Patent ID: 12200565

DETAILED DESCRIPTION

Reference will now be made in detail to the present embodiments of the invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the description to refer to the same or like parts.

Referring toFIG.1. In one aspect, the present disclosure is directed to a beam domain based localization system100. This system may be easily integrated into a beam domain indoor localization technology of multi-antenna systems and may be applicable or readily adaptable to all technologies. Accordingly, the beam domain based localization system100has advantages. Herewith the beam domain based localization system100is described below withFIG.1.

The subject disclosure provides the beam domain based localization system100in accordance with the subject technology. Various aspects of the present technology are described with reference to the drawings. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. It can be evident, however, that the present technology can be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing these aspects. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

FIG.1is a block diagram of the beam domain based localization system100according to one embodiment of the present disclosure. As shown inFIG.1, the beam domain based localization system100includes a wireless transceiver120and a computer device110. In structure, the computer device110is electrically connected to the wireless transceiver120. It should be noted that when an element is referred to as being “connected” or “coupled” to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being “directly connected” or “directly coupled” to another element, there are no intervening elements present. For example, the computer device110may be a built-in control circuit that is directly connected to the wireless transceiver120, or the computer device110may be an external server that is indirectly connected to the wireless transceiver120through the network.

In practice, for example, the wireless transceiver120can be a base station, a wireless access point, a multi-antenna communication device, or the like.

In practice, for example, the computer device110can be a built-in control circuit of the wireless transceiver120, a server disposed outside the wireless transceiver120, a computer host or other computer equipment. The server can be remotely managed in a manner that provides accessibility, consistency, and efficiency. Remote management removes the need for input/output interfaces in the servers. An administrator can manage a large data centers containing numerous rack servers using a variety of remote management tools, such as simple terminal connections, remote desktop applications, and software tools used to configure, monitor, and troubleshoot server hardware and software.

As used herein, “around”, “about” or “approximately” shall generally mean within 20 percent, preferably within 10 percent, and more preferably within 5 percent of a given value or range. Numerical quantities given herein are approximate, meaning that the term “around”, “about” or “approximately” can be inferred if not expressly stated.

As shown inFIG.1, the computer device110includes a storage device111and a processor112. For example, the storage device111can be a hard disk, a flash storage device or another storage medium, and the processor112can be a central processing unit, a controller or another circuit. In structure, the storage device111is electrically connected to the processor112. In use, the storage device111can store the beam domain received power map (BDRPM), program instructions and other information, and the processor112can execute the program instructions to implement beam domain based localization methods (e.g., a location prediction, a depth learning, etc.).

In one embodiment of the present disclosure, the computer device110obtains a beam selection result associated with a mobile device190(e.g., a mobile phone, an electronic tag, etc.) through the wireless transceiver, and the computer device110locates the mobile device190according to the beam selection result. Thus, the beam domain based localization system100have the characteristics of low system complexity, and can only use a single wireless transceiver120(e.g., a base station, a wireless access point, etc.) to provide accurate localization services to the mobile device. Moreover, the present disclosure can be extended to use multiple wireless transceivers120to further improve the localization accuracy.

For a more complete understanding of the beam selection result, refer toFIG.1,FIG.2AandFIG.2B.FIG.2Ais a beam received power matrix200according to one embodiment of the present disclosure.FIG.2Bis a beam domain received power map210transformed from the beam received power matrix200ofFIG.2A. It should be noted that, in order to simplify the diagram, in the following different embodiments, for example, the beam domain received power map210in the training phase or the testing phase of deep learning can be taken as an example to assist in the description. The input beam selection data in the training phase or the beam received power matrix in the testing phase can be taken as an example of the beam received power matrix200to assist in the description.

In one embodiment of the present disclosure, the beam selection result collects the beam received power matrix200when the wireless transceiver120provides a communication service to the mobile device190and uses the beam received power matrix200as a spatial feature of the mobile device, and the computer device110transforms the beam selection result into the beam domain received power map210associated with the mobile device190and then locates the mobile device190based on the beam domain received power map210.

In practice, for example, the beam domain received power map210uses different colors or gray-scale intensities to represent numerical ranges of different received powers, so as to facilitate subsequent deep learning. For example, the row201and the column202in the beam received power matrix200correspond to the location of the mobile device190, and therefore the row201and the column202in the beam domain received power map210also correspond to the location of the mobile device190.

For a more complete understanding of the deep learning, refer toFIG.3.FIG.3is a block diagram of a deep learning model300according to one embodiment of the present disclosure. In one embodiment of the present disclosure, the computer device110establishes a fingerprinting database and the deep learning model300in the training phase, and the fingerprinting database includes a BDRPM set (e.g., a plurality of beam domain received power maps). An autoencoder301of the deep learning model300performs a feature extraction and a feature training on the plurality of the beam domain received power maps Bsto obtain information of a latent space Lsand uses the information of the latent space Lsas an input of a predictor330of the deep learning model300, thereby increasing an accuracy of a location prediction of the predictor330.

As to above BDRPM set, in one embodiment of the present disclosure, the computer device110transforms a plurality of input beam selection data into the plurality of the beam domain received power maps in the training phase, where the plurality of the beam domain received power maps serve as a basis of training of the deep learning model300. In practice, for example, the input beam selection data include but not limited to raw data, or the input beam selection data can be information after weight training or other data processing. Deep learning algorithms include but are not limited to software, hardware, another algorithm known to assist in machine learning, artificial intelligence, deep learning, neural-like networks, other equivalent algorithms, mathematical formulas, or determination methods.

Moreover, in one embodiment of the present disclosure, after the training phase is finished, the computer device100uses the deep learning model300to locate the mobile device190according to the beam domain received power map210associated with the mobile device190.

For a more complete understanding of a beam domain based localization method performed by the beam domain based localization system100, referringFIGS.1-4,FIG.4is a flow chart of the beam domain based localization method400according to one embodiment of the present disclosure. As shown inFIG.4, the beam domain based localization method400includes operations S401to S408. However, as could be appreciated by persons having ordinary skill in the art, for the steps described in the present embodiment, the sequence in which these steps are performed, unless explicitly stated otherwise, can be altered depending on actual needs; in certain cases, all or some of these steps can be performed concurrently.

The beam domain based localization method400may take the form of a computer program product on a computer-readable storage medium having computer-readable instructions embodied in the medium. Any suitable storage medium may be used including non-volatile memory such as read only memory (ROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), and electrically erasable programmable read only memory (EEPROM) devices; volatile memory such as SRAM, DRAM, and DDR-RAM; optical storage devices such as CD-ROMs and DVD-ROMs; and magnetic storage devices such as hard disk drives and floppy disk drives.

The embodiment of the beam domain based localization method400takes indoor localization applications as an example, but is not limited to indoor localization applications. The wireless transceiver120(e.g., the base station/the wireless access point) can provide the mobile device190with the localization service while providing the communication service to the mobile device190. By using the result of beam selection by the wireless transceiver120, the beam domain based localization method400can utilize the beam selection result to provide the mobile device190with the localization service while providing the communication service to the mobile device190. The present disclosure can use a single wireless transceiver120to achieve an accurate indoor localization, and the present disclosure can also extend to use multiple wireless transceivers120to further improve positioning accuracy. The beam domain based localization method400roughly includes four parts: the collection of beam domain received power maps Bs, the training of the autoencoder301, the training of the predictor330, and the localization prediction of the mobile device190, where the autoencoder301may include an encoder310and a decoder320. The beam selection result is transformed into the beam domain received power maps Bsas the spatial features of the mobile device190and the input information of the subsequent positioning model (e.g., the deep learning model). The beam domain received power maps Bsare input to the autoencoder301for weighting training. The information of the latent space of the autoencoder301Lsis input to the predictor330for weight training. The location prediction of the mobile device190is performed through the trained autoencoder301and the trained predictor330. The autoencoder301transforms the beam domain received power maps into the information of the latent space with more spatial characteristics, and the information of the latent space with more spatial characteristics can be used as the input information of the predictor330, so as to accurately predict the location of mobile device190.

The processes of beam domain based localization method400can be roughly divided into two phases: the training phase and the testing phase. In the training phase, the beam selection result is collected as the spatial feature (i.e., a fingerprint) of a location of the mobile device190, where the mobile device190is disposed at the location in space. The collected beam selection result is transformed into the beam domain received power map as the input information of the encoder310. In order to accelerate the convergence speed of the encoder301and the predictor330, the beam domain based localization method400normalizes and preprocesses the data of the beam domain received power maps, and then the preprocessed beam domain received power map data are input to the autoencoder301for training. The autoencoder301transforms the features of aforesaid data into the information of the latent space with more spatial characteristics, so that the predictor330can be more easily converged. After the training of the autoencoder301and the predictor330in the training phase is finished, the prediction service of the mobile device190can be provided in the test phase.

In the training phase, in operation S401, a plurality of input beam selection data is obtained. In practice, for example, the plurality of input beam selection data can be different beam selection results corresponding to different locations of the mobile device190.

Then, in operation S402, the plurality of input beam selection data are transformed into a plurality of beam domain received power maps, so as to complete the collection of the plurality of beam domain received power maps.

Regarding the collection of beam domain received power maps, the wireless transceiver120uses a predefined three-dimensional (3D) codebook for beamforming, and constructs a three-dimensional beam by combining horizontal beams and vertical beams. The number of horizontal and vertical beams are represented as NBShand NBSvrespectively (as shown inFIG.2A), and therefore the 3D codebook size is NBS=NBSh×NBSv. When the wireless transceiver120selects different beams from the 3D codebook to provide the communication service to the mobile device190, the mobile device190has different received powers because the different beams used have different directivities and different channel transmission characteristics. In order to provide the mobile device190with the best communication service quality, the wireless transceiver120usually selects the beam with the maximum received power to perform the communication service for the mobile device190. Therefore, the beam domain based localization method400uses the collected beam received power matrix as the spatial feature of the mobile device190of the user in the indoor space when the communication service is provided for the mobile device190. The beam domain based localization method400transforms the collected beam received power matrix into the beam domain received power map210(shown inFIG.2B). In this way, after the mobile device190completes the beam selection, the beam domain received power map is obtained through above transforming process. It is observed that a special relationship between the beam domain received power map and the indoor spatial position of the mobile device190. Therefore, the beam domain based localization method400uses the beam domain received power map as the spatial feature for subsequent operations.

In operation S403, the fingerprinting database is established, which includes the BPRPM set (i.e., the plurality of the beam domain received power maps) that is used as a basis of subsequent training of the deep learning model. In operation S404, the deep learning model300is established to implement supervised learning. In operation S405, the computer device110is used for training, in which the autoencoder301of the deep learning model performs the feature extraction and feature training on the plurality of the beam domain received power maps to obtain information of the latent space Ls, and the information of the latent space Lsserves as an input of a predictor330of the deep learning model300, thereby increasing an accuracy of the location prediction of the predictor330. In this way, the computer device110generates the trained deep learning model300.

Regarding the training of the autoencoder301, the wireless transceiver120uses the 3D codebook for beam selection when serving the mobile device190. Therefore, the beam domain based localization method400can obtain the beam received power matrix through the beam selection mechanism and then can transform the beam received power matrix into a beam domain received power map Bs, where the beam domain received power map Bscan be viewed as a special image describing spatial characteristics. The present disclosure proposes the deep learning model300, such as a convolutional autoencoder neural network (CAR-Net). The deep learning model300mainly includes the encoder310, a decoder320and the predictor330. Therefore, the deep learning model300can include three parts: the encoder310, the decoder320, and the predictor330, as shown inFIG.3. Since the beam domain received power map is a two-dimensional image, the beam domain based localization method400uses a two-dimensional convolutional neural network (2DCNN) as the basic structure of the deep learning model300. The encoder310includes a plurality of two-dimensional convolutional neural networks312and a maximum pooling layer311. The decoder320includes an upsampling layer321and a plurality of two-dimensional convolutional neural networks322. The architecture of the encoder310and the decoder320performs the feature extraction and the feature training on the beam domain received power map Bs. With the architecture of the encoder310and the decoder320, the beam domain based localization method400can obtain the information of the latent space Lswith more spatial characteristics, and the information of the latent space Lsserves as the input of the predictor330.

The operation of the encoder310satisfies the following relationship: Ls=fe(Bs), where fe( ) is the function of the encoder310.

The operation of the decoder320satisfies the following relationship: {circumflex over (B)}S=fd(Ls), where fd( ) is the function of the decoder320, and {circumflex over (B)}sis the result of restoring the beam domain received power map.

Regarding the training of the predictor330, in this embodiment, the mobile device190can exist anywhere in the indoor space, and the beam domain based localization method400formulates the indoor localization problem as a regression problem. The information of latent space Lsextracted by the encoder310is input to the predictor330constructed by fully connected layers331, and the location prediction of the mobile device190is performed to obtain the predicted coordinate position Rêsof the mobile device190.

The operation of the predictor330satisfies the following relationship: {circumflex over (R)}s=fr(vec(Ls)), where fr( ) is the function of the predictor330, and vec( ) is a vectorization operation.

The loss parameter of the deep learning model300satisfies the following relationship: LS(RS, BS)=λBMSEB(BS)+λRMSEL(RS), where λBand λRare the hyperparameters of the restored beam domain received power map and the hyperparameters of the location prediction respectively, MSEB(Bs) and MSEL(Rs) are the loss function of the restored beam domain received power map and the loss function of the location prediction respectively, MSEB(Bs)=∥{circumflex over (B)}s−BS∥F2, and MSEL(Rs)=∥{circumflex over (R)}s−Rs∥2.

After the training phase is finished, in the testing phase, in short, in the beam domain based localization method400, the beam selection result associated with the mobile device190is obtained through the wireless transceiver120, and the mobile device190is located according to the beam selection result.

Specifically, in the testing phase, in operation S406, the beam selection result associated with the mobile device190is obtained through the wireless transceiver120. In one embodiment of the present disclosure, the beam selection result collects the beam received power matrix when the wireless transceiver120provides a communication service to the mobile device190and uses the beam received power matrix as the spatial feature of the mobile device190. In operation S407, the beam selection result is transformed into a beam domain received power map associated with the mobile device190.

Then, in operation S408, the location prediction is performed, which is based on the beam domain received power map to locate the mobile device190. Specifically, in operation S408, the trained deep learning model is based on the beam domain received power map associated with the mobile device190to locate the mobile device190.

Regarding the location prediction of the mobile device190, in this embodiment, after the deep learning model300in the beam domain based localization method400is trained in the training phase, when the communication service is provided for the mobile device190in the testing phase, a real-time location prediction service can be provided for the mobile device190by using the beam domain received power map obtained by the beam selection mechanism.

In practice, for example, with the 5G internet of things (IoT), a large number of IoT devices are arranged, and many of them have valuable assets. In order to provide the location service while communicating with the mobile device190, the beam domain based localization method400transforms the selection result of the beam mechanism into a two-dimensional image as an important feature of the device in space and introduces a deep learning mechanism to effectively identify the position of the device in space through the two-dimensional image. This localization technology can be performed simultaneously while providing the communication service for the mobile device190, thereby improving the localization accuracy and the robustness to environmental changes.

In view of the above, according to the present disclosure, the beam domain based localization system100and the beam domain based localization method400have the characteristics of low system complexity, and can only use a single wireless transceiver120(e.g., a base station, a wireless access point, etc.) to provide accurate localization services to the mobile device. Moreover, the present disclosure can be extended to use multiple wireless transceivers120to further improve the localization accuracy.

It will be apparent to those skilled in the art that various modifications and variations can be made to the structure of the present invention without departing from the scope or spirit of the invention. In view of the foregoing, it is intended that the present invention cover modifications and variations of this invention provided they fall within the scope of the following claims.