KR102308982B1 - Scalable sequence creation, detection method and apparatus in UAV cellular network - Google Patents

Abstract

A method for generating and detecting a scalable sequence for a UAV cellular network and an apparatus therefor are disclosed. A sequence detection method in an unmanned aerial vehicle comprises the steps of: (a) determining a channel state with a base station; and (b) receiving a signal from the base station by changing a bandwidth according to the determined channel state.

Description

Scalable sequence creation, detection method and apparatus in UAV cellular network

The present invention relates to a method and apparatus for detecting a scalable sequence for an Unmanned Aerial Vehicle (UAV) cellular network.

UAVs have been developed for military technical use to reduce pilot losses in hostile areas. In the commercial sector, the use of UAVs in areas such as remote sensing, delivery, surveillance, search and rescue, aerial photography, agriculture and telecommunication relay is increasing significantly. UAVs mainly use two types of control schemes: autonomous control and standalone control. The former uses an onboard computer to track a predefined route. However, when immediate intervention is required, real-time control is not possible. It is used to remotely control the UAV in real time using WiFi over the air in standalone control. However, the WiFi connection may be lost if it is not a Line-Of-Sight (LOS) communication environment. Accordingly, the cellular network is considered to provide ubiquitous coverage using the mobile communication operator's infrastructure that provides wide area, high speed and secure wireless access.

For UAV cellular communication, the rate at which UAVs will be in the same condition as the cell edge is much higher than that of terrestrial terminals (UEs). This is because UAVs receive downlink interference signals from a larger number of cells than typical terrestrial UEs due to their high LoS propagation probability. In addition, a large interference signal is generated in the LoS propagation path in the air, and the UAV causes more uplink interference to neighboring cells. To solve the interference problem in UAV cellular networks, a method of tracking the LoS direction between a base station (BS) and a UAV using a 3D beamforming array is being considered. In this approach, a 3D beamformer at the BS tracks the LoS direction of the UAV and adjusts the antenna boresight towards the UAV.

Also, because current cellular networks are optimized for terrestrial users, UAVs are likely to communicate using the sidelobe of the BS antenna array. Because the BS antenna is oriented towards the ground to limit cell radius and mitigate inter-cell interference, the nature of the radio link to a UAV is different from that of a terrestrial cell connection. In practice, UAVs will receive different signal strengths with different altitudes, as the received signal strengths depend on the pattern of the sidelobe. In addition, when the UAV is located at the nulling point between the sidelobe and the sidelobe and cannot receive a signal, it can receive a signal through a distant BS (with a different cell ID) rather than a geographically close BS. In this case, the path loss increases due to the long distance. Therefore, the UAV experiences large path loss changes and needs to connect with other BSs depending on the altitude.

Another challenge in operating unmanned aerial vehicles (UAVs) is the limited battery capacity. UAV flight time is limited to around 20 minutes depending on onboard battery capacity. Therefore, it is necessary to develop a communication technique that consumes less power in order to increase the flight time in consideration of the very limited battery capacity.

(01) Republic of Korea Patent Publication No. 10-2009-0111855 (2009.10.27.)

An object of the present invention is to provide a method and apparatus for detecting a scalable sequence for an Unmanned Aerial Vehicle (UAV) cellular network.

Another object of the present invention is to provide a scalable sequence detection method and apparatus for a UAV (Unmanned Aerial Vehicle) cellular network capable of reducing battery consumption of the UAV.

Another object of the present invention is to provide a scalable sequence detection method and an apparatus for a UAV (Unmanned Aerial Vehicle) cellular network capable of detecting a preamble by selectively adjusting an available bandwidth according to a channel condition (state).

According to one aspect of the present invention, a method for detecting a scalable sequence and a method for generating a scalable sequence for a UAV cellular network are provided.

According to an embodiment of the present invention, there is provided a sequence detection method in an unmanned aerial vehicle, the method comprising: (a) determining a channel state with a base station; and (b) receiving a preamble signal from the base station by changing a bandwidth according to the determined channel state.

The step (b) may include: receiving a preamble signal from the base station using a maximum available bandwidth when the channel state is less than or equal to a reference; and receiving a preamble signal from the base station using a partial bandwidth smaller than the maximum bandwidth when the channel condition exceeds the reference.

The method may further include detecting a preamble by using the received preamble signal.

The detecting of the preamble may include: generating a time domain reference sequence; and detecting a preamble including a cell ID and a synchronization parameter by correlating the always received signal with the time domain reference sequence.

The time domain reference sequence is generated by performing an N-point inverse discrete Fourier transform (IDFT) on a (NL) zero-padded frequency domain reference sequence in an L-th length sequence, wherein N is the total length of the preamble transmitted by the base station. .

The sequence may be at least one of a maximum-length sequence, a zadoff-Chu sequence, and a linear frequency modulated sequence.

When the sequence is a maximum-length sequence, a bandwidth corresponding to L subcarriers may be used to detect the preamble.

When the sequence is a zadoff-Chu sequence, N, which is the length of the sequence, is 16 or more, and may be generated to be a power of 2.

When the sequence is a linear frequency modulated sequence, a cell ID may be generated and detected by a combination of up-chirp and down-chirp patterns.

According to an embodiment of the present invention, there is provided a method for generating a scalable sequence, the method comprising: generating a scalable sequence having a length of N; generating a time domain sequence by performing an N-point inverse discrete Fourier transform on the scalable sequence; and transmitting the time domain sequence as a preamble.

When the scalable sequence is a Maximum-length sequence or a zadoff-Chu sequence, generating a time domain sequence includes generating a Maximum-length sequence or a zadoff-Chu sequence having a length of N to generate N subcarriers in the frequency domain. After being assigned to , it is possible to generate a time domain sequence by performing an inverse discrete Fourier transform.

In the case of the zadoff-Chu sequence, N is greater than or equal to 16, and may be generated to be a power of 2.

When the scalable sequence is a linear frequency modulated sequence, a cell ID may be generated through a combination of up-chirp and down-chirp patterns.

According to another aspect of the present invention, an apparatus for scalable sequence detection for a UAV cellular network is provided.

According to an embodiment of the present invention, a channel state analyzer for determining a channel state with a base station; a communication unit configured to receive a preamble signal from the base station by changing a bandwidth according to the determined channel state; and a preamble detector configured to detect a preamble by using the received preamble signal.

The communication unit receives the preamble signal from the base station using the maximum available bandwidth when the channel condition is less than or equal to the reference, and when the channel condition is greater than the reference, using a partial bandwidth smaller than the maximum bandwidth A preamble signal from the base station may be received.

The preamble detector may generate a time domain reference sequence and detect a preamble including a cell ID and a synchronization parameter by correlating the received signal with the time domain reference sequence.

By providing a scalable sequence detection method and an apparatus for a UAV (Unmanned Aerial Vehicle) cellular network according to an embodiment of the present invention, the preamble is detected by selectively adjusting the available bandwidth according to the channel condition (state) of the UAV. can do.

Through this, the present invention has the advantage of reducing the battery consumption of the UAV.

일 때 LFM 신호의 상관 관계를 나타낸 도면.
도 16은 본 발명의 또 다른 실시예에 따른 도플러 시프트가 존재하는 경우, LFM 신호의 최대 상관값을 나타낸 도면.
도 17은 본 발명의 일 실시예에 따른 도플러 시프트가 존재할 때, 세 가지 스케일러블 시퀀스의 성능을 비교한 도면.
도 18은 본 발명의 일 실시예에 따른 다른 길이 L에 대한 스케일러블 시퀀스의 검출 확률을 나타낸 그래프.
도 19는 본 발명의 일 실시예에 따른 도플러 시프트가 존재하는 경우 스케일러블 시퀀스의 검출 확률을 나타낸 그래프.
도 20은 본 발명의 일 실시예에 따른 스케일러블 시퀀스를 생성하여 전송하는 기지국의 구성을 개략적으로 도시한 블록도.
도 21은 본 발명의 일 실시예에 따른 무인 비행기의 내부 구성을 개략적으로 도시한 블록도.1 is a diagram schematically illustrating a UAV cellular network system according to an embodiment of the present invention;
2 is a flowchart illustrating a method of generating and transmitting a scalable sequence according to an embodiment of the present invention.
3 is a flowchart illustrating a method for detecting a scalable sequence in a UAV according to an embodiment of the present invention.
4 is a flowchart illustrating a method for detecting a preamble according to an embodiment of the present invention.
5 and 6 are diagrams illustrating autocorrelation characteristics of an M-sequence according to an embodiment of the present invention.
7 and 8 are diagrams illustrating correlations between the case where the cell IDs are the same and the case where the cell IDs are different in the M-sequence according to an embodiment of the present invention.
9 and 10 are diagrams showing autocorrelation characteristics of a ZC-sequence according to another embodiment of the present invention.
11 and 12 are diagrams illustrating correlations between the case where the cell IDs of the ZC-sequence are the same and different from those of the ZC-sequence according to another embodiment of the present invention.
13 is a view showing an LFM-sequence according to another embodiment of the present invention.
14 is a diagram illustrating a correlation between LFM signals when L=NLFM according to another embodiment of the present invention.
15 is a diagram according to another embodiment of the present invention;

A diagram showing the correlation of LFM signals when .
16 is a diagram illustrating a maximum correlation value of an LFM signal in the presence of a Doppler shift according to another embodiment of the present invention.
17 is a diagram comparing the performance of three scalable sequences in the presence of a Doppler shift according to an embodiment of the present invention.
18 is a graph illustrating a detection probability of a scalable sequence for another length L according to an embodiment of the present invention.
19 is a graph illustrating a detection probability of a scalable sequence in the presence of a Doppler shift according to an embodiment of the present invention.
20 is a block diagram schematically illustrating a configuration of a base station that generates and transmits a scalable sequence according to an embodiment of the present invention.
21 is a block diagram schematically illustrating an internal configuration of an unmanned aerial vehicle according to an embodiment of the present invention.

As used herein, the singular expression includes the plural expression unless the context clearly dictates otherwise. In this specification, terms such as “consisting of” or “comprising” should not be construed as necessarily including all of the various components or various steps described in the specification, some of which components or some steps are It should be construed that it may not include, or may further include additional components or steps. In addition, terms such as "...unit" and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented as hardware or software, or a combination of hardware and software. .

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram schematically illustrating a UAV cellular network system according to an embodiment of the present invention.

Referring to FIG. 1 , a UAV cellular network system according to an embodiment of the present invention includes a plurality of base stations 110 and a plurality of unmanned aerial vehicles 120 (hereinafter, referred to as UAVs).

In a cellular network, the base station 110 typically faces downward in the antenna direction to reduce inter-channel interference and to limit the cell coverage area. Accordingly, the unmanned aerial vehicle 120 transmits and receives a signal through a sidelobe of the base station 110 . Due to this, the unmanned aerial vehicle 120 receives signals of various strengths depending on whether the side lobe matches the direction and the altitude.

That is, when the location of the unmanned aerial vehicle 120 coincides with the sidelobe direction, the unmanned aerial vehicle 120 may receive a relatively strong signal. However, if the location of the unmanned aerial vehicle 120 is located above the null of the side lobe, the unmanned aerial vehicle 120 cannot receive a signal. In this case, the unmanned aerial vehicle 120 may receive a signal from another base station by handover to another adjacent base station. As described above, in UAV communication using a sidelobe of a base station in a cellular network, channel characteristics change significantly, and handover to another base station is required according to altitude.

In an embodiment of the present invention, a scalable sequence capable of minimizing the battery power of the unmanned aerial vehicle 120 is generated by receiving the preamble by changing the available bandwidth according to the channel condition (state) of the unmanned aerial vehicle 120, can be detected. This will be described in more detail below.

In an embodiment of the present invention, since there is no power constraint, the base station 110 may generate and transmit a preamble having a length of N using the entire available bandwidth.

Since the unmanned aerial vehicle 120 needs to communicate while minimizing battery power consumption, the preamble may be received by varying the bandwidth according to the channel state (condition).

For example, when the channel state is good (ie, when the strength of a signal received from the base station is greater than or equal to the reference value), the unmanned aerial vehicle 120 may receive the preamble by using a portion of the available bandwidth. That is, in order to reduce power consumption, the unmanned aerial vehicle 120 may use only a portion L of the preamble sequence as a reference signal in the unmanned aerial vehicle 120 to detect a synchronization parameter and a cell ID.

However, if the channel state is not good (ie, when the strength of a signal received from the base station is less than the reference value), the unmanned aerial vehicle 120 may receive the preamble using the entire available bandwidth. As described above, by using the entire bandwidth, it is possible to increase the detection probability of the synchronization parameter cell ID when detecting a preamble having an overall length of N.

In general, energy consumption of the unmanned aerial vehicle 120 may be composed of propulsion energy and communication-related energy. Since the propulsion energy is related to the propulsion of the unmanned aerial vehicle 120 , it will not be considered in the present invention. However, since communication-related energy consumption is linearly proportional to the bandwidth, using a smaller bandwidth to reduce communication energy is advantageous in terms of reducing energy consumption. Accordingly, when the unmanned aerial vehicle 120 receives a signal using a smaller bandwidth, communication energy consumption is reduced.

In one embodiment of the present invention, there is an advantage in that the energy consumption of the unmanned aerial vehicle 120 can be reduced by allowing the unmanned aerial vehicle 120 to detect a preamble by receiving a signal using a different bandwidth according to a channel condition (state).

2 is a flowchart illustrating a method of generating and transmitting a scalable sequence according to an embodiment of the present invention. In FIG. 2, a description will be given of generating and transmitting a scalable sequence in the base station.

As already described above, since there is no power constraint in the base station 110, a preamble having a length of N is generated and transmitted using the entire available bandwidth. This will be described in more detail.

In step 210, the base station 110 generates a scalable sequence of length N. In an embodiment of the present invention, at least one of a maximum-length sequence, a zadoff-Chu sequence, and a linear frequency modulated sequence may be used as a scalable sequence. Each sequence will be separately described below.

In step 215, the base station 110 performs an N-point inverse discrete Fourier transform (IDFT) on the generated scalable sequence (hereinafter referred to as IDFT). Since the IDFT method itself is obvious to those skilled in the art, a separate description thereof will be omitted.

In step 220, the base station 110 transmits the IDFT scalable sequence as a preamble.

일 때 LFM 신호의 상관 관계를 나타낸 도면이며, 도 16는 본 발명의 또 다른 실시예에 따른 도플러 시프트가 존재하는 경우, LFM 신호의 최대 상관값을 나타낸 도면이고, 도 17은 본 발명의 일 실시예에 따른 도플러 시프트가 존재할 때, 세 가지 스케일러블 시퀀스의 성능을 비교한 도면이며, 도 18은 본 발명의 일 실시예에 따른 다른 길이 L에 대한 스케일러블 시퀀스의 검출 확률을 나타낸 그래프이며, 도 19는 본 발명의 일 실시예에 따른 도플러 시프트가 존재하는 경우 스케일러블 시퀀스의 검출 확률을 나타낸 그래프이다. 3 is a flowchart illustrating a scalable sequence detection method in a UAV according to an embodiment of the present invention, FIG. 4 is a flowchart illustrating a preamble detection method according to an embodiment of the present invention, and FIGS. 5 and 6 are It is a view showing the autocorrelation characteristics of the M-sequence according to an embodiment of the present invention, and FIGS. 7 and 8 are diagrams showing the correlation between the case where the cell IDs are the same and the case where the cell IDs are different in the M-sequence according to an embodiment of the present invention 9 and 10 are diagrams showing autocorrelation characteristics of a ZC-sequence according to another embodiment of the present invention, and FIGS. 11 and 12 are diagrams showing the same cell ID of the ZC-sequence according to another embodiment of the present invention. It is a diagram showing the correlation between cases and other cases, FIG. 13 is a diagram showing an LFM-sequence according to another embodiment of the present invention, and FIG. 14 is an LFM when L=NLFM according to another embodiment of the present invention. It is a diagram showing the correlation of signals, and FIG. 15 is a diagram according to another embodiment of the present invention.

It is a diagram showing the correlation of LFM signals when It is a diagram comparing the performance of three scalable sequences when there is a Doppler shift according to an example, and FIG. 18 is a graph showing the detection probability of a scalable sequence for another length L according to an embodiment of the present invention, FIG. 19 is a graph showing a detection probability of a scalable sequence in the presence of a Doppler shift according to an embodiment of the present invention.

In step 310 , the unmanned aerial vehicle 120 determines a channel state with the base station 110 .

In step 315, the unmanned aerial vehicle 120 determines whether the determined channel state is equal to or greater than a reference value. For example, the unmanned aerial vehicle 120 may determine a channel state using the strength of a signal received from the base station 110 . That is, when the intensity of the received signal is less than the reference value, it is determined that the channel state is not good, and when the channel state is greater than the reference value, it can be determined that the channel state is good. In one embodiment of the present invention, for convenience of understanding and explanation, the channel state is classified into two states, good and bad, and the channel state can be divided in more detail. Changes can also be subdivided.

If it is equal to or greater than the reference value, in step 320 , the unmanned aerial vehicle 120 receives a signal (a time domain sequence signal (preamble)) from the base station 110 using a portion of the available total bandwidth.

However, if the channel state is less than the reference value, in step 325 , the unmanned aerial vehicle 120 receives a signal (a time domain sequence signal) from the base station 110 using the entire available bandwidth.

In step 330 , the unmanned aerial vehicle 120 detects a preamble using a signal received from the base station 110 .

A method of detecting the preamble will be described with reference to FIG. 4 .

In step 410, the unmanned aerial vehicle 120 generates a scalable sequence of length L as a reference sequence. Here, L may be less than or equal to the length N of the scalable sequence transmitted from the base station. That is, the unmanned aerial vehicle 120 may use a sequence that is a part (length L) of a scalable sequence having a length of N in the time domain.

In step 415, the unmanned aerial vehicle 120 generates a scalable sequence having a length of L, and then zero-pads the length (N-L) to generate a frequency domain reference sequence. The frequency domain reference sequence has a total length of N, but a sequence after the length L of the front end is a zero-padded sequence.

In step 420, the unmanned aerial vehicle 120 converts the frequency domain reference sequence into a time domain reference sequence by performing an N-point inverse discrete Fourier transform.

Thereafter, in step 425 , the unmanned aerial vehicle 120 may detect the preamble (cell ID and timing) by correlating the time domain reference sequence and the received signal (time domain sequence).

In an embodiment of the present invention, at least one of a maximum-length sequence, a zadoff-Chu sequence, and a linear frequency modulated sequence may be used as a sequence.

Hereinafter, each sequence will be described.

Maximum-length sequence (hereinafter referred to as M- sequence to be called)

In an embodiment of the present invention, the base station 110 generates the M-sequence in the same way as the NR-PSS, which is the NR synchronization signal. Here, the M-sequence has a length of N and may be allocated to N subcarriers in the frequency domain. After IDFT of the M-sequence, the base station 110 transmits a time domain sequence having a length of N as a preamble.

As already described above, since there is no power limitation, the base station 110 may transmit a time domain M-sequence of length N as a preamble using the entire available bandwidth.

The unmanned aerial vehicle 120 receives the preamble by varying the bandwidth according to the channel state. Even when the preamble is received by changing the bandwidth according to the channel state, the UAV 120 uses a bandwidth corresponding to an L (L<=N) subcarrier in order to detect an accurate timing parameter and a cell ID.

The drone 120 maps an M-sequence of length L (using the L length in front of an M-sequence having length N) in the frequency domain, and zero-pads it by (NL) to generate a frequency domain reference sequence. .

The time domain reference sequence may be obtained by performing N-point IDFT of the frequency domain reference sequence. Accordingly, the timing parameter and the cell ID can be detected by correlating a received time domain sequence signal of length N with a time domain reference sequence having the same length.

로 표현될 수 있다. 여기서, c는 셀 아이디를 나타낸다. 길이 N인 시간 도메인 시퀀스는

을 IDFT하여 획득될 수 있다. 이를 수학식으로 나타내면 수학식 1과 같다. Whether the M-sequence has a scalable sequence characteristic will be described. First, the vector assigned to the N subcarriers in the frequency domain is

can be expressed as Here, c represents a cell ID. A time domain sequence of length N is

can be obtained by IDFT. If this is expressed as an equation, it is the same as in Equation 1.

이며,

이고,

이다. here,

is,

ego,

am.

)는 길이가 L인 M-시퀀스와 주파수 도메인에서 (N-L)의 제로패딩을 통해 생성될 수 있다. 주파수 도메인 레퍼런스 시퀀스 벡터를 IDFT하면 시간 도메인 레퍼런스 시퀀스 벡터(

)가 획득될 수 있다. 따라서, 수신단(즉, UAV)에서 L개의 서브 캐리어에 대응하는 대역폭에 상관 없이 시간 도메인 레퍼런스 시퀀스의 길이는 N이 된다. frequency domain reference sequence vector (

) can be generated through an M-sequence of length L and zero-padding of (NL) in the frequency domain. IDFT of the frequency domain reference sequence vector gives the time domain reference sequence vector (

) can be obtained. Accordingly, the length of the time domain reference sequence becomes N regardless of the bandwidth corresponding to the L subcarriers at the receiving end (ie, the UAV).

)과 수신단(UAV)에서 생성된 레퍼런스 신호(

) 사이의 선형 상관(

)은 수학식 2와 같이 나타낼 수 있다. The preamble received from the base station (

) and the reference signal (

) a linear correlation between (

) can be expressed as Equation (2).

이고,

이며,

이다. here,

ego,

is,

am.

Equation 2 can be expressed as Equation 3.

(

)을 수학식 4와 같이 나타낼 수 있다. using Equation 3

(

) can be expressed as Equation (4).

일 때,

과

을 수학식 5와 같이 나타낼 수 있다. Likewise,

when,

class

can be expressed as Equation 5.

임을 알 수 있다. From Equation 5, the maximum value of the correlation function is when m = 0,

it can be seen that

Therefore, when the preamble is detected using the maximum-length sequence, it can be seen that the peak always appears at the correct timing (m=0) regardless of the value of L.

5 and 6 are diagrams illustrating correlation characteristics when L=N and L=N/4. It is assumed that the cell ID of the M-sequence generated at the transmitting end (ie, the base station) is the same as the cell ID of the reference signal at the receiving end. That is, the reference signals may be generated in the same sequence.

5 is a view showing the autocorrelation characteristics in the case of having a length of L = N = 1023, Figure 6 is L ? It is a diagram showing autocorrelation characteristics when N/4 (L=256 and N-L=767). The bandwidth of the receiver (UAV) is indicated by a left hatch in FIGS. 5 and 6, respectively. It can be seen that the correlation obtained by Equation 4 is consistent with the experimental results, and it can be seen that the peak occurs at the correct timing and the peak decreases according to the bandwidth size.

7 and 8 are graphs showing the correlation between the preamble and the reference signal when the cell ID is the same and when the cell ID is different. Signals are generated with different cell IDs through different cyclic-shifts in the frequency domain. 7 and 8, the case of L = N/2 (N = 1023, L = 512) was considered.

As can be seen from FIGS. 7 and 8 , it can be seen that the preamble is detected because the same cell ID is used. As a result, as shown in FIGS. 5 to 8 , in the case of the M-sequence, it can be seen that there is no restriction on the length (L) of the sequence.

zadoff - Chu sequence (below, ZC - sequence to be called)

The ZC sequence is widely used in preamble design of OFDM systems due to its good correlation characteristics and low peak-to-average power ratio (PAPR) characteristics. Since the ZC sequence becomes another ZC sequence having a different root index after DFT/IDFT is applied, it is suitable for the OFDM system.

A method of generating a ZC-sequence as a scalable sequence in an OFDM-based cellular network system will be described.

ZC-sequences are generated similar to how M-sequences are generated. A ZC sequence of length N is generated in the base station and then allocated to N subcarriers in the frequency domain. In the ZC-sequence, the cell ID of the base station is mapped to the root index of the ZC-sequence. A time domain ZC-sequence may be generated by performing N-point IDFT on the frequency domain ZC-sequence. The base station may transmit the corresponding time domain ZC-sequence as a preamble.

The receiving end (ie, UAV) generates a frequency domain reference ZC-sequence by generating a ZC-sequence having a length of L in the same way as the M-sequence and then zero-padding by (N-L). Thereafter, the unmanned aerial vehicle 120 may generate a time domain reference ZC-sequence by performing N-point IDFT on the frequency domain reference ZC-sequence.

The ZC-sequence can be expressed as Equation (6).

The ZC-sequence has the same characteristics as in Equation 7 when N>=4 and is a power of 2.

이다. 수학식 7을 이용하여 ZC-시퀀스를 IDFT하면, 수학식 8과 같이 나타낼 수 있다. here,

am. If the ZC-sequence is IDFTed using Equation 7, it can be expressed as Equation 8.

이다. 수학식 8에서 4개의 항이 더해져 있는

는 k가 4의 배수일 때, 0(zero)이 아닌 값을 가지며, 그 외의 경우 값이 0(zero)인 것을 알 수 있다. 따라서, 수학식 8은 수학식 9와 같이 다시 나타낼 수 있다. here,

am. In Equation 8, four terms are added

It can be seen that when k is a multiple of 4, it has a value other than 0 (zero), and in other cases, the value is 0 (zero). Therefore, Equation 8 can be expressed as Equation 9 again.

이다. 수학식 9는 길이가 N인 ZC-시퀀스를 N 포인트 IDFT 수행했을 때 획득되는 시간 도메인에서의 프리앰블 신호를 나타낸다. 수학식 9에서

는 c의 모듈로 역수를 나타낸다. L=N/4일 때 주파수 도메인 레퍼런스 신호는 ZC-시퀀스를 L개의 서브 캐리어에 할당하고, 주파수 도메인에서 할당된 길이가 N이 될때까지 제로 패딩하여 획득된다. L=N/4일 때의 시간 도메인 레퍼런스 시퀀스는 수학식 10과 같이 주파수 도메인 레퍼런스 신호를 IDFT 수행하여 획득될 수 있다.here,

am. Equation 9 represents a preamble signal in the time domain obtained when N-point IDFT is performed on a ZC-sequence of length N. in Equation 9

is the modulo reciprocal of c. When L=N/4, the frequency domain reference signal is obtained by allocating a ZC-sequence to L subcarriers, and zero-padding until the allocated length in the frequency domain becomes N. The time domain reference sequence when L=N/4 may be obtained by performing IDFT on the frequency domain reference signal as shown in Equation (10).

이다. here,

am.

Equation 10 can be expressed again as Equation 11.

이며,

이다. here,

is,

am.

는 1/4를 제외하고 수학식 10의

와 같다. in Equation 11

is in Equation 10 except for 1/4

same as

과 다르기 때문에, ZC-시퀀스의 의사 무작위 성질 때문에 두 신호간 상호 상관의 크기가 낮다. 수신단에서, 타이밍 및 셀 아이디는 길이 N의 수신된 프리앰블의 동일한 길이의 시간 도메인 레퍼런스 신호와 상관시킴으로써 검출될 수 있다.The remaining terms in Equation 11 are in Equation 10

, the magnitude of the cross-correlation between the two signals is low due to the pseudo-random nature of the ZC-sequence. At the receiving end, the timing and cell ID can be detected by correlating with a time domain reference signal of the same length of the received preamble of length N.

는 시간 도메인 동기화 신호이며, 수학식 10의

는 레퍼런스 신호이기 때문에,

과

사이의 상관은 타이밍과 셀 아이디가 일치할 때 N/4만큼의 피크를 가지고, 그렇지 않은 경우 낮은 크기를 가진다. 따라서, ZC-시퀀스는 길이가 N인 시퀀스가 N>=16이며, N이 2의 거듭제곱인 경우 생성 가능하다. of Equation 9

is the time domain synchronization signal, in Equation 10

Since is the reference signal,

class

The correlation between has a peak of N/4 when the timing and cell ID match, and has a low magnitude otherwise. Therefore, a ZC-sequence can be generated when a sequence of length N is N>=16, and N is a power of two.

9 and 10 are diagrams illustrating autocorrelation characteristics of a ZC-sequence according to an embodiment of the present invention. 9 shows the autocorrelation characteristics of the ZC-sequence when L=N, and FIG. 10 shows the autocorrelation characteristics of the ZC-sequence when L=N/2.

11 is a diagram illustrating correlation characteristics when cell IDs are the same, and FIG. 12 is a diagram illustrating correlation characteristics when cell IDs are different. As shown in FIG. 11 , it can be seen that the correlation between the received preamble and the reference signal has a peak at the correct timing as the cell IDs match. On the other hand, it can be seen that FIG. 12 shows a low correlation because the cell IDs are different.

Accordingly, as described above, when the ZC-sequence is used, it can be seen that the scalable sequence can detect the correct timing and cell ID when the condition of length N is satisfied.

linear frequency modulated sequence (below, LFM - sequence to be called)

LFM signals are widely used in surveillance systems such as radar and sonar because of their robustness to Doppler shift. LFM signals have been used for preamble detection for target detection, but have not been used in cellular networks.

In a cellular network, since a mobile station must be able to distinguish neighboring base stations, cell ID information must be included in a preamble.

However, it was not easy to include the ID of the transmitter through the LFM signal, so it was not applied, but in an embodiment of the present invention, the LFM-sequence is proposed as a sequence that enables preamble detection by varying the bandwidth according to the channel state in the cellular network. do. This will be described in detail.

In the continuous time domain, the LFM signal can be expressed as Equation (12).

와

는 각각 심볼 주기와 스위핑 파라미터를 나타낸다. 수학식 12를 시간 도메인에서 이산화하면 수학식 13과 같이 나타낼 수 있다. here,

Wow

denotes a symbol period and a sweeping parameter, respectively. If Equation 12 is discretized in the time domain, it can be expressed as Equation 13.

이며,

이고,

는 각각 샘플링 주기, 부반송파 간격, LFM 신호에서의 샘플 수를 나타낸다. 또한,

는

와

의 곱을 나타낸다.

에 대해 상승(up-chirp)과 하강(down-chirp)의 두 타입만 생성 가능하다. 본 발명의 일 실시예에 따른 LFM-시퀀스는

을 고정하고 상승(up-chirp)과 하강(down-chirp) 패턴을 통해 다른 셀 아이디를 생성할 수 있다. here,

is,

ego,

denotes the sampling period, the subcarrier interval, and the number of samples in the LFM signal, respectively. In addition,

Is

Wow

represents the product of

Only two types, up-chirp and down-chirp, can be created for . LFM-sequence according to an embodiment of the present invention

can be fixed and different cell IDs can be created through up-chirp and down-chirp patterns.

인 것을 가정하기로 한다. 13 shows an LFM-sequence consisting of one pattern among 16 possible cell IDs: up-chirp, up-chirp, down-chirp, and up-chirp. 13, the length of the LFM-sequence is

It is assumed that

를 가지는 상이한 패턴의 LFM-신호가 레퍼런스 신호로서 생성되고, 수신된 프리앰블 신호와 상관된다. 타이밍과 셀 아이디는 병렬 상관기에서 레퍼런스 신호를 통해 검출될 수 있다. 셀 아이디는 수신된 프리앰블 신호의 패턴과 레퍼런스 신호의 패턴이 일치할 때 검출된다. 그러나, UAV의 LFM-시퀀스는 채널 상태에 따라 신호를 받는 대역폭을 다르게 하여 프리앰블을 수신할 수 있다. 다라서, 주어진

의 값과 LFM 심볼 주기가 비례하기 때문에, 길이 L인 레퍼런스 LFM-시퀀스는 채널 상태에 따라 변한다. 예를 들어, UAV가 전체 대역폭으로 신호를 수신할 때, L의 길이는

과 같이 설정되며, UAV가 1/4만큼의 대역폭으로 신호를 수신할 때, L의 길이는

와 같이 설정될 수 있다. At the receiving end (UAV), the same

A LFM-signal of a different pattern with ? is generated as a reference signal and correlated with the received preamble signal. Timing and cell ID can be detected through a reference signal in the parallel correlator. The cell ID is detected when the pattern of the received preamble signal matches the pattern of the reference signal. However, the LFM-sequence of the UAV may receive the preamble by varying the bandwidth for receiving the signal according to the channel state. Therefore, given

Since the value of and the LFM symbol period are proportional, the reference LFM-sequence of length L varies according to the channel condition. For example, when a UAV receives a signal with full bandwidth, the length of L is

is set as , and when the UAV receives a signal with a bandwidth of 1/4, the length of L is

can be set as

는 송신단(기지국)에서 길이

인 프리앰블을 의미하고,

는 수신단(UAV)에서 길이 L인 레퍼런스 시퀀스(신호)를 의미한다.

일 때, 프리앰블과 레퍼런스 신호 사이의 상관 관계는 수학식 14와 같다. In order to confirm the scalable characteristics of the LFM-sequence, we will analyze the correlation between the preamble using the full bandwidth and the reference signal using a partial bandwidth.

is the length at the transmitting end (base station)

In preamble means,

denotes a reference sequence (signal) of length L at the receiving end (UAV).

When , the correlation between the preamble and the reference signal is expressed by Equation (14).

일 때와

일 때 각각의 상관 결과는 수학식 15 및 수학식 16과 같다.

when and

When , each correlation result is as shown in Equations 15 and 16.

일 때,

인 최대값을 가진다. 도 14는

일 때, 수학식 15와 수학식 16의 결과를 비교한 도면이다. 실험 결과는 분석된 결과와 잘 일치하는 것을 알 수 있다.

의 조건에서 타이밍이 올바르게 검출되는 것을 확인할 수 있다.

when,

has a maximum value of 14 is

, a diagram comparing the results of Equation 15 and Equation 16. It can be seen that the experimental results agree well with the analyzed results.

It can be confirmed that the timing is correctly detected under the condition of .

의 프리앰블을 길이 L의 레퍼런스 신호로 상관함으로써 분석할 수 있다.

일 때, 프리앰블과 레퍼런스 신호 사이의 상관 관계는 수학식 17과 같다. The scalable nature of the LFM-sequence is the length

can be analyzed by correlating the preamble of L with a reference signal of length L.

When , the correlation between the preamble and the reference signal is expressed by Equation (17).

Here, when the range of m is different, the correlation result is as shown in Equations 18 to 20.

일 때, 수학식 18 내지 수학식 20의 수식적으로 유도된 결과와 실험 결과를 비교한 그래프이다. 레퍼런스 신호의 길이가

일 때, 정확한 타이밍을 검출할 수 있음을 알 수 있다. 15 is

When , it is a graph comparing the experimental results with the mathematically derived results of Equations 18 to 20. The length of the reference signal is

, it can be seen that accurate timing can be detected.

인 LFM 신호의 도플러가 존재하는 환경에서 자기 상관은 수학식 21과 같이 나타낼 수 있다. In the UAV cellular network, the Doppler shift occurs due to the fast movement of the UAV, so the autocorrelation characteristic of the LFM signal under the Doppler environment will be described. length

In an environment where Doppler of the LFM signal exists, the autocorrelation can be expressed as Equation 21.

은 정규화된 주파수 오프셋을 나타낸다. 수학식 19의

일 때와

일 때의 상관 결과는 수학식 22 및 수학식 23과 같이 나타낼 수 있다. here,

denotes a normalized frequency offset. of Equation 19

when and

The correlation result when ? can be expressed as Equations 22 and 23.

를 변화시키며 비교한 결과이다. 여기서,

에 대해서는 최대 상관값만 도시되어 있다. 도 15에서 보여지는 바와 같이, 실험 결과와 수식적 결과가 일치하는 것을 알 수 있다. 즉, 프리앰블은 높은 도플러 시프트가 발생하는 상황에서도 모든

에 대해 최대 상관값이 0.9를 넘기 때문에 검출이 가능한 것을 알 수 있다. 16 shows the results of Equations 22 and 23 and the experimental results.

is the result of comparing and changing here,

For , only the maximum correlation value is shown. As shown in FIG. 15 , it can be seen that the experimental results and the mathematical results are consistent with each other. In other words, the preamble is all-inclusive even in a situation where a high Doppler shift occurs.

It can be seen that detection is possible because the maximum correlation value exceeds 0.9.

인 하나의 LFM 신호에 대해서만 분석하였으나, 길이가 N이고 다른 패턴을 가지는 LFM-시퀀스에 대해서도 유사한 결과가 획득되었다. In FIG. 16, for convenience of notation, the length is

Although only one LFM signal was analyzed, similar results were obtained for LFM-sequences of length N and other patterns.

의 변화에 따른 상술한 세가지 스케일러블 시퀀스(Maximum-length 시퀀스, zadoff-Chu 시퀀스 및 linear frequency modulated 시퀀스)에 대한 최대 상관값을 크기를 나타낸 도면이다. 도 17에서 보여지는 바와 같이, LFM 신호의 최대 상관값은 모든

에 대해 0.9 이상의 값을 가지는 것을 알 수 있다. 따라서, LFM 기반의 프리앰블이 도플러 환경 하에서 가장 좋은 성능을 제공한다. 그러나, 본 발명의 일 실시예에 따른 LFM-시퀀스는 상승(up-chirp)과 하강(down-chirp)의 두가지 셀 아이디만 제공할 수 있기 때문에 스케일러블 시퀀스로서는 적합하지 않다. 도 17의 (b)는 도플러 시프트가 있을 때, 실제 타이밍과 추정된 타이밍의 차이를 나타낸다. 타이밍은 최대 상관값이 발생한 순간으로 추정되었다(LFM 신호 제외). 세가지 스케일러블 시퀀스 중에서 LFM-시퀀스가 가장 좋은 성능을 보인다. 17 is

It is a diagram showing the magnitude of the maximum correlation value for the above-described three scalable sequences (maximum-length sequence, zadoff-Chu sequence, and linear frequency modulated sequence) according to the change of . As shown in Fig. 17, the maximum correlation value of the LFM signal is

It can be seen that it has a value greater than 0.9 for . Therefore, the LFM-based preamble provides the best performance under the Doppler environment. However, the LFM-sequence according to an embodiment of the present invention is not suitable as a scalable sequence because only two cell IDs of up-chirp and down-chirp can be provided. 17B shows the difference between the actual timing and the estimated timing when there is a Doppler shift. Timing was estimated as the moment at which the maximum correlation value occurred (except for the LFM signal). Among the three scalable sequences, the LFM-sequence shows the best performance.

18 shows the detection probability of three scalable sequences at the receiving end when a Doppler shift is present. Here, when accurate timing and cell ID are detected, it corresponds to successful detection. The scalable sequence performance was compared for two cases, when the length of L was N and N/4. When the receiving end uses the maximum bandwidth (L = N), the detection probabilities for the three scalable sequences are almost the same. In addition, the transmitted preamble can be accurately detected even when using 1/4 of the total bandwidth at low SNR (L = N/4). In a good channel environment, since 1/4 of the total bandwidth is used, a decrease in performance occurs as shown in FIG. 18, but this can be compensated for through a small path loss (good channel condition). In this case, the UAV can save battery power by using a small bandwidth. On the other hand, the performance gain obtained by a scalable sequence with full bandwidth will be offset by a large path loss (poor channel). In this case, as shown in FIG. 17 , a weak signal received from the UAV may be compensated by a gain.

19 shows the detection probability of three scalable sequences when there is a Doppler shift (ε = 0, 0.5, 1). Simulation was performed using 3D Spatial Channel Model (SCM), and a Rician fading channel with a K-factor of 25 dB was considered. The Rician fading channel was constructed considering the LoS path and 20 non-LoS paths. In the simulation, the height of the UAV was assumed to be 70m above the ground, and the height of the base station was assumed to be 30m. In addition, it is assumed that the UAV is 51m away from the base station. When ε = 0, the detection probabilities of the three scalable sequences are similar. When ε = 1, the performance decrease of the LFM-sequence is negligible. However, the detection probabilities of the M-sequence and ZC-sequence generally decrease as the magnitude of ε increases and converge to zero. As can be expected from FIG. 19 , it can be seen that the LFM-SS exhibits the highest detection probability in a Doppler environment.

Table 1 shows the characteristics of the above-described three scalable sequences. Since the M-sequence and ZC-sequence are sensitive to Doppler shift, it is preferable to use them in an environment with a small Doppler shift. When using the ZC-sequence, the condition for the length of the ZC-sequence must be satisfied. When the Doppler shift is high, it is desirable to use an LFM-sequence that is less sensitive to the Doppler shift.

sequence Doppler Sensitivity Sequence length limit Cell-ID mapping M-sequence very weak no limit circular movement ZC-sequence weakness

16이고, N이 2의 거듭제곱 SS length N

16, and N is a power of 2 root index LFM-sequence on Doppler
toughness no limit Up-chirp and down-chirp patterns

20 is a block diagram schematically illustrating a configuration of a base station that generates and transmits a scalable sequence according to an embodiment of the present invention.

Referring to FIG. 20 , the base station 110 according to an embodiment of the present invention is configured to include a communication unit 2010 , a preamble generator 2020 , a memory 2030 , and a processor 2040 .

The communication unit 2010 is a means for transmitting and receiving data with another device (eg, the unmanned aerial vehicle 120 ) through a communication network. The communication unit 2010 may transmit the time domain scalable sequence as a preamble under the control of the processor 2040 .

The preamble generator 2020 is a means for generating a preamble by generating a scalable sequence and converting it into a time domain sequence. Since this is the same as that described in FIG. 2 , a redundant description thereof will be omitted. As already described above, the scalable sequence may be generated by at least one of a maximum-length sequence, a zadoff-Chu sequence, and a linear frequency modulated sequence.

However, in the case of the ZC-sequence, in order to be accurately detected by the unmanned aerial vehicle 120 as a scalable sequence, the length N of the sequence is 16 or more, and may be generated to be a power of 2. Since this is the same as described above, the overlapping description will be omitted.

The memory 2030 is a means for storing various instructions (program codes) for performing the method for generating a scalable sequence according to an embodiment of the present invention, and various data derived from this process.

The processor 2040 is a means for controlling internal components (eg, the communication unit 1910 , the preamble generation unit 1920 , the memory 1930 , etc.) of the base station 110 according to an embodiment of the present invention. am. The base station 110 performs various functions in addition to, and includes various configurations. In an embodiment of the present invention, only functions necessary for generating and detecting a scalable sequence for UAV cellular network communication will be described.

21 is a block diagram schematically illustrating an internal configuration of an unmanned aerial vehicle according to an embodiment of the present invention.

Referring to FIG. 21 , the unmanned aerial vehicle 120 according to an embodiment of the present invention is configured to include a communication unit 2110 , a preamble detection unit 2120 , a memory 2130 , and a processor 2140 .

The communication unit 2110 is a means for communication with another device (eg, the base station 110) through a communication network. The communication unit 2110 may receive a signal (preamble) from the base station. In this case, the communication unit 2110 may receive a signal by changing the bandwidth differently according to the channel state with the base station. That is, when the channel state is good, a signal may be received using a part of the total available bandwidth. On the other hand, when the channel state is not good, the communication unit 2110 may receive a signal using the entire available bandwidth. Here, the received signal may be a time domain scalable sequence. As already described above, the scalable sequence may be at least one of a maximum-length sequence, a zadoff-Chu sequence, and a linear frequency modulated sequence.

The preamble detection unit 2120 is a means for detecting a preamble using a signal received from the base station and a reference signal.

This is the same as that described with reference to FIGS. 3 to 19 , and thus a redundant description thereof will be omitted.

The memory 2130 stores instructions (program codes) for performing a method of detecting a preamble by changing a bandwidth differently according to a channel state according to an embodiment of the present invention, various data derived from this process, and the like. Save.

The processor 2140 is a means for controlling internal components (eg, the communication unit 2110, the preamble detection unit 2120, the memory 2130, etc.) of the unmanned aerial vehicle 120 according to an embodiment of the present invention. am.

The apparatus and method according to an embodiment of the present invention may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions recorded on the computer readable medium may be specially designed and configured for the present invention, or may be known and available to those skilled in the computer software field. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floppy disks. - Includes magneto-optical media and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

Up to now, the present invention has been looked at focusing on the embodiments thereof. Those of ordinary skill in the art to which the present invention pertains will understand that the present invention can be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the disclosed embodiments should be considered in an illustrative rather than a restrictive sense. The scope of the present invention is indicated in the claims rather than the foregoing description, and all differences within the scope equivalent thereto should be construed as being included in the present invention.

110: base station
120: drone

Claims (23)

A method for detecting a sequence in an unmanned aerial vehicle, the method comprising:
(a) determining a channel state with a base station, wherein the channel state is determined according to the strength of a signal received from the base station; and
(b) receiving a preamble signal from the base station by changing a bandwidth according to the determined channel state;
(c) generating a time domain reference sequence and correlating the received preamble signal with the time domain reference sequence to detect a preamble including a cell ID and a synchronization parameter,
Step (b) is,
receiving a preamble signal from the base station using a maximum available bandwidth when the strength of the signal received from the base station is less than a reference value and the channel state is less than the reference value; and
When the strength of the signal received from the base station is greater than or equal to a reference value and the channel state is greater than or equal to the reference value, receiving a preamble signal from the base station using a partial bandwidth smaller than the maximum bandwidth,
The time domain reference sequence is generated by performing an N-point inverse discrete Fourier transform (IDFT) on a (NL) zero-padded frequency domain reference sequence in the L-th length sequence, wherein N is the total length of the preamble included in the preamble signal. A scalable sequence detection method, characterized in that
delete delete delete delete According to claim 1,
The sequence is
A scalable sequence detection method, characterized in that it is any one of a maximum-length sequence, a zadoff-Chu sequence, and a linear frequency modulated sequence.
7. The method of claim 6,
When the sequence is a maximum-length sequence, a bandwidth corresponding to an L subcarrier is used to detect the preamble.
7. The method of claim 6,
When the sequence is a zadoff-Chu sequence, the length N of the sequence is 16 or more, and the scalable sequence detection method is characterized in that it is generated to be a power of 2.
7. The method of claim 6,
When the sequence is a linear frequency modulated sequence, a cell ID is generated and detected by a combination of up-chirp and down-chirp patterns.
A computer-readable recording medium recording program code for performing the method according to claim 1 .
a channel state analyzer for determining a channel state with a base station;
a communication unit configured to receive a preamble signal from the base station by changing a bandwidth according to the determined channel state; and
A preamble detection unit generating a time domain reference sequence and correlating the received preamble signal with the time domain reference sequence to detect a preamble including a cell ID and a synchronization parameter,
The communication unit,
When the strength of the signal received from the base station is less than the reference value and the channel state is less than the reference value, receiving the preamble signal from the base station using the maximum available bandwidth,
When the strength of the signal received from the base station is greater than or equal to the reference value and the channel state is greater than or equal to the reference value, receiving the preamble signal from the base station using a partial bandwidth smaller than the maximum bandwidth,
The time domain reference sequence is generated by performing an N-point inverse discrete Fourier transform (IDFT) on a (NL) zero-padded frequency domain reference sequence in the L-th length sequence, wherein N is the total length of the preamble included in the preamble signal. Unmanned aerial vehicle, characterized in that.
delete delete delete 12. The method of claim 11,
The sequence is
An unmanned aerial vehicle, characterized in that it is any one of a maximum-length sequence, a zadoff-Chu sequence, and a linear frequency modulated sequence.
16. The method of claim 15,
When the sequence is a Maximum-length sequence, a bandwidth corresponding to L subcarriers is used to detect the preamble.
16. The method of claim 15,
When the sequence is a zadoff-Chu sequence, the length N of the sequence is 16 or more, and the unmanned aerial vehicle is generated to be a power of 2.
16. The method of claim 15,
When the sequence is a linear frequency modulated sequence, the cell ID is generated and detected by a combination of up-chirp and down-chirp patterns.
A method for generating a scalable sequence, comprising:
generating a scalable sequence of length N;
generating a time domain sequence by performing an N-point inverse discrete Fourier transform on the scalable sequence; and
transmitting the time domain sequence as a preamble;
When the scalable sequence is a Maximum-length sequence or a zadoff-Chu sequence, generating a time domain sequence comprises:
The maximum-length sequence or zadoff-Chu sequence of length N is generated and allocated to N subcarriers in the frequency domain, and then inverse discrete Fourier transform is performed to generate a time domain sequence,
In the case of the zadoff-Chu sequence, the N is greater than or equal to 16, and the scalable sequence generation method is characterized in that it is generated to be a power of 2.
delete delete delete A method for generating a scalable sequence, comprising:
generating a scalable sequence of length N;
generating a time domain sequence by performing an N-point inverse discrete Fourier transform on the scalable sequence; and
transmitting the time domain sequence as a preamble;
When the scalable sequence is a linear frequency modulated sequence, generating the time domain sequence comprises generating a cell ID through a combination of up-chirp and down-chirp patterns. How to create a sequence.

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