Method and apparatus for CFO estimation based on orthogonal chirp signal in wireless communication system

Abstract

According to various embodiments of the present disclosure, a method performed by a user equipment (UE) in a wireless communication system can comprise the steps of receiving, from a base station (BS), a first reception signal based on a first orthogonal chirp signal and a first conjugate signal which is a conjugate signal of the first orthogonal chirp signal; obtaining the first orthogonal chirp signal and the first conjugate signal based on the first reception signal; obtaining a first correlation based on the first orthogonal chirp signal and a second orthogonal chirp signal related to the first orthogonal chirp signal; obtaining a second correlation based on the first conjugate signal and a second conjugate signal which is a conjugate signal of the second orthogonal chirp signal; obtaining a peak value of the first correlation; obtaining a peak value of the second correlation; and obtaining a carrier frequency offset (CFO) value for the first reception signal based on the peak value of the first correlation and the peak value of the second correlation.

Description

Technical Field

[0001] This disclosure relates to wireless communication systems. More specifically, this disclosure relates to apparatus and methods for estimating carrier frequency offset (CFO) based on orthogonal chirped signals in wireless communication systems. Background Technology

[0002] Wireless access systems have been widely deployed to provide various types of communication services such as voice and data. Typically, a wireless access system is a multiple access system capable of supporting communication with multiple users by sharing available system resources (bandwidth, transmission power, etc.). Examples of multiple access systems include Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Orthogonal Frequency Division Multiple Access (OFDMA), and Single Carrier Frequency Division Multiple Access (SC-FDMA).

[0003] In particular, as more and more communication devices require greater communication capacity, enhanced mobile broadband (eMBB) communication technologies have been proposed compared to existing radio access technologies (RAT). Additionally, massive machine-type communication (mMTC) systems are being proposed to connect multiple devices and objects to provide various services anytime, anywhere, as well as communication systems considering reliability and latency-sensitive services / user equipment (UE). Various technical configurations for this purpose are being proposed.

[0004] Among the factors that degrade performance in terahertz (THz) band communication systems, carrier frequency offset (CFO) and phase noise (PN) are representative. CFO can typically be generated by RF impairments and the Doppler effect, while PN can be generated by RF impairments. The presence of CFO and PN makes it difficult for the UE to detect PSS (Power Segmentation). Summary of the Invention

[0005] Technical issues

[0006] To address the aforementioned problems, this disclosure provides an apparatus and method for estimating carrier frequency offset (CFO) using orthogonal chirped signals in the THz band.

[0007] Technical solution

[0008] A method performed by a user equipment (UE) in a wireless communication system according to embodiments of the present disclosure may include the following steps: receiving from a base station (BS) a first received signal based on a first orthogonal chirp signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirp signal; obtaining the first orthogonal chirp signal and the first conjugate signal based on the first received signal; and obtaining a first correlation based on the first orthogonal chirp signal and a second orthogonal chirp signal associated with the first orthogonal chirp signal.

[0009] The method may include the following steps: obtaining a second correlation based on a first conjugate signal and a second conjugate signal that is a second orthogonal chirped signal; obtaining a peak value of the first correlation; obtaining a peak value of the second correlation; and obtaining a carrier frequency offset (CFO) value for a first received signal based on the peak value of the first correlation and the peak value of the second correlation.

[0010] Obtaining a first orthogonal chirped signal and a first conjugate signal based on a first received signal may include: performing oversampling on the first orthogonal chirped signal with respect to a sampling factor; and performing oversampling on the first conjugate signal with respect to a sampling factor.

[0011] Obtaining the CFO value for the first received signal based on the peak value of the first correlation and the peak value of the second correlation may include: obtaining the CFO value for the first received signal based on the peak value of the first correlation, the peak value of the second correlation, and the sampling factor.

[0012] The method may also include the following steps: performing synchronization with the base station based on the peak of the first correlation and the peak of the second correlation.

[0013] A user equipment (UE) operating in a communication system according to embodiments of the present disclosure may include: one or more transceivers; one or more processors controlling one or more transceivers; and a memory including one or more instructions executed by the one or more processors. The one or more instructions may include: receiving from a base station (BS) a first received signal based on a first orthogonal chirp signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirp signal; obtaining the first orthogonal chirp signal and the first conjugate signal based on the first received signal; obtaining a first correlation based on the first orthogonal chirp signal and a second orthogonal chirp signal associated with the first orthogonal chirp signal; obtaining a second correlation based on the first conjugate signal and the second conjugate signal that is a conjugate signal of the second orthogonal chirp signal; obtaining a peak value of the first correlation; obtaining a peak value of the second correlation; and obtaining a carrier frequency offset (CFO) value for the first received signal based on the peak value of the first correlation and the peak value of the second correlation.

[0014] Obtaining a first orthogonal chirped signal and a first conjugate signal based on a first received signal may include: performing oversampling on the first orthogonal chirped signal with respect to a sampling factor; and performing oversampling on the first conjugate signal with respect to a sampling factor.

[0015] Obtaining the CFO value for the first received signal based on the peak value of the first correlation and the peak value of the second correlation may include: obtaining the CFO value for the first received signal based on the peak value of the first correlation, the peak value of the second correlation, and the sampling factor.

[0016] One or more instructions may also include: performing synchronization with the base station based on the peak of the first correlation and the peak of the second correlation.

[0017] A method performed by a base station (BS) in a wireless communication system according to embodiments of the present disclosure may include the following steps: generating a first received signal based on a first orthogonal chirp signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirp signal; transmitting the first received signal to a user equipment (UE); and performing synchronization with the UE based on the first received signal. Synchronization may be performed based on the peak value of a first correlation and the peak value of a second correlation, the first correlation being obtained based on the first orthogonal chirp signal and a second orthogonal chirp signal associated with the first orthogonal chirp signal, the second correlation being obtained based on the first orthogonal chirp signal, the first conjugate signal, and the second conjugate signal associated with the first conjugate signal.

[0018] Generating a first received signal based on a first orthogonal chirped signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirped signal may include: performing upconversion on the first orthogonal chirped signal; performing upconversion on the first conjugate signal; and generating the first received signal by summing the upconverted first orthogonal chirped signal and the upconverted first conjugate signal.

[0019] A base station (BS) operating in a communication system according to embodiments of the present disclosure may include: one or more transceivers; one or more processors controlling the one or more transceivers; and a memory including one or more instructions executed by the one or more processors. The one or more instructions may include: generating a first received signal based on a first orthogonal chirp signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirp signal; transmitting the first received signal to a user equipment (UE); and performing synchronization with the user equipment based on the first received signal. Synchronization may be performed based on the peak value of a first correlation and the peak value of a second correlation, the first correlation being obtained based on the first orthogonal chirp signal and a second orthogonal chirp signal associated with the first orthogonal chirp signal, the second correlation being obtained based on the first orthogonal chirp signal, the first conjugate signal, and the second conjugate signal associated with the first conjugate signal.

[0020] Generating a first received signal based on a first orthogonal chirped signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirped signal may include: performing upconversion on the first orthogonal chirped signal; performing upconversion on the first conjugate signal; and generating the first received signal by summing the upconverted first orthogonal chirped signal and the upconverted first conjugate signal.

[0021] An apparatus according to embodiments of the present disclosure may include: one or more memories; and one or more processors functionally coupled to one or more memories. One or more processors may be configured to cause the apparatus to: receive from a base station (BS) a first received signal based on a first orthogonal chirp signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirp signal; obtain the first orthogonal chirp signal and the first conjugate signal based on the first received signal; obtain a first correlation based on the first orthogonal chirp signal and a second orthogonal chirp signal associated with the first orthogonal chirp signal; obtain a second correlation based on the first conjugate signal and the second conjugate signal that is a conjugate signal of the second orthogonal chirp signal; obtain a peak value of the first correlation; obtain a peak value of the second correlation; and obtain a carrier frequency offset (CFO) value for the first received signal based on the peak value of the first correlation and the peak value of the second correlation.

[0022] A non-transitory computer-readable medium storing one or more instructions according to embodiments of the present disclosure may be configured to: receive a first received signal from a base station (BS) based on a first orthogonal chirp signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirp signal; obtain the first orthogonal chirp signal and the first conjugate signal based on the first received signal; obtain a first correlation based on the first orthogonal chirp signal and a second orthogonal chirp signal associated with the first orthogonal chirp signal; obtain a second correlation based on the first conjugate signal and the second conjugate signal that is a conjugate signal of the second orthogonal chirp signal; obtain a peak value of the first correlation; obtain a peak value of the second correlation; and obtain a carrier frequency offset (CFO) value for the first received signal based on the peak value of the first correlation and the peak value of the second correlation.

[0023] Beneficial effects

[0024] Based on this disclosure, CFO and time synchronization can be easily estimated. Attached Figure Description

[0025] The accompanying drawings are provided to aid in understanding this disclosure and to provide embodiments of the disclosure together with the detailed description. However, the technical features of this disclosure are not limited to the specific drawings, and the features disclosed in the various drawings can be combined with each other to form new embodiments. Reference numerals in the various drawings may denote structural elements.

[0026] Figure 1 An example of a communication system applicable to this disclosure is shown.

[0027] Figure 2 Examples of wireless devices applicable to this disclosure are shown.

[0028] Figure 3 A method for processing transmitted signals applicable to this disclosure is shown.

[0029] Figure 4 Another example of a wireless device applicable to this disclosure is shown.

[0030] Figure 5 An example of a handheld device applicable to this disclosure is shown.

[0031] Figure 6 The physical channel applicable to this disclosure and the signal transmission method using it are shown.

[0032] Figure 7 The structure of a radio frame applicable to this disclosure is shown.

[0033] Figure 8 The time slot structure applicable to this disclosure is shown.

[0034] Figure 9 Examples of communication architectures available in a 6G system applicable to this disclosure are shown.

[0035] Figure 10 An example of the structure of a perceptron is shown.

[0036] Figure 11 An example of the structure of a multilayer perceptron is shown.

[0037] Figure 12 An example of a deep neural network is shown.

[0038] Figure 13 An example of a convolutional neural network is shown.

[0039] Figure 14 An example of filter operations in a convolutional neural network is shown.

[0040] Figure 15 An example of a neural network structure with circular loops is shown.

[0041] Figure 16 An example of the operational structure of a recurrent neural network is shown.

[0042] Figure 17 The electromagnetic spectrum applicable to this disclosure is shown.

[0043] Figure 18 A THz communication method applicable to this disclosure is shown.

[0044] Figure 19 A THz wireless communication transceiver applicable to this disclosure is shown.

[0045] Figure 20 A method for generating THz signals applicable to this disclosure is shown.

[0046] Figure 21 A wireless communication transceiver applicable to this disclosure is shown.

[0047] Figure 22 A transmitter structure applicable to this disclosure is shown.

[0048] Figure 23 A modulator structure applicable to this disclosure is shown.

[0049] Figure 24 An example of ICI generation based on carrier frequency offset (CFO) in a wireless communication system is shown.

[0050] Figure 25 An example of the ICI effect according to CFO in a wireless communication system is shown.

[0051] Figure 26 This illustrates a linear chirped signal on the time / magnitude axis in a wireless communication system.

[0052] Figure 27 This illustrates a linear chirped signal on the time / frequency axis of a wireless communication system.

[0053] Figure 28 An example of a typical chirp signal in a wireless communication system is shown.

[0054] Figure 29 An example of an orthogonal chirped signal in a wireless communication system is shown.

[0055] Figure 30 An example of an OFDM signal in a wireless communication system is shown.

[0056] Figure 31 An example of an OCDM signal in a wireless communication system is shown.

[0057] Figure 32 and Figure 33 An example of autocorrelation in a wireless communication system is shown.

[0058] Figure 34 and Figure 35 An example of the first signal in a wireless communication system is shown.

[0059] Figure 36 and Figure 37 An example of a third signal in a wireless communication system is shown.

[0060] Figure 38 and Figure 39 An example of the correlation when a positive CFO is inserted in a wireless communication system is shown.

[0061] Figure 40 and Figure 41 An example of the correlation when a negative CFO is inserted in a wireless communication system is shown.

[0062] Figure 42 and Figure 43 An example of a fifth signal in a wireless communication system is shown.

[0063] Figure 44 and Figure 45 An example of a CFO estimation method according to an embodiment of this disclosure is shown.

[0064] Figure 46 An example diagram illustrating a CFO estimation method according to an embodiment of the present disclosure.

[0065] Figure 47 This is a flowchart illustrating a method for transmitting and receiving signals according to an embodiment of the present disclosure.

[0066] Figure 48 This is a flowchart illustrating a method for transmitting and receiving signals according to another embodiment of the present disclosure. Detailed Implementation

[0067] The embodiments of this disclosure described below are combinations of the elements and features of this disclosure in specific forms. Unless otherwise stated, elements or features are to be considered selective. Individual elements or features may be practiced without combination with other elements or features. Furthermore, embodiments of this disclosure may be constructed by combining some elements and / or features. The order of operations described in the embodiments of this disclosure may be rearranged. Some constructions or elements of any embodiment may be included in another embodiment and may be replaced by corresponding constructions or features of another embodiment.

[0068] In the description of the accompanying drawings, processes or steps that unnecessarily obscure the scope of this disclosure will be omitted, as will processes or steps that are understandable to those skilled in the art.

[0069] Throughout this specification, when a part "comprises" or "includes" a component, this indicates that other components are not excluded, and may include other components unless otherwise specified. The terms "unit," "device," and "module" described herein refer to a unit for performing at least one function or operation, which may be implemented by hardware, software, or a combination thereof. Furthermore, unless otherwise indicated in this specification or unless the context clearly indicates otherwise, in the context of this disclosure (more specifically, in the context of the following claims), the terms "a," "the," etc., may include both singular and plural representations.

[0070] In the embodiments of this disclosure, the data transmission and reception relationship between a base station (BS) and a mobile station is primarily described. A BS refers to a terminal node of a network that communicates directly with a mobile station. Specific operations described as being performed by the BS can be performed by upper-layer nodes of the BS.

[0071] That is, it is obvious that in a network consisting of multiple network nodes, including the BS, various operations performed to communicate with the mobile station can be performed by the BS or network nodes other than the BS. The term "BS" can be replaced by fixed station, node B, evolved Node B (eNode B or eNB), advanced base station (ABS), access point, etc.

[0072] In embodiments of this disclosure, the term "terminal" may be replaced by UE, mobile station (MS), subscriber station (SS), mobile subscriber station (MSS), mobile terminal, advanced mobile station (AMS), etc.

[0073] A transmitter is a fixed and / or mobile node that provides data or voice services, and a receiver is a fixed and / or mobile node that receives data or voice services. Therefore, on the uplink (UL), a mobile station can act as a transmitter and a BS can act as a receiver. Similarly, on the downlink (DL), a mobile station can act as a receiver and a BS can act as a transmitter.

[0074] Implementations of this disclosure can be supported by standard specifications disclosed for at least one wireless access system, including IEEE 802.xx systems, 3GPP systems, 3GPP Long Term Evolution (LTE) systems, 3GPP 5th Generation (5G) New Radio (NR) systems, and 3GPP2 systems. In particular, implementations of this disclosure can be supported by standard specifications 3GPP TS 36.211, 3GPP TS 36.212, 3GPP TS 36.213, 3GPP TS 36.321, and 3GPP TS 36.331.

[0075] Furthermore, the embodiments of this disclosure are applicable to other radio access systems, and are not limited to the systems described above. For example, the embodiments of this disclosure are applicable to systems used after the 3GPP 5G NR system, and are not limited to any specific system.

[0076] That is, no steps or parts described to elucidate the technical features of this disclosure are supported by these documents. Furthermore, all terms set forth herein are self-explanatory in standard documents.

[0077] Embodiments of this disclosure will now be described in detail with reference to the accompanying drawings. The detailed description given below with reference to the drawings is intended to illustrate exemplary embodiments of this disclosure, and not to show only embodiments that can be implemented according to this disclosure.

[0078] The following detailed description includes specific terminology in order to provide a full understanding of this disclosure. However, it will be apparent to those skilled in the art that specific terms may be replaced with other terms without departing from the technical spirit and scope of this disclosure.

[0079] The embodiments disclosed herein can be applied to various radio access systems, such as Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single Carrier Frequency Division Multiple Access (SC-FDMA), etc.

[0080] In the following description, for the purpose of clarification, the description is based on 3GPP communication systems (e.g., LTE, NR, etc.), but the technical spirit of this disclosure is not limited thereto. LTE may refer to technologies after 3GPP TS 36.xxx Release 8. Specifically, LTE technologies after 3GPP TS 36.xxx Release 10 may be referred to as LTE-A, and LTE technologies after 3GPP TS 36.xxx Release 13 may be referred to as LTE-A pro. 3GPP NR may refer to technologies after TS 38.xxx Release 15. 3GPP 6G may refer to technologies TS Release 17 and / or Release 18. “xxx” may refer to the detailed number of the standard document. LTE / NR / 6G may be collectively referred to as the 3GPP system.

[0081] For the background techniques, terms, abbreviations, etc. used in this disclosure, please refer to the descriptions in the standard documents previously published for this disclosure. For example, refer to standard documents 36.xxx and 38.xxx.

[0082] Communication systems applicable to this disclosure

[0083] Without being limited thereto, the various descriptions, functions, processes, proposals, methods and / or operation flowcharts disclosed herein are applicable to various fields that require wireless communication / connectivity (e.g., 5G).

[0084] A more detailed description will be given below with reference to the accompanying drawings. In the following drawings / description, unless otherwise indicated, the same reference numerals may refer to the same or corresponding hardware blocks, software blocks, or functional blocks.

[0085] Figure 1 An example of a communication system applicable to this disclosure is shown. (Refer to...) Figure 1The communication system 100 applicable to this disclosure includes wireless devices, base stations, and networks. Wireless devices refer to devices used to perform communication using radio access technologies (e.g., 5G NR or LTE) and may be referred to as communication / wireless / 5G devices. Without limitation, wireless devices may include robots 100a, vehicles 100b-1 and 100b-2, extended reality (XR) devices 100c, handheld devices 100d, home appliances 100e, Internet of Things (IoT) devices 100f, and artificial intelligence (AI) devices / servers 100g. For example, vehicles may include vehicles with wireless communication capabilities, autonomous vehicles, vehicles capable of performing vehicle-to-vehicle communication, etc. Vehicles 100b-1 and 100b-2 may include unmanned aerial vehicles (UAVs) (e.g., drones). XR device 100c includes augmented reality (AR) / virtual reality (VR) / mixed reality (MR) devices and can be implemented in the form of head-mounted displays (HMDs), head-up displays (HUDs) installed in vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signage, vehicles, or robots. Handheld device 100d may include smartphones, smart tablets, wearable devices (e.g., smartwatches or smart glasses), computers (e.g., laptops), etc. Home appliances 100e may include TVs, refrigerators, washing machines, etc. IoT device 100f may include sensors, smart meters, etc. For example, base station 120 and network 130 may be implemented by wireless devices, and a particular wireless device 120a may operate as a base station / network node of another wireless device.

[0086] Wireless devices 100a to 100f can connect to network 130 via base station 120. AI technology is applied to wireless devices 100a to 100f, and wireless devices 100a to 100f can connect to AI server 100g via network 130. Network 130 can be configured using 3G, 4G (e.g., LTE), or 5G (e.g., NR) networks. Wireless devices 100a to 100f can communicate with each other via base station 120 / network 130, or perform direct communication (e.g., sidelink communication) without using base station 120 / network 130. For example, vehicles 100b-1 and 100b-2 can perform direct communication (e.g., vehicle-to-vehicle (V2V) / vehicle-to-everything (V2X) communication). Additionally, IoT device 100f (e.g., a sensor) can perform direct communication with another IoT device (e.g., a sensor) or other wireless devices 100a to 100f.

[0087] Wireless communication / connections 150a, 150b, and 150c can be established between wireless devices 100a to 100f / base station 120 and base station 120 / base station 120. Here, wireless communication / connections can be established via uplink / downlink communication 150a, sidelink communication 150b (or D2D communication), or communication between base stations 150c (e.g., relay, various radio access technologies such as integrated access backhaul (IAB) (e.g., 5G NR). Wireless devices and base stations / wireless devices or base stations can transmit / receive radio signals to / from each other via wireless communication / connections 150a, 150b, and 150c. For example, wireless communication / connections 150a, 150b, and 150c can achieve signal transmission / reception via various physical channels. To this end, based on various proposals of this disclosure, at least some of the following can be performed: various configuration information setting processes for radio signal transmission / reception, various signal processing processes (e.g., channel coding / decoding, modulation / demodulation, resource mapping / demapping, etc.), resource allocation processes, etc.

[0088] Communication systems applicable to this disclosure

[0089] Figure 2 Examples of wireless devices applicable to this disclosure are shown.

[0090] Reference Figure 2 The first wireless device 200a and the second wireless device 200b can transmit and receive radio signals via various radio access technologies (e.g., LTE or NR). Here, {first wireless device 200a, second wireless device 200b} can correspond to... Figure 1 {Wireless device 100x, base station 120} and / or {Wireless device 100x, wireless device 100x}.

[0091] The first wireless device 200a may include one or more processors 202a and one or more memories 204a, and may also include one or more transceivers 206a and / or one or more antennas 208a. The processors 202a may be configured to control the memories 204a and / or the transceivers 206a and implement the descriptions, functions, processes, proposals, methods, and / or operational flowcharts disclosed herein. For example, the processors 202a may process information in the memories 204a to generate first information / signals, and then transmit a radio signal including the first information / signals via the transceivers 206a. Additionally, the processors 202a may receive radio signals including second information / signals via the transceivers 206a, and then store information obtained from signal processing of the second information / signals in the memories 204a. The memories 204a may be connected to the processors 202a and store various information related to the operation of the processors 202a. For example, memory 204a may store software code, including instructions for performing all or some of the processing controlled by processor 202a or for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed herein. Here, processor 202a and memory 204a may be part of a communication modem / circuit / chip designed to implement wireless communication technologies (e.g., LTE or NR). Transceiver 206a may be connected to processor 202a to transmit and / or receive radio signals via one or more antennas 208a. Transceiver 206a may include a transmitter and / or a receiver. Transceiver 206a may be used interchangeably with a radio frequency (RF) unit. In this disclosure, wireless device may refer to a communication modem / circuit / chip.

[0092] The second wireless device 200b may include one or more processors 202b and one or more memories 204b, and may also include one or more transceivers 206b and / or one or more antennas 208b. The processor 202b may be configured to control the memories 204b and / or the transceivers 206b and implement the descriptions, functions, processes, proposals, methods, and / or operational flowcharts disclosed herein. For example, the processor 202b may process information in the memories 204b to generate third information / signals, and then transmit the third information / signals via the transceivers 206b. Additionally, the processor 202b may receive radio signals including fourth information / signals via the transceivers 206b, and then store information obtained from signal processing of the fourth information / signals in the memories 204b. The memories 204b may be connected to the processor 202b to store various information related to the operation of the processor 202b. For example, memory 204b may store software code, including instructions for performing all or some of the processing controlled by processor 202b or for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed herein. In this document, processor 202b and memory 204b may be part of a communication modem / circuit / chip designed to implement wireless communication technologies (e.g., LTE or NR). Transceiver 206b may be connected to processor 202b to transmit and / or receive radio signals via one or more antennas 208b. Transceiver 206b may include a transmitter and / or a receiver. Transceiver 206b may be used interchangeably with a radio frequency (RF) unit. In this disclosure, wireless device may refer to a communication modem / circuit / chip.

[0093] The hardware components of wireless devices 200a and 200b will be described in more detail below. Without limitation, one or more protocol layers may be implemented by one or more processors 202a and 202b. For example, one or more processors 202a and 202b may implement one or more layers (e.g., functional layers such as PHY (Physical), MAC (Media Access Control), RLC (Radio Link Control), PDCP (Packet Data Convergence Protocol), RRC (Radio Resource Control), SDAP (Service Data Adaptation Protocol)). One or more processors 202a and 202b may generate one or more Protocol Data Units (PDUs) and / or one or more Service Data Units (SDUs) according to the descriptions, functions, processes, proposals, methods, and / or operation flowcharts disclosed herein. One or more processors 202a and 202b may generate messages, control information, data, or information according to the descriptions, functions, processes, proposals, methods, and / or operation flowcharts disclosed herein. One or more processors 202a and 202b may generate PDUs, SDUs, messages, control information, data, or information according to the functions, processes, proposals, and / or methods disclosed herein, and provide PDUs, SDUs, messages, control information, data, or information to one or more transceivers 206a and 206b. One or more processors 202a and 202b may receive signals (e.g., baseband signals) from one or more transceivers 206a and 206b and acquire PDUs, SDUs, messages, control information, data, or information according to the descriptions, functions, processes, proposals, methods, and / or operation flowcharts disclosed herein.

[0094] One or more processors 202a and 202b may be referred to as controllers, microcontrollers, microprocessors, or microcomputers. One or more processors 202a and 202b may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), one or more digital signal processing devices (DSPDs), programmable logic devices (PLDs), or one or more field-programmable gate arrays (FPGAs) may be included in one or more processors 202a and 202b. The descriptions, functions, processes, proposals, methods, and / or operation flowcharts disclosed herein may be implemented using firmware or software, and the firmware or software may be implemented as modules, processes, functions, etc. Firmware or software configured to perform the descriptions, functions, processes, proposals, methods, and / or operation flowcharts disclosed herein may be included in one or more processors 202a and 202b or stored in one or more memories 204a and 204b to be driven by one or more processors 202a and 202b. The descriptions, functions, processes, proposals, methods, and / or operation flowcharts disclosed herein are implemented using firmware or software in the form of code, commands, and / or command sets.

[0095] One or more memories 204a and 204b may be connected to one or more processors 202a and 202b to store various types of data, signals, messages, information, programs, code, instructions, and / or commands. One or more memories 204a and 204b may consist of read-only memory (ROM), random access memory (RAM), erasable programmable read-only memory (EPROM), flash memory, hard disk drive, registers, cache memory, computer-readable storage media, and / or combinations thereof. One or more memories 204a and 204b may be located internally and / or externally to one or more processors 202a and 202b. Additionally, one or more memories 204a and 204b may be connected to one or more processors 202a and 202b via various technologies such as wired or wireless connections.

[0096] One or more transceivers 206a and 206b may transmit user data, control information, radio signals / channels, etc., as described in the method and / or operation flowcharts of this disclosure to one or more other devices. One or more transceivers 206a and 206b may receive user data, control information, radio signals / channels, etc., as described in the method and / or operation flowcharts of this disclosure from one or more other devices. For example, one or more transceivers 206a and 206b may be connected to one or more processors 202a and 202b to transmit / receive radio signals. For example, one or more processors 202a and 202b may perform control such that one or more transceivers 206a and 206b transmit user data, control information, or radio signals to one or more other devices. Additionally, one or more processors 202a and 202b may perform control such that one or more transceivers 206a and 206b receive user data, control information, or radio signals from one or more other devices. Additionally, one or more transceivers 206a and 206b may be connected to one or more antennas 208a and 208b, and one or more transceivers 206a and 206b may be configured to transmit / receive user data, control information, radio signals / channels, etc., as described herein in the descriptions, functions, processes, proposals, methods, and / or operation flowcharts disclosed herein via one or more antennas 208a and 208b. In this disclosure, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers 206a and 206b may convert received radio signals / channels, etc., from RF band signals to baseband signals for processing by one or more processors 202a and 202b. One or more transceivers 206a and 206b may convert user data, control information, radio signals / channels, etc., processed using one or more processors 202a and 202b from baseband signals to RF band signals. For this purpose, one or more transceivers 206a and 206b may include (analog) oscillators and / or filters.

[0097] Figure 3 A method for processing transmitted signals applicable to this disclosure is illustrated. For example, the transmitted signal may be processed by a signal processing circuit. In this case, the signal processing circuit 300 may include a scrambler 310, a modulator 320, a layer mapper 330, a pre-encoder 340, a resource mapper 350, and a signal generator 360. For example, Figure 3 Operations / functions can be provided by Figure 2 The processors 202a and 202b and / or transceivers 206a and 206b execute. Additionally, for example, Figure 3 The hardware components can be found in Figure 2Processors 202a and 202b and / or Figure 2 Implemented in transceivers 206a and 206b. For example, blocks 1010 to 1060 can be... Figure 2 Implemented in processors 202a and 202b. Additionally, blocks 310 to 350 can be... Figure 2 Implemented in processors 202a and 202b, block 360 can be... Figure 2 It is implemented in transceivers 206a and 206b, but is not limited to the embodiments described above.

[0098] Code can be passed Figure 3 The signal processing circuit 300 converts the signal into a radio signal. Here, a codeword is an encoded bit sequence of an information block. The information block may include a transport block (e.g., a UL-SCH transport block or a DL-SCH transport block). The radio signal can be transmitted via... Figure 6 Various physical channels (e.g., PUSCH and PDSCH) are used for transmission. Specifically, the codeword can be converted into a bit sequence scrambled by scrambler 310. A scrambling sequence for scrambling is generated based on initial values, which may include the wireless device's ID information, etc. The scrambled bit sequence can be modulated into a modulation symbol sequence by modulator 320. Modulation methods may include pi / 2-binary phase shift keying (pi / 2-BPSK), m-phase shift keying (m-PSK), m-quadrature amplitude modulation (m-QAM), etc.

[0099] A complex modulation symbol sequence can be mapped to one or more transmission layers by layer mapper 330. The modulation symbols of each transmission layer can be mapped to the corresponding antenna port by precoder 340 (precoding). The output z of precoder 340 can be obtained by multiplying the output y of layer mapper 330 by N. The M precoding matrix W is obtained. Here, N can be the number of antenna ports, and M can be the number of transmission layers. The precoder 340 can perform precoding after transform precoding (e.g., Discrete Fourier Transform (DFT)) of the complex modulation symbols. Alternatively, the precoder 340 can perform precoding without performing transform precoding.

[0100] Resource mapper 350 maps modulation symbols from each antenna port to time-frequency resources. The time-frequency resources may include multiple symbols in the time domain (e.g., CP-OFDMA symbols and DFT-s-OFDMA symbols) and multiple subcarriers in the frequency domain. Signal generator 360 generates radio signals from the mapped modulation symbols, and the generated radio signals can be transmitted to another device via the respective antennas. For this purpose, signal generator 360 may include an inverse fast Fourier transform (IFFT) module, a cyclic prefix (CP) inserter, a digital-to-analog converter (DAC), a frequency uplink converter, etc.

[0101] The signal processing procedure for received signals in a wireless device can be configured as follows: Figure 3 The signal processing process is the reverse process from 310 to 360. For example, wireless devices (e.g., Figure 2 The 200a or 200b transceiver can receive radio signals from an external source via its antenna port / transceiver. The received radio signals are converted to baseband signals by a signal restorer. For this purpose, the signal restorer may include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a Fast Fourier Transform (FFT) module. Subsequently, the baseband signal is recovered into codewords through a resource demapping process, a post-encoding process, a demodulation process, and a descrambling process. The codewords are then decoded to recover the original information blocks. Therefore, the signal processing circuitry (not shown) used for the received signals may include a signal restorer, a resource demapping unit, a post-encoder, a demodulator, a descrambler, and a decoder.

[0102] Structure of the wireless device applicable to this disclosure

[0103] Figure 4 Another example of a wireless device applicable to this disclosure is shown.

[0104] Reference Figure 4 The wireless device 400 can correspond to Figure 2 Wireless devices 200a and 200b are included, and various elements, components, units / parts, and / or modules are incorporated. For example, wireless device 400 may include a communication unit 410, a control unit (controller) 420, a memory unit (memory) 430, and additional components 440. The communication unit may include communication circuitry 412 and transceiver 414. For example, communication circuitry 412 may include... Figure 2 One or more processors 202a and 202b and / or one or more memories 204a and 204b. For example, transceiver 414 may include Figure 2 The device may have one or more transceivers 206a and 206b and / or one or more antennas 208a and 208b. The control unit 420 may be electrically connected to the communication unit 410, the memory unit 430, and the add-on components 440 to control the overall operation of the wireless device. For example, the control unit 420 may control the electrical / mechanical operation of the wireless device based on programs / code / instructions / information stored in the memory unit 430. Additionally, the control unit 420 may use the communication unit 410 via a wireless / wired interface to transmit information stored in the memory unit 430 to an external source (e.g., another communication device) via the wireless / wired interface, or to store information received from an external source (e.g., another communication device) via the wireless / wired interface using the communication unit 410 in the memory unit 430.

[0105] The additional component 440 can be configured differently depending on the type of wireless device. For example, the additional component 440 may include at least one of a power supply unit / battery, an input / output unit, a drive unit, or a computing unit. Without limitation, the wireless device 400 may be a robot (…). Figure 1 ,100a), vehicles ( Figure 1 , 100b-1 and 100b-2), XR device ( Figure 1 ,100c), handheld device ( Figure 1 ,100d), household appliances ( Figure 1 ,100e), IoT devices ( Figure 1 ,100f), digital broadcasting terminals, holographic devices, public safety equipment, MTC devices, medical devices, financial technology devices (financial devices), security devices, climate / environmental devices, AI servers / devices ( Figure 1 ,140), base station ( Figure 1 It can be implemented in the form of network nodes, etc. Depending on the use case / service, the wireless device can be mobile or can be used in a fixed location.

[0106] exist Figure 4 In the wireless device 400, various elements, components, units / parts, and / or modules can be connected to each other via wired interfaces, or at least some of them can be wirelessly connected via communication unit 410. For example, in the wireless device 400, control unit 420 and communication unit 410 can be wired connected, and control unit 420 and first unit (e.g., 130 or 140) can be wirelessly connected via communication unit 410. Additionally, the various elements, components, units / parts, and / or modules of the wireless device 400 may also include one or more elements. For example, control unit 420 may consist of a collection of one or more processors. For example, control unit 420 may consist of a collection of communication control processors, application processors, electronic control units (ECUs), graphics processing processors, memory control processors, etc. In another example, memory unit 430 may consist of random access memory (RAM), dynamic RAM (DRAM), read-only memory (ROM), flash memory, volatile memory, non-volatile memory, and / or combinations thereof.

[0107] Handheld devices applicable to this disclosure

[0108] Figure 5 An example of a handheld device applicable to this disclosure is shown.

[0109] Figure 5A handheld device applicable to this disclosure is shown. The handheld device may include a smartphone, smartpad, wearable device (e.g., a smartwatch or smart glasses), and handheld computer (e.g., a laptop computer, etc.). The handheld device may be referred to as a mobile station (MS), user terminal (UT), mobile subscriber station (MSS), subscriber station (SS), advanced mobile station (AMS), or wireless terminal (WT).

[0110] Reference Figure 5 The handheld device 500 may include an antenna unit (antenna) 508, a communication unit (transceiver) 510, a control unit (controller) 520, a memory unit (memory) 530, a power supply unit (power supply) 540a, an interface unit (interface) 540b, and an input / output unit 540c. The antenna unit (antenna) 508 may be part of the communication unit 510. Blocks 510 to 530 / 540a to 540c may respectively correspond to... Figure 4 Blocks 410 to 430 / 440.

[0111] Communication unit 510 can send and receive signals (e.g., data, control signals, etc.) to and from other wireless devices or base stations. Control unit 520 can control components of handheld device 500 to perform various operations. Control unit 520 may include an application processor (AP). Memory unit 530 can store data / parameters / programs / code / instructions required to drive handheld device 500. In addition, memory unit 530 can store input / output data / information, etc. Power supply unit 540a can supply power to handheld device 500 and includes wired / wireless charging circuitry, battery, etc. Interface unit 540b can support connection between handheld device 500 and another external device. Interface unit 540b may include various ports for connecting to external devices (e.g., audio input / output ports and video input / output ports). Input / output unit 540c can receive or output video information / signals, audio information / signals, data and / or user input information. Input / output unit 540c may include a camera, microphone, user input unit, display 540d, speaker and / or haptic module.

[0112] For example, in the case of data communication, the input / output unit 540c can acquire user input information / signals (e.g., touch, text, voice, image, or video) from the user and store the user input information / signals in the memory unit 530. The communication unit 510 can convert the information / signals stored in the memory into radio signals and transmit the converted radio signals directly to another wireless device or to a base station. Alternatively, the communication unit 510 can receive radio signals from another wireless device or a base station and then recover the received radio signals into the original information / signals. The recovered information / signals can be stored in the memory unit 530 and then output through the input / output unit 540c in various forms (e.g., text, voice, image, video, and haptic feedback).

[0113] Physical channels and general signal transmission

[0114] In a radio access system, the UE receives information from the base station on the DL and transmits information to the base station on the UL. The information transmitted and received between the UE and the base station includes general data information as well as various control information. Many physical channels exist depending on the type / purpose of the information transmitted and received between the base station and the UE.

[0115] Figure 6 The physical channel applicable to this disclosure and the signal transmission method using it are shown.

[0116] When a UE is turned on again from a closed state or enters a new cell, it performs an initial cell search operation in S611, such as obtaining synchronization with the base station. Specifically, the UE performs synchronization with the base station by receiving the primary synchronization channel (P-SCH) and secondary synchronization channel (S-SCH) from the base station, and obtains information such as the cell identifier (ID).

[0117] Subsequently, the UE can receive the Physical Broadcast Channel (PBCH) signal from the base station and obtain intra-cell broadcast information. Furthermore, the UE can receive the Downlink Reference Signal (DL RS) and check the downlink channel status during the initial cell search step. The UE that has completed the initial cell search can receive the Physical Downlink Control Channel (PDCCH) and Physical Downlink Control Channel (PDSCH) in step S612 based on the Physical Downlink Control Channel information, thereby obtaining more detailed system information.

[0118] Subsequently, the UE can execute random access procedures such as steps S613 to S616 to complete access to the base station. To this end, the UE can transmit a preamble via the Physical Random Access Channel (PRACH) (S613) and receive a Random Access Response (RAR) to the preamble via the Physical Downlink Control Channel and the corresponding Physical Downlink Shared Channel (S614). The UE can use the scheduling information in the RAR to transmit the Physical Uplink Shared Channel (PUSCH) (S615) and perform contention resolution procedures such as receiving the Physical Downlink Control Channel signal and the corresponding Physical Downlink Shared Channel signal (S616).

[0119] The UE that has performed the above process can perform the reception of physical downlink control channel signals and / or physical downlink shared channel signals (S617) and the transmission of physical uplink shared channel (PUSCH) signals and / or physical uplink control channel (PUCCH) signals (S618) as a general uplink / downlink signal transmission process.

[0120] Control information sent from the UE to the base station is collectively referred to as uplink control information (UCI). UCI includes Hybrid Automatic Repeat Request Acknowledgment / Negative ACK (HARQ-ACK / NACK), Scheduling Request (SR), Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), Rank Indicator (RI), Beam Indicator (BI), etc. UCI is typically sent periodically via PUCCH, but in some implementations it can be sent via PUSCH (e.g., when control information and service data are sent simultaneously). Additionally, the UE can send UCI aperiodically via PUSCH according to network requests / instructions.

[0121] Figure 7 The structure of a radio frame applicable to this disclosure is shown.

[0122] UL and DL transmission based on NR system can be based on Figure 7 The frame is shown. Here, a radio frame has a length of 10 ms and can be defined as two 5 ms half-frames (HF). A half-frame can be defined as five 1 ms subframes (SF). A subframe can be divided into one or more time slots, and the number of time slots in a subframe depends on the subscriber spacing (SCS). Here, depending on the cyclic prefix (CP), each time slot can include 12 or 14 OFDM(A) symbols. If a normal CP is used, each time slot can include 14 symbols. If an extended CP is used, each time slot can include 12 symbols. Here, the symbols can include OFDM symbols (or CP-OFDM symbols) and SC-FDMA symbols (or DFT-s-OFDM symbols).

[0123] Table 1 shows the number of symbols per slot, the number of slots per frame, and the number of slots per subframe according to the SCS when using normal CP. Table 2 shows the number of symbols per slot, the number of slots per frame, and the number of slots per subframe according to the SCS when using extended CP.

[0124] [Table 1]

[0125] [Table 2]

[0126] In Tables 1 and 2 above, It can indicate the number of symbols in a time slot. It can indicate the number of time slots in a frame. It can indicate the number of time slots in a subframe.

[0127] Furthermore, in the systems to which this disclosure applies, the OFDM(A) parameter set (e.g., SCS, CP length, etc.) can be set differently among multiple cells merged into a single UE. Therefore, the (absolute time) period of time resources (e.g., SF, time slots, or TTI) consisting of the same number of symbols (collectively referred to as time units (TU) for convenience) can be set differently among the merged cells.

[0128] NR can support multiple parameter sets (or subscriber spacing (SCS)) to support a variety of 5G services. For example, a 15kHz SCS supports wide areas in traditional cellular bands, a 30kHz / 60kHz SCS supports dense urban areas, lower latency, and wider carrier bandwidth, and a SCS of 60kHz or higher can support bandwidths greater than 24.25 GHz to overcome phase noise.

[0129] The NR band is defined as frequency ranges of two types (FR1 and FR2). FR1 and FR2 can be configured as shown in the table below. Additionally, FR2 can refer to millimeter wave (mmW).

[0130] [Table 3]

[0131] Furthermore, for example, in the communication system to which this disclosure applies, the above parameter set can be set differently. For example, the terahertz (THz) band can be used as a band higher than FR2. In the THz band, the SCS can be set higher than in the NR system, and the number of time slots can be set differently, not limited to the above embodiments. The THz band will be described below.

[0132] Figure 8 The time slot structure applicable to this disclosure is shown.

[0133] In the time domain, a time slot comprises multiple symbols. For example, in normal CP, a time slot comprises seven symbols, while in extended CP, a time slot comprises six symbols. In the frequency domain, a carrier comprises multiple subcarriers. In the frequency domain, a resource block (RB) can be defined as multiple (e.g., 12) consecutive subcarriers.

[0134] In addition, the bandwidth portion (BWP) is defined as multiple consecutive (P)RBs in the frequency domain and may correspond to a set of parameters (e.g., SCS, CP length, etc.).

[0135] A carrier may include up to N (e.g., five) BWPs. Data communication is performed through the enabled BWPs, and only one BWP may be enabled for a UE. In the resource grid, each element is called a resource element (RE) and may be mapped to a complex symbol.

[0136] 6G communication system

[0137] 6G (wireless communication) systems have objectives such as: (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) reduced power consumption for battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) interconnected intelligence with machine learning capabilities. The vision for 6G systems can include four aspects, such as “intelligent connectivity,” “deep connectivity,” “holographic connectivity,” and “ubiquitous connectivity,” and 6G systems can meet the requirements shown in Table 4 below. That is, Table 4 shows the requirements for 6G systems.

[0138] [Table 4]

[0139] At this point, 6G systems can possess key features such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), massive machine-type communication (mMTC), AI-integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.

[0140] Figure 9 Examples of communication architectures available in a 6G system applicable to this disclosure are shown.

[0141] Reference Figure 96G systems will have 50 times more simultaneous wireless communication connections than 5G systems. As a key feature of 5G, URLLC will become even more important in 6G communication by providing end-to-end latency of less than 1 ms. At this point, unlike the frequently used domain spectral efficiency, 6G systems can have better volumetric spectral efficiency. 6G systems can offer advanced battery technology for energy harvesting and very long battery life, so mobile devices may not need separate charging in 6G systems. Additionally, new network characteristics in 6G include the following.

[0142] - Satellite-integrated networks: To provide global mobile coverage, 6G will be integrated with satellites. For 6G, integrating terrestrial waves, satellites, and public networks into a single wireless communication system may be crucial.

[0143] - Connected Intelligence: Unlike previous generations of wireless communication systems, 6G is innovative and its wireless evolution can be updated from "connected things" to "connected intelligence." AI can be applied to every step of the communication process (or the various signal processing procedures described below).

[0144] - Seamless integration of wireless messaging and power transfer: 6G wireless networks can transfer power to charge the batteries of devices such as smartphones and sensors. Therefore, wireless messaging and power transfer (WIET) will be integrated.

[0145] - Ubiquitous Ultra 3D Connectivity: Network and core network functions that access drones and very low Earth orbit satellites will establish ubiquitous ultra 3D connectivity in 6G.

[0146] Among the new network features of 6G, several general requirements are as follows.

[0147] - Small Cell Networks: The concept of small cell networks was introduced to improve received signal quality as a result of improvements in throughput, energy efficiency, and spectral efficiency in cellular systems. Consequently, small cell networks are a fundamental feature of 5G and beyond 5G (5GB) communication systems. Therefore, 6G communication systems also utilize the characteristics of small cell networks.

[0148] - Ultra-dense heterogeneous networks: Ultra-dense heterogeneous networks will be another important feature of 6G communication systems. Multi-layered networks composed of heterogeneous networks improve overall QoS and reduce costs.

[0149] - High-capacity backhaul: Backhaul connections are characterized by high-capacity backhaul networks to support high-capacity services. High-speed fiber optic and free-space optics (FSO) systems may be a possible solution to this problem.

[0150] - Radar technology integrated with mobile technology: High-precision positioning (or location-based services) via communication is one of the functions of 6G wireless communication systems. Therefore, radar systems will be integrated with 6G networks.

[0151] - Software and virtualization: Software and virtualization are two important features that form the basis of the design process in 5GB networks to ensure flexibility, reconfigurability and programmability.

[0152] Core implementation technologies of 6G systems

[0153] - Artificial Intelligence (AI)

[0154] The most important new technology to be introduced in 6G systems is AI. AI was not involved in 4G systems. 5G systems will support some or very limited AI. However, 6G systems will support AI to the fullest extent of automation. Advances in machine learning will create smarter networks in 6G for real-time communication. When AI is introduced into communication, real-time data transmission can be simplified and improved. AI can use countless analyses to determine methods for performing complex tasks. That is, AI can increase efficiency and reduce processing latency.

[0155] Time-consuming tasks such as switching, network selection, or resource scheduling can be performed immediately using AI. AI can also play a significant role in M2M, machine-to-human, and human-to-machine communication. Furthermore, AI can enable rapid communication in brain-computer interfaces (BCIs). AI-based communication systems can be supported by metamaterials, smart structures, smart networks, smart devices, intelligent identification radios, self-maintaining wireless networks, and machine learning.

[0156] Recently, attempts have been made to integrate AI with wireless communication systems at the application or network layers, but deep learning has been primarily focused on wireless resource management and allocation. However, these studies are gradually expanding to the MAC and physical layers, with particular efforts emerging to combine deep learning in the physical layer with wireless transmission. AI-based physical layer transmission refers to the application of AI-driven signal processing and communication mechanisms, rather than traditional communication frameworks within basic signal processing and communication mechanisms. Examples include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based multiple-input multiple-output (MIMO) mechanisms, and AI-based resource scheduling and allocation.

[0157] Machine learning can be used for channel estimation and channel tracking, and for power allocation and interference cancellation in the physical layer of deep learning. Additionally, machine learning can be used for antenna selection, power control, and symbol detection in MIMO systems.

[0158] However, applying deep neural networks (DNNs) for transmission at the physical layer may present the following problems.

[0159] Deep learning-based AI algorithms require large amounts of training data to optimize training parameters. However, due to limitations in acquiring data for training in specific channel environments, much of this training data is used offline. Static training using data specific to a particular channel environment can lead to a trade-off between the diversity and dynamic characteristics of the radio channel.

[0160] Furthermore, current deep learning primarily targets real-world signals. However, the signals at the physical layer of wireless communication are complex signals. To match the characteristics of wireless communication signals, it is necessary to research neural networks for detecting complex signals.

[0161] Machine learning will be described in more detail below.

[0162] Machine learning refers to a series of operations that train machines to perform tasks that humans cannot or find difficult to perform. Machine learning requires data and learning models. In machine learning, data learning methods can be broadly divided into three types: supervised learning, unsupervised learning, and reinforcement learning.

[0163] Neural network learning aims to minimize output error. It involves repeatedly inputting training data into a neural network, calculating the error between the network's output and the target value, propagating this error back from the output layer to the input layer to reduce it, and updating the weights of each node in the neural network.

[0164] Supervised learning can use training data labeled with correct answers, while unsupervised learning can use training data without labeled correct answers. That is, for example, in supervised learning for data classification, the training data can be labeled with categories. The labeled training data can be input into a neural network, and the output (category) of the neural network can be compared with the labels of the training data to calculate the error. The calculated error is backpropagated from the neural network (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network can be updated based on the backpropagation. The changes in the updated connection weights of each node can be determined based on the learning rate. The computation of backpropagation of the input data and the error by the neural network can be configured into learning cycles (epochs). The learning data can be applied differently depending on the number of repetitions of the neural network's learning cycles. For example, a high learning rate can be used in the early learning stages of the neural network to increase efficiency, allowing the neural network to quickly secure a certain level of performance; in the later learning stages, a low learning rate can be used to increase accuracy.

[0165] Learning methods can vary depending on the characteristics of the data. For example, to accurately predict the data transmitted from the transmitter in a receiver of a communication system, supervised learning can be used instead of unsupervised learning or reinforcement learning.

[0166] The learning model corresponds to the human brain and can be considered the most basic linear model. However, the machine learning paradigm that uses highly complex neural network structures (e.g., artificial neural networks) as learning models is called deep learning.

[0167] The core neural networks used as learning methods can be broadly categorized into deep neural network (DNN) methods, convolutional deep neural network (CNN) methods, and recurrent Boltzmann machine (RNN) methods. This learning model is applicable.

[0168] Artificial neural networks are examples of connecting multiple perceptrons.

[0169] Figure 10 An example of the structure of a perceptron is shown.

[0170] Reference Figure 10 When the input vector x = (x1, x2, ..., xd) is input, each component is multiplied by a weight (W1, W2, ..., Wd), and all results are summed. Then, the activation function σ( The entire process is called a perceptron. Large artificial neural network structures are scalable. Figure 10 The simplified perceptron structure shown illustrates how input vectors can be applied to different multidimensional perceptrons. For ease of illustration, input or output values ​​are referred to as nodes.

[0171] Figure 10 The perceptron structure shown can be described as consisting of a total of three layers based on the input and output values. Figure 11 An artificial neural network is shown, where, as an example, the number of (d+1)-dimensional perceptrons between the first and second layers is H, and the number of (H+1)-dimensional perceptrons between the second and third layers is K. Figure 11 An example of the structure of a multilayer perceptron is shown.

[0172] The layer containing the input vector is called the input layer, the layer containing the final output value is called the output layer, and all layers between the input and output layers are called hidden layers. As an example, Figure 11 Three layers are shown. However, since the number of layers in an artificial neural network excludes the input layer count, it can be considered as a total of two layers. An artificial neural network is constructed using a perceptron that connects basic blocks in two dimensions.

[0173] The input, hidden, and output layers described above can be combined in various artificial neural network architectures, such as CNNs, RNNs, and multilayer perceptrons, which will be described later. The greater the number of hidden layers, the deeper the artificial neural network, and the machine learning paradigm that uses sufficiently deep artificial neural networks as learning models is called deep learning. Furthermore, the artificial neural networks used for deep learning are called deep neural networks (DNNs).

[0174] Figure 12 The deep neural network shown is a multilayer perceptron consisting of eight hidden layers and eight output layers. The multilayer perceptron structure is represented as a fully connected neural network. In a fully connected neural network, there are no connections between nodes in the same layer; connections only exist between nodes in adjacent layers. DNNs have a fully connected neural network structure and consist of a combination of multiple hidden layers and activation functions, thus they can be usefully applied to understanding the correlation characteristics between inputs and outputs. Correlation characteristics can refer to the joint probability of the input and output.

[0175] Based on how multiple perceptrons are connected to each other, various artificial neural network structures different from the DNNs mentioned above can be formed.

[0176] In a DNN, nodes within a layer are arranged along a one-dimensional vertical direction. However, in Figure 13 In this case, we can assume that w nodes horizontally and h nodes vertically are arranged in two dimensions ( Figure 6 (Convolutional neural network structure). In this case, since weights are assigned to each connection during the connection process from an input node to the hidden layer, a total of h×w weights need to be considered. Since there are h×w nodes in the input layer, a total of h²w² weights are needed between two adjacent layers.

[0177] Figure 13 Convolutional neural networks have the problem that the number of weights increases exponentially with the number of connections. Therefore, instead of considering all connections between nodes in adjacent layers, we assume the existence of small-sized filters and perform weighted summations and activation function computations on the overlapping portions of the filters, such as... Figure 14 As shown.

[0178] A filter has a number of weights corresponding to its size, and the weights can be learned so that a certain feature on the image can be extracted and output as a factor. Figure 14 In the process, a 3×3 filter is applied to the top left 3×3 region of the input layer, and the output value obtained by performing weighted summation and activation function calculations on the corresponding nodes is stored in z22.

[0179] As the filter scans the input layer, it moves horizontally and vertically by predetermined intervals while performing weighted summations and activation function calculations, placing the output value at the current filter's position. This computational method is similar to the convolution operation on images in computer vision. Therefore, deep neural networks with this structure are called convolutional neural networks (CNNs), and the hidden layers generated as a result of the convolution operation are called convolutional layers. Furthermore, neural networks with multiple convolutional layers are called deep convolutional neural networks (DCNNs).

[0180] At the node where the current filter is located in the convolutional layer, the number of weights can be reduced by calculating a weighted sum that only includes nodes located within the region covered by the filter. Therefore, a single filter can be used to focus on features in a local region. Thus, CNNs can be effectively applied to image data processing where physical distance is an important criterion in 2D regions. In CNNs, multiple filters can be applied immediately before the convolutional layer, and multiple outputs can be generated through the convolution operations of each filter.

[0181] Depending on the data attributes, there may be data with important sequence characteristics. The structure of an artificial neural network that considers the variability in the length of the sequence data and the relational input elements of the data sequence at each time step, and whose output vector (hidden vector) of the hidden layer is output at a specific time step along with the next element of the data sequence, is called a recurrent neural network structure.

[0182] Figure 15 An example of a neural network structure with circular loops is shown.

[0183] Reference Figure 15 A recurrent neural network (RNN) is a structure in which, when any element (x1(t), x2(t), ..., xd(t)) at any time step "t" in a data sequence is input into a fully connected neural network, the hidden vector (z1(t-1), z2(t-1), ..., zH(t-1)) is input together with the element at the previous time step (t-1) to apply a weighted sum and activation function. The reason for passing the hidden vector to the next time step is that the information in the input vector from the previous time step is considered to be accumulated in the hidden vector at the current time step.

[0184] Figure 16 An example of the operational structure of a recurrent neural network is shown.

[0185] Reference Figure 16 Recurrent neural networks operate in a predetermined time sequence relative to the input data sequence.

[0186] When the input vector (x1(t), x2(t), ..., xd(t)) at time step 1 is input into the recurrent neural network, the hidden vector (z1(1), z2(1), ..., zH(1)) is input together with the input vector (x1(2), x2(2), ..., xd(2)) at time step 2 to determine the hidden layer vector (z1(2), z2(2), ..., zH(2)) through weighted summation and activation functions. This process is repeated at time steps 2, 3, ..., T.

[0187] When multiple hidden layers are set in a recurrent neural network, it is called a deep recurrent neural network (DRNN). Recurrent neural networks are designed to be usefully applied to sequential data (e.g., natural language processing).

[0188] Besides DNN, CNN, and RNN, the core neural networks used as learning methods include various deep learning methods such as Restricted Boltzmann Machines (RBM), Deep Belief Networks (DBN), and Deep Q-Networks, and can be applied to fields such as computer vision, speech recognition, natural language processing, and speech / signal processing.

[0189] Recently, attempts to integrate AI with wireless communication systems have emerged, but these have focused on wireless resource management and allocation at the application and network layers, particularly deep learning. However, this research is gradually expanding to the MAC and physical layers, with attempts to combine deep learning with wireless transmission emerging specifically at the physical layer. AI-based physical layer transmission refers to the application of AI-driven signal processing and communication mechanisms, rather than traditional communication frameworks based on fundamental signal processing and communication mechanisms. Examples include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanisms, and AI-based resource scheduling and allocation.

[0190] Terahertz (THz) communication

[0191] THz communication is suitable for 6G systems. For example, data rates can be increased by increasing bandwidth. This can be achieved by using sub-TH communication with wide bandwidth and applying advanced massive MIMO technology.

[0192] Figure 17 The electromagnetic spectrum applicable to this disclosure is shown. For example, refer to... Figure 17 The THz wave, known as submillimeter radiation, typically indicates a frequency band between 0.1 THz and 10 THz, corresponding to wavelengths ranging from 0.03 mm to 3 mm. The 100 GHz to 300 GHz frequency band (sub-THz band) is considered the main portion of the THz band used for cellular communications. 6G cellular communication capacity increases when the sub-THz band is added to the mmWave band. The defined 300 GHz to 3 THz THz band falls within the far-infrared (IR) band. This 300 GHz to 3 THz band is part of the optical band, but lies at its boundary, just behind the RF band. Therefore, the 300 GHz to 3 THz band shares similarities with RF.

[0193] Key characteristics of THz communication include (i) a wide bandwidth available to support very high data rates and (ii) high path loss occurring at high frequencies (making highly directional antennas indispensable). The narrow beamwidth generated in highly directional antennas reduces interference. The small wavelength of THz signals allows for the integration of a greater number of antenna elements with devices and base stations operating in this band. Therefore, advanced adaptive placement techniques that can overcome range limitations can be used.

[0194] Optical wireless technology

[0195] In addition to RF-based communication, Optical Wireless Communication (OWC) technology is planned for 6G communication to enable network access for all possible devices. This network connects to the backhaul / fronthaul network. OWC technology has been used since 4G communication systems but will be more widely used to meet the requirements of 6G communication systems. Broadband-based OWC technologies (e.g., high-fidelity / visible light communication, optical camera communication, and free-space optical (FSO) communication) are well-known. Optical wireless communication can provide very high data rates, low latency, and secure communication. Light detection and ranging (LiDAR) can also be used for ultra-high resolution 3D mapping in broadband-based 6G communication.

[0196] FSO Backhaul Network

[0197] The transmitters and receivers in an FSO system have characteristics similar to those in a fiber optic network. Therefore, data transmission in an FSO system is similar to that in a fiber optic system. Thus, FSO may be a good technology for providing backhaul connectivity in conjunction with fiber optic networks in 6G systems. When using FSO, very long-distance communication is possible even at distances of 10,000 km or more. FSO supports large-scale backhaul connectivity for both remote and non-remote areas (e.g., ocean, space, underwater, and isolated islands). FSO also supports cellular base station connectivity.

[0198] Massive MIMO technology

[0199] One of the core technologies used to improve spectral efficiency is MIMO (Multi-channel Mixing). Improvements in MIMO technology lead to improvements in spectral efficiency. Therefore, massive MIMO will be crucial in 6G systems. Since MIMO uses multiple paths, multiplexing and beamforming techniques suitable for the THz band should be carefully considered to enable data signal transmission through one or more paths.

[0200] Blockchain

[0201] Blockchain will be a crucial technology for managing massive amounts of data in future communication systems. Blockchain is a distributed ledger technology, where a distributed ledger is a database distributed across numerous nodes or computing devices. Each node replicates and stores an identical copy of the ledger. Blockchain is managed through a peer-to-peer (P2P) network. This can exist without centralized institutions or servers for management. Blockchain data is collected together and organized into blocks. Blocks are linked together and protected using encryption. Blockchain fully complements large-scale IoT through improved interoperability, security, privacy, stability, and scalability. Therefore, blockchain technology offers multiple capabilities such as interoperability between devices, traceability of large volumes of data, autonomous interaction between different IoT systems, and the large-scale connectivity stability of 6G communication systems.

[0202] 3D Internet

[0203] The 6G system integrates terrestrial and public networks to support the vertical expansion of user communications. 3D BS will be provided via low-Earth orbit satellites and UAVs. Adding new dimensions in terms of altitude and associated degrees of freedom makes 3D connectivity significantly different from existing 2D networks.

[0204] Quantum communication

[0205] In the context of 6G networks, unsupervised reinforcement learning for networks shows promise. Supervised learning methods cannot label the massive amounts of data generated in 6G. Unsupervised learning does not require labeling. Therefore, this technique can be used to autonomously construct representations for complex networks. Combining reinforcement learning with unsupervised learning can enable networks to operate in a truly autonomous manner.

[0206] Unmanned aerial vehicles

[0207] Unmanned aerial vehicles (UAVs), or drones, will be a crucial element in 6G wireless communication. In most cases, UAV technology is used to provide high-speed data wireless connectivity. Base station entities are installed within UAVs to provide cellular connectivity. UAVs possess certain characteristics not found in fixed base station infrastructure, such as ease of deployment, strong line-of-sight links, and controllable degrees of freedom in mobility. In emergencies such as natural disasters, the deployment of terrestrial telecommunications infrastructure is economically infeasible and sometimes unable to provide service in turbulent environments. UAVs can easily address these situations. UAVs will represent a new paradigm in wireless communication. This technology facilitates the three fundamental requirements of wireless networks such as eMBB, URLLC, and mMTC. UAVs can also serve numerous purposes such as network connectivity improvement, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, and accident monitoring. Therefore, UAV technology is considered one of the most important technologies for 6G communication.

[0208] Cellular communication

[0209] The tight integration of multi-frequency and heterogeneous communication technologies is crucial in 6G systems. As a result, users can seamlessly move from one network to another without any manual configuration within the device. The optimal network is automatically selected from available communication technologies. This breaks the limitations of the cellular concept in wireless communication. Currently, user movement from one cell to another in high-density networks results in too many handovers, leading to handover failures, handover latency, data loss, and the ping-pong effect. 6G cellular-free communication will overcome all these problems and provide better QoS. Cellular-free communication will be achieved through multi-connectivity and multi-layer hybrid technologies, as well as different heterogeneous radios within the device.

[0210] Wireless Information and Power Transfer (WIET)

[0211] WIET uses the same fields and waves as wireless communication systems. Specifically, sensors and smartphones will use wireless power transmission to charge during communication. WIET is a promising technology for extending the lifespan of batteries used to charge wireless systems. Therefore, battery-free devices will be supported in 6G communications.

[0212] Integration of sensing and communication

[0213] Autonomous wireless networks are functions used to continuously detect dynamically changing environmental conditions and exchange information between different nodes. In 6G, sensing will be tightly integrated with communication to support autonomous systems.

[0214] Integration of access to backhaul network

[0215] In 6G, the density of access networks will be enormous. These access networks will be connected via fiber optic and backhaul connections, such as FSO networks. To handle the sheer number of access networks, tight integration between access and backhaul networks will be essential.

[0216] Holographic beamforming

[0217] Beamforming is the signal processing procedure of adjusting an antenna array to transmit radio signals in a specific direction. It is a subset of smart antennas or advanced antenna systems. Beamforming technology offers several advantages, such as high signal-to-noise ratio, interference prevention and suppression, and high network efficiency. Holographic beamforming (HBF) is a novel beamforming method that differs significantly from MIMO systems because it uses software-defined antennas. HBF will be a highly effective method for efficiently and flexibly transmitting and receiving signals in multi-antenna communication devices in 6G.

[0218] Big data analytics

[0219] Big data analytics is the complex processing of various large datasets or big data. This processing seeks information such as hidden data, unknown correlations, and customer actions to ensure comprehensive data management. Big data is collected from a variety of sources such as videos, social networks, images, and sensors. This technology is widely used to process the massive amounts of data in 6G systems.

[0220] Large Intelligent Surfaces (LIS)

[0221] In the THz band, due to its high linearity, numerous shadowed areas may exist due to obstacles. Installing Light Insulators (LISs) near these shadowed areas is crucial for extending communication coverage, enhancing communication stability, and allowing for additional optional services. An LIS is an artificial surface made of electromagnetic materials that alters the propagation of incoming and outgoing radio waves. While LISs can be considered an extension of Massive MIMO, they differ in array structure and operating mechanism. Furthermore, LISs offer advantages such as low power consumption because they operate as reconfigurable reflectors with passive components; that is, signals are passively reflected without the use of an active RF chain. Additionally, the phase shift of the incident signal must be independently adjusted by each passive reflector in the LIS, which can benefit the wireless communication channel. By appropriately adjusting the phase shift via the LIS controller, the reflected signal can be collected at the target receiver to enhance the received signal power.

[0222] THz wireless communication

[0223] Figure 18 A THz communication method applicable to this disclosure is shown.

[0224] Reference Figure 18 THz wireless communication uses THz waves with frequencies ranging from approximately 0.1 to 10 THz (1 THz = 10¹² Hz), which can refer to wireless communication in the terahertz (THz) band using very high carrier frequencies of 100 GHz or higher. THz waves lie between the radio frequency (RF) / millimeter (mm) and infrared bands, (i) penetrating non-metallic / non-polarized materials better than visible / infrared light and having shorter wavelengths than RF / millimeter waves, thus exhibiting high linearity and the ability to beam converge.

[0225] Furthermore, the photon energy of THz waves is only a few meV, therefore harmless to the human body. The frequency bands used for THz wireless communication can be the D band (110 GHz to 170 GHz) or the H band (220 GHz to 325 GHz), where propagation loss is low due to molecular absorption in the air. Besides 3GPP, standardization discussions regarding THz wireless communication are primarily conducted within the IEEE 802.15 THz Working Group (WG), and the standard documents published by the IEEE 802.15 Task Group (TG) (e.g., TG3d, TG3e) specify and supplement the descriptions in this disclosure. THz wireless communication can be applied to wireless awareness, sensing, imaging, wireless communication, and THz navigation.

[0226] Specifically, refer to Figure 18 THz wireless communication scenarios can be categorized into macro networks, micro networks, and nanoscale networks. In macro networks, THz wireless communication can be applied to vehicle-to-vehicle (V2V) connections and backhaul / fronthaul connections. In micro networks, THz wireless communication can be applied to near-field communication such as indoor small cells, fixed point-to-point or multipoint connections such as wireless connections in data centers, or kiosk downloads. Table 5 below shows examples of technologies that can be used in THz waves.

[0227] [Table 5]

[0228] Figure 19 A THz wireless communication transceiver applicable to this disclosure is shown.

[0229] Reference Figure 19 THz wireless communication can be classified based on the methods of generating and receiving THz. THz generation methods can be classified into technologies based on optical or electronic devices.

[0230] At this time, methods for generating THz using electronic devices include methods using semiconductor devices such as resonant tunneling diodes (RTDs), methods using local oscillators and multipliers, monolithic microwave integrated circuit (MMIC) methods using integrated circuits based on compound semiconductor high electron mobility transistors (HEMTs), and methods using Si-CMOS based integrated circuits. Figure 19 In such cases, multipliers (frequency multipliers, triplers, dynodes) are used to increase the frequency, and radiation is performed by the antenna via a subharmonic mixer. Since the THz band forms a high frequency, multipliers are essential. Here, a multiplier is a circuit whose output frequency is N times the input frequency, matched to the desired harmonic frequency, and filters out all other frequencies. Additionally, by adjusting the frequency multiplier... Figure 19 Beamforming is achieved through the application of antenna arrays and other methods. Figure 19In the diagram, IF represents intermediate frequency, tripler and multiplier represent multipliers, PA represents power amplifier, LNA represents low-noise amplifier, and PLL represents phase-locked loop.

[0231] Figure 20 A method for generating THz signals applicable to this disclosure is shown. Figure 21 A wireless communication transceiver applicable to this disclosure is shown.

[0232] Reference Figure 20 and Figure 21 THz wireless communication technology based on optical devices refers to methods for generating and modulating THz signals using optical devices. Specifically, THz signal generation technology uses lasers and optical modulators to generate ultra-high-speed optical signals and then converts them into THz signals using ultra-high-speed photodetectors. Compared to technologies using only electronic devices, this technology allows for easier frequency scaling, the generation of high-power signals, and the attainment of a flat response characteristic over a wide bandwidth. To generate THz signals based on optical devices, such as... Figure 20 As shown, a laser diode, a broadband optical modulator, and an ultra-high-speed photodetector are required. Figure 20 In this case, optical signals from two lasers with different wavelengths are combined to generate a THz signal corresponding to the wavelength difference between the lasers. Figure 20 In optics, an optical coupler is a semiconductor device that uses light waves to transmit electrical signals, providing electrical isolation from a circuit or system. A single-carrier photodetector (UTC-PD) is one type of photodetector that uses electrons as active charge carriers and reduces electron travel time through bandgap gradation. UTC-PDs are capable of photoelectric detection at 150 GHz or higher. Figure 21 In this context, Erbium-Doped Fiber Amplifier (EDFA) refers to an fiber amplifier with added erbium; Photodetector (PD) refers to a semiconductor device that can convert optical signals into electrical signals; OSA refers to an optical sub-component in which various optical communication functions (e.g., photoelectric conversion, electroacoustic conversion, etc.) are modularized into a single component; and DSO refers to a digital storage oscilloscope.

[0233] Figure 22 A transmitter structure applicable to this disclosure is shown. Figure 23 A modulator structure applicable to this disclosure is shown.

[0234] Reference Figure 22 and Figure 23Typically, the phase of a laser light source can be altered by passing it through an optical waveguide. Data is then carried by changing electrical properties via microwave contact or similar means. Therefore, the output of the optical modulator is formed as a modulated waveform. An optoelectronic modulator (O / E converter) can generate THz pulses based on the optical rectification operation of a nonlinear crystal, the photoelectric conversion (O / E conversion) of a photoconductive antenna, and the emission of a relativistic electron beam. Terahertz pulses (THz pulses) generated in this manner can have lengths ranging from femtoseconds to picoseconds. The optoelectronic modulator (O / E converter) utilizes the nonlinearity of the device to perform down-conversion.

[0235] Given the intended use of the THz spectrum, multiple adjacent GHz bands are likely to be used for fixed or mobile services of terahertz systems. According to outdoor scenario standards, available bandwidth can be classified within a spectrum of up to 1 THz based on an oxygen attenuation of 10^2 dB / km. Therefore, a framework consisting of multiple band blocks of available bandwidth can be considered. As an example of this framework, if the length of a terahertz pulse (THz pulse) of a carrier (carrier) is set to 50 ps, ​​the bandwidth (BW) is approximately 20 GHz.

[0236] Effective downconversion from the infrared to the terahertz band depends on how the nonlinearity of the O / E converter is utilized. That is, to downconvert to the desired terahertz band (THz band), the design of the photoelectric converter (O / E converter) needs to have optimal nonlinearity to move to the corresponding terahertz band (THz band). If an O / E converter that is unsuitable for the target band is used, the probability of errors in amplitude and phase relative to the corresponding pulse is high.

[0237] In a single-carrier system, a single opto-converter can be used to implement a terahertz transmit / receive system. In a multi-carrier system, the number of opto-converters may be as many as the number of carriers, which may vary depending on the channel environment. This is particularly pronounced when multiple wideband multi-carrier systems are used according to plans related to the spectrum usage described above. In this regard, a frame structure for multi-carrier systems can be considered. Down-converted signals based on opto-converters can be transmitted in specific resource regions (e.g., specific frames). The frequency domain of a specific resource region may include multiple blocks. Each block may consist of at least one component carrier (CC).

[0238] In addition to LTE, NR, and 6G, the wireless communication technologies implemented in the wireless devices 200a and 200b of this disclosure may include narrowband Internet of Things (IoT) for low-power communication. In this case, for example, NB-IoT technology may be an example of low-power wide-area network (LPWAN) technology and may be implemented as standards such as LTE Cat NB1 and / or LTE Cat NB2, not limited to the aforementioned names. Alternatively or additionally, the wireless communication technologies implemented in the wireless devices of this disclosure may perform communication based on LTE-M technology. In this case, as an example, LTE-M technology may be an example of LPWAN and may be referred to by various names including enhanced machine-type communication (eMTC). For example, LTE-M technology may be implemented as at least any of various standards, such as 1) LTE Cat 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-bandwidth limited (non-BL), 5) LTE-MTC, 6) LTE machine-type communication, and / or 7) LTE M. Alternatively or additionally, considering low-power communication, the wireless communication technologies implemented in the wireless devices 200a and 200b of this disclosure may include at least one of ZigBee, Bluetooth, and Low-Power Wide Area Network (LPWAN), and are not limited to the aforementioned names. As an example, ZigBee technology may generate personal area networks (PANs) associated with small / low-power digital communication based on various standards including IEEE 802.15.4, and may be referred to by various names. Background Technology

[0239] Representative factors that degrade the performance of communication systems in the THz band include carrier frequency offset (CFO) and phase noise (PN). CFO is typically generated by RF impairments and the Doppler effect, while PN can be generated by RF impairments. The CFO normalized by OFDM SCS can be represented by Equation 1 below.

[0240] [Formula 1]

[0241] In Equation 1, It can be a CFO normalized through OFDM SCS. It could be the deviation between the transmitter's carrier and the receiver's carrier. It could be the receiver's moving speed. It could be the speed of light, and It can be an OFDM subcarrier frequency.

[0242] Referring to Equation 1, the CFO can increase with the increase in the offset between the transmitter's carrier and the receiver's carrier, the increase in the receiver's moving speed, and the increase in the OFDM subcarrier frequency. CFO can cause performance degradation during the signal acquisition phase required for synchronization between the transmitter and receiver. Specifically, in THz band communication systems utilizing high carrier frequencies, performance during the signal acquisition phase due to CFO may be further degraded. In existing CFO estimation methods, when CFO is less than the subcarrier spacing (SCS), the cyclic prefix (CP), which is the repetitive signal in OFDM, can be used to estimate CFO, and when CFO is greater than SCS, a scanning method can be used to estimate CFO. In the scanning method, system overhead can increase significantly.

[0243] Implementation of this disclosure

[0244] Symbols / Abbreviations / Terms

[0245] The symbols / abbreviations / terms used in this disclosure are as follows.

[0246] - AWGN: Additive White Gaussian Noise

[0247] - CP: Cyclic prefix

[0248] - CFO: Channel Frequency Offset

[0249] - DFnT: Discrete Fresnel Transform

[0250] - ICI: Intercarrier Interference

[0251] - OCDM: Orthogonal Chirped Multiplexing

[0252] - OFDM: Orthogonal Frequency Division Multiplexing

[0253] - PN: Phase noise

[0254] - PSS: Master Synchronization Signal

[0255] - SCS: Subcarrier Spacing

[0256] - THz: Terahertz

[0257] Problems to be solved

[0258] Figure 24 An example of ICI generation based on CFO is shown in a wireless communication system. Figure 25 An example of the ICI effect according to CFO in a wireless communication system is shown.

[0259] More specifically, Figure 24This illustrates an example of ICI generation when the orthogonality between orthogonal frequencies is broken in an OFDM system with the presence of CFO. Figure 25 The graph shows the impact of ICI on BER and SNR performance based on the normalized CFO of the OFDM system.

[0260] Reference Figure 24 If the orthogonality between frequencies is maintained, the signal of consecutive frequencies becomes zero. However, if orthogonality is violated by the CFO, components of consecutive frequencies are received and can act as interference. Referring to Equation 1 above, the CFO typically increases with the speed of the moving object, the carrier frequency of the base station, and the deviation between the carriers of the base station and the UE. Depending on the application, the probability of an increase in CFO increases in satellite communications or future sub-THz or THz communication systems because they involve ultra-high-speed movement and high carrier frequencies. In existing LTE or 5G NR, the cyclic prefix (CP) of the OFDM system can be used to estimate residual CFO components with CFO values ​​less than 1. However, for CFO components with CFO values ​​greater than 1, the only available method is to manually search for them by scanning nearby frequencies, which has the disadvantage of very high complexity.

[0261] When there is an ICI due to CFO, the received symbol and the ICI due to CFO can be represented as shown in Equations 2 and 3 below, respectively.

[0262] [Equation 2]

[0263] [Formula 3]

[0264] In Equations 2 and 3, r can be a received symbol, x can be a transmitted symbol, w can be an AWGN, I can be an ICI, and N can be the number of subcarriers. It could be the CFO.

[0265] Reference Figure 25 As CFO increases, ICI may increase, leading to an increase in terms within the summation symbol of Equation 2 (i.e., interference). Therefore, it can be observed that BER performance further deteriorates at the same SNR. To recover from this deterioration, additional algorithms are required, and the overhead caused by these algorithms may be unavoidable.

[0266] Detailed description of this disclosure

[0267] Orthogonal chirping signal

[0268] Figure 26 An example of a linear chirped signal on the time / magnitude axis in a wireless communication system. Figure 27 This illustrates a linear chirped signal on the time / frequency axis of a wireless communication system.

[0269] Reference Figure 26 and Figure 27 The chirped signal can be a signal used in the LoRA (Remote Response) standard for long-range transmission of frequency modulated continuous wave (FMCW) radar or Internet of Things (IoT) devices. Due to the characteristics of the chirped spread spectrum, the chirped signal can be robust to both CFO and PN. The chirped signal can include linear and nonlinear chirped signals. A linear chirped signal can be represented by Equation 4 below.

[0270] [Formula 4]

[0271] In Equation 4, It can be a linear chirped signal. It can be the chirp rate, and It can be the initial phase value. The chirp rate can be a variable that determines the slope of the chirped signal. Generally, the higher the chirp rate, the steeper the slope (e.g., ...). Figure 27 The slope of the straight line in the equation may be more steep along the time / frequency axis.

[0272] Figure 28 An example of a typical chirp signal in a wireless communication system is shown. Figure 29 An example of an orthogonal chirped signal in a wireless communication system is shown. Figure 30 An example of an OFDM signal in a wireless communication system is shown. Figure 31 An example of an OCDM signal in a wireless communication system.

[0273] Reference Figure 28 and Figure 29 Generally, chirped signals will use different frequency bands simultaneously, but orthogonal chirped signals can use multiple chirped signals by overlapping them in the same time and frequency band. The characteristics of orthogonal chirped signals can be considered in the same way as the method used in OFDM systems to utilize frequency orthogonality. The orthogonal chirped signals used in OFDM systems can be represented by Equation 5 below.

[0274] [Formula 5]

[0275] In Equation 5, It can be the k-th orthogonal chirp signal, N can be the number of orthogonal chirps included in the orthogonal chirp signal, and T can be the transmission period. Referring to Equation 5, the chirp rate of all orthogonal chirp signals can be related to... same.

[0276] Reference Figure 30 and Figure 31 For example, when there are 17 subcarriers, the OFDM signal can be as follows: Figure 31 As shown, and when N is 17 in Equation 5, the OCDM signal can be as follows: Figure 32 As shown.

[0277] CFO Estimation Method Using Orthogonal Chirped Signals

[0278] Figure 32 and Figure 33 An example of autocorrelation in a wireless communication system is shown. Figure 34 and Figure 35 An example of the first signal in a wireless communication system is shown. Figure 36 and Figure 37 An example of a third signal in a wireless communication system is shown. Figure 38 and Figure 39 An example of the correlation when a positive CFO is inserted in a wireless communication system is shown. Figure 40 and Figure 41 An example of the correlation when a negative CFO is inserted in a wireless communication system is shown. Figure 42 and Figure 43 An example of a fifth signal in a wireless communication system is shown. Figure 44 and Figure 45 An example of a CFO estimation method according to an embodiment of this disclosure is shown. Figure 46 An example diagram illustrating a CFO estimation method according to an embodiment of the present disclosure.

[0279] More specifically, Figure 32 and Figure 33 The results show the autocorrelation properties of the CFO based on the presence or absence of any chirped signal. Figure 32 Example the autocorrelation of the first signal of any chirped signal, and Figure 33 Example of the autocorrelation of the third signal for any chirped signal. In Figure 32 In this context, L1 is when the CFO is not present ( The autocorrelation characteristics are the result when CFO = 0), and L2 is the result when CFO exists ( The results of the autocorrelation characteristics when =2). Figure 33 In this context, L3 is when the CFO is not present ( The autocorrelation characteristics are the result when CFO = 0), and L4 is the result when CFO exists ( The results of the autocorrelation characteristics when =2).

[0280] Figure 34 The first signal in the time domain is shown, and Figure 35 The first signal in the time-frequency domain is shown. Figure 36 The third signal in the time domain is shown, and Figure 37The third signal in the time-frequency domain is shown.

[0281] Figure 38 and Figure 39 The correlation between the first and third signals and the correlation between the second and fourth signals are shown when a positive CFO is inserted.

[0282] exist Figure 38 and Figure 39 In the middle, L5 (L 51 To L 54 () can represent the correlation between the first and third signals. 51 To L 54 This can be used to represent the correlation when CFO is 0, 0.5, 1, and 3, respectively. L6 (L 61 To L 64 () can represent the correlation between the second and fourth signals. L 61 To L 64 These can be used to represent the correlation when CFO is 0, 0.5, 1, and 3, respectively.

[0283] Figure 40 and Figure 41 The correlation between the first and third signals and the correlation between the second and fourth signals are shown when a negative CFO is inserted.

[0284] exist Figure 40 and Figure 41 In the middle, L7 (L 71 To L 74 () can represent the correlation between the first and third signals. 71 To L 74 This can be used to represent the correlation when CFO is 0, 0.5, 1, and 3, respectively. L8 (L 81 To L 84 () can represent the correlation between the second and fourth signals. L 81 To L 84 These can be used to represent the correlation when CFO is 0, 0.5, 1, and 3, respectively.

[0285] Reference Figures 32 to 41 When CFO is added to the quadrature chirp signal in Equation 5, the quadrature chirp signal can be represented as shown in Equation 6 below.

[0286] [Formula 6]

[0287] In Equation 6, It could be the kth orthogonal chirped signal of the CFO.

[0288] The first to fourth signals can be represented as shown in Equations 7 to 10 below.

[0289] [Formula 7]

[0290] [Formula 8]

[0291] [Formula 9]

[0292] [Formula 10]

[0293] In equations 7 to 10, , , and These can be the first to the fourth signals, respectively.

[0294] Reference Figure 38 and Figure 39 It can be seen that L5 and L6 have peak values ​​at the left and right symmetrical points based on lags=0. Based on this, the receiver can detect not only the residual CFO, but also the common CFO.

[0295] Reference Figure 40 and Figure 41 It can be seen that L7 and L8 have peak values ​​at the left and right symmetrical points based on lags=0. Based on this, the receiver can detect not only the residual CFO, but also the common CFO.

[0296] The fifth signal can be a signal generated by combining the first and second signals, and the sixth signal can be a signal generated by combining the third and fourth signals. The fifth and sixth signals can be represented as shown in Equations 11 and 12 below, respectively.

[0297] [Equation 11]

[0298] [Equation 12]

[0299] In Equations 11 and 12, It could be the fifth signal, and It can be the sixth signal. The value of k in the fifth signal can be 1, but this is just an example and not a limitation.

[0300] based on Figures 38 to 41 The method for estimating CFO based on the relevant results shown in the figure can be expressed as Equation 13 below.

[0301] [Equation 13]

[0302] In Equation 13, It can be CFO, v can be the oversampling factor, and and This could be the peak position (i.e., the lag point) of the correlation values ​​between Equations 7 and 11, and Equations 8 and 11. In this case, and It can be symmetric about 0.

[0303] Reference Figure 44 For example, when v is 16 and and When the values ​​are 16 and -16 respectively, CFO can be 1, and this value can mean that CFO is equal to 1 times the subcarrier spacing (SCS).

[0304] Reference Figure 45 For example, when v is 16 and and When the values ​​are 8 and -8 respectively, the CFO can be -0.5.

[0305] The larger the value of v, the more accurate the CFO value can be estimated by the receiver, but the system complexity increases accordingly. Therefore, it is necessary to set it to an appropriate value. Since the receiver can use the midpoint information between the two peak positions to estimate time synchronization, the receiver can estimate both time synchronization and CFO simultaneously.

[0306] Figure 47 This is a flowchart illustrating a method for transmitting and receiving signals according to an embodiment of the present disclosure.

[0307] Reference Figure 47 In S4710, the user equipment (UE) can receive a first received signal from the base station. The UE can receive a first received signal based on a first orthogonal chirped signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirped signal from the base station.

[0308] In S4720, the UE can obtain a first quadrature chirp signal and a first conjugate signal based on the first received signal. The UE can oversample the first quadrature chirp signal with respect to a sampling factor, and oversample the first conjugate signal with respect to a sampling factor, to obtain the first quadrature chirp signal and the first conjugate signal.

[0309] In S4730, the UE can obtain a first correlation based on the first orthogonal chirped signal and the second orthogonal chirped signal associated with the first orthogonal chirped signal.

[0310] In S4740, the UE can obtain a second correlation based on the first conjugate signal and the second conjugate signal, which is the conjugate signal of the second orthogonal chirped signal.

[0311] In S4750, the UE can obtain the carrier frequency offset (CFO) value for the first received signal. The UE can obtain the peak value of the first correlation and the peak value of the second correlation, and obtain the CFO value based on the peak value of the first correlation and the peak value of the second correlation.

[0312] According to the implementation method, the UE can obtain the CFO value for the first received signal based on the peak value of the first correlation, the peak value of the second correlation, and the sampling factor.

[0313] The UE can perform synchronization with the base station based on the peak of the first correlation and the peak of the second correlation.

[0314] Figure 48 This is a flowchart illustrating a method for transmitting and receiving signals according to another embodiment of the present disclosure.

[0315] In S4810, the base station can generate a first received signal. The base station can generate a first received signal based on a first orthogonal chirped signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirped signal.

[0316] According to the implementation method, the base station can perform upconversion for the first orthogonal chirped signal, perform upconversion for the first conjugate signal, and generate the first received signal by summing the upconverted first orthogonal chirped signal and the upconverted first conjugate signal.

[0317] In S4820, the base station can send the first received signal to the user equipment (UE).

[0318] In S4830, the base station can perform synchronization with the UE based on a first received signal. Synchronization can be performed based on the peak of a first correlation and the peak of a second correlation, wherein the first correlation is obtained based on a first orthogonal chirp signal and a second orthogonal chirp signal associated with the first orthogonal chirp signal, and the second correlation is obtained based on the first orthogonal chirp signal, a first conjugate signal, and a second conjugate signal associated with the first conjugate signal.

[0319] The embodiments described above are combinations of the elements and features of this disclosure. Unless otherwise stated, elements or features are to be considered selective. Individual elements or features may be practiced without combination with other elements or features. Furthermore, embodiments of this disclosure may be constructed by combining some elements and / or features. The order of operations described in the embodiments of this disclosure may be rearranged. Some constructions of any embodiment may be included in another embodiment and may be replaced by corresponding constructions of another embodiment. It will be apparent to those skilled in the art that claims not expressly referenced in each other in the appended claims may be presented as combinations of embodiments of this disclosure, or may be included as new claims by subsequent amendments after filing the application.

[0320] The embodiments of this disclosure can be implemented by various means, such as hardware, firmware, software, or combinations thereof. In a hardware configuration, the methods according to the embodiments of this disclosure can be implemented by 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), processors, controllers, microcontrollers, microprocessors, etc.

[0321] In firmware or software configurations, embodiments of this disclosure can be implemented as modules, processes, functions, etc. For example, software code can be stored in memory units and executed by a processor. The memory can be located inside or outside the processor and can send data to and receive data from the processor via various known means.

[0322] Those skilled in the art will understand that this disclosure may be practiced in other specific ways besides those described herein without departing from the spirit and essential characteristics of this disclosure. Therefore, the above embodiments are to be construed as illustrative in all respects and not restrictive. The scope of this disclosure should be determined by the appended claims and their legal equivalents (rather than the foregoing description), and all changes falling within the meaning and scope of the appended claims are intended to be covered therewith.

Claims

1. A method performed by a user equipment (UE) in a wireless communication system, the method comprising the following steps: A first received signal is received from a base station (BS) based on a first orthogonal chirp signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirp signal. The first orthogonal chirped signal and the first conjugate signal are obtained based on the first received signal; A first correlation is obtained based on the first orthogonal chirped signal and the second orthogonal chirped signal related to the first orthogonal chirped signal; A second correlation is obtained based on the first conjugate signal and the second conjugate signal, which is the conjugate signal of the second orthogonal chirp signal; Obtain the peak value of the first correlation; Obtain the peak value of the second correlation; as well as The carrier frequency offset (CFO) value for the first received signal is obtained based on the peak values ​​of the first correlation and the second correlation.

2. The method according to claim 1, wherein, Obtaining the first orthogonal chirped signal and the first conjugate signal based on the first received signal includes: Oversampling is performed on the first orthogonal chirped signal with respect to the sampling factor; and Oversampling is performed on the first conjugate signal with respect to the sampling factor.

3. The method according to claim 2, wherein, Obtaining the CFO value for the first received signal based on the peak value of the first correlation and the peak value of the second correlation includes: The CFO value for the first received signal is obtained based on the peak value of the first correlation, the peak value of the second correlation, and the sampling factor.

4. The method according to claim 1, further comprising the following steps: Synchronization with the base station is performed based on the peak values ​​of the first correlation and the second correlation.

5. A user equipment (UE) operating in a communication system, the UE comprising: One or more transceivers; One or more processors, wherein the one or more processors control the one or more transceivers; as well as The memory includes one or more instructions that are executed by the one or more processors. Wherein, the one or more instructions include: A first received signal is received from a base station (BS) based on a first orthogonal chirp signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirp signal. The first orthogonal chirped signal and the first conjugate signal are obtained based on the first received signal; A first correlation is obtained based on the first orthogonal chirped signal and the second orthogonal chirped signal related to the first orthogonal chirped signal; A second correlation is obtained based on the first conjugate signal and the second conjugate signal, which is the conjugate signal of the second orthogonal chirp signal; Obtain the peak value of the first correlation; Obtain the peak value of the second correlation; and The carrier frequency offset (CFO) value for the first received signal is obtained based on the peak values ​​of the first correlation and the second correlation.

6. The UE according to claim 5, wherein, Obtaining the first orthogonal chirped signal and the first conjugate signal based on the first received signal includes: Oversampling is performed on the first orthogonal chirped signal with respect to the sampling factor; and Oversampling is performed on the first conjugate signal with respect to the sampling factor.

7. The UE according to claim 6, wherein, Obtaining the CFO value for the first received signal based on the peak value of the first correlation and the peak value of the second correlation includes: The CFO value for the first received signal is obtained based on the peak value of the first correlation, the peak value of the second correlation, and the sampling factor.

8. The UE according to claim 5, wherein, The one or more instructions further include: performing synchronization with the base station based on the peak value of the first correlation and the peak value of the second correlation.

9. A method performed by a base station (BS) in a wireless communication system, the method comprising the following steps: A first received signal is generated based on a first orthogonal chirped signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirped signal; Send the first received signal to the user equipment (UE); as well as Synchronization with the UE is performed based on the first received signal. The synchronization is performed based on the peak values ​​of a first correlation and a second correlation. The first correlation is obtained based on the first orthogonal chirp signal and a second orthogonal chirp signal associated with the first orthogonal chirp signal. The second correlation is obtained based on the first orthogonal chirp signal, the first conjugate signal, and a second conjugate signal associated with the first conjugate signal.

10. The method according to claim 9, in, The first received signal, which generates a signal based on the first orthogonal chirped signal and the first conjugate signal that is a conjugate signal of the first orthogonal chirped signal, includes: Perform upconversion on the first orthogonal chirped signal; Perform upconversion on the first conjugate signal; and The first received signal is generated by summing the upconverted first orthogonal chirped signal and the upconverted first conjugate signal.

11. A base station (BS) operating in a communication system, the BS comprising: One or more transceivers; One or more processors, wherein the one or more processors control the one or more transceivers; as well as The memory includes one or more instructions that are executed by the one or more processors. Wherein, the one or more instructions include: A first received signal is generated based on a first orthogonal chirped signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirped signal; Send the first received signal to the user equipment (UE); and Synchronization with the user equipment is performed based on the first received signal. The synchronization is performed based on the peak values ​​of a first correlation and a second correlation. The first correlation is obtained based on the first orthogonal chirp signal and a second orthogonal chirp signal associated with the first orthogonal chirp signal. The second correlation is obtained based on the first orthogonal chirp signal, the first conjugate signal, and a second conjugate signal associated with the first conjugate signal.

12. The base station according to claim 11, wherein, The first received signal, which generates a signal based on the first orthogonal chirped signal and the first conjugate signal that is a conjugate signal of the first orthogonal chirped signal, includes: Perform upconversion on the first orthogonal chirped signal; Perform upconversion on the first conjugate signal; and The first received signal is generated by summing the upconverted first orthogonal chirped signal and the upconverted first conjugate signal.

13. An apparatus, said apparatus comprising: One or more memory units; as well as One or more processors, said one or more processors being functionally coupled to said one or more memories. The one or more processors are configured to cause the device to: A first received signal is received from a base station (BS) based on a first orthogonal chirp signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirp signal. The first orthogonal chirped signal and the first conjugate signal are obtained based on the first received signal; A first correlation is obtained based on the first orthogonal chirped signal and the second orthogonal chirped signal related to the first orthogonal chirped signal; A second correlation is obtained based on the first conjugate signal and the second conjugate signal, which is the conjugate signal of the second orthogonal chirp signal; Obtain the peak value of the first correlation; Obtain the peak value of the second correlation; and The carrier frequency offset (CFO) value for the first received signal is obtained based on the peak values ​​of the first correlation and the second correlation.

14. A non-transitory computer-readable medium storing one or more instructions and configured to: A first received signal is received from a base station (BS) based on a first orthogonal chirp signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirp signal. The first orthogonal chirped signal and the first conjugate signal are obtained based on the first received signal; A first correlation is obtained based on the first orthogonal chirped signal and the second orthogonal chirped signal related to the first orthogonal chirped signal; A second correlation is obtained based on the first conjugate signal and the second conjugate signal, which is the conjugate signal of the second orthogonal chirp signal; Obtain the peak value of the first correlation; Obtain the peak value of the second correlation; as well as The carrier frequency offset (CFO) value for the first received signal is obtained based on the peak values ​​of the first correlation and the second correlation.

15. An apparatus, the apparatus comprising: One or more memory units; as well as One or more processors, said one or more processors being functionally coupled to said one or more memories. The one or more processors are configured to cause the device to: A first received signal is generated based on a first orthogonal chirped signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirped signal; Send the first received signal to the user equipment (UE); Synchronization with the UE is performed based on the first received signal. The synchronization is performed based on the peak values ​​of a first correlation and a second correlation. The first correlation is obtained based on the first orthogonal chirp signal and a second orthogonal chirp signal associated with the first orthogonal chirp signal. The second correlation is obtained based on the first orthogonal chirp signal, the first conjugate signal, and a second conjugate signal associated with the first conjugate signal.

16. A non-transitory computer-readable medium storing one or more instructions and configured to: A first received signal is generated based on a first orthogonal chirped signal and a first conjugate signal that is a conjugate signal of the first orthogonal chirped signal; Send the first received signal to the user equipment (UE); and Synchronization with the UE is performed based on the first received signal. in, The synchronization is performed based on the peak of a first correlation and the peak of a second correlation. The first correlation is obtained based on the first orthogonal chirp signal and a second orthogonal chirp signal associated with the first orthogonal chirp signal. The second correlation is obtained based on the first orthogonal chirp signal, the first conjugate signal, and a second conjugate signal associated with the first conjugate signal.

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