KR102430193B1 - Method and apparatus for tracking aerial node based on flight sensor data and beamforming information - Google Patents

Abstract

The present disclosure relates to a method and apparatus for tracking an aerial node based on flight sensor data and beamforming information. A method for a ground node to track a position of an aerial node according to an embodiment of the present disclosure includes: setting a set of candidates for spatial angles based on flight sensor data of the aerial node; deriving a predicted value of a received signal magnitude to which reception beamforming is applied with respect to an updated spatial angle that does not belong to the spatial angle candidates; determining an optimal spatial angle by repeating the update of the spatial angle and the derivation of the predicted value of the received signal magnitude; and determining the position of the aerial node based on the optimal spatial angle.

Description

Aerial node tracking method and apparatus based on flight sensor data and beamforming information

The present disclosure relates to tracking a moving object, and more particularly, to a method and apparatus for tracking an aerial node based on flight sensor data and beamforming information.

In a wireless communication system, a technology utilizing an aerial node such as an unmanned aerial vehicle (UAV) is being discussed for the purpose of extending network coverage, resolving shadow areas, and providing data hotspot services. The public node has a low introduction cost and a high degree of freedom in deployment, so that it can flexibly adjust a communication service provision area or support artificially or selectively using a more favorable transmission/reception channel environment.

For efficient wireless communication, it is required to track the exact location of a public node. Conventionally, there has been an attempt to track the position of an aerial node using beamforming information of a wireless communication system or flight sensor data such as GPS (Global Positioning System) information of an aviation system. However, the aviation system and the wireless communication system individually had a problem in that the accuracy and efficiency of location tracking were not high. Therefore, there is a need for a new method for tracking an aerial node by complementing and integrating flight sensor data and beamforming information.

It is an object of the present disclosure to provide an integrated beam tracking method and apparatus in consideration of characteristics of a wireless communication system and an aviation system.

A further technical problem of the present disclosure is to provide a method and apparatus for an air node to efficiently determine transmit beamforming without feedback from a terrestrial node.

An additional technical problem of the present disclosure is to provide a method and apparatus for dynamically tracking a position of an aerial node while reducing overhead of reception beamforming in a channel estimation process by a terrestrial node.

The technical problems to be achieved in the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those of ordinary skill in the art to which the present disclosure belongs from the description below. will be able

A method for a ground node to track a location of an aerial node according to an aspect of the present disclosure includes the steps of: setting a set of spatial angle candidates based on flight sensor data of the aerial node; deriving a predicted value of a received signal magnitude to which reception beamforming is applied with respect to an updated spatial angle that does not belong to the spatial angle candidates; determining an optimal spatial angle by repeating the update of the spatial angle and the derivation of the predicted value of the received signal magnitude; and determining the position of the aerial node based on the optimal spatial angle.

A method for an aerial node to transmit a signal according to a further aspect of the present disclosure includes: determining a transmission beamforming vector based on flight sensor data of the aerial node; and transmitting a signal to a terrestrial node based on the transmit beamforming vector.

The features briefly summarized above with respect to the present disclosure are merely exemplary aspects of the detailed description of the present disclosure that follows, and do not limit the scope of the present disclosure.

According to the present disclosure, an integrated beam tracking method and apparatus in consideration of characteristics of a wireless communication system and an aviation system may be provided.

According to the present disclosure, a method and apparatus for a public node to efficiently determine transmit beamforming without feedback from a terrestrial node may be provided.

According to the present disclosure, a method and apparatus for a terrestrial node to dynamically track a location of an aerial node while reducing the overhead of reception beamforming in a channel estimation process can be provided.

Effects obtainable in the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned may be clearly understood by those of ordinary skill in the art to which the present disclosure belongs from the description below. will be.

1 is a system model structural diagram to which the present disclosure can be applied.
2 is an xy plane projection for explaining a spatial angle according to the present disclosure.
3 is a view for explaining the timing of flight sensor data and communication signals according to the present disclosure.
4 is a diagram for explaining an example of a beam space channel to which the present disclosure can be applied.
5 is a flowchart illustrating a method for tracking a public node according to the present disclosure.
6 is a diagram illustrating the configuration of a ground node device and an air node device according to the present disclosure.
7 and 8 are diagrams illustrating simulation results according to the present disclosure.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art to which the present disclosure pertains can easily implement them. However, the present disclosure may be embodied in several different forms and is not limited to the embodiments described herein.

In describing the embodiments of the present disclosure, if it is determined that a detailed description of a well-known configuration or function may obscure the gist of the present disclosure, a detailed description thereof will be omitted. And, in the drawings, parts not related to the description of the present disclosure are omitted, and similar reference numerals are attached to similar parts.

In the present disclosure, when a component is "connected", "coupled" or "connected" to another component, it is not only a direct connection relationship, but also an indirect connection relationship in which another component exists in the middle. may also include. Also, when it is said that a component includes "includes" or "has" another component, it means that another component may be further included without excluding other components unless otherwise stated. .

In the present disclosure, terms such as first, second, etc. are used only for the purpose of distinguishing one component from other components, and unless otherwise specified, do not limit the order or importance between the components. Accordingly, within the scope of the present disclosure, a first component in one embodiment may be referred to as a second component in another embodiment, and similarly, a second component in one embodiment is referred to as a first component in another embodiment. may also be called

In the present disclosure, components distinct from each other are for clearly describing respective characteristics, and do not necessarily mean that the components are separated. That is, a plurality of components may be integrated to form one hardware or software unit, or one component may be distributed to form a plurality of hardware or software units. Accordingly, even if not specifically mentioned, such integrated or dispersed embodiments are also included in the scope of the present disclosure.

In the present disclosure, components described in various embodiments do not necessarily mean essential components, and some may be optional components. Accordingly, an embodiment composed of a subset of components described in one embodiment is also included in the scope of the present disclosure. In addition, embodiments including other components in addition to components described in various embodiments are also included in the scope of the present disclosure.

The present disclosure relates to communication between network nodes in a wireless communication system. The network node may include one or more of a base station, a terminal, or a relay. The term base station (BS) may be replaced with terms such as fixed station, Node B, eNodeB (eNB), ng-eNB, gNodeB (gNB), and access point (AP). . A terminal may be replaced with terms such as User Equipment (UE), Mobile Station (MS), Mobile Subscriber Station (MSS), Subscriber Station (SS), and non-AP station (non-AP STA).

A wireless communication system may support communication between a base station and a terminal or may support communication between terminals. In communication between the base station and the terminal, a downlink (DL) means communication from the base station to the terminal. Uplink (UL) means communication from the terminal to the base station. Inter-terminal communication may include various communication methods or services such as Device-to-Device (D2D), Vehicle-to-everything (V2X), Proximity Service (ProSe), and sidelink communication. In the communication between terminals, the terminal may be implemented in the form of a sensor node, a vehicle, a disaster warning device, or the like.

In addition, examples of the present disclosure may be applied to a wireless communication system including a relay or a relay node (RN). When a relay is applied to communication between a base station and a terminal, the relay may function as a base station for the terminal, and the relay may function as a terminal for the base station. Meanwhile, when a relay is applied to communication between terminals, the relay may function as a base station for each terminal.

The present disclosure may be applied to various multiple access schemes of a wireless communication system. For example, multiple access schemes include Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), and Single Carrier-FDMA (SC-FDMA). , OFDM-FDMA, OFDM-TDMA, OFDM-CDMA, Non-Orthogonal Multiple Access (NOMA), and the like. In addition, a wireless communication system to which the present disclosure may be applied may support a Time Division Duplex (TDD) scheme in which uplink and downlink communications use time resources that are distinguished from each other, and Frequency Division (FDD) uses distinct frequency resources. Duplex) method may be supported.

In the present disclosure, transmitting or receiving a channel includes the meaning of transmitting or receiving information or a signal through a corresponding channel. For example, transmitting the control channel means transmitting control information or a signal through the control channel. Similarly, to transmit a data channel means to transmit data information or a signal over the data channel.

Hereinafter, a method for tracking a public node according to the present disclosure will be described.

In the present disclosure, aerial node tracking refers to determining or predicting the direction (or transmission beamforming) of the transmission beam of the aerial node, and determining the direction (or reception beamforming) of the reception beam of the ground node corresponding to the transmission beam of the aerial node. determining or predicting, determining or predicting the location of an aerial node in space, and the like.

For aerial node tracking, a beamforming-based method of a wireless communication system may be utilized. For example, in a cellular communication system or a wireless local area network (LAN) communication system, a beam management method or a beam training method based on a Discrete Fourier Transform (DFT) matrix may be utilized for tracking a moving object.

According to the present disclosure, by utilizing aerial sensor data (eg, GPS location data or INS (Inertia Navigation System) attitude data, etc.) used in an aviation system in combination with an existing beamforming-based method in a communication system, the aerial It can improve the accuracy of node tracking.

In addition, rather than determining an optimal beam direction by actually performing beamforming and measuring a signal, a channel can be predicted without measuring a beamforming signal by using a Gaussian Process Regression (GPR) technique.

That is, according to the present disclosure, aerial node tracking can be performed more accurately and quickly by integrating and utilizing aerial sensor data and beamforming information and applying the GPR technique.

Specifically, according to the embodiments of the present disclosure, by newly defining a system that integrates and utilizes aerial sensor data and beamforming information and an operation method thereof, position tracking using an existing single communication system or an existing single aviation system Compensating for the shortcomings, it is possible to track air nodes efficiently and accurately.

In addition, there are studies that suggest a methodology for using aerial sensor data in a wireless communication system, but a beam tracking method for an integrated system in consideration of the characteristics of the aerial system and the signals of the wireless communication system is not specifically prepared.

In the existing aviation system, aerial nodes are tracked by using the values of flight sensor data. For this purpose, GPS and/or INS information mainly used has a long period of provision and a relatively large error. To compensate for this, even if there is no GPS and INS information or even if there is GPS and INS information, by utilizing beamforming information in an aerial node-based communication system, it is possible to accurately and continuously track aerial nodes or beams.

Specifically, in order to combine the aviation system and the wireless communication system, the period of the flight control signal and the communication signal must be considered. Since the GPS signal period is generally longer than the communication signal, a response is required in the absence of a GPS signal. GPS and Inertia Measurement Unit (IMU) signals included in flight sensor data can support position and channel estimation with lower complexity. However, since GPS has a large measurement error of about 5 to 15 m, it is not suitable for accurate beam alignment, so a correction method is required. Since directional beamforming capable of securing high antenna gain is a prerequisite in public node-based communication, it may be considered to utilize a beamforming signal for correction.

Hereinafter, various embodiments of the present disclosure will be described.

In order to build an actual air node-based communication system, a ground node should be able to continuously maintain signal transmission/reception by beamforming to a target (ie, air node). To support a moving aerial node, the ground node must align the beam while continuing to track the target. Beam alignment means determining the reception beamforming of the terrestrial node which optimally corresponds to the transmission beamforming of the aerial node.

1 is a system model structural diagram to which the present disclosure can be applied.

The ground node may receive location information (eg, GPS information, etc.) of the air node. The ground node may receive the location information of the air node directly from the air node, or may receive it through a flight control system (eg, a GPS repeater).

A terrestrial node may estimate an effective channel from a signal received from an air node through reception beamforming. An air node may determine transmit beamforming based on flight sensor data without feedback on the wireless communication system from the ground node (eg, channel state information (CSI) including precoding matrix indicator (PMI), etc.). For example, the aerial node may determine a precoding vector (or a beamforming vector) using the attitude information and position information included in the flight sensor data, and transmit a beamformed signal to the ground node based on this. The flight sensor data utilized by the aerial node may be obtained from equipment (eg, Embedded GPS/INS (EGI)) owned by the aerial node, or may be obtained by the aerial node from an external flight control system.

Assume that the terrestrial node has a planar array antenna (UPA) comprising N _x *Ny antenna elements. The terrestrial node may be fixed at a specific location, but is not limited thereto, and may be a mobile base station or a repeater. In the case of a mobile terrestrial node, information on the location of the terrestrial node may be provided to the public node. Assume that the aerial node is equipped with a linear array antenna (ULA) comprising N _U antenna elements. Assume a situation in which a terrestrial node tracks an aerial node.

1, n-frame is a frame in which the reference point is the antenna array of the ground node based on geodetic coordinates, and the u-frame is a frame in which the reference point is the center of gravity of the UAV and is parallel to the n-frame, a- The frame is defined as a frame in which the reference point is the center of gravity of the UAV and the u-frame is rotated in the direction of the UAV's linear antenna axis.

Ground nodes are relative to n-frames, their positions are p _G ⁿ = [0, 0, h], and the inter-antenna spacing d = λ/2. The central position of the air node is p _U ⁿ relative to the n-frame. = [x, y, h _u ].

2 is an xy plane projection for explaining a spatial angle according to the present disclosure.

The spatial angle between the ground node and the antenna array (a-frame) of the aerial node may be expressed as the directional angle and the attitude of the aerial node as in Equation (1).

In Equation 1, u _a may correspond to a departure space angle in the antenna array of the UAV. In addition, g _x ^a and g _y ^a correspond to the x, y coordinates transformed from the (0, 0) coordinates of the ground node in the a-frame, respectively. Φ is the angle between the ground node and the center of the air node, Φ _v is the angle with respect to the direction of the air node (i.e., heading direction or velocity direction), and α is the attitude (i.e. yaw) information of the air node .

The spatial angle between the ground node and the center of the air node may be expressed as Equation (2).

In Equation 2, u and v are spatial angles expressed by the location of the UAV, which are main components of a channel model to be described later. Also, u and v may be expressed as an azimuth angle (Φ) and an elevation angle (θ).

A channel between the ground node and the UAV may be expressed as a Line of Sight (LoS) path. For example, a channel (ie, H(k)) in the k-th communication block (ie, time unit) may be expressed as in Equation 3.

In Equation 3, μ(k) corresponds to the normalized channel gain in the k-th block.

In Equation 3, a _g (u(k), v(k)) is an antenna array response vector according to an angle of arrival at a ground node. An antenna array response vector refers to a vector indicating characteristics of a corresponding antenna array.

는 크로네커 곱을 의미한다.In Equation 3, a _g (u(k), v(k)) is a _x (u(k)), which is an antenna array response vector in the x-axis direction, and the antenna array in the y-axis direction, as shown in Equation 4 It can be expressed as a response vector a _y (v(k)). in Equation 4

is the Kronecker product.

In Equation 3, a _u (u _a (k)) is an antenna array response vector according to a departure angle from an aerial node, and is expressed as Equation 5. In Equation 3, a _u ^H (u _a (k)) corresponds to the Hermitian matrix of a _u (u _a (k)).

3 is a view for explaining the timing of flight sensor data and communication signals according to the present disclosure.

In the example of FIG. 3, flight sensor data, and transmission/reception timing of a communication signal are shown.

Flight sensor data may include INS signals and GPS signals. The INS signal and/or GPS signal may be generated by the air node itself and transmitted to the ground node, the air node may obtain from an external flight control system and transmitted to the ground node, or the air node may be transmitted to the ground node from the external flight control system. may be Alternatively, the public node may utilize itself to determine transmit beamforming without providing the INS information to the base station.

The communication signal may include a synchronization signal (SS) and data transmission. The synchronization signal may include a pilot signal (or reference signal) transmitted from the air node to the ground node, and may be transmitted as a beamforming signal to which a beamforming vector determined by the air node is applied. In addition, one or more synchronization signal blocks (SSB) may be included in one synchronization signal burst (SS burst), and the SS burst may be repeatedly transmitted at a predetermined period (T _p ). Data transmission may include payload data exchanged between the public node and the land node through uplink and/or downlink.

In general, the signal period of the flight sensor data is longer than the period of the communication signal. For example, the period (T _GPS ) of the GPS signal is about 50 to 100 ms, and the period (T _INS ) of the INS signal is about 20 ms. The period (T _p ) of the SS burst may be about 0.3 to 40 ms (eg, assuming a 5G NR system), but in this example, it is assumed to have a period of about 10 ms.

The ground node may receive the GPS value, which is the location information of the aerial node, every T _GPS (from the aerial node or from a GPS repeater). In this case, the GPS value may have an error of about 5 to 15 m when used independently, and an error of about 2 to 5 m when a correction process is performed through other sensor data.

The terrestrial node may set beam candidates for reception beamforming every T _p based on the received GPS signal. A public node may determine a precoding vector for transmission beamforming by acquiring its own location information and latitude information for each T _INS of the mounted EGI.

The terrestrial node may determine a reception beamforming matrix by referring to GPS information and track the aerial node by using a communication signal (eg, a synchronization signal) every T _p . Accordingly, even in a section without a GPS signal, a ground node can continuously and accurately track an air node.

That is, the ground node may determine the reception beamforming matrix and estimate the channel based on the communication signal from the aerial node, even if the flight sensor data does not exist or the previously received flight sensor data is invalid. Of course, since the ground node sets candidates for the reception beamforming matrix using flight sensor data, it is possible to more efficiently determine the reception beamforming matrix and perform channel estimation.

In addition, the aerial node may determine the transmit beamforming vector (or precoding matrix) without feedback (eg, CSI) from the ground node, based on the flight sensor data. Of course, when the aerial node receives feedback from the ground node, the transmission beamforming vector can be determined more efficiently, but in this embodiment, the aerial node determines the transmission beamforming vector based on the flight sensor data without feedback from the ground node. Therefore, it is possible to reduce system overhead for feedback from terrestrial nodes and increase resource utilization.

Assuming that the public node moves at a constant velocity during one communication block, the position shift of the public node in the k+1-th communication block based on the position and velocity in the k-th communication block can be expressed as Equation (6).

In Equation 6, Tp corresponds to the period of the communication signal block, and the velocity v _u and the direction angle Φ _v of the aerial node can be expressed as in Equation 7.

In Equation 7, ρ corresponds to a correlation coefficient, ∈v corresponds to Gaussian noise for velocity, and ∈d corresponds to Gaussian noise for direction.

The position prediction of the aerial node in the ground node is expressed as Equation 8 when there is a GPS signal at the corresponding time point, and is expressed as Equation 9 when there is no GPS signal at the corresponding time point.

로 표현되고, 이에 기초하여 산출된 값은

로 표현되며, 산출값 중에서 최종 추정값이 아닌 그 이전의 예측값은

로 표현된다. 예를 들어,

는 k 번째 시간 블록에서의 공중 노드의 위치에 대한 예측값을 나타내며, 후술하는 바와 같이 특정 조건을 만족하는 예측값이 최종 추정값이 될 수 있다.The values measured in the present disclosure are

is expressed as, and the value calculated based on this is

It is expressed as , and among the calculated values, the previous predicted value that is not the final estimate is

is expressed as for example,

denotes a predicted value for the position of a public node in the k-th time block, and as will be described later, a predicted value that satisfies a specific condition may be the final estimated value.

In Equations 8 and 9, k is an index of a communication block (or a time unit of a synchronization signal), and m corresponds to a time index of a GPS signal (or a time unit of a GPS signal).

That is, the ground node predicts the position of the aerial node based on the GPS measurement value when there is a GPS signal, and in the absence of a GPS signal, the position of the aerial node can be predicted by adding the velocity measurement value to the position estimated in the previous frame. .

Since the spatial angle of the antenna array varies according to the attitude data of the aerial node, it is important to know the attitude data. Through the onboard EGI, the aerial node can obtain the spatial angle of the antenna array. Assuming that the aerial node flies at a constant altitude and a linear array is mounted, it is the heading angle and yaw posture that affect the spatial angle. The spatial angle in the a-frame obtained using the heading angle (Φ _v ) and the yaw posture is expressed as Equation (10).

는 INS로부터 획득된 yaw 값이고, INS/GPS 컨트롤러의 시간 단위는 (n)으로 표현된다. 선형 안테나 어레이를 가정하였으므로, 자세 데이터로서 yaw 값만을 고려한다.in Equation 10

is the yaw value obtained from the INS, and the time unit of the INS/GPS controller is expressed as (n). Since a linear antenna array is assumed, only the yaw value is considered as attitude data.

The aerial node may perform beamforming in the direction of the calculated spatial angle, and the precoding vector f for transmission beamforming is expressed as Equation (11).

As such, based on the values measured by the flight controller (or flight sensor), the aerial node can perform beam alignment without feedback from the ground node. That is, the air node may determine the precoding vector without a feedback process from the terrestrial node for downlink beamforming (or transmit beamforming).

The terrestrial node must estimate the precoded effective channel from the air node. The spatial angle obtained by the terrestrial node from the GPS sensor may be expressed as Equation 12 below, similar to Equation 2 below.

Based on the spatial angle calculated as shown in Equation 12, the ground node may set the angle candidate set D _s as shown in Equation 13.

D _s corresponds to the training data set established based on the predicted positions of aerial nodes. That is, D _s is a set of peripheral angles of a spatial angle (ie, Equation 12) calculated based on a GPS measurement value, and the range may be set to 2B. Δ may correspond to the grid spacing of the codebook, that is, the spacing of spatial angle candidates, and 2B may correspond to the main lobe beam width of the antenna array. Accordingly, only angles near the beam width of the main lobe can be included in the training data.

The Gaussian process regression (GPR) method is a non-parametric Bayesian prediction technique that predicts the posterior distribution using the prior distribution and the observed value, and can obtain the estimated value and the estimated variance using the mean and variance.

In the present disclosure, the spatial angle grid D _s may be input to the GPR, and the magnitude of the beamformed signal at the corresponding angle (ie, the received signal magnitude) may be set as the output of the GPR. Accordingly, it is possible to track the aerial node by determining the optimal reception beam angle by predicting the beamformed signal size without actually performing reception beamforming and measuring the signal size, that is, without an actual beamforming procedure.

For example, if a set D _s composed of an input x = [uv] ^T is X _t and a reception beamforming matrix is W, the reception signal y _t may be expressed as in Equation 13.

In Equation 14, ∈ corresponds to an error, s corresponds to a signal component, and n corresponds to a noise component.

Using the Gaussian process regression technique, it is possible to predict the size of a beamformed signal without actually performing beamforming for a new spatial angle other than the spatial angle of the input data. The output y _t for the new input x ^* can be predicted as a Gaussian distribution, that is, the mean and variance. That is, the size f ^* of the beamformed reception signal for the new input x ^* = [u ^* v ^* ] ^T can be predicted by using the correlation of X _t , which is the existing training data, and the observed value y _t . The predicted received signal magnitude f ^* may be expressed as in Equation (15).

로 표현된다. In Equation 15, the average of the random variable f ^* is

is expressed as

The average (E) of the predicted received signal magnitude (f ^* ) is the kernel matrix K(X _t ,X _t ) consisting of the existing training data, and the kernel correlation matrix K(x ^* ,X) between the existing training data and the new input. _t ), and the measured value y _t . In addition, the covariance (cov) is a kernel matrix K(X _t ,X _t ) consisting of the existing training data, a kernel correlation matrix K(x ^* ,X _t ) between the existing training data and the new input, and the new input values. It may be calculated based on the formed kernel matrix K(x ^* ,x ^* ), the kernel correlation matrix K(X _t ,x ^* ) between the new input and the existing training data, and the measured value. Summarizing this, it can be expressed as Equation 16.

In Equation 16, σ _s ² is the variance for the signal and may correspond to the width of the Gaussian function. I represents the identity matrix. Also, k(x,x') corresponds to a scalar value indicating a correlation between x and x'.

4 is a diagram for explaining an example of a beam space channel to which the present disclosure can be applied.

Fig. 4(a) shows an example of an actual beam space channel for u and v. In the beam spatial channel model, the spatial angle parameters u and v may have extreme values at positions. That is, in the illustrated example, the angle corresponding to the extreme value part may correspond to the channel parameters u and v.

4( b ) exemplarily shows spatial angle parameters predicted based on a training data set D _s and a new input value x ^* .

In the case of having an output value obtained by codebook beamforming, an output for an angle other than the codebook can be predicted through Gaussian process regression (GPR). The position of the extreme value, which is channel information, can be found through the predicted value. To this end, a technique such as gradient descent may be applied. For example, the extreme value can be reached using the derivative of the predicted mean value for the new input x ^* .

Hereinafter, a method of deriving spatial angle parameters u and v in a hybrid beamforming structure and an analog beamforming structure using a Gaussian process regression method will be described.

For channel estimation in the hybrid beamforming structure, we select the initial training data X _t as the input of the Gaussian process regression. In addition, the interval Δ in Equation (13) is expressed by l, which is a quantization bit of the phase changer, as in Equation (17).

The codebook range B may have a value of 2/N _x or 2/N _y , which is the null-to-null beamwidth of the main lobe (or N _x = N _y ). That is, the reception beamforming matrix W is set with spatial angles around the poles of the beam spatial channel. The magnitude of the beamformed reception signal obtained through the set reception beamforming matrix W is expressed by Equation 14 above.

The differentiation with respect to the average of the magnitude of the received signal predicted with respect to the new input value x ^* may be expressed as Equation (18).

The extreme value of the spatial angle may be reached based on the differential value as in Equation (18). Since the beam spatial channel is unimodal in the main lobe section, if the gradient descent technique is applied at spatial angles near the global optimum, the local optima becomes the final optimum. There is no risk of falling into a decision error.

The spatial angle to be applied to the beamforming matrix may be updated as in Equation (19).

In Equation 19, η is the update interval size, and i is the index of the iteration order. By changing and updating the spatial angle and performing beamforming based on the updated spatial angle, the actually measured value may be included in the training data as shown in Equation (20). Accordingly, a channel function value for a new spatial angle can be predicted.

This process proceeds until the output function value converges.

In the channel estimation in the analog beamforming structure, since the analog phase shifter uses a quantized phase value according to the phase resolution, it is difficult to perform beamforming for an updated spatial angle and obtain an actual measurement value. Therefore, the extrema of the beam space channel can be obtained using the Gaussian process regression method as shown in Equations 18 and 19 using only the output values of the beam codebook. The initial training data X _t is used as it is, and the output function value converges without adding training data as in Equation 20.

The x, y coordinate position of the aerial node can be estimated as in Equation 21 by using the spatial angle derived from the channel estimation methods assuming the above-described hybrid or analog beamforming.

5 is a flowchart illustrating a method for tracking a public node according to the present disclosure.

First, the operation of the ground node will be described.

In step S510, the ground node may predict the location of the aerial node based on flight sensor data related to the aerial node. For example, the ground node may predict the location of the aerial node as shown in Equations 8 and 9 by using the GPS measurement value for the aerial node.

In step S520, the terrestrial node may determine a reception beamforming matrix based on the predicted position of the aerial node. For example, as shown in Equation 13, an appropriate reception beamforming matrix W may be determined from among elements belonging to Ds corresponding to a set of candidates of spatial angles u and v.

In step S530, the terrestrial node may receive a pilot signal (or a synchronization signal or a reference signal) from the aerial node based on the determined reception beamforming matrix. The received signal may be expressed as Equation (14). That is, a measurement value or an observation value of a received signal may be obtained by actually performing reception beamforming.

In step S540, the ground node may set training data for applying a Gaussian process regression (GPR) technique. For example, a set Ds of candidates of spatial angles u and v as in Equation 13 may be set or updated.

In step S550, the terrestrial node may perform channel prediction and spatial angle update. For example, as shown in Equation 15, a predicted value of the received signal magnitude with respect to a new input value may be calculated. In addition, according to the hybrid beamforming or analog beamforming method, the optimal spatial angle can be derived by adding training data to the initial training data or using the differential value of the predicted value as in Equation 18 based on only the initial training data. can

In step S560, the ground node may estimate the optimal spatial angle as the final spatial angle, and estimate the position of the aerial node as shown in Equation 21 based on the final spatial angle.

In this way, the ground node may refer to the flight sensor data of the aerial node, and also by deriving an optimal value for the spatial angle for the aerial node using a machine learning method such as the GPR technique without actually performing reception beamforming and signal reception. , the location of aerial nodes can be dynamically and continuously tracked.

On the other hand, the air node may determine the transmission beamforming vector based on the flight sensor data in step S515. For example, the air node may determine an optimal transmit beamforming vector based on GPS values and INS measurements.

In step S525, the aerial node may perform precoding on a pilot signal (or a synchronization signal or a reference signal) based on the determined transmission beamforming vector and transmit the precoding to the ground node.

As such, the air node may perform transmit beamforming based on the flight sensor data without feedback from the ground node.

6 is a diagram illustrating the configuration of a ground node device and an air node device according to the present disclosure.

The terrestrial node device 600 may include a processor 610 , an antenna unit 620 , a transceiver 630 , and a memory 640 .

The processor 610 performs baseband-related signal processing and may include an upper layer processing unit 611 and a physical layer processing unit 615 . The higher layer processing unit 611 may process the operation of the MAC layer, the RRC layer, or higher layers. The physical layer processor 615 may process PHY layer operations (eg, downlink transmission signal processing, uplink reception signal processing, etc.). The processor 610 may control the overall operation of the terrestrial node device 600 in addition to performing baseband-related signal processing.

The antenna unit 620 may include one or more physical antennas, and when it includes a plurality of antennas, it may support MIMO transmission/reception. The transceiver 630 may include an RF transmitter and an RF receiver. The memory 640 may store information processed by the processor 610 , software related to the operation of the terrestrial node device 600 , an operating system, an application, and the like, and may include components such as a buffer.

The processor 610 of the terrestrial node device 600 may be configured to implement the operation of the terrestrial node in the embodiments described in the present invention.

For example, the upper layer processing unit 611 of the processor 610 of the ground node device 600 may include a flight sensor data obtaining unit 612 and a position determining unit 613 .

The flight sensor data acquisition unit 612 may acquire flight sensor data such as GPS, INS, etc. for the aerial node device 650 .

The position determiner 613 may set a set of candidates for the spatial angle based on the flight sensor data. Also, the position determiner 613 may derive a predicted value of the received signal magnitude to which the receive beamforming is applied with respect to a new or updated spatial angle that does not belong to the spatial angle candidates. In addition, the position determiner 613 may determine the optimal spatial angle by repeating the update of the new spatial angle and the derivation of the received signal magnitude predicted value corresponding thereto. Also, the location determining unit 613 may determine the location of the aerial node based on the optimal spatial angle.

The aerial node device 650 may include a processor 660 , an antenna unit 670 , a transceiver 680 , and a memory 690 .

The processor 660 performs baseband-related signal processing and may include an upper layer processing unit 661 and a physical layer processing unit 665 . The higher layer processing unit 661 may process operations of the MAC layer, the RRC layer, or higher layers. The physical layer processing unit 665 may process operations of the PHY layer (eg, downlink reception signal processing, uplink transmission signal processing, etc.). The processor 660 may control the overall operation of the public node device 660 in addition to performing baseband-related signal processing.

The antenna unit 670 may include one or more physical antennas, and when it includes a plurality of antennas, it may support MIMO transmission/reception. The transceiver 680 may include an RF transmitter and an RF receiver. The memory 690 may store information processed by the processor 660 , software related to the operation of the public node device 650 , an operating system, an application, and the like, and may include components such as a buffer.

The processor 660 of the public node device 650 may be configured to implement the operation of the public node in the embodiments described in the present invention.

For example, the upper layer processing unit 661 of the processor 660 of the air node device 650 may include a flight sensor data acquisition unit 662 and a transmission beamforming determination unit 663 .

Flight sensor data acquisition unit 662 may acquire information such as GPS, INS.

Transmission beamforming determination unit 623 may determine the transmission beamforming vector based on the obtained flight sensor data. Here, the transmit beamforming vector/matrix may be determined without feedback information from a terrestrial node.

The lower layer processing unit 665 may transmit a signal to the terrestrial node based on the determined transmission beamforming vector.

In the operation of the terrestrial node device 600 and the public node device 650, the descriptions of the terrestrial node and the public node in the examples of the present invention may be equally applied, and overlapping descriptions will be omitted.

7 and 8 are diagrams illustrating simulation results according to the present disclosure.

Methods compared to the exemplary method of the present disclosure assumed perturbation based beamforming, a GPS-only system, and codebook based tracking.

Perturbation-based beamforming estimates the slope of a beam spatial channel through beamforming, but actually receives a signal to determine reception beamforming. It is assumed that not. The GPS-only system assumes a method of tracking a location using only the GPS sensor values. Codebook-based tracking assumes a method of tracking a location using only a communication signal without considering flight sensor data.

In the example of FIG. 7 , it is assumed that the accuracy of the GPS, EGI, and INS sensors is relatively higher than that of the example of FIG. 8 .

In the example of FIG. 7 , it was confirmed that the GPS-only algorithm performed better than the other communication-based algorithms except for the method of the present disclosure. It is assumed that the time unit interval is Tp, and the GPS-only algorithm obtains a measurement value every 5 Tp.

It was confirmed that the method of the present disclosure has higher performance than the GPS-only algorithm and the communication signal-only algorithms because the channel parameter is estimated using GPR based on the GPS measurement value and estimated for each Tp.

Since the example of FIG. 8 assumes that the accuracy of the GPS, EGI, and INS sensors is relatively low compared to the example of FIG. 7 , it was confirmed that the perturbation-based beamforming and the method of the present disclosure performed better than the GPS alone algorithm.

Example methods of the present disclosure are expressed as a series of operations for clarity of description, but this is not intended to limit the order in which the steps are performed, and if necessary, each step may be performed simultaneously or in a different order. In order to implement the method according to the present disclosure, other steps may be included in addition to the illustrated steps, other steps may be included except some steps, or additional other steps may be included except some steps.

The various embodiments of the present disclosure do not list all possible combinations, but are intended to describe representative aspects of the present disclosure, and matters described in various embodiments may be applied independently or in combination of two or more.

In addition, various embodiments of the present disclosure may be implemented by hardware, firmware, software, or a combination thereof. For implementation by hardware, one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general purpose It may be implemented by a processor (general processor), a controller, a microcontroller, a microprocessor, and the like.

The scope of the present disclosure includes software or machine-executable instructions (eg, operating system, application, firmware, program, etc.) that cause operation according to the method of various embodiments to be executed on a device or computer, and such software or and non-transitory computer-readable media in which instructions and the like are stored and executed on a device or computer.

Claims (13)

A method for a ground node to track a location of an air node, the method comprising:
setting a set of spatial angle candidates based on the flight sensor data of the aerial node;
deriving a predicted value of a received signal magnitude to which reception beamforming is applied with respect to an updated spatial angle that does not belong to the spatial angle candidates;
determining an optimal spatial angle by repeating updating the spatial angle and deriving a predicted value of the received signal magnitude; and
determining the location of the aerial node based on the optimal spatial angle.
The method of claim 1,
The step of deriving the predicted value of the received signal magnitude comprises:
and setting the spatial angle candidates as an input of Gaussian process regression (GPR), and setting the received signal magnitude to which the receive beamforming is applied as an output of the GPR.
3. The method of claim 2,
Based on the GPR, a predicted value of the received signal magnitude to which the received beamforming is applied with respect to the updated spatial angle is derived.
4. The method of claim 3,
The spatial angle is expressed as (u, v),
The set of candidates for the spatial angle is,

is defined as
k is the time unit index and Δ is the interval of spatial angle candidates.
5. The method of claim 4,
The received signal size is

is expressed as
W is a receive beamforming matrix, Xt corresponds to a set Ds, H corresponds to a channel from the air node to the terrestrial node, f is a transmit beamforming vector, s is a signal component, and n is a noise component. In, way.
6. The method of claim 5,
Determining the optimal spatial angle is
and deriving an extremal value of a derivative of the mean of the predicted values of the received signal magnitude.
7. The method of claim 6,
The derivative with respect to the average of the predicted values of the received signal magnitude is

is expressed as
x ^* corresponds to the updated spatial angle,

corresponds to the predicted value of the received signal, K corresponds to the kernel matrix, σ ² corresponds to the variance, and I is the identity matrix.
The method of claim 1,
The flight sensor data includes one or more of position information and attitude information of the aerial node.
A method for a public node to transmit a signal, comprising:
determining a transmission beamforming vector based on flight sensor data of the aerial node; and
and transmitting a signal to a terrestrial node based on the transmit beamforming vector.
10. The method of claim 9,
The transmit beamforming vector is determined without receiving feedback from the terrestrial node of information on a channel state from the air node to the terrestrial node.
11. The method of claim 10,
The flight sensor data includes one or more of position information and attitude information of the aerial node.
A ground node device for tracking the position of an aerial node, comprising:
transceiver;
Memory; and
including a processor;
the processor
set a set of spatial angle candidates based on the flight sensor data of the aerial node;
deriving a predicted value of a received signal magnitude to which reception beamforming is applied with respect to an updated spatial angle that does not belong to the spatial angle candidates;
determining an optimal spatial angle by repeating the update of the spatial angle and the derivation of the predicted value of the received signal magnitude; and
and determine the position of the aerial node based on the optimal spatial angle.
A public node device for transmitting a signal, comprising:
transceiver;
Memory; and
including a processor;
The processor is
determine a transmit beamforming vector based on flight sensor data of the air node; and
and to transmit a signal to a terrestrial node based on the transmit beamforming vector.

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