, the inverse covariance matrix, and a set of indices; Based on the initialized variables, calculate the first matrix and the second matrix for indices not included in the set of indices; Select columns using the first and second matrices calculated; Calculate the PDP tap value corresponding to the column with the first index; Update the set of indexes using the first index; and Repeat the calculation, selection, calculation, and update process until the predetermined conditions are met.

6. The method according to claim 2, wherein, Estimating the PDP of the narrowband reference signal includes using a block sparse Bayesian learning-based method to estimate the PDP of the narrowband reference signal.

7. The method according to claim 6, wherein, Estimating the PDP of the narrowband reference signal using the block sparse Bayesian learning-based method includes: Initialize variables, including the estimated PDP, the inverse covariance matrix, and a set of indices; Based on the initialized variables, calculate the first matrix and the second matrix for indices not included in the set of indices; Select columns using the first and second matrices calculated; Calculate the PDP tap value corresponding to the column with the first index and the second index; Update the set of indexes using the first and second indexes; and Repeat the calculation, selection, calculation, and update process until the predetermined conditions are met.

8. The method of claim 1, further comprising time-adaptive delay extension DS.

9. The method according to claim 8, wherein, DS adaptation over time includes: Set the initial DS value; and The initial DS value is increased over time based on predetermined conditions.

10. The method according to claim 9, wherein, The predetermined conditions include: ， in, Let L represent the estimated power delay distribution (PDP) of the narrowband reference signal, L represent the number of PDP taps, and thresh represent a predetermined threshold.

11. The method according to claim 8, wherein, DS adaptation over time includes: Set the initial DS value; and Based on predetermined conditions, the initial DS value is decreased over time.

12. The method according to claim 11, wherein, The predetermined condition includes the presence of at least one trailing zero in the estimated power delay distribution (PDP) of the narrowband reference signal.

13. An electronic device, comprising: transceiver; and The processor is configured as follows: The narrowband reference signal is received via the transceiver. The narrowband reference signal is used to estimate the frequency domain correlation matrix, and The estimated frequency domain correlation matrix is ​​used to perform narrowband channel estimation on the narrowband reference signal.

14. The electronic device according to claim 13, wherein, The processor is also configured to use the narrowband reference signal to estimate the frequency domain correlation matrix in the following manner: The narrowband reference signal is used to calculate the sample covariance matrix; The power delay distribution (PDP) of the narrowband reference signal is estimated using the sample covariance matrix. as well as The frequency domain correlation matrix is ​​estimated using the estimated PDP of the narrowband reference signal.

15. The electronic device according to claim 14, wherein, The processor is also configured to use the least squares method to estimate the PDP of the narrowband reference signal.

16. The electronic device according to claim 14, wherein, The processor is also configured to estimate the PDP of the narrowband reference signal using a sparse Bayesian learning-based method.

17. The electronic device according to claim 14, wherein, The processor is also configured to estimate the PDP of the narrowband reference signal using a block sparse Bayesian learning-based method.

18. The electronic device according to claim 13, wherein, The processor is also configured to adapt the delay extension DS over time.

19. The electronic device according to claim 18, wherein, The processor is also configured to adapt to the DS over time in the following manner: Set the initial DS value; as well as Based on predetermined conditions, the initial DS value is increased over time. The predetermined conditions include: ,and in, Let L represent the estimated power delay distribution (PDP) of the narrowband reference signal, L represent the number of PDP taps, and thresh represent a predetermined threshold.

20. The electronic device according to claim 18, wherein, The processor is also configured to adapt to the DS over time in the following manner: Set the initial DS value; and Based on predetermined conditions, the initial DS value decreases over time. The predetermined condition includes the presence of at least one trailing zero in the estimated power delay distribution (PDP) of the narrowband reference signal.

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