Methods for compressing multiple digital signals, computer program products for the same, non-temporary computer-readable storage media, processing units, wireless devices and chips

Abstract

A method (100) for compressing a first plurality of digital signals obtained from a first plurality (NRX) frequency division multiplexing, FDM, signal received by a receiving device, comprising: (110) obtaining two or more channel estimation matrices (H1, H2, ..., HK) related to the propagation channels of the FDM signal; and (120) applying a function (F) to the two or more channel estimation matrices to obtain a resulting matrix (RM), wherein the resulting matrix (RM) is obtained from applying (120), and the resulting matrix (RM) is obtained from a first decomposition matrix (U) containing a first coefficient vector (U1, U2, ..., UN) and a second different from the first decomposition matrix (U) containing a second coefficient vector (Λ1, Λ2, ..., ΛN). A method (100) comprising: decomposing a first decomposition matrix (Λ) into (130) a first decomposition matrix (U) being a unitary eigenvector matrix containing one or more eigenvectors; determining a spatial filter coefficient vector (T1, T2, ..., TN) from a first coefficient vector (U1, U2, ..., UN) (140); selecting a subset (T1, ..., TM) of the spatial filter coefficient vector (T1, T2, ..., TN) (145) a subset (T1, ..., TM) relating to the most dominant eigenvector of the first decomposition matrix (U); and compressing a first plurality (NRX) digital signal using only the subset (T1, ..., TM) (151). Corresponding computer program products, processing units, wireless devices and chips are also disclosed.

Description

[[Technical Field]]

[0001] Technical Field The present disclosure relates to a method of compressing a (first) plurality of digital signals, a computer program product therefor, a processing unit, a wireless device and a chip.

[0002] More specifically, the present disclosure relates to a method of compressing a (first) plurality of digital signals, a computer program product, a processing unit, a wireless device and a chip as defined in the preamble of an independent claim. [[Background Art]]

[0003] Background Art U.S. Patent No. 10833751B2 discloses that precoding coefficients can be compressed based on a signal-to-interference-plus-noise ratio or path loss of user equipment in a fronthaul cloud radio access network system.

[0004] Further, in the case of a fully digital beamforming receiver architecture, the signal received on subcarrier k at each receive (Rx) antenna is y k =H k x k +w k can be modeled as follows, where [[Math.]] is a vector including inputs on subcarrier k from each of N rx spatially distributed Rx antennas, [[Math.]] is a channel matrix representing a radio channel between each of N tx transmit (Tx) (antenna) ports and N rx receive (Rx) antennas on subcarrier k, [[Math.]] is N tx a vector comprising modulation symbols transmitted on subcarrier k from each of N Tx (antenna) ports ("MIMO layers"), and

Math

[0005] A minimum mean square error (MMSE) equalizer for a fully digital beamforming receiver is

Math

Math

[0006] However, with this approach, since signals received at all RX antennas are passed to the baseband, a high data rate is required, which may consequently result in high power consumption. Furthermore, the maximum rank of H k is min(N rx , N tx ), but the actual rank may be lower depending on the radio channel. Furthermore, to mitigate the influence of an ill-conditioned matrix (especially when the least squares (LS) method is used instead of the MMSE method (in this case, the regularizing identity matrix I in G k does not exist)), G kSingular value decomposition (SVD) may be used to derive the inverse or pseudo-inverse matrix in the calculation. When implementing digital beamforming in receivers such as wireless devices (WDs), a fully digital beamformer (receiver architecture) becomes very complex. Therefore, there is a need for a method and / or device / unit with reduced complexity (e.g., with performance nearly the same as a fully digital beamforming receiver).

[0007] Furthermore, there is a need for methods and / or devices / units that increase the flexibility in representing radio channels and their variations spatially, spectrally, and / or temporally.

[0008] U.S. Patent Application Publication 2012 / 0062421A1 discloses a mechanism for mitigating user-to-user interference in a multi-user wireless communication environment. [Overview of the project] [Problems that the invention aims to solve]

[0009] overview The purpose of this disclosure is to mitigate, reduce, or eliminate one or more of the above-mentioned defects and shortcomings of the prior art, and / or to solve at least the above-mentioned problems or other problems. [Means for solving the problem]

[0010] According to the first aspect, a method for converting / compressing a plurality of (first) digital signals obtained from a plurality of (first) frequency division multiplexing, FDM, signals transmitted from a plurality of (second) transmitting antenna ports of a transmitting device and received by a receiving device, comprising: obtaining two or more channel estimation matrices related to the propagation channels of the FDM signal; and applying a function to the two or more channel estimation matrices to obtain a resulting matrix, wherein the resulting matrix is ​​obtained by applying the function, and the resulting matrix is ​​divided into a first decomposition matrix containing a first coefficient vector, A method is provided which includes decomposing a first decomposition matrix into a second decomposition matrix distinct from the first decomposition matrix, the first decomposition matrix being a unitary eigenvector matrix containing one or more eigenvectors; determining a spatial filter coefficient vector from the first coefficient vector; selecting a subset of the spatial filter coefficient vector, the subset relating to one or more of the four most dominant eigenvalues ​​of the second decomposition matrix; and using only the subset, converting a plurality of (first) digital signals into a plurality of (third) virtual antenna streams using a spatial filter.

[0011] According to some embodiments, the number of virtual antenna streams is greater than the number of transmitting antenna ports.

[0012] According to some embodiments, the FDM signal is an orthogonal frequency division multiplexing (OFDM) signal.

[0013] According to some embodiments, the function is a quadratic function.

[0014] According to some embodiments, the subset relates to coefficients associated with the 1, 2, or 4 most dominant eigenvectors of the first decomposition matrix (only).

[0015] According to some embodiments, a plurality of (first) FDM signals are transmitted from a plurality of (second) transmitting antenna ports of a transmitting device such as a wireless device or a base station.

[0016] According to some embodiments, the method further includes transmitting a plurality of (third) virtual antenna streams to a baseband (BB) processor for further processing / beamforming.

[0017] According to some embodiments, the method further includes dividing a (third) number of virtual antenna streams into a first subset and a second subset, and acquiring (120), performing (130), determining (140), and compressing (150) are performed for the first and second subsets, respectively.

[0018] According to some embodiments, decomposition includes performing singular value decomposition, or SVD.

[0019] According to some embodiments, decomposition includes performing eigenvalue decomposition, spectral decomposition, or eigendecomposition of the resulting matrix.

[0020] According to some embodiments, determining the spatial filter coefficient vectors involves selecting the spatial filter coefficient vectors as one or more column vectors of a first decomposition matrix.

[0021] According to some embodiments, the spatial filter coefficient vector is selected to be an orthonormal spatial filter coefficient vector.

[0022] According to some embodiments, obtaining one or more channel estimation matrices includes estimating one or more channel estimation matrices for each subcarrier, estimating one or more channel estimation matrices for each resource block, or estimating one or more channel estimation matrices for each frequency range.

[0023] According to some embodiments, the function includes weights, and obtaining them involves setting weights for each subcarrier, setting weights for each resource block, setting weights for each frequency range, and / or setting weights for each TX (antenna) port (Q).

[0024] According to some embodiments, obtaining two or more channel estimation matrices [H] at a first time point is possible. k The objective is to determine (t) and, at the second time point, to determine two or more channel estimation matrices [H k The process involves determining (t-τ), and the function is a function of the squares of two or more channel estimation matrices at the first time point and the squares of two or more channel estimation matrices at the second time point.

[0025] According to some embodiments, the receiving device receives information (from the transmitting device) regarding the number of transmitting antenna ports of the transmitting device.

[0026] According to a second aspect, a program product is provided which, when executed on at least one processor of a processing device, causes the processing device to perform a method according to the first aspect or any of the embodiments referred to herein.

[0027] According to a third aspect, a non-temporary computer-readable storage medium is provided for storing one or more programs configured to be executed by one or more processors of a processing device, wherein the one or more programs, when executed by the processing device, include instructions causing the processing device to perform a method according to the first aspect or any of the embodiments referenced herein.

[0028] According to a fourth aspect, a processing unit is provided which performs the following steps: obtaining two or more channel estimation matrices related to one or more propagation channels of an FDM signal; applying a function such as a quadratic function (QF) to the two or more channel estimation matrices to obtain a resulting matrix, the resulting matrix being obtained from the application; matrix decomposition of the resulting matrix into a first decomposition matrix containing a first coefficient vector and a second decomposition matrix different from the first decomposition matrix containing a second coefficient vector; determining spatial filter coefficient vectors from the first coefficient vector; selecting a subset of spatial filter coefficient vectors, the subset relating to one or more of the four most dominant eigenvalues ​​of the second decomposition matrix, such as coefficients relating to the 1, 2, or 4 most dominant eigenvalues ​​of the second decomposition matrix; and converting a plurality of (first) digital signals into a plurality of virtual antenna streams using only the subset.

[0029] According to some embodiments, the number of virtual antenna streams is greater than the number of transmitting antenna ports of the transmitting device used to transmit multiple FDM signals.

[0030] According to a fifth aspect, a wireless device (WD) is provided which includes the processing unit of the fourth aspect.

[0031] According to the sixth aspect, a chip is provided that includes the processing unit of the fourth aspect.

[0032] The effects and features of the second, third, fourth, fifth, and sixth embodiments are entirely or to a considerable extent similar to those described above in relation to the first embodiment, and vice versa.

[0033] Embodiments mentioned in relation to the first aspect are fully or substantially compatible with the second, third, fourth, fifth, and sixth aspects, and vice versa.

[0034] The advantages of some embodiments include, for example, increased freedom and / or flexibility in representing radio channels and their variations spatially, spectrally, and temporally compared to conventional MRC-based receivers.

[0035] Another advantage of some embodiments is that power consumption (in wireless devices) is reduced or optimized.

[0036] A further advantage of some embodiments is that they provide a less complex system / receiver compared to, for example, a fully digital beamforming receiver (with nearly the same or equivalent performance).

[0037] Another advantage of some embodiments is that a low level of complexity is achieved.

[0038] Another advantage of some embodiments is that implementation is simplified.

[0039] Another further advantage of some embodiments is the reduction in complexity.

[0040] Other advantages include potentially improved, more robust, and / or more accurate beamforming, and / or improved signal quality.

[0041] This disclosure should be evident from the detailed description set forth below. The detailed description and specific examples disclose preferred embodiments of this disclosure for illustrative purposes only. The guidance in the detailed description will be understood by those skilled in the art to show that variations and modifications may be made within the scope of this disclosure.

[0042] Therefore, it should be understood that the disclosures disclosed herein are not limited to any particular component of the device described or any step of the method described, because such devices and methods may vary. It should also be understood that the terms used herein are intended solely to describe and not to limit any particular embodiment. It should be noted that, as used herein and in the appended claims, the articles “a,” “an,” “its,” and “the foregoing” are intended to mean that there is one or more elements unless explicitly indicated otherwise in the context. Thus, for example, a reference to “unit” or “its unit” may include several devices, etc. Furthermore, the terms “include,” “contain,” “seat,” and similar expressions do not exclude other elements or steps. Furthermore, the terms “configured” or “adapted” are intended to mean that a unit or similar is shaped, sized, connected, connectable, or otherwise adapted for a particular purpose.

[0043] Brief explanation of the drawing The above-mentioned objectives, as well as any additional objectives, features, and advantages of this disclosure, should be understood more in detail by referring to the following exemplary and non-limiting detailed description of exemplary embodiments of this disclosure in conjunction with the accompanying drawings. [Brief explanation of the drawing]

[0044] [Figure 1A] This is a schematic diagram showing a wireless device according to several embodiments. [Figure 1B] A flowchart shows several method steps according to several embodiments. [Figure 2] This is a schematic diagram showing computer-readable (storage) media according to several embodiments. [Figure 3] This flowchart shows action / method steps performed by a wireless device or its processing unit according to several embodiments. [Figure 4]A flowchart shows several method steps according to several embodiments. [Figure 5] A flowchart shows several method steps according to several embodiments. [Figure 6] A flowchart shows several method steps according to several embodiments. [Figure 7] A flowchart shows several method steps according to several embodiments. [Figure 8] This is a schematic diagram showing chips according to several embodiments. [Figure 9] This is a schematic diagram showing a multi-antenna receiver configuration according to several embodiments. [Figure 10] This is a schematic diagram showing a system including wireless devices and transceiver nodes according to several embodiments. [Modes for carrying out the invention]

[0045] Detailed explanation The present disclosure will now be described with reference to the accompanying drawings illustrating preferred exemplary embodiments thereof. However, the present disclosure may be embodied in other forms and should not be construed as being limited to the embodiments disclosed herein. The disclosed embodiments are provided to adequately convey the scope of the present disclosure to those skilled in the art.

[0046] term In this specification, the term "processor / processing unit" is used. A processor may be a digital processor. Alternatively, a processor may be a microprocessor, microcontroller, central processing unit, coprocessor, graphics processing unit (GPU), digital signal processor (DSP), image signal processor, quantum processing unit, or analog signal processor. A processing unit may include one or more processors and optionally other units such as a control unit. Thus, a processor may be implemented as a single-processor, dual-processor system, or multi-processor system. Furthermore, the present invention may also be implemented in a distributed computing environment in which specific tasks are performed by remote processing devices linked to one or more local processors via a communication network, e.g., 5G. In a distributed computing environment, program modules may be located on both local and remote memory storage devices. Furthermore, some processing (e.g., for the data plane) may be moved to a centralized node, such as a centralized transceiver node (TNode). For example, higher-layer processing, such as baseband processing and / or processing above the physical layer, may be moved to a cloud, such as an mmW RAN cloud (where processing is performed by cloud processors). Such (mmW) cloud deployments can result in significant cost savings for operators due to the combination of centralized processing, cooperative wireless processing, and the availability of inexpensive commodity hardware.

[0047] This specification refers to baseband (BB) processors / processing units. A BB processor is a processor specifically adapted for processing baseband signals / data.

[0048] This specification refers to millimeter-wave (mmW) utilization, mmW communication, mmW communication capability, and mmW frequency range. The mmW frequency range is 24.25 gigahertz (GHz) to 71 GHz, or more commonly, 24 to 300 GHz. The mmW frequency range may also be called frequency range 2 (FR2).

[0049] This specification refers to centimeter-wave (cmW) utilization, cmW communication, cmW communication capability, and cmW frequency range. The cmW frequency range is 10 gigahertz (GHz) to 30 GHz.

[0050] In this specification, the term "chip" refers to an integrated circuit (chip) or monolithic integrated circuit (chip), and may also be called an IC or microchip.

[0051] In this specification, the term "wireless device (WD)" refers to any device that can transmit or receive signals wirelessly. Some examples of wireless devices include user equipment (UE), mobile phones, cell phones, smartphones, Internet of Things (IoT) devices, vehicle-to-vehicle / vehicle-to-infrastructure (V2X) devices, vehicle-to-infrastructure (V2I) devices, vehicle-to-network (V2N) devices, vehicle-to-vehicle (V2V) devices, vehicle-to-pedestrian (V2P) devices, vehicle-to-device (V2D) devices, vehicle-to-grid (V2G) devices, fixed wireless access (FWA) points, and tablets.

[0052] This specification refers to Transmit Configuration Instructor (TCI) states. A TCI state includes one or two downlink reference signals and parameters for establishing a pseudo-collocation relationship between a demodulation reference signal (DM-RS) port of a physical downlink shared channel (PDSCH), a DM-RS port of a physical downlink control channel (PDCCH), and / or a channel status information reference signal (CSI-RS) port of a CSI-RS resource.

[0053] In this specification, the term "transceiver node" (TNode) is used. A TNode may be a radio unit (RU), remote radio unit (RRU), repeater, radio node or radio base station (RBS), a base station (BS) such as a node B, an advanced node B (eNB), or gNodeB (gNB). Thus, a TNode may be a network (NW) node. Furthermore, a TNode may be a BS of an adjacent cell, a BS of a handover (HO) candidate cell, a radio unit (RRU), a distributed unit (DU), a base station (BS) of another WD (e.g., a remote WD) or (active / deactivated) secondary cell (SCell) or serving / primary cell (PCell, e.g., one associated with an active TCI state), a laptop, a radio station, a relay, a repeater device, a reconfigurable intelligent surface, or a large intelligent surface.

[0054] In this specification, we refer to an antenna unit. An antenna unit may be a single antenna. However, an antenna unit may also be a dual antenna, such as a dual patch antenna, which has a first (e.g., horizontal) polarization and a second (e.g., vertical) polarization, and thus functions as two separate antennas or an antenna unit with two ports. Furthermore, if, for example, analog beamforming is performed, the antenna unit may be an antenna array.

[0055] Antenna polarization refers to the direction of the electric field of the radio waves transmitted by that antenna, and is determined by the physical structure and orientation of the antenna. For example, an antenna consisting of a straight conductor oriented vertically (such as a dipole antenna or whip antenna) will produce vertical polarization, while if it is oriented horizontally, the polarization of the same antenna will be horizontal.

[0056] In this specification, the term "vector" refers to a mathematical vector or tuple (not a physical vector with direction).

[0057] Other methods In addition to the MMSE method in the fully digital beamforming receiver / architecture described above, other methods can also be applied. One such method is to introduce a spatial filter into the two-stage beamformer. Using the spatial filter, N rx The number of antenna streams is N s ≤N rx It is converted into individual virtual antenna streams. The spatial filter has dimension N. rx ×N s It can be represented as a column of matrix T. The relationship between the antenna stream and the virtual antenna stream can be described as follows: s k =T H y k =T H H k x k +T H w k (N s ×1) The MMSE equalizer is,

number

number

[0058] Furthermore, T H H k The maximum rank is min(N s ,N tx However, the rank may be lower depending on the wireless channel. Furthermore, the ill-condition matrix (especially when using the LS method instead of the MMSE method (in this case, G k To mitigate the effect of the absence of a regularizing identity matrix I in G, k SVD may be used to derive the inverse or pseudo-inverse matrix in the calculation.

[0059] The equivalent representation of a fully digital beamforming receiver is N s =N rxA number of filters, i.e., dimension N rx ×N rx This can be derived by using a matrix T and selecting filters such that matrix T is unitary, for example, matrix T is the identity matrix. Then all the information received by the antenna is passed to the baseband for a second stage / further processing / beamforming.

[0060] However, a drawback of using a fully digital beamforming receiver / architecture is the need for very high data rates between the radio unit and the baseband unit. Another drawback is the significant baseband processing required in terms of fast Fourier transform (FFT), channel estimation, and / or channel equalization. One advantage is that a fully digital beamforming receiver / architecture can handle any variations in spatial-spectral characteristics.

[0061] Furthermore, an equivalent representation of antenna selection where the entire beamforming is left in the baseband is N s <N rx It can be defined by using orthogonal filters (where all but one coefficient of each filter is zero).

[0062] The disadvantage of antenna selection is N s <N rx Because energy can only be taken from individual antennas, the total energy received by all antennas cannot be utilized. One advantage is that a selected subset of antennas can address any variations in spatial-spectral characteristics.

[0063] Furthermore, the equivalent representation of the Maximum Ratio Combination (MRC) digital beamformer is such that each filter represents the conjugate of the average spatial channel characteristics across the spectrum for each TX (antenna) port. s =N tx This can be derived by using N filters. sThe spatially coupled information in each stream is passed to the baseband for further beamforming, such as a second stage of beamforming.

[0064] The disadvantage of the MRC digital beamformer is the number of streams N S However, the number of observable Tx (antenna) ports N tx This is limited by the fact that it restricts the degrees of freedom when dealing with variations in spatial-spectral characteristics. One advantage of MRC digital beamformers is that (unlike antenna selection) N rx From individual antennas to N s =N tx This means that energy can be taken into each individual stream, N s <N rx As a result of processing individual streams, the complexity is reduced compared to what is required in a fully digital beamforming receiver.

[0065] When implementing digital beamforming in receivers such as wireless devices (WDs), fully digital beamformers are disadvantageous due to their high complexity, and antenna selection is disadvantageous because this method does not support energy acquisition from all antennas, which can result in poor signal quality or signal quality below the desired threshold. Therefore, in many cases, the only feasible alternative is to utilize MRC beamformers. However, MRC beamformers are limited in terms of degrees of freedom and / or flexibility because filters can only be derived by observing the radio channels from each TX (antenna) port to their respective RX (antenna) ports. Thus, the number of filters used when implementing an MRC beamformer is limited (e.g., fixed) by the number of TX (antenna) ports.

[0066] Furthermore, since spatial filters are derived sparsely, for example every 20 milliseconds (ms), and the radio channel may change somewhat during that time, and / or the spatial characteristics may change for each subcarrier, it is desirable to define filters that allow for a higher degree of freedom, i.e., filters that provide greater flexibility in representing the radio channel and its variations spatially, spectrally, and / or temporally than the flexibility achievable, for example, when using MRC-based methods in a two-stage digital beamforming architecture.

[0067] Therefore, there is a need for methods and / or devices / units that increase the flexibility in representing radio channels and their variations spatially, spectrally, and / or temporally.

[0068] Furthermore, there is a need for a method and / or apparatus / unit that has nearly the same performance as a fully digital beamforming receiver, but is (for example, significantly) less complex than a fully digital beamforming receiver.

[0069] Basic concepts The basic concept of this invention is that a spatial filter is used for the N of wireless channels. S ≥N tx The goal is to derive the ability to capture the number of spatial-spectral principal components. In some embodiments, (for example, with N degrees of freedom) tx A spatial filter for a two-stage beamformer (which allows for a larger and higher degree of freedom) is derived. s is N txIf greater than , the degrees of freedom are higher compared to the degrees of freedom achievable by a purely maximum ratio synthesis-based digital beamformer. The increased degrees of freedom achieved by the present invention as described herein leads to an increased possibility of accommodating spatial-spectral variations in the radio channel, i.e., the weighted combination of spatial filters allows for a more accurate description of the radio channel as perceived across different Rx antennas and different subcarriers, including any variations over time between opportunities for the spatial filters to be updated. Thus, improved, more robust, and / or more accurate beamforming can be provided. This reduces power consumption and / or improves signal quality. In some embodiments, the spatial components of the spatial-spectral principal components are determined via the following eigenvalue decomposition.

number

[0070] Therefore, the spatial filter has a maximum size of N. s Assuming that the eigenvalues ​​are extracted from the columns UC1, UC2, ..., UCM of the resulting unitary eigenvector matrix U, and arranged in descending order of the eigenvalues ​​on the diagonal of Λ, then the following holds:

number

[0071] When designed in this way, the columns of T are orthonormal, so the extended MMSE equalizer will look like this:

number

[0072] Embodiment The following describes embodiments, with Figure 1A showing a wireless device according to several embodiments, and Figure 1B showing several method steps according to several embodiments. Figure 1A shows a wireless device (WD) 302. WD302 includes a processing unit 300. Furthermore, in some embodiments, the wireless device includes a multi-antenna receiver configuration 400 (shown in Figure 9) and / or one, more, or all of its components. Method 100 is for compressing a plurality (i.e., NRX) of digital signals (or antenna streams) by a receiving device (e.g., WD302). The digital signals are obtained from a plurality (i.e., NRX) of frequency division multiplexing (FDM) signals received by the receiving device. In some embodiments, the FDM signals are orthogonal frequency division multiplexing (OFDM) signals. Alternatively, the FDM signals are non-orthogonal frequency division multiplexing (N-OFDM) signals. As an alternative form, the FDM signal includes a mixture of OFDM signals and N-OFDM signals, i.e., the FDM signal includes one or more OFDM signals and one or more N-OFDM signals. In some embodiments, method 100 includes a receiving device receiving a plurality of FDM signals (transmitted from a plurality of transmitting antenna ports of a transmitting device, e.g., NTX). In some embodiments, the receiving device receives (from the transmitting device) information about the number (NTX) of transmitting antenna ports of the transmitting device (used to transmit the FDM signals). Furthermore, in some embodiments, method 100 includes a receiving device (or its analog-to-digital converter) obtaining a plurality of digital signals (e.g., one digital signal for each received FDM signal) from the plurality of received FDM signals. Furthermore, in some embodiments, the receiving device is WD302. Method 100 includes a processing unit 300 obtaining one or more channel estimation matrices H1, H2, ... HK related to the propagation channels (one or more) for / related to the FDM signals 110.In some embodiments, a propagation channel is a channel through which an FDM signal is transmitted from NTX (or a second or more) transmit (antenna) ports of a transmission / transmitting device (e.g., a base station, BS, or other transceiver node) to NRX (a first or more) antenna / antenna ports of a receiving device / WD302. In some embodiments, obtaining one or more channel estimation matrices 110 includes estimating one or more channel estimation matrices 112 per subcarrier, for example, one or more subgroups of 12 subcarriers. Alternatively, obtaining one or more channel estimation matrices 110 includes estimating one or more channel estimation matrices 114 per resource block. As another alternative, obtaining one or more channel estimation matrices 110 includes estimating one or more channel estimation matrices 116 per frequency range. The frequency ranges may vary across the system bandwidth (i.e., the span of one frequency range may differ from the span of another frequency range). This may be useful for high-dispersion channels, i.e., improved performance for high-dispersion channels may be achieved. Some frequency ranges (e.g., the first and / or second) may cover only one or a few subcarriers, while other frequency ranges (e.g., the third and / or fourth) may cover one or more resource blocks.

[0073] Furthermore, the method includes applying a function F 120 to two or more channel estimation matrices H1, H2, ... HK by a processing unit 300 or a baseband (BB) processor to obtain the resulting matrix RM. The resulting matrix RM is the matrix obtained from the application 120. In some embodiments, the function F is a quadratic function QF. An example of such a QF is:

number

number

number

[0074] Furthermore, in some embodiments (shown in Figure 5), the function F includes weights. In these embodiments, application 120 includes setting a weight for each subcarrier (SC). Alternatively or additionally, application 120 includes setting a weight for each resource block. As another alternative or additional form, application 120 includes setting a weight for each frequency range αk. As yet another alternative or additional form, application 120 includes setting a weight for each TX (antenna) port Q. Furthermore, the method includes, for example, processing unit 300, decomposing the resulting matrix RM into a first decomposition matrix U and a second decomposition matrix Λ (for example, by using matrix decomposition). The first decomposition matrix U includes (one or more) first coefficient vectors U1, U2, ..., UN (or a first set of coefficient vectors U1, U2, ..., UN). In some embodiments, the first coefficient vectors U1, U2, ..., UN are the column vectors UC1, UC2, ..., UCM of the first decomposition matrix U. The second decomposition matrix Λ is different from the first decomposition matrix U. In some embodiments, the second decomposition matrix Λ is a diagonal matrix. Furthermore, the second decomposition matrix Λ includes (one or more) second coefficient vectors Λ1, Λ2, ..., ΛN (or a second set of coefficient vectors Λ1, Λ2, ..., ΛN). In some embodiments, each second vector (or each vector in the second set of vectors) either contains no non-zero coefficients or contains one non-zero coefficient (i.e., all coefficients or all but one of the coefficients of each second vector are zero). Thus, in these embodiments, each second vector is either a single non-zero coefficient / value (e.g., an eigenvalue) or contains one, or is not a non-zero coefficient / value or does not contain one. Furthermore, the first decomposition matrix U is a unitary eigenvector matrix containing one or more eigenvectors. In some embodiments, the second decomposition matrix Λ is a diagonal matrix, where the diagonal elements of the diagonal matrix are eigenvalues ​​corresponding to one or more eigenvectors (i.e., the diagonal matrix contains eigenvalues ​​corresponding to one or more eigenvectors).Method 100 includes determining the spatial filter coefficient vectors T1, T2, ..., TN from a first coefficient vector U1, U2, ..., UN, for example by a processing unit 300 140. In some embodiments, the spatial filter coefficient vectors T1, T2, ..., TN are determined directly from the first coefficient vectors U1, U2, ..., UN and / or determined solely from the first coefficient vectors U1, U2, ..., UN. Alternatively, the spatial filter coefficient vectors T1, T2, ..., TN are determined directly from the first coefficient vectors U1, U2, ..., UN without referring to any second coefficient vectors Λ1, Λ2, ..., ΛN. In some embodiments, determining the spatial filter coefficient vectors T1, T2, ..., TN 140 includes selecting the spatial filter coefficient vectors T1, T2, ..., TN as one or more column vectors of a first decomposition matrix U, for example by a processing unit 300 142. Alternatively, determining the spatial filter coefficient vectors T1, T2, ..., TN 140 includes, for example, by a processing unit 300 selecting the spatial filter coefficient vectors T1, T2, ..., TN as one or more row vectors of a first decomposition matrix U 144. Furthermore, method 100 includes, for example, by a processing unit 300 selecting a subset T1, ..., TM of the spatial filter coefficient vectors T1, T2, ..., TN 145. The (selected) subset T1, ..., TM relates to one or more of the (e.g., four) most dominant eigenvectors of the first decomposition matrix U and therefore also relates to the most dominant eigenvectors of the second decomposition matrix (since each eigenvector of the first decomposition matrix has a corresponding eigenvalue of the second decomposition matrix Λ). In some embodiments, the (selected) subset T1, ..., TM is associated with one or more of the 1, 2, 3, or 4 most dominant eigenvectors of the first decomposition matrix U (and one or more of the 1, 2, 3, or 4 most dominant eigenvalues ​​of the second decomposition matrix Λ).As an example, subsets T1, ..., TM relate to the most dominant eigenvector / eigenvalue, second most dominant eigenvector / eigenvalue, third most dominant eigenvector / eigenvalue, and fourth most dominant eigenvector / eigenvalue of the first / second decomposition matrix U / Λ (and optionally one or more other eigenvectors / eigenvalues ​​of the most dominant eigenvector / eigenvalue, e.g., 10 or 20). As another example, subsets T1, ..., TM relate to the second most dominant eigenvector / eigenvalue, third most dominant eigenvector / eigenvalue, fourth most dominant eigenvector / eigenvalue, and fifth most dominant eigenvector / eigenvalue of the first / second decomposition matrix U / Λ (and optionally one or more other eigenvectors / eigenvalues ​​of the most dominant eigenvector / eigenvalue, e.g., 10 or 20). As yet another example, subsets T1, ..., TM relate to the most dominant eigenvector / eigenvalue and the second most dominant eigenvector / eigenvalue of the first / second decomposition matrix U / Λ (and optionally one or more other eigenvectors / eigenvalues ​​of, for example, five, ten, or twenty most dominant eigenvectors / eigenvalues). As yet another example, subsets T1, ..., TM relate to the second most dominant eigenvector / eigenvalue and the third most dominant eigenvector / eigenvalue of the first / second decomposition matrix U / Λ (and optionally one or more other eigenvectors / eigenvalues ​​of, for example, five, ten, or twenty most dominant eigenvectors / eigenvalues). As yet another example, subsets T1, ..., TM relate to the most dominant eigenvector / eigenvalue and the third most dominant eigenvector / eigenvalue of the first / second decomposition matrix U / Λ (and optionally one or more other eigenvectors / eigenvalues ​​of, for example, five, ten, or twenty most dominant eigenvectors / eigenvalues). As yet another example, subsets T1, ..., TM relate to the most dominant eigenvector / eigenvalue, second most dominant eigenvector / eigenvalue, third most dominant eigenvector / eigenvalue and / or fourth most dominant eigenvector / eigenvalue of the first / second decomposition matrix U / Λ (and optionally one or more other eigenvectors / eigenvalues ​​of the most dominant eigenvector / eigenvalue, e.g., five, ten, or twenty).Furthermore, method 100 includes, for example, compressing a plurality (i.e., NRX) of digital signals by a processing unit 300 using only a subset T1, ..., TM. Thus, in some embodiments, a plurality (i.e., NRX) of digital signals are compressed by using only a subset T1, ..., TM as coefficients in a spatial filter. In some embodiments, a plurality of FDM signals are transmitted from a plurality (i.e., NTX) of transmitting (antenna) ports (and / or transmitters) of a transmitting device (and received by NRX / first plurality of receiving antennas / antenna ports that may be spatially distributed). In these embodiments, compression 150 may include, for example, converting a plurality (i.e., NRX) of digital signals into a plurality (i.e., NS) of virtual antenna streams by a processing unit 300. The third plurality is greater than the second plurality, i.e., the number of virtual antenna streams is greater than the number of transmitting antenna ports (NS > NTX). Furthermore, in some embodiments, the receiving device receives information from the transmitting device (or intermediate device) regarding the number of transmitting antenna ports of the transmitting device. In some embodiments (for example, when the receiving device receives information from the transmitting device regarding the number of transmitting antenna ports of the transmitting device), the number of virtual antenna streams is selected by the receiving device (or processing unit 300) to be greater than the number of transmitting antenna ports. In some embodiments, the transformation 152 is a reversible transformation (for example, by utilizing only a subset of the spatial filter coefficient vectors), and therefore multiple digital signals are compressed into multiple virtual antenna streams. Alternatively, the transformation 152 is a reversible transformation.

[0075] Furthermore, in some embodiments, compression 150 further includes ignoring all other eigenvectors of the first decomposition matrix and / or ignoring all spatial filter coefficient vectors T1, T2, ..., TN other than subset T1, ..., TM, i.e., ignoring spatial filter coefficient vectors TM+1, ..., TN that do not belong to subset T1, ..., TM. By compressing the (first) multiple digital signals using only subset T1, ..., TM, it is ensured that the most dominant component (of the resulting matrix) is utilized. Furthermore, by compressing the (first) multiple digital signals using only subset T1, ..., TM, complexity is reduced. Furthermore, in some embodiments, method 100 includes transmitting a (third) plurality (i.e., NS) of virtual antenna streams to a baseband, BB, processor for further processing / beamforming 156. In some embodiments, the third plurality (i.e., NS) are known parameters, e.g., user-defined, standard-defined, or system-defined. In these embodiments, method 100 may include splitting (a third) number of (i.e., NS) virtual antenna streams into a first subset and a second subset (for example, before acquiring 110) 154. Furthermore, in some embodiments, one or more of acquiring 110, applying 120, decomposing 130, determining 140, selecting 145, and compressing 150 are performed for each of the first and second subsets. As an example, if there are two TX ports (a first and a second TX port), splitting 154 may be utilized. Thus, the user (or system) may define two spatial filters for receiving from the first TX port and two other (different) spatial filters for receiving from the second TX port. Alternatively, splitting 154 may not be utilized, and the user (or system) defines four spatial filters to be used at reception, regardless of whether the reception is from the first or second TX port. By utilizing the division 154, reception and / or transmission are adapted to be performed on a specific TX port (only) when communicating, for example, in the case of multiple transmit and receive points (multi-TRP).Furthermore, in some embodiments, the spatial filter coefficient vectors T1, T2, ..., TN and / or subset vectors T1, ..., TM are selected to be orthonormal spatial filter coefficient vectors.

[0076] According to some embodiments, a computer program product is provided that includes a non-temporary computer-readable medium 200, such as a punch card, compact disc (CD) ROM, read-only memory (ROM), digital versatile disc (DVD), embedded drive, plug-in card, or universal serial bus (USB) memory. Figure 2 shows an exemplary computer-readable medium in the form of a compact disc (CD) ROM 200. The computer-readable medium stores a computer program that includes program instructions. The computer program can be loaded into a data processor (PROC) 220, which may be included in a computer or computing device or control unit 210, for example. Once loaded into the data processor 220, the computer program may be stored in memory (MEM) 230 associated with or included in the data processor 220. According to some embodiments, when loaded into and executed by the data processor 220, the computer program can cause a method step, for example, the method shown in Figure 1B as described herein. Furthermore, in some embodiments, a computer program product is provided that, when executed in at least one processor of a processing device, includes instructions that cause the processing device to perform the method shown in Figure 1B. Furthermore, in some embodiments, a non-temporary computer-readable storage medium is provided that stores one or more programs configured to be executed by one or more processors of a processing device, wherein the one or more programs, when executed by the processing device, include instructions that cause the processing device to perform the method shown in Figure 1B.

[0077] Figure 3 shows the action / method steps performed by the processing unit 300 (as described above in relation to Figures 1A and 1B) which is included in or may be included in the WD302 (as described above in relation to Figures 1A and 1B). In some embodiments, the processing unit 300 is configured to cause a receiving device to receive a plurality of FDM signals (transmitted from a plurality of transmitting devices, e.g., from NTX transmitting antenna ports). For this purpose, the processing unit 300 may be associated with (e.g., operably connectable or connectable to) a first receiving unit (e.g., a first receiving circuit or a first receiver). Furthermore, in some embodiments, the processing unit 300 is configured to cause a receiving device (or its analog-to-digital converter) to obtain a plurality of digital signals (e.g., one digital signal for each received FDM signal) from the plurality of received FDM signals. For this purpose, the processing unit 300 may be associated with (e.g., operationally connectable to or can be connected to) a first or second acquisition unit (e.g., a first or second acquisition circuit or a first / second acquisition device). The processing unit 300 is configured to perform the acquisition 310 of two or more channel estimation matrices H1, H2, ..., HK related to the propagation channels of the FDM signal. For this purpose, the processing unit 300 may be associated with (e.g., an operationally connectable to or can be connected to) a first acquisition unit (e.g., a first acquisition circuit or a first acquisition device). Furthermore, the processing unit 300 is configured to perform the application 320 of two or more channel estimation matrices to obtain a resulting matrix RM. The resulting matrix RM is obtained from the application 320. For this purpose, the processing unit 300 may be associated with (for example, operably connectable to or can be connected to) a first application unit (e.g., a first application circuit, a first application device, or a second processor such as a BB processor).Furthermore, the processing unit 300 is configured to decompose the resulting matrix RM into a first decomposition matrix U containing first coefficient vectors U1, U2, ..., UN, and a second decomposition matrix Λ different from the first decomposition matrix U, containing second coefficient vectors Λ1, Λ2, ..., ΛN. For this purpose, the processing unit 300 may be associated with (for example, operationally connectable or connectable to) a first decomposition unit (e.g., a second processor such as a first decomposition circuit, a first decomposer, or a BB processor). The processing unit 300 is configured to determine the spatial filter coefficient vectors T1, T2, ..., TN from the first coefficient vectors U1, U2, ..., UN. For this purpose, the processing unit 300 may be associated with (for example, operationally connectable or connectable to) a first decision unit (e.g., a second processor such as a first decision circuit, a first decisioner, or a BB processor). Furthermore, the processing unit 300 is configured to perform the selection of a subset T1, ..., TM of spatial filter coefficient vectors T1, T2, ..., TN, where the subset T1, ..., TM is related to the most dominant eigenvectors of the first decomposition matrix U (and therefore related to the most dominant eigenvalues ​​of the second decomposition matrix), such as the coefficients related to the 1, 2, or 4 most dominant eigenvectors of the first decomposition matrix U (and therefore related to the coefficients related to the 1, 2, or 4 most dominant eigenvalues ​​of the second decomposition matrix). For this purpose, the processing unit 300 may be associated with (for example, operably connectable or connectable to) a first selection unit (e.g., a first selection circuit, a first selector, or a second processor such as a BB processor). Furthermore, the processing unit 300 is configured to perform the compression of a plurality (i.e., NRX) of digital signals using only the subset T1, ..., TM. For this purpose, the processing unit 300 may be associated with (for example, operably connectable to or can be connected to) a first compression unit (e.g., a first compression circuit, a first compressor, or a spatial filter such as a spatial filter, which will be described later in relation to Figure 9).Furthermore, in some embodiments, the processing unit 300 is configured to cause a plurality (i.e., NS) of (third) virtual antenna streams to be transmitted to a baseband, BB, processor for further processing / beamforming 356. For this purpose, the processing unit 300 may be associated with (e.g., operationally connectable or connectable to) a first transmitting unit (e.g., a first transmitting circuit or a first transmitter). In some embodiments, the processing unit 300 is configured to cause a plurality (i.e., NS) of (third) virtual antenna streams to be split into a first subset and a second subset 354 (e.g., before acquisition 310). For this purpose, the processing unit 300 may be associated with (e.g., operationally connectable or connectable to) a first splitting unit (e.g., a first splitting circuit or a first splitter). In some embodiments, acquiring 320, decomposing 330, determining 340, and compressing 350 are performed for each of the first and second subsets. In addition, in some embodiments, the processing unit 300 is configured to perform one or more action / method steps corresponding to the method steps described below in relation to Figures 4 to 7.

[0078] Figures 4 to 7 illustrate several method steps according to several embodiments. Figure 4 shows that in some embodiments, obtaining one or more channel estimate matrices 110 includes estimating one or more channel estimate matrices for each subcarrier 112. Alternatively, as can be seen from Figure 4, obtaining one or more channel estimate matrices 110 includes estimating one or more channel estimate matrices for each resource block 114. As another alternative form (shown in Figure 4), obtaining one or more channel estimate matrices 110 includes estimating one or more channel estimate matrices for each frequency range 116.

[0079] Furthermore, in some embodiments, obtaining 110 is possible at a first time point t, where two or more channel estimation matrices H k(t) is determined, and at the second time point t-τ, two or more channel estimation matrices H k This includes determining (t-τ) 118. In these embodiments, the function F (as applied by applying 120) is the square of two or more channel estimate matrices at the first time point.

number

number

[0080] Figure 6 shows that in some embodiments, determining the spatial filter coefficient vectors T1, T2, ..., TN 140 includes selecting the spatial filter coefficient vectors T1, T2, ..., TN as one or more column vectors of a first decomposition matrix U 142. Alternatively (as shown in Figure 6), determining the spatial filter coefficient vectors T1, T2, ..., TN 140 includes selecting the spatial filter coefficient vectors T1, T2, ..., TN as one or more row vectors of a first decomposition matrix U 144.

[0081] In some embodiments, the (first) multiple FDM signals are transmitted from (second) multiple transmit (antenna) ports of a transmitting device (i.e., NTX transmit antennas and / or transmitters) (and received by NRX receive antennas / antenna ports which may be spatially distributed). In these embodiments, as shown in Figure 7, method 100 or compressing method step 150 includes converting the (first) multiple (i.e., NRX) digital signals into a (third) multiple (i.e., NS) virtual antenna streams 152, where the third multiple is greater than the second multiple (i.e., NS > NTX). Furthermore, in some embodiments, as shown in Figure 7, method 100 includes transmitting the (third) multiple (i.e., NS) virtual antenna streams to a baseband, BB, processor for further processing / beamforming 156 (e.g., after or as part of compressing 150).

[0082] As described above, weighting or assignments may be applied when deriving spatial filters or their coefficients. Therefore, the spatial characteristics of some subcarriers (SCs) may be given lower weights than those of other subcarriers. In one non-restrictive example, when forming the matrix to be decomposed, a weight of 0 ≤ α_k ≤ 1 may be assigned to each subcarrier, i.e., each subcarrier SC1, SC2, ..., SCk may be associated with its respective weight α1, α2, ..., αk, where each weight α1, α2, ..., αk has a value in the range of 0 to 1. The value of each weight α1, α2, ..., αk may depend on the signal-to-noise plus interference ratio (SINR) of the corresponding subcarrier SC1, SC2, ..., SCk / the signal-to-noise plus interference ratio (SINR) associated with the corresponding subcarrier SC1, SC2, ..., SCk, the rank of the corresponding subcarrier SC1, SC2, ..., SCk / the rank associated with the corresponding subcarrier SC1, SC2, ..., SCk, the importance of the subband explicitly stated as the subcarrier range, and / or the interference received in the subband. If it is desirable for a particular subcarrier SCn to contribute more to the spatial filter, the value of the corresponding weight αn for that subcarrier SCn is increased, and if it is desirable for a particular subcarrier SCm to contribute less to the spatial filter, the value of the corresponding weight αm for that subcarrier is decreased.

[0083] Furthermore, weighting can be applied when deriving a spatial filter by assigning / setting different weights to the spatial characteristics of different Tx ports, i.e., by setting a weight for each TX port (Q). In a non-restrictive example, a weight diagonal matrix Q of size NTX×NTX is used. All diagonal elements of the weight diagonal matrix Q are between 0 and 1. All other elements of the weight diagonal matrix Q are zero. Furthermore, the p-th diagonal element constitutes the weight imposed on the contribution of the p-th Tx port in forming the resulting matrix, i.e., the matrix to be decomposed. Thus, a lower value results in less influence on the spatial filter, and a higher value results in greater influence on the spatial filter. It should be noted that other forms of weighting can be performed. For example, different diagonal matrices can be used for different subcarriers to allow different weighting in different parts of the spectrum. This is just one example of weighting. It should be noted that weighting can be performed in many different ways. Regardless of the method, the desired net effect is not to emphasize some subcarriers more than others when deriving a spatial filter.

[0084] In some embodiments, the decomposition performed (during decomposition 130) to derive the spatial filter at a certain time t is all N rx The channel estimates for each of the Rx antennas are calculated based on information from one or more previous time points t-τ1, t-τ2, ..., t-τk and / or from one or more previous time points t-τ1, t-τ2, ..., t-τk from which the spatial filters were derived. As an example, the decomposition is as follows, based on a weighted average, 0 < β ≤ 1:

number

[0085] As another example, the decomposition is based on a recursive equation / algorithm, 0 < β < 1, M(t < 0) = 0, and is as follows:

number

[0086] The above examples are just two examples of how to include information / statistics from one or more previous time points when deriving a spatial filter. Regardless of the method, the desired net effect is that previous spatial characteristics have some influence (via the weighting coefficient β) when deriving a spatial filter based on the current spatial characteristics. One reason for doing so is to account for the higher variability of radio channels than what is observed at a single time point when deriving the spatial filter. Another reason is to reduce noise in channel estimation by averaging over two or more time points. Whether or not to include information from previous time points may depend on whether the transmit setting instruction (TCI) state at the previous time point is the same as at the current time point. If the TCI state at the previous time point is not the same as at the current time point, one may choose to derive a spatial filter based only on the spatial characteristics from the current time point, i.e., without including information from previous time points.

[0087] Whether or not to include information from previous points in time depends, in some embodiments, on the time τ between the current point in time and the previous point in time. In some embodiments, if τ is longer than a time threshold, e.g., 200 ms, the spatial filter is selected based only on the spatial characteristics from the current point in time (i.e., without including information from previous points in time), and if τ is shorter than a time threshold, e.g., 200 ms, the spatial filter is selected based on the spatial characteristics from the current point in time and spatial characteristics from one or more previous points in time. Alternatively, instead of fixing the time threshold (value), the time threshold (value) depends on the acquired / measured / detected channel characteristics, for example, the smaller the variability of the radio channel (over time), the larger the time threshold.

[0088] Whether to include information from a previous point in time, either alternatively or additionally, depends on the received reference signal, synchronization signal, or, for example, H k The channel estimation depends on the (instantaneous) SINR of other known signals used when forming the channel. If the SINR is lower than the SINR threshold, e.g., 0 dB, information from previous points in time is included; if the SINR is higher than the SINR threshold, information from previous points in time is not included.

[0089] The derived spatial filter may, in some embodiments, be merely approximately orthogonal, rather than perfectly orthogonal, as a result of mapping the derived filter coefficients to the spatial filter coefficients in fixed-point representation using a limited number of integer and fractional bits. A key essence of the present invention is a method for deriving a spatial filter based on spatial-spectral principal components. In this case, necessary engineering considerations, such as mapping the results to a fixed and potentially smaller number of bit representations using floating-point or fixed-point, may result in small inaccuracies due to quantization and adaptation to fixed-point arithmetic. Therefore, in some embodiments, the (derived) spatial filter is scaled, for example, mutually orthogonal and / or mutually orthonormal. In some embodiments, the (derived) spatial filter is scaled mutually orthogonal (but not necessarily orthonormal), i.e., scaled such that the scaled spatial filter is mutually orthogonal (but not necessarily orthonormal). Furthermore, in some embodiments, due to finite-precision and / or fixed-point representation, the derived / obtained orthogonal (basis) vectors may be merely approximately orthogonal, rather than perfectly orthogonal. The fixed-point representation of the filter coefficients can be improved by scaling the spatial filter (for example, orthogonally).

[0090] As mentioned above, a third number of filters (i.e., NS filters) are derived from the spatial-spectral characteristics of all Tx ports. However, in some (alternative) embodiments, the Tx ports are divided into subsets, and for each subset of Tx ports, a corresponding subset of filters is derived. Furthermore, in some (alternative) embodiments, the subcarriers are divided into subsets, and for each subset of subcarriers, a corresponding subset of filters is derived.

[0091] Figure 8 shows a chip 990 according to several embodiments. The chip 990 includes a processing unit 300. Furthermore, in some embodiments, the chip 900 includes a multi-antenna receiver configuration 400 (and / or one or more of its components) which is described below in relation to Figure 9.

[0092] Figure 9 shows a multi-antenna receiver configuration 400. As can be seen from Figure 9, the multi-antenna receiver configuration 400 includes a first number of N (NRX) receivers / transmitters 500, 501, ..., 515 (N=NRX) configured to receive a first number of NRX analog radio signals via a first number of NRX antenna units / ports 700, 701, ..., 715. In some embodiments, the multi-antenna receiver configuration 400 includes a first number of NRX antenna units / ports 700, 701, ..., 715 for receiving analog radio signals. Furthermore, the multi-antenna receiver configuration 400 includes a fourth number of analog-to-digital converters (ADCs) 600, 601, ..., 615 configured to convert a first number of NRX analog radio signals into a first number of NRX digital (baseband) signals. The (fourth) plurality (l) may be equal to the (first) plurality (NRX), i.e., there is one ADC for each receiver / transceiver / analog signal (I=NRX). However, in other embodiments, the (fourth) plurality is twice the (first) plurality (i.e., 2N), i.e., there are two ADCs for each analog signal, for example, one for an in-phase (I) branch and one for a quadrature-phase (Q) branch (I=2N or I=2*NRX). Furthermore, the multi-antenna receiver configuration 400 includes an extraction unit 900 configured to extract physical resources used to estimate channel characteristics, such as a reference signal, from each of the (first) plurality (NRX) digital signals. In some embodiments, the extraction unit 900 includes the (first) plurality (NRX) sub-extraction units 901, 902, ..., 916, i.e., one sub-extraction unit for each digital signal. The multi-antenna receiver configuration 400 includes a channel analyzer 920. The channel analyzer 920 is configured to determine the characteristics of each of the (first) multiple (NRX) digital signals based on the extracted reference signal. Furthermore, the multi-antenna receiver configuration 400 includes (second) multiple (m or NTX) spatiotemporal or spatial filters 800, ..., 807.The spatiotemporal / spatial filters 800, ..., 807 are configured to process or to process a plurality of (first) digital signals (NRX) in order to obtain a plurality of (third) composite signals (NS). In some embodiments, the plurality of (first) signals (NRX) is greater than the plurality of (third) signals (NS), i.e., NRX > NS). In some embodiments, the plurality of (third) signals (NS) is two or more, e.g., three. In some embodiments, the plurality of (first) signals is three or more, e.g., sixteen. In some embodiments, the multi-antenna receiver configuration 400 includes a conversion unit 940. The conversion unit 940 is configured to convert each of the plurality of (third) composite signals (NS) into the frequency domain. In some embodiments, the conversion unit 940 is or includes a plurality of (third) conversion subunits (NS). Each conversion subunit is configured to process each of the (third) multiple (NS) composite signals (as adapted for connections and other configurations). In some embodiments, the conversion unit serially converts each of the composite signals. In some embodiments, each of the (third) multiple conversion subunits processes NRX / NS signals. Furthermore, in some embodiments, the multi-antenna receiver configuration 400 includes a post-processing unit 960. The post-processing unit 960 is configured to or performs post-processing of the frequency-domain converted signals to obtain a (fifth) multiple (k) frequency-domain processed signals. Furthermore, in some embodiments, a (first) multiple (NRX) analog radio signals are encoded. Thus, in some embodiments, the multi-antenna receiver configuration 400 includes a decoder 980. The decoder 980 is configured to or performs decoding of a (fifth) multiple (k) frequency-domain processed signals (to obtain information signals). The (3rd) plurality (NS) is greater than / more than the (5th) plurality (k), i.e., NS > k. In some embodiments, the spatiotemporal filters (spatial filters) 800, ..., 807 in Figure 9 are constructed, for example, by the processing unit 300 using a subset (T1, ..., TM) of the spatial filter coefficient vector (T1, T2, ..., TN) described above.Therefore, in some embodiments, the coefficients of the spatiotemporal or spatial filters 800, ..., 807 are determined / selected according to the method 100 described above, and the spatiotemporal or spatial filters 800, ..., 807 are used to compress (first) multiple (i.e., NRX) digital signals using only subsets T1, ..., TM. In some embodiments, the WD400 includes a multi-antenna receiver configuration 302.

[0093] Figure 10 shows system 999. System 999 may be a wireless / cellular communication system, cellular network, mobile network, telecommunications network, cellular wireless system, digital cellular network, mobile phone network, mobile phone cellular network, e.g., 1G, 2G, 3G, 4G, 5G, 6G, or similar. Furthermore, system 999 includes one or more wireless devices (WDs) 302, 303, ..., 308. Furthermore, system 999 includes one or more transceiver nodes (TNodes) 397, 398, 399. One or more transceiver nodes (TNodes) 397, 398, 399 may be base stations (gNB, eNB, RBS), remote radio units (RRUs), or remote radio nodes. In some embodiments, WD302 (and WD303, ..., 308) are configured to communicate with one or more remote transceiver nodes (TNode) 397, 398, 399 (for example, to transmit and / or receive signals such as radio signals including baseband / information signals to such nodes). In some embodiments, the system includes one or more transmitting devices (e.g., 303, 304, ..., 308, 397, 398) and one or more receiving devices (e.g., 302, 303, 397, 399).

[0094] A non-zero vector v of dimension N is an eigenvector of a square N×N matrix A if, for some scalar λ, it satisfies a linear equation of the following form: Av=λv Here, the scalar (λ) is called the eigenvalue corresponding to v (i.e., each eigenvector has a corresponding eigenvalue). Geometrically speaking, the eigenvectors of A are simply the vectors that A contracts or expands, and the amount of contraction / expansion is the eigenvalue. The above equation is called the eigenvalue equation or eigenvalue problem. Furthermore, as mentioned above in "Basic Concepts", the columns UC1, UC2, ..., UCM (i.e., eigenvectors) of the resulting unitary eigenvector matrix U are N s It is associated with n eigenvalues. Therefore, each column (or eigenvector) of the resulting unitary eigenvector matrix is ​​associated with (or has a corresponding eigenvalue of) the corresponding eigenvalue.

[0095] List of examples: Example 1. A multi-antenna transmitter and receiver configuration, a method (100) for controlling MATARA(400), wherein MATARA(400) can be included in a wireless device, WD(402), and includes a (first) plurality of transceivers (500, 501, ..., 515), the method being The first PLL (520) is configured to control the center frequency generation of the first transceiver set (500, 501) (110), The second PLL (522) is configured to control the center frequency generation of the second transceiver set (514, 515) (120), MATARA(400) receives a first radio signal transmitted by a first remote transceiver node, TNode(398), at a first center frequency (CF1) during a first period (T1) (130), Using the first set of filter coefficients, construct a (second) set of multiple spatiotemporal filters (800, ..., 807) (140), During the second period (T2) following the first period, it is determined whether or not it is necessary for the MATARA (400) to receive a second radio signal transmitted by a second remote TNode (398, 399) at a second center frequency (CF2) different from the first center frequency (CF1) (150), During the third period (T3) following the second period (T2), it is determined whether or not it is necessary to receive the first radio signal at a third center frequency (CF3) different from the second center frequency (CF2) using MATARA (400) (160), When it is determined that it is necessary to receive a second radio signal at a second center frequency (CF2) during a second period (T2) and to receive a first radio signal at a third center frequency (CF3) during a third period (T3), a second set of spatiotemporal filters (800, ..., 807) is constructed using a second set of filter coefficients (170), wherein the second set of filter coefficients is selected such that signals from the first transceiver set (500, 501) are combined only with signals from the first transceiver set (500, 501), and signals from the second transceiver set (514, 515) are combined only with signals from the second transceiver set (514, 515) (170) Method (100), including the method (100).

[0096] Example 2. The method according to Example 1, wherein the first radio signal received during the first period (T1) and the first radio signal received during the third period (T3) have the same transmit setting instruction, TCI, and state.

[0097] Example 3. The method according to Example 1 or 2, wherein the second period (T2) is the measurement gap.

[0098] Example 4. The method according to Example 1 or 2, wherein MATARA(400) is configured to monitor only the first bandwidth portion, BWP, during the first and third periods (T1, T3), and MATARA(400) is configured to monitor only the second BWP, which is different from the first BWP, during the second period (T2).

[0099] Example 5. The method described in any one of Examples 1 to 5, wherein the third center frequency (CF3) is the same as the first center frequency (CF1).

[0100] Example 6. A computer program product comprising a non-temporary computer-readable medium (200) storing a computer program including program instructions, wherein the computer program is loadable into a data processing unit (220), and is configured to cause the data processing unit (220) to execute the method described in any one of Examples 1 to 5 when the computer program is executed.

[0101] Example 7. Control unit (995) for a multi-antenna transmitter and receiver configuration, MATARA(400), wherein MATARA(400) can be included in a wireless device, WD(402), and MATARA(400) comprises a plurality of (first) transceivers (500, 501, ..., 515) configured to receive a plurality of (first) analog radio signals via a plurality of (first) antenna units (700, 701, ..., 715), and a plurality of analog-to-digital converters configured to convert the plurality of (first) analog radio signals into a plurality of (first) digital signals. The control unit (995) includes a first digital signal (600, 601, ..., 615), an extraction unit (900) configured to extract a reference signal from each of the (first) multiple digital signals, a channel analyzer (920) configured to determine the characteristics of each of the (first) multiple digital signals based on the extracted reference signals, a second spatiotemporal filter (800, ..., 807) configured to process the (first) multiple digital signals to obtain a second composite signal, a first phase-locked loop (PLL) (520), and a second PLL (522). The first PLL (520) is configured (310) to control the center frequency generation of the first transceiver set (500, 501), The second PLL (522) is configured to control the center frequency generation of the second transceiver set (514, 515) (320), MATARA(400) receives (330) a first radio signal transmitted by a first remote transceiver node, TNode(398), at a first center frequency (CF1) during a first period (T1). Using the first set of filter coefficients, construct a (second) set of multiple spatiotemporal filters (800, ..., 807) (340), During the second period (T2) following the first period (T1), it is determined (350) whether or not it is necessary for MATARA (400) to receive a second radio signal, which is different from the first radio signal, transmitted by a second remote TNode (398, 399) at a second center frequency (CF2) different from the first center frequency (CF1), During the third period (T3) following the second period (T2), it is determined whether or not it is necessary to receive the first radio signal at a third center frequency (CF3) different from the second center frequency (CF2) using MATARA (400) (360), When it is determined that it is necessary to receive a second radio signal at a second center frequency (CF2) during a second period (T2) and a first radio signal at a third center frequency (CF3) during a third period (T3), a second set of spatiotemporal filters (800, ..., 807) is constructed using a second set of filter coefficients (370), wherein the second set of filter coefficients is selected such that signals from the first transceiver set (500, 501) are combined only with signals from the first transceiver set (500, 501), and signals from the second transceiver set (514, 515) are combined only with signals from the second transceiver set (514, 515) (370). A control unit (995) configured to perform the following actions.

[0102] Example 8. Multi-antenna transmitter and receiver configuration, MATARA(400), which can be included in a wireless device, WD(402). A plurality of transceivers (500, 501, ..., 515) (first) configured to receive a plurality of analog radio signals (first) via a plurality of antenna units (700, 701, ..., 715), Multiple analog-to-digital converters (600, 601, ..., 615) configured to convert (first) multiple analog radio signals into (first) multiple digital signals, An extraction unit (900) configured to extract a reference signal from each of the (first) multiple digital signals, A channel analyzer (920) configured to determine the characteristics of each of the (first) digital signals based on the extracted reference signal, A plurality of spatiotemporal filters (800, ..., 807) configured to process the plurality of digital signals (first) in order to obtain a plurality of (second) composite signals, The first phase-locked loop, PLL(520), The second PLL (522), Optionally, a plurality of (first) antenna units (700, 701, ..., 715) and The control unit (995) described in Example 8 and MATARA(400) includes this.

[0103] Example 9. A wireless device, WD(402), comprising the multi-antenna transmitter and receiver configuration, MATARA(300), described in Example 8, and a plurality of (first) antenna units (700, 701, ..., 715).

[0104] Example 10. A chip (990) including the control unit (995) described in Example 7.

[0105] In general, all terms used herein should be interpreted according to their ordinary meanings in the relevant art unless a different meaning is explicitly given and / or implicitly indicated by the context in which they are used. Various embodiments are referenced herein. However, those skilled in the art will recognize many variations of the embodiments described, which still fall within the scope of the claims. For example, embodiments of the methods described herein disclose exemplary methods with steps performed in a specific order. However, it should be recognized that these sequences of events may be performed in a different order without departing from the scope of the claims. Furthermore, some action / method steps may be performed in parallel, even if they are described as being performed in order. Therefore, the steps of any method disclosed herein do not need to be performed in the exact order disclosed unless it is explicitly stated that one step follows or precedes another, and / or it is implicitly indicated that one step must follow or precede another. Similarly, it should be noted that in the descriptions of embodiments, dividing functional blocks into specific units is not intended to be limiting. On the contrary, these divisions are merely examples. A functional block described herein as a single unit may be divided into two or more units. Furthermore, a functional block described herein as being implemented as two or more units may be integrated into fewer units (e.g., a single unit). Any feature of any embodiment / appearance disclosed herein may be applied to any other embodiment / appearance where appropriate. Similarly, any advantage of any embodiment may be applied to any other embodiment, and vice versa. Therefore, it should be understood that the details of the embodiments described are merely examples presented for illustrative purposes and all variations included in the claims are intended to be encompassed therein.

[0106] A list of some acronyms and abbreviations that may appear in the description. 3GPP - Third Generation Partnership Project 5G - Fifth Generation 5G-NR (5G-New Radio) is a new RAT developed by 3GPP for 5G mobile networks. ADC - Analog-to-Digital Converter AGC - Automatic Gain Controller BB - Bassband BF - Beamforming BW - Bandwidth BWP - Bandwidth portion cmW - centimeter wave CSI-RS - Channel Status Information Reference Signal CU - Control Unit DAC - Digital-to-Analog Converter DCI - Downlink Control Information DIC - Digital Interface Chip DL-PRS - Downlink Positioning Reference Signal DM-RS - Demodulation reference signal DS - Downsampling FDM - Frequency Division Multiplexing FFT - Fast Fourier Transform FR1 - Frequency Range 1 FR1.5 - Frequency range 1.5 FR2 - Frequency Range 2 Fe - Frontend FWA - Fixed Wireless Access GNSS - Global Navigation Satellite System GPS - Global Positioning System IF - Intermediate Frequency I / O - Input / Output L1 - Layer 1 LNA - Low Noise Amplifier LO - Local Oscillator LoS - Line of Sight LTE - Long-Term Evolution MAC - Media Access Control MATARA - Multi-antenna transmitter and receiver configuration MIMO - Multiple Input, Multiple Output MMSE - Least Mean Squared Error mmW - millimeter wave MRC - Maximum Ratio Combining NAS - Non-Accessible Stratum nLoS - Outside of line of sight NRX - Number of received signals NS - Number of streams NTX - Number of transmit ports OFDM - Orthogonal Frequency Division Multiplexing PA - Power Amplifier PBCH - Physical Broadcast Channel PCB - Printed Circuit Board PCell - Primary Cell PDCCH - Physical Downlink Control Channel PDP - Power Delay Profile PDSCH - Physical Downlink Shared Channel PHY - Physical layer PLL - Phase-Locked Loop PSCell - Primary / Secondary Cell PSS - Main Sync Signal PT-RS - Phase tracking reference signal PUCCH - Physical Uplink Control Channel PUSCH - Physical uplink shared channel QCL - Pseudo-collocation QoS - Quality of Service RAT - Wireless Access Technology RRC - Wireless Resource Control RSRP - Reference signal received power RSRQ - Reference Signal Received Quality RSSI - Received Signal Strength Indicator SCell - Secondary Cell SNR - Signal-to-Noise Ratio SSB - Synchronization Signal Block SRS - Sounding Reference Signal SSS - Secondary sync signal STEF - Spatiotemporal Filter STF - Spatial Transmission Filter TCI - Transmission settings instruction TNode - Transceiver Node VGA - Variable Gain Amplifier WD - Wireless Devices

Claims

1. A method (100) for converting multiple digital signals obtained from multiple (NRX) frequency division multiplexing, FDM, signals transmitted from multiple (NTX) transmitting antenna ports of a transmitting device and received by the receiving device, using a receiving device including spatial filters (800, ..., 807), Obtaining two or more channel estimation matrices (H1, H2, ..., HK) related to the propagation channel of the FDM signal (110), To obtain a resulting matrix (RM), the process involves applying a function (F) to the two or more channel estimation matrices (120), wherein the resulting matrix (RM) is obtained from the application (120) and the application (120), Decomposing the resulting matrix (RM) into a first decomposition matrix (U) containing a first coefficient vector (U1, U2, ..., UN) and a second decomposition matrix (Λ) different from the first decomposition matrix (U), wherein the first decomposition matrix (U) is a unitary eigenvector matrix containing one or more eigenvectors, decomposition (130), Determining the spatial filter coefficient vector (T1, T2, ..., TN) from the first coefficient vector (U1, U2, ..., UN) (140), Selecting (145) a subset (T1, ..., TM) of the spatial filter coefficient vector (T1, T2, ..., TN), wherein the subset (T1, ..., TM) is related to one or more of the four most dominant eigenvalues ​​of the second decomposition matrix (U), The spatial filters 800, ..., 807 convert the multiple (NRX) digital signals into multiple (NS) virtual antenna streams using only the subset (T1, ..., TM) (151), wherein the number of virtual antenna streams is greater than the number of transmitting antenna ports (152). A method (100) including the following.

2. The method according to claim 1, wherein the plurality (NRX) FDM signals are a plurality of orthogonal frequency division multiplexing, OFDM signals, and the function (F) is a quadratic function.

3. The method according to claim 2, further comprising transmitting the plurality (NS) virtual antenna streams to a baseband, BB, processor for further processing / beamforming (156).

4. The method according to claim 2 or 3, further comprising dividing the plurality (NS) virtual antenna streams into a first subset and a second subset (154), wherein acquiring (120), performing (130), determining (140), and compressing (150) is performed for each of the first and second subsets.

5. The method according to any one of claims 1 to 4, wherein decomposition (130) includes performing singular value decomposition, SVD, or eigenvalue decomposition.

6. The method according to any one of claims 1 to 5, wherein determining a spatial filter coefficient vector (T1, T2, ..., TN) (140) includes selecting the spatial filter coefficient vector (T1, T2, ..., TN) as one or more column vectors of the first decomposition matrix (U) (144).

7. The method according to any one of claims 1 to 6, wherein the spatial filter coefficient vector (T1, T2, ..., TN) is selected to be an orthonormal spatial filter coefficient vector.

8. The method according to any one of claims 1 to 7, wherein obtaining one or more channel estimation matrices (110) includes estimating one or more channel estimation matrices for each subcarrier (112), estimating one or more channel estimation matrices for each resource block (114), or estimating one or more channel estimation matrices for each frequency range (116).

9. The method according to any one of claims 1 to 8, wherein the function (F) includes weights, and obtaining (120) includes setting the weights for each subcarrier (122), setting the weights for each resource block (124), setting the weights for each frequency range (αk) (126), and / or setting the weights for each TX port (Q) (128).

10. To obtain (120) is to obtain the two or more channel estimation matrices (H) at the first time point (t). ｋ (t)) to determine (117), and at the second time point (t-τ), the two or more channel estimation matrices (H ｋ The function (F) includes determining (t-τ) (118), wherein the function (F) is the square of the two or more channel estimation matrices at the first time point. [Math 1] and the squares of the two or more channel estimation matrices at the second time point. [Math 2] The method according to any one of claims 1 to 9, which is a function of .

11. A computer program product that, when executed by one or more processors of a processing device, includes instructions causing the processing device to perform the method according to any one of claims 1 to 10.

12. A non-temporary computer-readable storage medium storing one or more programs configured to be executed by one or more processors of a processing device, wherein the one or more programs, when executed by the processing device, include instructions causing the processing device to perform the method according to any one of claims 1 to 10.

13. Obtaining two or more channel estimation matrices (H1, H2, ..., HK) associated with one or more propagation channels of an FDM signal (310), To obtain a resulting matrix (RM), a function (F) is applied (320) to the two or more channel estimation matrices, wherein the resulting matrix (RM) is obtained from the application (320) and the application (320), The resulting matrix (RM) is matrix-decomposed into a first decomposition matrix (U) containing a first coefficient vector (U1, U2, ..., UN) and a second decomposition matrix (Λ) different from the first decomposition matrix (U), containing a second coefficient vector (Λ1, Λ2, ..., ΛN) (330), Determining the spatial filter coefficient vector (T1, T2, ..., TN) from the first coefficient vector (U1, U2, ..., UN) (340), Selecting (345) a subset (T1, ..., TM) of the spatial filter coefficient vector (T1, T2, ..., TN), wherein the subset (T1, ..., TM) is related to one or more of the four most dominant eigenvalues ​​of the second decomposition matrix (U), Using only the subset (T1, ..., TM) (351), converting multiple (NRX) digital signals into multiple (NS) virtual antenna streams (352) A processing unit (300) configured to perform the following actions.

14. The processing unit according to claim 13, wherein the function (F) is a quadratic function.

15. The processing unit according to claim 13 or 14, wherein the number of virtual antenna streams is greater than the number of transmitting antenna ports of the transmitting device used to transmit the plurality of FDM signals.

16. Wireless device, WD (302), comprising a processing unit (300) according to any one of claims 13 to 15.

17. A chip (990) comprising a processing unit (300) according to any one of claims 13 to 15.

Images