Channel state information processing method and apparatus

Abstract

The channel state information processing method and apparatus are applicable to wireless local area network systems supporting 802.11 series protocols, such as IEEE 802.11ax next-generation Wi-Fi protocols such as 802.11be, Wi-Fi 7, or EHT, or 802.11be next-generation protocols such as Wi-Fi 8 or UHR, and may also be applied to UWB-based wireless personal area network systems, sensing systems, or the like. The method includes: a transmitting end determining a CSI report based on a transformation matrix and transmitting the CSI report; and a receiving end correspondingly receiving the CSI report and processing a first CSI included in the CSI report based on the transformation matrix. The first CSI included in the CSI report is obtained based on the second CSI and the transformation matrix, where the transformation matrix is ​​a complex matrix with M rows and N columns, and the absolute value of the elements in the transformation matrix is ​​1.

Description

[Technical Field]

[0001] The present application relates to the field of communication technologies, and more particularly to a channel state information (CSI) processing method and apparatus. [Background technology]

[0002] [CROSS-REFERENCE TO RELATED APPLICATIONS] This application claims priority to Chinese Patent Application No. 202211104120.7, filed with the State Intellectual Property Office of China on September 9, 2022, and entitled "Channel State Information Processing Method and Apparatus," which is incorporated herein by reference in its entirety.

[0003] [background] In recent years, wireless traffic has been booming, and users' requirements for communication service quality, such as low latency and ultra-reliability, are becoming increasingly high. As a key technology for carrying wireless traffic services, wireless local area networks (WLANs) are continuously developing and evolving to meet users' higher requirements for wireless transmission. Currently, technologies such as massive multiple input multiple output (MIMO) and millimeter wave are expected to become the key technologies driving WLAN development. However, both the increase in the number of antennas and the increase in frequency bandwidth lead to an increase in the number of subcarriers requiring channel detection. As a result, the channel state information (CSI) feedback overhead increases, degrading transmission performance.

[0004] Orthogonal frequency division multiplexing (OFDM) is one of the core WLAN technologies. The basic principle of OFDM technology is to convert a serial high-speed transmission bit stream into multiple parallel low-speed transmission bit streams and modulate the data onto various orthogonal subcarriers. In the 802.11ac protocol, a 20-MHz bandwidth contains 64 subcarriers. However, in the 802.11ax protocol, the number of 20-MHz subcarriers is increased to 256, resulting in a four-fold increase in CSI feedback overhead. In the transmission process, the channel coefficient for each subcarrier needs to be estimated, and an increase in the number of subcarriers means an increase in CSI feedback overhead. MIMO is another core WLAN technology. MIMO means that, assuming the same bandwidth and the same number of subcarriers, multiple transmit antennas and multiple receive antennas can be used at the transmitting end and the receiving end, respectively, so that signals are transmitted and received through multiple antennas at the transmitting end and multiple antennas at the receiving end, thereby improving communication quality. In MIMO, estimation and feedback are required for the channel between each transmit antenna and each receive antenna, and an increase in the number of antennas also means an increase in CSI feedback overhead. Both OFDM and MIMO lead to an increase in CSI feedback overhead, and combining OFDM and MIMO in WLAN leads to an exponential increase in CSI overhead.

[0005] Therefore, there is an urgent need to find a way to perform CSI compression processing to reduce the CSI feedback overhead. Summary of the Invention

[0006] The embodiments of the present application provide a CSI processing method and apparatus, which can not only effectively reduce the CSI feedback overhead but also effectively improve the accuracy of CSI compression.

[0007] According to a first aspect, an embodiment of the present application provides a CSI processing method, which includes: determining a CSI report; and transmitting the CSI report, where the CSI report includes a first CSI, and the first CSI is obtained based on a second CSI and a transformation matrix, the transformation matrix being a complex matrix having M rows and N columns, where an absolute value of an element in the transformation matrix is ​​1, M is greater than N, M is the number of elements in the second CSI, and N is the number of elements in the first CSI.

[0008] In this embodiment of the present application, the transmitting end compresses the second CSI by using a transform matrix to obtain the first CSI. Here, the number of rows in the transform matrix corresponds to the number of elements in the second CSI (which may also be understood as uncompressed CSI), and the number of columns in the transform matrix corresponds to the number of elements in the first CSI (which may also be understood as compressed CSI). In the method for compressing CSI by using a transform matrix according to this embodiment of the present application, since M is greater than N, the overhead occupied by the compressed CSI is less than the overhead occupied by the uncompressed CSI. Therefore, the CSI feedback overhead can be effectively reduced. Furthermore, this embodiment of the present application can be further applied to different M and N to achieve a relatively long compression length and address massive MIMO and multi-subcarrier scenarios.

[0009] Generally, the absolute value of uncompressed CSI changes slowly. That is, uncompressed CSI has similar (or approximate) absolute values. Therefore, by making the absolute values ​​of elements in the transformation matrix the same and setting them all to 1, it is possible to ensure that when the receiving end reconstructs CSI based on the compressed CSI and the transformation matrix, the absolute values ​​of elements in the reconstructed CSI also have similar (or approximate) values. Therefore, the accuracy of the CSI reconstructed by the receiving end is guaranteed, and the accuracy of CSI compression is effectively improved.

[0010] In a possible implementation, the angles of the elements in at least one column in the transformation matrix vary periodically, with the angles of elements in different columns varying with different periods.

[0011] In this embodiment of the present application, the angle of the uncompressed CSI changes periodically. Therefore, by ensuring that the angle of the elements in at least one column in the transformation matrix changes periodically, the periodic angle change of the CSI restored by the receiving end based on the transformation matrix and the compressed CSI can be made as similar as possible to the periodic angle change of the uncompressed CSI, thereby improving the accuracy of CSI restoration at the receiving end. The angles of the elements in the uncompressed CSI have different frequency components. That is, the periodic angle change of the uncompressed CSI is not completely regular (this can also be understood as not being an exactly periodic change). The angle changes of the elements in different columns in the transformation matrix are designed with different periods, thereby effectively matching the angle change rule (sometimes called the phase characteristic) of the uncompressed CSI. Therefore, the difference between the CSI restored by the receiving end and the uncompressed CSI is minimized, which ensures as much as possible that the receiving end can restore the uncompressed CSI based on the compressed CSI and the transformation matrix, thereby improving the accuracy of CSI compression.

[0012] In a possible implementation, the angles of the elements in the transformation matrix are determined based on the angle period of M, N, and the second CSI, or the angles of the elements in the transformation matrix are determined based on the frequency components of a discrete Fourier transform (DFT) of the angles of M, N, and the second CSI.

[0013] In this embodiment of the present application, the angles of the elements in the transformation matrix are determined based on the number of elements in the first CSI (i.e., N), the number of elements in the second CSI (i.e., M), and the angle period of the second CSI (or the frequency component of the DFT at the phase of the second CSI), so that the angles of the elements in the transformation matrix can be effectively combined with the periodic angle change of the second CSI. Therefore, the transformation matrix is ​​constructed by using the angle change rule of the uncompressed CSI, thereby effectively improving the compression performance of CSI compression. Therefore, the difference between the CSI restored by the receiving end and the uncompressed CSI is minimized, the error between the CSI restored by the receiving end and the uncompressed CSI is reduced, and the accuracy of CSI compression is improved.

[0014] In a possible implementation, the angle of the element in the mth row and nth column in the transformation matrix satisfies the following equation:

[0015]

number

[0016] where T0 relates to the angle of the second CSI, m is an integer greater than 0 and less than or equal to M, n is an integer greater than 0 and less than or equal to N, and k(n) is a function of n.

[0017]

number

[0018] may be equivalent to the angular velocity. The variable k(n) may be used to allow the angles of elements in different columns in the transformation matrix to have different angular velocities, thereby improving the accuracy of the CSI compression.

[0019] In a possible implementation, T0 is determined based on the frequency components of the DFT of the angle of the second CSI.

[0020] In a possible implementation, T0 satisfies the following equation:

[0021]

number

[0022] Here, f0 represents the frequency component corresponding to the maximum absolute value of the coefficients in the frequency components of the DFT of the angle of the second CSI.

[0023] In this embodiment of the present application, the angle period of the second CSI is determined by using the frequency component corresponding to the maximum value of the absolute value of the coefficients in the frequency components of the DFT of the angle of the second CSI, so that the transformation matrix can be better combined with the change rule of the angle period of the second CSI, thereby effectively improving the accuracy of CSI compression.

[0024] In a possible implementation, the function of n satisfies the following equation:

[0025]

number

[0026] or

[0027]

number

[0028] where α is greater than 0 and β is greater than 0.

[0029] In a possible implementation, the CSI report further includes at least one of the following information: M, N, T0, and f0.

[0030] According to a second aspect, an embodiment of the present application provides a CSI processing method, which includes: receiving a CSI report, the CSI report including a first CSI; and processing the first CSI based on a transformation matrix to obtain a second CSI, the transformation matrix being a complex matrix having M rows and N columns, an absolute value of an element in the transformation matrix being 1, M being greater than N, M being the number of elements in the second CSI, and N being the number of elements in the first CSI.

[0031] In a possible implementation, the method further includes: obtaining at least one of information of M, N, T0, and f0, where T0 is related to the angle of the second CSI and f0 is determined based on T0; and processing the first CSI based on a transformation matrix to obtain the second CSI includes: determining a transformation matrix based on T0 or f0, and M and N; and processing the first CSI based on the transformation matrix to obtain the second CSI.

[0032] In a possible implementation, the angles of the elements in at least one column in the transformation matrix vary periodically, with the angles of elements in different columns varying with different periods.

[0033] In a possible implementation, the angle of the element in the mth row and nth column in the transformation matrix satisfies the following equation:

[0034]

number

[0035] where m is an integer greater than 0 and less than or equal to M, n is an integer greater than 0 and less than or equal to N, and k(n) is a function of n.

[0036] In a possible implementation, T0 satisfies the following equation:

[0037]

number

[0038] Here, f0 represents the frequency component corresponding to the maximum absolute value of the coefficients in the frequency components of the DFT of the angle of the second CSI.

[0039] In a possible implementation, the function of n satisfies the following equation:

[0040]

number

[0041] or

[0042]

number

[0043] where α is greater than 0 and β is greater than 0.

[0044] According to a third aspect, an embodiment of the present application provides a communication device configured to perform the method according to the first aspect or any one of the possible implementations of the first aspect. The communication device includes a unit for performing the method according to the first aspect or any one of the possible implementations of the first aspect. For example, the communication device may include a processing unit and a transceiver unit.

[0045] According to a fourth aspect, an embodiment of the present application provides a communication device configured to perform the method according to the second aspect or any one of the possible implementations of the second aspect. The communication device includes a unit for performing the method according to the second aspect or any one of the possible implementations of the second aspect. For example, the communication device may include a processing unit and a transceiver unit.

[0046] According to a fifth aspect, an embodiment of the present application provides a communications device, the communications device including a processor configured to perform the method according to the first aspect or any one of the possible implementations of the first aspect, or the processor configured to execute a program stored in a memory, the program being executed to perform the method according to the first aspect or any one of the possible implementations of the first aspect.

[0047] In a possible implementation, the memory is located external to the communication device.

[0048] In a possible implementation, the memory is located within the communication device.

[0049] In this embodiment of the present application, the processor and memory may alternatively be integrated into one device, in other words, the processor and memory may alternatively be integrated together.

[0050] In a possible implementation, the communication device further includes a transceiver configured to receive and / or transmit signals.

[0051] According to a sixth aspect, an embodiment of the present application provides a communications device, the communications device including a processor configured to perform the method according to the second aspect or any one of the possible implementations of the second aspect, or the processor configured to execute a program stored in a memory, the program being executed to perform the method according to the second aspect or any one of the possible implementations of the second aspect.

[0052] In a possible implementation, the memory is located external to the communication device.

[0053] In a possible implementation, the memory is located within the communication device.

[0054] In this embodiment of the present application, the processor and memory may alternatively be integrated into one device, in other words, the processor and memory may alternatively be integrated together.

[0055] In a possible implementation, the communication device further includes a transceiver configured to receive and / or transmit signals.

[0056] According to a seventh aspect, an embodiment of the present application provides a communication device, the communication device including a logic circuit and an interface, wherein the logic circuit is coupled to the interface, the logic circuit is configured to determine a CSI report, and the interface is configured to output the CSI report.

[0057] It can be understood that the communication device shown in the seventh aspect may be a chip or a device including a chip. For a detailed description of the seventh aspect, please refer to the first aspect.

[0058] According to an eighth aspect, an embodiment of the present application provides a communication device. The communication device includes a logic circuit and an interface. The logic circuit is coupled to the interface. The interface is configured to input a CSI report. The logic circuit is configured to process the first CSI based on a transformation matrix to obtain a second CSI.

[0059] In a possible implementation, the logic circuit is specifically configured to: obtain at least one of information of M, N, T0, and f0, where T0 is related to the angle of the second CSI and f0 is determined based on T0; determine a transformation matrix based on T0 or f0 and M and N; and process the first CSI based on the transformation matrix to obtain the second CSI.

[0060] It can be understood that the communication device shown in the eighth aspect may be a chip or a device including a chip. For a detailed description of the eighth aspect, please refer to the second aspect.

[0061] According to a ninth aspect, an embodiment of the present application provides a computer-readable storage medium configured to store a computer program which, when run on a computer, performs the method according to the first aspect or any one of the possible implementations of the first aspect.

[0062] According to a tenth aspect, an embodiment of the present application provides a computer-readable storage medium configured to store a computer program which, when run on a computer, performs the method according to the second aspect or any one of the possible implementations of the second aspect.

[0063] According to an eleventh aspect, an embodiment of the present application provides a computer program product, the computer program product comprising a computer program or computer code, which, when run on a computer, performs the method according to the first aspect or any one of the possible implementations of the first aspect.

[0064] According to a twelfth aspect, an embodiment of the present application provides a computer program product, the computer program product comprising a computer program or computer code, which, when run on a computer, performs the method according to the second aspect or any one of the possible implementations of the second aspect.

[0065] According to a thirteenth aspect, an embodiment of the present application provides a computer program which, when run on a computer, performs the method according to the first aspect or any one of the possible implementations of the first aspect.

[0066] According to a fourteenth aspect, an embodiment of the present application provides a computer program which, when run on a computer, performs the method according to the second aspect or any one of the possible implementations of the second aspect.

[0067] According to a fifteenth aspect, an embodiment of the present application provides a communication system, the communication system including a transmitting end and a receiving end, the transmitting end configured to perform the method according to the first aspect or any one of the possible implementations of the first aspect, and the receiving end configured to perform the method according to the second aspect or any one of the possible implementations of the second aspect. [Brief explanation of the drawings]

[0068] [Figure 1] 1 illustrates an architecture of a communication system according to an embodiment of the present application; [Figure 2] 1 illustrates an architecture of a communication system according to an embodiment of the present application; [Figure 3] FIG. 10 illustrates the performance of a subcarrier grouping method according to an embodiment of the present application. [Figure 4] 1 is a schematic flowchart illustrating a CSI processing method according to an embodiment of the present application. [Figure 5a] FIG. 1 illustrates a phase change of CSI according to an embodiment of the present application. [Figure 5b] FIG. 1 illustrates the frequency components of the DFT of the phase of the CSI according to an embodiment of the present application. [Figure 6] FIG. 1 illustrates the performance of a CSI compression method based on a recursive polynomial method, according to an embodiment of the present application. [Figure 7a] FIG. 1 illustrates a performance comparison between various CSI compression methods, according to an embodiment of the present application. [Figure 7b] FIG. 1 illustrates a performance comparison between various CSI compression methods, according to an embodiment of the present application. [Figure 7c]FIG. 1 illustrates a performance comparison between various CSI compression methods, according to an embodiment of the present application. [Figure 8a] FIG. 1 illustrates a performance comparison between various CSI compression methods, according to an embodiment of the present application. [Figure 8b] FIG. 1 illustrates a performance comparison between various CSI compression methods, according to an embodiment of the present application. [Figure 9] FIG. 1 illustrates a configuration of a communication device according to an embodiment of the present application. [Figure 10] FIG. 1 illustrates a configuration of a communication device according to an embodiment of the present application. [Figure 11] FIG. 1 illustrates a configuration of a communication device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0069] The terms "first," "second," and the like in the specification, claims, and accompanying drawings of this application are used merely to distinguish different objects, and not to describe a particular order. In addition, the terms "include," "have," and any other variations thereof are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include additional steps or units that are not listed, or may optionally include other steps or units that are inherent to the process, method, product, or device.

[0070] In this specification, a reference to "one embodiment" means that a particular feature, configuration, or characteristic described with reference to an embodiment can be incorporated into at least one embodiment of the present application. Phrases appearing in various places in this specification may not necessarily refer to the same embodiment, and are not an embodiment that is exclusive, independent of, or an alternative to another embodiment. It can be explicitly and implicitly understood by those skilled in the art that the embodiments described in this specification can be combined with other embodiments.

[0071] In this application, "at least one (item)" means one or more, "plurality" means two or more, "at least two (items)" means two, three, or more, and "and / or" is used to describe an association relationship between related objects and indicates that three relationships may exist. For example, "A and / or B" may indicate the following three cases: when only A is present; when only B is present; and when both A and B are present. Here, A and B may be singular or plural. "Or" indicates that two relationships may exist, e.g., when only A is present and when only B is present. If A and B are not mutually exclusive, it may indicate that three relationships exist, e.g., when only A is present, when only B is present, and when both A and B are present. The character " / " generally indicates an "or" relationship between related objects. "At least one of the following" or similar expressions means any combination of these items. For example, at least one of a, b, or c can represent a, b, c, "a and b," "a and c," "b and c," or "a and b and c."

[0072] The technical solutions provided in this application may be applied to WLAN systems, such as Wi-Fi. For example, the methods provided in this application may be applied to IEEE 802.11 series protocols, such as 802.11a / b / g protocols, 802.11n protocols, 802.11ac protocols, 802.11ax protocols, 802.11be protocols, or next-generation protocols. These examples are not listed one by one in this specification. The technical solutions provided in this application may also be applied to wireless personal area networks (WPANs) based on UWB technology. For example, the methods provided in this application may be applied to IEEE 802.15 series protocols, such as 802.15.4a protocols, 802.15.4z protocols, 802.15.4ab protocols, or future-generation UWB WPAN protocols. These examples are not listed one by one in this specification. The technical solutions provided in the present application may be further applied to various other communication systems, for example, Internet of Things (IoT) systems, vehicle-to-everything (V2X) systems, and narrowband Internet of Things (NB-IoT) systems, or may be further applied to devices in Internet of Things (Vehicles), Internet of Things nodes, sensors, and the like in Internet of Things (IoT), smart cameras, smart remote controls, and smart water / electricity meters in smart homes, sensors in smart cities, and the like, or may be further applied to long term evolution (LTE) systems, fifth generation (5G) communication systems, new communication systems emerging in future communication developments, and the like.

[0073] Although the embodiments of the present application primarily use WLANs, particularly networks conforming to the IEEE 802.11 series of standards, as an example for illustration, such as systems supporting Wi-Fi 7, sometimes referred to as "extremely high throughput (EHT)," and systems supporting Wi-Fi 8, sometimes referred to as "ultra high reliability (UHR)" or "ultra high reliability and throughput (UHRT)," as another example, those skilled in the art will readily understand that aspects of the present application can be extended to various standards or protocols, such as Bluetooth, high performance radio LAN (HIPERLAN) (a wireless standard similar to the IEEE 802.11 standard and primarily used in Europe), or wide area networks (WANs), or other networks known or developed in the future. Therefore, various aspects provided in the present application can be applied to any suitable wireless network, regardless of the coverage area and wireless access protocol used.

[0074] The methods provided in the present application may be implemented by a communication device in a wireless communication system, for example, an access point (AP) or a station (STA).

[0075] An access point is a device with wireless communication capabilities that supports communication or sensing using a WLAN protocol and communicates with or senses other devices (e.g., stations or other access points) in a WLAN network. An access point may also have the capability of communicating with or sensing other devices. Alternatively, an access point may correspond to a bridge connecting a wired network and a wireless network. The main function of an access point is to connect various wireless network clients together and then connect the wireless network to Ethernet. In a WLAN system, an access point may be referred to as an access point station (AP STA). A device with wireless communication capabilities may be an entire device, or a chip, processing system, or the like integrated into the entire device. A device equipped with a chip or processing system may implement the methods and functions in the embodiments of the present application under the control of the chip or processing system. An AP in the embodiments of the present application is a device that provides services to STAs and may support the 802.11 series of protocols, or subsequent protocols, or the like. For example, an access point may be an access point for a terminal (e.g., a mobile phone, etc.) to access a wired (or wireless) network, and is mainly deployed in homes, buildings, and campuses. A typical coverage radius is tens of meters to hundreds of meters. Indeed, an access point may alternatively be deployed outdoors. As another example, an AP may be a communication entity such as a communication server, a router, a switch, or a network bridge. An AP may include various forms of macro base stations, micro base stations, relay stations, and the like. Indeed, an AP may alternatively be a chip and processing system within these various forms of devices, which implements the methods and functions in the embodiments of the present application.

[0076] A station is a device with wireless communication capabilities, supports communication or sensing by using a WLAN protocol, and has the capability of communicating with or sensing another station or an access point in a WLAN network. In a WLAN system, a station may be referred to as a non-access point station (non-AP STA). For example, a STA is any user communication device that enables a user to communicate with an AP or perform sensing of the AP and communicate with the WLAN. A device with wireless communication capabilities may be an entire device, or a chip, processing system, or the like incorporated in the entire device. A device incorporating a chip or processing system may implement the methods and functions in the embodiments of the present application under the control of the chip or processing system. For example, a station may be a wireless communication chip, a wireless sensor, or a wireless communication terminal, and may also be referred to as a user. As another example, the station may be a mobile phone that supports Wi-Fi communication functionality, a tablet computer that supports Wi-Fi communication functionality, a set-top box that supports Wi-Fi communication functionality, a smart television that supports Wi-Fi communication functionality, a smart wearable device that supports Wi-Fi communication functionality, an in-vehicle communication device that supports Wi-Fi communication functionality, or a computer that supports Wi-Fi communication functionality.

[0077] WLAN systems can provide high-speed and low-latency transmission. With the continuous development of WLAN application scenarios, WLAN systems are being applied in more scenarios and industries, such as the Internet of Things industry, the Internet of Vehicles industry, the banking industry, corporate offices, stadiums and exhibition halls, concert halls, hotel rooms, dormitories, hospital wards, classrooms, shopping malls and supermarkets, squares, streets, workshops, and warehouses. Indeed, a device (e.g., an access point or station) supporting WLAN communication or sensing may be a sensor node in a smart city (e.g., a smart water meter, a smart electricity meter, or a smart air detection node), a smart device in a smart home (e.g., a smart camera, a projector, a display, a television, a speaker, a refrigerator, or a washing machine), a node in the Internet of Things, an entertainment terminal (e.g., a wearable device such as an augmented reality (AR) or virtual reality (VR) device), a smart device in a smart office (e.g., a printer, a projector, a loudspeaker, or a speaker), an Internet of Vehicles appliance in the Internet of Vehicles, infrastructure in everyday life scenarios (e.g., a vending machine, a self-service navigation console in a shopping mall or supermarket, a self-checkout machine, or a self-service ordering machine), a device in a large stadium or music venue, or the like. Illustratively, for example, the access points and stations may be devices used in the Internet of Vehicles, Internet of Things nodes or sensors in the Internet of Things, smart cameras, smart remote controls, and smart water or electricity meters in a smart home, or sensors in a smart city. The specific forms of the STAs and APs are not limited in the embodiments of the present application and are merely examples for the purposes of illustration herein.

[0078] For example, a communication system to which the methods provided in the present application can be applied may include an access point and a station. For example, the present application is applicable to a communication or sensing scenario between an AP and a STA, between APs, or between STAs in a WLAN. This is not limited to the embodiments of the present application. Optionally, an AP may communicate with or sense a single STA, or an AP may communicate with or sense multiple STAs simultaneously. Specifically, communication or sensing between an AP and multiple STAs may be further divided into downlink transmission, in which the AP simultaneously transmits signals to multiple STAs, and uplink transmission, in which multiple STAs transmit signals to the AP. WLAN communication protocols may be supported between an AP and a STA, between APs, or between STAs. The communication protocols may include the IEEE 802.11 series of protocols, such as the 802.11be standard and certainly standards beyond 802.11be.

[0079] 1 is a diagram illustrating the architecture of a communication system according to one embodiment of the present application. The communication system may include one or more APs and one or more STAs. FIG. 1 illustrates two access points, such as AP1 and AP2, and three stations, such as STA1, STA2, and STA3. It may be understood that one or more APs may communicate with one or more STAs. Indeed, APs may communicate with each other, and STAs may communicate with each other.

[0080] In Fig. 1, an example is used in which the STA is a mobile phone and the AP is a router, but it can be understood that this does not mean that the types of APs and STAs in the present application are limited. Also, Fig. 1 shows only two APs and three STAs as an example. There may be more or fewer APs or STAs. This is not limited in this embodiment of the present application.

[0081] FIG. 2 is a diagram illustrating the architecture of a communication system according to an embodiment of the present application. As shown in FIG. 2, the communication system may include at least one network device and at least one terminal device, for example, terminal device 1 to terminal device 4 in FIG. 2. For example, terminal device 3 and terminal device 4 shown in FIG. 2 may communicate directly with each other. For example, direct communication between the terminal devices may be implemented by using D2D technology. For example, terminal device 1 to terminal device 4 may communicate individually with the network device. For example, terminal device 3 and terminal device 4 may communicate directly with the network device, or may communicate indirectly with the network device, for example, through another terminal device (not shown in FIG. 2). It should be understood that FIG. 2 illustrates an example of one network device and four terminal devices, and communication links between the communication devices. Optionally, the communication system may include multiple network devices, and a different number of terminal devices, for example, more or fewer terminal devices, may be included in the coverage area of ​​each network device. This is not limited to this embodiment of the present application. The terminal devices and network devices are described in detail below.

[0082] A terminal device is a device having wireless transmission and reception capabilities. The terminal device may communicate with access network equipment (sometimes called access equipment) in a radio access network (RAN). The terminal device may also be called user equipment (UE), access terminal, terminal, subscriber unit, subscriber station, mobile station, remote station, remote terminal, mobile device, user terminal, user agent, user equipment, or the like. In possible implementations, the terminal device may be deployed on land, including indoor, outdoor, handheld, or vehicle-mounted devices, or on water (e.g., on a ship, etc.). In possible implementations, the terminal device may be a handheld device with wireless communication capabilities, an in-vehicle device, a wearable device, a sensor, a terminal in the Internet of Things, a terminal in the Internet of Vehicles, an unmanned aerial vehicle, any form of terminal device in a 5G network or future network, or the like. This is not limited to this embodiment of the present application. It may be understood that the terminal devices shown in this embodiment of the present application may include vehicles (e.g., automobiles) in the Internet of Vehicles, and may also include in-vehicle devices, in-vehicle terminals, or the like in the Internet of Vehicles. The specific form of the terminal devices used in the Internet of Vehicles is not limited in this embodiment of the present application. It may be understood that the terminal devices shown in this embodiment of the present application may communicate with each other by using D2D, V2X, M2M, or the like. The communication method between the terminal devices is not limited in this embodiment of the present application.

[0083] The network equipment may be a device deployed in a radio access network and providing wireless communication services to terminal devices. The network equipment may also be referred to as access network equipment, access equipment, RAN equipment, or the like. For example, the network equipment may be a next generation NodeB (gNB), a next generation evolved NodeB (ng-eNB), network equipment in 6G communication, or the like. The network equipment may be any device having radio transmission and reception capabilities, including but not limited to the base stations listed above (including base stations deployed on satellites). Alternatively, the network equipment may be a device having base station functionality in 6G. Optionally, the network equipment may be an access node, a radio relay node, a radio backhaul node, or the like in a Wi-Fi system. Optionally, the network equipment may be a radio controller in a cloud radio access network (CRAN) scenario. Optionally, the network equipment may be a wearable device, an in-vehicle device, or the like. Optionally, the network equipment may be a small cell, a transmission reception point (TRP) (also called a transmission point), or the like. Alternatively, it may be understood that the network equipment may be a base station, a satellite, or the like in a future evolved public land mobile network (PLMN). Alternatively, the network equipment may be a communication device that functions as a base station in a non-terrestrial communication system, D2D, V2X, or M2M, or the like. The specific type of network equipment is not limited in this embodiment of the present application. In systems using different radio access technologies, the name of a communication device having the function of network equipment may be different and is not enumerated in this embodiment of the present application.Optionally, in some deployments of the network equipment, the network equipment may include a central unit (CU), a distributed unit (DU), and the like. In some other deployments of the network equipment, the CU may be further divided into a CU control plane (CP), a CU user plane (UP), and the like. Alternatively, in still some deployments of the network equipment, the network equipment may be an open radio access network (ORAN) architecture, or the like. The specific deployment aspects of the network equipment are not limited to this embodiment of the present application.

[0084] The network architectures and service scenarios described in the embodiments of the present application are intended to more clearly explain the technical solutions in the embodiments of the present application, but do not constitute any limitations on the technical solutions provided in the embodiments of the present application. Those skilled in the art may recognize that even if network architectures evolve and new service scenarios emerge, the technical solutions provided in the embodiments of the present application can still be applied to similar technical problems.

[0085] Currently, a subcarrier grouping method exists, in which multiple adjacent subcarriers report one CSI, thereby reducing the feedback overhead of CSI reporting. In this method, CSI from equally spaced subcarriers is selected for transmission. After the receiving end receives the compressed CSI, the receiving end performs initial CSI reconstruction. This method has the advantage of low computational complexity. However, the accuracy of this method is relatively low, which affects subsequent applications such as beamforming or Wi-Fi sensing, and fails to meet the CSI compression quality requirements. Figure 3 illustrates the subcarrier grouping method for CSI (also called channel coefficients) received between a pair of transmitting and receiving antennas. In Figure 3, "uncompressed" refers to uncompressed CSI, and "compressed" refers to CSI reconstructed by the receiving end based on the compressed CSI received by the receiving end. The "compressed" symbol in Figure 3 may also be understood to refer to "CSI reconstructed after compression." As can be seen from Figure 3, when the compression rate is relatively high (for example, 128 CSIs are compressed to 8 CSIs), the subcarrier grouping method has obvious deviations. The reason for the obvious deviations is as follows: As the compression rate increases, the interval between selected subcarriers becomes larger, and the continuity between subcarriers is lost. As a result, the accuracy of CSI recovery performed at the receiving end decreases.

[0086] However, the subcarrier grouping method ignores the CSI variation rules between subcarriers, and CSI compression is directly performed by using a one-out-of-many approach, resulting in relatively poor compression quality. In practical applications, compression quality is usually achieved by dividing two to four subcarriers into one group. Therefore, relatively long compression lengths cannot be implemented, and massive MIMO and multi-subcarrier scenarios cannot be met. With the advancement of WLAN technology, OFDM subcarrier spacing has become narrower and frequency bandwidth has become wider. Both of these factors increase the number of subcarriers requiring channel estimation. Furthermore, the number of MIMO antennas continues to increase. These three factors add up to an increase in CSI feedback overhead, consuming time that would otherwise be used for data transmission and resulting in degradation of network performance.

[0087] In view of this, an embodiment of the present application provides a CSI processing method and apparatus for effectively reducing CSI feedback overhead. Reducing the CSI feedback overhead can effectively improve cases where excessively high CSI feedback overhead consumes time used for data transmission, thereby ensuring network performance. Optionally, the method provided in this embodiment of the present application can further effectively use a CSI variation rule between subcarriers, thereby effectively reducing the difference between the recovered CSI at the receiving end and the uncompressed CSI at the transmitting end, improving compression accuracy and improving the compression performance of CSI compression. Optionally, the method provided in this embodiment of the present application can more effectively meet massive MIMO and multi-subcarrier scenarios. Optionally, the CSI feedback overhead is mainly related to the number of MIMO antennas and the number of OFDM subcarriers. The method provided in this embodiment of the present application can effectively alleviate the problem of increased CSI feedback overhead due to the use of at least one of MIMO technology and OFDM technology.

[0088] For example, CSI feedback plays an important role in radio frequency sensing applications such as Wi-Fi positioning. In wireless environments, different human behaviors result in different multipath variations. Therefore, by observing human movements in wireless sensor networks and the resulting CSI, it is possible to reconstruct the real physical world through radio frequency sensing and thereby provide a wide range of new services. CSI compression can reduce the number of data pieces that need to be transmitted, thereby reducing transmission delays and improving the timeliness of CSI feedback and wireless sensing.

[0089] FIG. 4 is a schematic flowchart illustrating a CSI processing method according to an embodiment of the present application. This method may be applied to the communication system shown in FIG. 1 or 2. This method may be applied to a transmitting end and a receiving end. The transmitting end may be understood as a communication device that transmits a CSI report, and the receiving end may be understood as a communication device that receives the CSI report. For example, a STA may be used as the transmitting end, and an AP may be used as the receiving end. As another example, an AP may be used as the transmitting end, and an STA may be used as the receiving end. As another example, a UE may be used as the transmitting end, and a base station may be used as the receiving end. As another example, a base station may be used as the transmitting end, and a UE may be used as the receiving end. As another example, a beamforming receiving end (beamformee) may be used as the transmitting end, and a beamforming transmitting end (beamformer) may be used as the receiving end. It may be understood that the beamforming transmitting end may be understood as a communication device that transmits a pilot signal, and the beamforming receiving end may be understood as a communication device that receives the pilot signal. For example, the beamforming receiving end may obtain a channel measurement result based on a pilot signal transmitted by the beamforming transmitting end and feed back CSI. The pilot signal may be understood as a signal used for channel detection, a signal used for channel estimation, or a signal used for channel measurement. In this embodiment of the present application, channel detection, channel estimation, and channel measurement may be understood to be interchangeable. For a description of the transmitting end and the receiving end in this embodiment of the present application, please refer to the above description of FIG. 1 and FIG. 2. Details will not be described again in this specification. In this embodiment of the present application, it is not limited whether another forwarding device is included between the transmitting end and the receiving end.

[0090] Before describing the method shown in FIG. 4, first, the transformation matrix in this embodiment of the present application will be described below.

[0091] The CSI processing method provided in this embodiment of the present application is related to the following optimization problem:

[0092]

number

[0093] where vector b represents a vector of uncompressed CSI, vector x represents a vector of compressed CSI, and elements in vector b and vector x are complex numbers. The number of elements in vector b is M, and the number of elements in vector x is N, where M and N are both positive integers, and M>N. Vector b is

[0094]

number

[0095] and the vector x can be expressed as

[0096]

number

[0097] It can be understood that matrix A represents a transformation matrix used for CSI compression, matrix A is a complex matrix having M rows and N columns, and matrix A can be expressed as

[0098]

number

[0099] In matrix A, the number of rows M corresponds to the number of uncompressed CSI (which may also be understood as the number of elements in vector b), and the number of columns N corresponds to the number of compressed CSI (which may also be understood as the number of elements in vector x). The compressed CSI referred to in this specification is compressed CSI (e.g., vector x) obtained by the transmitting end based on the uncompressed CSI (e.g., vector b), and "compressed" in the accompanying drawings in the embodiments of the present application may be understood to represent CSI restored by the receiving end based on vector x and transformation matrix A, i.e., Ax. Therefore, "compressed" in the accompanying drawings should not be understood as compressed CSI obtained by the transmitting end based on the uncompressed CSI and transformation matrix in the embodiments of the present application.

[0100] It can be understood that the uncompressed CSI in this embodiment of the present application can be understood as the CSI obtained by the transmitting end through channel estimation for subcarrier groups between the transmitting antennas and the receiving antennas. For example, vector b may correspond to a vector of CSI obtained based on the transmitting antennas, the receiving antennas, and the subcarrier groups. The number of subcarriers specifically included in the subcarrier groups is not limited in this embodiment of the present application. For example, the subcarrier groups may include 64 subcarriers, 242 subcarriers, or the like, which are not listed one by one. Accordingly, M shown in this embodiment of the present application can be understood as the number of CSIs obtained on the subcarrier groups for the transmitting antennas and the receiving antennas.

[0101] When performing CSI compression, the transmitting end may compress M CSIs of consecutive subcarrier groups into N CSIs. Optionally, the number of elements M in each group of uncompressed CSI may be equal to the number of consecutive subcarriers in the bandwidth M C and the number of subcarriers M corresponding to the compression performance threshold. P , i.e., M=min{M C ,M P}. M Pdenotes the corresponding number of subcarriers if the minimum CSI compression performance requirement is met. C represents the number of contiguous subcarriers within a bandwidth of 20 MHz, 40 MHz, 80 MHz, 160 MHz, or 320 MHz, or a resource unit (RU) having a different size. Optionally, for a bandwidth, direct current (DC) subcarriers (sometimes referred to as DC subcarriers for short) within the bandwidth (near the center frequency) are usually not used to transmit data. Therefore, due to the presence of the DC subcarriers, the M subcarriers shown in this embodiment of the present application may be discontinuous. However, because the DC subcarriers themselves are not used to transmit data, the subcarrier discontinuity caused by the DC subcarriers within the bandwidth is ignored, and ignoring the DC subcarriers is equivalent to regarding the subcarriers within the bandwidth as contiguous. Optionally, although the loss of CSI at the DC subcarrier position (near the center frequency) within the bandwidth causes a loss of phase continuity of two CSI data on either side of the DC subcarrier position, the transmitting end may perform an interpolation process at the DC position to obtain CSI of consecutive subcarriers within the bandwidth. Accordingly, when restoring CSI based on the compressed CSI and the transformation matrix, the receiving end may restore more than M CSI, and then remove the CSI at the DC position to obtain the initial CSI.

[0102] A transmitting antenna shown in this embodiment of the present application may be understood to be an antenna configured to transmit a pilot signal (e.g., an antenna configured to transmit a pilot signal at a receiving end, etc.), and a receiving antenna may be understood to be an antenna configured to receive a pilot signal (e.g., an antenna configured to receive a pilot signal at a receiving end, etc.).

[0103] At the transmitting end, for a given vector b of uncompressed CSI, the above equation (1) can be understood as a 2-norm minimization problem, which belongs to the convex optimization problem. Therefore, the result of the optimization problem can be solved by using a convex optimization algorithm, and the compressed CSI vector x is obtained.

[0104] For the receiving end, the receiving end may recover the uncompressed CSI based on the following equation:

[0105]

number

[0106] Here, A i represents the i-th column vector of matrix A, where i is an integer greater than or equal to 1 and less than or equal to N. In other words, the receiving end may multiply each column of matrix A by the corresponding element of the compressed CSI, and then obtain a cumulative sum to estimate the uncompressed CSI. It can be understood that the approximation sign is used in equation (2) because the CSI recovered by the receiving end based on the compressed CSI and matrix A may differ from the uncompressed CSI obtained by the transmitting end. For example, this difference may arise due to the optimization problem in equation (1). For example, the vector x obtained by the transmitting end may be expressed as

[0107]

number

[0108] may not be equal to 0. As another example, the difference may be caused by errors in quantization and coding. These examples are not listed one by one in this specification.

[0109] The matrix A in this embodiment of the present application may satisfy at least one of the following conditions:

[0110] Condition 1: The absolute value of the elements in matrix A is 1. This can also be understood as all elements in each column of matrix A having unit absolute value.

[0111] In general, the absolute values ​​(sometimes referred to as values) of uncompressed CSI change slowly. That is, uncompressed CSI has similar (or approximate) absolute values. The absolute values ​​in vector b are similar (or approximate). Therefore, by making the absolute values ​​of elements in vector A the same and setting them all to 1, it is possible to effectively adjust the absolute values ​​of elements in vector x to achieve similarity (or approximation) between the absolute values ​​of elements in vector Ax and the absolute values ​​of elements in vector b. Therefore, the computational complexity of optimizing Equation (1) by the transmitting end can be effectively reduced, and the difference between the CSI recovered by the receiving end and the uncompressed CSI is minimized, thereby improving the compression performance of CSI compression.

[0112] Condition 1 can also be understood as the case where the element in the m-th row and n-th column in matrix A satisfies the following equation:

[0113]

number

[0114] Here, θ mn can be understood as the angle of the element at the mth row and nth instance in matrix A.

[0115] Condition 2: The angles (which may also be understood as phases) of elements in at least one column in matrix A vary periodically, with the angles of elements in different columns varying with different periods.

[0116] In practical applications, the phase of uncompressed CSI may change periodically, or may be understood to be periodic. Figure 5a is a diagram illustrating CSI phase changes according to one embodiment of the present application. In Figure 5a, the abscissa represents the subcarrier sequence number, and the ordinate represents the phase, expressed in units of π. Figure 5a illustrates CSI phase changes shown by using an example in which a subcarrier group has 242 subcarriers and corresponds to one transmit antenna and one receive antenna. However, the number of subcarriers and the number of antennas shown in Figure 5a should not be understood as limitations on this embodiment of the present application. It may be understood that the CSI phase changes shown in Figure 5a are general. It may be understood that the angles and phases in this embodiment of the present application may be understood to be equivalent.

[0117] Since the angles of the uncompressed CSI change periodically, the angles of the elements in vector b also change periodically. Therefore, at least one column in matrix A (e.g., A i By ensuring that the angles of the elements in i to the coefficient x i After multiplying by x i A i The periodic angular variation of the elements in vector b can be as close as possible to the periodic angular variation of the elements in vector b. i The weight x corresponding to i By adjusting Ax=b, Ax=b can be realized as much as possible, thereby improving the accuracy of CSI compression.

[0118] The angles of the elements in vector b have different frequency components. That is, the periodic angle change of vector b is not completely regular (it can also be understood as not being an exactly periodic change). Therefore, the angles of the elements in different columns in matrix A correspond to different periods, which can effectively match the angle change rule (sometimes called the phase characteristic) of the uncompressed CSI, thereby improving the accuracy of CSI compression. Therefore, the difference between the CSI restored by the receiving end and the uncompressed CSI is minimized, and it is ensured as much as possible that the receiving end can restore the uncompressed CSI based on the compressed CSI and the transformation matrix.

[0119] Condition 2 can be extended to each column in matrix A. For example, the angle of the elements in each column in matrix A changes periodically. Based on the relationship between the period and the angular velocity, Condition 2 can also be expressed as follows: the angular velocity of the angles of the elements in at least one column in matrix A remains unchanged (a constant angular velocity can also be understood as the same angular velocity), and the angular velocities of the angles of the elements in different columns are different. When Condition 2 is extended to each column in matrix A, the angular velocity of the angles of the elements in each column in matrix A remains unchanged, and the angular velocities of the angles of the elements in different columns are different.

[0120] Condition 3: The angle of the elements in matrix A may be determined based on M, N, and the phase period of the uncompressed CSI.

[0121] The angle of the element in the m-th row and n-th row in matrix A satisfies the following formula:

[0122]

number

[0123] where T0 represents the phase period of the uncompressed CSI, m is a positive integer less than or equal to M, and k(n) is a function of n, where n is a positive integer less than or equal to N.

[0124] In equation (4),

[0125]

number

[0126] may correspond to angular velocity. By using the variable n, adjusting the angular velocity of the elements may better combine equation (4) with respect to columns in matrix A, so that the angles of elements in different columns in matrix A have different angular velocities, thereby improving the accuracy of CSI compression.

[0127] Optionally, T0 may be understood as the number of subcarriers included in a 2π phase change of the uncompressed CSI. Optionally, T0 may be understood as the minimum period of a phase change of the uncompressed CSI. Optionally, T0 may be understood as a reference period. Optionally, T0 may be understood as an optimal period of the optimization problem shown in equation (5). T0 is related to the angle of the uncompressed CSI, i.e.,

[0128]

number

[0129] is.

[0130] For an explanation of the parameters in equation (5), please refer to equation (1), and the details will not be repeated here.

[0131] Based on the relationship between the phase period of the uncompressed CSI and the frequency components of the DFT of the phase, condition 3 may also be understood as the angle of the elements in matrix A may be determined based on M, N, and the frequency components of the DFT of the phase of the uncompressed CSI.

[0132] Figure 5b is a diagram showing frequency components of a DFT of the phase of CSI according to one embodiment of the present application. In Figure 5b, the abscissa represents the frequency components, and the ordinate represents the absolute values ​​of coefficients (sometimes called weights) corresponding to the frequency components. In general, the coefficients corresponding to the frequency components after the DFT are complex numbers, and the ordinate shown in Figure 5b represents the absolute values ​​of the coefficients. Based on the relationship between Figures 5a and 5b, the abscissa in Figure 5b can also be understood as the number of periods over which the phase of the uncompressed CSI (vector b) changes, and the number of periods can be understood to correspond to the number of diagonal lines in Figure 5a.

[0133] After the DFT of the phase of the uncompressed CSI, the highest point on the x-axis can be understood as the frequency component with the largest absolute value of the coefficient. As shown in Figure 5b, the highest point on the x-axis is 5. Since the frequency corresponding to the first number after the DFT of the phase of the uncompressed CSI is 1, to ensure the correspondence between frequency and coefficient, the frequency component of the uncompressed CSI shown in Figure 5a is 5-1=4.

[0134] The frequency components of the DFT of the phase of the uncompressed CSI may satisfy the following equation:

[0135]

number

[0136] where,

[0137]

number

[0138] represents the maximum value of the absolute values ​​of the coefficients corresponding to different frequency components of the DFT of the phase of the uncompressed CSI (i.e., vector b), or may be understood as the ordinate corresponding to the highest point of the DFT of the phase of the uncompressed CSI, or may be understood as the maximum weight of the DFT of the phase of the uncompressed CSI.

[0139]

number

[0140] represents the frequency component corresponding to the maximum value mentioned above, or the abscissa corresponding to the highest point mentioned above, or the frequency component corresponding to the maximum weight mentioned above.

[0141]

number

[0142] represents the frequency component of the phase of the uncompressed CSI.

[0143] T0 in this embodiment of the present application will be described below.

[0144] In a possible implementation, T0 may satisfy the following equation:

[0145]

number

[0146] is.

[0147] Since the number of elements in the uncompressed CSI is M, T0 may be obtained based on M and f0.

[0148] 5a and 5b are used as an example. When M=242 and f0=4, T0=242 / 4=60.5. Therefore, the minimum period of the phase change of the uncompressed CSI shown in FIG. 5a may be 60.5. Or, the number of subcarriers included in the 2π phase change of the uncompressed CSI is 60.5. Or, T0 that optimizes the optimization problem shown in Equation (5) is 60.5.

[0149] In another possible implementation, T0 may satisfy the following equation:

[0150]

number

[0151] is.

[0152] For a detailed explanation of equation (8), please refer to equation (7) or equation (6).

[0153] In yet another possible implementation, T0 may satisfy the following equation:

[0154]

number

[0155] ,or,

[0156]

number

[0157] is.

[0158] It can be understood that the numerators in equations (7) and (8) are determined based on the number of elements in the uncompressed CSI, and the numerators in equations (9) and (10) are determined based on the length of the uncompressed CSI.

[0159] It can be understood that in specific implementation, the above formulas (7) to (10) can be further adjusted, for example, rounding up or rounding down to ensure that T0 is an integer, thereby reducing the calculation complexity as much as possible.

[0160] In yet another possible implementation, T0 may satisfy the following equation:

[0161]

number

[0162] is.

[0163] Generally, the frequency component index calculated by using the DFT

[0164]

number

[0165] is an integer, and the phase change frequency of the uncompressed CSI may actually have decimal places. Therefore, by using Equation (11), T can be obtained by searching T within the above range to optimize Equation (5).

[0166] In yet another possible implementation, T0 may satisfy the following equation:

[0167]

number

[0168] is.

[0169] T0 may satisfy the following equation:

[0170]

number

[0171] is.

[0172] In yet another possible implementation, f0 may satisfy the following equation:

[0173]

number

[0174] is.

[0175] T0 may satisfy the following equation:

[0176]

number

[0177] is.

[0178] It can be understood that T0 and f0 shown in this embodiment of the present application can be combined with each other. For example, Equation (6) can be combined with Equation (13) or with Equation (14). As another example, Equation (12) can be combined with any one of Equations (7) to (10). As another example, Equation (14) can be combined with any one of Equations (7) to (10). These examples are not listed one by one.

[0179] The k(n) in the embodiment of the present application will be explained below.

[0180] Due to the presence of a cyclic prefix in the long training field (LTF) used for channel estimation, the actual time-domain sampling positions may be shifted to the left, which corresponds to a phase shift in the frequency domain.

[0181]

number

[0182] As shown in FIG. 5a, in this embodiment of the present application,

[0183]

number

[0184] There is no limitation on the specific value of . Therefore, in this embodiment of the present application,

[0185]

number

[0186] is.

[0187] In this embodiment of the present application, min{k(n)}=0 and max{k(n)}=1. Therefore, the frequency range between different columns in matrix A can be limited by using k(n), where min{k(n)}=0 indicates a frequency of 0 and max{k(n)}=1 indicates the maximum frequency.

[0188]

number

[0189] According to the formula (16), the frequency in this case is f0. For ease of representation, the value interval of k(n) is set to unit length, for example, k(1)=1 and k(N)=1. The following formulas (16) to (21) can be understood to be shown by using k(1)=0 and k(N)=1 as examples. An example where min{k(n)}=k(N)=0 and max{k(n)}=k(1)=1 can be obtained by converting the formulas shown below. Therefore, in this embodiment of the present application, these examples will not be shown one by one.

[0190] In a possible implementation, k(n) may satisfy the following equation:

[0191]

number

[0192] where α is greater than 0 and β is greater than 0.

[0193] In another possible implementation, α=β=1, and k(n) may satisfy the following equation:

[0194]

number

[0195] is.

[0196] According to equation (17), if n is 1, 2, 3, ..., N, respectively,

[0197]

number

[0198] are equal to 0, 1 / 2, 2 / 3, ..., 1-N / N, respectively. Accordingly, with reference to Equation (17) and Equation (4), N rotation coefficients can be constructed with periods of ∞, 2T(1 / 2*f), 3 / 2T(2 / 3*f), 4 / 3T(3 / 4*f), ..., N / (N-1)T(1-N / N*f), respectively. These rotation coefficients can be understood as angle changes with a constant absolute value. Therefore, the constructed N rotation coefficients effectively ensure that different n correspond to different angular velocities, and Equation (16) can be effectively combined with the phase change characteristics of uncompressed CSI. Furthermore, the constructed N rotation coefficients have more values ​​near f0, which can effectively ensure that the phase change periods of elements in more column vectors in matrix A are near T0, so that the phase change rules of elements in matrix A are as close as possible to the phase change rules of uncompressed CSI, thereby improving the accuracy of CSI compression.

[0199] In yet another possible implementation, k(n) may satisfy the following equation:

[0200]

number

[0201] is.

[0202] According to equation (18), when α=1 and n is 1, 2, 3, ..., N, respectively,

[0203]

number

[0204] is equal to 0, 1 / N, 2 / N, ..., 1-N / N, i.e., N rotation factors with evenly distributed periods can be constructed, which is simple and convenient.

[0205] In yet another possible implementation, k(n) may satisfy the following equation:

[0206]

number

[0207] is.

[0208] In yet another possible implementation, k(n) may satisfy the following equation:

[0209]

number

[0210] is.

[0211] For an explanation of equations (19) and (20), see equation (18).

[0212] In yet another possible implementation, k(n) may satisfy the following equation:

[0213]

number

[0214] is.

[0215] Regarding equation (21), it can be understood that as the parameter α increases, more data in the sequence k(n) are distributed around k(n) = 1. According to the above equation (4), it indicates that in matrix A, the phase change period of the elements in more column vectors is around T0.

[0216] It may be understood that the method for compressing CSI based on matrix A shown in this embodiment of the present application may also be referred to as a CSI compression method based on a modified DFT matrix. Currently, there is another CSI compression method based on a recursive polynomial. The transformation matrix B used in the CSI compression method based on a recursive polynomial is a real matrix with M rows and N columns, and B mn =m n Therefore, matrix B is a real matrix, while the uncompressed CSI vector x and the compressed CSI vector b are both complex vectors. Therefore, the missing imaginary part of matrix B causes a degradation in compression quality and an increase in compression error. FIG. 6 is a diagram illustrating the performance of a CSI compression method based on a recursive polynomial method according to an embodiment of the present application. As shown in FIG. 6, "uncompressed" represents the uncompressed CSI (i.e., vector b), and "compressed" represents the CSI (i.e., Ax) restored by the receiving end based on the compressed CSI received by the receiving end. As can be seen from FIG. 6, B mn =m n Since matrix B is simply a real matrix and cannot be effectively combined with the phase characteristics of the uncompressed CSI, in the case of a high compression rate (e.g., 242 CSIs compressed to 8 CSIs), the compressed CSI obtained by using the CSI compression method based on the recursive polynomial method will produce an obvious deviation between the CSI restored by the receiving end and the uncompressed CSI obtained by the transmitting end. It may be understood that the explanation of "compressed" in Figure 6 may refer to the above explanation in Figure 3 or the above explanation in Equation (1).

[0217] The matrix A in this embodiment of the present application is a complex matrix, and the matrix A effectively utilizes the phase continuity and periodicity of the uncompressed CSI, thereby effectively improving the accuracy of the CSI compression. In the matrix B, all elements in the first row are 1, and the elements in the last row and the last column are M N As M and N increase,

[0218]

number

[0219] increases the complexity and error in solving the optimization problem. In this embodiment of the present application, the absolute values ​​of all elements in matrix A are 1. Therefore, the computational complexity of the optimization problem is effectively reduced, and the optimization estimation is implemented with high efficiency and low complexity.

[0220] The method provided in the embodiment of the present application is described below. As shown in Figure 4, the method includes the following steps:

[0221] 401: The transmitting end determines a CSI report.

[0222] The CSI report includes a first CSI, where the first CSI is obtained based on the second CSI and a transformation matrix, where the transformation matrix is ​​a complex matrix having M rows and N columns, where an absolute value of an element in the transformation matrix is ​​1, M is greater than N, where M is the number of elements in the second CSI, and where N is the number of elements in the first CSI.

[0223] Furthermore, the transmitting end determining the CSI report may also be understood as: the transmitting end generating a CSI report; or the transmitting end performing CSI compression based on the channel detection result (or channel estimation result), compressing the number of CSIs from M to N, and obtaining a CSI report based on the compressed CSI; or the transmitting end performing channel detection based on a reference signal to obtain M CSIs, compressing the M CSIs into N CSIs by using a CSI compression method based on a modified DFT matrix, and obtaining a CSI report based on the N CSIs. The reference signal is a signal used for channel detection. The reference signal may be transmitted by the receiving end to the transmitting end or the like. The source of the reference signal is not limited in this embodiment of the present application.

[0224] In general, the channel detection result may be determined based on the channel matrix and the number of subcarriers, or the channel detection result may be understood as being related to the channel matrix and the amount of subcarriers. The channel matrix represents channel information between all transmit antennas and all receive antennas. M shown in this embodiment of the present application may be understood as the channel detection result for M subcarriers between the transmit antennas and the receive antennas, or (for example only) as the transmit end performing CSI compression by using every M CSI as one group. Accordingly, N in this embodiment of the present application may be understood as the number of CSIs obtained after the channel detection result for M subcarriers between the transmit antennas and the receive antennas is compressed, or as the number of CSIs obtained after CSI compression is performed for every M CSI. M in each CSI group shown in this embodiment of the present application may be understood as just an example. The values ​​of M in different CSI groups may be the same or may certainly be different. This is not limited in this embodiment of the present application.

[0225] It should be noted that the uncompressed CSI shown in this embodiment of the present application includes M CSI. The M CSI may be CSI obtained by the transmitting end based on M consecutive subcarriers between the transmitting antenna and the receiving antenna. Alternatively, the M CSI may be CSI obtained by the transmitting end based on more than M consecutive subcarriers between the transmitting antenna and the receiving antenna. For example, the transmitting end may obtain more than M CSI and then select M CSI for feedback.

[0226] In one example, the CSI report may include the first CSI, M, N, and T, or the CSI report may include the first CSI, M, N, and f, where T or f is clearly indicated in the CSI report, allowing the receiving end to easily and clearly learn the phase period of the second CSI.

[0227] In another example, the CSI report may include the first CSI, so that after receiving the CSI report, the receiving end may learn M, N, and f0 (or T0) based on the CSI report including M, N, and f0 (or T0) before the CSI report, thereby effectively reducing signaling overhead.

[0228] In yet another example, the CSI report may include the first CSI, M, and N. Thus, after receiving the CSI report, the receiving end may learn f0 or T0 based on the CSI report that includes f0 or T0 before the CSI report, thereby effectively reducing signaling overhead.

[0229] It may be understood that the first CSI shown in this embodiment of the present application may be understood as compressed CSI (e.g., vector x shown above), and the second CSI may be understood as uncompressed CSI (e.g., vector b shown above).

[0230] For example, the CSI report may be included in a CSI frame in the media access control (MAC) layer. For example, in this embodiment of the present application, a CSI report field of a WLAN physical layer packet may be used to support a function of transmitting CSI from a transmitting end (e.g., a beamformee) to a receiving end (e.g., a beamformer) in a CSI frame in the MAC layer in an explicit feedback scheme. When quantization and encoding are performed on the compressed CSI, there may be two methods: quantization using the same number of bits and quantization using different numbers of bits, depending on whether different CSIs are quantized by using the same number of bits. In this embodiment of the present application, the quantization and encoding methods are not described in detail.

[0231] For example, for compressed CSI that is quantized by using the same number of bits, the CSI report may be shown in Table 1, where:

[0232]

number

[0233] is the Nth order of the MIMO channel matrix r Row and N c Represents the column number.

[0234] M indicates that when performing CSI compression, every M CSIs are compressed as one CSI group.

[0235] N indicates that each CSI group is compressed into N pieces of CSI during CSI compression.

[0236] N b is the number of bits determined by the coefficient size field of the MIMO control field, and represents the number of bits required when quantization and coding are performed on the real or imaginary part of one CSI.

[0237] N c represents the number of columns in the channel matrix.

[0238] N r represents the number of rows in the channel matrix.

[0239] N s represents the number of subcarriers for each receive antenna.

[0240] When the transmitting end needs to feed back the CSI report shown in Table 1, the CSI report can be understood to include the initial CSI, M, N, and T. For example, the CSI report is the compressed result of the CSI of multiple subcarriers between each transmitting antenna and each receiving antenna, i.e., N in Table 1. r ×N c The T0 corresponding to the compression results of the first CSIs corresponding to the compression results of the CSIs is calculated by using M CSIs as one group.

[0241]

number

[0242] Corresponds to.

[0243] Optionally, the CSI report may include CSI compression results for multiple subcarriers among all transmit and receive antennas in the channel matrix, and M, N, and T0 corresponding to each group of CSI compression results, as shown in Table 1. It may be understood that T0 is only used as an example in Table 1 and should not be construed as a limitation on this embodiment of the present application.

[0244] Optionally, the CSI report may include a compressed CSI result of multiple subcarriers between some transmit antennas and all receive antennas in the channel matrix. Optionally, the CSI report may include a compressed CSI result of multiple subcarriers between all transmit antennas and some receive antennas in the channel matrix. Optionally, the CSI report may include a compressed CSI result of multiple subcarriers between the transmit antennas and receive antennas in the channel matrix. In the above three cases, the CSI report may not include M, N, and T0 (or f0). The receiving end may obtain M, N, and f0 (or T0) based on a CSI report that includes M, N, and f0 (or T0) before the CSI report, thereby effectively reducing signaling overhead.

[0245] [Table 1]

[0246] Optionally, the CSI report may include M and N, which are CSI compression results of multiple subcarriers between all transmit antennas and receive antennas in the channel matrix. In this case, the receiving end may obtain T0 corresponding to each group of CSI compression results based on the CSI report including T0 (or f0) before the CSI report. Therefore, the receiving end can acquire the CSI compression rate based on M and N in the CSI report. Furthermore, if it is determined that the CSI compression rate in the CSI report is the same as the CSI compression rate before the CSI report, the receiving end can obtain T0 corresponding to each group of CSI compression results based on the CSI report including T0 (or f0) before the CSI report.

[0247] Optionally, the CSI report may further include indication information, which may be used to indicate a method for performing CSI compression. For example, the field in which the indication information is located may occupy 1 bit. For example, if the value of the field in which the indication information is located is 0, it may indicate that the indication information is used to indicate that the method for performing CSI compression is the subcarrier grouping method. As another example, if the value of the field in which the indication information is located is 1, it may indicate that the indication information is used to indicate that the method for performing CSI compression is the CSI compression method based on a modified DFT matrix. As another example, the field in which the indication information is located may occupy 2 bits. For example, if the value of the field in which the indication information is located is 01, it may indicate that the indication information is used to indicate that the method for performing CSI compression is the subcarrier grouping method. As another example, if the value of the field in which the indication information is located is 10, it may indicate that the indication information is used to indicate that the method for performing CSI compression is the recursive polynomial method. As another example, if the value of the field in which the indication information is located is 11, it may indicate that the indication information is used to indicate that the method for performing CSI compression is a CSI compression method based on a modified DFT matrix. Alternatively, the number of bits occupied by the field in which the indication information is located may be 3 bits, or the like. These examples are not listed one by one. It may be understood that the explanation between the values ​​and meanings described above is merely an example and should not be construed as a limitation on this embodiment of the present application.

[0248] For example, the procedure for the transmitting end to compress the CSI may be specifically as follows: N in the channel matrix r For each of the rows, { N in the channel matrix c For each of the rows, { N s The CSI of consecutive subcarriers needs to be compressed.

[0249] N s Calculate the parameter T0 based on the CSI of consecutive subcarriers, select the compression parameters M and N according to the protocol, and generate the transformation matrix.

[0250] N s For the CSI of consecutive subcarriers, every M CSIs are compressed to N, and if the remaining CSIs are less than M, padding is performed.

[0251] For each real and imaginary part of the compressed CSI, N b Quantization and encoding of bits is performed.

[0252] } } }

[0253] For a description of the parameters used in the above-mentioned procedure, please refer to Table 1. Details will not be repeated herein. With reference to the transformation matrices shown in this embodiment of the present application, the transmitting end may determine the transformation matrices according to Equations (1), (3), and (4), and perform CSI compression based on the transformation matrices determined by the transmitting end. For methods for determining T0 and f0 in the transformation matrices, please refer to Equations (5), (6), (7), (8), (9), (10), (11), (12), (13), (14), or (15). For methods for determining k(n) in the transformation matrices, please refer to Equations (16), (17), (18), (19), (20), and (21).

[0254] 402: The transmitting end transmits a CSI report, and in response, the receiving end receives the CSI report.

[0255] 403: The receiving end processes the first CSI based on the transformation matrix to obtain second CSI.

[0256] It may be understood that the CSI (e.g., Ax shown above) recovered by the receiving end based on the transformation matrix and the first CSI is different from the second CSI (e.g., vector b shown above). For example, the difference may arise due to the optimization problem in Equation (1). For example, if the vector x obtained by the transmitting end is

[0257]

number

[0258] may not be equal to 0. As another example, the difference may be caused by errors in quantization and encoding. In this specification, these examples are not listed one by one. It may be understood that the CSI restored by the receiving end based on the transformation matrix and the first CSI may also be understood as "compressed" in the accompanying drawings in the embodiments of the present application.

[0259] In the above formula (2),

[0260]

number

[0261] If m is used to represent the m-th element of CSI vector b, then b(m) may satisfy the following equation:

[0262]

number

[0263] is.

[0264] It can be understood that Equation (2) and Equation (22) can be understood to be equivalent. Therefore, for the explanation of Equation (22), please refer to the above explanation. The details will not be described again in this specification.

[0265] Referring to equations (4) and (17), b(m) may satisfy the following equation:

[0266]

number

[0267] is.

[0268] It can be understood that equation (23) is used only as an example and should not be construed as a limitation on this embodiment of the present application.

[0269] Any

[0270]

number

[0271] For , equation (23) is the element at the same position in each periodic sequence.

[0272]

number

[0273] , which can approach and approximate the element b(m) at the same position in the uncompressed CSI sequence. Thus, the receiving end can better approach and approximate the uncompressed CSI such as b(m).

[0274] With reference to the transformation matrices shown in the present application, the receiving end may determine the transformation matrix based on Equation (2) (or Equation (22)) and Equation (3), and process the first CSI based on the transformation matrix to restore the second CSI. For methods for determining T0 and f0 in the transformation matrix, see Equation (5), Equation (6), Equation (7), Equation (8), Equation (9), Equation (10), Equation (11), Equation (12), Equation (13), Equation (14), or Equation (15). For methods for determining k(n) in the transformation matrix, see Equation (16), Equation (17), Equation (18), Equation (19), Equation (20), and Equation (21).

[0275] It may be understood that the methods for determining T or f for the transmitting end and the receiving end need to be the same (e.g., T or f are determined by using the same formula), and the methods for determining k(n) need to be the same (e.g., k(n) is determined by using the same formula). For example, the transmitting end may store Equation (1), Equation (3), and Equation (4), and the receiving end may store Equation (2), Equation (3), and Equation (4). As another example, both the transmitting end and the receiving end may store Equation (1), Equation (3), and Equation (4), and the receiving end may determine Equation (2) based on Equation (1). According to the method provided in this embodiment of the present application, the transmitting end performs CSI compression based on the channel detection result to compress the number of CSIs from M to N. Then, the transmitting end feeds back the compressed CSI vector x and the compression parameters M, N, and T. The receiving end constructs a transformation matrix A based on the compression parameters M, N, and T0, and multiplies the received compressed CSI vector x by the constructed matrix A to obtain an estimate of the actual CSI vector b.

[0276] It will be understood that for the description of the transformation matrices in this embodiment of the present application, reference may be made to equations (1) through (21).

[0277] In one example, referring to equations (4) and (17), when M=6 and N=3, the transformation matrix A 6×3is as follows:

[0278]

number

[0279] is.

[0280] It can be understood that Equation (24) is merely an example. In a specific implementation, after recognizing the parameters M and N, the transformation matrix A can be extended to any dimension. In this embodiment of the present application, the specific forms of the transformation matrix are not listed one by one.

[0281] Optionally, all elements in the first row of the transformation matrix may be equal. For example, all elements in the first column of the transformation matrix are equal, and all elements in the first row of the transformation matrix are equal. Referring to equations (4) and (17),

[0282]

number

[0283] is as shown below:

[0284]

number

[0285] That is,

[0286]

number

[0287] is.

[0288] It should be understood that Equation (25) is merely an example. For example, all elements in the last row of a transformation matrix may be equal. These examples are not listed one by one in this specification. Based on the design principle of the transformation matrix in this embodiment of the present application, all changes to the transformation matrix fall within the scope of protection of the embodiment of the present application. For example, after obtaining a transformation matrix based on Equation (3) and Equation (4), setting all elements in at least one column of the transformation matrix to be equal, or setting all elements in at least one row of the transformation matrix to be equal, or setting at least two columns of the transformation matrix to be equal, or setting at least two rows of the transformation matrix to be equal, is a transformation matrix variation. As another example, performing column permutations, row permutations, and the like on a transformation matrix also belongs to the transformation matrix variation. The transformation matrix variations are not listed one by one.

[0289] For example, if M=32 and N=8, the uncompressed CSI vector b is: b=[0.451378116153413+0.161206470054790j,0.483619410164371+0.0322412940109581j, 0.515860704175329-0.0322412940109581j,0.548101998186287-0.0967238820328741j, 0.548101998186287-0.161206470054790j,0.515860704175329-0.225689058076706j, 0.515860704175329-0.290171646098622j,0.483619410164371-0.322412940109581j, 0.419136822142455-0.386895528131497j,0.354654234120539-0.419136822142455j, <h2 style=";text-align:left;direction:ltr">0.290171646098622-0.451378116153413j,0.225689058076706-0.515860704175329j,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> 0.161206470054790-0.515860704175329j,0.00000000000000-0.548101998186287j,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> -0.0967238820328741-0.515860704175329j,-0.161206470054790-0.419136822142455j,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> -0.193447764065748-0.419136822142455j,-0.257930352087664-0.354654234120539j,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> -0.354654234120539-0.290171646098622j,-0.419136822142455-0.193447764065748j,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> -0.483619410164371-0.0322412940109581j, -0.483619410164371+0.0322412940109581j,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> -0.515860704175329+0.128965176043832j,-0.451378116153413+0.257930352087664j,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> -0.386895528131497+0.386895528131497j,-0.290171646098622+0.483619410164371j,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> -0.257930352087664+0.548101998186287j, -0.193447764065748+0.644825880219161j,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> -0.0967238820328741+0.741549762252035j,0.0644825880219161+0.806032350273951j,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> 0.193447764065748+0.934997526317784j,0.354654234120539+0.934997526317784j]<h2 style=";text-align:left;direction:ltr"> T <h2 style=";text-align:left;direction:ltr"> The vector b is a column vector, and the vector representations shown herein can be understood to be normalized by using the maximum absolute value of the sequence.

[0290] A phase period T0=61 may be calculated based on the uncompressed CSI vector b. Then, a transformation matrix A may be calculated based on the parameters M, N, and T0. After performing compression optimization on vector b based on the transformation matrix, a compressed CSI vector x having a length of N=8 may be obtained as follows: x=[-2.34816644461573+20.1896480067683j,720.803500484608-634.73829124619 7j,-4862.01245868484+2795.95573115259j,4466.28697492887-3770.19074366863 j,3768.86799665697+1628.18866505662j,22141.6789052461+612.943304910265j ,-57028.7979219074-2148.65375745227j,30795.9221407766+1496.50237265181j] T The vectors can be understood to be column vectors.

[0291] It may be understood that for a method for the receiving end to acquire parameters related to the transformation matrix, reference may be made to the related description in step 401. For example, the receiving end may acquire M, N, and T0 (or f0) based on the CSI report received in step 402. As another example, the receiving end may acquire M, N, and f0 (or T0) based on a CSI report including M, N, and f0 (or T0) before the CSI report received in step 402. As another example, the receiving end may acquire f0 or T0 based on a CSI report including f0 or T0 before the CSI report received in step 402, and may acquire M and N based on the CSI report received in step 402.

[0292] Optionally, if the CSI report includes indication information, the receiving end may obtain, based on the indication information, a compression method used when the transmitting end performs CSI compression, and process the first CSI in the CSI report based on the corresponding compression method.

[0293] For example, after obtaining the second CSI, the receiving end may perform beamforming by using the second CSI. As another example, after obtaining the second CSI, the receiving end may perform sensing by using the second CSI. The specific function of the second CSI is not limited in this embodiment of the present application.

[0294] In this embodiment of the present application, the transmitting end compresses the second CSI using a transform matrix to obtain the first CSI. Here, the number of rows in the transform matrix corresponds to the number of elements in the second CSI (which may also be understood as uncompressed CSI), and the number of columns in the transform matrix corresponds to the number of elements in the first CSI (which may also be understood as compressed CSI). In the method for compressing CSI by using a transform matrix according to this embodiment of the present application, since M is greater than N, the overhead occupied by the compressed CSI is smaller than the overhead occupied by the uncompressed CSI. Therefore, the CSI feedback overhead can be effectively reduced. Furthermore, this embodiment of the present application can be further applied to different M and N to implement a relatively long compression length and meet massive MIMO and multi-subcarrier scenarios.

[0295] Generally, the absolute value of uncompressed CSI changes slowly. That is, uncompressed CSI has similar (or approximate) absolute values. Therefore, by setting the absolute values ​​of the elements in the transformation matrix to be the same and all to 1, it is possible to ensure that when the receiving end reconstructs CSI based on the compressed CSI and the transformation matrix, the absolute values ​​of the elements in the reconstructed CSI are also similar (or approximate). Therefore, the accuracy of the CSI reconstructed by the receiving end is ensured, and the compression performance of CSI compression is improved.

[0296] Simulation results provided in this embodiment of the present application are described below.

[0297] For simplicity, in this embodiment of the present application, the CSI of a subcarrier group on a pair of transmit and receive antennas is considered. A normalization process is performed for each CSI group. That is, each CSI is divided by the maximum absolute value of the multiple CSIs in the group. Figures 7a to 7c respectively show performance comparisons at different compression ratios. Subcarrier grouping method: CSI data is not transmitted per subcarrier, but the CSI of multiple subcarriers on each antenna is grouped and only one CSI is transmitted for each group. Recursive polynomial method: It is a CSI compression method based on a recursive polynomial matrix and a mathematical model of 2-norm minimization.

[0298] It may be understood that "uncompressed" shown in each of Figures 7a to 7c represents a specific number of marked CSIs from 32 CSIs, 128 CSIs, or 242 CSIs, and that the circles shown in Figures 7a to 7c should not be understood as the number of uncompressed CSIs. In other words, the circles shown in Figures 7a to 7c are merely examples of marked CSIs. It may be understood that the description of "compressed" in Figures 7a, 7b, and 7c may refer to the description of Figure 3 above or the description of Equation (1) above.

[0299] FIG. 7a shows the compression performance when 32 subcarrier groups of CSI are compressed into 8 CSI groups using different methods. As can be seen from FIG. 7a, the subcarrier grouping method and the recursive polynomial method have obvious errors at the boundary points. For the subcarrier grouping method, in practical applications, two to four subcarriers are usually grouped into one group to ensure compression quality. As the compression rate increases, the compression quality obviously decreases. The recursive polynomial method improves the CSI compression rate at the expense of higher complexity. The CSI compression method based on the modified DFT matrix according to this embodiment of the present application effectively combines with the phase change characteristics of the uncompressed CSI, thereby effectively improving the compression performance of the CSI compression. Even when the compression rate is increased, the accuracy of the CSI compression can still be ensured by using the method provided in this embodiment of the present application.

[0300] Figure 7b shows the compression performance when 128 subcarrier group CSIs are compressed to 8 CSIs for different methods. As can be seen from Figure 7b, as the compression ratio increases, larger errors occur in subcarrier grouping, especially for the completely separated raw data near the boundary points.

[0301] Figure 7c shows the compression performance of different methods when 242 subcarrier groups of CSI are compressed to 8 CSI. As can be seen from Figure 7b, the regression polynomial method also generates a relatively large error and cannot meet the compression quality requirements.

[0302] 7a, 7b, and 7c, the CSI compression method based on the modified DFT matrix according to this embodiment of the present application can be effectively combined with the angle change rule of the uncompressed CSI, so that the compression quality can always be satisfied and the CSI compression performance can be better. The CSI compression method based on the modified DFT matrix according to this embodiment of the present application is applicable to different compression ratios, and the CSI compression performance can be ensured for any compression ratio.

[0303] It can be understood that "compressed" shown in Figures 7a, 7b, and 7c refers to CSI restored by the receiving end by using different methods, and "uncompressed" refers to the initial CSI obtained by the transmitting end by performing channel estimation by using a pilot channel.

[0304] 8a and 8b show a comparison of the average performance of compression methods in a laboratory environment and a simulator environment, respectively, considering 10,000 groups of channel data samples, where each group of channel data samples may include a CSI group. In this application, the mean squared error (MSE) between the CSI recovered by the receiving end and the uncompressed CSI data is used as a performance indicator for comparison. In this application, for a group of CSI samples,

[0305]

number

[0306] . Considering the excessively large difference between the values ​​of different methods, the y-axis is converted to dB units by logarithmic operation and preceded with a negative sign. Therefore, in Figures 8a and 8b, the higher the bar, the lower the MSE and the better the performance. As shown in Figure 8a, in the laboratory environment, as the compression ratio increases, the MSE performance of the compression method based on the modified DFT matrix according to this embodiment of the present application is found to be the best at any compression ratio. At 242 to 8 compression, compared with the regression polynomial method, the method provided in this embodiment of the present application effectively reduces the error by about 15 dB. As shown in Figure 8b, in the simulator environment, compared with the experiment in the laboratory empty room environment, the performance of all CSI compression methods decreases to some extent. However, the method provided in this embodiment of the present application still has the best MSE performance at any compression ratio. At 242 to 8 compression, compared with the regression polynomial method, the method provided in this embodiment of the present application effectively reduces the error by about 7 dB. Optionally, the compression quality threshold is set to -15 dB. As can be seen from Figure 8a, the subcarrier grouping method can meet the quality requirements for 64:8 compression, the recursive polynomial method can meet the quality requirements for 128:8 compression, and the method provided in this embodiment of the present application can meet the quality requirements for 242:8 compression. As can be seen from Figure 8b, the recursive polynomial method can meet the quality requirements for 64:8 compression, and the method provided in this embodiment of the present application can meet the quality requirements for 128:8 compression. Therefore, the compression rate is improved according to the method provided in this embodiment of the present application. For a given compression length, the method provided in this embodiment of the present application has a smaller mean square error. Therefore, the error is effectively reduced and the compression accuracy is improved.

[0307] A communication device provided in one embodiment of the present application is described below.

[0308] In the present application, the communication device may be divided into functional modules based on the above-described method embodiment. For example, the functional modules may be obtained through a one-to-one division of functions, or two or more functions may be integrated into one processing module. The integrated module may be implemented in the form of hardware or in the form of a software functional module. It should be noted that the module division in the present application is an example. This division is merely a logical division of functions, and other divisions may be used in actual implementation. The communication device in the embodiment of the present application will be described in detail below with reference to Figures 9 to 11.

[0309] 9 is a diagram illustrating a configuration of a communication device according to an embodiment of the present application. As shown in FIG. 9, the communication device includes: a processing unit 901 and a transceiver unit 902.

[0310] In some embodiments of the present application, the communication device may be a transmitting end or the chip shown above, which may be located at the transmitting end. In other words, the communication device may be configured to perform steps, functions, or the like performed by the transmitting end in the method embodiments described above (including FIG. 4).

[0311] The processing unit 901 is configured to determine a CSI report, and the transceiver unit 902 is configured to transmit the CSI report.

[0312] The processing unit 901 may be configured to determine a CSI report and output the CSI report, whereby the transceiver unit 902 may be understood to transmit the CSI report.

[0313] In some other embodiments of the present application, the communication device may be the receiving end or the chip shown above, which may be located at the receiving end. In other words, the communication device may be configured to perform the steps, functions, or the like performed by the receiving end in the method embodiments described above (including FIG. 4).

[0314] The transceiver unit 902 is configured to receive a CSI report, and the processing unit 901 is configured to process the first CSI report based on a transformation matrix to obtain a second CSI.

[0315] It may be understood that the processing unit 901 may be configured to input a CSI report and process the first CSI report based on a transformation matrix to obtain a second CSI.

[0316] Optionally, the processing module 901 is specifically configured to: determine a transformation matrix based on T0 or f0, and M and N; and process the first CSI based on the transformation matrix to obtain the second CSI.

[0317] It can be understood that the detailed description of the transceiver unit and the processing unit shown in this embodiment of the present application is merely an example. For the specific functions, steps, or the like performed by the transceiver unit and the processing unit, please refer to the above-mentioned method embodiments. The details will not be described again in this specification.

[0318] In the above-mentioned embodiment, for descriptions of the first CSI, the second CSI, the transformation matrix, M, N, T0, or f0, or the like, please refer to the descriptions in the above-mentioned method embodiment, and the details will not be described again in this specification.

[0319] The communication device in this embodiment of the present application has been described above. Possible product forms of the communication device will be described below. It should be understood that any form of product having the function of the communication device in FIG. 9 falls within the scope of protection of the embodiment of the present application. Furthermore, it should be understood that the following description is merely an example, and the product form of the communication device in this embodiment of the present application is not limited thereto.

[0320] In a possible implementation, in the communication device shown in FIG. 9, the processing unit 901 may be one or more processors. The transceiver unit 902 may be a transceiver. Alternatively, the transceiver unit 902 may be a transmitting unit and a receiving unit. Here, the transmitting unit may be a transmitter, the receiving unit may be a receiver, or the transmitting unit and the receiving unit may be integrated into one component, such as a transceiver. In this embodiment of the present application, the processor and the transceiver may be combined, or the like. In this embodiment of the present application, the connection method between the processor and the transceiver is not limited. In the process of executing the above-mentioned method, the process of transmitting information in the above-mentioned method may be understood as a process of outputting information by the processor. When outputting information, the processor outputs the information to the transceiver, which then transmits the information. After the information is output by the processor, other processes may need to be performed on the information before it arrives at the transceiver. Similarly, the process of receiving information in the above-mentioned method may be understood as a process of receiving input information by the processor. When the processor receives input information, the transceiver receives the information and inputs the information to the processor. Furthermore, after the transceiver receives the information, it may need to perform other processes on the information, and then the processed information is input to the processor.

[0321] As shown in FIG. 10, the communications device 100 includes one or more processors 1020 and a transceiver 1010 .

[0322] In some embodiments of the present application, the communication device may be configured to perform steps, functions, or the like performed by the transmitting end in the method embodiments described above (including FIG. 4).

[0323] The processor 1020 is configured to determine the CSI report, and the transceiver 1010 is configured to transmit the CSI report.

[0324] In some other embodiments of the present application, the communication device may be configured to perform steps, functions, or the like performed by the receiving end in the method embodiments described above (including FIG. 4).

[0325] The transceiver 1010 is configured to receive a CSI report, and the processor 1020 is configured to process the first CSI report based on a transformation matrix to obtain a second CSI.

[0326] Optionally, processor 1020 is specifically configured to: determine a transformation matrix based on T0 or f0, and M and N; and process the first CSI based on the transformation matrix to obtain the second CSI.

[0327] It can be understood that the detailed description of the transceiver and the processor shown in this embodiment of the present application is merely an example. For specific functions, steps, or the like performed by the transceiver and the processor, please refer to the above-mentioned method embodiments. This specification will not be described again in detail.

[0328] In the above-mentioned embodiment, for descriptions of the first CSI, the second CSI, the transformation matrix, M, N, T0, or f0, or the like, please refer to the descriptions in the above-mentioned method embodiment, and the details will not be described again in this specification.

[0329] In each implementation of the communication apparatus shown in Figure 10, the transceiver may include a receiver and a transmitter. The receiver is configured to perform receiving functions (or operations), and the transmitter is configured to perform transmitting functions (or operations). The transceiver is configured to communicate with another device / apparatus over a transmission medium.

[0330] Optionally, the communication device 100 may further include one or more memories 1030 configured to store program instructions, data, or the like. The memory 1030 is coupled to the processor 1020. The coupling in this embodiment of the present application may be an indirect coupling between devices, units, or modules in an electrical, mechanical, or other form, or may be a communication connection used for information exchange between the devices, units, or modules. The processor 1020 may cooperate with the memory 1030. The processor 1020 may execute programs stored in the memory 1030. Optionally, at least one of the one or more memories may be included in the processor. Optionally, the one or more memories may store at least one of Equations (1) through (25). Optionally, the one or more memories may store a transformation matrix in a specific form. For example, for a given M and N, the form of the transformation matrix may be fixed.

[0331] In the embodiment of the present application, a specific connection medium between the transceiver 1010, the processor 1020, and the memory 1030 is not limited. In the embodiment of the present application, the memory 1030, the processor 1020, and the transceiver 1010 are connected via a bus 1040 in FIG. 10. In FIG. 10, the bus is represented by a thick line. The connection manner of other components is merely an example for explanation and is not limited thereto. The bus may be classified as an address bus, a data bus, a control bus, and the like. For ease of representation, only one thick line is used to represent the bus in FIG. 10, but this does not mean that there is only one bus or only one type of bus.

[0332] In the present embodiments of the present application, the processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or the like, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor, any conventional processor, or the like. The steps of the methods disclosed with reference to the embodiments of the present application may be performed and completed directly by using a hardware processor, or may be performed or completed by using a combination of hardware in a processor, a software module, or the like.

[0333] In this embodiment of the application, memory may include, but is not limited to, non-volatile memory, such as a hard disk drive (HDD) or solid-state drive (SSD), random access memory (RAM), erasable programmable read-only memory (EPROM), read-only memory (ROM), or compact disc read-only memory (CD-ROM). Memory is any storage medium that can be used to carry or store program code in the form of instructions or data structures and that can be read and / or written by a computer (e.g., a communication device shown in this application), although the application is not limited thereto. Memory in this embodiment of the application may alternatively be a circuit or any other device capable of implementing a storage function and configured to store program instructions and / or data.

[0334] For example, the processor 1020 is primarily configured to process communication protocols and communication data, control the entire communication device, execute software programs, and process data of the software programs. The memory 1030 is primarily configured to store software programs and data. The transceiver 1010 may include a control circuit and an antenna. The control circuit is primarily configured to convert between baseband signals and radio frequency signals and process radio frequency signals. The antenna is primarily configured to transmit and receive radio frequency signals in the form of electromagnetic waves. An input / output device, such as a touch screen, a display, or a keyboard, is primarily configured to receive data input by a user and output data to a user.

[0335] After the communication device is powered on, the processor 1020 may read the software program in the memory 1030, interpret and execute the instructions of the software program, and process data of the software program. When data needs to be transmitted wirelessly, the processor 1020 performs baseband processing on the data to be transmitted and then outputs the baseband signal to the radio frequency circuit. The radio frequency circuit performs radio frequency processing on the baseband signal and then transmits the radio frequency signal in the form of electromagnetic waves via an antenna. When data is to be transmitted to the communication device, the radio frequency circuit receives the radio frequency signal via the antenna, converts the radio frequency signal to a baseband signal, and outputs the baseband signal to the processor 1020. The processor 1020 converts the baseband signal to data and processes the data.

[0336] In another implementation, the radio frequency circuitry and antenna may be located independently of the processor that performs the baseband processing, e.g., in a distributed scenario, the radio frequency circuitry and antenna may be located independently and remotely from the communication device.

[0337] It can be understood that the communication device shown in this embodiment of the present application may further have more components than those in FIG. 10 and similar components. This is not limited in this embodiment of the present application. The method performed by the processor and the transceiver is merely an example. For specific steps performed by the processor and the transceiver, please refer to the method described above.

[0338] In another possible implementation, in the communication device shown in FIG. 9, the processing unit 901 may be one or more logic circuits. The transceiver unit 902 may be an input / output interface, also referred to as a communication interface, interface circuit, interface, or the like. Alternatively, the transceiver unit 902 may be a transmitting unit and a receiving unit. The transmitting unit may be an output interface, and the receiving unit may be an input interface. The transmitting unit and the receiving unit are integrated into one unit, for example, an input / output interface. As shown in FIG. 11, the communication device shown in FIG. 11 includes a processing circuit 1101 and an interface 1102. In other words, the processing unit 901 may be implemented by using the logic circuit 1101, and the transceiver unit 902 may be implemented by using the interface 1102. The logic circuit 1101 may be a chip, a processing circuit, an integrated circuit, a system-on-chip (SoC) chip, or the like. The interface 1102 may be a communication interface, an input / output interface, a pin, or the like. 11 shows an example in which the communication device is a chip. The chip includes a logic circuit 1101 and an interface 1102.

[0339] In this embodiment of the present application, the logic circuit and the interface may be coupled to each other, and the specific connection manner between the logic circuit and the interface is not limited in this embodiment of the present application.

[0340] In some embodiments of the present application, the communication device may be configured to perform steps, functions, or the like performed by the transmitting end in the method embodiments described above (including FIG. 4).

[0341] Logic circuit 1101 is configured to determine a CSI report, and interface 1102 is configured to output the CSI report.

[0342] Optionally, the communication device may further include a memory, which may be configured to store at least one of equations (1) to (25).

[0343] In some other embodiments of the present application, the communication device may be configured to perform steps, functions, or the like performed by the receiving end in the method embodiments described above (including FIG. 4).

[0344] The interface 1102 is configured to receive the CSI report, and the logic circuit 1101 is configured to process the first CSI report based on the transformation matrix to obtain the second CSI.

[0345] Optionally, the logic circuit 1101 is specifically configured to: determine a transformation matrix based on T0 or f0, and M and N; and process the first CSI based on the transformation matrix to obtain the second CSI.

[0346] Optionally, the communication device may further include a memory, which may be configured to store at least one of equations (1) to (25).

[0347] For the specific manner of the transformation matrices stored in the transmitting end and the receiving end, please refer to the above description, and the details will not be described again in this specification.

[0348] It can be understood that the detailed description of the logic circuits and interfaces shown in this embodiment of the present application is merely an example. For the specific functions, steps, or the like performed by the logic circuits and interfaces, please refer to the above-mentioned method embodiments. The details will not be described again here.

[0349] In the above-mentioned embodiment, for descriptions of the first CSI, the second CSI, the transformation matrix, M, N, T0, or f0, or the like, please refer to the descriptions in the above-mentioned method embodiment, and the details will not be described again in this specification.

[0350] It can be understood that the communication device shown in this embodiment of the present application may implement the method provided in the embodiment of the present application in the form of hardware, or may implement the method provided in the embodiment of the present application in the form of software, which is not limited in this embodiment of the present application.

[0351] An embodiment of the present application further provides a wireless communication system, which includes a transmitting end and a receiving end, and the transmitting end and the receiving end can be configured to perform the method of any one of the above-mentioned embodiments (as shown in FIG. 4).

[0352] Furthermore, the present application further provides a computer program, which is used to implement the operations and / or processes performed by the sending end in the methods provided in the present application.

[0353] The present application further provides a computer program, which is used to implement the operations and / or processes performed by the receiving end in the methods provided in the present application.

[0354] The present application further provides a computer-readable storage medium that stores computer code that, when executed on a computer, enables the computer to perform the operations and / or processes performed by the sending end in the methods provided herein.

[0355] The present application further provides a computer-readable storage medium that stores computer code that, when executed on a computer, enables the computer to perform the operations and / or processes performed by the receiving end in the methods provided herein.

[0356] The present application further provides a computer program product, which includes computer code or a computer program that, when executed on a computer, performs the operations and / or processes performed by the sending end in the methods provided herein.

[0357] The present application further provides a computer program product, which includes computer code or a computer program that, when executed on a computer, performs the operations and / or processes performed by the receiving end in the methods provided herein.

[0358] In some embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the device embodiments described above are merely examples. For example, the division of units is merely a logical division of function, and in actual implementation, other divisions may be used. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not implemented. Furthermore, the shown or described mutual couplings or direct couplings or communication connections may be implemented by using some interfaces. Indirect couplings or communication connections between devices or units may be implemented in electronic, mechanical, or other forms.

[0359] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one location or distributed over multiple network units. Some or all of the units may be selected based on actual requirements to achieve the technical effects of the solutions provided in the embodiments of the present application.

[0360] Furthermore, the functional units in the embodiments of the present application may be integrated into one processing unit, and each of the units may exist physically alone, or two or more units may be integrated into one unit. The integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0361] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, the integrated unit may be stored in a computer-readable storage medium. Based on this understanding, the technical solution in the present application, or a portion contributing to the prior art, or all or a portion of the technical solution may essentially be implemented in the form of a software product. The computer software product is stored in a readable storage medium and includes a plurality of instructions for instructing a computer device (which may be a personal computer, a server, or a network device) to perform all or a portion of the steps of the method described in the embodiments of the present application. The readable storage medium includes any medium capable of storing program code, such as a USB flash drive, a removable hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0362] The above description is merely a specific implementation of the present application, and the scope of protection of the present application is not limited thereto. Any modifications or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present application shall be included in the scope of protection of the present application. Therefore, the scope of protection of the present application shall be subject to the scope of protection of the claims.

Claims

1. A channel state information (CSI) processing method, comprising: determining a CSI report, the CSI report including a first CSI, the first CSI being obtained based on a second CSI and a transformation matrix, the transformation matrix being a complex matrix having M rows and N columns, an absolute value of an element in the transformation matrix being 1, M being greater than N, M being the number of elements of the second CSI, and N being the number of elements in the first CSI; transmitting the CSI report; A method comprising:

2. The method of claim 1 , wherein the angles of elements in at least one column in the transformation matrix vary periodically, with angles of elements in different columns varying with different periods.

3. the angles of the elements in the transformation matrix are determined based on M, N, and the angular period of the second CSI; or the angles of the elements in the transformation matrix are determined based on M, N, and the frequency components of a Discrete Fourier Transform (DFT) of the angles of the second CSI; The method according to claim 1 or 2.

4. The angle of the element at row m and column n in the transformation matrix is ​​given by the following formula: [Equation 1] and T ０ is related to the angle of the second CSI, m is an integer greater than 0 and less than or equal to M, n is an integer greater than 0 and less than or equal to N, and k(n) is a function of n.

4. The method according to any one of claims 1 to 3.

5. T ０ The method of claim 4 , wherein σ is determined based on frequency components of the DFT of the angle of the second CSI.

6. T ０ is expressed as follows: [Equation 2] and f ０ 6. The method of claim 5, wherein σ represents a frequency component corresponding to a maximum value of the absolute value of the coefficients in the frequency components of the DFT of the angle of the second CSI.

7. The function of n is given by the following formula: [Equation 3] ,or [Equation 4] 7. The method according to claim 4, wherein: α is greater than 0; and β is greater than 0.

8. The CSI report includes the following information: M, N, T ０ , and f ０ 8. The method of claim 1, further comprising at least one of:

9. A channel state information (CSI) processing method, comprising: receiving a CSI report, the CSI report including a first CSI; processing the first CSI based on a transformation matrix to obtain a second CSI, the transformation matrix being a complex matrix having M rows and N columns, an absolute value of an element in the transformation matrix being 1, M being greater than N, M being the number of elements in the second CSI, and N being the number of elements in the first CSI; A method comprising:

10. The following information: M, N, T ０ , and f ０ obtaining at least one of T ０ is related to the angle of the second CSI, and f ０ Is T ０ Steps determined based on Furthermore, The step of processing the first CSI based on the transformation matrix to obtain the second CSI includes: T ０ or f ０ determining the transformation matrix based on M and N; processing the first CSI based on the transformation matrix to obtain the second CSI; Including, 10. The method of claim 9.

11. The method of claim 10 , wherein the angles of elements in at least one column in the transformation matrix vary periodically, with angles of elements in different columns varying with different periods.

12. The angle of the element at row m and column n in the transformation matrix is ​​given by the following formula: [Equation 5] 12. The method of claim 9, wherein m is an integer greater than 0 and less than or equal to M, n is an integer greater than 0 and less than or equal to N, and k(n) is a function of n.

13. T ０ is expressed as follows: [Equation 6] and f ０ 13. The method of claim 12, wherein Θ represents a frequency component corresponding to a maximum absolute value of coefficients in the frequency components of the DFT of the angle of the second CSI.

14. The function of n is given by the following formula: [Equation 7] ,or [Equation 8] 14. The method according to claim 12 or 13, wherein: a is greater than 0; and β is greater than 0.

15. A communication device comprising a unit adapted to carry out the method according to any one of claims 1 to 14.

16. A communication device comprising a processor and a memory, the memory is configured to store instructions; The processor is configured to execute the instructions to thereby perform the method of any one of claims 1 to 14. Communication equipment.

17. 1. A communication device comprising a logic circuit and an interface, the logic circuit coupled to the interface; The interface is configured to input and / or output code instructions, and the logic circuit is configured to execute the code instructions, thereby performing the method of any one of claims 1 to 14. Communication equipment.

18. 15. A computer-readable storage medium configured to store a computer program, the computer program being operable, when executed, to perform the method of any one of claims 1 to 14.

19. A communication system, comprising a transmitting end and a receiving end, the transmitting end configured to perform a method according to any one of claims 1 to 8, and the receiving end configured to perform a method according to any one of claims 9 to 14.

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