Signaling method and apparatus

Abstract

This disclosure relates to 5G or 6G communication systems for supporting higher data rates than 4G communication systems such as LTE. Embodiments of this disclosure disclose signal transmission methods and apparatus. A method performed by a user equipment (UE) in a wireless communication system includes: acquiring third information associated with a first matrix, wherein the dimension of the first matrix is ​​associated with the length of the first information, and the first information is associated with information to be reported; determining a first matrix based on the third information; processing the first information based on the first matrix to obtain second information; and transmitting a signal associated with the second information. A method performed by a base station in a wireless communication system includes: receiving the second information transmitted by the UE; processing the second information based on a first neural network to obtain fourth information, wherein the fourth information is associated with the first information, and the first information is associated with information to be reported by the UE. This disclosure can improve system performance.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of wireless communication technology, and more specifically to signal transmission methods and devices. Background Technology

[0002] Given the successive generations of wireless communication development, these technologies have primarily been developed for human-oriented services such as voice calls, multimedia services, and data services. With the commercialization of 5th-generation (5G) communication systems, the number of connected devices is expected to grow exponentially. These will increasingly connect to communication networks. Examples of the Internet of Things (IoT) can include vehicles, robots, drones, home appliances, displays, smart sensors connected to various infrastructures, construction machinery, and factory equipment. Mobile devices are expected to evolve in various forms, such as augmented reality glasses, virtual reality headsets, and holographic devices. Efforts are underway to develop improved 6G communication systems to provide a wide range of services by connecting hundreds of billions of devices and things in the 6th-generation (6G) era.

[0003] The 6G communication system, expected to be commercially available around 2030, will offer significant improvements in all aspects compared to existing 5G systems. Its peak speed will reach at least 50 Gbit / s, user experience speed will reach at least 300 Mbit / s, air interface latency will be less than 1 ms, and air interface reliability will reach [missing information]. In addition to the basic communication indicators mentioned above, 6G communication systems will also have sensing capabilities, AI-related capabilities, and better security, interoperability, and sustainability.

[0004] To achieve the aforementioned performance indicators for 6G communication systems, more advanced air interface and network technologies are needed. Currently, the evolution of extreme multiple input multiple output (extreme MIMO) is being considered, including the use of very large-scale antenna arrays, the development and evolution of distributed antenna systems, and the design of MIMO air interface algorithms assisted by artificial intelligence (AI). This technology can achieve higher spectral efficiency, greater coverage, and more precise positioning and sensing capabilities. Furthermore, technologies that contribute to improving high-frequency coverage, such as metamaterial-based lenses and antennas, novel antenna architectures, and reconfigurable intelligence surfaces (RIS), also require further evolution and development.

[0005] To meet the new functionalities added to 6G communication systems, it is necessary to develop new technologies in areas such as network energy saving, air interface security, and network security, while also studying the feasibility of converged technologies such as integrated communication and sensing.

[0006] In addition, to improve spectrum efficiency and overall network performance, the following technologies have been developed for 6G communication systems: full-duplex technology to enable uplink and downlink transmissions to use the same frequency resources simultaneously; network technologies that utilize satellites, high-altitude platform stations (HAPS), etc., in a comprehensive manner; improved network architecture to support mobile base stations, etc., and to enable network operation optimization and automation; dynamic spectrum sharing technology based on spectrum usage prediction and conflict avoidance; the use of artificial intelligence (AI) in wireless communication to improve overall network operation by utilizing AI from the design phase of 6G development and internalizing end-to-end AI support functions; and next-generation distributed computing technologies that overcome the computing power limitations of user equipment (UE) by leveraging ultra-high-performance communication and computing resources available on the network, such as mobile edge computing (MEC), cloud, etc. Furthermore, efforts are continuing to enhance connectivity between devices, optimize networks, promote the software-defined networking of network entities, and increase the openness of wireless communications by designing new protocols to be used in 6G communication systems, developing mechanisms for achieving hardware-based secure environments and secure data use, and developing technologies for maintaining privacy.

[0007] The research and development of 6G communication systems, encompassing both person-to-machine (P2M) and machine-to-machine (M2M) hyper-connectivity, is expected to deliver the next wave of hyper-connected experiences. Specifically, services such as truly immersive extended reality (XR), high-fidelity mobile holograms, and digital replicas are anticipated to be provided through 6G communication systems. Furthermore, services such as remote surgery for enhanced security and reliability, industrial automation, and emergency response will be available via 6G communication systems, enabling the technology to be applied across a wide range of sectors including industry, healthcare, automotive, and home appliances. Summary of the Invention

[0008] The embodiments of this disclosure present a signal processing method.

[0009] In a first aspect, embodiments of this disclosure provide a method executed by a user equipment (UE) in a wireless communication system, comprising: acquiring third information associated with a first matrix, wherein the dimension of the first matrix is ​​associated with the length of the first information, and the first information is associated with information to be reported; determining a first matrix based on the third information; processing the first information based on the first matrix to obtain second information, and sending a signal associated with the second information.

[0010] In some embodiments, obtaining third information related to the first matrix includes: receiving configuration information from a base station, wherein the configuration information includes third information related to the first matrix.

[0011] In some embodiments, the third information includes positional and numerical information of at least one element in the first matrix, or index information of the first matrix, or parameter information used to generate the first matrix.

[0012] In some embodiments, determining a first matrix based on third information includes: replacing or adding elements in a second matrix based on the position information and numerical information of at least one element in the first matrix contained in the third information to obtain a first matrix, wherein the second matrix is ​​the first matrix previously used by the UE or a predefined matrix.

[0013] In some embodiments, the parameter information for generating the first matrix includes at least one of the following: sequence information for generating the first matrix, reporting precision parameters, configuration information for reporting resources, and row and / or column indexes for generating the first matrix.

[0014] In some embodiments, determining a first matrix based on third information includes at least one of the following: obtaining the first matrix using a random number generation algorithm based on sequence information used to generate the first matrix; obtaining the first matrix using a second neural network based on reporting precision parameters and / or configuration information of resources used for reporting, wherein the second neural network is predefined or configured by the base station; and extracting elements from a third matrix to form the first matrix based on row index and / or column index, wherein the third matrix is ​​predefined or configured by the base station.

[0015] In some embodiments, the first matrix is ​​the matrix in the candidate matrix set whose number of columns is closest to the number of elements in the first information, or whose number of rows is closest to the total number of physical resources contained in the transmission resources associated with the second information.

[0016] In some embodiments, determining the first matrix based on third information includes: obtaining at least two matrices based on the third information and using the result of multiplying the obtained matrices as the first matrix; or obtaining at least two matrices based on the third information and using the result of concatenating the obtained matrices according to rows and / or columns as the first matrix.

[0017] In some embodiments, the method further includes: performing inverse quantization and / or scaling processing based on the numerical information of at least one element in the first matrix contained in the third information, and obtaining the first matrix based on the numerical values ​​of the processed elements.

[0018] In some embodiments, the method further includes: determining parameters of the transmission resources associated with the second information based on the number of rows in the first matrix.

[0019] In some embodiments, determining the parameters of the transmission resources associated with the second information based on the number of rows in the first matrix includes: obtaining a benchmark of the total number of physical resources based on the number of rows in the first matrix, wherein if the first matrix is ​​a real matrix, the benchmark is half the number of rows, and if the first matrix is ​​a complex matrix, the benchmark is the number of rows; and selecting, based on the benchmark, the transmission resource whose total number of physical resources contained in the candidate transmission resource set is closest to the benchmark.

[0020] In some embodiments, selecting the transmission resource whose total number of physical resources contained in the transmission resource is closest to the benchmark from the candidate transmission resource set includes: selecting the transmission resource whose total number of physical resources contained in the transmission resource is closest to the benchmark and is greater than or equal to the benchmark from the candidate transmission resource set; or selecting the transmission resource whose total number of physical resources contained in the transmission resource is closest to the benchmark and is less than or equal to the benchmark from the candidate transmission resource set.

[0021] In some embodiments, the first information includes any of the following: channel state information; statistics calculated based on the channel state information; a precoding matrix obtained based on the channel state information; HARQ-ARQ feedback; and a scheduling request.

[0022] In some embodiments, processing first information based on a first matrix to obtain second information includes: performing a multiplication operation between the first information and the first matrix to obtain the second information.

[0023] In some embodiments, the method further includes updating the second information by at least one of the following methods: performing an addition operation on the second information and a first vector associated with the first matrix to update the second information, wherein the first vector is predefined or configured by the base station; or processing the second information through a first function associated with the first matrix to update the second information, wherein the first function is predefined or configured by the base station; or performing quantized and / or normalized signal processing on the second information to update the second information.

[0024] In some embodiments, the method further includes: if the third information includes information related to at least one fourth matrix; for each fourth matrix, the method further includes: performing a multiplication operation on the second information and the fourth matrix to update the second information; and / or performing an addition operation on the updated second information and a second vector associated with the fourth matrix to update the second information, wherein the second vector is predefined or configured by the base station; and / or processing the updated second information through a second function associated with the fourth matrix to update the second information, wherein the second function is predefined or configured by the base station.

[0025] Secondly, embodiments of this disclosure provide a method executed by a base station in a wireless communication system, comprising: receiving second information sent by a UE; processing the second information based on a first neural network to obtain fourth information, wherein the fourth information is associated with the first information, and the first information is associated with information that the UE needs to report.

[0026] Thirdly, embodiments of this disclosure provide a UE, characterized in that it includes: a transceiver; and a processor coupled to the transceiver and configured to perform the method of any of the first aspects.

[0027] Fourthly, embodiments of this disclosure provide a base station, characterized in that it includes: a transceiver; and a processor coupled to the transceiver and configured to perform the method of any of the second aspects.

[0028] Fifthly, embodiments of this disclosure provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements any of the methods described above.

[0029] The signal processing method provided in the embodiments of this disclosure, under the same signal-to-interference-plus-noise ratio (SINR) conditions, can achieve information reporting with the same accuracy using fewer communication resources compared to traditional methods, and can achieve higher accuracy information reporting using the same communication resources. The information reporting process places a lower computational burden on the sending end and less signaling overhead for transmitting the required configuration information, thereby further improving system performance. Attached Figure Description

[0030] Figure 1 An example wireless network according to an embodiment of the present disclosure is shown; Figure 2 An example base station according to an embodiment of the present disclosure is shown; Figure 3 An example user equipment according to an embodiment of the present disclosure is shown; Figure 4-1A flowchart is shown showing a method performed by a UE in a wireless communication system according to an embodiment of the present disclosure; Figure 4-2 A flowchart is shown showing a method performed by a base station in a wireless communication system according to an embodiment of the present disclosure; Figure 5 A schematic diagram of a first neural network and a second neural network according to an embodiment of the present disclosure is shown; Figure 6-1 and Figure 6-2 A flowchart of a method performed by a communication device according to an embodiment of the present disclosure is shown; Figure 7-1 This illustrates a UE procedure for reporting Channel State Information (CSI); Figure 7-2 The process of obtaining the projection matrix based on relevant configuration is shown; Figure 7-3 The process of generating a candidate matrix based on parameters in the configuration is shown; Figure 7-4 The process of obtaining a candidate matrix based on at least one pair of values ​​and positions in the configuration is shown; Figure 7-5 The process based on configuring the projection matrix is ​​shown; Figure 8 A schematic diagram of the device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0031] Before proceeding with the following detailed description, it may be advantageous to define certain words and phrases used throughout the patent literature. The term “connection” and its derivatives refer to any direct or indirect communication between two or more elements, regardless of whether those elements are physically in contact with each other. The terms “transmit,” “receive,” and “transmit,” and their derivatives encompass both direct and indirect communication. The terms “comprise” and “include,” and their derivatives mean inclusion without limitation. The term “or” is concurrent, meaning both and / or. The phrase “associated with,” and its derivatives mean including, being included in, interconnected with, containing, being contained within, connected to or connected with, coupled to or coupled with, able to communicate with, cooperate with, intertwine, juxtapose, proximate, bound to or bound with, having, possessing attributes, having a relationship with, or having a relationship with, etc. The term “controller” means any device, system, or part thereof that controls at least one operation. Such a controller may be implemented in hardware or in a combination of hardware and software and / or firmware. The functionality associated with any particular controller, whether local or remote, can be centralized or distributed. The phrase "at least one" when used to list items means that different combinations of one or more of the listed items can be used, and it is possible that only one item in the list is needed. For example, "at least one of A, B, and C" includes any of the following combinations: A, B, C; A and B; A and C; B and C; and only A, only B, and only C. Similarly, the term "set" means one or more. Therefore, a set of items can be a single item or a set of two or more items.

[0032] Furthermore, the various functions described below can be implemented or supported by one or more computer programs, each function being formed by computer-readable program code and embodied in a computer-readable medium. The terms "application" and "program" refer to one or more computer programs, software components, instruction sets, procedures, functions, objects, classes, instances, associated data, or portions thereof suitable for implementation in appropriate computer-readable program code. The phrase "computer-readable program code" includes any type of computer code, including source code, object code, and executable code. The phrase "computer-readable medium" includes any type of medium accessible by a computer, such as read-only memory (ROM), random access memory (RAM), hard disk drive, compact disc (CD), digital video disc (DVD), or any other type of storage. "Non-transitory" computer-readable media excludes wired, wireless, optical, or other communication links that transmit transient electrical or other signals. Non-transitory computer-readable media include media in which data can be permanently stored and media such as rewritable optical discs or erasable memory devices in which data can be stored and later rewritten.

[0033] Definitions for certain other words and phrases are provided throughout this patent document. Those skilled in the art will understand that, in many, if not the most, instances, such definitions apply to both prior and future use of the words and phrases defined in this way.

[0034] The figures and various embodiments included herein, used to illustrate the principles of this disclosure, are merely illustrative and should not be construed in any way as limiting the scope of this disclosure. Furthermore, those skilled in the art will understand that the principles of this disclosure can be implemented in any suitably arranged wireless communication system.

[0035] The following Figures 1 to 3 Various embodiments of this disclosure implemented in wireless communication systems are described. Figures 1 to 3 The description does not imply any physical or architectural limitations on the ways in which different embodiments can be implemented. Different embodiments of this disclosure can be implemented in any suitably arranged communication system.

[0036] Figure 1 An example wireless network according to an embodiment of this disclosure is shown. Figure 1 The embodiments of the wireless network shown are for illustrative purposes only. Other embodiments of the wireless network 100 may be used without departing from the scope of this disclosure.

[0037] like Figure 1As shown, the wireless network includes a base station (nextgeneration node B, gNB or gNodeB) 101, gNB 102, and gNB 103. gNB 101 communicates with gNB 102 and gNB 103. gNB 101 also communicates with at least one network 130 such as the Internet, Internet Protocol (IP) networks, or other data networks.

[0038] gNB102 provides wireless broadband access to network 130 to multiple first user equipments (UEs) within coverage area 120 of gNB102. The multiple first UEs include UE111, which may be located in a small business (SB); UE112, which may be located in an enterprise (E); UE113, which may be located in a WiFi hotspot (HS); UE114, which may be located in a first residence (R1); UE115, which may be located in a second residence (R2); and UE116, which may be a mobile device (M) such as a cellular phone, wireless laptop, or wireless personal digital assistant (PDA). gNB103 provides wireless broadband access to network 130 to multiple second UEs within coverage area 125 of gNB103. The multiple second UEs include UE115 and UE116, and subscriber stations (SS, such as UEs) 117, 118, and 119. In some embodiments, one or more of gNB101-103 may communicate with each other and UE111-116 using existing wireless communication technologies, and one or more of UE111-119 may communicate directly with each other (e.g., UE117-119) using other existing or proposed wireless communication technologies.

[0039] Depending on the network type, the term "base station" or "BS" can refer to any component (or set of components) configured to provide wireless access to a network, such as a transmitter point (TP), a transmitter-receive point (TRP), an enhanced (or "evolved") base station (eNodeB or eNB), a 5G base station (gNB), a macro cell, a femtocell, a wireless fidelity (WiFi) access point (AP), or other wirelessly capable devices. A base station can provide wireless access according to one or more wireless communication protocols, such as 3GPP 5G new radio (NR), Long Term Evolution (LTE), LTE-Advanced (LTE-A), High Speed ​​Packet Access (HSPA), Wi-Fi 802.11a / b / g / n / ac, etc. For convenience, various names for base station type devices and functions may be used interchangeably in this patent document to refer to network infrastructure components that provide wireless access to remote terminals. Furthermore, depending on the network type, the term "User Equipment" (UE) can refer to any component such as a mobile station (MS), user station (SS), remote terminal, wireless terminal, receiving point, or user device. For convenience, various names for user equipment type devices and functions may be used interchangeably in this patent document to refer to remote wireless devices that wirelessly access the BS regardless of whether the UE is a mobile device (such as a mobile phone or smartphone) or is generally considered a fixed device (such as a desktop computer or vending machine).

[0040] The dashed lines indicate the approximate extent of coverage areas 120 and 125, which are shown as roughly circular for illustrative and explanatory purposes only. It should be clearly understood that coverage areas such as 120 and 125 associated with the gNB can have other shapes, including irregular shapes, depending on the configuration of the gNB and variations in the wireless environment associated with natural and man-made obstacles.

[0041] As described in more detail below, one or more of UE111-119 includes circuitry, programming, or a combination thereof. In some embodiments, one or more of gNB101-103 includes circuitry, programming, or a combination thereof.

[0042] although Figure 1 An example of a wireless network is shown, but more can be found on... Figure 1Various modifications can be made. For example, wireless network 100 can include any number of gNBs and any number of UEs in any suitable arrangement. Furthermore, gNB 101 can communicate directly with any number of UEs and provide those UEs with wireless broadband access to network 130. Similarly, each gNB 102-103 can communicate directly with network 130 and provide UEs with direct wireless broadband access to network 130. Additionally, gNBs 101, 102, and / or 103 can provide access to other or additional external networks, such as external telephone networks or other types of data networks.

[0043] Figure 2 An example base station according to an embodiment of the present disclosure is shown. Figure 2 The embodiment of gNB102 shown is for illustrative purposes only, and Figure 1 The gNB101 and 103 can have the same or similar configurations. However, gNBs come in a variety of configurations, and Figure 2 This disclosure is not intended to limit the scope to any particular implementation of gNB.

[0044] like Figure 2 As shown, gNB102 includes multiple antennas 200a-200n, multiple radio frequency (RF) transceivers 201a-201n, transmit (TX) processing circuitry 203, and receive (RX) processing circuitry 204. gNB102 also includes a controller / processor 205, a memory 206, and a backhaul or network interface (IF) 207.

[0045] RF transceivers 201a-201n receive incoming RF signals from antennas 200a-200n, such as signals transmitted by the UE in network 100. RF transceivers 201a-201n down-convert the incoming RF signals to generate intermediate frequency (IF) or baseband signals. The IF or baseband signal is sent to RX processing circuitry 204, which generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband or IF signal. RX processing circuitry 204 sends the processed baseband signal to controller / processor 205 for further processing.

[0046] The TX processing circuit 203 receives analog or digital data (such as voice data, web data, email, or interactive video game data) from the controller / processor 205. The TX processing circuit 203 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. RF transceivers 201a-201n receive the processed baseband or IF signal from the TX processing circuit 203 and up-convert the baseband or IF signal into an RF signal transmitted via antennas 201a-201n.

[0047] The controller / processor 205 may include one or more processors or other processing devices that control the overall operation of the gNB 102. For example, the controller / processor 205 may control the reception of forward channel signals and the transmission of reverse channel signals by the RF transceivers 201a-201n, the RX processing circuit 204, and the TX processing circuit 203, according to known principles. The controller / processor 205 may also support additional functions, such as more advanced wireless communication functions.

[0048] For example, the controller / processor 205 can support beamforming or directional routing operations, where signals emitted from multiple antennas 200a-200n are weighted differently to effectively redirect the emitted signals in the desired direction. Any of a variety of other functions can be supported in the gNB102 via the controller / processor 205.

[0049] The controller / processor 205 is also capable of executing programs and other processes located in the memory 206, such as the operating system (OS). The controller / processor 205 can move data into or out of the memory 206 as needed by the executing process.

[0050] The controller / processor 205 is also connected to a backhaul or network interface 207. The backhaul or network interface 207 allows the gNB 102 to communicate with other devices or systems via a backhaul connection or over a network. Interface 207 can support communication via any suitable wired or wireless connection(s). For example, when the gNB 102 is implemented as part of a cellular communication system (such as a cellular communication system supporting 5G, LTE, or LTE-A), interface 207 can allow the gNB 102 to communicate with other gNBs via a wired or wireless backhaul connection. When the gNB 102 is implemented as an access point, interface 207 can allow the gNB 102 to communicate via a wired or wireless local area network or via a wired or wireless connection to a larger network (such as the Internet). Interface 207 includes any suitable structure that supports communication via wired or wireless connections such as Ethernet or RF transceivers.

[0051] Memory 206 is connected to controller / processor 205. A portion of memory 206 may include random access memory (RAM), and another portion of memory 206 may include flash memory or other read-only memory (ROM).

[0052] although Figure 2 An example of gNB102 is shown, but it is possible to see more. Figure 2 Various changes can be made. For example, gNB102 can include any number of Figure 2 Each component is shown in the diagram. As a specific example, an access point may include multiple interfaces 207, and the controller / processor 205 may support routing functionality to route data between different network addresses. As another specific example, although shown as a single instance of TX processing circuitry 203 and a single instance of RX processing circuitry 204, gNB102 may include multiple instances of each (such as one per RF transceiver). For example, Figure 2 The various components can be combined, further subdivided, or omitted, and additional components can be added as needed.

[0053] Figure 3 An example user equipment according to an embodiment of the present disclosure is shown. Figure 3 The embodiment of UE116 shown is for illustrative purposes only, and Figure 1 UEs 111-115 and 117-119 can have the same or similar configurations. However, UEs appear in multiple configurations, and Figure 3 This disclosure is not intended to limit the scope to any particular implementation of the UE.

[0054] like Figure 3 As shown, UE116 includes an antenna 301, a radio frequency (RF) transceiver 302, a TX processing circuit 303, a microphone 304, and a receive (RX) processing circuit 305. UE116 also includes a speaker 306, a controller or processor 307, an input / output (I / O) interface (IF) 308, an input device 309, a touchscreen display 310, and a memory 311. The memory 311 includes an OS 312 and one or more applications 313.

[0055] RF transceiver 302 receives incoming RF signals transmitted by gNB of network 100 from antenna 301. RF transceiver 302 down-converts the incoming RF signals to generate an IF or baseband signal. The IF or baseband signal is sent to RX processing circuitry 305, which generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband or IF signal. RX processing circuitry 305 sends the processed baseband signal to speaker 306 (e.g., for voice data) or processor 307 for further processing (e.g., for web browsing data).

[0056] TX processing circuit 303 receives analog or digital voice data from microphone 304 or other outgoing baseband data (such as web data, email, or interactive video game data) from processor 307. TX processing circuit 303 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. RF transceiver 302 receives the processed baseband or IF signal from TX processing circuit 303 and up-converts the baseband or IF signal into an RF signal transmitted via antenna 301.

[0057] Processor 307 may include one or more processors or other processing devices and executes OS 312 stored in memory 311 to control the overall operation of UE 116. For example, processor 307 may control the reception of forward channel signals and the transmission of reverse channel signals by RF transceiver 302, RX processing circuit 305 and TX processing circuit 303 according to known principles. In some embodiments, processor 307 includes at least one microprocessor or microcontroller.

[0058] Processor 307 is also capable of executing other processes and programs located in memory 311, such as processes for CSI (Channel State Information) reporting on the uplink channel. Processor 307 can move data into or out of memory 311 as needed for executing processes. In some embodiments, processor 307 is configured to execute application 313 based on OS 312 or in response to signals received from gNB or operator. Processor 307 is also coupled to I / O interface 308, which provides UE 116 with the ability to connect to other devices such as laptops and laptops. I / O interface 308 is the communication path between these accessories and processor 307.

[0059] The processor 307 is also connected to the touchscreen display 310. Users of the UE116 can use the touchscreen display 310 to input data into the UE116. The touchscreen display 310 can be a liquid crystal display, a light-emitting diode display, or other display capable of rendering text and / or at least limited graphics such as those from a website.

[0060] Memory 311 is connected to processor 307. A portion of memory 311 may include RAM, and another portion of memory 311 may include flash memory or other ROM.

[0061] although Figure 3 An example of UE116 is shown, but it is possible to modify it. Figure 3 Make various changes. For example, Figure 3 The various components can be combined, further subdivided, or omitted, and additional components can be added as needed. As a specific example, processor 307 can be divided into multiple processors, such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs). Moreover, although... Figure 3 The UE116 is shown configured as a mobile phone or smartphone, but the UE can be configured to operate as other types of mobile or fixed devices.

[0062] In wireless communication systems, the transmission of information (e.g., the transmission of information from the Physical Downlink Control Channel (PDCCH), Physical Downlink Shared Channel (PDSCH), Physical Uplink Shared Channel (PUSCH), and Physical Uplink Control Channel (PUCCH)) occurs on at least one resource element (RE) of physical resources (e.g., multiple time points, multiple frequency points, multiple antennas, and various combinations of time, frequency, and antennas). Physical resources are resource entities in a communication system that can be used for signal transmission; for example, they can be time-domain physical resources, frequency-domain physical resources, and antenna-domain physical resources. The state of the RE occupied by the signal has a comprehensive impact on the amplitude and phase of the signal transmitted along the entire wireless communication link. Without loss of generality, this disclosure refers to the comprehensive impact as channel state information on the corresponding RE.

[0063] To meet the requirements of wireless data transmission, it is necessary to obtain the channel state information of the RE (Relay Entity) occupied by the signal at the receiving end and / or transmitting end. In existing systems, the method to obtain the channel state information of a certain RE is to transmit a reference signal (RS) on it and estimate the channel state information based on it. For example, the reference signal is a transmitted signal consisting of a generated sequence, the content of which, along with the transmission time (time domain resource) and frequency (frequency domain resource), are shared by both the transmitting and receiving ends. The reference signal can also be called a pilot signal, training signal, etc.

[0064] Without loss of generality, in existing systems, information transmission occurs on more than one RE. The method for obtaining the channel state information on all the REs used is to select a subset of these REs according to a certain pattern to transmit reference signals (RS), and then estimate the channel state information on all REs based on this. Specifically, the transmitter transmits the reference signal determined by the configuration information on the REs specified by the configuration information, based on the reference signal configuration information known to both the transmitter and receiver. The receiver can then obtain the channel state information from the corresponding REs based on this configuration information and further estimate the channel state information on all REs.

[0065] In a wireless communication system, the terminal converts uplink control information (e.g., channel state information obtained by the terminal) into a corresponding information sequence, which is then transmitted to the base station via a PUCCH or PUSCH channel. Specifically, firstly, a bit generation method (also known as source coding, quantization, etc.) is used to convert the uplink control information into a bit-level information sequence. Then, the obtained bit-level information sequence undergoes further processing, including channel coding (also known as error correction coding) and modulation, to obtain a symbol-level information sequence representing the uplink control information (or complex information sequence, complex-valued modulated symbols, complex-valued symbols, symbols, etc., these descriptions are used interchangeably in this disclosure). Finally, the symbol-level information sequence is mapped onto the corresponding RE in the PUCCH or PUSCH channel for transmission, thereby enabling the information payload to be transmitted on the carrier signal.

[0066] For any uplink control information, wireless communication systems typically need to perform the three steps mentioned above to report it, which is highly complex. Therefore, signal transmission enhancement is necessary.

[0067] On the other hand, the inventors recognized a significant difference in computing power between base stations and terminals in existing systems. As the transmitting end in the reporting process, the terminal has far less computing power available for signal processing than the base station, which acts as the receiving end. Therefore, in some embodiments of this disclosure, the signal processing structure of the transmitting end is further simplified, enabling the transmission of corresponding uplink control information on the carrier signal through one or two simple steps, thereby reducing the computing power burden on the transmitting end.

[0068] The method according to at least one embodiment of this disclosure can improve the efficiency and reliability of communication. The improved communication efficiency is manifested in that, under the same signal-to-interference-plus-noise ratio (SINR) conditions, the method according to at least one embodiment of this disclosure can achieve the same accuracy in information reporting, such as channel state information reporting, using fewer communication resources compared to conventional methods. The improved communication reliability is manifested in that, under the same SINR conditions, the method according to at least one embodiment of this disclosure can achieve higher accuracy in information reporting, such as channel state information reporting, using the same communication resources compared to conventional methods. Furthermore, in some aspects, the method according to at least one embodiment of this disclosure places a lower computational burden on the sending end during the information reporting process and reduces the signaling overhead for transmitting the required configuration information, thereby further improving system performance.

[0069] It should be understood that the technical problems that this disclosure can solve are not limited to those mentioned in the foregoing description, but may include any technical problems that can be solved based on the substance and principles of the technical content described herein, all of which are within the scope of this disclosure. Furthermore, this disclosure is not required to solve all the technical problems expressly listed.

[0070] Embodiments of this disclosure will now be described in more detail with reference to examples.

[0071] Figure 4-1 and Figure 4-2 A flowchart of a method performed by a communication device according to an embodiment of this disclosure is shown. For ease of description, the following description will primarily focus on a scenario of communication devices reporting Channel State Information (CSI). It is understood that this description is merely exemplary and does not limit the types of uplink control information to which the method is applicable to to CSI, but can cover situations where various other types of uplink control information are reported.

[0072] According to embodiments, the communication device can be any device capable of transmitting and receiving signals and performing signal detection. For example, the communication device can be a base station or a terminal, but is not limited thereto. For example, the communication device can also be a user equipment with a large-scale antenna port, and so on.

[0073] like Figure 4-1As shown, method 400 is executed by a communication device (e.g., a terminal) that needs to report information, and the method includes steps S410, S420, and S430.

[0074] Step S410: Obtain third information related to the first matrix. The dimension of the first matrix is ​​related to the length of the first information, and the first information is related to the information that needs to be reported. Step S420: Determine the first matrix based on the third information; Step S430: Based on the first matrix, process the first information to obtain the second information, and send the signal associated with the second information.

[0075] The above method enables better adaptation of information processing or transmission to the transmission channel, and higher accuracy of information reporting can be achieved by utilizing the same communication resources, thereby improving the performance of information transmission and / or reception.

[0076] The contents of steps S410 to S430 described above will be described in detail below.

[0077] Reference Figure 4-1 In step S410, third information related to the first matrix is ​​obtained. The dimension of the first matrix is ​​related to the length of the first information, and the first information is related to the information that needs to be reported.

[0078] In some optional implementations of this embodiment, configuration information is received from the base station, wherein the configuration information includes third information related to the first matrix.

[0079] The third information can be obtained through configuration information and / or preset information. Configuration information is the information received by the UE from the base station required to complete its own signal processing, such as configuration information obtained through the Radio Resource Control (RRC) procedure, configuration information contained in the Downlink Control Information (DCI), etc. Preset information refers to information that the device already knows without additional configuration; it can be predefined in the standard, or it can come from the device's factory settings or subsequent manual configuration. For the sake of convenience in the following description, in this disclosure, the third information can come from the above-mentioned configuration information and / or preset information.

[0080] In step S420, a first matrix is ​​determined based on the third information. In some optional implementations of this embodiment, the third information includes the position and numerical information of at least one element in the first matrix, or the index information of the first matrix, or parameter information used to generate the first matrix.

[0081] The first matrix can be obtained directly from the positional and numerical information of at least one element indicated in the third information.

[0082] The index information of the first matrix can be used to query the first matrix in the candidate matrix set. The index information, also known as the identification information, is used to indicate the corresponding matrix in the candidate matrix set, and its definition is not limited here.

[0083] A first matrix can be generated based on the parameter information. The parameter information for generating the first matrix includes at least one of the following: sequence information for generating the first matrix (e.g., initialization parameters and / or sequence identifier parameters, etc.), reporting precision parameters, configuration information for reporting resources, and row index and / or column index for generating the first matrix.

[0084] In some optional implementations of this embodiment, determining the first matrix based on the third information includes: replacing or adding elements in the second matrix based on the position information and numerical information of at least one element in the first matrix contained in the third information to obtain the first matrix, wherein the second matrix is ​​the first matrix previously used by the UE or a predefined matrix.

[0085] In some optional implementations of this embodiment, determining the first matrix based on third information includes at least one of the following: obtaining the first matrix through a random number generation algorithm based on sequence information used to generate the first matrix (e.g., the initial value of the shift register in the random number algorithm, parameter information used to distinguish the sequence generated by the algorithm); obtaining the first matrix through a second neural network based on reporting precision parameters and / or configuration information of resources used for reporting, wherein the second neural network is predefined or configured by the base station; and extracting elements from the third matrix to form the first matrix based on row index and / or column index, wherein the third matrix is ​​predefined or configured by the base station.

[0086] In some optional implementations of this embodiment, the first matrix is ​​the matrix in the candidate matrix set whose number of columns is closest to the number of elements in the first information, or whose number of rows is closest to the total number of physical resources contained in the transmission resources associated with the second information.

[0087] In some optional implementations of this embodiment, determining the first matrix based on the third information includes: obtaining at least two matrices based on the third information and using the result of multiplying the obtained matrices as the first matrix; or obtaining at least two matrices based on the third information and using the result of concatenating the obtained matrices according to rows and / or columns as the first matrix.

[0088] In some optional implementations of this embodiment, the method further includes: performing inverse quantization and / or scaling processing based on the numerical information of at least one element in the first matrix contained in the third information, and obtaining the first matrix based on the numerical values ​​of the processed elements.

[0089] In some optional implementations of this embodiment, the method further includes: determining parameters of the transmission resources associated with the second information based on the number of rows in the first matrix.

[0090] In some optional implementations of this embodiment, determining the parameters of the transmission resources associated with the second information based on the number of rows of the first matrix includes: obtaining a benchmark for the total number of physical resources based on the number of rows of the first matrix, wherein if the first matrix is ​​a real matrix, the benchmark is 1 / 2 of the number of rows, and if the first matrix is ​​a complex matrix, the benchmark is the number of rows; and selecting, based on the benchmark, the transmission resource whose total number of physical resources contained in the candidate transmission resource set is closest to the benchmark.

[0091] In some optional implementations of this embodiment, selecting the transmission resource whose total number of physical resources contained in the transmission resource is closest to the benchmark from the candidate transmission resource set includes: selecting the transmission resource whose total number of physical resources contained in the transmission resource is closest to the benchmark and is greater than or equal to the benchmark from the candidate transmission resource set; or selecting the transmission resource whose total number of physical resources contained in the transmission resource is closest to the benchmark and is less than or equal to the benchmark from the candidate transmission resource set.

[0092] In some optional implementations of this embodiment, the first information includes any of the following: channel state information; statistics calculated based on the channel state information; precoding matrix obtained based on the channel state information; HARQ-ARQ feedback; scheduling request.

[0093] In some optional implementations of this embodiment, processing the first information based on the first matrix to obtain the second information includes: performing a multiplication operation between the first information and the first matrix to obtain the second information.

[0094] In some optional implementations of this embodiment, the method further includes updating the second information by at least one of the following methods: performing an addition operation on the second information and a first vector associated with the first matrix to update the second information, wherein the first vector is predefined or configured by the base station; or processing the second information through a first function associated with the first matrix to update the second information, wherein the first function is predefined or configured by the base station; or performing quantized and / or normalized signal processing on the second information to update the second information.

[0095] In some optional implementations of this embodiment, the method further includes: if the third information includes information related to at least one fourth matrix (for example, the information related to the fourth matrix may include positional information and numerical information of at least one element); for each fourth matrix, the method further includes: performing a multiplication operation on the second information and the fourth matrix to update the second information; and / or performing an addition operation on the updated second information and a second vector associated with the fourth matrix to update the second information, wherein the second vector is predefined or configured by the base station; and / or processing the updated second information through a second function associated with the fourth matrix to update the second information, wherein the second function is predefined or configured by the base station.

[0096] In some examples, the first matrix is ​​a real or complex matrix with at least one non-zero element in each row, and its dimension is related to the length of the first information. In some embodiments, the first matrix is ​​a matrix with real elements, and its number of columns is twice the number of elements in the first information. In other embodiments, the first matrix is ​​a matrix with complex elements, and its number of columns is the same as the number of elements in the first information. Furthermore, in some embodiments, the number of rows in the first matrix is ​​only similar to but not consistent with the total number of physical resources contained in the transmission resources; specific processing methods are detailed in subsequent sections.

[0097] In some examples, the first matrix can be a special matrix with certain structures. In some embodiments, the first matrix is ​​a block diagonal matrix constructed from one or more smaller matrices, wherein the number of rows / columns of the first matrix is ​​an integer multiple of the number of rows / columns of the smaller matrices, and each smaller matrix may be the same or different. In some embodiments, the first matrix is ​​a Toeplitz matrix constructed based on a sequence, where each row is a cyclic shift of the sequence, and the cyclic shift length of each row is different. In some embodiments, the first matrix is ​​a sparse matrix with most elements being 0, guaranteeing that each row has at least one non-zero element. For example, the first matrix is ​​a sparse matrix containing only 0 and 1 elements. Each row of the first matrix randomly selects columns with elements of 1, and the remaining elements are 0, and it is guaranteed that the selected columns in each row are different.

[0098] In some examples, the third information includes the position and value information of at least one element in the first matrix. For example, the third information includes the values ​​of all elements in the first matrix. In some embodiments, the positions of the elements included in the third information need to be further calculated and determined. For example, the third information includes the values ​​of all elements in a complete small matrix and the repetition count a0 of that small matrix, and a block diagonal matrix composed of a0 such small matrices (the positions of multiple elements corresponding to each value are calculated based on the repetition count a0) is used as the first matrix. In other embodiments, the third information includes the position indices and values ​​of some elements in the first matrix, and the first matrix is ​​obtained by updating the elements of the corresponding position indices of the second matrix, wherein the second matrix is ​​the first matrix previously used by the UE, or a predefined matrix. For example, based on the position indices given in the third information, the elements of the corresponding position indices in the second matrix are replaced with the values ​​given in the third information. Another example: based on the position indices given in the third information, the elements of the corresponding position indices in the second matrix are added to the values ​​given in the third information to obtain the first matrix.

[0099] In some examples, the process of obtaining the elements in the first matrix based on the values ​​contained in the third information may still include signal processing procedures such as dequantization and scaling. In some embodiments, the values ​​of the elements in the first matrix are obtained by multiplying the values ​​provided in the configuration information by preset information to give scaling parameters. In some embodiments, the configuration information provides the a1 bit of the parameter, and the communication device obtains the a2 bit through preset information and / or random noise, etc. The value of the element of the first matrix is ​​the value corresponding to all bits formed by combining the a1 bit and the a2 bit.

[0100] In some examples, the first matrix is ​​obtained based on parameters (e.g., sequence information) contained in the third information. In some embodiments, each element in the first matrix can be generated by the UE's random number generation algorithm based on the initialization information given in the third information. The initialization information can be RNTI information, cell ID information, sequence ID, or other information used for initialization in the random number generation algorithm. The distribution satisfied by the random number generation algorithm used to generate each element in the first matrix can be a Gaussian random distribution, a uniform random distribution, etc. The random number generation algorithm can be an existing random number table-based random number generation algorithm or other random number generation algorithms known to the UE.

[0101] In some embodiments, the elements in the first matrix may come from a pseudo-random sequence with the same length as the number of elements in the first matrix. This pseudo-random sequence may be an m-sequence, a gold sequence, a ZC sequence, etc., and the specific sequence can be determined based on parameters included in the third information used to distinguish sequences. For example, an m-sequence with the same length as the number of elements in the first matrix may be determined based on the sequence ID provided in the configuration information, and this pseudo-random sequence may be rearranged based on the number of rows and columns of the first matrix to form the first matrix.

[0102] In some embodiments, the first matrix can be generated by extracting elements at corresponding positions from the third matrix based on the row and / or column indices given in the third information. The third matrix can be obtained from the third information or from other methods, such as generation using a pre-agreed algorithm, which is not limited here. For example, the third matrix is ​​a matrix with dimension M1. The first matrix is ​​formed by extracting elements at corresponding positions in the third matrix based on the row and / or column index information given in the configuration information.

[0103] In some embodiments, the first matrix can be obtained based on third information through a second neural network, wherein the structure and parameters of the second neural network can be obtained through the third information. For example, the reporting accuracy and reporting resource configuration given in the configuration information, together with the first information, are input into the second neural network, and the output of the second neural network is used as the first matrix.

[0104] In some examples, a matrix satisfying certain conditions is selected as the first matrix from a set of candidate matrices based on the index information of the first matrix contained in the third information, wherein the set of candidate matrices is obtained from pre-configuration of the base station and / or predefined in other ways. In some embodiments, the first matrix is ​​a matrix in the set of candidate matrices that directly corresponds to an index contained in the third information. For example, based on a second index a1 contained in the configuration information, the matrix corresponding to the second index a1 is selected as the first matrix. In some embodiments, the index information used to determine the first matrix can also be associated with the number of elements in the first information, wherein the number of elements in the first information can be obtained from the third information. For example, based on the third information, the total number of elements b0 in the first information can be obtained, and a matrix with b0 columns is selected from the set of candidate matrices as the first matrix. In some embodiments, the index information used to determine the first matrix can also be associated with the total number of physical resources used to transmit the second information, wherein the total number of physical resources used to transmit the second information can be obtained from the third information. For example, based on the total number of physical resources b1 used to transmit the second information, a matrix with b1 rows is selected from the set of candidate matrices as the first matrix. For example: Based on the third information, obtain the table index a2 and the number of elements b2 in the first information, and select the first matrix with the number of columns b2 from the corresponding subset a2 of the candidate matrix set.

[0105] It should be noted that the selection of c1 that is closest to the value c0 in this disclosure refers not only to the absolute value of the difference between the two values ​​( The minimum can also include: c1 is the value in the set {c1,c2…cn} that has the smallest absolute value of the numerical difference with c0 and is greater than or equal to c0. c1 is the value in the set {c1,c2…cn} that has the smallest absolute value of the numerical difference with c0 and is less than or equal to c0.

[0106] In some examples, based on the third information, at least two matrices can be obtained, and the result of multiplying the obtained matrices consecutively is used as the first matrix. For example, based on the two second indices a3 and a4 obtained from the third information, the corresponding matrices d3 and d4 are selected from the candidate matrix set, and the result of multiplying matrices d3 and d4 is used as the first matrix. As another example, based on the two second indices a5 and a6 obtained from the third information, the corresponding matrices d5 and d6 are selected from the candidate matrix set, and the matrix obtained by concatenating the rows of matrices d5 and d6 (for example, the number of rows in the concatenated matrix remains the same, and the number of columns is the sum of the number of columns of the two matrices) is used as the first matrix.

[0107] In some examples, when the first information is HARQ feedback information, the two sub-matrices constituting the first matrix—the first sub-matrix and the second sub-matrix—can come from different sets of candidate matrices and play different roles. The first sub-matrix can be a column orthogonal matrix with the same number of columns as the length of the first information and more rows than columns, which can convert the first information into a sparse vector. The second sub-matrix has the same number of columns as the first sub-matrix and the same number of rows as the number of physical resources used to transmit the second information, which can effectively constrain the length of the second information. In this way, the terminal can transmit HARQ information with lower complexity.

[0108] In step S430, based on the first matrix, the first information is processed to obtain the second information, and a signal associated with the second information is sent. Specifically, this step may include: determining a first processing procedure based on the first matrix; obtaining the second information through the first processing procedure based on the first information; mapping the second information to the physical resources associated with the second information and sending the corresponding signal.

[0109] In some examples, the physical resources used to transmit the second information are directly determined based on the third information. In some embodiments, the configuration information includes direct indication information such as the location of the aforementioned physical resources. In other embodiments, the configuration information includes the total number of the aforementioned physical resources, and the location of the physical resources used to transmit data is determined based on this number. In some embodiments, the third information includes an index about the aforementioned physical resources, and the location of the aforementioned physical resources is determined by looking up a set of transmission resource parameters based on this index, wherein the set of transmission resource parameters may come from the third information. For example, the set of transmission resource parameters can be configured first through an RRC procedure, and then the index can be provided in a subsequent RRC procedure.

[0110] In some examples, parameters related to physical resources used for transmitting the second information are obtained based on a first matrix. Specifically, this may include: obtaining a quantity benchmark of physical resources used for transmitting the second information based on the number of rows in the first matrix; and selecting the physical resources from a set of transmission resource parameters based on this quantity benchmark, wherein the set of transmission resource parameters may be derived from third information. For example, if the first matrix is ​​a real matrix, the quantity benchmark is half the number of rows. Or, if the first matrix is ​​a complex matrix, the quantity benchmark is the same as the number of rows. In some embodiments, the physical resources are selected from the transmission resource parameters by selecting transmission resources from the transmission resource parameter set whose total number of physical resources is closest to the aforementioned quantity benchmark. For example, if the number of rows in the first matrix is ​​m², then transmission resources with a total number of physical resources of m² are selected from the transmission resource parameter set. In some embodiments, the physical resources are selected from the transmission resource parameter set by selecting transmission resources from the transmission resource parameter set whose total number of physical resources is closest to and greater than or equal to the quantity benchmark. In some embodiments, the physical resource is selected from the set of transmission resource parameters by selecting the transmission resource containing the total number of physical resources that is closest to and less than or equal to the quantity benchmark.

[0111] It should be noted that the various pieces of information from the third information mentioned above can come from the same and / or different third information. In some embodiments, the configuration information provided by the RRC procedure may be a set of candidate matrices containing multiple matrices, while the configuration information provided by the DCI provides an index to determine the first matrix. The UE selects the matrix indicated by the index in the DCI configuration information from the set of candidate matrices provided by the RRC procedure as the first matrix. In some embodiments, preset information provides a second matrix, and the configuration information provided by the RRC procedure includes updated values ​​and position indices. The elements at the corresponding positions of the second matrix are updated, thereby obtaining the first matrix.

[0112] According to the embodiment, the first information is the information that the UE needs to report, which can be a bit sequence, a symbol sequence, or a numerical sequence. Specifically, it can include: information related to resource requests (e.g., SR (scheduling request) and / or BSR (buffer status report) used to indicate uplink data transmission resource scheduling requests); and feedback information on data reception (e.g., HARQ-ACK (Hybrid Automatic Repeat Quest - Acknowledgement) information indicating whether data has been received correctly, including detailed feedback such as retransmission versions). Information includes: CG-UCI (Configured Grant-Uplink Control Information); channel feedback-related information (e.g., Full Channel State Information (CSI) such as Explicit CSI, Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), CSI-RS Resource Indicator (CRI), SS / PBCH Resource Indicator (SSBRI), Transport Layer Indicator (LI), Channel Rank Indicator (RI), Received Signal Strength (RSRP, RSRQ, etc.); resource scheduling information (e.g., transmission resource indicators given in DCI); communication equipment behavior control information; user data information, etc. For example, full channel state information can relate to the combined effect of the state of REs occupied by the signal on the amplitude and phase of the signal transmitted along the entire wireless communication link. For example, full channel state information can also be called raw channel state information, or, for example, it can represent channel state information that has not been quantized or otherwise processed.

[0113] In some examples, the elements in the first information can be bits, which can be binary bits with values ​​from 0 to 1, or bits of other bases (such as -1, 0, 1 bits in ternary), or bit groups composed of multiple of the aforementioned bits.

[0114] In some examples, the elements in the first information can be symbols, which can be real-valued symbols and / or complex-valued symbols, or a sequence, vector, matrix, or tensor composed of the aforementioned symbols. Real-valued and / or complex-valued symbols are represented by real-valued and / or complex values ​​within a fixed range. The values ​​of the real-valued and / or complex-valued symbols can be discrete or continuous. Discrete-valued symbols are represented by quantizing the value range into finite fixed values, while continuous-valued symbols are represented by any value within the value range. For example, discrete-valued complex-valued symbols can be BPSK modulation symbols, with a value range of {(1+j) / ,(-1-j) / Two numerical values. The range of values ​​for a continuously taking complex value sign can be [(1+j) / ， (-1-j) / The symbol can be any value within that range.

[0115] In this disclosure, for ease of description, Channel State Information (CSI) related information is used as an example of the first information. It is understood that this is merely exemplary, and the information to be transmitted in the method, scheme, or table can be replaced with any of the above information. Furthermore, the information to be transmitted can be not only control information but also data information, and the corresponding transmission channel can be an uplink control channel (PUCCH), a downlink control channel (PDCCH), a downlink broadcast channel (PBCH), an uplink shared channel (PUSCH), a downlink shared channel (PDSCH), or other channels.

[0116] According to an embodiment, the first information may include any of the following: channel state information; statistics calculated based on the channel state information; and a precoding matrix obtained based on the channel state information. In some examples, the first information may be channel state information (CSI), specifically, it may be a vector, matrix, or tensor with at least one dimension used to describe the influence of the channel on the signal. The at least one dimension may include at least one dimension in the time domain, frequency domain, and spatial domain. For example, the granularity between elements in the time domain dimension may include symbols, slots, subframes, frames, etc.; the granularity between elements in the frequency domain dimension may include subcarriers, resource blocks, multiple resource blocks of fixed length, subbands, widebands, etc.; the granularity between elements in the spatial domain dimension may include antennas, antenna ports, antenna panels, beams, etc.

[0117] In some examples, the first piece of information can be a statistic calculated based on the channel state information, such as the expectation of the channel state information or the autocorrelation matrix of the channel state information. For example, the channel state information matrix can be multiplied by the original channel state information matrix after being conjugate transposed in the antenna domain, and the result of the multiplication can be averaged in the time domain and / or frequency domain to obtain the autocorrelation matrix of the channel state information.

[0118] In some examples, the first information can be a precoding matrix obtained based on channel state information. For instance, the channel state information can be decomposed using SVD, and one or more eigenvectors obtained from the decomposition can be used as the first information. Furthermore, precoding methods can also include zero-forcing precoding (ZF precoding), block diagonal precoding (BD precoding), minimum mean square error precoding (MMSE precoding), Tomlinson-Harashimap coding (TH precoding), and precoding methods based on Discrete Fourier Transform (DFT), etc.

[0119] In some examples, before computation, one or more dimensions of the first information can be transformed using a transformation algorithm. This transformation algorithm can include one of the following methods: Fourier transform, Laplace transform, wavelet transform, Hilbert-Huang transform, bispectral transform, etc. For example, the antenna domain dimension of the first information can be Fourier transformed to convert it into angular domain information, and the transformed vector becomes the new first information.

[0120] In some examples, the first information can undergo signal processing procedures such as sparsification, normalization, signal whitening, filtering, and signal reconstruction before computation. For instance, the first information can be grouped along a certain dimension, where the group length and dimension are determined based on preset and / or configuration information. The elements in each group are averaged, and the averaged results of all groups are combined to form a new first information. Another example is combining the real and imaginary parts of the complex elements in the first information into a new real vector, using this vector as the new first information, and then performing subsequent processing and / or computations based on this new first information.

[0121] In some examples, based on the first information and a first matrix, second information is obtained through a first processing procedure, including: multiplying the first information with the first matrix to obtain the second information. Specifically, it can be described as follows: ,in, It is a vector composed of the elements in the first piece of information. It is the first matrix. This refers to the generated second information. In some embodiments, the second information is the result of a weighted sum of multiple sets of coefficients based on the first information, where each set of coefficients is an element of a row or column of the first matrix. Specifically, the process can be described as follows: extract a row or column of elements from the first matrix as coefficients; select some / all elements from the first information; perform a weighted sum of the selected elements based on a set of coefficients; repeat the above steps until all rows / columns of the first matrix have been traversed; and combine all results into the second information.

[0122] In some embodiments, the first matrix can be divided into multiple smaller matrices, each containing elements from at least one row of the first matrix. Each smaller matrix is ​​multiplied with the first information to obtain the result, and the combination of all results constitutes the second information. In some embodiments, the first matrix is ​​a block diagonal matrix, where each block can be treated as a smaller matrix. Each smaller matrix is ​​multiplied only with a subset of elements in the corresponding position of the first information, and the results are combined to obtain the second information. This approach significantly reduces the computational cost of obtaining the second information.

[0123] In some examples, the first processing step described above may further include adding the second information to the first vector to obtain new second information, wherein the first vector is a real or complex vector with the same length as the second information, obtained through the third information, and the specific method of obtaining the third information is the same as the method of obtaining the first matrix described above. Specifically, it can be described as follows: ,in, It is a vector composed of the elements in the first piece of information. It is the first matrix. Corresponding to the second information obtained based on the first matrix and the first information, It is the first vector. This is the updated second piece of information.

[0124] In some examples, the first processing step described above may further include updating the second information using a first function. Specifically, the first function may be: tanh function, ReLU function, eLU function, seLU function, ceLU function, preLU function, geLU function, LeakyReLU function, Sigmoid function, Softmax function, or Softplus function, and its specific function expression is determined by the third information. For example, the second information may be processed using the tanh function so that the amplitude of each element in the second information is between [-1, +1].

[0125] In some examples, the first processing step described above may further include quantizing, normalizing, and performing other signal processing procedures on the second information to obtain new second information. In some embodiments, the new second information is the result of normalizing the input second information. Specifically, this process may... ,in The i-th element of the second input information. and This is the normalization parameter of the i-th element, which may be the same or different among all elements, and can be obtained through third information and / or calculated based on input data. In some embodiments, the second information is the result of quantization processing of the first information described above. Specifically, the sign and / or phase and / or amplitude of each element in the second information are used as new second information. Each element in the second information and / or its phase and / or amplitude is mapped to the element in the first element set that is closest to it, and all the converted signs are combined as new second information. Here, the first element set is a set consisting of K fixed elements, which can be obtained based on the third information, and the distance can be Euclidean distance, l1 norm distance, infinite norm distance, etc. For example, the first element set contains three elements {-2, 0, 2}, and all elements greater than 1 in the second information are converted to 2, all elements less than -1 are converted to -2, and other elements are converted to 0.

[0126] In some examples, the first processing step described above may further include converting the real elements of the second information into complex elements. For example, combining every two real elements of the second information as their real and imaginary parts respectively into a complex element, and combining all the complex elements to obtain new second information. ,in, and It has 2 real elements. For complex elements.

[0127] Optionally, in some examples, step S430 may further include: performing at least one first processing procedure on the second information based on at least one fourth matrix to obtain updated second information, wherein the relevant information of the fourth matrix (e.g., the relevant information of the fourth matrix may include the position information and numerical information of at least one element) can be obtained based on the third information, and the method of obtaining it refers to the method of obtaining the first matrix described above. For ease of description, the different fourth matrices used in each execution of the first processing procedure can be sequentially named the first value of the fourth matrix, the second value of the fourth matrix, ... . Specifically, after the first information obtains the second information through the first processing procedure, the updated second information is obtained based on the second information and the fourth matrix through the first processing procedure. The above process is repeated multiple times, and the fourth matrix used in each first processing procedure is sequentially named the first value of the fourth matrix, the second value of the fourth matrix, ..., until all fourth matrices have been used.

[0128] Optionally, in some examples, step S430 above can also be equivalently described as: obtaining a third neural network based on a first matrix and / or at least one fourth matrix; obtaining second information through the third neural network from the first information, and sending a signal associated with the second information, wherein the third neural network includes at least one fourth matrix and at least one second function. Specifically, the first matrix and / or each fourth matrix and its corresponding first function and / or second function constitute a layer of the third neural network. For example, the first matrix and its corresponding first function can be a layer in the third neural network, the first value of the fourth matrix and its corresponding second function constitute the second layer, the second value of the fourth matrix and its corresponding second function constitute the third layer, and so on. The second function can be: tanh function, ReLU function, eLU function, seLU function, ceLU function, preLU function, geLU function, LeakyReLU function, Sigmoid function, Softmax function, Softplus function, and its specific function expression is determined by the third information. For example, when the first and / or fourth matrices are typically dense real-valued matrices, they can be considered as DNN or MLP layers; when they are block diagonal matrices, they can be considered as multiple parallel MLP or FFN (feedforward neural network) layers. When the first and / or fourth matrices are Toeplitz matrices, they can be considered as CNN layers. When the first and / or fourth matrices are the product of multiple Toeplitz matrices, they can be considered as a combination of multiple CNN layers, and so on. The first and / or second functions in each layer can be considered as activation functions; if they are not processed by the first and / or second functions, they are considered to have a constant activation function or no activation function. Each time the first information (or second information) passes through a layer, the aforementioned first processing procedure is repeated once. In addition, other signal processing procedures such as sparsification, normalization, etc., which perform dimensionality reduction, feature extraction, and feature combination on the input, can also be added to the preprocessing of each neural network layer.

[0129] In some examples, the mapping of each element in the second information to the corresponding physical resource can be done by selecting the corresponding physical resource sequentially in the order of frequency domain first and then time domain and / or time domain first and then frequency domain.

[0130] In some examples, the mapping of each element in the second information to the corresponding physical resource can be performed according to the priority of the physical resource from high to low, where the priority can be derived from the third information. For example, physical resources located near the reference signal have higher priority, so the elements in the second information are mapped to these physical resources first, and then the remaining elements are mapped to other lower priority physical resources.

[0131] In some examples, the mapping of each element in the second information to its corresponding physical resource can be performed according to the order indicated by the third information. For example, the configuration information specifies that the elements at the beginning of the second information are mapped to the physical resources located on OFDM symbols 4 and 6 in the transport resources, the elements at the end of the second information are mapped to the physical resources located on OFDM symbols 2 and 7 in the transport resources, and the remaining elements are mapped to the physical resources on the other OFDM symbols in the transport resources in sequence.

[0132] In some examples, the transmission resource contains more than one antenna port. The second information will be divided into multiple signal segments, each corresponding to one antenna port. The signal on each antenna port is mapped to the corresponding physical resource according to the aforementioned mapping method.

[0133] In some examples, the second information can be encoded before mapping. For instance, each element in the second information can be quantized into its corresponding bit, and then polar encoding can be applied to these bits to obtain the new second information.

[0134] In some examples, the second information can undergo signal processing such as modulation, interleaving, and combination before mapping. For example, QPSK modulation can be used to map each two-bit group in the second information to a corresponding QPSK symbol, thereby forming new second information.

[0135] In some examples, the second information may undergo a rate matching process before mapping. In some embodiments, if the number of elements in the second information is less than the total number of physical resources in the transmission resources, then after mapping all elements in the second information, the element at the beginning of the second information can be mapped to the remaining physical resources in the transmission resources. In some embodiments, if the number of elements in the second information is greater than the total number of physical resources in the transmission resources, then the element at the end of the second information can be discarded, and the remaining elements can be mapped to the physical resources of the transmission resources sequentially.

[0136] Reference Figure 4-2 Method 500 is performed by a communication device (e.g., a base station) that needs to receive the reported information. The method includes: Step S440: Receive the second information sent by the UE. The UE sends the second information via steps S410-S430.

[0137] Step S450: The received second information is processed based on the first neural network to obtain fourth information, wherein the fourth information is associated with the aforementioned first information, and the first information is associated with the information that the UE needs to report.

[0138] In some examples, the received second information may undergo signal processing procedures such as denoising, channel equalization, interference suppression, whitening, and signal reconstruction before being input into the first neural network. For example, the real and imaginary parts of the complex elements in the received second information may be combined into a new real vector, which is then used as the input to the first neural network. Another example is that the received second information may be multiplied by the channel equalization matrix, and the result is used as the input to the first neural network. Specifically, this process can be described as follows: ,in, This is the second message received. It is the channel equalization matrix. This is the signal that will be input into the first neural network. The channel equalization matrix can be obtained based on the channel experienced by the second information, which can be obtained based on a reference signal transmitted along with the second information. For example, the channel influence experienced by the second information... Then the MMSE algorithm can be used to calculate the corresponding channel equalization matrix. .

[0139] In some examples, the base station may have one or more neural networks. Based on relevant information of the second information (e.g., which terminal it comes from, the length of the second information, the signal-to-interference-plus-noise ratio of the currently received signal, etc.), a corresponding first neural network is obtained for processing the received second information. In some embodiments, the base station may determine the first neural network to be used based on a first processing method used to generate the second information. Based on the random seed used by the first processing method to generate the first matrix, a neural network corresponding to the random seed is selected as the first neural network. In some implementations, the base station may determine the first neural network to be used based on the signal-to-interference-plus-noise ratio of the currently received second information. When the noise is too strong, a neural network with improved denoising performance is selected as the first neural network; when the interference is too strong, a neural network with stronger interference suppression performance is selected as the first neural network.

[0140] In some examples, auxiliary information and the received second information can be input together into a first neural network to obtain fourth information. In some embodiments, the auxiliary information may include information related to the first information, including at least one of the following: multipath delay spread information of the channel corresponding to the first information, correlation time information of the channel corresponding to the first information, etc. For example, the multipath delay spread information of the channel may be the distribution pattern of the channel multipath, which can be preset by the base station installers or derived by the base station based on its own long-term operating data analysis.

[0141] In some examples, the fourth information can also be the result of signal processing procedures such as normalization, filtering, signal combination, Fourier transform, and inverse Fourier transform. For example, the fourth information can be divided into two vectors, with the real part and the dashed part combined into a complex vector, which is then used as the fourth information. Another example is performing an inverse Fourier transform on the fourth information in one or more dimensions. By combining the elements of the transform result into a vector, a new fourth information can be obtained. This operation can restore the fourth information from the angular time-delay domain to time-frequency domain data usable for subsequent processing. It should be noted that the Fourier transform can be replaced by other signal processing-related transforms, which may include one of the following methods: Fourier transform, inverse Fourier transform, Laplace transform, wavelet transform, Hilbert-Huang transform, bispectral transform, etc. Yet another example is dividing the fourth information by its l2 norm, and using the resulting normalized vector as the fourth information.

[0142] The following complete embodiment will be used to better illustrate steps S410, S420, and S430 performed by the UE. It is understood that this description is merely exemplary and not limiting. All specific values, structures, processing methods, etc., described below can be replaced with other values, other structures, other processing methods, and related expressions, etc. It should be noted that the execution order of the various steps described in the embodiments of this disclosure is not unique. Those skilled in the art can adjust, combine, or partially omit the execution order of the steps according to the actual application scenario or implementation requirements, as long as the technical effects of this disclosure can be achieved.

[0143] 1. The terminal obtains one or more of the following information from the configuration information obtained during the RRC procedure: a) Relevant information for the CSI information (first information) that needs to be reported. For example, the number of CSIs reported n1 (e.g., the number of sub-bands), the number of CSI transmit antenna ports n2, and the total length of the CSI information calculated based on this as n=n1. n2 n3, where n3 is the number of receiving antennas of the terminal or the number of layers reported.

[0144] b) The total number of transmission resources used for reporting CSI information is m. c) Generate configuration information for the first matrix, such as a random seed d used to generate the compressed matrix.

[0145] d) The first matrix is ​​based on a row number of 2 m, with 2 columns n. Based on the random seed d, using the terminal's own random number table, generate (e.g., satisfy a Gaussian distribution) real random numbers to fill the first matrix.

[0146] 2. Concatenate the real and imaginary parts of the complex elements of the first information to form a sequence of length 2. The vector of n is used as the new first information, for example... ,in It is the complex vector corresponding to the first piece of information. It is the real part of the first information. It is the imaginary part of the first information. It is a concatenated real number vector.

[0147] 3. Multiply the first information by the first matrix to produce a matrix of length 2. The vector of m is used as the second piece of information, for example... ,in, It is a combined real number vector. It is the first matrix. This is the second piece of information generated. This operation can be achieved by inputting a specific AI model or other methods.

[0148] 4. Divide the second information into two sub-vectors of length m, and combine them as the real and imaginary parts to form a vector of length m as the second information. For example, ,in , yes The first m elements, yes The last m elements.

[0149] 5. Normalize the power of the second information to obtain new second information, for example, ,in This represents the average power of all elements in the second information.

[0150] 6. Map each complex element in the second information onto the transmission resources in sequence according to a specific mapping rule (e.g., frequency domain first, then time domain) and send the relevant signals.

[0151] The following description, using another embodiment, better illustrates steps S410, S420, and S430 performed by the UE. It is understood that this description is merely exemplary and not limiting; all specific values, structures, processing methods, etc., described below can be replaced with other values, other structures, other processing methods, and related expressions, etc. It should be noted that the execution order of the various steps described in the embodiments of this disclosure is not unique. Those skilled in the art can adjust, combine, or partially omit the execution order of the steps according to actual application scenarios or implementation requirements, as long as the technical effects of this disclosure can be achieved.

[0152] 1. The terminal obtains the following information from the configuration information obtained during the RRC procedure: a) Calculate the total length of the CSI information (first information) n = n1 based on the number of CSI information items (n1) to be reported and the number of CSI ports n2. n2 n3, where n3 is the number of receiving antennas of the terminal. b) The total number of transmission resources, m, used for reporting CSI information. 2. The terminal obtains the first value of the fourth matrix with m1 rows and m2 columns from the RRC configuration information. For example, the configuration information gives the value of each element of the first value of the fourth matrix.

[0153] 3. The terminal selects the first matrix with n columns and m2 rows from the candidate matrix set.

[0154] 4. The terminal constructs a neural network (e.g., an MLP neural network) based on the first matrix and the corresponding first function (e.g., the geLU function), and passes the first information through the MLP neural network to obtain the second information. For example, the first information is multiplied by the first matrix, and the result of the multiplication is processed by the geLU function to obtain the second information.

[0155] 5. The terminal constructs a neural network (e.g., an MLP neural network) based on the first value of the fourth matrix and the corresponding second function (e.g., the tanh function). The second information is then passed through this MLP neural network to obtain new second information. For example, the terminal multiplies the second information with the second value of the fourth matrix, and then processes the result of the multiplication through the tanh function. The result obtained is used as the updated second information.

[0156] 6. The terminal performs power normalization on the updated second information to obtain normalized second information, for example, ,in This represents the average power of all elements in the second information.

[0157] 7. Divide the normalized second information into two sub-vectors of length m1 / 2, and combine them as the real and imaginary parts to form a vector of length m as the second information to be sent. For example... ,in , yes The first m1 / 2 elements, yes The last m1 / 2 elements.

[0158] 8. Map each complex element in the second information to be sent onto the transmission resources in sequence according to the rule of first the frequency domain and then the time domain, and send the relevant signals.

[0159] This embodiment is merely an example and does not limit the implementation order and steps of the solution. For instance, some steps may be optional (e.g., the execution of steps 5-7 is optional). Furthermore, the execution order of the steps can be adjusted (e.g., the execution order of steps 5-7 can be swapped). Also, some steps may be executed multiple times (e.g., if two fourth matrices are obtained through step 3, step 5 can be executed twice, each time using a different fourth matrix and the corresponding second function).

[0160] In some embodiments, the first function and the second function may be, for example, the tanh function, ReLU function, eLU function, seLU function, ceLU function, preLU function, geLU function, LeakyReLU function, Sigmoid function, Softmax function, Softplus function, etc.

[0161] Figure 5 A schematic diagram of a first neural network and a second neural network according to an embodiment of the present disclosure is shown.

[0162] Neural networks can be implemented by the same physical entity or by combining and / or independently of different physical units. The physical entity (or physical unit) can be composed of hardware, software, or a combination thereof. Those skilled in the art can configure it according to actual needs.

[0163] like Figure 5 As shown, a neural network may include an information processing unit and multiple neural network layers. The information processing unit can convert input information and / or auxiliary information into at least one of data vectors, matrices, and tensors that can be used by the neural network layers for computation. Neural network layers may include... Figure 5The diagram shows at least one of an input layer, a hidden layer / intermediate layer (intermediate hidden layer), and an output layer. Furthermore, each neural network layer can consist of multiple neurons n, and at least one activation function can be used to activate the neurons, as an example and not a limitation, such as tanh, ReLU, eLU, seLU, ceLU, preLU, geLU, LeakyReLU, Sigmoid, Softmax, Softplus, etc. Each neural network layer can perform operations on its input data to achieve functions such as matrix transformation, data dimensionality reduction, data feature extraction, and data feature combination. As an example and not a limitation, multiple neural network layers can be connected in series or parallel to form different neural network structures, including but not limited to multilayer perceptrons (MLP), multilayer perceptron mixers (MLP-mixers), convolutional neural networks (CNN), deep neural networks (DNN), recurrent neural networks (RNN), restricted Boltzmann machines (RBM), deep belief networks (DBN), bidirectional recurrent deep neural networks (BRDNN), generative adversarial networks (GAN), and transform networks (Transformer).

[0164] For example, in Figure 5 In the case where the neural network shown is the neural network mentioned above, the input information can be the received second information or the first information, and the information processing unit can convert the channel state information and auxiliary information into at least one of vectors, matrices and tensors for neural network layer calculations.

[0165] In some examples, the neural network can be a deep convolutional neural network. The input data can be transformed and stacked into a tensor form before being input into the neural network. The neural network can output a matrix of data whose shape corresponds to the shape and dimensions of the first piece of information (e.g., channel state information), serving as feature information. In other embodiments of this example, the neural network can employ a Transformer architecture, where the input data can be split into multiple groups. Each data point is processed and then superimposed with a position token corresponding to its RE position. This, along with a pre-trained query token, is then input into the neural network. The neural network outputs the data of the channel containing the query token as feature information.

[0166] As an example, and not a limitation, the set of channel state information is defined as follows: First, the information processing unit processes the known channel state information using numerical mapping methods (e.g., normalization, standardization, etc.) to make its numerical scale or distribution suitable for neural network computation. More specifically, taking min-max standardization as an example, it can first calculate... The maximum value of all elements in and minimum value After that, Each element in Perform the following processing:

[0167] After performing the above numerical mapping, the processed elements can be... The set Convert to at least one of vectors, matrices, and tensors corresponding to the input size of the neural network layer.

[0168] In some examples, the information processing unit may also perform positional encoding on the input information and convert the positionally encoded information into at least one of vectors, matrices, and tensors for neural network computation. Optionally, positional encoding may be a process of adding and / or multiplying the input information with codewords in a predetermined codebook. As an example, and not a limitation, when the input information is channel state information, positional encoding using a codebook may include the following processes: First, a set of known channel state information is mapped using a numerical mapping method. Processing yields a set It contains several elements that have undergone numerical mapping. Subsequently, the codebook for positional encoding is obtained according to the following equation.

[0169]

[0170] Where n represents the location of the channel state information in the physical resource (such as time domain, frequency domain, and spatial domain), d represents the physical resource dimension of the channel state information, and N is a set of values ​​greater than 1000. The integer contains any positive number of elements, where i represents the index of the physical resource dimension. Then, through the equation... For known channel state information elements Perform position encoding to obtain a set Finally, the processed elements will be... The set Convert the vector into at least one of vectors, matrices, and tensors corresponding to the input size of the neural network layer, and then input the vector into the neural network layer.

[0171] like Figure 5As shown, the information processing unit can process the input second information and auxiliary information (such as the mapping and position encoding described above), thereby enabling the neural network to acquire the implicit prior information, thus helping the neural network improve the accuracy and flexibility of the acquired results. Using a neural network to obtain feature information can make the obtained feature information more accurate. Optionally, the information processing unit can also directly input its input into the neural network layer without any additional processing. Furthermore, it should be noted that the operations in the above-described neural network unit are merely exemplary, and this disclosure is not limited thereto.

[0172] Figure 6-1 and Figure 6-2 A flowchart of a method 600 performed by a communication device according to an embodiment of this disclosure is shown. For ease of description, the following description will primarily focus on a scenario where communication devices report channel state information (CSI) (also known as a CSI feedback scenario). It is understood that this description is merely exemplary and does not limit the types of uplink control information to which method 600 is applicable to to only CSI, but may cover situations where various other types of uplink control information are reported.

[0173] According to embodiments, the communication device can be any device capable of transmitting and receiving signals and performing signal detection. For example, the communication device can be a base station or a terminal, but is not limited thereto. For example, the communication device can also be a user equipment with a large-scale antenna port, and so on.

[0174] like Figure 6-1 As shown, method 600 is executed by a communication device (e.g., a terminal) that needs to report information, and the method includes steps S610, S620, and S630.

[0175] Step S610: Obtain configuration information related to CSI feedback, wherein the relevant configuration information is obtained based on the signal received by the communication device and / or the preset information of the communication device, and its specific meaning is consistent with the third information in method 400; Step S620: Based on the configuration information related to CSI feedback, a first method and related parameters are determined, wherein the first method is the method used in step S630 to process the first information. This disclosure provides a first method for signal transmission, which mainly achieves efficient processing of information to be reported by operating a first matrix. The first method may include the following steps: First, according to system configuration or protocol specifications, a specific implementation method for the first matrix is ​​selected (e.g., through random number generation, neural network training, or index extraction); then, the first matrix is ​​obtained and determined according to the selected specific implementation method; finally, the first matrix is ​​used to compress the first information, and the processed second information is reported to the base station. In this way, this disclosure can effectively reduce the computational load and transmission overhead of the UE while ensuring information accuracy.

[0176] Step S630: Based on the first method, process the first information to obtain the second information, and send a signal associated with the second information, wherein the first information is the CSI information that needs to be fed back, and the second information is the compressed CSI information.

[0177] The above method enables better adaptation of information processing or transmission channels, and higher accuracy of information reporting can be achieved by utilizing the same communication resources, thereby improving the performance of information transmission and / or reception.

[0178] The contents of steps S610 to S630 described above will be described in detail below.

[0179] Reference Figure 6-1 Step S610: Obtain configuration information related to CSI feedback, wherein the configuration information is obtained based on the signal received by the communication device and / or the preset information of the communication device; In some examples, the relevant configuration information may be information received from the base station that is needed to complete its own signal processing process, such as configuration information obtained through the Radio Resource Control (RRC) procedure, MAC layer signaling, configuration information contained in the Downlink Control Information (DCI), etc.

[0180] In some examples, preset information refers to information that the communication device already knows without requiring additional configuration. For example, it could be information predefined in the standard, or it could come from the communication device's factory settings or subsequent manual configuration.

[0181] In some examples, CSI feedback configuration information may include CSI-related information that needs to be fed back, such as the number of elements in the CSI, CSI-related subband information, antenna shape-related information, the number of downlink antenna ports, the number of receive antennas, the number of layers or streams corresponding to the CSI, and the type of CSI (e.g., feature vector-based CSI, explicit CSI based on MIMO channels, etc.). The CSI-related subband information includes at least one of the following: the number of subbands, the subband location, and the number of RBs included in the subband.

[0182] In some embodiments of this example, the number of subbands in CSI may include the number of subbands corresponding to the CSI that needs to be fed back in this transmission, or the maximum / minimum number of subbands corresponding to the CSI that needs to be fed back under all possible configuration scenarios, or the number of subbands corresponding to the CSI that needs to be fed back under all possible configuration scenarios.

[0183] In some embodiments of this example, the subband position in the CSI may include the subband position corresponding to the CSI that needs to be fed back in this transmission, or it may include information related to all possible subband positions corresponding to the CSI that needs to be fed back under all possible configuration scenarios. It may also include a subband position configuration list corresponding to each group of CSIs in multiple feedback scenarios, where each item in the subband position configuration list corresponds to a group of subband positions.

[0184] In some embodiments of this example, the number of downlink antenna ports may include the number of downlink antenna ports corresponding to the CSI that needs to be fed back in this transmission, or the maximum / minimum number of downlink antenna ports corresponding to the CSI that needs to be fed back under all possible configurations, or the number of downlink antenna ports corresponding to the CSI that needs to be fed back under all possible configurations.

[0185] In some embodiments of this example, the layer or stream number information corresponding to the CSI may include the layer or stream number information corresponding to the CSI that needs to be fed back in this transmission, or the maximum / minimum value of the layer or stream number corresponding to the CSI that needs to be fed back under all possible configurations, or the layer or stream number corresponding to the CSI that needs to be fed back under all possible configurations.

[0186] In some embodiments of this example, the type of CSI may include explicit CSI based on the MIMO channel, such as a matrix describing the impact of the channel on physical transmission resources; precoding based on CSI, such as a precoding matrix obtained by performing ZF or MMSE precoding on CSI; channel features calculated based on CSI, such as eigenvectors and / or eigenvalues ​​obtained by eigenvalue decomposition of CSI, and the autocorrelation matrix of CSI.

[0187] In some examples, CSI feedback configuration information may include information about the timing of CSI feedback. For example: periodic CSI feedback, semi-periodic CSI feedback, and aperiodic CSI feedback.

[0188] In some examples, CSI feedback configuration information may include information related to the physical resources used to send the second information. For example, the quantity and / or location information of the physical resources used to transmit the second information, and information about the physical channels used for signal transmission.

[0189] In some embodiments of this example, the information related to the physical channel used to send the second information may be channel indication information used to send the second information, such as indication information for transmitting the second information on the PUSCH, indication information for transmitting the second information on the PUCCH, etc.

[0190] In some embodiments of this example, the information related to the physical channel used to transmit the second information may be information related to the PUCCH resources used to transmit the second information, including: PUCCH resource location, PUCCH format, etc. This information may be information used in this transmission, or information from all possible scenarios.

[0191] In some embodiments of this example, the information related to the physical channel used to send the second information may be information related to the PUSCH resources used to send the second information, including: the location of the PUSCH resources, the number and / or location of REs allocated to the second information, etc. This information may be information related to the current transmission, or information related to all possible scenarios.

[0192] In some examples, information related to the resources used to send the second information may include information on the quantity and / or location of the physical resources used to indicate / configure the second information.

[0193] In some embodiments of this example, the information related to the physical resources used to transmit the second information may include parameter information for calculating the quantity and / or location of uplink physical resources used to transmit the CSI. For example, the information related to the physical resources used to transmit the second information may include one or more factor parameters. The communication device calculates the number and / or location of REs used to transmit the second information (i.e., the compressed CSI) based on the factor parameters and one or more of the following: the MCS (modulation and coding scheme) of the PUSCH or PUCCH channel, the transport block size, the type of other uplink control information (UCI), and / or the payload.

[0194] Specifically, the communication device receives at least one of the following configured information: the maximum number of physical resources Amax for transmitting the second information, the minimum number of physical resources Amin for transmitting the second information, the physical resource A for transmitting the second information, and the maximum number of physical resources B occupied by all UCIs. For example, it can be at least one of the following: The communication equipment receives and determines the amount of physical resources used to send the compressed CSI as the minimum value of the output dimension of the first matrix and B; The communication equipment receives and determines the number of physical resources used to send the compressed CSI as B, which is the value after removing the number of physical resources of UCI with a priority higher than the second transmission information; The communication device receives and determines the minimum value of the physical resources B used to send the compressed CSI after removing the physical resources of the UCI with a priority higher than the second information to be sent, and Amax or A. The communication device receives and determines the physical resource quantity (B) for transmitting the compressed CSI, excluding the physical resource quantity of UCIs with a priority higher than the second transmitted information, and the maximum value of Amin or A. The communication device determines the physical resource quantity (A) for transmitting the compressed CSI, and uses the physical resource quantity (B) after deducting the number of REs (Resources) of the UCIs transmitting the second information from the maximum physical resource quantity (B) occupied by all UCIs to determine the physical resource quantity for other UCI transmissions. When the determined physical resource quantity for transmitting the compressed CSI is less than the number of complex CSI values, some complex values ​​are punctured according to predefined rules to match the physical resource quantity for transmission. For example, some complex values ​​with the lowest priority can be punctured according to a predefined finite level, such as the last compressed output value or the compressed output value corresponding to the lowest layer.

[0195] When the number of physical resources available for sending the compressed CSI exceeds the number of complex elements in the compressed CSI, the compressed complex number is transmitted repeatedly according to predefined rules. For example, the compressed complex number is re-mapped in sequence.

[0196] In some examples, the CSI feedback configuration information may include configuration information related to the first method. Specifically, the configuration information may include: information instructing the communication device to obtain the second information based on the neural network, information instructing the communication device to obtain the second information based on the first matrix, and information instructing the communication device to obtain the second information based on the codebook.

[0197] In some embodiments of this example, the relevant information that instructs the communication device to obtain the second information based on the first matrix may be related information about the number of rows and / or columns of the first matrix, or related information for obtaining the element values ​​in the first matrix, etc. For details, please refer to the relevant content in the subsequent step S620.

[0198] In some embodiments of this example, the relevant information that instructs the communication device to obtain the second information based on the neural network may be related information about the structure of the neural network, or related information about the parameters in the neural network, etc. For details, please refer to the relevant content in the subsequent step S620.

[0199] In some examples, this step may also include the communication device reporting its own CSI feedback capabilities. Specifically, the communication device may report the relevant processes it can perform to obtain the second information. For example, the communication device may report the structure and complexity of the neural network it can run. Or, for example, the communication device may report that it only supports obtaining the second information based on the first matrix.

[0200] In some embodiments of this example, the CSI feedback capability reported by the communication device can be a computing power-related value, such as floating point operations per second (FLOPS).

[0201] In some embodiments of this example, the CSI feedback capability reported by the communication device may be the neural network it supports (or the type of neural network, etc.). For example, each neural network may have its corresponding number, and the correspondence between the number and the neural network is obtained based on configuration information or preset information. The communication device indicates the neural network structure or type it supports by reporting the number.

[0202] In some embodiments of this example, the CSI feedback capabilities reported by the communication device can be capability levels. For example, a communication device at capability level 0 may only support codebook-based CSI feedback; a communication device at capability level 1 may support CSI feedback based on a first matrix; and a communication device at capability level 2 may support CSI feedback based on a neural network. As another example, a communication device at capability level 0 may be in a low-power mode and may only support CSI feedback based on the first matrix; a communication device at capability level 1 may be in a medium-power mode and may support CSI feedback based on relatively simple neural networks (e.g., 1-3 layer DNN structures, 1-4 layer CNN structures, etc.); and a communication device at capability level 2 may be in a high-power mode and may support CSI feedback based on more complex neural networks.

[0203] In step S620, based on the configuration information related to CSI feedback, a first method and related parameters are determined, wherein the first method is the method used in step S630 to process the first information. Specifically, the first method may include the following steps: processing the first information based on a specified first matrix; processing the first information based on a specified second neural network; and processing the first information based on a specified codebook.

[0204] In some examples, the communication device determines the first method based on configuration information related to CSI feedback.

[0205] In some embodiments of this example, the communication device determines a first method based on indications of corresponding configuration parameters in the configuration information related to CSI feedback. For example, the corresponding configuration parameters indicate that the first method is to process the first information using a first matrix. As another example, the communication device obtains various sets of CSI feedback configurations in the relevant configuration information during the RRC process, each CSI feedback configuration set corresponding to a different first method. The communication device determines the corresponding first method and related parameters based on the configuration set indicated in the DCI. As yet another example, the corresponding configuration parameters indicate that the first method is to process the first information using a first matrix, and the first matrix is ​​generated based on the parameters in the configuration information.

[0206] In some embodiments of this example, the communication device determines the first method based on parameters related to the first method included in the configuration information related to CSI feedback. For example, when the configuration information includes parameters related to a second neural network, the first method is to process the first information using the second neural network. As another example, when the configuration information does not include parameters related to a second neural network but includes parameters related to a first matrix, the first method is to process the first information using the first matrix.

[0207] In some examples, such as Figure 6-2 As shown, this step may include the following steps: Step S621, obtaining at least one candidate matrix based on the configuration information related to CSI feedback; Step S622, obtaining a first matrix (also known as a projection matrix) based on the configuration information related to CSI feedback and all candidate matrices. The first matrix can be a matrix obtained by pruning and / or splicing the candidate matrices according to predefined rules and / or configuration. For example, the candidate matrix may be specific to a particular number of transmission ports (e.g., 64 Tx) or a number of subbands (e.g., 13 subbands). According to predefined rules, the first 32 elements of the candidate matrix are pruned to obtain the first matrix, used for a matrix with 32 Tx transmission ports. For another example, the configuration information related to CSI feedback may include information indicating the number or location of subbands. Based on this indication, the communication device prunes the candidate matrix into a projection matrix corresponding to the number of subbands. For example, 13 subbands may be pruned into 7 subbands. For example, the candidate matrix is ​​the matrix designed for each layer. When the feedback CSI is multi-layered (e.g., 4 layers) or multi-receiver antenna (e.g., 4 receiving antennas), the 4 candidate matrices can be spliced ​​together to form the first matrix for compression.

[0208] This simplification simplifies protocol design and avoids designing / configuring different compression matrices for different configurations, such as the number of sending ports and the number of subbands.

[0209] In some examples, the specific content of step S621 is consistent with the relevant content of determining the first matrix based on the third information in step S420.

[0210] In some examples, the communication device can determine the number of rows and columns of the candidate matrix based on configuration information related to CSI feedback. The number of rows in the candidate matrix is ​​associated with the length of the first information, which is associated with the CSI that needs to be fed back.

[0211] In some embodiments of this example, the number of columns and / or rows of the candidate matrix is ​​obtained directly from the configuration information related to CSI feedback. For example, when step S630 is performed based on complex number operations, the number of columns and / or rows of the candidate matrix is ​​a value obtained based on the configuration information related to CSI feedback. As another example, when step S630 is performed based on real number operations, the number of columns and / or rows of the candidate matrix is ​​twice the value obtained based on the configuration information related to CSI feedback.

[0212] In some embodiments of this example, the number of rows in the candidate matrix is ​​based on the amount of physical resources used to transmit the second information. For example, the number of rows in the candidate matrix is ​​the maximum amount of physical resources used for signal transmission, where the amount of physical resources can be obtained based on the product of the number of OFDM symbols and the number of physical resources contained in each OFDM. As another example, the number of rows in the candidate matrix is ​​twice the maximum amount of physical resources used for signal transmission.

[0213] In some embodiments of this example, the number of columns in the candidate matrix is ​​obtained based on the maximum number of subbands and / or the maximum number of ports and / or the maximum number of layers of CSIs requiring feedback. For example, the number of columns could be the product of the maximum number of subbands and the maximum number of ports of CSIs requiring feedback. Another example is that the number of columns could be twice the product of the maximum number of subbands and the maximum number of ports of CSIs requiring feedback. Yet another example is that the number of columns could be the product of the maximum number of subbands and the maximum number of ports of CSIs requiring feedback, and the maximum number of layers of CSIs requiring feedback.

[0214] In some examples, the communication device generates a (pseudo)random sequence with a length equal to the product of the number of rows and columns of the candidate matrix, based on parameters in the CSI feedback-related configuration information. Elements from this pseudo-random sequence are then sequentially filled into the candidate matrix in a row-first, column-second (or column-first, row-second) order. The specific details can be consistent with those in step S420. For example, based on the sequence ID in the CSI feedback-related configuration information, a gold sequence with a length equal to the number of elements in the candidate matrix is ​​generated. Elements from this gold sequence are then sequentially filled into the corresponding positions in the candidate matrix, forming the candidate matrix. The specific details can be consistent with those in step S420.

[0215] In some embodiments of this example, the relevant parameters used for the (pseudo)random sequence for each candidate matrix can be relevant or different. These relevant parameters can be common parameters for each communication device, such as cell ID, RNTI in PDCCH, etc.; or they can be specific parameters for each communication device and / or candidate matrix, such as the sequence ID of this CSI feedback, the sequence ID of this communication device, etc.

[0216] In some embodiments of this example, the communication device uses BPSK modulation to convert 0 and 1 elements in the candidate matrix into +1 and -1 elements.

[0217] In some examples, the communication device updates the corresponding positions of the base matrix based on the element positions and corresponding values ​​contained in the configuration information related to CSI feedback, thereby obtaining candidate matrices. The base matrix can come from predefined information (e.g., a pure zero matrix or an empty matrix), or it can be a candidate matrix obtained through the aforementioned process, or it can be a previous candidate matrix or a first matrix. For example, a first matrix is ​​obtained based on the information of each element at each position in the first matrix contained in the configuration information related to CSI feedback. The specific details can be consistent with the relevant content in step S420.

[0218] In some embodiments of this example, the communication device directly replaces the element at the indicated location with the value in the configuration information.

[0219] In some embodiments of this example, the communication device directly uses the result of adding the value in the configuration information to the element at the indicated location as the new element at that location.

[0220] In some embodiments of this example, the communication device directly uses the result of multiplying the value in the configuration information by the element at the indicated location as the new element at that location.

[0221] In some examples, the communication device obtains only one candidate matrix in this CSI feedback, and that candidate matrix is ​​the first matrix used in this CSI feedback.

[0222] In some examples, the communication device selects multiple candidate matrices as the first value, second value, etc. of the first matrix based on relevant configuration information.

[0223] In some examples, the communication device can obtain multiple candidate matrices, and can select the corresponding candidate matrix as the first matrix based on the configuration information related to the CSI feedback. The specific details can be consistent with the relevant content in step S420.

[0224] In some embodiments of this example, the communication device selects the candidate matrix corresponding to the corresponding index (e.g., matrix number) from the candidate matrices as the first matrix based on the matrix index information contained in the configuration information related to CSI feedback.

[0225] In some embodiments of this example, the communication device selects a corresponding candidate matrix as the first matrix based on physical resource information for transmitting the second information included in the configuration information related to CSI feedback. For example, the candidate matrix with the number of rows closest to the number of physical resources is selected as the first matrix. Another example is selecting a candidate matrix corresponding to the format of the PUCCH used for transmitting the second information (e.g., PUCCH format 3) as the first matrix.

[0226] In some embodiments of this example, the communication device selects a corresponding candidate matrix as the first matrix based on the CSI feedback-related configuration information. For example, the candidate matrix with the number of columns closest to the total number of elements in the CSI is selected as the first matrix. Alternatively, the candidate matrix corresponding to the type of CSI requiring feedback (e.g., the eigenvector obtained by SVD decomposition of the CSI) is selected as the first matrix.

[0227] In some examples, this step also includes combining / concatenating the selected candidate matrices to obtain a first matrix. The selected candidate matrices can be obtained using the method described above for selecting the first matrix, and these candidate matrices can be the same or different matrices. For example, based on the number of downlink antenna ports in the required CSI feedback, candidate matrices with the same number of columns are selected. These selected candidate matrices are then repeated based on the number of subbands in the required CSI feedback, and all repeated candidate matrices are combined along the column dimension to form the first matrix. As another example, based on the subband location information in the required CSI feedback, candidate matrices corresponding to each subband location are selected, and these selected candidate matrices are combined along the column dimension to form the first matrix.

[0228] In some embodiments of this example, based on the structure of the CSI to be fed back, contained in the configuration information related to CSI feedback, multiple candidate matrices are combined into a first matrix. Specifically, based on one or more quantities of the structure of the CSI to be fed back (e.g., the number of sub-bands, the number of layers, the number of antenna ports, etc.), multiple candidate matrices are combined into a block diagonal matrix, and the new block diagonal matrix is ​​used as the first matrix. For example, a selected candidate matrix is ​​repeated N times, and these repeated matrices are combined into the first matrix based on the block diagonal structure, where N is the number of layers of the CSI to be fed back. Another example is that N selected candidate matrices are combined into the first matrix based on the block diagonal structure, where N is the number of sub-bands of the CSI to be fed back, with each candidate matrix obtained based on the aforementioned process.

[0229] In some examples, step S630 further includes rearranging the columns of the first matrix based on the structure of the first information, and using the rearranged matrix as the first matrix. For example, the mapping order of the CSI elements of the first information may be sub-band first, then antenna port, but each block in the block diagonal structure of the first matrix corresponds to all antenna ports of a sub-band. Therefore, the columns of the first matrix need to be rearranged so that each column corresponds to the correct element position in the first information during calculation.

[0230] In some examples, step S630 further includes rearranging the rows of the first matrix based on the physical resource structure for transmitting the second information, and using the rearranged matrix as the first matrix. For example, the rows of the first matrix are rearranged such that the rows of each block in the block diagonal structure are evenly distributed rather than clustered together.

[0231] In some examples, this step also involves extracting / deleting / reordering rows of the first matrix based on row indices to obtain a new first matrix.

[0232] In some embodiments of this example, the row index can be obtained based on the number of REs used for signal transmission and / or location information. For example, with row indices of 0, 1…L, the communication device extracts the first L rows of the first matrix to form a new first matrix, where L is the total number of physical resources used for the second information transmission. Another example is a row index of… The communication device extracts the rows corresponding to the indices from the first matrix to form a new first matrix, where k belongs to the set of RE sequence numbers of the resources used to transmit the second information. It belongs to the set of OFDM sequence numbers of the resources used to transmit the second information. It is the number of REs contained in each OFDM symbol in the resources used to transmit the second information.

[0233] In some embodiments of this example, the row index can be obtained based on the number of REs used for signal transmission and / or location information and relevant information about the CSI that needs to be fed back. For example, with row indices of 0, 1…L, the communication device extracts the first L rows of the first matrix to form a new first matrix, where L is the number of resources used for signal transmission configured by the communication device divided by the number of CSI layers.

[0234] In some examples, this step also includes extracting / deleting / reordering columns of the first matrix based on column indexes to obtain a new first matrix, wherein the column indexes of the first matrix can be obtained based on configuration information related to CSI feedback.

[0235] In some embodiments of this example, the column indices are 0, 1...L, and the communication device extracts the first L columns of the first matrix to form a new first matrix, where L is the number of elements in the CSI.

[0236] In some embodiments of this example, each element in the CSI corresponds to a column in the first matrix, and the column index of the first matrix is ​​determined based on the element of the CSI that currently needs to be fed back. For example, the column index is... The communication device extracts the columns corresponding to the indices from the first matrix to construct a new first matrix, where... The set of antenna port numbers (or antenna pair numbers) belonging to CSI feedback. The set of subband numbers belonging to CSI feedback. This is the maximum number of antenna ports (or antenna pair numbers) reported by CSI. For example, the column index is... The communication device extracts the columns corresponding to the indices from the first matrix to construct a new first matrix, where... The set of antenna port numbers (or antenna pair numbers) belonging to CSI feedback. The set of subband numbers belonging to CSI feedback, and the set of layer numbers belonging to CSI feedback. This is the maximum number of antenna ports (or antenna pair numbers) reported by CSI. This is the maximum number of layers for CSI feedback.

[0237] In some examples, the method for obtaining the candidate matrix is ​​determined based on relevant configuration information fed back by CSI.

[0238] In some embodiments of this example, when the CSI feedback configuration information does not contain information such as the values ​​of elements in the candidate matrix, a candidate matrix is ​​generated based on relevant parameters. Conversely, when the CSI feedback configuration information contains information such as the values ​​of elements in the candidate matrix, a candidate matrix is ​​obtained based on the values ​​and positions of the elements in the candidate matrix.

[0239] In some embodiments of this example, when the timing of CSI feedback in the CSI feedback-related configuration information is aperiodic CSI feedback, a candidate matrix is ​​generated based on the relevant parameters in the CSI feedback-related configuration. Conversely, when the timing of CSI feedback in the CSI feedback-related configuration information is periodic or semi-periodic CSI feedback, a candidate matrix is ​​obtained based on the numerical and positional information of the elements in the candidate matrix in the CSI feedback-related configuration.

[0240] Step S630: Based on the first method, process the first information to obtain the second information, and send a signal associated with the second information, wherein the first information is the CSI information that needs to be fed back, and the second information is the compressed CSI information.

[0241] In some examples, the specific content of step S630 is consistent with the relevant content of step S430.

[0242] In some examples, step S630 may also include reconstructing the first information (i.e., CSI-related information) based on the CSI feedback-related configuration information to match it with the first matrix and / or the second neural network.

[0243] In some embodiments of this example, to match the measured CSI to be fed back with the first matrix, the communication device can pad some rows and / or columns of the measured CSI to be fed back with zeros according to relevant information, predefined rules, and / or base station configuration (such as third information), so that its length matches the number of columns in the first matrix. Then, the first matrix is ​​compressed to obtain the second information (i.e., the compressed CSI information). For example, if the CSI to be fed back only contains elements at subband positions #0, #1, and #5, and the number of columns in the first matrix corresponds to the length of 7 subbands, zeros can be padded at positions #2, #3, #4, and #6 based on relevant configuration information, so that the length of the CSI to be fed back (i.e., the first information) matches the number of columns in the first matrix. This method can be implemented without pruning or splicing the candidate matrix, and can directly use the candidate matrix for compression.

[0244] In some embodiments of this example, the first information is divided into one or more sub-information, wherein the length of each sub-information is the same as the number of columns of the first matrix. The communication device processes each sub-information using the first matrix and combines the processed sub-information into second information, wherein the first matrix used to process each sub-information may be the same or different. For example, the communication device splits the first information based on the layer of the CSI to be fed back, multiplying the CSI of each layer as a sub-information with the same first matrix, and combining all the processed sub-information into second information. As another example, the communication device splits the CSI to be fed back based on sub-bands, multiplying the CSI of each sub-band as a sub-information with a different first matrix, and combining all the processed sub-information into second information.

[0245] In some examples, step S630 further includes deleting elements from the second information. Specifically, the communication device deletes elements from the second information sequentially from back to front until the number of elements in the second information matches the number of physical resources for transmitting the second information in the configuration information.

[0246] In some examples, step S630 further includes repeating elements in the second information. Specifically, the communication device copies elements in the second information from front to back and adds them to the end of the second information until the number of elements in the second information matches the number of physical resources for transmitting the second information in the configuration information.

[0247] Figure 7-1 A possible Channel State Information (CSI) reporting procedure is shown. To reduce the computational load on the UE-side equipment, the first method employs a projection matrix approach.

[0248] Step 1: Determine the Channel State Information (CSI) Compression Method Based on Configuration

[0249] Step 1.1, UE capability reporting: The UE reports its desired CSI feedback scheme based on its own computing power and energy consumption status.

[0250] Level 0 (Lv0): Supports codebook-based CSI feedback schemes.

[0251] Level 1 (Lv1): Supports compressed reporting of Channel State Information (CSI) via projection matrix.

[0252] Level 2 (Lv2): Supports compressed reporting of Channel State Information (CSI) using an artificial intelligence model.

[0253] Step 1.2: Obtain the parameters required for Channel State Information (CSI) compression based on the received configuration.

[0254] Step 2: Obtain the projection matrix, where the number of columns is related to Channel State Information (CSI), the number of rows is related to resources, and the method for obtaining the projection matrix is ​​determined by configuration. See details below. Figure 7-2 .

[0255] Step 3: Compress the complex value of channel state information (CSI) using the projection matrix. Specifically, this includes recombining the channel state information (CSI) into a channel state information (CSI) vector; multiplying the channel state information (CSI) vector with the projection matrix to obtain the compressed channel state information (CSI); and obtaining the transmission symbol based on the compressed channel state information (CSI).

[0256] Step 4: Transmit compressed Channel State Information (CSI)

[0257] By following the steps above, performance similar to that of a dual-side model (where both the UE and base station sides use artificial intelligence models) can be achieved without a high computational load on the UE device.

[0258] like Figure 7-2 This illustrates the process of obtaining the projection matrix based on relevant configurations. The projection matrix used in the UE must correspond to the artificial intelligence model used in the base station (BS).

[0259] Step 1.2.1: Obtain relevant configurations, which may include: number of candidate matrices (default value is 1); candidate matrix generation configuration; channel state information (CSI) reporting resource configuration; channel state information (CSI) reporting configuration; and index information for subsequent selection of candidate matrices.

[0260] Step 1.2.2: Obtain at least one candidate matrix

[0261] Step 1.2.2.1: Obtain the size of each candidate matrix, where the number of matrix rows is related to resources, such as one of the possible resource numbers reported by Channel State Information (CSI); the number of matrix columns is related to Channel State Information (CSI), such as the product of the maximum number of subbands and the maximum number of antenna ports in Channel State Information (CSI).

[0262] Step 1.2.2.2: Obtain the elements of each candidate matrix, including: obtaining the candidate matrix based on the parameters in the configuration; and obtaining the candidate matrix based on at least one pair of values ​​and positions in the configuration. See details for the process. Figure 7-3 and Figure 7-4 .

[0263] Step 1.2.3: Select one candidate matrix from the candidate matrices as the projection matrix, including: selecting the only available candidate matrix; selecting the matrix corresponding to the index in the configuration; selecting the matrix whose number of rows is closest to the number of resources; and selecting the matrix whose number of columns is closest to the number of Channel State Information (CSI) elements.

[0264] Step 1.2.4: Prune the projection matrix according to the sequence requirements, which may include: retaining the corresponding rows in the projection matrix based on resource configuration; and retaining the corresponding columns in the projection matrix based on the reported configuration. See details for the specific process. Figure 7-5 .

[0265] In summary, this method provides a concise solution for obtaining the projection matrix used for channel state information (CSI) compression.

[0266] Figure 7-3 The process of generating a candidate matrix based on parameters in the configuration is illustrated. To minimize additional downlink overhead, the UE autonomously generates a candidate matrix that meets specified requirements.

[0267] Step 1: Obtain initialization parameters from the configuration, where each candidate matrix can be generated using the same or different initialization parameters; the initialization parameters can be general parameters: cell ID, Radio Network Temporary Identifier (RNTI) of Physical Downlink Control Channel (PDCCH); or they can be UE-specific parameters: sequence identifier reported by Channel State Information (CSI), UE sequence identifier, and other UE-related identification information.

[0268] Step 2: Generate a sequence based on the parameters using a (pseudo) random sequence generation algorithm, where the number of elements in the sequence is equal to the product of the number of rows and columns of the matrix; the sequence can be an m-sequence, a Gold sequence, a ZC sequence, a sequence that satisfies a Gaussian random distribution, etc.; in addition, binary phase shift keying (BPSK) modulation can be used to convert the binary sequence (0 and 1) into a bipolar sequence (+1 and -1).

[0269] Step 3: Reassemble the generated sequences into a candidate matrix, where the reassembly order can be either row-major or column-major. This method has the following advantages: it can obtain the candidate matrix without additional overhead; and multiple UEs can reuse uplink resources through UE-specific parameters.

[0270] Figure 7-4 The procedure for obtaining a candidate matrix based on at least one pair of values ​​and locations in the configuration is shown. For optimal performance, the UE should use the candidate matrix directly provided by the base station (BS), at the cost of consuming downlink resources.

[0271] Step 1: Obtain the reference matrix, which may include: the reference matrix can be obtained from a preset configuration; the reference matrix can be obtained through... Figure 7-3 The process shown yields candidate matrices; the reference matrix can be a default matrix (such as an all-zero matrix); the reference matrix can be a previous candidate matrix or a projection matrix.

[0272] Step 2: Obtain at least one pair of values ​​and locations from the configuration information. For example, the configuration information can be obtained through the Radio Resource Control (RRC) process or predefined by the standard. Alternatively, the configuration information can be obtained from the system's broadcast or multicast information (such as the System Information Block (SIB)).

[0273] Step 3: Update the corresponding element in the reference matrix using the obtained value, including: replacing the element at the corresponding position with the obtained value; adding the obtained value to the element at the corresponding position; and multiplying the obtained value by the element at the corresponding position.

[0274] Compared to Figure 7-3 The method described above produces a matrix that achieves a high compression ratio and superior transmission performance.

[0275] Figure 7-5 The process of pruning the projection matrix based on configuration is illustrated. The core idea is to modify the dimensions of the projection matrix according to the Channel State Information (CSI) structure and uplink resource structure to better adapt to the current task. The specific process is as follows: Step 1: Based on the row index and / or column index, retain (or remove) the corresponding rows and / or columns from the projection matrix. The row index and / or column index can be obtained from the configuration; if the indicated row index and / or column index is all or none, the projection matrix will not be pruned.

[0276] Step 2: Determine the index of the reserved column based on the Channel State Information (CSI) feedback required. For example, the column index can be 0, 1…L, where L is the number of elements in the Channel State Information (CSI). Another example is that the column index can be represented as { 1 + },in The set of antenna port numbers that belong to the channel state information (CSI) feedback. The set of subband numbers belonging to the Channel State Information (CSI) feedback, and 1 represents the maximum number of antenna ports for Channel State Information (CSI) feedback.

[0277] Step 3: Determine the index of the reserved row based on the quantity and / or location of the transmission resources. For example, the row index can be 0, 1…L, where L is the quantity of resources used for signal transmission. Another example is that the row index can be represented as { 1 + },in The set of the number of resource elements (REs) belonging to a resource. The set of orthogonal frequency division multiplexing (OFDM) symbols belonging to resources, while 1 represents the number of resource elements (REs) per orthogonal frequency division multiplexing (OFDM) symbol.

[0278] This process can achieve better performance with low overhead.

[0279] The core idea behind compressing the complex values ​​of Channel State Information (CSI) using a projection matrix is ​​that the base station (BS) sends a configuration instruction to the UE to select an appropriate projection matrix. The specific process is as follows: Step 1: Based on the configuration, reorganize the elements of the Channel State Information (CSI) into at least one vector, the length of which is equal to the number of columns in the projection matrix. If the projection matrix is ​​a real matrix, the real and imaginary parts of the CSI vector need to be concatenated to convert it into a real vector. Specifically, this process may include: reorganizing all elements of the CSI into a single vector; reorganizing the elements of the CSI based on layers, i.e., CSI elements of the same layer forming a single vector; and reorganizing the elements of the CSI based on subbands, i.e., CSI elements of the same subband forming a single vector. Furthermore, according to the configuration, zeros are padded in the CSI vector at positions that do not need to be reported (not obtained) to meet the corresponding vector length requirements (e.g., the requirement for consistent vector lengths). Step 2: Obtain compressed Channel State Information (CSI) based on the projection matrix and Channel State Information (CSI) vectors. Specifically, the compressed CSI is a combination of the projection matrix and the product of each CSI vector; that is, the compressed CSI is a weighted sum of the elements in the CSI vector based on multiple sets of coefficients, where the elements of each row of the projection matrix constitute a set of coefficients. If the projection matrix is ​​a real matrix, the compressed CSI needs to be converted into complex values, with half of the elements as the real part and the other half as the imaginary part. This step may also include repeating, pruning, or discarding the compressed CSI according to the amount of physical resources; and power normalization of the compressed CSI, etc.

[0280] This process enables lossless compression of channel state information (CSI) with extremely low computational complexity.

[0281] like Figure 8 The diagram shown is of a device according to an embodiment of the present disclosure. The device includes: UE801 and base station 802.

[0282] UE801 includes a transceiver and a processor, wherein the processor is coupled to the transceiver and configured to execute all method steps performed by the UE throughout the document, including but not limited to steps S410-S430 of method 400, steps S610-S630 of method 600, and sub-steps in various embodiments.

[0283] Base station 802 includes a transceiver and a processor, wherein the processor is coupled to the transceiver and configured to perform all method steps performed by the base station throughout the document, including but not limited to steps S440-S450 of method 500 and sub-steps in various embodiments.

Claims

1. A method performed by a user equipment (UE) in a wireless communication system, comprising: Obtain third information related to the first matrix, wherein the dimension of the first matrix is ​​associated with the length of the first information, and the first information is associated with the information to be reported; Based on the third information, the first matrix is ​​determined; Based on the first matrix, the first information is processed to obtain the second information, and a signal associated with the second information is sent.

2. The method according to claim 1, wherein, Obtain third information related to the first matrix, including: Configuration information is received from the base station, wherein the configuration information includes third information related to the first matrix.

3. The method according to claim 1 or 2, wherein, The third information includes the position and value information of at least one element in the first matrix, or the index information of the first matrix, or the parameter information used to generate the first matrix.

4. The method according to claim 3, wherein, Determining the first matrix based on the third information includes: Based on the position and value information of at least one element in the first matrix contained in the third information, the elements in the second matrix are replaced or added to obtain the first matrix. The second matrix is ​​the first matrix previously used by the UE or a predefined matrix.

5. The method according to claim 3, wherein, The parameter information used to generate the first matrix includes at least one of the following: The sequence information used to generate the first matrix, the reported precision parameters, the configuration information of the reported resources, and the row index and / or column index used to generate the first matrix.

6. The method according to claim 5, wherein, Based on the third information, the first matrix is ​​determined, including at least one of the following: The first matrix is ​​obtained by a random number generation algorithm based on the initialization parameters and / or sequence identifier parameters used to generate the first matrix; Based on the reporting accuracy parameters and / or the configuration information of the resources used for reporting, the first matrix is ​​obtained through a second neural network, wherein the second neural network is predefined or configured by the base station; Based on the row index and / or column index, elements are extracted from the third matrix to form the first matrix, wherein the third matrix is ​​predefined or configured by the base station.

7. The method according to claim 2, wherein, The first matrix is ​​the matrix in the candidate matrix set whose number of columns is closest to the number of elements in the first information, or whose number of rows is closest to the total number of physical resources contained in the transmission resources associated with the second information.

8. The method according to claim 2, wherein, Determining the first matrix based on the third information includes: Based on the third information, at least two matrices are obtained, and the result of multiplying the obtained matrices is used as the first matrix; or Based on the third information, at least two matrices are obtained, and the obtained matrices are concatenated according to rows and / or columns as the first matrix.

9. The method according to claim 3, wherein, The method further includes: Based on the numerical information of at least one element in the first matrix contained in the third information, inverse quantization and / or scaling are performed, and the first matrix is ​​obtained based on the numerical values ​​of the processed elements.

10. The method according to claim 2, further comprising: Based on the number of rows in the first matrix, the parameters of the transmission resources associated with the second information are determined.

11. The method according to claim 10, wherein, The step of determining the parameters of the transmission resources associated with the second information based on the row number of the first matrix includes: The total number of physical resources is obtained based on the number of rows of the first matrix, wherein if the first matrix is ​​a real matrix, the benchmark is 1 / 2 of the number of rows; if the first matrix is ​​a complex matrix, the benchmark is the number of rows. Based on the benchmark, select the transmission resource whose total number of physical resources contained in the candidate transmission resource set is closest to the benchmark.

12. The method according to claim 11, wherein, The step of selecting the transmission resource whose total number of physical resources is closest to the benchmark from the candidate transmission resource set includes: From the candidate transmission resource set, select the transmission resource whose total number of physical resources is closest to and greater than or equal to the benchmark; or Select the transmission resource whose total number of physical resources is closest to the benchmark and is less than or equal to the benchmark from the candidate transmission resource set.

13. A method performed by a base station in a wireless communication system, comprising: Receive the second information sent by the UE; The second information is processed based on the first neural network to obtain the fourth information, wherein the fourth information is associated with the first information, and the first information is associated with the information that the UE needs to report.

14. A UE, characterized in that, include: transceiver; as well as A processor, coupled to the transceiver and configured to perform the method of any one of claims 1-12.

15. A base station, characterized in that, include: transceiver; as well as A processor, coupled to the transceiver and configured to perform the method of claim 13.

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