WIRELESS COMMUNICATION APPARATUS FOR ADAPTIVE BEAMFORMING AND OPERATING METHOD THEREOF

Provided is an operating method of a first apparatus communicating with a second apparatus in a wireless local area network (WLAN) system including the first apparatus and the second apparatus. The operating method of the first apparatus includes obtaining channel characteristic data with the second apparatus based on a null data packet (NDP) frame received from the second apparatus; generating beamforming feedback information and a feedback frame based on a class of a channel determined by applying a machine learning algorithm to the channel characteristic data; and transmitting the feedback frame to the second apparatus.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on and claims priority under 35 U.S.C. § 119 to Korean Patent Application No. 10-2022-0146387, filed on Nov. 4, 2022, Korean Patent Application No. 10-2023-0002516, filed on Jan. 6, 2023, and Korean Patent Application No. 10-2023-0033458, filed on Mar. 14, 2023, in the Korean Intellectual Property Office, the disclosures of which are incorporated by reference herein in their entireties.

BACKGROUND

The present disclosure relates to wireless communication, and more particularly, to a wireless communication apparatus supporting beamforming feedback by using machine learning, and an operating method of the wireless communication apparatus.

A wireless local area network (WLAN), which is an example of wireless communication, links two or more apparatuses by using a wireless signal transmission method. For example, a WLAN technology may be based on and/or may conform to a wireless communication standard, such as, but not limited to an Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard. The 802.11 standard may refer to several versions of the standard (e.g., 802.11b, 802.11a, 802.11g, 802.11n, 802.11ac, 802.11ax, and the like), which may support a transmission rate of up to 1 gigabyte/second based on orthogonal frequency-division multiplexing (OFDM).

In a WLAN based on the IEEE 802.11ac standard, data may be simultaneously transmitted to a plurality of users through a multi-user multi-input multi-output (MU-MIMO) scheme. In another WLAN based on the IEEE 802.11be standard, which may be referred to as extremely high throughput (EHT), and/or a next-generation protocol standard after EHT (e.g., EHT+), the WLAN may provide support for a six (6) gigahertz (GHz) unlicensed frequency band, utilization of bandwidth of up to 320 megahertz (MHz) per channel, introduction of hybrid automatic repeat and request (HARM), and/or support for up to 16×16 MIMO.

Also, in a MU-MIMO communication environment of a related WLAN, a beamforming process may be used to potentially improve communication performance. For example, a beamformer (or an access point) that performs a beamforming process may perform beamforming based on feedback on a channel received from a beamformee (or a station).

There exists a need for further improvements in wireless communication technology, as the need for improvements in the performance of a wireless communication system may be constrained by a lack of support of beamforming technology by a wireless communication apparatus. Alternatively or additionally, there may be a need for a beamforming feedback generation method for a wireless communication apparatus that may provide for a base station (or access point) to perform beamforming that may be suitable for a channel state of the wireless communication apparatus.

SUMMARY

Example embodiments provide a wireless communication apparatus that may adaptively adjust beamforming feedback resources by using machine learning in a wireless communication system, and an operating method of the wireless communication apparatus.

According to an aspect of an example embodiment, an operating method of a first apparatus communicating with a second apparatus through a wireless local area network (WLAN) is provided. The operating method includes obtaining channel characteristic data with the second apparatus based on a null data packet (NDP) frame received from the second apparatus; generating beamforming feedback information and a feedback frame based on a class of a channel determined by applying a machine learning algorithm to the channel characteristic data; and transmitting the feedback frame to the second apparatus.

According to an aspect of an example embodiment, an operating method of a second apparatus communicating with a first apparatus through a WLAN is provided. The operating method includes transmitting an NDP frame to the first apparatus; receiving a channel information frame from the first apparatus; obtaining channel characteristic data with the first apparatus based on the channel information frame; generating beamforming feedback information based on a class of a channel determined by applying a machine learning algorithm to the channel characteristic data; and transmitting the beamforming feedback information to the first apparatus.

According to an aspect of an example embodiment, a first apparatus communicating with a second apparatus through a WLAN is provided. The first apparatus includes a processing circuit configured to obtain channel characteristic data based on an NDP frame received from the second apparatus, and generate beamforming feedback information and a feedback frame based on a class of a channel determined by applying a machine learning algorithm to the channel characteristic data, wherein the processing circuit is further configured to control a transceiver to transmit the feedback frame to the second apparatus.

DETAILED DESCRIPTION

Hereinafter, various embodiments of the present disclosure are described with reference to the accompanying drawings.

FIG.1is a block diagram illustrating a wireless communication system, according to an embodiment.

The block diagram ofFIG.1illustrates a beamformer10and a beamformee20communicating with each other in a wireless communication system30. Each of the beamformer10and the beamformee20may be and/or may include an apparatus communicating in the wireless communication system30. In an embodiment, each of the beamformer10and the beamformee20may be referred to as an apparatus for wireless communication. In some embodiments, each of the beamformer10and the beamformee20may be and/or may include an access point and/or a station of a wireless local area network (WLAN) system, such as the wireless communication system30.

Referring toFIG.1, the beamformer10may include a controller11, a beamforming circuit12, and a plurality of first antennas AT_11to AT_x1, where x is a positive integer greater than zero (0). The controller11and the beamforming circuit12may be referred to as a processing circuit of the beamformer10. The beamformee20may include a channel estimator21, a decomposer22, a compressor23, and a plurality of second antennas AT_12to AT_y2, where y is a positive integer greater than zero (0). The channel estimator21, the decomposer22, and the compressor23may be referred to as a processing circuit of the beamformee20.

In an embodiment, the beamformee20may receive a null data packet (NDP) frame through the plurality of second antennas AT_12to AT_y2. The channel estimator21may estimate a channel by using a reference signal included in the received NDP frame. In some embodiments, the NDP frame may be referred to as a sounding packet. The NDP frame ykreceived by the channel estimator21for channel estimation may be expressed by an equation similar to Equation 1.

Referring to Eq. 1, Hkmay represent a channel matrix, xkmay represent a transmission data stream, and nkmay denote thermal noise. k may represent a subcarrier index of a channel, and may have a range of one (1) to NFFT. Accordingly, a size of Hkfor each subcarrier may be Nr×Nt, where Nrmay represent the number of second antennas AT_12to AT_y2, and Ntmay represent the number of first antennas AT_11to AT_x1. Each element of Eq. 1 may be defined as a matrix and/or a vector. The transmission data stream xkmay have a size of, for example, Nt×1, where Ntmay represent the number of transmission streams. The thermal noise nkmay refer to white Gaussian noise. The thermal noise nkmay have a size of Nr×1.

In an embodiment, the channel estimator21may generate channel state information based on the estimated channel. For example, the channel state information may include at least one of a channel quality indicator (CQI), a precoding matrix indicator (PMI), and a rank indicator (RI).

In some embodiments, a class of a channel between the beamformer10and the beamformee20may be classified into a plurality of classes. For example, in a binary-level classification, a class may be classified into a first channel class (e.g., additive white Gaussian noise (AWGN) and Ch.A of an IEEE 802.11ac/ax/be WLAN system) and a second channel class (e.g., Ch.B, Ch.C, Ch.D, Ch.E, and Ch.F of an IEEE 802.11ac/ax/be WLAN system). In such an example, the second channel class may have a greater multipath fading than the first channel class. Multipath fading may refer to interference that may occur due to signals that may be received along different paths may have different amplitudes and/or phases due to reflection, and the like.

In contrast, in the case of multi-level classification, a class may be classified into a first channel class (e.g., AWGN and Ch.A of the IEEE 802.11ac/ax/be WLAN system), a second channel class (e.g., Ch.B and Ch.C of the IEEE 802.11ac/ax/be WLAN system), and a third channel class (e.g., Ch.D, Ch.E, and Ch.F of the IEEE 802.11ac/ax/be WLAN system). In such an example, the second channel class may have a greater multipath fading than the first channel class, and the third channel class may have greater multipath fading than the second channel class.

Although a class of a channel has been described as being in a binary-level classification and/or a multi-level classification, the present disclosure is not limited thereto. For example, the number of channel classes and/or the classification methods may be set differently (e.g., different numbers of classes, different classification methods).

In some embodiments, the beamformee20may obtain first channel characteristic data from the channel estimated based on the NDP frame. The first channel characteristic data may refer to data for determining a class of a channel. The first channel characteristic data may include at least one of a variance of a channel frequency response, a channel delay spread, and a signal-to-noise ratio (SNR) of a channel.

The variance σH2of the channel frequency response may be represented as an equation similar to Equation 2.

Referring to Equation 2, Ĥk(i, j) may represent an (i, j)thelement of a channel matrix estimated by using the NDP frame.

The channel delay spread τHmay be represented as an equation similar to Equation 3.

Referring to Equation 3, IDFT may represent an inverse discrete Fourier transform, Δ may represent a subcarrier index, Ĥ*k(i,j) may represent a complex conjugate of Ĥk(i, j), and E{ . . . } may represent an arithmetic mean operator.

The SNR of the channel may be represented as an equation similar to Equation 4.

Referring to Equation 4, PNmay represent a power of thermal noise, and PSmay represent a power of the NDP frame received by the beamformee20.

In some embodiments, the beamformee20may determine a class of a channel by applying a machine learning algorithm to the first channel characteristic data. An example of an operation of the machine learning algorithm is described with reference toFIGS.6to8. The beamformee20may generate beamforming feedback information based on a determination result obtained from the machine learning algorithm. The beamforming feedback information may refer to information about feedback frame resources.

The beamforming feedback information may include subcarrier grouping information and codebook size information. The subcarrier grouping information may refer to information used to group a specific number of subcarriers. The subcarrier grouping information is described with reference toFIG.2.

The codebook size information may refer to the number of bits corresponding to quantization angle information described below with reference toFIG.1. Also, as described with reference toFIG.4, a resolution of a demodulated signal of a fine codebook having a large codebook size may be higher than another resolution of a coarse codebook having a small codebook size.

The beamformee20may generate a feedback frame according to the beamforming feedback information. For example, when the subcarrier grouping information has information corresponding to four (4), the beamformee20may generate a feedback frame by grouping four (4) subcarriers.

Also, when the codebook size information has information corresponding to a fine codebook, the beamformee20may generate a feedback frame having a larger codebook size than in a coarse codebook.

Accordingly, the beamformee20may adjust feedback frame resources according to the class of the channel, and may reduce beamforming feedback overhead.

The decomposer22may perform singular value decomposition on the channel Ĥest,kestimated by the channel estimator21using an equation similar to Equation 5.

Referring to Equation 5, Ukmay represent a left singular matrix, and Vkmay represent a right singular matrix, and may include a Hermitian operator. Σkmay represent a diagonal matrix including non-negative singular values.

A size of the left singular matrix Ukmay be Nr×NSS. A size of the right singular matrix Vkmay be Nt×NSS. Also, a size of Σkmay be NSS×NSS. The right singular matrix Vkmay be referred to as an initial beam steering matrix. In a wireless communication system30(e.g., an IEEE 802.11ac/ax/be WLAN system), according to some embodiments, because the beamformer10may transmit a signal to the beamformee20through orthogonal frequency-division multiplexing (OFDM) modulation in which NFFTsubcarriers in one symbol may be guaranteed to be orthogonal to each other, a channel estimation operation of the channel estimator21and a singular value decomposition operation of the decomposer22may be performed for each subcarrier.

The decomposer22may apply a diagonal matrix D for performing a common phase shift to the initial beam steering matrix Vkusing an equation similar to Equation 6, without transmitting the initial beam steering matrix Vkto the beamformer10in order to reduce feedback overhead transmitted to the beamformer10.

Referring to Eq. 6, Qkmay represent a beam steering matrix, and a first diagonal matrix D may represent a matrix for allowing an element of a last row of each column of the beam steering matrix Qkto have a real value. For example, the first diagonal matrix D may be (e−jϕ(Ntx,1), . . . , e−jϕNtx-1,Ntx-1)), where e−jϕ(Ntx,1)may represent a phase value of an element corresponding to an Ntxthrow and a first column of the initial beam steering matrix Vk. In some embodiments, the first diagonal matrix D may include a phase value of an element of a last row of each column of the initial beam steering matrix Vk.

The compressor23may obtain angle information ϕ, ψ about the beam steering matrix Qkgenerated by the decomposer22using equations similar to Equations 7 to 9.

Referring to Equation 7, 1i-1may represent a vector including 1s having a length i-1. {hacek over (I)}Nt×Nrmay represent an identity matrix having a size of Nt×Nr.

Continuing to refer to Equation 7, Di(1i-1, ejϕi,i, . . . , ejϕNt-1i, 1) may be expressed as a second diagonal matrix using an equation similar to Equation 8.

Continuing to refer to Equation 7, Gli(ψ) may represent a Givens rotation matrix and may be expressed using an equation similar to Equation 9.

The compressor23may generate quantization angle information by quantizing the obtained angle information ϕ, ψ. Pieces of quantization angle information {circumflex over (ϕ)} and {circumflex over (ψ)} may be respectively quantized using equations similar to Equations 10 and 11.

Referring to Equation 10, bϕmay represent the number of bits used to quantize ϕ.

Referring to Equation 11, bψmay denote the number of bits used to quantize ψ.

The compressor23, according to an embodiment, may generate a feedback frame including the quantization angle information. As used herein, the quantization angle information may be referred to as information about the channel estimated by the channel estimator21. In an embodiment, the beamformee20may transmit the feedback frame to the beamformer10through a transceiver and the plurality of second antennas AT_12to AT_y2of the beamformee20.

The beamformer10may receive the feedback frame from the beamformee20through a transceiver and the plurality of first antennas AT_11to AT_x1. The controller11may control an overall operation for communication of the beamformer10. For example, the controller11may generate the NDP frame, and/or may process information included in the feedback frame so that the beamforming circuit12may use the information.

The beamforming circuit12, according to an embodiment, may determine beamforming matrices for performing beamforming according to the information included in the feedback frame.

The beamformer10may transmit a signal beamformed according to the beamforming matrices determined by the beamforming circuit12to the beamformee20through the transceiver and the first antennas AT_11to AT_x1.

In another embodiment, the beamformer10may generate beamforming feedback information. For example, according to the IEEE 802.11ac/ax/be standard, in a case of multi-user beamforming (e.g., MU-MIMO), the beamformer10may generate beamforming feedback information and may transmit the beamforming feedback information to the beamformee20. The beamformee20may generate a feedback frame according to the received beamforming feedback information. In contrast, in a case of single-user beamforming, the beamformee20may generate beamforming feedback information similar to the above-described embodiment.

When the beamformer10generates the beamforming feedback information, the beamformee20may generate a channel information frame by using the reference signal included in the NDP frame received from the beamformer10. The beamformer10may receive the channel information frame from the beamformee20. The beamformer10may obtain second channel characteristic data based on the channel information frame. The second channel characteristic data may refer to data for determining a class of a channel as described above. The second channel characteristic data may include at least one of a variance of a channel frequency response, a channel delay spread, and an SNR of a channel, similar to the first channel characteristic data.

The beamformer10may determine a class of a channel by applying a machine learning algorithm to the second channel characteristic data. An example operation of the machine learning algorithm is described with reference toFIGS.13and14. The beamformer10may generate beamforming feedback information based on a determination result. In an embodiment, the beamforming feedback information may include subcarrier grouping information and codebook size information as described previously.

The beamformer10may transmit the beamforming feedback information to the beamformee20. In an embodiment, the beamformee20may generate a feedback frame according to the beamforming feedback information. For example, when the subcarrier grouping information has information corresponding to four (4), the beamformee20may generate a feedback frame by grouping four (4) subcarriers.

Also, when the codebook size information has information corresponding to a fine codebook, the beamformee20may generate a feedback frame having a larger codebook size than in a coarse codebook.

Accordingly, the beamformee20may efficiently adjust feedback frame resources according to the class of the channel, and may reduce beamforming feedback overhead.

FIG.2is a diagram depicting subcarrier grouping, according to an embodiment.FIGS.2to4are described with reference toFIG.1.

Subcarrier grouping may refer to technology used in a WLAN standard, such as, but not limited to IEEE 802.11ac/ax/be. Subcarrier grouping may refer to the beamformee20feeding back quantization angle information corresponding to only some subcarriers without feeding back quantization angle information corresponding to all subcarriers by using channel frequency correlations.

When subcarrier grouping information Ng is determined, the beamformee20may feed back, to the beamformer10, quantization angle information corresponding to one subcarrier100from among consecutive subcarriers of the subcarrier grouping information Ng. The subcarrier grouping information Ng may be determined by the beamformer10and/or the beamformee20. When the beamformer10generates beamforming feedback information, for example, in a case of multi-user beamforming, the beamformer10may transmit the subcarrier grouping information Ng to the beamformee20. The beamformee20may feed back quantization angle information according to the received subcarrier grouping information Ng. In contrast, when the beamformee20generates beamforming feedback information, for example, in a case of single-user beamforming, the beamformee20may set the subcarrier grouping information Ng by itself.

The beamformer10may receive quantization angle information according to the subcarrier grouping information Ng from the beamformee20, and then may infer quantization angle information for the remaining subcarriers by using interpolation. For example, the IEEE 802.11ac may define subcarrier grouping information Ng as having a value of one (1), two (2), or four (4). Alternatively or additionally, the IEEE 802.11ax/be may define subcarrier grouping information Ng as having a value of four (4) or sixteen (16). However, the present disclosure is not limited in this regard, for example, the subcarrier grouping information Ng may be set to other values.

FIGS.3A and3Billustrate tables describing codebook size information, according to an embodiment. As described with reference toFIG.1, codebook size information may refer to the numbers of bits bϕand bψcorresponding to quantization angle information.

FIG.3Aillustrates codebook size information in a case of binary-level classification according to the IEEE 802.11ac/ax/be standard.FIG.3Billustrates codebook size information in a case of multi-level classification.

Referring toFIG.3A, in the case of a first channel class, two (2) and four (4) bits corresponding to a coarse codebook may be allocated as the numbers of bits bϕand bψof quantization angle information. A channel corresponding to the coarse codebook may be a frequency flat channel in which multipath fading may occur over a wide frequency band.

As further shown inFIG.3A, in a case of a second channel class, four (4) and six (6) bits corresponding to a fine codebook may be allocated as the numbers of bits bϕand bψof quantization angle information. A channel corresponding to the fine codebook may have a frequency selective channel in which multipath fading may occur in a specific frequency band.

Referring toFIG.3B, in a case of a first channel class, two (2) and four (4) bits corresponding to a coarse codebook may be allocated as the numbers of bits bϕand bψof quantization angle information. A channel corresponding to the coarse codebook may be and/or may include a frequency flat channel.

In a case of a second channel class, four (4) and six (6) bits corresponding to a low fine codebook may be allocated as the numbers of bits bϕand bψof quantization angle information. A channel corresponding to the low fine codebook may be a low frequency selective channel in which multipath fading occurs in a specific frequency band.

In a case of a third channel class, six (6) and eight (8) bits corresponding to a high fine codebook may be allocated as the numbers of bits bϕand bψof quantization angle information. A channel corresponding to the high fine codebook may be a high frequency selective channel in which multipath fading may occur in a narrower frequency band than in the low fine codebook.

FIG.4is a diagram depicting a resolution of a beam steering matrix, according to an embodiment.FIG.4illustrates a demodulated beam steering matrix {circumflex over (Q)} according to a subcarrier index. The demodulated beam steering matrix {circumflex over (Q)} may refer to a beam steering matrix generated when the beamformer10demodulates pieces of quantization angle information received from the beamformee20.

Referring toFIG.4, a resolution of a beam steering matrix in a case of a coarse codebook may be lower than the resolution in a fine codebook, and thus, the amount of information lost in a demodulation process may be large. However, because a fine codebook requires more bits than a coarse codebook as described with reference toFIG.3A, feedback overhead may be greater.

FIG.5illustrates signal exchange illustrating an operating method of a beamformer and a beamformee, according to an embodiment. Beamformer510and beamformee520ofFIG.5may include and/or may be similar in many respects to the beamformer10and the beamformee20described above with reference toFIG.1, respectively, and may include additional features not mentioned above. Consequently, repeated descriptions of the beamformer510and beamformee520described above with reference toFIG.1may be omitted for the sake of brevity.

The signal exchange ofFIG.5may correspond to an operating method when the beamformee520generates beamforming feedback information, for example, in a case of single-user beamforming. Also, the signal exchange ofFIG.5shows operations of a beamformer510as an access point and a beamformee520as a station over time. As shown inFIG.5, the operating method of the beamformer510and the beamformee520may include a plurality of operations S110to S190.

Referring toFIG.5, in operation S110, the beamformer510may generate an NDP frame. The NDP frame may include a reference signal for channel estimation between the beamformer510and the beamformee520.

In operation S120, the beamformee520may receive the NDP frame from the beamformer510.

In operation S130, the beamformee520may obtain channel characteristic data based on the NDP frame. The beamformee520may obtain the channel characteristic data by estimating a channel by using the reference signal included in the NDP frame. The channel characteristic data may include at least one of a variance of a channel frequency response, a channel delay spread, and an SNR of a channel, as described with reference toFIG.1.

In some embodiments, operation S130may include an operation of extracting pieces of information included in the NDP frame by identifying the NDP frame, and an operation of obtaining the channel characteristic data between the beamformee520and the beamformer510by using the extracted pieces of information.

In operation S140, the beamformee520may determine a class of a channel by applying a first machine learning algorithm to the channel characteristic data. The first machine learning algorithm may refer to supervised learning of a machine learning to infer at least one function from training data. The first machine learning algorithm may be and/or may include, for example, a K-nearest neighbor (KNN) algorithm as described with reference toFIGS.6and7, and/or an artificial intelligence network algorithm as described with reference toFIG.8. The beamformee520may determine the class of the channel by applying the first machine learning algorithm to the channel characteristic data. For example, in a case of binary-level classification, the beamformee520may determine whether the class of the channel is a first channel class or a second channel class. Alternatively or additionally, in a case of multi-level classification, the beamformee520may determine whether the class of the channel is a first channel class, a second channel class, or a third channel class.

In operation S150, the beamformee520may generate beamforming feedback information based on a determination result of operation S140. The beamforming feedback information may include subcarrier grouping information and codebook size information.

In operation S160, the beamformee520may generate a feedback frame according to the beamforming feedback information. For example, when the subcarrier grouping information has information corresponding to four (4), the beamformee520may generate a feedback frame by grouping four (4) subcarriers. Also, when the codebook size information has information corresponding to a fine codebook, the beamformee520may generate a feedback frame having a larger codebook size than in a coarse codebook.

In operation S170, the beamformee520may transmit the feedback frame to the beamformer510. The feedback frame may include pieces of quantization angle information.

In operation S180, the beamformer510may perform beamforming by demodulating the pieces of quantization angle information included in the feedback frame.

In operation S190, the beamformee520may receive a beamformed signal from the beamformer510.

FIG.6is a flowchart for determining a channel class based on a first machine learning algorithm, according to an embodiment.FIGS.6to8are described with reference toFIG.5.

A first machine learning algorithm, according to an embodiment, may include operations S141to S144.

In operation S141, the beamformee520may obtain pieces of channel training data including pieces of first channel class data and pieces of second channel class data. In some embodiments, the beamformee520may obtain the pieces of channel training data based on a channel simulation result with the beamformer510.

In operation S142, the beamformee520may determine K pieces of KNN data closest to channel characteristic data from among the pieces of channel training data, where K is a positive integer greater than zero (0). For example, the beamformee520may determine K pieces of KNN data closest to the channel characteristic data in a three-dimensional (3D) domain including an SNR of a channel, a variance of a channel frequency response, and a channel delay spread, as described with reference toFIG.7.

In operation S143, the beamformee520may compare the number of pieces of first channel class data with the number of pieces of second channel class data from among the pieces of KNN data. For example, when K is three (3), the beamformee520may check channel classes of the pieces of KNN data, and may compare one first channel class with two second channel classes.

In operation S144, the beamformee520may determine a class of a channel corresponding to the channel characteristic data to be a class of a channel corresponding to a greater number as a comparison result. For example, when the number of second channel classes from among the pieces of KNN data is greater, the beamformee520may determine the class of the channel corresponding to the channel characteristic data to be a second channel class.

FIG.7is a diagram depicting a first machine learning algorithm, according to an embodiment.

FIG.7illustrates pieces of first channel class data, pieces of second channel class data, and channel characteristic data that is new data, in a 3D domain including an SNR of a channel, a variance of a channel frequency response, and a channel delay spread.

According to a KNN algorithm, when K is three (3), channel classes of pieces of KNN data closest to the channel characteristic data may be one piece of first channel class data and two pieces of second channel class data. Accordingly, because the number of second channel classes is greater than that of first channel classes from among the pieces of KNN data, the channel characteristic data may be determined to be a second channel class.

FIG.8is a diagram illustrating a first machine learning algorithm, according to an embodiment.

The beamformee520may determine a class of a channel by applying an artificial neural network algorithm to channel characteristic data as shown inFIG.8. A method of determining a class of a channel by applying an artificial neural network algorithm is described with reference toFIG.8.

The beamformee520may include an artificial neural network200as shown inFIG.8. The artificial neural network200may have a structure including an input layer210, hidden layers (e.g., first hidden layer220, second hidden layer230, and third hidden layer240), and an output layer250. The artificial neural network200may perform an operation based on received input data (e.g., a variance of a channel frequency response, a channel delay spread, and/or an SNR of a channel). For example, the artificial neural network200may determine a class of a channel based on an operation result.

The artificial neural network200may be and/or may include an n-layers neural network and/or a deep neural network (DNN) including two or more hidden layers. For example, as shown inFIG.8, the artificial neural network200may be a DNN including the input layer210, the first to third hidden layers220to240, and the output layer250. The DNN may include, but is not limited to, a convolutional neural network (CNN), a recurrent neural network (RNN), a deep belief network, a restricted Boltzmann machine, and the like.

Although, as shown inFIG.8. the artificial neural network200includes five (5) layers (e.g., input layer210, first hidden layer220, second hidden layer230, third hidden layer240, and output layer250), the present disclosure is not limited in this regard. That is, the artificial neural network200illustrated inFIG.8is merely an example, and the artificial neural network200may include more (e.g., six (6) or more layers) or fewer layers (e.g., four (4) or less layers). Also, the artificial neural network200may include layers having any of various structures different from that shown inFIG.8.

Each of the layers (e.g., input layer210, first hidden layer220, second hidden layer230, third hidden layer240, and output layer250) included in the artificial neural network200may include a plurality of neurons. The neurons may correspond to a plurality of artificial nodes, that may be known or referred to as processing elements (PEs), units, or similar terms. For example, as shown inFIG.8, the input layer210may include three neurons (nodes), and each of the first to third hidden layers220to240may include four neurons (nodes). However, this is merely an example, and each of the layers included in the artificial neural network200may include any of various numbers of neurons (nodes).

The neurons included in the layers included in the artificial neural network200may be connected to each other to exchange data. One neuron may receive data from other neurons, may perform an operation on the data, and may output an operation result to other neurons.

An input and an output of each of the neurons (nodes) may be respectively referred to as an input activation and an output activation. That is, an activation may be a parameter that is an output of one neuron and an input of neurons included in a next layer. Each neuron may determine its activation based on activations and weights received from neurons included in a previous layer. A weight may refer to a parameter used to calculate an output activation of each neuron, and may be a value allocated to a connection between neurons.

Each neuron may be processed by a computational unit and/or a processing element that receives an input activation and outputs an output activation, and the input activation and the output activation of each neuron may be mapped. For example, σjimay represent an activation function of a jthneuron of an ithlayer, and wj,kimay represent a weight value from a kthneuron included in an (i-1)thlayer to the jthneuron included in the ithlayer. αjimay be referred to as an activation of the jthneuron of the ithlayer, in other words, a post activation. The post activation αjimay be calculated by using Equation 12.

As shown inFIG.8, a post activation of a first neuron of the first hidden layer220may be expressed as α12. Also, α12may have a value of α12=σ12(w1,12×α11+w1,22×α21+w1,32×α31), according to Equation 12. That is, a post activation may be a value obtained by applying an activation function to a sum of activations received from a previous layer. However, Equation 12 is merely an example illustrating an activation and a weight used to process data in a neural network, and the present disclosure is not limited thereto.

The artificial neural network200may perform the operations described above on the first to third hidden layers220to240and the output layer250. For example, the artificial neural network200may determine a class of a channel based on a result of the operation. That is, in a case of binary-level classification, the artificial neural network200may determine a class of a channel as a first channel class or a second channel class. Alternatively or additionally, in a case of multi-level classification, the artificial neural network200may determine a class of a channel as a first channel class, a second channel class, or a third channel class.

FIGS.9A and9Billustrate tables describing subcarrier grouping information, according to an embodiment.

FIG.9Aillustrates subcarrier grouping information in a case of binary-level classification.FIG.9Billustrates subcarrier grouping information in a case of multi-level classification.

Referring toFIG.9A, in a case of a first channel class Class I, sixteen (16) subcarriers may be grouped to generate a feedback frame (e.g., Ng=16). In an embodiment, the first channel class Class I may correspond to Ch.A of a WLAN system that conforms to the IEEE 802.11ac/ax/be standard. Alternatively or additionally, the first channel class Class I may refer to a frequency flat channel in which multipath fading may occur over a wide frequency band.

In contrast, in a case of a second channel class Class II, four (4) subcarriers may be grouped to generate a feedback frame (e.g., Ng=4). The second channel class Class II may correspond to Ch.B, Ch.C, Ch.D, Ch.E, and Ch.F of the WLAN system that conforms to the IEEE 802.11ac/ax/be standard. Alternatively or additionally, the second channel class Class II may refer to a frequency selective channel in which multipath fading may occur in a specific frequency band.

Referring toFIG.9B, in a case of a first channel class Class I, a total number NSTof subcarriers may be grouped to generate a feedback frame (e.g., Ng=NST). The first channel class Class I may correspond to Ch.A of a WLAN system that conforms to the IEEE 802.11ac/ax/be standard. Alternatively or additionally, the first channel class Class I may refer to a frequency flat channel.

In a case of a second channel class Class II, sixteen (16) subcarriers may be grouped to generate a feedback frame (e.g., Ng=16). The second channel class Class II may correspond to Ch.B and Ch.C of the WLAN system that conforms to the IEEE 802.11ac/ax/be standard. Alternatively or additionally, the second channel class Class II may refer to a low frequency selective channel in which multipath fading may occur in a specific frequency band.

In a case of a third channel class Class III, four (4) subcarriers may be grouped to generate a feedback frame (e.g., Ng=4). The third channel class Class III may correspond to Ch.D, Ch.E, and Ch.F of the WLAN system that conforms to the IEEE 802.11ac/ax/be standard. Alternatively or additionally, the third channel class Class III may refer to a high frequency selective channel in which multipath fading may occur in a narrower frequency band than in a low fine codebook.

FIG.10is a diagram illustrating subcarrier grouping, according to an embodiment.FIG.10is described with reference toFIG.1.

FIG.10is a diagram of a first channel class as described with reference toFIG.9B. That is, as shown inFIG.10, a total number NSTof subcarriers may be grouped to generate a feedback frame (e.g., Ng=NST). For example, the beamformee20may feed back quantization angle information corresponding to one subcarrier110from among the total number NSTof subcarriers to the beamformer10.

FIG.11is a diagram illustrating the total number NSTof subcarriers according to a bandwidth of a channel, according to an embodiment. As shown inFIG.11, the total number NSTof subcarriers may vary according to a bandwidth of a channel. For example, a total number NSTof subcarriers for a 20 MHz bandwidth may be 242 (e.g., NST=242), a total number NSTof subcarriers for a 40 MHz bandwidth may be 484 (e.g., NST=484), a total number NSTof subcarriers for a 80 MHz bandwidth may be 996 (e.g., NST=996), a total number NSTof subcarriers for a 80+80 MHz bandwidth may be 1,992 (e.g., NST=1992), and a total number NSTof subcarriers for a 160 MHz bandwidth may be 3,984 (e.g., NST=3984).

FIG.12illustrates an example of a signal exchange depicting an operating method of a beamformer and a beamformee, according to an embodiment. Beamformer810ofFIG.12may include and/or may be similar in many respects to at least one of the beamformer10ofFIG.1and the beamformer510described above with reference toFIGS.5and6, and may include additional features not mentioned above. Beamformee820ofFIG.12may include and/or may be similar in many respects to at least one of the beamformee20ofFIG.1and the beamformee520described above with reference toFIGS.5and6, and may include additional features not mentioned above. Consequently, repeated descriptions of the beamformer810and beamformee820described above with reference toFIGS.1,5, and6may be omitted for the sake of brevity.

The signal exchange ofFIG.12may correspond to an operating method when the beamformer810generates beamforming feedback information (e.g., in a case of multi-user beamforming). That is, the signal exchange ofFIG.12illustrates operations of the beamformer810as an access point and a beamformee820as a station over time. As shown inFIG.12, the operating method of the beamformer810and the beamformee820may include a plurality of operations S210to S290.

Referring toFIG.12, in operation S210, the beamformer810may generate an NDP frame. The NDP frame may include a reference signal for channel estimation between the beamformer810and the beamformee820.

In operation S220, the beamformer810may transmit the NDP frame to the beamformee820.

In operation S230, the beamformer810may receive a channel information frame from the beamformee820.

In operation S240, the beamformer810may obtain channel characteristic data based on the channel information frame. The channel characteristic data may include at least one of a variance of a channel frequency response, a channel delay spread, and an SNR of a channel. In operation S250, the beamformer810may determine a class of a channel by applying a second machine learning algorithm to the channel characteristic data. The second machine learning algorithm may refer to unsupervised learning of a machine learning to infer at least one function without training data. The second machine learning algorithm may be and/or include, for example, a K-mean algorithm as described with reference toFIG.13. In an embodiment, the beamformer810may determine the class of the channel by applying the second machine learning algorithm to the channel characteristic data. For example, in a case of binary-level classification, the beamformer810may determine whether the class of the channel is a first channel class or a second channel class. Alternatively or additionally, in a case of multi-level classification, the beamformer810may determine whether the class of the channel is a first channel class, a second channel class, or a third channel class.

In operation S260, the beamformer810may generate beamforming feedback information based on a determination result of operation S250. The beamforming feedback information may include subcarrier grouping information and codebook size information.

In operation S270, the beamformer810may transmit the beamforming feedback information to the beamformee820.

In operation S280, the beamformee820may generate a feedback frame according to the beamforming feedback information. For example, when the subcarrier grouping information has information corresponding to four (4), the beamformee820may generate a feedback frame by grouping four (4) subcarriers. Also, when the codebook size information has information corresponding to a fine codebook, the beamformee820may generate a feedback frame having a larger codebook size than in a coarse codebook. In some embodiments, the feedback frame may include pieces of quantization angle information corresponding to the channel characteristic data.

In operation S290, the beamformer810may receive the feedback frame generated in operation S280from the beamformee820.

FIG.13is a flowchart for determining a channel class based on a second machine learning algorithm, according to an embodiment.FIGS.13and14are described with reference toFIG.12.

A second machine learning algorithm may include operations S251to S253.

In operation S251, the beamformer810may obtain pieces of first channel training data including pieces of initial center point data. The pieces of first channel training data may refer to pieces of data obtained when the beamformer810measures a channel with the beamformee820. For example, the beamformer810may set an average of an arbitrary number of pieces of data from among pieces of measured data as initial first channel class center point data and initial second channel class center point data. The initial first channel class center point data and the initial second channel class center point data may be referred to as the pieces of initial center point data.

In operation S252, the beamformer810may determine nearest center point data closest to channel characteristic data. For example, as shown inFIG.14, the beamformer810may determine the initial center point data closest to the channel characteristic data as nearest center point data in a 3D domain including an SNR of a channel, a variance of a channel frequency response, and a channel delay spread.

In operation S253, the beamformer810may determine a class of a channel corresponding to the channel characteristic data to be a class of a channel corresponding to the nearest center point data. For example, when the nearest center point data is the initial second channel class center point data, the beamformer810may determine the class of the channel corresponding to the channel characteristic data to be a second channel class.

In some embodiments, the second machine learning algorithm may further include an operation of updating pieces of center point data based on pieces of second channel training data obtained by adding the channel characteristic data to the pieces of first channel training data. For example, when the beamformer810determines the class of the channel corresponding to the channel characteristic data to be the second channel class, the beamformer810may update second channel class center point data to the pieces of second channel training data including the channel characteristic data.

FIG.14is a diagram illustrating a second machine learning algorithm, according to an embodiment.

FIG.14illustrates pieces of measured data, pieces of first channel class center point data, pieces of second channel class center point data, and channel characteristic data that is new data, in a 3D domain including an SNR of a channel, a variance of a channel frequency response, and a channel delay spread.

According to a K-mean algorithm, the beamformer810may set an initial first channel class center point data and an initial second channel class center point data by measuring a channel with the beamformee820.

The beamformer810may determine a nearest initial center point data closest to channel characteristic data from among pieces of initial center point data. For example, when the initial second channel class center point data is the initial center point data closest to the channel characteristic data, the beamformer810may determine the nearest initial center point data to be the initial second channel class center point data.

The beamformer810may determine a class of a channel corresponding to the channel characteristic data to be a class of a channel corresponding to the nearest initial center point data. For example, when the nearest initial center point data is the initial second channel class center point data, the beamformer810may determine a class of a channel corresponding to the channel characteristic data to be a second channel class.

FIG.15is a conceptual diagram illustrating an Internet of things (IoT) network system to which embodiments of the present disclosure may be applied.

Referring toFIG.15, an IoT network system1000may include a plurality of IoT devices (e.g., home gadgets1100, home appliances1120, entertainment devices1140, and vehicles1160), an access point1200, a gateway1250, a wireless network1300, and a server1400. The IoT network system1000may refer to a network between objects using wired/wireless communication.

Each of the IoT devices1100to1160may form a group according to characteristics of each IoT device. For example, the IoT devices may be grouped into a home gadget group1100, a home appliance/furniture group1120, an entertainment group1140, a vehicle group1160, and/or the like. The plurality of IoT devices1100,1120, and1140may be connected to each other through a communication network and/or may be connected to other IoT devices through the access point1200. In an embodiment, the access point1200may be provided in one IoT device. The gateway1250may change a protocol to connect the access point1200to an external wireless network1300. That is, the IoT devices1100,1120, and1140may be connected to an external communication network1300through the access point1200and the gateway1250. The external communication network1300may include the Internet and/or public network. The plurality of IoT devices1100,1120,1140, and1160may be connected to the server1400that may provide a certain service through the wireless network1300, and a user may use the service through at least one of the plurality of IoT devices1100,1120,1140, and1160.

According to embodiments, the plurality of IoT devices1100to1160may adaptively adjust beamforming feedback resources by using machine learning as described above with reference toFIGS.1to14. Accordingly, the IoT devices1100to1160may efficiently adjust feedback frame resources, thereby potentially reducing beamforming feedback overhead.

Embodiments have been described with reference to the drawings. While embodiments have been described by using specific terms, the terms have merely been used to explain the present disclosure and should not be construed as limiting the scope of the present disclosure defined by the claims. Hence, it may be understood by one of ordinary skill in the art that various modifications and other equivalent embodiments may be made therefrom. Accordingly, the scope of the present disclosure should be defined by the following claims.