Communication method and communication device

Abstract

The embodiment of the invention provides a communication method and a communication device. In this application, a new concept of "reference channel" is proposed. A receiving device may obtain information of K reference channels, the information of the K reference channels relating to a set of environmental parameters. The receiving device also obtains a channel estimate of the first channel, and then sends the first information according to the similarity metric value. The similarity metric value may be calculated from the information of the K reference channels and the channel estimate of the first channel. The first information indicates a similarity metric value between the first channel and a first reference channel. The first reference channel may be a "representative" of the first channel. The receiving device transmits the first information rather than the channel estimate of the first channel, which may reduce the overhead of information feedback for the first channel.

Description

Cross Reference to Related Applications

[0001] This application is related to U.S. Provisional Patent Application No. 63 / 507,279, filed on June 9, 2023, entitled “A NEW CSI ACQUISITION MECHANISM”, and claims priority to the U.S. Provisional Patent Application. The disclosure of the above application is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] Embodiments of the present application relate to the field of wireless technology, and more particularly, to a communication method and a communication apparatus. BACKGROUND

[0003] For wireless systems, multi-user multiple-in-multiple-out (MU-MIMO) is usually used in downlink (DL), where the base station (BS) is the transmitting device and multiple user equipments (UEs) are the corresponding receiving devices. The MIMO channels of multiple UEs are paired by a common precoder to multiplex on frequency and time resources. To achieve higher throughput and system efficiency, modern MU-MIMO systems deploy a large number of antenna ports on a wider frequency band. For example, in a terabit-MIMO (T-MIMO) system, the BS is expected to have 3072 antenna ports, while the UE has 64 antenna ports on a 400 MHz bandwidth. The MIMO channel becomes a three-dimensional tensor.

[0004] In a large-scale MIMO system, such as a T-MIMO system, how to estimate and report the channel estimation of the downlink (DL) channel with reasonable overhead is a challenging problem that needs to be solved. SUMMARY

[0005] Embodiments of the present application provide a communication method and a communication apparatus, which reduce the overhead of the receiving device in estimating and reporting the DL channel information in the MIMO system.

[0006] In a first aspect, a communication method is provided, which can be performed by a receiving device or a chip installed in the receiving device. The method comprises: obtaining information of K reference channels, wherein the K reference channels are related to a set of environmental parameters, and K is a positive integer; obtaining a channel estimation of a first channel; and transmitting information of the first channel according to a similarity measure value, wherein the similarity measure value is calculated according to the information of the K reference channels and the channel estimation of the first channel.

[0007] In some embodiments of the present disclosure, a new concept of “reference channel” is proposed. A receiving device can obtain information of K reference channels, which are related to a set of environmental parameters. The receiving device further obtains a channel estimation of a first channel, and then transmits first information according to a similarity measure value. The similarity measure value can be calculated according to the information of the K reference channels and the channel estimation of the first channel. The first information indicates a similarity measure value between the first channel and a first reference channel. The first reference channel can be regarded as a “representative” of the first channel. The receiving device transmits the first information instead of the channel estimation of the first channel, which can reduce the overhead of information feedback of the first channel.

[0008] In an implementation form of the first aspect, the method further includes obtaining common information, wherein the common information is used to determine the information of the first channel; and the similarity measure value is calculated according to the common information, the information of the K reference channels and the channel estimation of the first channel.

[0009] In a second aspect, a communication method is provided, which can be performed by a transmitting device or a chip installed in the transmitting device. The method includes receiving information of a first channel, wherein the information of the first channel is determined according to a similarity measure value, and the similarity measure value is calculated according to information of K reference channels and a channel estimation of the first channel, and the K reference channels are related to a set of environmental parameters.

[0010] The technical effects of the second aspect can refer to those of the first aspect, which are not repeated here.

[0011] In an implementation form of the first aspect or the second aspect, the information of the first channel includes an index of a first reference channel in the K reference channels, and a similarity measure value between the first channel and the first reference channel is less than or equal to a threshold value.

[0012] The information of the first channel (i.e., a DL channel) can be an index of a first reference channel, which greatly reduces the overhead of channel estimation feedback of the DL channel in a MIMO system.

[0013] In the following, some examples of the similarity measure value are provided.

[0014] In an implementation form of the first aspect or the second aspect, the similarity measure value indicates a distance between the first channel and the first reference channel.

[0015] In an implementation form of the first aspect or the second aspect, the similarity measure value indicates a likelihood of the first channel being inside or near a subspace of the first reference channel.

[0016] In an implementation form of the first aspect or the second aspect, the similarity measure value indicates a score value representing a distance between the first channel and the first reference channel and calculated by a score function.

[0017] In an implementation form of the first aspect or the second aspect, each of the K reference channels or the first reference channel is an uplink channel, a downlink channel, a sidelink channel, a satellite-to-Earth link channel, or a channel between two integrated access backhaul (IAB) nodes.

[0018] In this implementation form, the proposed scheme can be applied in many wireless communication systems, including not only MIMO systems, but also sidelink systems, non-terrestrial network (NTN) systems, and IAB systems, etc.

[0019] In a third aspect, a communication apparatus is provided, having a function or unit to perform the method in the first aspect and any possible implementation form of the first aspect.

[0020] In a fourth aspect, a communication apparatus is provided, having a function or unit to perform the method in the second aspect and any possible implementation form of the second aspect.

[0021] In a fifth aspect, a chip (or chip system) is provided. The chip includes at least one processor coupled to at least one memory. The at least one memory is configured to store one or more instructions and / or executable computer code. The at least one processor is configured to invoke the one or more instructions and / or executable computer code, so that a communication apparatus in which the chip is installed performs the method in the first aspect and any possible implementation form of the first aspect, or the communication apparatus performs the method in the second aspect and any possible implementation form of the second aspect. Optionally, the chip can further include the at least one memory. Optionally, the chip can further include a communication interface configured to input and / or output information or data.

[0022] In a sixth aspect, a communication apparatus is provided. The communication apparatus includes one or more circuits and one or more communication interfaces. The one or more communication interfaces can include a first interface for receiving (i.e., inputting) information and / or data to be processed by the one or more circuits and a second interface for transmitting (i.e., outputting) information and / or data processed by the one or more circuits. The one or more circuits are configured to process the information and / or data to be processed to cause the communication apparatus to perform the method of the first aspect and any implementation of the first aspect, or to perform the method of the second aspect and any implementation of the second aspect.

[0023] In a seventh aspect, a communication system is provided. The communication system can include the communication apparatus of the third aspect and the communication apparatus of the fourth aspect.

[0024] In an eighth aspect, a computer storage medium storing executable computer code for performing one or more instructions of the method of the first aspect or any possible implementation of the first aspect, or the method of the second aspect or any possible implementation of the second aspect is provided.

[0025] In a ninth aspect, a computer program product including one or more instructions, which when executed on a computer, cause the computer to perform the method of the first aspect or any possible implementation of the first aspect, or the method of the second aspect or any possible implementation of the second aspect is provided. BRIEF DESCRIPTION OF DRAWINGS

[0026] One or more embodiments are exemplarily described by the corresponding drawings, which do not constitute a limitation on the embodiments. Elements with the same reference signs in the drawings are shown as similar elements, and the drawings are not limited to scale, in which: Figure 1 is a schematic diagram of an application scenario of the present application.

[0027] Figure 2 An example of a communication system is shown.

[0028] Figure 3 Another example of an electronic device (ED) and a base station is shown.

[0029] Figure 4 is an example of a channel model of a MIMO system.

[0030] Figure 5 is a flowchart of a communication method proposed in the present application.

[0031] Figure 6 An example of vectorizing a tensor-form MIMO channel sample provided by the embodiments of the present application is shown.

[0032] Figure 7 An example of column-wise vectorizing and juxtaposing column-wise vectorized channel data samples into a matrix is shown.

[0033] Figure 8 An equivalent low-dimensional space is shown.

[0034] Figure 9 An example of a scoring function for measuring the distance between two channel data samples in the equivalent low-dimensional latent space is shown.

[0035] Figure 10 An example of a DNN-based scoring function for measuring the distance between two channel data samples in the equivalent low-dimensional latent space is shown.

[0036] Figure 11 is an example of a method provided by embodiments of the present application.

[0037] Figure 12 is a schematic block diagram of a communication apparatus provided by embodiments of the present application.

[0038] Figure 13 is a schematic block diagram of a communication apparatus provided by embodiments of the present application.

[0039] Figure 14 is a schematic block diagram of a communication apparatus provided by embodiments of the present application.

[0040] Figure 15 is a schematic block diagram of a communication apparatus provided by embodiments of the present application.

[0041] Figure 16 A TMIMO channel dimension is shown.

[0042] Figure 17 An example of vectorizing a tensor-form MIMO channel sample is shown.

[0043] Figure 18 An example of selecting representative nodes according to a "distance" map between channel data samples is shown.

[0044] Figures 19 to 21 is an example of an embodiment of the present application.

[0045] Figure 22 An example of a basic module structure provided by embodiments of the present application is shown. DETAILED DESCRIPTION

[0046] In order to more clearly understand the features and technical contents of the embodiments of the present application, the following will be a detailed description of the implementation modes of the embodiments of the present application in conjunction with the drawings, which are only used for reference and illustration, and do not limit the embodiments of the present application. In the following technical description, many details will be described in order to provide a thorough understanding of the disclosed embodiments.

[0047] In order to facilitate the understanding of the embodiments of the present application, the communication system shown in Figures 1 to 3 The communication system to which the embodiments of the present application are applicable will be described in detail.

[0048] Reference is made to Figure 1 , a simplified schematic diagram of a communication system is provided as an illustrative example but without limitation. The communication system 100 includes a wireless access network 120. The wireless access network 120 can be a next generation (e.g., sixth generation (6G) or higher) wireless access network or a legacy (e.g., 5G, 4G, 3G, or 2G) wireless access network. One or more communication electric devices (EDs) 110a to 110j (generally referred to as 110) can be interconnected to each other or to one or more network nodes (170a, 170b, generally referred to as 170) in the wireless access network 120. A core network 130 can be part of the communication system, which can be dependent or independent of the wireless access technology used in the communication system 100. In addition, the communication system 100 includes a public switched telephone network (PSTN) 140, the Internet 150, and other networks 160.

[0049] Figure 2 An exemplary communication system 100 is shown. In general, the communication system 100 is capable of enabling multiple wireless or wireline elements to communicate data and other content. The communication system 100 can be used to provide voice, data, video, and / or text content, among other content, through broadcast, multicast, and unicast, among other techniques. The communication system 100 can operate through sharing of resources, such as carrier frequency spectrum bandwidth, among other resources, between its constituent elements. The communication system 100 can include a terrestrial communication system and / or a non-terrestrial communication system. The communication system 100 can provide a wide range of communication services and applications (e.g., earth monitoring, remote sensing, passive sensing and positioning, navigation and tracking, autonomous delivery and mobility, among others). The communication system 100 can provide high availability and robustness through joint operation of terrestrial and non-terrestrial communication systems. For example, integration of non-terrestrial communication systems (or components thereof) into a terrestrial communication system can enable a heterogeneous network comprising multiple tiers. The heterogeneous network can achieve better overall performance compared to legacy communication networks through efficient multi-link joint operation, more flexible function sharing, and faster physical layer link switching between terrestrial and non-terrestrial networks.

[0050] The ground communication system and the non-terrestrial communication system can be considered as subsystems of a communication system. In the illustrated example, the communication system 100 includes electronic devices (EDs) 110a-110d (generally referred to as EDs 110), radio access networks (RANs) 120a and 120b, a non-terrestrial communication network 120c, a core network 130, a public switched telephone network (PSTN) 140, the Internet 150, and other networks 160. The RANs 120a and 120b include respective base stations (BSs) 170a and 170b, which can be generally referred to as terrestrial transmit and receive points (T-TRPs) 170a and 170b. The non-terrestrial communication network 120c includes an access node 120c, which can be generally referred to as a non-terrestrial transmit and receive point (NT-TRP) 172.

[0051] Alternatively or additionally, any of the EDs 110 can be configured to connect with, access, or communicate with any other T-TRPs 170a and 170b, the NT-TRP 172, the Internet 150, the core network 130, the PSTN 140, the other networks 160, or any combination of the foregoing. In some examples, the ED 110a can communicate uplink and / or downlink transmissions with the T-TRP 170a over an interface 190a. In some examples, the EDs 110a, 110b, and 110d can also communicate directly with each other over one or more sidelink air interfaces 190b. In some examples, the ED 110d can communicate uplink and / or downlink transmissions with the NT-TRP 172 over an interface 190c.

[0052] The air interfaces 190a and 190b can use similar communication techniques, for example, any suitable wireless access technique. For example, the communication system 100 can implement one or more channel access methods in the air interfaces 190a and 190b, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), or single-carrier FDMA (SC-FDMA), for example. The air interfaces 190a and 190b can utilize other multi-dimensional signal spaces that can include combinations of orthogonal and / or non-orthogonal dimensions.

[0053] The air interface 190c can enable communication between the ED 110d and one or more NT-TRPs 172 through a wireless link or simply a link. For some examples, the link is a dedicated connection for unicast transmissions, a connection for broadcast transmissions, or a connection between a group of EDs and one or more NT-TRPs for groupcast transmissions.

[0054] The RANs 120a and 120b are in communication with the core network 130 to provide the EDs 110a, 110b, and 110c with access to various services, such as voice, data, and other services. The RANs 120a and 120b and / or the core network 130 can be in direct or indirect communication with one or more other RANs (not shown) that can or can not be of the same

[0055] Figure 3Another example of an ED 110 and base stations 170a, 170b, and / or 170c is shown. The ED 110 is used to connect people, objects, machines, etc. The ED 110 can be widely used in various scenarios, such as cellular communication, device-to-device (D2D), vehicle to everything (V2X), peer-to-peer (P2P), machine-to-machine (M2M), machine-type communication (MTC), internet of things (IOT), virtual reality (VR), augmented reality (AR), industrial control, autonomous driving, telemedicine, smart grid, smart home, smart office, smart wearable device, smart transportation, smart city, drone, robot, remote sensing, passive sensing, positioning, navigation and tracking, automatic distribution, mobility, etc.

[0056] Each ED 110 represents any suitable end user device for wireless operation, which can include these devices as (or can be referred to as) a UE, WTRU, mobile station, fixed or mobile subscriber unit, cellular phone, STA, MTC device, PDA, smartphone, notebook computer, computer, tablet computer, wireless sensor, consumer electronics, smartbook, vehicle, car, truck, bus, train, or IoT device, industrial device, or means (e.g., a communication module, modem, or chip) of the above devices, etc. Future generation ED 110 can be referred to using other terms. The base stations 170a and 170b are T-TRPs, which will be referred to as T-TRPs 170 hereinafter. Also shown in Figure 3 NT-TRPs, which will be referred to as NT-TRPs 172 hereinafter. Each ED 110 connected to the T-TRPs 170 and / or NT-TRPs 172 can be dynamically or semi-statically turned on (i.e., established, activated, or enabled), turned off (i.e., released, deactivated, or disabled), and / or configured in response to one or more of connection availability and connection necessity.

[0057] The ED 110 includes a transmitter 201 and a receiver 203 coupled to one or more antennas 204. Only one antenna 204 is shown in the figure. One, some or all of the antennas can also be panels. The transmitter 201 and receiver 203 can be, for example, integrated as a transceiver. The transceiver is used to modulate data or other content for transmission, e.g., by at least one antenna 204 or a network interface controller (NIC). The transceiver is also used to demodulate data or other content received by the at least one antenna 204. Each transceiver includes any suitable structure for generating a signal for wireless or wired transmission and / or for processing a signal received via wireless or wired transmission. Each antenna 204 includes any suitable structure for transmitting and / or receiving wireless or wired signals.

[0058] The ED 110 includes at least one memory 208. The memory 208 stores instructions and data used, generated, or collected by the ED 110. For example, the memory 208 could store software

[0059] instructions or modules used to implement part or all of the functionality described herein and executed by the processing unit 210. Each memory 208 includes any suitable volatile and / or non-volatile storage and retrieval devices. Any suitable type of memory can be used, such as random access memory (RAM), read only memory (ROM), hard disk, optical disk, subscriber identity module (SIM) card, memory stick, secure digital (SD) memory card, processor cache, etc. Figure 1 The ED 110 can also include one or more input / output devices (not shown) or interfaces (e.g., wired interfaces to the Internet 150). The input / output devices enable interaction with users or other devices via input and output operations. Each input / output device includes any suitable structure for providing information to or receiving information from a user, such as a speaker, microphone, keypad, keyboard, display, or touch screen, including network interface communications.

[0060] The ED 110 also includes a processor 210 for performing operations including operations related to preparing uplink transmissions to be sent to the NT-TRPs 172 and / or the T-TRPs 170, operations related to processing downlink transmissions received from the NT-TRPs 172 and / or the T-TRPs 170, and operations related to processing sidelink transmissions to and from another ED 110. The processing operations related to preparing uplink transmissions to be sent can include operations such as encoding, modulating, transmit beamforming, and generating symbols for transmission. The processing operations related to processing downlink transmissions can include operations such as receive beamforming, demodulating received symbols, and decoding received symbols. According to embodiments, the downlink transmissions can be received by the receiver 203, possibly using receive beamforming, and the processor 210 can extract signaling (e.g., by detecting and / or decoding the signaling) from the downlink transmissions. An example of the signaling can be reference signals transmitted by the NT-TRPs 172 and / or the T-TRPs 170. In some embodiments, the processor 276 implements transmit beamforming and / or receive beamforming according to an indication of a beam direction, e.g., beam angle information (BAI), received from the T-TRPs 170. In some embodiments, the processor 210 can perform operations related to network access (e.g., initial access) and / or downlink synchronization, such as operations related to detecting synchronization sequences, decoding and acquiring system information, etc. In some embodiments, the processor 210 can perform channel estimation using reference signals received from the NT-TRPs 172 and / or the T-TRPs 170, for example.

[0061] Although not shown, the processor 210 can form part of the transmitter 201 and / or the receiver 203. Although not shown, the memory 208 can form part of the processor 210.

[0062] The processor 210, and processing components in the transmitter 201 and the receiver 203, respectively, can be implemented by the same or different one or more processors for executing instructions that are stored in a memory (e.g., the memory 208). Alternatively, some or all of the processor 210, and processing components in the transmitter 201 and the receiver 203, can be implemented using specially designed circuitry, e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a graphical processing unit (GPU), or the like.

[0063] In some embodiments, T-TRP 170 can be referred to by other names, such as a base station, a base transceiver station (BTS), a wireless base station, a network node, a network device, a network-side device, a transmission-reception node, a NodeB, an evolved NodeB (eNodeB or eNB), a Home eNodeB, a next Generation NodeB (gNB), a transmission point (TP), a site controller, an access point (AP), or a wireless router, a relay, a radio remote unit, a ground node, a ground network device, or a ground base station, a BBU, a RRU, a radio unit (RU), an AAU, a RRH, a CU, a DU, a positioning node, etc. T-TRP 170 can be a macro base station, a micro base station, a relay node, a donor node, etc., or a combination thereof. T-TRP 170 can refer to the above devices or an apparatus (e.g., a communication module, a modem, or a chip) in the above devices.

[0064] In some embodiments, CU (or CU control plane (CP) and CU user plane (UP)), DU, or RU can be referred to by other names. For example, in an open RAN (ORAN) system, CU can also be referred to as an open CU (O-CU), DU can also be referred to as an open DU (O-DU), CU-CP can also be referred to as an open CU-CP (O-CU-CP), CU-UP can also be referred to as an open CU-UP (O-CU-CP), and RU can also be referred to as an open RU (O-RU). Any of CU (or CU-CP, CU-UP), DU, or RU can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0065] In some embodiments, various parts of T-TRP 170 can be distributed. For example, some modules of T-TRP 170 can be located at a location remote from a device that houses the antennas of T-TRP 170, and can be coupled to the device that houses the antennas by a communication link (not shown), sometimes referred to as front-haul, such as common public radio interface (CPRI). Thus, in some embodiments, the term "T-TRP 170" can also refer to modules that perform the processing operations of ED 110 position determination, resource allocation (scheduling), message generation and encoding / decoding, etc. on the network side, which are not necessarily part of the device that houses the antennas of T-TRP 170. These modules can also be coupled to other T-TRPs. In some embodiments, T-TRP 170 can actually be multiple T-TRPs that work together, e.g., through coordinated multipoint transmission, to serve ED 110.

[0066] The T-TRP 170 includes at least one transmitter 252 and at least one receiver 254 coupled to one or more antennas 256. Only one antenna 256 is shown in the figure. One, some or all of the antennas can also be panels. The transmitter 252 and receiver 254 can be integrated as a transceiver. The T-TRP 170 also includes a processor 260 for performing various operations, including operations related to preparing downlink transmissions to the ED 110, processing uplink transmissions received from the ED 110, preparing backhaul transmissions to the NT-TRP 172, and processing transmissions received from the NT-TRP 172 over the backhaul. The processing operations related to preparing the downlink or backhaul transmissions for transmission can include operations such as encoding, modulation, precoding (e.g., MIMO precoding), transmit beamforming, and generating symbols for transmission. The processing operations related to processing received transmissions in the uplink or over the backhaul can include operations such as receive beamforming, demodulating received symbols, and decoding received symbols. The processor 260 can also perform operations related to network access (e.g., initial access) and / or downlink synchronization, such as generating contents of a synchronization signal block (SSB), generating system information, etc. In some embodiments, the processor 260 also generates an indication of a beam direction, e.g., a BAI, which can be scheduled for transmission by the scheduler 253. The processor 260 performs other network-side processing operations described herein, e.g., determining a location of the ED 110, determining a location to deploy the NT-TRP 172, etc. In some embodiments, the processor 260 can generate signaling, e.g., to configure one or more parameters of the ED 110 and / or one or more parameters of the NT-TRP 172. Any signaling generated by the processor 260 is transmitted by the transmitter 252. It should be noted that “signaling” used herein can also be referred to as control signaling. Dynamic signaling can be transmitted in a control channel such as a physical downlink control channel (PDCCH), and static or semi-static higher layer signaling can be included in packets transmitted in a data channel such as a physical downlink shared channel (PDSCH).

[0067] The scheduler 253 can be coupled to the processor 260. The scheduler 253, which can be included within or operate separately from the T-TRP 170, can schedule uplink, downlink, and / or backhaul transmissions, including issuing scheduling grants and / or configuring grant-free (“configured grant”) resources. The T-TRP 170 also includes memory 258 for storing information and data. The memory 258 stores instructions and data used, generated, or collected by the T-TRP 170. For example, the memory 258 can store software

[0068] Although not shown, the processor 260 can form part of the transmitter 252 and / or the receiver 254. Further, although not shown, the processor 260 can implement the scheduler 253. Although not shown, the memory 258 can form part of the processor 260.

[0069] The processor 260, the scheduler 253, and the processing components in the transmitter 252 and the receiver 254 can each be implemented by one or more processors executing instructions stored in memory (e.g., the memory 258). Alternatively, some or all of the processor 260, the scheduler 253, and the processing components in the transmitter 252 and the receiver 254 can be implemented using special-purpose circuitry, such as FPGA, GPU, or ASIC.

[0070] Although NT-TRP 172 is shown as a drone merely as an example, NT-TRP 172 can be implemented through any suitable non-ground-based form. Also, in some embodiments, NT-TRP 172 can go by other names, such as non-ground node, non-ground network device, or non-ground base station. NT-TRP 172 includes a transmitter 272 and a receiver 274 coupled to one or more antennas 280. Only one antenna 280 is shown in the figure. One, some or all of the antennas can also be panels. Transmitter 272 and receiver 274 can be integrated as a transceiver. NT-TRP 172 also includes a processor 276 for performing various operations, including operations related to preparing downlink transmissions to ED 110, processing uplink transmissions received from ED 110, preparing backhaul transmissions to T-TRP 170, and processing transmissions received from T-TRP 170 over the backhaul. Processing operations related to preparing downlink transmissions or backhaul transmissions can include operations such as encoding, modulation, precoding (e.g., MIMO precoding), transmit beamforming, and generating symbols for transmission. Processing operations related to processing received transmissions in the uplink or over the backhaul can include operations such as receive beamforming, demodulating received symbols, and decoding received symbols. In some embodiments, processor 276 implements transmit beamforming and / or receive beamforming according to beam direction information (e.g., BAI) received from T-TRP 170. In some embodiments, processor 276 can generate signaling, such as for configuring one or more parameters of ED 110. In some embodiments, NT-TRP 172 implements physical layer processing but not higher layer functions such as functions of a medium access control (MAC) layer or a radio link control (RLC) layer. Since this is merely one example, NT-TRP 172 can generally implement higher layer functions in addition to physical layer processing.

[0071] NT-TRP 172 also includes a memory 278 for storing information and data. Although not shown, processor 276 can form part of transmitter 272 and / or receiver 274. Although not shown, memory 278 can form part of processor 276.

[0072] The processor 276, and processing components of the transmitter 272 and receiver 274, respectively, can be implemented by the same or different one or more processors that execute instructions stored in memory (e.g., memory 278). Alternatively, some or all of the processor 276, and processing components of the transmitter 272 and receiver 274, can be implemented using specially designed hardware (e.g., FPGA, GPU, ASIC, etc.) programmed for specific functionality. In some embodiments, the NT-TRP 172 can actually be multiple NT-TRPs that work together, e.g., through coordinated multipoint transmission, to serve the ED 110.

[0073] The T-TRP 170, NT-TRP 172, and / or ED 110 can include other components, which for brevity, have been omitted.

[0074] MIMO technology can enable antenna arrays composed of multiple antennas to transmit and receive signals to meet high transmission rate requirements. The ED 110 and T-TRP 170 and / or NT-TRP described above use MIMO to communicate over wireless resource blocks. MIMO utilizes multiple antennas at a transmitting device and / or a receiving device to transmit and receive parallel wireless signals over wireless resource blocks. MIMO can beamform parallel wireless signals to enable reliable multipath transmission over wireless resource blocks. MIMO can bundle parallel wireless signals that transmit different data to increase data rates over wireless resource blocks.

[0075] In recent years, MIMO (massive MIMO) wireless communication systems in which the above T-TRPs 170 and / or NT-TRPs 172 are configured with a large number of antennas have attracted extensive attention from academia and industry. In a massive MIMO system, the T-TRPs 170 and / or NT-TRPs 172 are usually configured with more than ten antenna units (e.g., 128 or 256), while serving tens of EDs 110 (e.g., 40). The large number of antenna units of the T-TRPs 170 and / or NT-TRPs 172 can greatly improve the spatial degrees of freedom of wireless communication, greatly improve the transmission rate, spectral efficiency and power efficiency, and largely eliminate the interference between cells. The increase in the number of antennas makes the size of each antenna unit smaller and the cost lower. With the spatial degrees of freedom provided by the large number of antenna units, the T-TRPs 170 and / or NT-TRPs 172 of each cell can communicate with multiple EDs 110 in the cell on the same time-frequency resources, thereby greatly improving the spectral efficiency. The large number of antenna units of the T-TRPs 170 and / or NT-TRPs 172 also enables each user to have better spatial directivity for uplink and downlink transmission. Therefore, the transmission power of the T-TRPs 170 and / or NT-TRPs 172 and the EDs 110 is reduced, and the power efficiency is improved. When the number of antennas of the T-TRPs 170 and / or NT-TRPs 172 is large enough, the random channels between each ED 110 and the T-TRPs 170 and / or NT-TRPs 172 can be close to orthogonality. The interference between cells and users and the influence of noise can be eliminated. The above-mentioned various advantages make the massive MIMO system have good application prospects.

[0076] A MIMO system can include a receiving device connected to a receive (Rx) antenna, a transmitting device connected to a transmit (Tx) antenna, and a signal processor connected to the transmitting device and the receiving device. Each of the Rx antenna and the Tx antenna can include a plurality of antennas. For example, the Rx antenna can have a uniform linear array (ULA) antenna array in which a plurality of antennas are arranged in a row at uniform intervals. When a radio frequency (RF) signal is transmitted through the Tx antenna, the Rx antenna can receive a signal that is reflected from a forward target and returns.

[0077] In this application, the central device can be one of the network nodes 170a or 170b in Figure 1 , and the user equipment can be one of the EDs 110a to 110j in Figure 1 ; or the central device can be one of the T-TRPs 170a and 170b and the NT-TRP 172 in Figure 2 , and the user equipment can be one of the EDs 110a to 110j in Figure 2one of the EDs 110a-110d in FIG. 1; or, the central device can be Figure 3 one of the T-TRPs 170 or the NT-TRPs 172 in FIG. 1, and the user device can be Figure 3 the ED 110 in FIG. 1.

[0078] Figure 4 is an example of a channel model of a MIMO system. The transmitting device is connected to four Tx antennas x1-x4, and the receiving device is connected to four Rx antennas y1-y4. A transmission channel can be formed between each Tx antenna and each Rx antenna. For example, an RF signal transmitted by x1 can be received by y2 through channel h21. An RF signal transmitted by x3 can be received by y1 through channel h13.

[0079] In a MIMO system, to implement system synchronization, channel information feedback, and data transmission, etc., channel estimation needs to be performed on the uplink channel or the downlink channel. Channel estimation refers to a process of reconstructing or recovering a received signal to compensate for signal distortion caused by channel fading and noise. In channel estimation, a reference signal transmitted by the transmitting device can be used to track changes in the time domain and / or the frequency domain of the channel, so as to reconstruct or recover the received signal. The reference signal can also be referred to as a pilot signal or a reference sequence, etc. For ease of understanding, it is described as a reference signal in the following description. For example, the reference signal includes a channel state information-reference signal (CSI-RS), a sounding reference signal (SRS), a demodulation reference signal (DMRS), a phase tracking reference signal (PT-RS), or a cell reference signal (CRS). The above listed reference signals are only examples and should not constitute any limitation on the present application. The present application does not exclude the possibility of defining other reference signals in future protocols to achieve the same or similar functions.

[0080] To facilitate understanding of the embodiments of the present application, the CSI-RS is described in detail below by way of example. The CSI-RS is mainly used for downlink channel estimation corresponding to a physical antenna port. For example, a receiving device (i.e., a user equipment) can perform channel estimation for each physical antenna port according to a CSI-RS transmitted by a transmitting device (i.e., a central device), to feed back channel state information (CSI) according to a channel estimation result. The CSI can include channel quality indicator (CQI), precoding matrix indicator (PMI), layer indicator (LI), and rank indicator (RI), and other related information. The CSI is used for reconstructing or precoding a downlink channel. In some embodiments, the process of the central device obtaining the CSI can include: the central device transmitting a reference signal to the UE; the UE obtaining a CSI estimation value according to the received reference signal; the UE selecting a precoding vector from a codebook according to the CSI estimation value; the UE feeding back an index of the precoding vector to the central device; and the central device determining a CSI reconstruction value with reference to the index of the precoding vector. The CSI reconstruction value can be a CSI closest to a true value of the CSI that the central device can obtain.

[0081] In one embodiment, the transmitting device maps a reference signal sequence to certain physical resources, and transmits the reference signal on the certain physical resources. The reference signal sequence and the physical resources are known to both the transmitting device and a receiving device that receives the reference signal. Therefore, the receiving device can perform channel estimation according to the known reference signal sequence and the received signal.

[0082] The transmitting device can map a sequence to physical resources to transmit the reference signal. The physical resources can include a plurality of resource elements, which are physical resources allocated for reference signal transmission. For example, when transmitting a DM-RS, the resource elements have common resource blocks allocated for physical downlink shared channel (PDSCH) transmission.

[0083] The location of the physical resources of the reference signal can be referred to as a reference signal pattern or a pilot pattern. The location of the physical resources is usually described by at least one of the following dimensions: a time dimension, a frequency dimension, and a spatial dimension.

[0084] The time dimension can be represented by one or more time domain resource units. A time domain resource unit can include, but is not limited to, a symbol, an orthogonal frequency division multiplexing (OFDM) symbol, and a time slot. In some embodiments, a time domain unit can be represented by a symbol index, an OFDM symbol index, or a time slot index.

[0085] The frequency dimension can be represented by one or more frequency domain resource units. A frequency domain resource unit can include, but is not limited to, a subcarrier or a subband. In some embodiments, a frequency domain unit can be represented by a subcarrier index or a subband index. In some embodiments, a frequency domain unit can also be represented by a resource element (RE) index, a resource block (RB) index, or a resource block group (RBG) index. An RE includes a symbol in the time domain and a subcarrier in the frequency domain, and an RE index can be used to indicate the location of a subcarrier. An RB includes a time slot in the time domain and 12 consecutive subcarriers in the frequency domain. An RB index can be used to indicate the location of 12 subcarriers. An RBG consists of a group of RBs, and an RBG index can be used to indicate the location of a group of subcarriers.

[0086] The spatial dimension can be represented by one or more spatial domain resource units. A spatial domain resource unit can be represented by an antenna port. In embodiments of the present application, an antenna port can be a Tx antenna. An antenna port can be identified by an antenna port index.

[0087] For the convenience of understanding the embodiments of the present application, in the following exemplary description, a symbol index is used to represent the location of a time domain resource unit, a subcarrier index is used to represent the location of a frequency domain resource unit, and an antenna port index is used to represent the location of a spatial domain resource unit.

[0088] The above channel estimation process is only exemplary and should not constitute any limitation on the present application. The channel estimation process is known in the prior art, and for the sake of brevity, the detailed description of the specific process is omitted herein.

[0089] The receiving device can be an ED (i.e., a user equipment), and the transmitting device can be a T-TRP or an NT-TRP (i.e., a central device); or the receiving device can be a T-TRP or an NT-TRP (i.e., a central device), and the transmitting device can be an ED (i.e., a user equipment). In some embodiments, when the reference signal in these embodiments is a downlink signal (e.g., a CSI-RS), the transmitting device can be a central device, and the receiving device can be a user equipment. When the reference signal in these embodiments is an uplink signal (e.g., an SRS), the transmitting device can be a user equipment, and the receiving device can be a central device. Although one transmitting device can transmit a reference signal to one or more receiving devices, the following embodiments focus on the method between one transmitting device and one receiving device; these examples are not intended to limit the scope of the present application.

[0090] In the following, the embodiments of the present application will be described in detail with reference to the accompanying drawings.

[0091] The proposed method described in the embodiments of the present application can be used in a T-MIMO system, in which the transmitting device and the receiving device have more antenna ports and larger bandwidth. The method can also be applied to other MIMO systems (e.g., a 5G MIMO system) or a single antenna system, which is not limited in the present application.

[0092] In the following, the proposed scheme of the present application will be described taking a T-MIMO wireless channel as an example, which is referred to as a wireless channel or a channel for short. It should be noted that the present application can be applied to a large-dimensional signal space other than T-MIMO.

[0093] Generally speaking, the embodiments of the present application propose a communication method, which focuses on how the receiving device reports the information of a DL MIMO channel in a MIMO scenario.

[0094] Figure 5 FIG. 5 is a flowchart of a communication method (500) proposed by the embodiments of the present application. The method (500) specifically includes the following steps 510 to 530: the steps of the method (500) can be correspondingly executed by a transmitting device or a receiving device or a chip installed in the transmitting device or the receiving device. The method can be applied to a MIMO system including one transmitting device and one or more receiving devices. In the following, the method of the embodiments will be described taking one transmitting device and one receiving device as an example. The transmitting device can be a BS, and the receiving device can be a UE.

[0095] In step 510, the receiving device obtains the information of K reference channels, K being a positive integer.

[0096] The receiving device can obtain the information of the K reference channels in various alternative ways.

[0097] In one implementation, the receiving device can obtain the information of the K reference channels from other devices, such as the transmitting device, or a remote data center. In this implementation, the transmitting device or the remote data center can obtain the information of the K information, and then send the information to the receiving device.

[0098] In another implementation, the receiving device can accumulate and process the channel data samples to obtain the information of the K reference channels.

[0099] In some implementations, the information of the K reference channels can include one or more of the following: data samples in the time domain of each of the K reference channels; data samples in the frequency domain of each of the K reference channels; multipath information of each of the K reference channels; an initial phase of a second reference channel corresponding to the first sub-band and a phase offset of a third reference channel corresponding to the second sub-band relative to the initial phase, wherein the K 2, the K reference channels include the second reference channel and the third reference channel.

[0100] In some implementations, the K reference channels can include a fourth reference channel, and the information of the K reference channels can include multipath information of the fourth reference channel. The multipath information of the fourth reference channel can include one or more of the following: the number of paths, the horizontal direction angle, the vertical direction angle, the delay, the Doppler, the power, the amplitude, and the phase of each of the paths, wherein the horizontal direction angle or the vertical direction angle includes the angle of departure and the angle of arrival.

[0101] The information of the K reference channels can be represented in the form of K vectors corresponding to the K reference channels.

[0102] It should be noted that ordinal numbers such as “first”, “second”, etc. are used in the embodiments, such as “first reference channel”, “second reference channel”, “first sub-band”, “second sub-band”, etc. The use of ordinal numbers is only to distinguish things of the same type, and should not limit the scope of the embodiments of the present application.

[0103] In some embodiments of the present application, new concepts of “channel data sample” and “reference channel” are proposed. In order to facilitate the understanding of the embodiments of the present application, some related technologies are introduced here.

[0104] First, the channel data sample is described in detail.

[0105] The wireless channel between the transmitting device and the receiving device is mainly determined by the environment in which the transmitting device and the receiving device are located. The inherent correlation between the environment and the wireless channel is an embodiment in the ray-tracing (RT) channel model, which generates the channel response according to the line of sight (LOS) and non-line of sight (NLOS) (reflection and / or diffusion) (i.e., a ray or a cluster of rays, plus some randomness). According to the RT channel model, the wireless channel is composed of a deterministic part due to RT and a random part due to random events. In an implementation of the present application, the deterministic part is some common characteristics among the channels in the nearby area, which can be learned or obtained and represented as common information.

[0106] The wireless channel can be caused by a multipath fading channel, which is more or less affected by the surrounding environment. The wireless ray or cluster of rays (or ray group) of the wireless channel can experience reflection and diffusion of wireless electromagnetic waves on the surrounding physical surfaces, edges or corners of buildings, roads, buses, tracks, people, etc., which can result in multiple wireless paths on the receiving device side. Some surfaces, edges and corners are fixed (buildings, bridges, utility poles, roads, sidewalks, etc.), while others are moving (e.g., moving vehicles, etc.), which can cause timing variations (or fading) on multiple wireless paths. In practice, most moving entities can follow a certain trajectory at a certain speed (e.g., vehicles only travel on roads), which can also be regulated by the surrounding environment composed of some immobile entities. Therefore, the wireless channel can be closely related to the environment in which the transmitting device and the receiving device are located. The environment can be a broad definition, and the environment can be represented as a set of environment parameters. The set of environment parameters can include one or more environment parameters. The one or more environment parameters can include one or more of the following: a spatial region, a frequency band, a duplex mode (e.g., time division duplex or frequency division duplex; half duplex or full duplex), a time or duration, weather, data traffic (e.g., a traffic mode or a non-traffic mode. The traffic mode refers to a period of time when the data traffic exceeds a certain threshold. The non-traffic mode refers to a period of time when the data traffic is less than or equal to the certain threshold), a precoder, etc.

[0107] Multiple wireless channels located in the same environment can share certain commonalities. The commonalities can be regarded as common environmental prior knowledge about the wireless channel. The common environmental prior knowledge can be represented in various forms, including but not limited to any one of the following: Alternative #1: represented by one or more statistical functions with parameters; Alternative #2: represented by one or more matrices; Alternative #3: represented by one or several trained artificial intelligent (AI) models (e.g., deep-learning neural network (DNN)).

[0108] The common environmental prior knowledge of multiple wireless channels between a transmitting device and multiple receiving devices in the same environment can be learned or acquired. The acquired environment-related common environmental prior knowledge can be validated and persistent, useful for wireless channels between transmitting devices and receiving devices entering the environment a period of time after the common environmental prior knowledge is acquired. Thus, the acquired common environmental prior knowledge can represent the spatial and temporal persistent commonality related to the environment.

[0109] The common information can be acquired or collected by a transmitting device (may be considered as a central device, e.g., a BS), a powerful receiving device (e.g., one or more UEs), or a remote data center, etc. Hereinafter, “device” is used to represent any one of the devices including the transmitting device, the receiving device, and the remote data center. Some examples are given below about the device acquiring the common information.

[0110] The device can acquire multiple pieces of common environmental prior knowledge, each of which is related to an environment. The environment can be a generalized definition including spatial region, frequency band, duplex, timing, weather, traffic, precoder, etc., thus, the environment can be represented by an environment parameter set including one or more environment parameters, e.g., spatial region, frequency band, duplex, timing, weather, traffic, precoder, etc. Among them, the environment parameter set (i.e., one or more environment parameters) can represent the wireless environment. In some embodiments of the present application, the common environmental prior knowledge can be learned or acquired from channel data samples related to the same environment (i.e., the environment parameter set). For example, the common environmental prior knowledge can be learned or acquired from M channel data samples, M is an integer. The common environmental prior knowledge can also be referred to as common information in the present application.

[0111] Different environments can overlap or not overlap in the physical spatial region; or different environments can overlap or not overlap between UL and DL; or different environments can overlap or not overlap in the frequency band.

[0112] It should be noted that “spatial region” can refer to a region on the space domain, and “physical spatial region” can refer to an actually existing region or space.

[0113] Some examples are given below about the device acquiring the common information.

[0114] Example #1: The device can acquire one piece of common information related to an environment.

[0115] Example #2: The device can obtain two pieces of common information. The first piece of common information is related to the wireless channel corresponding to a first spatial region, and the second piece of common information is related to the wireless channel corresponding to a second spatial region. The two spatial regions can overlap or not overlap, be adjacent or apart, and the spatial regions can be designated as sectors.

[0116] Example #3: The device can obtain two pieces of common information. The first piece of common information is related to the wireless channel corresponding to a first physical spatial region, and the second piece of common information is related to the wireless channel corresponding to a second physical spatial region. The first spatial region can include the second spatial region.

[0117] Example #4: The device can obtain two pieces of common information. The first piece of common information is related to the wireless channel between the receiving device and the transmitting device that can apply a first Tx decoder, and the second piece of common information is related to the wireless channel between the receiving device and the transmitting device that can apply a second Tx decoder. The transmitting device can apply both Rx decoders to the receiving device.

[0118] Example #5: The device can obtain two pieces of common information. The first piece of common information is related to the wireless channel of a first frequency band between the receiving device and the transmitting device, and the second piece of common information is related to the wireless channel of a second frequency band between the receiving device and the transmitting device. The two frequency bands can overlap or not overlap, and can be adjacent or apart.

[0119] Example #6: The device can obtain two pieces of common information. The first piece of common information is related to the UL wireless channel between the receiving device and the transmitting device, and the second piece of common information is related to the DL wireless channel between the receiving device and the transmitting device.

[0120] In addition, the common information obtained by the device can be a combination of the above examples. In addition, the common information can change over time.

[0121] Further, any of the above pieces of common information can be obtained from a plurality of channel data samples (also referred to as channel samples, data sample sets, learning data sets, training data sets, etc.), such as M channel data samples, which can be accumulated and prepared in the following ways, including but not limited to any of the following: Alternative #1: The channel data samples can be measured by the transmitting device or the receiving device or both and accumulated in a history record. For example, the transmitting device can use UL-SRS to probe the channel to accumulate the channel data samples. The receiving device can estimate the DL channel and report the channel estimation of the DL channel to the transmitting device, and the transmitting device can accumulate and process the channel estimation of the DL channel to obtain the channel data samples.

[0122] Alternative #2: Channel data samples can be reported by some physical reference users, which can be deployed at some key locations or random locations in the target environment. The physical reference users can receive DL signals from the transmitter, estimate the DL channel, and then report the channel estimation of the DL channel (preferably in compressed format) to the transmitter. The transmitter can accumulate and process the channel estimation of the DL channel to obtain the channel data samples.

[0123] Alternative #3: Channel data samples can be virtually generated by a digital environment simulator, which can be referred to as a digital twin of the target environment.

[0124] In some implementations, the channel data samples can be accumulated, preferably in a dynamic combination of the above alternatives, without limitation.

[0125] Next, the reference channel is described in detail.

[0126] The reference channel can be a channel data sample in the embodiments of the present application. In some embodiments, the K reference channels can be K channel data samples selected from the M channel data samples, or the K reference channels can be K compressed channel data samples corresponding to the K reference channels in the M channel data samples. The M channel data samples are related to the set of environment parameters, where K≤M, K and M are positive integers. From another perspective, each reference channel can be data or information of a channel that can exist between the transmitter and the receiver.

[0127] The channel data sample can be data or information representing the channel state of the channel, which can be measured or accumulated by a communication device (e.g., a transmitter, a receiver, or a remote data center), or virtually generated by a simulator.

[0128] In some embodiments, the channel data sample can include one or more of the following: Channel data samples in the time domain; Channel data samples in the frequency domain; Projections of channel data samples on subspaces.

[0129] In some embodiments of the present application, the common information related to the environment (i.e., the set of environment parameters) can be represented in various forms, such as the aforementioned statistical-based form, matrix-based form, AI-based form (e.g., DNN-based form), etc. Further, the matrix-based common information includes common information based on channel space bases, common information based on orthogonal matrices, or common information based on non-orthogonal matrices, etc.

[0130] In one embodiment, the common information can be represented by statistical values about the wireless channel, e.g., the statistical values can be calculated by using a statistical-based function or an empirical formula. For example, the statistical values include coherence time, coherence frequency, root-mean-square (RMS) delay, etc.

[0131] In yet another embodiment, the common information can be based on a matrix and / or AI, which can be learned or obtained from channel data samples. The channel data samples contain multiple channel data samples, which can include channel states, channel measurements, channel coefficients, etc.

[0132] If the common information is based on a matrix, the following general or main steps can be taken on a device (which can be considered as a central device, e.g., a BS), a powerful receiving device (e.g., one or more UEs), or a remote data center, etc. Hereinafter, “device” is used to represent any one of the devices including the transmitting device, the receiving device, and the remote data center. The common information can be calculated by the following steps: Step #1: If the channel data samples are in the form of a matrix or a tensor (e.g., a MIMO channel data sample is a three-dimensional tensor, RE x Tx Ant (Tx antenna port) x Rx Ant (Rx antenna port)), the device can vectorize the channel data samples, where the device can apply a fixed vectorization order to all the channel data samples and save or memorize the vectorization order.

[0133] Figure 6 An example of vectorizing a MIMO channel sample in the form of a tensor is shown. The following is an example of vectorizing a three-dimensional tensor into a vector. The MIMO channel sample is represented as a three-dimensional tensor by x x and is vectorized into a column vector x represented by as shown in Figure 6 The vectorization order is: RE, then Tx, then Rx, where RE represents the number of resource elements (REs), Tx represents the number of Tx antenna ports at the transmitting device side, and Rx represents the number of Rx antenna ports at the receiving device side. Since the first MIMO channel data sample in the tensor is represented by x x , the device can vectorize this sample into in the order of RE, then Tx, then Rx.​ × It means that, among them, ), that is, the first columnar vector; since the second MIMO channel data sample in the tensor is composed of × × Indicated Therefore, the device can vectorize the sample in the same order. (use × It means that, among them, ), that is, the second columnar vector; and so on, until the device vectorizes all M MIMO channel data samples in the tensor into columnar vectors.

[0134] Step #2: The device can concatenate all columnar vectorized channel data samples into a matrix and then decompose the matrix. If the channel data samples are vectorized into columnar vectors, they can be concatenated row by row. These two concatenations are mathematically equivalent. Columnar vectorization and columnar concatenation are used in the following discussion. Decomposition can be used to compute the basis of the matrix. This basis can be called the channel space basis, representing the common environmental prior knowledge obtained from the channel data samples. Decomposition can be singular value decomposition (SVD), such that the resulting channel space basis is an orthogonal or unitary matrix. Other methods can be used to decompose the matrix so that the resulting channel space basis is a non-orthogonal matrix.

[0135] In one example (make) The vectorized MIMO channel data samples are juxtaposed into a single vector. × matrix: The order of the channel data samples is not important.

[0136] Figure 7 An example is shown where the columnar vectorized channel data sample columnar representation is juxtaposed into a matrix. According to... Figure 7 The decomposition is a reduced-rank SVD: , and It is a unitary matrix or an orthonormal matrix. It is a diagonal matrix. If If set as a columnar vector, then by × Indicated It is the channel space basis, representing all Public information shared by data samples from each MIMO channel. As mentioned above, the target environment consists of accumulation and preparation. The position and manner of a MIMO channel data sample are defined. In this sense, the environment (i.e., the set of environment parameters) can be regarded as a channel data sample.

[0137] It should be noted that in the embodiments of the present application, is set as a column vector, without loss of generality, if is set as a row vector, as , , is the channel spatial basis, representing the common environment prior knowledge of the channel. They are mathematically exactly the same. In the following discussion, the column vector will be used as an example.

[0138] With the channel spatial basis, any vectorized channel data sample can be represented (i.e., compressed, encoded or projected) by a weighted linear combination of the columns of the channel spatial basis , where the weighted coefficients are called the spectral coefficient vector : , is ×1 vector. Although the channel spatial basis is a tall and thin matrix, i.e., , the spectral coefficient vector (represented by ×1) is much smaller than (represented by × ). The spectral coefficient vector is mathematically an equivalent low-dimensional space of . It lets certain storage, representation or computation on be equivalently performed on .

[0139] Figure 8 The equivalent low-dimensional space is shown. Alternatively and preferably, the device that computes the channel spatial basis from multiple channel data samples can project each vectorized channel data sample into an equivalent low-dimensional space representation by the inverse of the channel spatial basis , which can be named as the low-dimensional spectral coefficient representation: where, is ×1 vector. If is a standard orthogonal matrix or a unitary matrix, the inverse of the channel spatial basis is the Hermitian transpose of the channel spatial basis (i.e., ): . Because contains All the key information, so the device can project the spectral coefficient representation back into the original channel data space: ,like Figure 8 As shown in the image. Compared to channel data samples. The equipment may be more inclined to use a channel space basis. Low-dimensional space representation Channel data samples are stored in the form of [format missing].

[0140] The device can collect data samples from M channels. The device selects a set of K channel data samples. This set of K channel data samples can be used as the set of the aforementioned K reference channels. Alternatively, the reference channels can also be called anchor channels or anchor channels, without restriction. The transmitting device can continuously update the set of K reference channels; the device can discard some old reference channels and requisition some new reference channels; the device can retain or change the size (K) of the set. The device can divide the set of K reference channels into several overlapping or non-overlapping subsets, wherein the division can be performed in any of the following ways: Alternative Solution #1: The device can randomly select K channel data samples from M channel data samples as the first subset, and then continue to randomly select from this set. ( ( ) channel data samples are used as the second subset.

[0141] Alternative Solution #2: The device can use segmentation algorithms such as K-means, random walk, or Gaussian mixture model (GMM) to segment from... M Select the first channel data sample One data sample is used as the first set, from M Select the second from the channel data samples One data sample is used as the second set. Alternatively, the device can select a data sample to be used in both the first and second sets.

[0142] Alternative Option #3: The device can score the "distance" in M ​​channel data samples, and then select... The highest degree channel data sample. "Degree" is a graph theory term representing the number of connections a node has in a graph. A node with a higher degree is called the "center" node in the graph. A higher degree node is considered more typical or representative. Note that each node in the graph represents a channel data sample from alternative scheme #3.

[0143] In any alternative, the device can select K channel data samples to a set. middle: ,in, Return to the above The original index of the selected channel data sample. Optionally, the device may not need to select from... Instead of selecting K channel data samples from the channel data samples, new channel data samples can be remeasured in the manner described above.

[0144] Unless explicitly required, the following embodiments will focus on solutions for a single set of K reference channels. Those skilled in the art will readily extend these embodiments to multiple sets of reference channels without difficulty.

[0145] Because in T-MIMO scenarios, the dimension of the reference channel ( The value can be very large, so in some implementations, the device can also handle a set of K reference channels. Projection (or compression) can be performed. Based on the above, the device can... Set of K reference channels The projection is a low-dimensional spectral coefficient vector. The device can project from the low-dimensional spectral space instead of the original space. The reference channel set is stored as And send it, where, yes ×1 vector.

[0146] In one implementation, the information about the K reference channels described in step 510 may include the original K reference channels. For example, when the receiving device receives information about the K reference channels from the transmitting device, the receiving device receives a set of the K reference channels from the transmitting device. Optionally, the transmitting device may transmit a portion of the set of the K reference channels to reduce the overhead of radio resources used to transmit the information about the reference channels, particularly when the transmitting device determines that the "representative" channel of the first channel may be a subset of the K reference channels.

[0147] In step 520, the receiving device acquires the channel estimate of the first channel.

[0148] The first channel can be the channel between the transmitting device and the receiving device, or it can be considered the channel between the Tx antenna on the transmitting device side and the Rx antenna on the receiving device side. Alternatively, from another perspective, the first channel is the channel through which the receiving device performs channel estimation. In one implementation, the first channel is a DL channel.

[0149] In one implementation, the receiving device uses a reference signal transmitted by the transmitting device to perform channel measurement on the first channel, thereby obtaining a channel estimate (i.e., a channel estimation result) for the first channel. The channel measurement in this embodiment is similar to that in the prior art and will not be described in detail here.

[0150] At step 530, the receiving device sends information of the first channel according to the similarity measure value, wherein the similarity measure value can be calculated according to information of the K reference channels and the channel estimation result of the first channel.

[0151] Correspondingly, the sending device receives the first information. The first information indicates a similarity measure value between the first channel and the first reference channel. The first reference channel can be any one of the K reference channels.

[0152] According to some embodiments of the present application, a "reference channel" and a "similarity measure value between a DL channel and a reference channel" are proposed. On this basis, the receiving device obtains information of K reference channels, and calculates a similarity measure value between a first channel (i.e. a DL channel) and a reference channel according to information of the K reference channels and the channel estimation result of the first channel. If the similarity measure value between the first channel and the first reference channel is less than or equal to a threshold value, the receiving device sends information of the first channel. The information of the first channel can be an index of the first reference channel. In this way, the first channel can be represented by the first reference channel, and the first reference channel can be determined by the similarity measure value.

[0153] The embodiments of the present application propose a new idea of reporting DL channel information. The receiving device reports a channel estimation result of a DL channel, so that the sending device obtains the channel state of the DL channel in the prior art. In the embodiments of the present application, the reference channel reported by the receiving device can be a "representative" of the DL channel.

[0154] Here, the "representative" of the DL channel can be a channel that is conditionally close to the DL channel. The "representative" can select, for example, K reference channels from a plurality of reference channels according to a similarity measure value. In some implementations, the "representative" can be a reference channel in the K reference channels corresponding to the minimum similarity measure value. The similarity measure value can represent the similarity between channels. Assuming that the receiving device can determine a reference channel (i.e. a first reference channel) as the "representative" of the DL channel, the information of the DL channel is no longer the absolute information of the DL channel, but the relative information of the DL channel and the first reference channel. Based on the relative information, since the information of the K reference channels including the first reference channel is obtained in advance, the sending device can also obtain the channel state of the DL channel.

[0155] Compared with the reporting method of the absolute information of the DL channel in the prior art, the reporting method of the relative information of the DL channel proposed in the embodiments of the present application has less overhead.

[0156] In one implementation, the similarity measure value can indicate the distance between the first channel and the reference channel.

[0157] In some embodiments of the present application, the concept of distance between two channels is proposed. Specifically, the distance between two channels can be the distance between the first channel and the reference channel.

[0158] The receiving device can measure or score the distance between the first channel and the reference channel by one or several scoring functions.

[0159] In the process of measuring the distance between the first channel and the reference channel, the common information described in the above embodiments can be adopted, and therefore the method (500) can further include step 540.

[0160] In step 540, the receiving device obtains the common information.

[0161] For example, the receiving device can receive the common information from the transmitting device. In some embodiments, the common information can be represented by a matrix, for example, represented by channel space basis . The common information can also be represented by the projection of channel space basis to a predefined matrix, for example, the predefined matrix can be a discrete Fourier transform (DFT) matrix. In other embodiments, the common information can be represented by DNN parameters, without limitation.

[0162] Returning to step 530, Figure 9 Examples of scoring functions for measuring the distance between two channels in an equivalent low-dimensional space are shown. In the following, the two channels can be illustrated by two channel data samples.

[0163] As Figure 9 shown, in the case where the device represents the common information by channel space basis , the device can project the channel data samples ( × , ) to a low-dimensional spectral space, i.e., the spectral coefficient vector ( ×1) multiplied by the channel space basis , , . In particular and preferably, when the channel space basis is orthonormal or unitary, , . Therefore, the device can score or measure the "distance" or "similarity" or "correlation" measure between any two channel data samples and by the scoring or measuring function , returning two input channel data samples The "distance", "similarity", or "relevance" scalar measure between If are equivalent, then , means that the score or metric can be equivalently done on the low-dimensional spectral space. The device can use to represent the distance between two channel data samples ( and ). The score or metric function may be equivalent, can include but is not limited to the following operations: Alternative #1: Euclidean function; Alternative #2: Normalized Mean Squared Error (NMSE); Alternative #3: Cosine loss between two vectors; Alternative #4: Inner product (or dot product) between two vectors; and so on.

[0164] Figure 10 An example of a DNN-based score function for measuring the distance between two channel data samples in an equivalent low-dimensional latent space is shown. As shown in Figure 10 , with the above representing the common information, the device can use a score or metric function on the latent layer output The score function can be implemented by another DNN ( ), where are parameters in the neurons that need to be trained. In this example, the common information can be represented by DNN parameters and .

[0165] In another implementation, the similarity measure value can indicate the likelihood of the first channel being within or near the subspace of the first reference channel.

[0166] For example, the likelihood can be calculated by a probability density function (PDF), a log PDF, a gradient of the log PDF, a likelihood ratio between two distributions, etc.

[0167] In yet another implementation, the similarity measure value can indicate a score value, which can be calculated by a score function, and can indicate the distance between two channels.

[0168] For example, the score value can be calculated by the following equation: .

[0169] In this equation, represents a function outputting one or more values of a variable minimizing the value of an objective function. The objective function can be wherein, . represents a spectral coefficient vector having elements, represents information of an th reference channel, , represents an index of a reference channel reported by a receiving device.

[0170] In embodiments of the present application, if the similarity measure value between the first channel and the reference channel is less than or equal to a threshold value, it indicates that the first channel can be represented by the reference channel. The threshold value can be predefined, configured by the sending device such as a BS, or specified by a communication standard.

[0171] The following gives a detailed example of the method (500).

[0172] Figure 11 is an example of the method (500) provided by the present application. The receiving device can obtain information of K reference channels. The receiving device can obtain the information of the K reference channels in any of the ways described in step 510. For example, the receiving device can receive the information of the K reference channels from the sending device. The receiving device can obtain common information used to determine the information of the DL channel. For example, the receiving device can receive the common information from the sending device. The sending device can send a reference signal used for channel measurement. The receiving device can use the reference signal to perform channel measurement on the DL channel (i.e. the first channel in the above embodiments) between the receiving device and the sending device, obtaining a channel estimate of the DL channel. The receiving device calculates the similarity measure value between the DL channel and the reference channel. If the similarity measure value between the DL channel and the first reference channel is less than or equal to a threshold value, the receiving device can report the information of the DL channel to the sending device. The information of the DL channel can be an index of the first reference channel.

[0173] As an example, the receiving device can calculate the similarity measure value between the DL channel and each reference channel, and report each reference channel corresponding to a similarity measure value less than or equal to a threshold value to the sending device. Each reference channel corresponding to a similarity measure value less than or equal to a threshold value is the first reference channel in this example. There can be one or more first reference channels. The information of the DL channel can be an index of one or more first reference channels.

[0174] As another example, the receiving apparatus can calculate similarity metric values between the DL channel and each of the K reference channels, and select one of the K reference channels corresponding to a minimum similarity metric value. The one reference channel is the first reference channel in this example. The information of the DL channel can be an index of the one reference channel.

[0175] As another example, the receiving apparatus can calculate similarity metric values between the DL channel and the reference channels, and can select the reference channels involved in the calculation randomly or in a certain order calculated by a function. Once the reference channels meet the requirement, the receiving apparatus can stop the calculation and report the reference channels meeting the requirement. For example, the requirement can be that the distance between the reference channel and the DL channel is less than or equal to a threshold. In this example, the information of the DL channel can be an index of the reference channel.

[0176] The transmitting apparatus can transmit a reference signal for tracking the first reference channel. The transmission of the reference signal can be periodic or aperiodic. The receiving apparatus performs channel measurement on the DL channel using the reference signal, and then transmits feedback information to confirm the first reference channel. The feedback information can indicate one or more first reference channels. For example, the feedback information can be an index corresponding to the first reference channel. Since the first reference channel can be a "representative" of the DL channel, the transmitting apparatus transmits the information of the DL channel according to the similarity metric value, which reduces the overhead in the channel estimation feedback of the DL channel.

[0177] The above describes in detail the method provided by the present application. The communication apparatus provided by the present application will be described in detail below.

[0178] Figure 12 is a schematic block diagram of the communication apparatus 10 provided by an embodiment of the present application. As shown in Figure 12 the apparatus 10 includes a receiving module 11, a processing module 12, and a transmitting module 13.

[0179] The receiving module 11 is configured to obtain information of K reference channels, wherein the K reference channels are related to a set of environmental parameters, and K is a positive integer.

[0180] The processing module 12 is configured to obtain a channel estimation of a first channel.

[0181] The transmitting module 13 is configured to transmit information of the first channel according to a similarity metric value, wherein the similarity metric value is calculated according to the information of the K reference channels and the channel estimation of the first channel.

[0182] In an implementation manner, the processing module 12 is configured to obtain common information, wherein the common information is used to determine the information of the first channel; and the similarity metric value is calculated according to the common information, the information of the K reference channels, and the channel estimation of the first channel.

[0183] The apparatus 10 according to embodiments of the present application can correspond to a receiving apparatus of any of the above-mentioned method embodiments, and the operation and / or function of the apparatus 10 is intended to implement the corresponding steps of the above-mentioned methods. For brevity, no further elaboration is made herein.

[0184] Optionally, the sending module 13 and the receiving module 11 can be implemented by a transceiver, and the processing module 12 can be implemented by a processor.

[0185] With reference to Figure 13 , the communication apparatus 20 can comprise a transceiver 21. Optionally, the communication apparatus can further comprise a processor 22 and a memory 23. The memory 23 can be used to store data, information, code or instructions, etc. to be executed by the processor 22, so as to make the communication apparatus 20 perform the operation of the receiving apparatus in the corresponding embodiments.

[0186] Figure 14 is a schematic block diagram of the communication apparatus provided by embodiments of the present application. As shown in Figure 14 , the apparatus 30 comprises a receiving module 31, a processing module 32 and a sending module 33.

[0187] The receiving module 31 is configured to receive information of a first channel, the information of the first channel being determined according to a similarity metric value, the similarity metric value being calculated according to information of K reference channels and a channel estimation of the first channel, the K reference channels being related to a set of environmental parameters.

[0188] The apparatus 30 according to embodiments of the present application can correspond to a sending apparatus of any of the above-mentioned method embodiments, and the operation and / or function of the apparatus 30 is intended to implement the corresponding steps of the above-mentioned methods. For brevity, no further elaboration is made herein.

[0189] Similarly, the sending module 33 and the receiving module 31 can be implemented by a transceiver.

[0190] With reference to Figure 15 , the communication apparatus 40 can comprise a transceiver 41. Optionally, the communication apparatus can further comprise a processor 42 and a memory 43. The memory 43 can be used to store data, information, code or instructions, etc. to be executed by the processor 42, so as to make the communication apparatus 40 perform the operation of the sending apparatus in the corresponding embodiments.

[0191] The processor 22 or the processor 42 can be an integrated circuit chip with signal processing capability. In the implementation process, each step in the above method embodiment can be realized by hardware integrated logic circuit in the processor or by instructions in the form of software. The processor 22 or the processor 42 can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. All methods, steps and logic block diagrams disclosed in the embodiments of the present application can be realized or executed. The general-purpose processor can be a microprocessor, or the processor can be any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly executed and completed by the hardware decoding processor, or executed and completed by using a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium known in the art such as random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), electrically erasable programmable memory or register. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with the hardware of the processor.

[0192] It can be appreciated that the memory 23 or the memory 43 in the embodiments of the present application can be a volatile memory or a non-volatile memory, and can also include a volatile memory and a non-volatile memory. The non-volatile memory can be a ROM, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a RAM used as an external cache. By way of example, and not limitation, many forms of RAM can be used, such as a static RAM (SRAM), a dynamic RAM (DRAM), a synchronous dynamic RAM (SDRAM), a double data rate SDRAM (DDR SDRAM), an enhanced SDRAM (ESDRAM), a synch link DRAM (SLDRAM), and a direct Rambus RAM (DR RAM). The storage of the system and method described in the specification is intended to include, but not be limited to, these and any other suitable storage.

[0193] The embodiments of the present application also provide a communication system. The communication system includes the communication device 10 and the communication device 30 described in any one of the above embodiments.

[0194] The embodiments of the present application also provide a computer storage medium, which can store one or more instructions for executing any one of the above methods.

[0195] Optionally, the storage medium can be specifically the memory 23 or 43.

[0196] The embodiments of the present application also provide a computer program product, which can store one or more instructions for executing any one of the above methods.

[0197] The "and / or" described in the embodiments of the application represents the association relationship between the associated objects, and indicates that there can be three relationships. For example, A and / or B can represent the following three cases: only A exists, A and B both exist, and only B exists. The character " / " generally represents an "or" relationship between associated objects. "At least one" means one or more. "At least one of A and B" is similar to "A and / or B", which describes the association relationship between the associated objects, and indicates that there can be three relationships. For example, at least one of A and B can represent the following three cases: only A exists, A and B both exist, and only B exists.

[0198] The technical terms such as "reference channel", "channel data sample" and the like can not be limited by specific names, but can be other names.

[0199] In addition, the singular forms "a", "an" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0200] Those of ordinary skill in the art will realize that each unit and algorithm step described in connection with the examples described in the embodiments disclosed in the specification can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether the described functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but it should not be considered that the embodiments exceed the scope of the present application.

[0201] Those skilled in the art can understand that, for the convenience and brevity, the detailed working processes of the above system, device and unit can refer to the corresponding processes in the above method embodiments, which will not be described herein.

[0202] In several embodiments provided in the present application, the disclosed system, device and method can be implemented in other ways. For example, the described device embodiments are only examples. For example, the unit division is a logical function division, and other division methods can also be used in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be omitted or not performed. In addition, the display or description of mutual coupling or direct coupling or communication connection can be realized through various communication interfaces. The indirect coupling or communication connection between devices or units can be realized through electronic, mechanical or other forms.

[0203] In addition, the functional units in the embodiments of the present application can be integrated into one processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0204] When these functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The technical solution of this application can be implemented as a software product. This software product is stored in a storage medium and includes several instructions to instruct a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in the embodiments of this application. The aforementioned storage medium includes any medium capable of storing program code, such as a USB flash drive, portable hard drive, ROM, RAM, magnetic disk, or optical disk.

[0205] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiment scheme according to actual needs. Furthermore, the functional units in the embodiments of this application may be integrated into one processing unit, or each unit may exist physically independently, or two or more units may be integrated into one unit.

[0206] The above descriptions are merely some specific embodiments of this application and are not intended to limit the scope of protection of this application. Any variations or substitutions that are readily conceived by those skilled in the art within the scope of the technology disclosed in this application are within the scope of protection of this application. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

[0207] New CSI Acquisition Mechanism

[0208] This invention generally relates to wireless communication.

[0209] Abbreviations

[0210] MIMO and MU-MIMO MIMO systems are widely used in modern wireless systems to improve system capacity and bandwidth efficiency by utilizing spatial diversity between antenna ports. For example, on a given subcarrier or RE, by One Tx antenna port and A transceiver consisting of Rx antenna ports constitutes... × MIMO channel, by × Complex matrix This indicates that the complex matrix can be decomposed using SVD [4]: ,in, yes × a square orthogonal matrix (such that ), is a square orthogonal matrix (such that ), is a square orthogonal matrix (such that ), is a rectangular diagonal matrix. The rank of is not greater than the smaller of and . According to the standard SVD, if the transmitter applies a precoding matrix and the receiver applies a receiving matrix , then the x MIMO channel will become independent parallel (orthogonal) sub-channels:

[0211] Each sub-channel has a scaling value channel response , i.e., the i-th diagonal element of (the singular value ). Accordingly, the SNR on the i-th sub-channel is defined as In a wireless system, only the sub-channels with SNR higher than a certain threshold are considered to be effectively used for transmission. These effective sub-channels are called MIMO streams.

[0212] The SNR-based truncated MIMO decomposition scheme decomposes the standard SVD into a reduced-rank SVD by discarding the sub-channels with SNR lower than a threshold: (reduced-rank SVD in [4]), where is a square orthogonal matrix (such that ), is a square orthogonal matrix (such that ), is a square orthogonal matrix (such that ), is a square diagonal matrix. The number of MIMO streams of When the transmitter applies a precoding matrix and the corresponding receiver applies a receiving matrix , x MIMO channel will become:

[0213] For rank-reduced SVD, is × a diagonal matrix.

[0214] Mathematically, the precoding matrix at the transmitter and the receiving matrix at the receiver are obtained by linear transformation on the MIMO channel to coordinate the whole MIMO channel on effective sub-channels. The MIMO gain or spatial diversity gain is denoted as SNR , which is due to the spatial diversity inherent in the MIMO channel between the transmitter and the receiver, and is related to the wireless environment. According to experience, in such a complex environment as the city center, the number of MIMO streams of the wireless channel is more than in a simple rural environment, because the high-rise buildings in the city center produce more spatial diversity due to higher radio reflectivity.

[0215] In order to obtain higher MIMO gain, the wireless system increases the number of antenna ports (i.e. and ), which improves the upper limit of the number of potential MIMO streams, because . But in fact, is much smaller than its upper limit . This prompted the deployment of MU-MIMO: if the number of MIMO streams generated by a MIMO channel is insufficient, multiple MIMO channels on the same RE can be multiplexed by a common precoder . Assuming that two MIMO channels and on the same RE are very different from each other, it is likely to find a common precoder to multiplex (separate) the two; while assuming that two MIMO channels and on the same RE are almost the same, it is less likely to find a common precoder to multiplex (separate) the two.

[0216] Mathematically, such a common precoder is related to the precoders and . The method widely used in practice is based on EZF. By concatenating the two precoders in the rank-reduced SVD on the MIMO channel into one, where is a × matrix. In the EZF way, their common precoder is , where is X Matrix. If and are orthogonal, then approximates the identity matrix , which means that the transmitter can continue to use the precoding matrix for UE-1 and the precoding matrix for UE-2, which are simultaneously multiplexed on that RE without MAI. If and are the same, then approximates the singular matrix (non-invertible), so no common precoder is available. These two UEs cannot be paired. In practice, most cases are in between the two extremes. Neither the identity matrix nor the singular matrix. The transmitter must compute common precoders for all possible combinations and then find the best one. However, this is an NP-hard problem. Suppose a transmitter has 200 candidate receivers. In theory, for different receiver combinations, the transmitter must perform an exhaustive search in different computations of common precoders . Furthermore, to improve the degree of approximation to the identity matrix and support more pairing or grouping of receivers, it is common to make , prompting wireless systems to employ more antenna ports, or more accurately, higher MIMO antenna port ratios between the transmitter and the receiver ( ).

[0217] After computing the common precoder , the transmitter multiplies the common precoder with the signal that the transmitter sends.

[0218] MU-MIMO engineering trade-offs For wireless systems, MU-MIMO is typically used in DL, where the BS is the transmitter and the UE is the receiver. The MIMO channels of multiple UEs are paired through a common precoder to multiplex on the same RE (frequency) and the same time duration (timing).

[0219] To achieve higher throughput and system efficiency, modern MU-MIMO systems deploy a large number of antenna ports over a wider frequency band. For example, in a T-MIMO system (of 6G), a BS is expected to have 1024 antenna ports, while a UE has 32 antenna ports over a 500 MHz bandwidth. The MIMO channel becomes a three-dimensional tensor ( X X ).

[0220] Figure 16 : TMIMO channel dimension Main trade-off #1: DL / UL channel reciprocity assumption While MU-MIMO should be paired on the DL channel between one BS and multiple UEs, it is impractical for each candidate UE to report or feedback its DL channel estimate to the BS, because of the huge UL feedback overhead due to the large dimension of the T-MIMO channel. In TDD systems, it is assumed that the DL channel between one BS and one UE can be approximated by the UL channel between the BS and the UE. In 4G and 5G-NR systems, the SRS UL channel is designated for UL channel measurement or estimation for this purpose. The SRS UL channel is shared by multiple UEs. These UEs transmit their own SRS reference signals on the SRS pilot locations so that the BS can estimate its own UL MIMO channel separately. In 5G-NR, sharing is achieved by code multiplexing the modulated signals.

[0221] Main trade-off #2: Random or quasi-random MU pairing implementation As mentioned before, the MU pairing is an NP-hard problem. Theoretically, the best pairing is the result of an exhaustive search (computation) of all possible combinations of candidate UEs (from two to all). However, the computation involving the pseudo-inverse of large matrices is too long for real-time signal processing during one TTI or a few TTIs. In particular, when more than a few hundred or even a few thousand UEs and pairing 10 or 20 UEs in a few TTIs, the pseudo-inverse of the matrix may be computationally prohibitive for most hardware implementations. Due to complexity, memory and latency constraints, the exhaustive search for the best pairing scheme is limited in practical implementations. Instead, a fixed number of paired UEs is first randomly or quasi-randomly selected from a large pool of candidates into , then the common precoding matrix EZF is computed . The selection can take into account the location of the candidate UEs, for example. For example, the empirical selection algorithm can tend to select paired UEs that are far apart from each other, as these UEs are more likely to have orthogonal MIMO channels. The number of pairs can simply be provided by empirical experience, system or hardware constraints, for example.

[0222] Strictly speaking, the trade-off does not implement pairing, but only computes the precoding matrix from any invertible .

[0223] 5G-NR SRS UL and CSI-RS for DL MIMO channel acquisition 5G-NR employs SRS UL channel to measure UL MIMO channel between BS (as a transmitter) and multiple UEs (as receivers). BS will assume UL MIMO channel of BS estimated from SRS UL channel of BS as DL MIMO channel between BS and UEs in TDD mode.

[0224] Specifically, SRS UL channel defines a set of uniform pilot (or reference signal) placement or location pattern in terms of RE (frequency), BS antenna port, and UE antenna port. 5G-NR standard specifies a uniform pilot placement pattern that both BS and UE must follow. One of the reasons for standardizing uniform pilot placement pattern is simplicity, i.e., only a few parameters are exchanged between transmitter and receiver to align the pattern to be used currently with each other.

[0225] Furthermore, to enable BS to measure more than one UE at the same time, a code multiplexing scheme is used on the pilot to enable more than one UE to mask their pilots with different codes to share the same pilot location. In 5G-NR, code multiplexing scheme of SRS UL channel is designed to support up to 16 UEs. If more than 16 UEs are required to share SRS UL channel, then new pilot locations must be consumed. Thus, 5G-NR has the capacity of SRS UL channel to measure multiple UEs at the same time.

[0226] UL / DL channels are not always reciprocal if RF and IF parts are considered.

[0227] Received UL signal strength from cell edge UEs to BS can be too weak to estimate. These UEs must feedback their DL MIMO channel instead of sending their pilots on SRS UL channel. Thus, 5G-NR provides CSI-RS, i.e., uniform pilot placement pattern, in DL channel for these UEs. UE will estimate channel coefficients on pilots (reference signals (RS)) in DL channel, then interpolate the whole channel coefficients from the estimated channel coefficients. UE will compress the whole channel estimation into CSI, then feedback to BS in UL channel. 5G standard not only defines pilot placement pattern of CSI-RS in DL channel, but also defines compression method. For example, CSI includes PMI and RI, which are indices in some preconfigured tables of precoding matrix and rank. It is expected that BS will decompress CSI into DL MIMO channel estimation, then proceed with subsequent MU-MIMO pairing and common precoding calculation. In general, CSI-RS DL channel results in CSI compression for reconstruction; specifically, CSI compression or encoder defined in 5G-NR is a lossy compression.

[0228] EZF-based MU-MIMO pairing and precoding matrix computation As mentioned in the background section, the pair search and the common precoding matrix computation are done together.

[0229] First, the computation of the common precoding matrix cannot be done until all SVDs on the candidate UEs are done. In particular, in T-MIMO, for each candidate UE, the BS needs to estimate their MIMO channel from the SRS UL channel or from the CSI feedback Then, a reduced rank SVD is computed on the large × matrix.

[0230] Second, the pseudo-inverse operation of is too complex to be done in a few milliseconds. For example, in T-MIMO, is a thousand by hundred complex matrix. It is almost impossible to compute on a large number of candidate in a TTI (2 ms).

[0231] Non-uniform pilot placement pattern 5G-NR SRS UL channels and CSI-RS DL channels both use uniform pilot placement patterns, partly because uniform pilot placement patterns are one of the safest ways to ensure channel estimation performance, especially when little is known about the current channel, and partly because they are easy to describe, standardize and align (configure) on transceivers. However, uniform pilot placement patterns are among the least efficient patterns. Their density must be designed for the worst case in statistics, which is rarely seen in practice. In other words, in most practical cases, the uniform pilot placement patterns specified in the 5G-NR standard can also be over-designed.

[0232] In 5G-NR, the average density of its uniform pilot placement patterns is about 7% to 17% of the wireless resources it uses for pilot or reference signals. For example, placing one reference signal per RB (composed of 12 consecutive REs) results in a pilot overhead of 8.33% (about 1 / 12). As Figure 1 shown, if T-MIMO uses the same uniform density as 5G-NR, the pilot overhead will be too large to handle, or at least prohibit cell-edge UEs from feeding back their T-MIMO CSI.

[0233] From the a priori knowledge that the common spatial basis is a DFT matrix, an approximately optimal non-uniform pilot placement pattern can be computed by performing a column rotation QRD on . . The several “strongest” column pivots (in a typical column-rotated QRD, the column pivots are ordered by their importance or contribution) will indicate the most important or most contributing positions for arranging the reference signal (or pilot) for reconstruction purposes.

[0234] The non-uniform pilot arrangement pattern indicated by the column rotation pivot in the middle will produce almost minimal pilot overhead, but still minimize the MSE with respect to reconstruction (or decoder, decompression) [6].

[0235] 5G-NR SRS UL and CSI-RS for DL MIMO channel acquisition The first major drawback stems from the assumption of reciprocity in UL / DL channels. Although due to information theory ( , There are two random variables. and The air portion of a MIMO channel typically satisfies UL / DL reciprocity, but the RF and IF components (analog circuitry) generally do not support this assumption. Therefore, this assumption will inevitably compromise overall performance. Furthermore, this assumption only applies to TDD mode, not FDD mode.

[0236] When the size of the MIMO channel is Figure 1 When reaching large numbers like T-MIMO, a second major drawback emerges. The BS must estimate the entire MIMO channel for all coded multiplexed UEs on the BS's SRS UL channel. First, the BS must estimate the channel coefficients on each individual pilot for each coded multiplexed UE. Second, the BS must interpolate the entire MIMO channel based on the estimated channel coefficients on the pilots for each UE. Third, the BS must attempt to pair all active UEs and compute a common precoder for these UEs. The size of a typical T-MIMO limits storage and computation.

[0237] The third major drawback is due to MAI (Multiplexing Activity) among coded multiplexed UEs sharing the same SRS UL channel. MAI is unavoidable. On the one hand, it will limit the maximum number of coded multiplexed UEs (upper capacity); on the other hand, it will compromise the accuracy (or performance) of channel estimation. This is why 5G-NR must limit the maximum number of UEs sharing the same SRS UL channel. However, the upper capacity on the SRS UL channel will represent the scheduling and overhead in 6G, where a single BS will accommodate significantly more active UEs compared to 5G-NR.

[0238] The fourth major drawback is due to mobility. It is well known that radio channels change significantly when a UE moves. Sometimes, even small positional displacements can lead to LOS losses, resulting in substantial channel variations. Since the SRS-UL channel is shared among all active UEs and has a capacity limit, performing SRS-UL channel estimation so frequently for a group of UEs and BS is inconvenient and power-intensive. Therefore, in practical applications, SRS-UL-based MU-MIMO is more sensitive to mobility.

[0239] The final major drawback involves the DL CSI-RS channel for cell-edge UEs. In fact, cell-edge UEs using CSI-RS will suffer even more severe performance degradation.

[0240] EZF-based MU-MIMO pairing and precoding matrix computation The first drawback is that calculations must be performed for any potential pairings. If no candidate UE is paired (only one is selected and the rest are not paired), radio overhead (SRS UL channel or CSI-RS channel, CSI feedback) and computational overhead (channel estimation, SVD, decompression) will be wasted.

[0241] The second drawback is that for any potential pairing attempt, calculation must be performed. The pseudo-inverse operation[5] is the widely used EZF method. If a candidate pair If no pair is selected (only one is selected and the rest are not matched), then computational and storage overhead will be wasted. ).

[0242] The final drawback is that pairing and precoding computations are sequential: Must be in the process of trying to match ( Before that, estimations and calculations are performed.

[0243] QRD-based non-uniform pilot placement and compression While this approach provides a good channel estimation and compression scheme with near-minimum pilot and compression overhead, it still aims to reconstruct the channel as reliably as possible. This objective requires minimal overhead in terms of the number of reference signals and compression ratio, both of which necessitate a common space basis (…). The minimum depth size of ). From the perspective of source coding, the common space basis ( ) is a codebook used to minimize MSE during reconstruction. × How much should be retained? This determines how many "details" need to be rebuilt. Due to the public space base ( ) is the result of SVD [4], which usually puts the columns of in order of their corresponding singular values, so the first column will be more important (in mathematical terms) than the second one, and so on.

[0244] The more columns that are kept in , the more "details" about the reconstruction will be provided, but from an energy point of view, "details" are not that important. In order to reconstruct the whole MIMO channel ( ) and non-uniform pilot pattern ( ), a large enough common spatial basis ( ) should be aligned between the BS and the UE. However, in the T-MIMO scenario, and are huge. Further, when a UE moves from one area to another, the current and must be updated to the new

[0245] and . Since the common spatial basis ( ) is learned from many data samples, the common spatial basis ( ) itself is a highly IPR entity. It is costly to collect and clean the data samples and to compute the common spatial basis (

[0246] ), especially for large dimensional data samples. Any party with the common spatial basis ( ) can optimize its non-uniform pilot pattern, or even compression scheme.

[0247] 4. Detailed description of the technical solutions of the present application Figure 1 The focus of the present application is on how to achieve MU-MIMO pairing and precoding matrix computation in the T-MIMO scenario. In general, the present application will involve how to estimate the DL MIMO channel for a moving UE, how to select the best pair or group (more than two UEs) among all candidate combinations, and how to compute the common precoding matrix with reasonable storage and computation complexity.

[0248] In more detail, the present application aims to solve the following main problems: 1) The method of the present invention no longer assumes UL / DL channel reciprocity; therefore, there is no performance loss and no distinction between cell edge UEs; in addition, since the CSI-RS DL channel can be naturally shared among an unlimited number of UEs simultaneously; ultimately, it can support FDD-MU-MIMO.

[0249] 2) The UE will estimate the DL MIMO channel through a CSI-RS DL channel with an ultra-sparse non-uniform pilot arrangement pattern instead of a 5G-NR CSI-RS DL channel with a uniform pilot arrangement pattern; the non-uniform pilot arrangement pattern of the present invention requires a pilot density that is several orders of magnitude lower than the uniform pilot density of 5G-NR.

[0250] 3) The UE can feed back highly compressed CSI to the BS, which consumes several orders of magnitude less CSI compression than 5G-NR; 4) BS does not decompress CSI, but continues to use compressed CSI to complete all the following operations, including SVD-based MIMO channel decomposition, EZF-based pairing and precoding matrix calculation, thus saving a lot of storage and reducing computational complexity.

[0251] 5) Pairing and precoding matrix calculation can be performed separately; furthermore, pairing or grouping will be performed before SVD channel decomposition; this means that only selected UEs will be notified to report their compressed CSI to the BS for the final common precoding matrix calculation; parallelism is achieved between pairing attempts and precoding calculation.

[0252] 6) It can simplify pairing to support high mobility.

[0253] To address the challenges and problems discussed in the previous section, we rely primarily on two fundamental principles: environment-dependent MIMO channels and equivalent low-dimensional signal spaces.

[0254] As is well known, the wireless channel between a transmitter and receiver is primarily determined by its environment. The inherent correlation between the environment and the wireless channel is reflected in RT channel models, which generate channel responses based on LOS and NLOS (reflection and / or scattering) (i.e., rays or ray clusters, plus some randomness). According to the RT channel model, the wireless channel consists of a deterministic component caused by RT and a random component caused by random events. The deterministic component consists of some common characteristics among channels in the vicinity, which can be learned and represented as a common orthogonal basis (orthogonal base). ), referred to as a basis in the following discussion. Any (vectorized) channel Both can be used as bases It is represented by a weighted linear combination of columns, where the weight coefficients are called the spectral coefficient vector. Although the common standard orthogonal basis ( ) is a fine and tall matrix ( ), but more than ( × A much smaller spectral coefficient vector ( × In mathematics, it is The equivalent low-dimensional space. It allows Some storage, representation, or computation on the can be equivalently performed on (Right now Execute on the equivalent low-dimensional signal space.

[0255] This IPR discloses the DL pilot layout pattern, channel estimation, and spatial reference channel.

[0256] In the following discussion, a T-MIMO wireless channel will be used as an example because of its large dimensionality, such as... Figure 1 As shown, T-MIMO wireless channels are abbreviated as wireless channels or channels. It should be noted that spatial reference (anchoring) channels can be applied to large-dimensional signal spaces other than T-MIMO.

[0257] 1: Common prior knowledge about wireless channels Wireless channels, also known as multipath fading channels, are more or less affected by the surrounding environment because their wireless paths, rays, or ray clusters (or groups of rays) are related to reflections and scattering from the physical surfaces, edges, or corners of buildings, roads, buses, tracks, people, etc. Some surfaces, edges, and corners are fixed (e.g., buildings, bridges, utility poles, roads, sidewalks, etc.); others are moving (e.g., moving vehicles and pedestrians). Generally, fixed factors contribute to certain deterministic components of the wireless channel, while moving factors contribute to a random component.

[0258] Prior to 5G-NR, wireless systems treated the deterministic and random components as a single wireless channel entity and assumed no prior knowledge of the wireless channel. Therefore, they had to incur pilot and measurement feedback overhead so that the transceiver could synchronously know what the current channel was.

[0259] Since most of the fixed factors attributable to the deterministic portion of a wireless channel are generally known or available in advance, this part of the channel is also known in advance to both the transmitter and receiver, leaving only the random portion for pilot and measurement feedback overhead, thus greatly improving effective bandwidth efficiency. In most practical cases, the deterministic portion of the wireless channel dominates the channel more consistently than the random portion; therefore, acquiring prior knowledge about the wireless channel is valuable and crucial. This prior knowledge can be expressed in various forms, such as: - Alternative #1: one or more statistical functions with variable parameters; - Alternative #2: one or several standard orthogonal bases; - Alternative #3: one or several DNNs; - and so on.

[0260] While it is possible to learn or acquire prior knowledge of a specific wireless channel between a transmitter and a receiver, in the context of cellular communications, it is more useful to learn or acquire common prior knowledge that covers multiple similar wireless channels within a specific spatial region. By doing so, the acquired prior knowledge will be shared and reused among any new wireless channels within that spatial region. In this sense, the acquired prior knowledge represents a spatial commonality that is closely related to that spatial region. A BS, as a transmitter or receiver, can have one or several common prior knowledge(s) that are related to one or more overlapping or non-overlapping spatial regions. Moreover, since different frequency bands correspond to different wavelengths, a BS can have one prior knowledge representation for one frequency band and another prior knowledge representation for another frequency band.

[0261] - Alternative #1: the BS has one common prior knowledge; - Alternative #2: the BS has several sectors, each with its own common prior knowledge; these sectors can or can not overlap; - Alternative #3: the BS has one common prior knowledge, but has several sectors, each with its own common prior knowledge; these sectors can or can not overlap; - Alternative #4: the BS has multiple pre-installed Tx precoders, each with its own common prior knowledge; - Alternative #5: the BS can have a prior knowledge that is specific to its associated UEs; it can be useful for certain fixed UEs; - and so on.

[0262] 2: Prepare data samples to learn or acquire common prior knowledge about wireless channels 1 The common spatial prior knowledge that is related to a given spatial region, as proposed in the Background section, is acquired or learned on data samples that are prepared in the following various ways: Alternative #1: acquire or learn the common prior knowledge from a set of data samples accumulated by the transmitter or receiver in history, a learning data set, or a training data set; initially, the BS, as a transmitter, must accumulate a sufficient amount of wireless channel data samples without prior knowledge, using some existing technology methods such as SRS sounding and / or CSI-RS, from which to learn the common prior knowledge.

[0263] Alternative Solution #2: Some reference units (reference UEs or sensing UEs) deployed in the area as receivers acquire or learn common prior knowledge from the feedback of data sample sets, learning datasets, or training datasets, and feed back their DL-estimated radio channels to the BS as transmitters, in order to accumulate a sufficient amount of radio channel data samples from which they learn common prior information.

[0264] Alternative Option #3: Acquire or learn common prior knowledge from sample datasets, learning datasets, or training datasets virtually generated by the digital twin; the digital twin generates virtual data samples based on 3D graphs / models or other environment-related information.

[0265] Alternative #4: Obtain or learn common prior knowledge from the sample dataset, learning dataset, or training dataset (a combination of Alternative #2 and Alternative #3); Initially, the digital twin generates an initial data sample set for the initial prior knowledge, which then triggers the first real measurement and feedback on the deployed perceived UE; The first measurement then partially replaces a portion of the data sample set with a second data sample set to obtain accurate second prior knowledge; Accurate prior knowledge triggers the second real measurement, and so on.

[0266] Alternative #5: Obtain or learn common prior knowledge from the sample dataset, learning dataset, or training dataset (a combination of Alternative #1, Alternative #2, and Alternative #3); initially, historical data and digital twins generate an initial data sample set for the initial prior knowledge, and then the initial prior knowledge triggers the first real measurement and feedback on the deployed perceived UE; then the first measurement partially replaces a portion of the data sample set with a second data sample set to obtain accurate second prior knowledge; accurate prior knowledge triggers the second real measurement, and so on.

[0267] 3: Representation and learning / acquisition of common prior knowledge based on orthonormal basis (unitary matrix) 1 The public spatial prior knowledge related to a given spatial region proposed in [the paper] can be represented in different forms: statistical, basis-based (unitary matrix), and DNN-based. In fact, existing wireless systems already use statistical functions or formulas to calculate key statistical values ​​about the wireless channel, such as coherence time, coherence frequency, and RMS delay. From [the paper]... 2 Basis-based and DNN-based representations are obtained from the prepared data samples. Generally, basis-based representations are linear, while DNN-based representations are nonlinear approximations of basis-based representations. This embodiment focuses on how to learn or acquire basis-based representations of common prior knowledge of wireless channels related to a specific spatial region.

[0268] A MIMO wireless channel is a three-dimensional tensor: × × It must be vectorized for matrix-based decomposition, such as... Figure 17 As shown.

[0269] Figure 17 Vectorize tensor MIMO channel samples If all MIMO radio channel samples are vectorized in the same dimensional order, the order itself is not important for subsequent learning performance. In this IPR, the first MIMO radio channel data sample in the tensor is... × × And vectorized into the first column vector in the order RE->Tx->Rx. ( × , The second MIMO wireless channel data sample in the tensor is × × And vectorized into a second column vector in the same order. ( × , ); and so on, until all of the tensor Each MIMO wireless channel sample is vectorized.

[0270] A sufficient quantity ( , making The vectorized MIMO wireless channel samples are arranged as follows: × matrix: (The order of the data samples is not important). Learning via reduced-rank SVD: ,in, yes × A unitary (orthogonal) matrix represents all elements related to a specific spatial region. Public (spatial) prior knowledge of each data sample.

[0271] (Note that in the above derivation, ) Set it as a column vector. Without sacrificing generality, if If set as a row vector, then -> -> -> Represents common (spatial) prior knowledge. Mathematically, the two are identical. The column vector version will be used in the following discussion. Use base ( ), each vectorized channel data sample can be projected (compressed or encoded) into an equivalent low-dimensional space, called the spectral coefficient representation: where is a 1 x 1 vector. contains all the essential information, because the spectral coefficient representation can be projected back (decompressed or decoded) into the original channel data space: .

[0272] 4: DNN-based representation and learning / acquiring common prior knowledge 2 The DNN-based prior knowledge representation in 2 is an approximation of the linear basis ( ) in . The encoding DNN ( 3 ) approximates ; while the decoding DNN ( ) approximates 3 . The output of the latent layer ( ) approximates the spectral coefficient representation of , i.e., 3 .

[0273] To approximate the rank-reduced SVD of that minimizes the MSE 3 , the DNN-based representation can be trained or learned by adjusting the neurons and in an SGD manner, with the training or learning objective set to minimize the MSE of all training data samples ( , , ).

[0274] 5: Scoring the distance between any two wireless channels According to the mathematical properties of SVD, 3 the basis represents the common (spatial) prior knowledge of all wireless channels associated with a particular spatial region. Any new MIMO wireless channel ( ) ( x , ) can be safely projected into a low-dimensional space, i.e., the spectral coefficient vector ( ) ( x 1) multiplied by the basis ( ), such that ,​ .

[0275] base It can handle any two wireless channels in an equivalent low-dimensional space. and The score or measure is based on the "distance (similarity, relevance, etc.)" between two wireless channels. This indicates that the result returns the distance between the two wireless channels ( and A score or measure of "distance", "similarity", or "relevance" between ( ). .if If it is linear, then This means that scoring or measurement can be performed equivalently in a low-dimensional spectral space. Scoring or measurement function It can be linear and simple: -Alternative Solution #1: Euclidean Function - Alternative Option #2: Inner Product -And so on.

[0276] exist 4 In the case of DNN-based representations, the score or metric function on the latent layer output will be another DNN ( ),in, It is a neuron.

[0277] 6: Generate and align pilot arrangement patterns and feedback spectral coefficients 3 base This represents the common (spatial) prior knowledge of all wireless channels associated with a specific spatial region. Any new MIMO wireless channel estimation... 1 ( () × , ) can be projected (compressed) into a low-dimensional spectral coefficient vector ( () ×1), making , .

[0278] Pilots for channel estimation In order to target 1 Channel estimation is performed on the random portion of the wireless channel. The pilot arrangement or location pattern or scheme should be clearly specified and aligned between the transmitter and receiver.

[0279] - Alternative Option #1: Use a traditional uniform pilot arrangement pattern; for example, in the 5G-NR specification, each RB has a pilot, and these pilots are continuously arranged in the RB direction; both the transmitter and receiver are specified in accordance with the 3GPP standard.

[0280] - Alternative #2: Adopt a pseudo-random pilot placement pattern, where pilot locations are generated by a function of a random seed; the pattern function and the random seed must be aligned explicitly or implicitly between the transmitter and the receiver.

[0281] - Alternative #3: Adopt a generative pilot placement pattern based on a basis ; an exemplary approach to approximate the best pattern is disclosed in [1] (with enhancements due to MIMO) and [2] (without enhancements); the generative function and the basis must be aligned explicitly or implicitly between the transmitter and the receiver, or the generative pattern must be aligned explicitly or implicitly between the transmitter and the receiver.

[0282] - Alternative #4: Adopt a pilot placement pattern output from a generative DNN; the generative DNN and its input must be aligned explicitly or implicitly between the transmitter and the receiver, or the generative pattern must be aligned explicitly or implicitly between the transmitter and the receiver.

[0283] - and so on Regardless of the generation method, the pilot placement pattern can be represented by a sampling (location or placement) matrix, each row of which has only one “1” to represent the location to be used as a pilot; the BS as the transmitter transmits pilots on the locations indicated by the sampling matrix ; The sampling (location or placement) matrix , each row of which has only one “1” to represent the location to be used as a pilot; the BS as the transmitter transmits pilots on the locations indicated by the sampling matrix ; the UE as the receiver estimates the channel coefficients (h ) on the locations indicated by the same sampling matrix In most practical cases of non-uniform placement schemes, the sampling matrix may be very sparse, i.e.

[0284] Therefore, in order to align the pilot placement scheme between the transmitter and the receiver, the system can: - Alternative #1: Adopt a pre-defined standard protocol, similar to 5G-NR; - Alternative #2: Adopt a random seed and a standardized method or a generative function of the random seed; - Alternative #3: If are available to both parties, adopt a generative function based on a basis ; - Alternative #4: Adopt a generative DNN and its input; - Alternative #4: Transmit the pilot placement matrix (or scheme) as the payload directly Channel estimation feedback ​ More interestingly, the sampling matrix can be used to "compress" the basis ( x ) into x , which is . Since is much smaller than (because ), and cannot reconstruct the basis , the can be a better substitute for . Moreover, the receiver can directly obtain the spectral coefficient vector: x ; the receiver does not need to interpolate from to ; is a better substitute for . Therefore, both the transmitter and the receiver have several alternatives to align their prior knowledge: - Alternative #1: the transmitter or the receiver sends the basis to the other party; - Alternative #2: the transmitter or the receiver sends the basis to the other party; - Alternative #3: the transmitter or the receiver sends the basis to the other party; If the DNN approximates the basis, both the transmitter and the receiver should align with 's 4 and .

[0285] To minimize the pilot and feedback overhead, both the transmitter and the receiver are preferably aligned through a random seed, a pseudo-randomly generated pilot placement function, and . In the T-MIMO scenario, the BS as the transmitter will broadcast or groupcast the common pilot placement scheme through a random seed and as the control payload in the DL, and transmit pilots according to the common pilot placement scheme. The candidate UE as the receiver will obtain the common pilot placement scheme and ; demodulate the pilots according to the pilot placement scheme, estimate the channel coefficients on the pilots, and compute the spectral coefficients according to the channel estimation on the pilots. Optionally, the UE can immediately feedback the spectral coefficients to the BS in the UL as the control payload after obtaining the spectral coefficients.

[0286] Figure 6 Exemplary process 7: Select (spatial) reference (anchoring) channel from​2 and 3 of training data samples Select ( A set of _____ wireless channels, used as spatial reference (or anchoring) channels. This set is dynamic and adaptive; it is kept updated over time; old reference channels are discarded and new reference channels are selected. Its size ( The set can be fixed or change over time. It can include several overlapping or non-overlapping subsets. Selection methods can include: -Alternative Solution #1: Given Random selection; - Alternative Option #2: Selection based on K-means, GMM and other classification algorithms.

[0287] -Alternative Option #3: Based on 5 The selection is based on the "distance" graph between the data samples mentioned in the text; for example, the "center" node with the highest degree on the graph is selected.

[0288] Figure 18 The center of the diagram is the most representative node. In any selection method, The wireless channel samples are selected from the set of spatial reference (anchored) channels: ,in, return 2 of The index of the selected data sample.

[0289] In the following discussion, unless explicitly stated otherwise, we will focus on a single set of spatial reference channels, as a single set can be easily extended to multiple sets.

[0290] 8: Compress the spatial reference channel and send it to the UE Compressed reference channel

[0291] BS, as the transmitter, should be in 7 Selected space reference channel ( A portion or the entire set of the reference channel is sent to the UE, which acts as the receiver. However, in T-MIMO scenarios, the dimension of the reference channel ( The value is too large to be sent via DL.

[0292] according to 3 The wireless channel can be equivalently projected (compressed or encoded) into a spectral coefficient vector: The projection will 7 Space reference channel in The set is compressed into ,in, yes ×1 vector. If 7 The space reference channels mentioned in the text have several sets or subsets, then all sets or subsets use... 3 The same base To compress their own space reference channels.

[0293] Preferably, the BS, acting as a transmitter, transmits the compressed space reference channel to the UE, acting as a receiver, via the DL in a broadcast, multicast, or even unicast manner. The complete set or a partial set. Optionally and preferably, when the BS acts as a transmitter, it transmits each compressed space reference channel. At that time, BS can send The former ( ) elements instead of All Each element, through sending and The instructions saved a significant amount of net DL load.

[0294] Compression basis

[0295] For BS or UE, TMIMO's ( and The dimension is too large to store all of them. Heki The system needs to compress them further.

[0296] MU-MIMO pairing is performed on an RBG basis, wherein, on an RBG comprising several consecutive RBs (each RB having 12 REs), on average... × Find a MU-MIMO pairing scheme and its precoding matrix on a MIMO channel. First, Reorder it into its tensor form: × × Multiply 3 Dimensional order: From the first RE to the... There are REs, each RE having × ( MIMO channel. If the previous The REs form the first RBG, and the first RBG is on × MIMO channel in the first : The above is averaged; then x MIMO channel is ; and so on.

[0297] Since the spectral coefficient vector can be expressed as a linear combination of the columns of the basis , linear tensorization can be ->

[0298] Express as x matrix, which is the th column of the basis tensorized and averaged over the th RBG. Therefore, there is no need to store , because it can be calculated by and . Since all reference channels share the basis , they also share : .

[0299] In addition, the QRD can also be QRD is a x standard orthogonal projection matrix and x upper triangular square matrix : By using the projection matrix to compress : where is a x square matrix.

[0300] The present application can be used to solve the pilot design problem of T-MIMO systems with a large number of transmit antenna ports and receive antenna ports and large bandwidth. The same method can also be applied to ordinary MIMO systems (for example, 5G MIMO systems) and even single antenna systems.

[0301] Through the present application, the system will have the following characteristics: > Need to know the channel state of the target environment in advance. That is, the system obtains the channel space basis of the target environment ( U ​) or similar channel state related representation. As the channel state of the target environment is known in advance, the pilot usage or overhead can be saved.

[0302] One or more pilot patterns are much sparser than one or more traditional pilot patterns (5G NR pilot design), and can be distributed non-uniformly along time-frequency-space resources.

[0303] New type of CSI acquisition mechanism, • Anchor channel data definition for MIMO communication: • Channel samples on time or frequency domain; • Projection of channel samples on subspace, obtained by BS and / or UE • Basis set of channel samples • Projection of basis set of channel samples on subspace, obtained by BS and / or UE • Similarity measure and threshold between measured channel data and anchor channel data ) need to be specified in the standard and configured by BS • The measure can be: • Alternative 1: Distance between measured channel and anchor channel • e.g. NMSE, SGCS (cosine loss between two vectors), dot product between two vectors, Euclidean distance, etc. • Alternative 2: Likelihood of measured channel being inside or close to the subspace of spatial reference channel • e.g. probability density function, log PDF, dlog PDF (gradient of log PDF), likelihood ratio between two distributions, etc.

[0304] • Alternative 3: Scoring function ) • The similarity measure can be the distance between measured channel and anchor channel in a given space • When the value of the similarity measure is less than a pre-defined threshold, it indicates that the measured channel can be represented by the anchor channel data.

[0305] Figure 19 Embodiment 1 Figure 20 Embodiment 2 Figure 21 Embodiment 3 [1] PCT / CN2022 / 126878 A Method And Apparatus of Channel Estimation for MIMO System [2] PCT / CN2022 / 094688 An Method to Design Transmission Dimensionality and Reference Signal Placement Scheme for a Dimensional Transmission Channel by its Prior Structures [3] Column rotation QRD: https: / / en.wikipedia.org / wiki / QR_decomposition [4] SVD: https: / / en.wikipedia.org / wiki / QR_decomposition [5] Pseudo-inverse: https: / / en.wikipedia.org / wiki / Moore%E2%80%93Penrose_inverse [6] MSE: https: / / en.wikipedia.org / wiki / Mean_squared_error [7] Condition number of a matrix: https: / / en.wikipedia.org / wiki / Condition_number 6G system structure 2.3 6G basic module structure One or more steps of the example methods provided herein can be performed by Figure 22 the corresponding units or modules shown. Figure 22Units or modules in the ED 110, T-TRP 170, or NT-TRP 172, etc. are shown. For example, a signal can be transmitted by a transmitting unit or a transmitting module. For example, a signal can be transmitted by a transmitting unit or a transmitting module. A signal can be received by a receiving unit or a receiving module. A signal can be processed by a processing unit or a processing module. Other steps can be performed by an artificial intelligence (AI) module or a machine learning (ML) module. The corresponding units or modules can be implemented using hardware, one or more components or devices executing software, or a combination thereof. For example, one or more of these units or modules can be an integrated circuit, such as a programmed FPGA, GPU, ASIC. It should be understood that in the case of using software to implement a module for processing by a processor, for example, these modules can be retrieved all or in part as needed by the processor, processed individually or together in a single or multiple instances, and these modules themselves can include instructions for further deployment and instantiation.

[0306] Other details about the ED 110, T-TRP 170, and NT-TRP 172 are known to those skilled in the art. Therefore, these details are omitted here.

[0307] A non-exhaustive list of possible units or possible configurable parameters or MIMO systems in some embodiments includes: Panel: a unit of an antenna group or an antenna array or an antenna subarray, whose Tx or Rx beam can be controlled independently.

[0308] Beam: a beam is formed by performing amplitude and / or phase weighting on data transmitted or received by at least one antenna port, and can also be formed by using other methods, such as by adjusting related parameters of antenna units. A beam can include a Tx beam and / or an Rx beam. A transmit beam represents the distribution of signal strength in different directions in space after the signal is transmitted by an antenna. A receive beam represents the distribution of signal strength in different directions in space of a wireless signal received from an antenna. Beam information can be a beam identifier, or an antenna port identifier, or a CSI-RS resource identifier, or an SSB resource identifier, or an SRS resource identifier, or other reference signal resource identifier.

[0309] 1 In IPR, the estimated value is capped.

Claims

1. A communication method, characterized in that, include: Obtain information from K reference channels, wherein the K reference channels are related to an environmental parameter set, and K is a positive integer; Obtain the channel estimate for the first channel; Information about the first channel is transmitted based on a similarity metric, wherein the similarity metric is calculated based on the information from the K reference channels and the channel estimate of the first channel.

2. The method according to claim 1, characterized in that, The method further includes: Obtain public information, wherein the public information is used to determine the information of the first channel; The similarity metric is calculated based on the information from the K reference channels and the channel estimate of the first channel, including: The similarity metric is calculated based on the public information, the information of the K reference channels, and the channel estimate of the first channel.

3. The method according to claim 1 or 2, characterized in that, The information of the first channel includes the index of the first reference channel among the K reference channels, and the similarity metric between the first channel and the first reference channel is less than or equal to a threshold.

4. The method according to claim 3, characterized in that, The similarity metric indicates the distance between the first channel and the first reference channel.

5. The method according to claim 3, characterized in that, The similarity metric indicates the likelihood that the first channel is within or near the subspace of the first reference channel.

6. The method according to claim 3, characterized in that, The similarity metric indicates a score value, which represents the distance between the first channel and the first reference channel and is calculated using a scoring function.

7. The method according to any one of claims 1 to 6, characterized in that, Each of the K reference channels, or the first channel, is an uplink channel, a downlink channel, a sidelink channel, a satellite-to-earth link channel, or a channel between two integrated access backhaul (IAB) nodes.

8. A communication method, characterized in that, include: Information from a first channel is received, wherein the information from the first channel is determined based on a similarity metric, which is calculated based on information from K reference channels and a channel estimate of the first channel, wherein the K reference channels are related to a set of environmental parameters.

9. The method according to claim 8, characterized in that, The information of the first channel includes the index of the first reference channel among the K reference channels, and the similarity metric between the first channel and the first reference channel is less than or equal to a threshold.

10. The method according to claim 9, characterized in that, The first information indicates the distance between the first channel and the first reference channel.

11. The method according to claim 9, characterized in that, The first information indicates the likelihood that the first channel is within or near the subspace of the first reference channel.

12. The method according to claim 9, characterized in that, The first information indicates a score value, which represents the distance between the first channel and the first reference channel and is calculated using a scoring function.

13. The method according to any one of claims 8 to 12, characterized in that, Each of the K reference channels, or the first reference channel, is an uplink channel, a downlink channel, a sidelink channel, a satellite-to-earth link channel, or a channel between two integrated access backhaul (IAB) nodes.

14. A communication device, characterized in that, The communication device includes a processor for executing one or more instructions stored in a memory, such that the communication device implements the method according to any one of claims 1 to 7 or implements the method according to any one of claims 8 to 13.

15. The communication device according to claim 14, characterized in that, The communication device includes the memory.

16. The communication device according to claim 14 or 15, characterized in that, The communication device includes a communication interface, which is used to input and / or output information or data.

17. A communication device, characterized in that, The communication device includes functions or units that perform the method according to any one of claims 1 to 7 or the method according to any one of claims 8 to 13.

18. A communication device, characterized in that, The communication device includes a circuit and a communication interface, the communication interface being used to receive information and / or data to be processed by the circuit and to send the information and / or data to the circuit; the circuit is used to perform the method according to any one of claims 1 to 7 or to perform the method according to any one of claims 8 to 13.

19. The communication device according to claim 18, characterized in that, The communication interface is also used to output the information and / or data processed by the circuit.

20. A communication system, characterized in that, It includes a transmitting device and a receiving device, wherein the receiving device performs the method according to any one of claims 1 to 7, and the transmitting device performs the method according to any one of claims 8 to 13.

21. A computer-readable storage medium, characterized in that, It includes one or more instructions, which, when executed on a computer, cause the computer to perform the method according to any one of claims 1 to 7 or the method according to any one of claims 8 to 13.

22. A computer program product, characterized in that, It includes one or more instructions, which, when executed on a computer, cause the computer to perform the method according to any one of claims 1 to 7 or the method according to any one of claims 8 to 13.

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