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

[0002] [Cross-references to related applications] This application was filed with the China National Intellectual Property Administration on September 9, 2022, and claims priority to Chinese Patent Application No. 202211104120.7, entitled “Method and Apparatus for Processing Channel State Information,” which is incorporated herein by reference in its entirety.

[0003] [background] In recent years, wireless traffic has surged, and user requirements for communication service quality, such as low latency and ultra-reliability, are increasing. As a crucial technology for carrying wireless traffic services, wireless local area networks (WLANs) are continuously being developed and evolved to meet users' higher requirements for wireless transmission. Currently, technologies such as multiple input multiple output (MIMO) and millimeter wave are expected to be key technologies driving WLAN development. However, both increases in the number of antennas and increases in frequency bandwidth lead to an increase in the number of subcarriers that require channel detection. As a result, channel state information (CSI) feedback overhead increases, and transmission performance deteriorates.

[0004] Orthogonal frequency division multiplexing (OFDM) technology is one of the core WLAN technologies. The basic principle of OFDM technology is to convert a serial high-speed transmission bitstream into multiple parallel low-speed transmission bitstreams and modulate the data into various orthogonal subcarriers. In the 802.11ac protocol, a 20 megahertz (MHz) bandwidth contains 64 subcarriers. However, in the 802.11ax protocol, the number of 20 MHz subcarriers is increased to 256, resulting in a fourfold increase in CSI feedback overhead. In the transmission process, the channel coefficient of each subcarrier needs to be estimated, and the 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 subcarrier number, multiple transmitting antennas and multiple receiving antennas can be used at the transmitting and receiving ends, respectively. This allows signals to be transmitted and received through multiple antennas at the transmitting and receiving ends, thereby improving communication quality. MIMO requires estimation and feedback for the channels between each transmitting and receiving 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 a WLAN leads to an exponential increase in CSI overhead.

[0005] Therefore, in order to reduce CSI feedback overhead, it is urgently necessary to find a way to perform CSI compression processing. [Overview of the project]

[0006] Embodiments of this application provide a CSI processing method and apparatus that can not only effectively reduce CSI feedback overhead but also effectively improve the accuracy of CSI compression.

[0007] According to a first aspect, one embodiment of the present application provides a CSI processing method. The method includes the following: Steps include determining the CSI report and sending the CSI report, where the CSI report includes a first CSI, which is obtained based on a second CSI and a transformation matrix, which is a complex matrix having M rows and N columns, where the absolute value of the elements in the transformation matrix is ​​1, M is greater than N, where 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 a second CSI to obtain a first CSI by using a transformation matrix, where the number of rows in the transformation matrix corresponds to the number of elements in the second CSI (which may also be understood as an uncompressed CSI), and the number of columns in the transformation matrix corresponds to the number of elements in the first CSI (which may also be understood as a compressed CSI). In the method for compressing a CSI by using a transformation 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. Thus, 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 relatively long compression lengths and address large-scale MIMO and multi-subcarrier scenarios.

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

[0010] In possible implementations, the angles of elements in at least one column of the transformation matrix change periodically, while the angles of elements in different columns change using different periods.

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

[0012] In possible implementations, the angles of the elements in the transformation matrix are determined based on the angular periods of M, N, and the second CSI. Alternatively, the angles of the elements in the transformation matrix are determined based on the frequency components of the discrete Fourier transform (DFT) of the angles of M, N, and the second CSI.

[0013] In this embodiment of the present application, the angle of the elements in the conversion matrix is 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 angular period of the second CSI (or the frequency component of the DFT in the phase of the second CSI), whereby the angle of the elements in the conversion matrix can be effectively combined with the periodic angular change of the second CSI. Therefore, the conversion matrix is constructed by using the angular change rule of the uncompressed CSI, thereby effectively improving the compression performance of CSI compression. Accordingly, 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 m-th row and n-th column in the conversion matrix satisfies the following formula. That is,

[0015]

Equation

[0016] where T0 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.

[0017]

Equation

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

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

[0020] In a possible implementation, T0 satisfies the following formula. That is,

[0021]

number

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

[0023] In this embodiment of the present application, the angular period of the second CSI is determined by using the frequency component corresponding to the maximum absolute value of the coefficient in the frequency component of the angle DFT of the second CSI, thereby allowing the transformation matrix to be more appropriately combined with the rule for change of the angular period of the second CSI, thereby effectively improving the accuracy of CSI compression.

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

[0025]

number

[0026] is or,

[0027]

number

[0028] Here, α is greater than 0, and β is greater than 0.

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

[0030] According to a second aspect, one embodiment of the present application provides a CSI processing method. The method includes the following: A step of receiving a CSI report, wherein the CSI report includes a first CSI; and a step of processing the first CSI based on a transformation matrix to obtain a second CSI, wherein the transformation matrix is ​​a complex matrix having M rows and N columns, the absolute value of the elements 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.

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

[0032] In possible implementations, the angles of elements in at least one column of the transformation matrix change periodically, while the angles of elements in different columns change using different periods.

[0033] In possible implementations, the angles of the elements in the mth row and nth column of the transformation matrix satisfy the following equation:

[0034]

number

[0035] Here, 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 possible implementations, T0 satisfies the following equation:

[0037]

number

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

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

[0040]

number

[0041] is or,

[0042]

number

[0043] Here, α is greater than 0, and β is greater than 0.

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

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

[0046] According to a fifth aspect, one embodiment of the present application provides a communication device. The communication device includes a processor configured to perform a method according to the first aspect or a possible implementation thereof. Alternatively, the processor is configured to execute a program stored in memory. Once the program is executed, a method according to the first aspect or a possible implementation thereof is performed.

[0047] In possible implementations, memory is located outside the communication device.

[0048] In possible implementations, memory is located inside the communication device.

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

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

[0051] According to a sixth aspect, one embodiment of the present application provides a communication device. The communication device includes a processor configured to perform a method according to the second aspect or a possible implementation thereof. Alternatively, the processor is configured to execute a program stored in memory. Once this program is executed, a method according to the second aspect or a possible implementation thereof is performed.

[0052] In possible implementations, memory is located outside the communication device.

[0053] In possible implementations, memory is located inside the communication device.

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

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

[0056] According to the seventh aspect, one embodiment of the present application provides a communication device. The communication device includes a logic circuit and an interface, where 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 a CSI report.

[0057] The communication device shown in the seventh aspect may be understood to 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 the eighth aspect, one embodiment of the present application provides a communication device. The communication device includes a logic circuit and an interface, where the logic circuit is coupled to the interface. The interface is configured to input a CSI report. The logic circuit is configured to process a first CSI based on a transformation matrix to obtain a second CSI.

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

[0060] The communication device shown in the eighth aspect may be understood to 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 the ninth aspect, one embodiment of the present application provides a computer-readable storage medium. The computer-readable storage medium is configured to store a computer program. When the computer program is executed on a computer, a method according to the first aspect, or any one of the possible implementations thereof, is performed.

[0062] According to the tenth aspect, one embodiment of the present application provides a computer-readable storage medium. The computer-readable storage medium is configured to store a computer program. When the computer program is executed on a computer, a method according to the second aspect, or any one of the possible implementations of the second aspect, is executed.

[0063] According to the eleventh aspect, one embodiment of the present application provides a computer program product. The computer program product includes a computer program or computer code. When the computer program product is executed on a computer, a method according to the first aspect or any one of the possible implementations of the first aspect is performed.

[0064] According to the twelfth aspect, one embodiment of the present application provides a computer program product. The computer program product includes a computer program or computer code. When the computer program product is executed on a computer, a method according to the second aspect, or any one of the possible implementations of the second aspect, is performed.

[0065] According to the thirteenth aspect, one embodiment of the present application provides a computer program. When the computer program is executed on a computer, a method according to the first aspect or any one of the possible implementations of the first aspect is performed.

[0066] According to the fourteenth aspect, one embodiment of the present application provides a computer program. When the computer program is executed on a computer, a method according to the second aspect, or any one of the possible implementations of the second aspect, is performed.

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

[0068] [Figure 1] This figure shows the architecture of a communication system according to one embodiment of the present application. [Figure 2] This figure shows the architecture of a communication system according to one embodiment of the present application. [Figure 3] This figure shows the performance of a subcarrier grouping method according to one embodiment of this application. [Figure 4] This is a schematic flowchart illustrating a CSI processing method according to one embodiment of this application. [Figure 5a] This figure shows the phase change of CSI according to one embodiment of the present application. [Figure 5b] This figure shows the frequency components of the DFT of the phase of the CSI according to one embodiment of this application. [Figure 6] This figure shows the performance of a CSI compression method based on a regressive polynomial method according to one embodiment of this application. [Figure 7a] This figure shows a performance comparison between various CSI compression methods according to one embodiment of this application. [Figure 7b] This figure shows a performance comparison between various CSI compression methods according to one embodiment of this application. [Figure 7c]This figure shows a performance comparison between various CSI compression methods according to one embodiment of this application. [Figure 8a] This figure shows a performance comparison between various CSI compression methods according to one embodiment of this application. [Figure 8b] This figure shows a performance comparison between various CSI compression methods according to one embodiment of this application. [Figure 9] This figure shows the configuration of a communication device according to one embodiment of the present application. [Figure 10] This figure shows the configuration of a communication device according to one embodiment of the present application. [Figure 11] This figure shows the configuration of a communication device according to one embodiment of the present application. [Modes for carrying out the invention]

[0069] The terms “first,” “second,” and similar terms in the specification, claims, and accompanying drawings of this application are used solely to distinguish different objects and not to describe any particular order. In addition, the terms “include,” “have,” and any other variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device comprising a set of steps or units may, at their discretion, further include steps or units not listed, or at their discretion, further include other steps or units specific to those processes, methods, products, or devices, without being limited to the listed steps or units.

[0070] In this specification, a reference to “an embodiment” means that certain features, configurations, or characteristics described with reference to an embodiment may be incorporated into at least one embodiment of this application. The terms used in various places in this specification do not necessarily refer to the same embodiment and are not exclusive, independent, or alternative embodiments of another embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein may 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 the relationship between related objects and indicates that three relationships may exist. For example, "A and / or B" may indicate the following three cases: that only A exists; that only B exists; and that both A and B exist. Here, A and B may be singular or plural. "Or" indicates that two relationships may exist, e.g., that only A exists and that only B exists. If A and B are not mutually exclusive, it may indicate that three relationships exist, e.g., that only A exists, that only B exists, and that both A and B exist. The letter " / " generally indicates an "or" relationship between related objects. The phrase "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 could mean a, b, c, "a and b", "a and c", "b and c", or "a, 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 further 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 this application may be further applied to various other communication systems, such as Internet of Things (IoT) systems, vehicle-to-everything (V2X) systems, and narrowband Internet of Things (NB-IoT) systems, or to devices in the Internet of Vehicles, Internet of Things (IoT) nodes, sensors, and the like, smart cameras, smart remote controls, and smart water / electricity meters in smart homes, sensors in smart cities, and the like, or to long-term evolution (LTE) systems, fifth-generation (5G) communication systems, new communication systems emerging in future communication developments, and the like.

[0073] The embodiments of this application primarily use, as illustrative examples, a WLAN, in particular a network applicable to the IEEE 802.11 series standards, such as a system supporting Wi-Fi 7, sometimes called extremely high throughput (EHT), and, as another example, a system supporting Wi-Fi 8, sometimes called ultra high reliability (UHR) or ultra high reliability and throughput (UHRT). However, those skilled in the art will readily understand that the embodiments of this application can be extended to various standards or protocols, such as Bluetooth, high-performance radio LAN (HIPERLAN), or wide area networks (WANs), or known or future networks. Therefore, regardless of the coverage used and the radio access protocol used, the various embodiments provided in this application are applicable to any suitable radio network.

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

[0075] An access point is a device having wireless communication capabilities that supports communication or sensing using the WLAN protocol and has the ability to communicate with or sense other devices within a WLAN network (e.g., a station or another access point). Indeed, an access point may also have the ability to communicate with or sense other devices. Alternatively, an access point is equivalent to a bridge connecting wired and wireless networks. The primary 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 is sometimes called an access point station (AP STA). A device having wireless communication capabilities may be an entire device, or a chip, processing system, or similar mounted on the entire device. A device on which a chip or processing system is mounted may implement the methods and functions of the embodiments of this application under the control of the chip or processing system. The AP in the embodiments of this application is a device that provides services to an STA and may support the 802.11 series protocol or later protocols or similar. For example, an access point may be an access point for a terminal (e.g., a mobile phone) to access a wired (or wireless) network, and is primarily deployed within homes, buildings, and premises. A typical coverage radius is tens to hundreds of meters. Indeed, access points may, alternatively, be deployed outdoors. Another example is that an AP may be a communications entity such as a communications server, router, switch, or network bridge. APs may include various forms of macro base stations, micro base stations, relay stations, and similar devices. Indeed, an AP may, alternatively, be a chip and processing system within these various forms of devices that implements the methods and functions of the embodiments of this application.

[0076] A station is a device having wireless communication capabilities that supports communication or sensing by using the WLAN protocol and has the ability to communicate with or sense other stations or access points within a WLAN network. In a WLAN system, a station is sometimes called a non-access point station (non-AP STA). For example, an STA is any user communication device that enables a user to communicate with an AP or sense one and communicate with the WLAN. A device having wireless communication capabilities may be an entire device, or a chip, processing system, or similar mounted on the entire device. A device on which a chip or processing system is mounted may implement the methods and functions of the embodiments of this 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 called a user. As another example, the station may be a mobile phone that supports Wi-Fi communication, a tablet computer that supports Wi-Fi communication, a set-top box that supports Wi-Fi communication, a smart television that supports Wi-Fi communication, a smart wearable device that supports Wi-Fi communication, an in-vehicle communication device that supports Wi-Fi communication, or a computer that supports Wi-Fi communication.

[0077] WLAN systems can provide high-speed and low-latency transmission. With the continued development of WLAN application scenarios, WLAN systems are being applied to a wider range of 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, devices that support WLAN communication or sensing (e.g., access points or stations) may be sensor nodes in smart cities (e.g., smart water meters, smart electricity meters, or smart air sensing nodes), smart devices in smart homes (e.g., smart cameras, projectors, displays, televisions, speakers, refrigerators, or washing machines), nodes in the Internet of Things, entertainment terminals (e.g., wearable devices such as augmented reality (AR) or virtual reality (VR) devices), smart devices in smart offices (e.g., printers, projectors, loudspeakers, or speakers), Internet of Vehicles devices in the Internet of Vehicles, infrastructure in everyday life scenarios (e.g., vending machines, self-service navigation consoles, self-checkout machines, or self-service ordering machines in shopping malls or supermarkets), devices in large stadiums or music venues, or similar. Exemplary examples include, for instance, access points and stations, which may be devices used in the Internet of Things in vehicles, Internet of Things nodes or sensors within 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. Specific forms of STAs and APs are not limited to those in the embodiments of this application, but are merely illustrative examples provided herein.

[0078] For example, communication systems to which the methods provided in this application may be applied may include access points and stations. For example, this application is applicable to communication or sensing scenarios between APs and STAs, between APs, or between STAs in a WLAN. This is not limited to the embodiments of this 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 transmissions, where the AP simultaneously transmits signals to multiple STAs, and uplink transmissions, where multiple STAs transmit signals to the AP. WLAN communication protocols may be supported between APs and STAs, between APs, or between STAs. Communication protocols may include protocols in the IEEE 802.11 series, e.g., the 802.11be standard and certainly later standards.

[0079] Figure 1 shows the architecture of a communication system according to one embodiment of the present application. This communication system may include one or more access points (APs) and one or more stations (STAs). Figure 1 shows two access points, such as AP1 and AP2, and three stations, such as STA1, STA2, and STA3. It can 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] Figure 1 uses an example where the STA is a mobile phone and the AP is a router, but this should be understood not to mean that the types of APs and STAs in this application are limited. Also, Figure 1 shows only two APs and three STAs as an example. There may be more or fewer APs or STAs. This is not limited to these embodiments of this application.

[0081] Figure 2 shows the architecture of a communication system according to one embodiment of the present application. As shown in Figure 2, this communication system may include at least one network device and at least one terminal device, for example, terminal devices 1 to 4 in Figure 2. For example, terminal devices 3 and 4 shown in Figure 2 may communicate directly with each other. For example, direct communication between terminal devices may be implemented by using D2D technology. For example, terminal devices 1 to 4 may communicate with the network device individually. For example, terminal devices 3 and 4 may communicate directly with the network device, or they may communicate indirectly with the network device, for example, through another terminal device (not shown in Figure 2). It should be understood that Figure 2 shows an example of a communication link between one network device and four terminal devices, as well as 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 the present embodiment of the present application. Terminal equipment and network equipment are described in detail below.

[0082] Terminal equipment is a device having wireless transmission and reception capabilities. Terminal equipment can communicate with access network equipment (sometimes called access equipment) within a radio access network (RAN). Terminal equipment 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 similar. In possible implementations, terminal equipment may be deployed on land, including indoor, outdoor, handheld, or in-vehicle devices, or on water (e.g., a ship). In possible implementations, terminal equipment 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 Vehicle Internet, an unmanned aerial vehicle, a terminal in any form within a 5G network or future network, or similar. This is not limited to the embodiments of this application. The terminal devices shown in this embodiment of the application may include vehicles (e.g., automobiles) in the Vehicle Internet, and may also include in-vehicle devices, in-vehicle terminals, or similar devices in the Vehicle Internet. The specific forms of terminal devices used in the Vehicle Internet are not limited in this embodiment of the application. The terminal devices shown in this embodiment of the application may be understood to be able to communicate with each other using D2D, V2X, M2M, or similar technologies. The method of communication between terminal devices is not limited in this embodiment of the application.

[0083] Network equipment can be devices deployed in a wireless access network that provide wireless communication services to terminal devices. Network equipment may also be called access network equipment, access equipment, RAN equipment, or similar. For example, network equipment may be a next-generation NodeB (gNB), a next-generation evolved NodeB (ng-eNB), network equipment in 6G communication, or similar. Network equipment may be any device with wireless transmit / receive functionality, including, but not limited to, the base stations described above (including base stations deployed on satellites). Alternatively, network equipment may be equipment with base station functionality in 6G. Optionally, network equipment may be an access node, wireless relay node, wireless backhaul node, or similar in a Wi-Fi system. Optionally, network equipment may be a wireless controller in a cloud radio access network (CRAN) scenario. Optionally, network equipment may be a wearable device, in-vehicle equipment, or similar. Optionally, network equipment may be small cells, transmission reception points (TRPs) (or referred to as transmission points), or similar. Alternatively, network equipment may be understood to be base stations, satellites, or similar in a future developed public land mobile network (PLMN). Alternatively, network equipment may be communication devices functioning as base stations in non-terrestrial communication systems, D2D, V2X, or M2M, or similar. The specific types of network equipment are not limited to these embodiments of this application. In systems using different radio access technologies, the names of communication devices having network equipment functions may differ and are not enumerated in these embodiments of this application.Optionally, in some deployments of network equipment, the network equipment may include a central unit (CU), a distributed unit (DU), and similar. In some other deployments of network equipment, the CU may be further divided into a CU control plane (CP), a CU user plane (UP), and similar. Alternatively, in some further deployments of network equipment, the network equipment may be an open radio access network (ORAN) architecture or similar. The specific configurations of the network equipment are not limited to these embodiments of the present application.

[0084] The network architectures and service scenarios described in the embodiments of this application are intended to more clearly illustrate the technical solutions in the embodiments of this application, but do not constitute any limitation on the technical solutions provided in the embodiments of this application. Those skilled in the art will recognize that even as network architectures evolve and new service scenarios emerge, the technical solutions provided in the embodiments of the present invention will remain applicable to similar technical problems.

[0085] Currently, subcarrier grouping methods exist, for example, where multiple adjacent subcarriers report a single CSI, reducing the feedback overhead of CSI reporting. In this method, the CSIs of equally spaced subcarriers are selected for transmission, and 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, as it cannot meet the CSI compression quality requirements. Figure 3 shows the subcarrier grouping method for the CSI (sometimes called channel coefficient) of a subcarrier group received between a pair of transmitting and receiving antennas. In Figure 3, "uncompressed" represents the uncompressed CSI, and "compressed" represents the CSI reconstructed by the receiving end based on the compressed CSI received by the receiving end. The "compressed" shown in Figure 3 can also be understood as "reconstructed CSI after compression." As can be seen from Figure 3, when the compression ratio is relatively high (for example, 128 CSIs are compressed into 8 CSIs), the subcarrier grouping method exhibits a clear deviation. The reason why the subcarrier grouping method exhibits a clear deviation is as follows: as the compression ratio increases, the spacing between selected subcarriers becomes larger, and the continuity between subcarriers is lost. As a result, the accuracy of CSI reconstruction performed at the receiving end decreases.

[0086] However, in subcarrier grouping methods, the CSI change rules between subcarriers are ignored, and CSI compression is performed directly using a one-out-of-many approach, resulting in relatively low compression quality. In practical applications, it is common to ensure compression quality by dividing two or four subcarriers into a single group. Therefore, relatively long compression lengths cannot be implemented, and large-scale MIMO and multi-subcarrier scenarios cannot be satisfied. With advances in WLAN technology, OFDM subcarrier spacing has become narrower and frequency bandwidth has become wider. Both of these increase the number of subcarriers that require channel estimation. Furthermore, the number of MIMO antennas is continuously increasing. These three factors add up, resulting in an increase in CSI feedback overhead, which consumes time that should be used for data transmission, leading to a degradation of network performance.

[0087] In view of this, embodiments of the present application provide a CSI processing method and apparatus for effectively reducing CSI feedback overhead. By reducing CSI feedback overhead, cases in which extremely high CSI feedback overhead consume time used for data transmission can be effectively improved, thereby ensuring network performance. Optionally, the method provided in these embodiments of the present application can further effectively utilize inter-subcarrier CSI change rules, thereby effectively reducing the difference between the CSI restored at the receiving end and the uncompressed CSI at the transmitting end, improving compression accuracy and enhancing the compression performance of CSI compression. Optionally, the method provided in these embodiments of the present application can more effectively satisfy large-scale MIMO and multi-subcarrier scenarios. Optionally, CSI feedback overhead is mainly related to the number of MIMO antennas and the number of OFDM subcarriers. The method provided in these embodiments of the present application can effectively mitigate the problem of increased CSI feedback overhead resulting from the use of at least one of MIMO or OFDM technologies.

[0088] For example, CSI feedback plays a crucial role in radio frequency sensing fields such as Wi-Fi positioning. In wireless environments, different human behavior leads to different multipath changes. Therefore, by observing human movement in a wireless sensor network and the resulting CSI, it is possible to reconstruct the real physical world through radio frequency sensing, thereby enabling the provision of a wide range of new services. CSI compression can reduce transmission delay by decreasing the amount of data that needs to be transmitted, thereby improving the timeliness of CSI feedback and wireless sensing.

[0089] Figure 4 is a schematic flowchart illustrating a CSI processing method according to one embodiment of the present application. This method may be applied to the communication system shown in Figure 1 or Figure 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 a CSI report. For example, an STA may be used as the transmitting end and an AP may be used as the receiving end. Another example is that an AP may be used as the transmitting end and an STA may be used as the receiving end. Another example is that an UE may be used as the transmitting end and a base station may be used as the receiving end. Another example is that a base station may be used as the transmitting end and a UE may be used as the receiving end. Another example is that a beamforming receiving end (beamformy) may be used as the transmitting end and a beamforming transmitting end (beamformer) may be used as the receiving end. 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 a pilot signal. For example, a beamforming receiver may obtain channel measurement results based on a pilot signal transmitted by a beamforming transmitter and feed back the CSI. The pilot signal may be understood as a signal used for channel detection, or a signal used for channel estimation, or a signal used for channel measurement. Channel detection, channel estimation, and channel measurement in this embodiment of the application may be understood to be interchangeable. For a description of the transmitter and receiver in this embodiment of the application, please refer to the above-described explanation in Figures 1 and 2. Details are not described again in this specification. In this embodiment of the application, it is not limited whether or not another transfer device is included between the transmitter and receiver.

[0090] Before describing the method shown in Figure 4, 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 relates to the following optimization problem:

[0092]

number

[0093] Here, vector b represents an uncompressed CSI vector, vector x represents a compressed CSI vector, and the elements in vectors b and x are complex numbers. Vector b has M elements, and vector x has N elements. Here, M and N are both positive integers, and M > N. Vector b is,

[0094]

number

[0095] It can be expressed as, and the vector x is,

[0096]

number

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

[0098]

number

[0099] It can be expressed as follows: In matrix A, the number of rows M corresponds to the number of uncompressed CSIs (which can also be understood as the number of elements in vector b), and the number of columns N corresponds to the number of compressed CSIs (which can also be understood as the number of elements in vector x). The compressed CSI as shown herein is a compressed CSI (e.g., vector x) obtained by the transmitting end based on an uncompressed CSI (e.g., vector b), and “compressed” in the accompanying drawings of embodiments of this application can be understood to represent the 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 a compressed CSI obtained by the transmitting end based on the uncompressed CSI and transformation matrix of embodiments of this application.

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

[0101] When CSI compression is performed, the transmitting end can compress M CSIs of a continuous subcarrier group into N CSIs. Optionally, the number of elements M in each group of uncompressed CSIs is equal to the number of continuous subcarriers in the bandwidth. C And the subcarrier number M corresponding to the compression performance threshold P It can be determined by the minimum value of, that is, M = min{M C M P} is M PThis represents the corresponding subcarrier number when the minimum CSI compression performance requirements are met. C This represents the number of continuous subcarriers within a bandwidth of 20 MHz, 40 MHz, 80 MHz, 160 MHz, or 320 MHz, or resource units (RUs) of different sizes. Optionally, for a given bandwidth, direct current (DC) subcarriers (sometimes abbreviated as DC subcarriers) within the bandwidth (near the center frequency) are not typically used to transmit data. Therefore, the presence of DC subcarriers may cause discontinuities among the M subcarriers shown in this embodiment of the application. However, since DC subcarriers themselves are not used to transmit data, any discontinuities in the subcarriers caused by DC subcarriers within the bandwidth are ignored, and ignoring DC subcarriers is equivalent to considering the subcarriers within the bandwidth to be continuous. Optionally, a loss of CSI at the DC subcarrier position (near the center frequency) within the bandwidth causes a loss of phase continuity between the two CSI data on either side of the DC subcarrier position. However, the transmitting end can perform interpolation at the DC position to obtain a continuous subcarrier CSI within the bandwidth. Accordingly, when reconstructing the CSI based on the compressed CSI and transformation matrix, the receiving end may reconstruct more than M CSIs, after which the receiving end removes the CSI at the DC position to obtain the original CSI.

[0102] The transmitting antenna shown in this embodiment of the application is an antenna configured to transmit a pilot signal (for example, sending The receiving antenna can be understood as an antenna configured to receive a pilot signal (for example, an antenna configured to transmit a pilot signal at the receiving end), and the receiving antenna as an antenna configured to receive a pilot signal (for example, an antenna configured to receive a pilot signal at the receiving end).

[0103] At the transmitting end, for a given vector b of uncompressed CSI, equation (1) above can be understood as a 2-norm minimization problem, and this problem belongs to the category of convex optimization problems. 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] At the receiving end, the receiving end can recover the uncompressed CSI based on the following formula:

[0105]

number

[0106] Here, A i represents the i-th column vector of matrix A, where i is an integer between 1 and N. In other words, the receiving end may estimate the uncompressed CSI by multiplying each column of matrix A by the corresponding element of the compressed CSI, and then obtaining the cumulative sum. The use of an approximation sign in equation (2) can be understood as being because the CSI reconstructed 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 from the optimization problem in equation (1). For example, the vector x obtained by the transmitting end is

[0107]

number

[0108] This may not result in a value equal to zero. As another example, this difference can be caused by errors in quantization and coding. These examples are not listed one by one in this specification.

[0109] 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 each element in matrix A is 1. This can also be understood as all elements in each column of matrix A having a unit absolute value.

[0111] Generally, the absolute values ​​(sometimes called values) of uncompressed CSI change gradually. That is, uncompressed CSI has similar (or approximate) absolute values. The absolute values ​​in vector b are similar (or approximate). Therefore, by setting the absolute values ​​of the elements in vector A to the same value and all to 1, it is possible to effectively adjust the absolute values ​​of the elements in vector x and achieve similarity (or approximation) between the absolute values ​​of the elements in vector Ax and the absolute values ​​of the elements in vector b. Thus, the computational complexity in the optimization of equation (1) by the transmitting end can be effectively reduced, the difference between the CSI restored by the receiving end and the uncompressed CSI can be minimized, and the compression performance of CSI compression is improved.

[0112] Furthermore, condition 1 can also be understood as meaning that the element in the mth row and nth column of matrix A may satisfy the following equation. ru. In other words,

[0113]

number

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

[0115] Condition 2: The angle (which can also be understood as phase) of an element in at least one column of matrix A changes periodically, and the angles of elements in different columns change using different periods.

[0116] In practical applications, the phase of the uncompressed CSI may change periodically or can be understood to be periodic. FIG. 5a is a diagram showing the phase change of CSI according to an embodiment of the present application. In FIG. 5a, the horizontal axis represents the subcarrier sequence number, the vertical axis represents the phase, and the unit is π. FIG. 5a shows the phase change of CSI shown by using an example where the subcarrier group is 242 subcarriers and corresponds to one transmitting antenna and one receiving antenna. However, the number of subcarriers and the number of antennas shown in FIG. 5a should not be understood as a limitation to the present embodiment of the present application. The phase change of CSI shown in FIG. 5a can be understood to be general. The angles and phases in the present embodiment of the present application can be understood to be equivalent ru.

[0117] Since the angle of the uncompressed CSI changes periodically, the angles of the elements in vector b also change periodically. Therefore, by ensuring that the angles of the elements in at least one column (for example, A i etc.) in matrix A change periodically, after multiplying A i by coefficient x i , the periodic angle change of x i A i can be made as close as possible to the periodic angle change of the elements in vector b. By adjusting the weights x i corresponding to different columns A i in matrix A, 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 angular changes of vector b are not perfectly regular (and can even be understood as not being precisely periodic). Therefore, the angles of elements in different columns of matrix A correspond to different periods, which allows for an effective matching of the angular change rules (sometimes called phase characteristics) of the uncompressed CSI, thereby improving the accuracy of CSI compression. Thus, the difference between the CSI reconstructed by the receiving end and the uncompressed CSI is minimized, and it is guaranteed, as far as possible, that the receiving end can reconstruct 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 angles of the elements in each column of matrix A change periodically. Based on the relationship between period and angular velocity, condition 2 can also be expressed as follows: The angular velocity of the angles of the elements in at least one column of matrix A remains constant (a constant angular velocity can also be understood as the same angular velocity), while 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 of matrix A remains constant, while the angular velocities of the angles of the elements in different columns are different.

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

[0121] The angles of the elements in the mth row and nth instance of matrix A satisfy the following equation:

[0122]

number

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

[0124] In equation (4),

[0125]

number

[0126] This can correspond to angular velocity. By using the variable n, the angular velocity of the elements can be adjusted, allowing equation (4) to be more appropriately applied to the 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 subcarrier number included in the 2π phase change of the incompressible CSI. Optionally, T0 may be understood as the minimum period of the phase change of the incompressible CSI. Optionally, T0 may be understood as the reference period. Optionally, T0 may be understood as the optimal period of the optimization problem shown in equation (5). T0 is related to the angle of the incompressible CSI. That is,

[0128]

number

[0129] That is the case.

[0130] For an explanation of the parameters in equation (5), please refer to equation (1). Further details are not provided in this specification.

[0131] Based on the relationship between the phase period of the uncompressed CSI and the frequency components of the phase DFT, condition 3 can also be understood as being determined based on M, N, and the frequency components of the phase DFT of the uncompressed CSI.

[0132] Figure 5b shows the frequency components of the DFT of the phase of a CSI according to one embodiment of the present application. In Figure 5b, the horizontal axis represents the frequency components, and the vertical axis represents the absolute value of the coefficients (sometimes called weights) corresponding to the frequency components. Generally, the coefficients corresponding to the frequency components after the DFT are complex numbers, and the vertical axis shown in Figure 5b represents the absolute value of the coefficients. Based on the relationship between Figures 5a and 5b, the horizontal axis in Figure 5b can also be understood as the number of periods during which the phase of the uncompressed CSI (vector b) changes, and the number of periods can be understood as corresponding to the number of shaded areas 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 uncompressed CSI may satisfy the following equation:

[0135]

number

[0136] That is the case. Here,

[0137]

number

[0138] This may be understood as representing the maximum absolute value of the coefficients corresponding to different frequency components of the DFT of the phase of the uncompressed CSI (i.e., vector b), or as the y-coordinate corresponding to the highest point of the DFT of the phase of the uncompressed CSI, or as the maximum weight of the DFT of the phase of the uncompressed CSI.

[0139]

number

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

[0141]

number

[0142] This represents the frequency components of the phase in uncompressed CSI.

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

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

[0145]

number

[0146] That is the case.

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

[0148] Figures 5a and 5b are used as examples. 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 Figure 5a may be 60.5. Alternatively, the subcarrier number included in the 2π phase change of the uncompressed CSI is 60.5. Or, the 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] That is the case.

[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] That is the case.

[0158] The numerators in equations (7) and (8) are determined based on the number of elements in the incompressible CSI. The numerators in equations (9) and (10) can be understood to be determined based on the length of the incompressible CSI.

[0159] In specific implementations, it can be understood that equations (7) through (10) described above may be further modified. For example, in rounding up or rounding down methods, it is guaranteed that T0 is an integer, thereby reducing computational complexity as much as possible.

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

[0161]

number

[0162] That is the case.

[0163] Generally, frequency component index calculated using DFT

[0164]

number

[0165] Since is an integer, the phase shift frequency of the uncompressed CSI may actually have decimal places. Therefore, by using equation (11) and searching for T0 within the range described above, an optimized T0 can be obtained for equation (5).

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

[0167]

number

[0168] That is the case.

[0169] T0 can satisfy the following equation:

[0170]

number

[0171] That is the case.

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

[0173]

number

[0174] That is the case.

[0175] T0 can satisfy the following equation:

[0176]

number

[0177] That is the case.

[0178] The T0 and f0 shown in this embodiment of the application may be understood to be able to be combined with each other. For example, formula (6) may be combined with formula (13) or with formula (14). As another example, formula (12) may be combined with any one of formulas (7) through (10). As yet another example, formula (14) may be combined with any one of formulas (7) through (10). These examples are not listed one by one.

[0179] The k(n) in the embodiments of this application will be described below.

[0180] Because the long training field (LTF) used for channel estimation contains cyclic prefixes, the actual time-domain sampling position may be shifted to the left, which in turn causes a phase shift in the frequency domain.

[0181]

number

[0182] It is expressed as follows. As shown in Figure 5a, in this embodiment of the present application,

[0183]

number

[0184] The specific value of is not limited. Therefore, in this embodiment of the present application,

[0185]

number

[0186] That is the case.

[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). Here, min{k(n)}=0 indicates a frequency of 0, and max{k(n)}=1 indicates the maximum frequency.

[0188]

number

[0189] According to this, the frequency in this case is f0. For ease of expression, the interval of values ​​for k(n) is set to a unit length, for example, k(1)=1 and k(N)=1. Equations (16) to (21) below can be understood as being shown by using k(1)=0 and k(N)=1 as examples. Examples of min{k(n)}=k(N)=0 and max{k(n)}=k(1)=1 can be obtained by transforming the equations shown below. Therefore, in this embodiment of the present application, these examples are not shown one by one.

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

[0191]

number

[0192] Here, 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] That is the case.

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

[0197]

number

[0198] These are equal to 0, 1 / 2, 2 / 3, ..., and 1-N / N, respectively. Accordingly, by referring to equations (17) and (4), N rotation coefficients can be constructed with periods of ∞, 2T0(1 / 2*f0), 3 / 2T(2 / 3*f0), 4 / 3T(3 / 4*f0), ..., and N / (N-1)T(1-N / N*f0), respectively. These rotation coefficients can be understood as angular changes with a constant absolute value. Thus, 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 the incompressible CSI. Furthermore, the constructed N rotation coefficients have more values ​​around f0, which effectively ensures that the phase change period of more elements in the column vectors of matrix A is around T0, thereby bringing the phase change rule of the elements in matrix A as close as possible to the phase change rule 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] That is the case.

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

[0203]

Number

[0204] is equal to 0, 1 / N, 2 / N, ..., 1 - N / N. That is, N rotation coefficients 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. That is,

[0206]

Number

[0207] is as follows.

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

[0209]

Number

[0210] is as follows.

[0211] For the explanations of Equation (19) and Equation (20), please refer to Equation (18).

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

[0213]

Number

[0214] is as follows.

[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 equation (4) above, this indicates that in matrix A, the phase change period of more elements in the column vectors is around T0.

[0216] The method for compressing CSI based on matrix A shown in this embodiment of the present application may be understood to sometimes be called a CSI compression method based on a modified DFT matrix. Currently, there is another CSI compression method based on a regressive polynomial. The transformation matrix B used in the CSI compression method based on a regressive polynomial is a real matrix having M rows and N columns, and B mn =m n Therefore, matrix B is a real matrix, but both the vector x of the uncompressed CSI and the vector b of the compressed CSI are complex vectors. Consequently, the loss of the imaginary part of matrix B causes a decrease in compression quality and an increase in compression error. Figure 6 shows the performance of a CSI compression method based on a regressive polynomial method according to one embodiment of the present application. As shown in Figure 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 Figure 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 high compression ratios (e.g., 242 CSIs are compressed to 8 CSIs), the compressed CSI obtained by using a CSI compression method based on a regressive polynomial method will result in a clear deviation between the CSI restored by the receiving end and the uncompressed CSI obtained by the transmitting end. The explanation of "compressed" in Figure 6 can be understood as referring to the explanation given above in Figure 3 or in equation (1).

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

[0218]

number

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

[0220] A method provided in the embodiments of this application is described below. As shown in Figure 4, the method includes the following steps.

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

[0222] The CSI report includes a first CSI, which is obtained based on a second CSI and a transformation matrix, where the transformation matrix is ​​a complex matrix with M rows and N columns, the absolute value of the elements 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.

[0223] Furthermore, the determination of the CSI report by the transmitting end can also be understood as follows: the transmitting end generates a CSI report; or the transmitting end performs CSI compression based on the channel detection result (or channel estimation result) to compress the number of CSIs from M to N, and obtains a CSI report based on the compressed CSIs; or the transmitting end performs channel detection based on a reference signal to obtain M CSIs, compresses the M CSIs to N CSIs by using a CSI compression method based on a modified DFT matrix, and obtains 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 similar. The source of the reference signal is not limited to this embodiment of the present application.

[0224] Generally, channel detection results can be understood as being determined based on the channel matrix and the subcarrier number, or as being related to the channel matrix and the subcarrier number. The channel matrix represents channel information between all transmitting and receiving antennas. M as shown in this embodiment of the application may be understood as the channel detection result for M subcarriers between the transmitting and receiving antennas, or (as a mere example) as the transmitting end performing CSI compression by using each M CSI as a group. Accordingly, N as shown in this embodiment of the application may be understood as the number of CSIs obtained after the channel detection result for M subcarriers between the transmitting and receiving antennas has been compressed, or as the number of CSIs obtained after CSI compression has been performed on each M CSI. M in each group of CSIs shown in this embodiment of the application may be understood as merely an example. The value of M in different CSI groups may be the same or certainly different. This is not limited to this embodiment of the application.

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

[0226] In one example, the CSI report may include a first CSI, M, N, and T0, or the CSI report may include a first CSI, M, N, and f0. In the CSI report, T0 or f0 is clearly indicated so that the receiving end can easily and clearly determine the phase period of the second CSI.

[0227] In another example, a CSI report may include a first CSI. Therefore, after receiving a CSI report, the receiving end can acquire the M, N, and f0 (or T0) based on the CSI report, which includes the M, N, and f0 (or T0) prior to the CSI report, thereby effectively reducing signaling overhead.

[0228] In yet another example, a CSI report may include a primary CSI, M, and N. Therefore, after receiving a CSI report, the receiving end can acquire f0 or T0 based on the CSI report, which includes the preceding f0 or T0, thereby effectively reducing signaling overhead.

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

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

[0231] For example, for the compressed CSI quantized by using the same number of bits, the CSI report can be shown in Table 1. Here,

[0232]

Number

[0233] represents the N r th row and N c th column of the MIMO channel matrix.

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

[0235] N indicates that when performing CSI compression, each CSI group is compressed into N CSIs.

[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 encoding are performed on the real or imaginary part of one CSI.

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

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

[0239] N s This represents the subcarrier frequency of each receiving antenna.

[0240] If 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 T0. 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 When CSI compression is performed, the corresponding T0 is in Table 1, which includes the compressed results of multiple initial CSIs corresponding to the compressed results of individual CSIs, and uses M CSIs as a single group.

[0241]

number

[0242] It corresponds to.

[0243] Optionally, the CSI report may include the compressed CSI results of numerous subcarriers between all transmitting and receiving antennas in the channel matrix, as shown in Table 1, and M, N, and T0 corresponding to each group of CSI compressed results. T0 is used only as an example in Table 1 and should not be construed as a limitation to this embodiment of the application.

[0244] Optionally, the CSI report may include the compressed result of the CSI of numerous subcarriers between some transmitting antennas and all receiving antennas in the channel matrix. Optionally, the CSI report may include the compressed result of the CSI of numerous subcarriers between all transmitting antennas and some receiving antennas in the channel matrix. Optionally, the CSI report may include the compressed result of the CSI of numerous subcarriers between the transmitting and receiving antennas in the channel matrix. In the three cases described above, 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 the CSI report that includes M, N, and f0 (or T0) prior to the CSI report, thereby effectively reducing signaling overhead.

[0245] [Table 1]

[0246] Optionally, the CSI report may include the compressed CSI results M and N of the numerous subcarriers between all transmitting and receiving antennas in the channel matrix. In this case, the receiving end may obtain T0 corresponding to each group of CSI compressed results based on the CSI report, which includes T0 (or f0) prior to the CSI report. Thus, the receiving end may learn the CSI compression ratio based on M and N in the CSI report, and further, if it is determined that the CSI compression ratio in the CSI report is the same as the CSI compression ratio prior to the CSI report, the receiving end may obtain T0 corresponding to each group of CSI compressed results based on the CSI report, which includes T0 (or f0) prior to the CSI report.

[0247] Optionally, the CSI report may further include indication information, which may be used to indicate the method of performing CSI compression. For example, the field where the indication information is located may occupy 1 bit. For example, if the value of the field where the indication information is located is 0, it may indicate that the indication information is used to indicate that the method of performing CSI compression is the subcarrier grouping method. As another example, if the value of the field where the indication information is located is 1, it may indicate that the indication information is used to indicate that the method of performing CSI compression is the modified DFT matrix-based CSI compression method. As yet another example, the field where the indication information is located may occupy 2 bits. For example, if the value of the field where the indication information is located is 01, it may indicate that the indication information is used to indicate that the method of performing CSI compression is the subcarrier grouping method. As yet another example, if the value of the field where the indication information is located is 10, it may indicate that the indication information is used to indicate that the method of performing CSI compression is the regressive polynomial method. As another example, if the value of the field where the instruction information is placed is 11, it may indicate that the instruction information is used to indicate that the method performing CSI compression is a CSI compression method based on a modified DFT matrix. Alternatively, the number of bits occupied by the field where the instruction information is placed may be 3 bits, or similar. These examples are not listed one by one. The explanations between the values ​​and meanings described above are merely examples and should not be interpreted as limitations on the embodiments of this application.

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

[0249] N s The parameter T0 is calculated based on the CSI of the individual continuous subcarriers, and the compression parameters M and N are selected according to the protocol to generate the transformation matrix.

[0250] N s For each continuous subcarrier CSI, every M CSI is compressed into N CSIs, 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 Performs bit quantization and encoding.

[0252] } } }

[0253] See Table 1 for a description of the parameters used in the procedure described above. Further details are not provided herein. Referring to the transformation matrix shown in this embodiment, the transmitting end may determine the transformation matrix according to equations (1), (3), and (4), and perform CSI compression based on the transformation matrix determined by the transmitting end. For methods of determining T0 and f0 in the transformation matrix, see equations (5), (6), (7), (8), (9), (10), (11), (12), (13), (14), or (15). For methods of determining k(n) in the transformation matrix, see equations (16), (17), (18), (19), (20), and (21).

[0254] 402: The transmitting end sends a CSI report, and the receiving end receives a CSI report accordingly.

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

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

[0257]

number

[0258] It may not be possible to make it equal to 0. Another example is that the difference may arise due to errors in quantization and coding. These examples are not listed one by one in this specification. The CSI reconstructed by the receiving end based on the transformation matrix and the first CSI may also be understood as "compressed" in the accompanying drawings of embodiments of this application. ru.

[0259] In equation (2) above,

[0260]

number

[0261] However, when used to represent the m-th element of a CSI vector b, b(m) can satisfy the following equation:

[0262]

number

[0263] That is the case.

[0264] Equations (2) and (22) can be understood as equivalent. ru. Therefore, please refer to the explanation above for the explanation of equation (22). Further details will not be explained in this specification.

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

[0266]

number

[0267] That is the case.

[0268] Formula (23) is used as an example only and should not be interpreted as a limitation on this embodiment of the present application.

[0269] any

[0270]

number

[0271] Regarding this, equation (23) is given by the elements at the same position in each periodic sequence.

[0272]

number

[0273] It can be considered a weighted sum of these elements, which can approximate an element b(m) at the same position in the uncompressed CSI sequence. Thus, the receiving end can more appropriately approach and approximate an uncompressed CSI like b(m).

[0274] Referring to the transformation matrix shown in this application, the receiving end uses equation (2) (or equation (22)) and equation (3 )Based on this, the transformation matrix can be determined, and the first CSI can be processed based on the transformation matrix to reconstruct the second CSI. For methods of determining T0 and f0 in the transformation matrix, see equations (5), (6), (7), (8), (9), (10), (11), (12), (13), (14), or (15). For methods of determining k(n) in the transformation matrix, see equations (16), (17), (18), (19), (20), and (21).

[0275] It can be understood that the method for determining T0 or f0 must be the same for the transmitting end and the receiving end (e.g., T0 or f0 is determined by using the same formula), and the method for determining k(n) must also be the same (e.g., k(n) is determined by using the same formula). For example, the transmitting end may store formulas (1), (3), and (4), and the receiving end may store formulas (2), (3), and (4). As another example, both the transmitting and receiving ends may store formulas (1), (3), and (4), and the receiving end may determine formula (2) based on formula (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 for each M through N. The transmitting end then feeds back the compressed CSI vector x and the compression parameters M, N, and T0. 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 estimated result of the actual CSI vector b.

[0276] The explanation of the transformation matrix in this embodiment of the application may be understood to involve reference to equations (1) to (21).

[0277] For example, referring to equations (4) and (17), if M=6 and N=3, the transformation matrix A 6×3 The following applies:

[0278]

number

[0279] That is the case.

[0280] Equation (24) can be understood as merely an example. In a concrete implementation, after knowing the parameters M and N, the transformation matrix A can be extended to any dimension. In this embodiment of the present application, 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 may be equal, and all elements in the first row of the transformation matrix may be equal. See equations (4) and (17),

[0282]

number

[0283] As shown below,

[0284]

number

[0285] It is adjusted to that. That is,

[0286]

number

[0287] That is the case.

[0288] Equation (25) can be understood as 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 principles of transformation matrices in this embodiment of the application, all variations of the transformation matrix are covered within the scope of protection of this embodiment. For example, after obtaining a transformation matrix based on equations (3) and (4), setting all elements in at least one column of the transformation matrix to be the same, or setting all elements in at least one row of the transformation matrix to be the same, or setting at least two columns of the transformation matrix to be the same, or setting at least two rows of the transformation matrix to be the same, are variations of the transformation matrix. As another example, performing column substitutions, row substitutions, and similar operations on a transformation matrix also belong to variations of the transformation matrix. Variations of transformation matrices are not listed one by one.

[0289] For example, if M=32 and N=8, the uncompressed CSI vector b is as follows: 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, 0.290171646098622-0.451378116153413j,0.225689058076706-0.515860704175329j, 0.161206470054790-0.515860704175329j,0.00000000000000-0.548101998186287j, -0.0967238820328741-0.515860704175329j,-0.161206470054790-0.419136822142455j, -0.193447764065748-0.419136822142455j,-0.257930352087664-0.354654234120539j, -0.354654234120539-0.290171646098622j,-0.419136822142455-0.193447764065748j, -0.483619410164371-0.0322412940109581j, -0.483619410164371+0.0322412940109581j, -0.515860704175329+0.128965176043832j,-0.451378116153413+0.257930352087664j, -0.386895528131497+0.386895528131497j,-0.290171646098622+0.483619410164371j, -0.257930352087664+0.548101998186287j, -0.193447764065748+0.644825880219161j, -0.0967238820328741+0.741549762252035j,0.0644825880219161+0.806032350273951j, 0.193447764065748+0.934997526317784j,0.354654234120539+0.934997526317784j] T Vector b is a column vector, and the vector representations presented herein can be understood to be normalized by using the greatest absolute value of the sequence.

[0290] The phase period T0=61 can be calculated based on the uncompressed CSI vector b. The transformation matrix A can then 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 with length N=8 can 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 Therefore, a vector can be understood as a column vector.

[0291] For methods by which the receiving end obtains parameters related to the transformation matrix, it may be understood to refer to the relevant explanation in step 401. For example, the receiving end may obtain M, N, and T0 (or f0) based on the CSI report received in step 402. As another example, the receiving end may obtain M, N, and f0 (or T0) based on a CSI report that includes the M, N, and f0 (or T0) prior to the CSI report received in step 402. As yet another example, the receiving end may obtain f0 or T0 based on a CSI report that includes the f0 or T0 prior to the CSI report received in step 402, and obtain M and N based on the CSI report received in step 402.

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

[0293] For example, after acquiring a second CSI, the receiving end may perform beamforming by using the second CSI. Alternatively, after acquiring a second CSI, the receiving end may perform sensing by using the second CSI. The specific function of the second CSI is not limited to these embodiments of the present application.

[0294] In this embodiment of the present application, the transmitting end compresses a second CSI to obtain a first CSI by using a transformation matrix, where the number of rows in the transformation matrix corresponds to the number of elements in the second CSI (which may also be understood as an uncompressed CSI), and the number of columns in the transformation matrix corresponds to the number of elements in the first CSI (which may also be understood as a compressed CSI). In the method for compressing a CSI by using a transformation 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. Thus, 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 relatively long compression lengths and satisfy large-scale MIMO and multi-subcarrier scenarios.

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

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

[0297] For simplicity, in this embodiment of the present application, CSIs of subcarrier groups on a pair of transmitting and receiving antennas are considered. Normalization is performed on each CSI group; that is, each CSI is divided by the largest absolute value of the multiple CSIs in the group. Figures 7a to 7c show performance comparisons at different compression ratios, respectively. Subcarrier grouping method: Instead of transmitting CSI data for each subcarrier, the CSIs of multiple subcarriers on each antenna are grouped, and only one CSI is transmitted for each group. Regressive polynomial method: This is a CSI compression method based on a regressive polynomial matrix and a mathematical model of 2-norm minimization.

[0298] The "uncompressed" shown in Figures 7a to 7c, respectively, represents a specific number of marked CSIs from 32, 128, or 242 CSIs, and 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. For the explanation of "compressed" in Figures 7a, 7b, and 7c, it may be understood that a reference can be made to the explanation in Figure 3 above, or to the explanation of equation (1) above.

[0299] Figure 7a shows the compression performance when 32 subcarrier groups of CSI are compressed into 8 CSI using different methods. As can be seen from Figure 7a, the subcarrier grouping method and the regressive polynomial method introduce clear errors at the boundary points. For the subcarrier grouping method, in practical applications, typically 2 to 4 subcarriers are grouped into one group to ensure compression quality. As the compression ratio increases, the compression quality clearly deteriorates. The regressive polynomial method increases the CSI compression ratio at the cost of higher complexity. The CSI compression method based on the modified DFT matrix according to this embodiment of the application is effectively combined with the phase shift characteristics of the uncompressed CSI, thereby effectively improving the compression performance of the CSI compression. Even when increasing the compression ratio, the accuracy of the CSI compression can still be ensured by using the method provided in this embodiment of the application.

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

[0301] Figure 7c shows the compression performance when 242 subcarrier groups of CSI are compressed into 8 CSI using different methods. Figure 7 c As can be seen, even with the regression polynomial method, relatively large errors occur, and it is not possible to meet the compression quality requirements.

[0302] As can be seen from Figures 7a, 7b, and 7c, the CSI compression method based on a modified DFT matrix according to this embodiment of the present application is effectively combined with the angular change rules of uncompressed CSI, and thus can always satisfy compression quality and have better CSI compression performance. The CSI compression method based on a modified DFT matrix according to this embodiment of the present application is applicable to different compression ratios, and CSI compression performance can be ensured for any compression ratio.

[0303] In Figures 7a, 7b, and 7c, "compressed" can be understood as referring to the CSI restored by the receiving end using a different method, while "uncompressed" refers to the initial CSI obtained by the transmitting end by performing channel estimation using a pilot channel.

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

[0305]

number

[0306] Therefore, considering the excessively large differences between values ​​in different methods, the y-axis is converted to dB units by logarithmic calculation and preceded by a negative sign. Thus, in Figures 8a and 8b, higher bars indicate lower MSE and better performance. As shown in Figure 8a, in a 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 application is found to be the best at any compression ratio. For 242:8 compression, the method provided in this embodiment of the application effectively reduces the error by approximately 15 dB compared to the regressive polynomial method. As shown in Figure 8b, in a simulator environment, the performance of all CSI compression methods is reduced to some extent compared to experiments in an empty laboratory environment. However, the method provided in this embodiment of the application still has the best MSE performance at any compression ratio. For 242:8 compression, the method provided in this embodiment of the application effectively reduces the error by approximately 7 dB compared to the regressive polynomial method. Optionally, the compression quality threshold is set to -15 dB. As can be seen from Figure 8a, the subcarrier grouping method can satisfy the quality requirements for 64:8 compression, the regression polynomial method can satisfy the quality requirements for 128:8 compression, and the method provided in this embodiment of the present application can satisfy the quality requirements for 242:8 compression. As can be seen from Figure 8b, the regression polynomial method can satisfy the quality requirements for 64:8 compression, and the method provided in this embodiment of the present application can satisfy the quality requirements for 128:8 compression. Therefore, the compression ratio is improved according to the method provided in this embodiment of the present application. For any compression length, the method provided in this embodiment of the present application has a smaller mean square error. Thus, the error is effectively reduced and the compression accuracy is improved.

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

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

[0309] Figure 9 shows the configuration of a communication device according to one embodiment of the present application. As shown in Figure 9, the communication device includes a processing unit 901 and a transceiver unit 902.

[0310] In some embodiments of this application, the communication device may be a transmitting end or the chip shown above, the chip of 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 embodiments of the method described above (including Figure 4).

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

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

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

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

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

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

[0317] The detailed description of the transceiver unit and processing unit shown in this embodiment of the application should be understood as merely an example. For specific functions, steps, or similar actions performed by the transceiver unit and processing unit, please refer to the embodiments of the method described above. Details are not described again in this specification.

[0318] For descriptions of the first CSI, the second CSI, the transformation matrix, M, N, T0 or f0, or similar in the embodiments described above, please refer to the descriptions in the embodiments of the methods described above. Further details are not described further in this specification.

[0319] As described above, the communication device in this embodiment of the present application has been explained. The possible product forms of the communication device are described below. It should be understood that any product having the functions of the communication device shown in Figure 9 is included within the scope of protection of the embodiment of this application. Furthermore, it should be understood that the following description is merely an example, and the product forms of the communication device in this embodiment of the present application are not limited to these.

[0320] In possible implementations, in the communication device shown in Figure 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, and the receiving unit may be a receiver, and the transmitting unit and the receiving unit may be integrated into a single component, such as a transceiver. In these embodiments of the application, the processor and the transceiver may be coupled, or similarly. In these embodiments of the application, the connection method between the processor and the transceiver is not limited. In a process that performs the method described above, the process of transmitting information in the method described above may be understood as a process in which the processor outputs information. When outputting information, the processor outputs the information to the transceiver, and the transceiver transmits the information. After the information is output by the processor, other processes may need to be performed on the information before the information arrives at the transceiver. Similarly, the process of receiving information in the method described above may be understood as a process in which the processor receives input information. When the processor receives input information, the transceiver receives the information and inputs it to the processor. Furthermore, after the transceiver receives the information, it may need to perform other processes on that information, and the processed information is then input to the processor.

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

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

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

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

[0327] The detailed description of the transceiver and processor shown in this embodiment of the application should be understood as merely an example. For specific functions, steps, or similar actions performed by the transceiver and processor, please refer to the embodiments of the method described above. This specification will not provide further details.

[0328] For descriptions of the first CSI, the second CSI, the transformation matrix, M, N, T0 or f0, or similar in the embodiments described above, please refer to the descriptions in the embodiments of the methods described above. Further details are not described further in this specification.

[0329] In each implementation of the communication device shown in Figure 10, the transceiver may include a receiver and a transmitter. The receiver is configured to perform a receiving function (or operation), and the transmitter is configured to perform a transmitting function (or operation). The transceiver is configured to communicate with another device / device through 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 memories 1030 are coupled to the processor 1020. The coupling in this embodiment of the application may be an indirect coupling between devices, units, or modules in an electrical, mechanical, or other form, or it may be a communication connection used for information exchange between devices, units, or modules. The processor 1020 may cooperate with the memories 1030. The processor 1020 may execute programs stored in the memories 1030. Optionally, at least one of the one or more memories may be included in the processor. Optionally, one or more memories may store at least one of equations (1) to (25). Optionally, one or more memories may store a transformation matrix in a particular form. For example, the form of the transformation matrix may be fixed for a given M and N.

[0331] In the embodiments of this application, the specific connection medium between the transceiver 1010, the processor 1020, and the memory 1030 is not limited. In the embodiments of this application, the memory 1030, the processor 1020, and the transceiver 1010 are connected via a bus 1040 in Figure 10. In Figure 10, the bus is represented by a thick line. The connection methods of other components are merely examples for illustrative purposes and are not limited thereto. Buses can be classified as address buses, data buses, control buses, and similar. For ease of representation, only a single thick line is used to represent a bus in Figure 10, but this does not mean that there is only one bus or only one type of bus.

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

[0333] In this embodiment of the present 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 from and / or written to by a computer (e.g., a communication device as shown in this application). However, this application is not limited thereto. Alternatively, memory in this embodiment of the present application may be a circuit or any other device that can implement a storage function and is configured to store program instructions and / or data.

[0334] For example, the processor 1020 is configured primarily to process communication protocols and communication data, control the entire communication device, execute software programs, and process data from the software programs. The memory 1030 is configured primarily to store software programs and data. The transceiver 1010 may include a control circuit and an antenna. The control circuit is configured primarily to perform conversions between baseband signals and radio frequency signals and to process radio frequency signals. The antenna is configured primarily to transmit and receive radio frequency signals in the form of electromagnetic waves. Input / output devices, such as touchscreens, displays, or keyboards, are configured primarily to receive data entered by the user and output data to the user.

[0335] After the communication device is powered on, the processor 1020 can read the software program in the memory 1030, interpret and execute the instructions of the software program, and process the data of the software program. If it is necessary to transmit data 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 the antenna. When data is transmitted to the communication device, the radio frequency circuit receives the radio frequency signal via the antenna, converts the radio frequency signal into a baseband signal, and outputs the baseband signal to the processor 1020. The processor 1020 converts the baseband signal into data and processes the data.

[0336] In an alternative implementation, the radio frequency circuitry and antennas may be located independently of the processor performing baseband processing. For example, in a distributed scenario, the radio frequency circuitry and antennas may be located remotely, independently of the communication equipment.

[0337] It can be understood that the communication device shown in this embodiment of the application may have more components than those shown in Figure 10 and similarly. This is not limited to this embodiment of the application. The methods performed by the processor and transceiver are merely examples. For specific steps performed by the processor and transceiver, please refer to the methods described above.

[0338] In another possible implementation, in the communication device shown in Figure 9, the processing unit 901 may be one or more logic circuits. The transceiver unit 902 may be an input / output interface, also called a communication interface, interface circuit, interface, or similar. Alternatively, the transceiver unit 902 may be a transmit unit and a receive unit. The transmit unit may be an output interface, and the receive unit may be an input interface. The transmit unit and the receive unit are integrated into a single unit, for example, an input / output interface. As shown in Figure 11, the communication device shown in Figure 11 includes a processing circuit 1101 and an interface 1102. In other words, the processing unit 901 may be implemented using a logic circuit 1101, and the transceiver unit 902 may be implemented using an interface 1102. The logic circuit 1101 may be a chip, processing circuit, integrated circuit, system-on-a-chip (SoC) chip, or similar. The interface 1102 may be a communication interface, input / output interface, pin, or similar. For example, Figure 11 shows an example where the communication device is a chip. This chip includes a logic circuit 1101 and an interface 1102.

[0339] In this embodiment of the present application, the logic circuits and interfaces may be coupled to each other. In this embodiment of the present application, the specific connection method between the logic circuits and interfaces is not limited.

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

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

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

[0343] In several other embodiments of this application, the communication device may be configured to perform steps, functions, or similar actions performed by the receiving end in the embodiments of the method described above (including Figure 4).

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

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

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

[0347] For specific details regarding the transformation matrices stored at the transmitting and receiving ends, please refer to the explanation above. Further details will not be provided in this specification.

[0348] The detailed description of the logic circuits and interfaces shown in this embodiment of the application should be understood as merely illustrative. For specific functions, steps, or similar actions performed by the logic circuits and interfaces, please refer to the embodiments of the method described above. Further details are not described herein.

[0349] For descriptions of the first CSI, the second CSI, the transformation matrix, M, N, T0 or f0, or similar in the embodiments described above, please refer to the descriptions in the embodiments of the methods described above. Further details are not described further in this specification.

[0350] The communication device shown in this embodiment of the application may implement the method provided in this embodiment in hardware form, or it may implement the method provided in this embodiment in software form. This is not limited to this embodiment of the application.

[0351] One embodiment of this application further provides a wireless communication system, which includes a transmitting end and a receiving end. The transmitting end and the receiving end may be configured to perform any one of the embodiments described above (as shown in Figure 4).

[0352] Furthermore, this application further provides a computer program used to implement operations and / or processes performed by the transmitting end in the method provided in this application.

[0353] This application further provides a computer program used to implement operations and / or processes performed by a receiving end in the manner provided in this application.

[0354] This application further provides a computer-readable storage medium that stores computer code. When the computer code is executed on a computer, the computer is able to perform operations and / or processes performed by the transmitting end in the manner provided in this application.

[0355] This application further provides a computer-readable storage medium that stores computer code. When the computer code is executed on a computer, the computer is able to perform operations and / or processes performed by the receiving end in the manner provided in this application.

[0356] This application further provides a computer program product, which includes computer code or a computer program. When the computer code or computer program is executed on a computer, operations and / or processes performed by the transmitting end are performed in the manner provided in this application.

[0357] This application further provides a computer program product, which includes computer code or a computer program. When the computer code or computer program is executed on a computer, operations and / or processes performed by a receiving end are performed in the manner provided in this application.

[0358] It should be understood that in some embodiments provided in this application, the disclosed systems, apparatus, and methods may be implemented in other ways. For example, the embodiments of the apparatus described above are merely examples. For example, the division of units is merely a logical functional division, 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 performed. Furthermore, the mutual coupling, direct coupling, or communication connection shown or described may be implemented by using some interfaces. Indirect coupling or communication connection between apparatus or units may be implemented in electronic, mechanical, or other forms.

[0359] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one location, or may be distributed across 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 this application.

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

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

[0362] The above description is merely a specific implementation of the present application, but the scope of protection of this application is not limited thereto. Any modification or substitution that is readily conceivable to a person skilled in the art within the scope of the technical scope disclosed in this application shall be included within the scope of protection of this application. Accordingly, the scope of protection of this application shall be subject to the scope of protection of the claims.

Claims

1. A method for processing channel status information (CSI), A step of determining a CSI report, wherein the CSI report includes a first CSI, the first CSI is obtained based on a second CSI and a transformation matrix, the transformation matrix is ​​a complex matrix having M rows and N columns, the absolute values ​​of the elements in the transformation matrix are 1, M is greater than N, M is the number of elements in the second CSI, N is the number of elements in the first CSI, and M and N are each positive integers greater than 0. The steps include sending the CSI report and Equipped with, 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 the frequency components of the discrete Fourier transform (DFT) of M, N, and the angles of the second CSI. The angle of an element in at least one column of the transformation matrix represents the phase change between the two elements when the second CSI element corresponding to that column is transformed by the transformation matrix into the first CSI element. method.

2. The method according to claim 1, wherein the angles of elements in at least one column of the transformation matrix change periodically, and the angles of elements in different columns change by different periods.

3. The angle of the element in the mth row and nth column of the transformation matrix is ​​given by the following formula, i.e. [Math 1] Satisfying T ０ k(n) represents the period corresponding to the phase period of the second CSI, related to the angle of the second CSI, 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. The method according to claim 1.

4. T ０ The method according to claim 3, wherein the second CSI is determined based on the frequency component of the DFT at the angle.

5. T ０ This is expressed by the following formula, namely [Math 2] Satisfying f ０ The method according to claim 4, wherein represents the frequency component corresponding to the maximum value of the absolute value of the coefficient in the frequency component of the DFT of the angle of the second CSI.

6. The function of n is given by the following equation, namely [Math 3] ,or [Math 4] The method according to claim 3, wherein the condition is met, α is greater than 0, and β is greater than 0.

7. The aforementioned CSI report contains the following information: M, N, T ０ , and f ０ The method according to claim 5, further comprising at least one of the following.

8. A method for processing channel status information (CSI), A step of receiving a CSI report, wherein the CSI report includes a first CSI, A step of processing a first CSI based on a transformation matrix to obtain a second CSI, wherein the transformation matrix is ​​a complex matrix having M rows and N columns, the absolute value of the elements in the transformation matrix is ​​1, M is greater than N, M is the number of elements in the second CSI, N is the number of elements in the first CSI, and M and N are each positive integers greater than 0. Equipped with, 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 the frequency components of the discrete Fourier transform (DFT) of M, N, and the angles of the second CSI. The angle of an element in at least one column of the transformation matrix represents the phase change between the two elements when the second CSI element corresponding to that column is transformed by the transformation matrix into the first CSI element. method.

9. The following information, namely M, N, T ０ , and f ０ obtaining at least one of them, wherein T ０ represents a period corresponding to the phase period of the second CSI related to the angle of the second CSI, and f ０ is determined based on T ０ step Furthermore, The step of processing the first CSI based on the transformation matrix to obtain the second CSI is: T ０ or f ０ , and a step of determining the transformation matrix based on M and N, A step of processing the first CSI based on the transformation matrix to obtain the second CSI, including, The method according to claim 8.

10. The method according to claim 9, wherein the angles of elements in at least one column of the transformation matrix change periodically, and the angles of elements in different columns change with different periods.

11. The angle of the element in the mth row and nth column of the transformation matrix is ​​given by the following formula, i.e. [Math 5] The method according to claim 9, wherein the following conditions are met: 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.

12. T ０ This is expressed by the following formula, namely [Math 6] Satisfying f ０ The method according to claim 11, wherein represents the frequency component corresponding to the maximum value of the absolute value of the coefficient in the frequency component of the DFT of the angle of the second CSI.

13. The function of n is given by the following equation, namely [Number 7] ,or [Number 8] The method according to claim 11, wherein the condition is met, α is greater than 0, and β is greater than 0.

14. A communication device comprising a unit configured to perform the method according to any one of claims 1 to 13.

15. A communication device comprising a processor and memory, The memory is configured to store instructions, The processor is configured to execute the instruction, thereby performing the method according to any one of claims 1 to 13. Communication device.

16. A communication device comprising a logic circuit and an interface, wherein the logic circuit is 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 according to any one of claims 1 to 13. Communication device.

17. A computer-readable storage medium, wherein the computer-readable storage medium is configured to store a computer program, and when the computer program is executed, the method according to any one of claims 1 to 13 is executed.

18. A communication system comprising a transmitting end and a receiving end, wherein the transmitting end is configured to perform the method according to any one of claims 1 to 7, and the receiving end is configured to perform the method according to any one of claims 8 to 13.

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