Communication device and method performed by the same and storage medium

Abstract

The present disclosure relates to a 5G communication system or a 6G communication system for supporting higher data rates than 4G communication systems such as Long Term Evolution (LTE). The present disclosure provides a communication device and a method performed by the same and a storage medium. A method performed by a communication device include: receiving first signals from respective resource elements (REs), and grouping the respective REs, wherein the first signals are associated with a plurality of antenna ports, each group includes at least one RE for carrying a reference signal; obtaining feature information of each group based on the reference signal on the at least one RE for carrying the reference signal in each group; combining the first signals to obtain a second signal based on the feature information of each group; performing signal detection based on the second signal.

Description

COMMUNICATION DEVICE AND METHOD PERFORMED BY THE SAME AND STORAGE MEDIUM

[0001] The present disclosure relates to a communication field and specifically, to a communication device and a method performed by the same, and a computer readable storage medium.

[0002] Considering the development of wireless communication from generation to generation, the technologies have been developed mainly for services targeting humans, such as voice calls, multimedia services, and data services. Following the commercialization of 5th-generation (5G) communication systems, it is expected that the number of connected devices will exponentially grow. Increasingly, these will be connected to communication networks. Examples of connected things may include vehicles, robots, drones, home appliances, displays, smart sensors connected to various infrastructures, construction machines, and factory equipment. Mobile devices are expected to evolve in various form-factors, such as augmented reality glasses, virtual reality headsets, and hologram devices. In order to provide various services by connecting hundreds of billions of devices and things in the 6th-generation (6G) era, there have been ongoing efforts to develop improved 6G communication systems.

[0003] 6G communication systems, which are expected to be commercialized around 2030, have various significantly improved metrics compared to the current 5G communication systems. The peak data rate will reach at least 50 Gbit / s, and the user experienced data rate will reach at least 300 Mbit / s, the air-interface latency will be less than 1 ms, and the air-interface reliability will reach . In addition to the above basic communication metrics, the 6G communication systems will also have sensing capabilities, AI-related capabilities, better security, better interoperability and better sustainability.

[0004] In order for the 6G communication systems to fulfill the above metrics, more advanced air-interface technologies and network technologies need to be developed. The evolution of extreme Multiple Input Multiple Output (extreme MIMO) has been already under consideration, including the use of ultra-large scale antenna arrays, the development and evolution of distributed antenna systems, and the design of MIMO air-interface algorithms assisted by Artificial Intelligence (AI). This technology enables higher spectral efficiency, greater coverage, and precise localization and sensing capabilities. Additionally, for technologies that contribute to improve high-frequency band coverage, including metamaterial-based lenses and antennas, new antenna architectures, and reconfigurable intelligent surface (RIS), etc., they also need to be better evolved and developed. In order to meet some of newly added functions of the 6G communication systems, new technologies need to be developed in the terms of network energy saving, air-interface security, and network security, meanwhile the feasibility of fusion technologies such as Integrated Sensing and Communication, needs to be studied.

[0005] Moreover, in order to improve the spectral efficiency and the overall network performances, the following technologies have been developed for 6G communication systems: a full-duplex technology for enabling an uplink transmission and a downlink transmission to simultaneously use the same frequency resource at the same time; a network technology for utilizing satellites, high-altitude platform stations (HAPS), and the like in an integrated manner; an improved network structure for supporting mobile base stations and the like and enabling network operation optimization and automation and the like; a dynamic spectrum sharing technology via collision avoidance based on a prediction of spectrum usage; an use of artificial intelligence (AI) in wireless communication for improvement of overall network operation by utilizing AI from a designing phase for developing 6G and internalizing end-to-end AI support functions; and a next-generation distributed computing technology for overcoming the limit of user equipment (UE) computing ability through reachable super-high-performance communication and computing resources (such as mobile edge computing (MEC), clouds, and the like) over the network. In addition, through designing new protocols to be used in 6G communication systems, developing mechanisms for implementing a hardware-based security environment and safe use of data, and developing technologies for maintaining privacy, attempts to strengthen the connectivity between devices, optimize the network, promote softwarization of network entities, and increase the openness of wireless communications are continuing.

[0006] It is expected that research and development of 6G communication systems in hyper-connectivity, including person to machine (P2M) as well as machine to machine (M2M), will allow the next hyper-connected experience. Particularly, it is expected that services such as truly immersive extended reality (XR), high-fidelity mobile hologram, and digital replica could be provided through 6G communication systems. In addition, services such as remote surgery for security and reliability enhancement, industrial automation, and emergency response will be provided through the 6G communication system such that the technologies could be applied in various fields such as industry, medical care, automobiles, and home appliances.

[0007] The present disclosure relates to a communication field and specifically, to a communication device and a method performed by the same, and a computer readable storage medium.

[0008] According to a first aspect of an embodiment of the present disclosure, there is provided a method performed by a communication device, the method includes: receiving first signals of respective resource elements (REs), and grouping the respective REs, wherein the first signals are associated with a plurality of antenna ports, each group includes at least one RE for carrying a reference signal; obtaining feature information of each group based on the reference signal on the at least one RE for carrying the reference signal in each group; combining the first signals to obtain a second signal based on the feature information of each group; performing signal detection based on the second signal.

[0009] Alternatively, the feature information of each group includes statistical information obtained based on channel state information associated with the reference signal of the group; or the feature information of each group includes feature information obtained by a neural network based on the channel state information associated with the reference signal of the group.

[0010] Alternatively, the grouping of the respective REs includes grouping the respective REs by at least one of following grouping methods:

[0011] grouping the respective REs based on frequency domain and / or time domain information of the respective REs;

[0012] grouping the respective REs based on a predefined grouping table;

[0013] selecting at least one RE, as a group of REs after grouping, based on a predefined RE selection rule,

[0014] wherein, for each grouping method, REs between respective groups after the grouping are different, partially the same, or all the same.

[0015] Alternatively, the obtaining of the feature information of each group based on the reference signal on the at least one RE for carrying the reference signal in each group includes: selecting RE(s) from each group as sample(s) and obtaining the feature information of the group based on the sample(s), wherein the sample(s) include at least a part of REs in the at least one RE for carrying the reference signal; wherein the RE(s) is / are selected as the sample(s) from each group using one of following sample selection methods: selecting all of REs in the group as the sample(s); selecting at least a part of the REs in the group as the sample(s) based on frequency domain and / or time domain information of the REs in the group; selecting at least a part of the REs from the group as the sample(s) based on a predefined selection table; selecting at least one RE from the group as the sample(s) based on a predefined RE selection rule.

[0016] Alternatively, the obtaining of the feature information of the group based on the sample(s) includes: obtaining channel state information corresponding to the sample(s); obtaining the feature information of the group based on the channel state information corresponding to the sample(s).

[0017] Alternatively, the obtaining of the channel state information corresponding to the sample(s) includes: obtaining the channel state information corresponding to the sample(s) based on a reference signal corresponding to the sample(s); or using pre-stored channel state information associated with the sample(s) as the channel state information corresponding to the sample(s).

[0018] Alternatively, the obtaining of the feature information of the group based on the channel state information corresponding to the sample(s) includes: obtaining the feature information of the group by performing a statistical operation and / or an algebraic operation on the channel state information corresponding to the sample(s); or obtaining the feature information of the group using a first neural network based on the channel state information corresponding to the sample(s) and auxiliary information, wherein the auxiliary information includes at least one of: information related to time domain resource(s) corresponding to the sample(s), information related to frequency domain resource(s) corresponding to the sample(s), and information related to a channel environment of the sample(s).

[0019] Alternatively, the information related to the channel environment of the sample(s) includes at least one of: a signal-to-noise ratio, multipath delay expansion information of a channel, and correlation time information of the channel.

[0020] Alternatively, the method further includes: updating the grouping based on at least one of the first signals, the second signal, a block error rate (BLER), a bit error rate (BER), and a signal-to-noise ratio.

[0021] Alternatively, the method further includes at least one of: grouping the respective REs based on at least one of the first signals, the second signal, a block error rate (BLER), a bit error rate (BER), and a signal-to-noise ratio; determining a number of the sample(s) based on at least one of the first signals, the second signal, and a result of performing the signal detection; determining a type of the feature information based on at least one of the first signals, the second signal, and the result of performing the signal detection; determining a sample selection method based on at least one of the first signals, the second signal, and the result of performing the signal detection.

[0022] Alternatively, the result of performing the signal detection includes at least one of: the BLER, the BER, the signal-to-noise ratio.

[0023] According to a second aspect of an embodiment of the present disclosure, there is provided a communication device, the communication device includes: a transceiver, and a processor coupled to the transceiver and configured to perform the above method.

[0024] According to a second aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium storing instructions that, when run by at least one processor, cause the at least one processor to perform the above method.

[0025] According to the technical solutions provided by the embodiments of the present disclosure, since after receiving the first signals of respective resource elements (REs), the respective REs are grouped and the feature information of each group is obtained, the first signals are combined based on the feature information of each group to obtain the second signal, so that the number of antenna ports associated with the second signal is less than the number of antenna ports associated with the first signals, thus not only the computational burden and processing delay during subsequent signal detection may be reduced and the signal detection efficiency may be improved, but also by allowing each RE in each group of REs to share the feature information of the group of REs (i.e., share consistent feature information), there is no need to calculate the feature information of respective REs separately and then perform the combining of the first signals of respective REs based on it, and thus the computational amount and complexity of performing signal combining before signal detection may be reduced, and the overall computational burden of the communication device is effectively reduced, which facilitates realization of a lightweight and low-cost communication device and at the same time, objectively avoids inaccuracy of the feature information of an individual RE from adversely affecting the subsequent combining and detection, and facilitates the improvement of the performance stability of signal detection.

[0026] It should be understood that the above general description and the detailed descriptions that follow are exemplary and explanatory only and do not limit the present disclosure.

[0027] The drawings herein incorporated into the specification form part of the specification, show example embodiments that conform to the present disclosure, and are used together with the specification to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0028] FIG. 1 illustrates an example wireless network according to an embodiment of the present disclosure.

[0029] FIG. 2 illustrates an example base station according to an embodiment of the present disclosure.

[0030] FIG. 3 illustrates an example user equipment according to an embodiment of the present disclosure.

[0031] FIG. 4 is a flowchart illustrating a method performed by a communication device according to an embodiment of the present disclosure.

[0032] FIG. 5 is a schematic diagram illustrating a neural network according to an embodiment of the present disclosure.

[0033] FIG. 6 is a block diagram illustrating a communication device according to an embodiment of the present disclosure.

[0034] Before undertaking the DETAILED DESCRIPTION below, it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The term “couple” and its derivatives refer to any direct or indirect communication between two or more elements, whether those elements are in physical contact with one another. The terms “transmit,” “receive,” and “communicate,” as well as derivatives thereof, encompass both direct and indirect communication. The terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation. The term “or” is inclusive, meaning and / or. The phrase “associated with,” as well as derivatives thereof, means to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like. The term “controller” means any device, system or part thereof that controls at least one operation. Such a controller may be implemented in hardware or a combination of hardware and software and / or firmware. The functionality associated with any particular controller may be centralized or distributed, whether locally or remotely. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. For example, “at least one of: A, B, and C” includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A and B and C. Likewise, the term “set” means one or more. Accordingly, a set of items can be a single item or a collection of two or more items.

[0035] Moreover, various functions described below can be implemented or supported by one or more computer programs, each of which is formed from computer readable program code and embodied in a computer readable medium. The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer readable program code. The phrase “computer readable program code” includes any type of computer code, including source code, object code, and executable code. The phrase “computer readable medium” includes any type of medium capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a compact disc (CD), a digital video disc (DVD), or any other type of memory. A “non-transitory” computer readable medium excludes wired, wireless, optical, or other communication links that transport transitory electrical or other signals. A non-transitory computer readable medium includes media where data can be permanently stored and media where data can be stored and later overwritten, such as a rewritable optical disc or an erasable memory device.

[0036] Definitions for other certain words and phrases are provided throughout this patent document. Those of ordinary skill in the art should understand that in many if not most instances, such definitions apply to prior as well as future uses of such defined words and phrases.

[0037] The figures included herein, and the various embodiments used to describe the principles of the present disclosure are by way of illustration only and should not be construed in any way to limit the scope of the disclosure. Further, those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged wireless communication system.

[0038] FIGS. 1-3 below describe various embodiments of the present disclosure implemented in wireless communication systems. The descriptions of FIGS. 1-3 are not meant to imply physical or architectural limitations to the manner in which different embodiments may be implemented. Different embodiments of the present disclosure may be implemented in any suitably-arranged communication system.

[0039] FIG. 1 illustrates an example wireless network according to an embodiment of the present disclosure. The embodiment of the wireless network shown in FIG. 1 is for illustration only. Other embodiments of the wireless network 100 could be used without departing from the scope of the present disclosure.

[0040] As shown in FIG. 1, the wireless network includes a base station (next generation nodeB, gNB or gNodeB) 101, a gNB 102, and a gNB 103. The gNB 101 communicates with the gNB 102 and the gNB 103. The gNB 101 also communicates with at least one network 130, such as the Internet, a proprietary Internet Protocol (IP) network, or other data network.

[0041] The gNB 102 provides wireless broadband access to the network 130 for a first plurality of user equipments (UEs) within a coverage area 120 of the gNB 102. The first plurality of UEs includes a UE 111, which may be located in a small business; a UE 112, which may be located in an enterprise (E); a UE 113, which may be located in a WiFi hotspot (HS); a UE 114, which may be located in a first residence (R1); a UE 115, which may be located in a second residence (R2); and a UE 116, which may be a mobile device (M), such as a cell phone, a wireless laptop, a wireless personal digital assistant (PDA), or the like. The gNB 103 provides wireless broadband access to the network 130 for a second plurality of UEs within a coverage area 125 of the gNB 103. The second plurality of UEs includes the UE 115 and the UE 116, as well as subscriber stations (SS, for example, UEs) 117, 118 and 119. In some embodiments, one or more of the gNBs 101-103 may communicate with each other and with the UEs 111-116 using existing wireless communication techniques, and one or more of the UE 111-119 may communicate directly with each other (e.g., UEs 117-119) using other existing or proposed wireless communication techniques.

[0042] Depending on the network type, the term “base station” or “BS” can refer to any component (or collection of components) configured to provide wireless access to a network, such as transmit point (TP), transmit-receive point (TRP), an enhanced (or “evolved”) base station (eNodeB or eNB), a 5G base station (gNB), a macrocell, a femtocell, a wireless fidelity (WiFi) access point (AP), or other wirelessly enabled devices. Base stations may provide wireless access in accordance with one or more wireless communication protocols, e.g., 3GPP 5G New Radio (NR), Long Term Evolution (LTE), LTE Advanced (LTE-A), high speed packet access (HSPA), Wi-Fi 802.11a / b / g / n / ac, etc. For the sake of convenience, the various names for a base station-type apparatus and functionality are used interchangeably in this patent document to refer to network infrastructure components that provide wireless access to remote terminals. Also, depending on the network type, the term “user equipment” (UE) can refer to any component such as a mobile station (MS), subscriber station (SS), remote terminal, wireless terminal, receive point, or user device. For the sake of convenience, the various names for a user equipment-type device and functionality are used interchangeably in this patent document to refer to remote wireless equipment that wirelessly accesses a BS, whether the UE is a mobile device (such as a mobile telephone or smartphone) or is normally considered a stationary device (such as a desktop computer or vending machine).

[0043] Dotted lines show the approximate extents of the coverage areas 120 and 125, which are shown as approximately circular for the purposes of illustration and explanation only. It should be clearly understood that the coverage areas associated with gNBs, such as the coverage areas 120 and 125, may have other shapes, including irregular shapes, depending upon the configuration of the gNBs and variations in the radio environment associated with natural and man-made obstructions.

[0044] As described in more detail below, one or more of the UEs 111-119 include circuitry, programing, or a combination thereof. In certain embodiments, and one or more of the gNBs 101-103 includes circuitry, programing, or a combination thereof.

[0045] Although FIG. 1 illustrates one example of a wireless network, various changes may be made to FIG. 1. For example, the wireless network could include any number of gNBs and any number of UEs in any suitable arrangement. Also, the gNB 101 could communicate directly with any number of UEs and provide those UEs with wireless broadband access to the network 130. Similarly, each gNB 102-103 could communicate directly with the network 130 and provide UEs with direct wireless broadband access to the network 130. Further, the gNBs 101, 102, and / or 103 could provide access to other or additional external networks, such as external telephone networks or other types of data networks.

[0046] FIG. 2 illustrates an example base station according to an embodiment of the present disclosure. The embodiment of the gNB 102 illustrated in FIG. 2 is for illustration only, and the gNBs 101 and 103 of FIG. 1 could have the same or similar configuration. However, gNBs come in a wide variety of configurations, and FIG. 2 does not limit the scope of the present disclosure to any particular implementation of a gNB.

[0047] As shown in FIG 2, the gNB 102 includes multiple antennas 200a-200n, multiple radio frequency (RF) transceivers 201a-201n, transmit (TX) processing circuitry 203, and receive (RX) processing circuitry 204. The gNB 102 also includes a controller / processor 205, a memory 206, and a backhaul or network interface 207.

[0048] The RF transceivers 201a-201n receive, from the antennas 200a-200n, incoming RF signals, such as signals transmitted by UEs in the network 100. The RF transceivers 201a-201n down-convert the incoming RF signals to generate intermediate frequency (IF) or baseband signals. The IF or baseband signals are sent to the RX processing circuitry 204, which generates processed baseband signals by filtering, decoding, and / or digitizing the baseband or IF signals. The RX processing circuitry 204 transmits the processed baseband signals to the controller / processor 205 for further processing.

[0049] The TX processing circuitry 203 receives analog or digital data (such as voice data, web data, electronic mail, or interactive video game data) from the controller / processor 205. The TX processing circuitry 203 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate processed baseband or IF signals. The RF transceivers 201a-201n receive the outgoing processed baseband or IF signals from the TX processing circuitry 203 and up-converts the baseband or IF signals to RF signals that are transmitted via the antennas 201a-201n.

[0050] The controller / processor 205 can include one or more processors or other processing devices that control the overall operation of the gNB 102. For example, the controller / processor 205 could control the reception of forward channel signals and the transmission of reverse channel signals by the RF transceivers 201a-201n, the RX processing circuitry 204, and the TX processing circuitry 203 in accordance with well-known principles. The controller / processor 205 could support additional functions as well, such as more advanced wireless communication functions.

[0051] For instance, the controller / processor 205 could support beam forming or directional routing operations in which outgoing signals from multiple antennas 200a-200n are weighted differently to effectively steer the outgoing signals in a desired direction. Any of a wide variety of other functions could be supported in the gNB 102 by the controller / processor 205.

[0052] The controller / processor 205 is also capable of executing programs and other processes resident in the memory 206, such as an operating system (OS). The controller / processor 205 can move data into or out of the memory 206 as required by an executing process.

[0053] The controller / processor 205 is also coupled to the backhaul or network interface 207. The backhaul or network interface 207 allows the gNB 102 to communicate with other devices or systems over a backhaul connection or over a network. The interface 207 could support communications over any suitable wired or wireless connection(s). For example, when the gNB 102 is implemented as part of a cellular communication system (such as one supporting 5G, LTE, or LTE-A), the interface 207 could allow the gNB 102 to communicate with other gNBs over a wired or wireless backhaul connection. When the gNB 102 is implemented as an access point, the interface 207 could allow the gNB 102 to communicate over a wired or wireless local area network or over a wired or wireless connection to a larger network (such as the Internet). The interface 207 includes any suitable structure supporting communications over a wired or wireless connection, such as an Ethernet or RF transceiver.

[0054] The memory 206 is coupled to the controller / processor 205. Part of the memory 206 could include a random access memory (RAM), and another part of the memory 206 could include a Flash memory or other read only memory (ROM).

[0055] Although FIG. 2 illustrates one example of gNB 102, various changes may be made to FIG. 2. For example, the gNB 102 could include any number of each component shown in FIG. 2. As a particular example, an access point could include a number of interfaces 207, and the controller / processor 205 could support routing functions to route data between different network addresses. As another particular example, while shown as including a single instance of TX processing circuitry 203 and a single instance of RX processing circuitry 204, the gNB 102 could include multiple instances of each (such as one per RF transceiver). Also, various components in FIG. 2 could be combined, further subdivided, or omitted and additional components could be added according to particular needs.

[0056] FIG. 3 illustrates an example user equipment according to an embodiment of the present disclosure. The embodiment of the UE 116 illustrated in FIG. 3 is for illustration only, and the UEs 111-115 and 117-119 of FIG. 1 could have the same or similar configuration. However, UEs come in a wide variety of configurations, and FIG. 3 does not limit the scope of the present disclosure to any particular implementation of a UE.

[0057] As shown in FIG. 3, the UE 116 includes an antenna 301, a radio frequency (RF) transceiver 302, TX processing circuitry 303, a microphone 304, and receive (RX) processing circuitry 305. The UE 116 also includes a speaker 306, a controller or processor 307, an input / output (I / O) interface (IF) 308, an input device 309, a touchscreen display 310, and a memory 311. The memory 311 includes an OS 312 and one or more applications 313.

[0058] The RF transceiver 302 receives, from the antenna 301, an incoming RF signal transmitted by a gNB of the network 100. The RF transceiver 302 down-converts the incoming RF signal to generate an IF or baseband signal. The IF or baseband signal is sent to the RX processing circuitry 305, which generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband or IF signal. The RX processing circuitry 305 transmits the processed baseband signal to the speaker 306 (such as for voice data) or to the processor 307 for further processing (such as for web browsing data).

[0059] The TX processing circuitry 303 receives analog or digital voice data from the microphone 304 or other outgoing baseband data (such as web data, e-mail, or interactive video game data) from the processor 307. The TX processing circuitry 303 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. The RF transceiver 302 receives the outgoing processed baseband or IF signal from the TX processing circuitry 303 and up-converts the baseband or IF signal to an RF signal that is transmitted via the antenna 301.

[0060] The processor 307 can include one or more processors or other processing devices and execute the OS 312 stored in the memory 311 in order to control the overall operation of the UE 116. For example, the processor 307 could control the reception of forward channel signals and the transmission of reverse channel signals by the RF transceiver 302, the RX processing circuitry 305, and the TX processing circuitry 303 in accordance with well-known principles. In some embodiments, the processor 307 includes at least one microprocessor or microcontroller.

[0061] The processor 307 is also capable of executing other processes and programs resident in the memory 311, such as processes for CSI reporting on uplink channel. The processor 307 can move data into or out of the memory 311 as required by an executing process. In some embodiments, the processor 307 is configured to execute the applications 313 based on the OS 312 or in response to signals received from gNBs or an operator. The processor 307 is also coupled to the I / O interface 308, which provides the UE 116 with the ability to connect to other devices, such as laptop computers and handheld computers. The I / O interface 308 is the communication path between these accessories and the processor 307.

[0062] The processor 307 is also coupled to the touchscreen display 310. The user of the UE 116 can use the touchscreen display 310 to enter data into the UE 116. The touchscreen display 310 may be a liquid crystal display, light emitting diode display, or other display capable of rendering text and / or at least limited graphics, such as from web sites.

[0063] The memory 311 is coupled to the processor 307. Part of the memory 311 could include RAM, and another part of the memory 311 could include a Flash memory or other ROM.

[0064] Although FIG. 3 illustrates one example of UE 116, various changes may be made to FIG. 3. For example, various components in FIG. 3 could be combined, further subdivided, or omitted and additional components could be added according to particular needs. As a particular example, the processor 307 could be divided into multiple processors, such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs). Also, while FIG. 3 illustrates the UE 116 configured as a mobile telephone or smartphone, UEs could be configured to operate as other types of mobile or stationary devices.

[0065] Exemplary embodiments of the present disclosure are further described below in conjunction with the accompanying drawings.

[0066] The text and accompanying drawings are provided as examples only to assist reader in understanding the present disclosure. They are not intended to and should not be construed as limiting the scope of the present disclosure in any way. Although certain embodiments and examples have been provided, based on what is disclosed herein, it will be apparent to those skilled in the art that changes may be made to the illustrated embodiments and examples without departing from the scope of the present disclosure.

[0067] In wireless communication systems, transmission of information (e.g., information transmission in a Physical Downlink Control Channel (PDCCH), a Physical Downlink Shared Channel (PDSCH), a Physical Uplink Shared Channel (Physical Uplink Shared Channel (PUSCH), a Physical Uplink Control Channel (PUCCH), etc.) will occur on at least one Resource Element (RE) (e.g., multiple time points, multiple frequency points, multiple antennas, and various combinations of the time, the frequency, and the antenna) of a physical resource. The physical resource is a resource entity in a communication system that may be used to perform signaling, and may be, for example, a time domain physical resource, a frequency domain physical resource, and an antenna domain physical resource. A state of a RE occupied by a signal has a comprehensive effect on an amplitude and a phase of the signal transmitted over an entire wireless communication link.

[0068] In order to meet a demand for higher rate wireless data transmission, a communication device (e.g., a base station) uses a larger antenna scale, a wider bandwidth, and a higher modulation order, resulting in a larger scale of received data to be processed by the communication device. In contrast, an amount of data that needs to be processed within a limited time delay in a subsequent signal detection process also grows rapidly, which in turn poses a serious challenge to a computational capability of a processor of the communication device. For example, in a LTE / NR system, a data amount of received signals by the base station in each slot is about N_f*N_ofdm*N_RE*N_bit*2, where N_RE is the number of receiving antenna ports, N_bit is the number of quantization bits, N_f is the number of subcarriers, and N_ofdm is the number of OFDM symbols. If the base station is upgraded from with a 4-port antenna to with a 64-port antenna, an amount of data to be processed will be upgraded to 16 times of the original data amount. On one hand, for cost considerations, existing processors have limited computing power, and thus they require more processing time to complete large-scale data, resulting in a surge in transmission delay, causing serious system performance loss or even system collapse. On the other hand, devices with high-speed computing for large-scale data are currently very expensive and it may be different to afford a large-scale deployment of base stations using such devices. Therefore, as the user demand for transmission rate continues to increase and the amount of data continues to grow, there is a need to compress received signals before signal detection to accommodate the increasing amount of data.

[0069] Signal combing among antenna ports reduces, by a method of combining signals on receiving antennas, the number of antenna ports corresponding to signals to be detected, i.e., performs antenna port compression, which reduces the amount of computation in performing signal detection. However, the current scheme of the signal combing among antenna ports requires additional computational steps and is sensitive to a channel state, so there is still a need for further optimization.

[0070] For example, the current scheme of the signal combing among antenna ports is highly sensitive to a channel environment in which a system is located and lacks performance stability, it may lead to serious performance degradation in a case of high noise, high interference, and low channel estimation accuracy, or even lead to failure of subsequent signal detection; for another example, the current scheme of signal combing among antenna ports computes information required to perform signal combing among antenna ports for a signal on each RE in the received signals respectively, which leads to additional computational burden and processing delay, and even when a bandwidth of the received signal is large, the additional computational burden generated by performing signal combing among antenna ports exceeds the computational burden reduced by the reduction in the number of antenna ports, thereby leading to an increase rather than a decrease in the overall complexity of the signal receiving process.

[0071] In response, the present disclosure proposes a method performed by a communication device to solve at least one of the above problems.

[0072] FIG. 4 is a flowchart illustrating a method performed by a communication device according to an embodiment of the present disclosure.

[0073] According to an embodiment, the communication device may be any device capable of acquiring a signal and performing signal detection, for example, the communication device may be a base station, but not limited to that, for example, it may even be a user equipment having a large scale of antenna ports, and so on. In the following, the method according to the embodiment of the present disclosure is described by taking an example that the communication device is a base station.

[0074] As shown in FIG. 4, at step S410, first signals from respective resource elements (REs) are received, and the respective REs are grouped, wherein the first signals are associated with a plurality of antenna ports, and each group includes at least one RE for carrying a reference signal; at step S420, feature information of each group is obtained based on the reference signal on the at least one RE for carrying the reference signal in each group; at step S430, the first signals are combined to obtain a second signal based on the feature information of each group; at step S440, signal detection is performed based on the second signal.

[0075] According to the above method, since after receiving the first signals of respective resource elements (REs), the respective REs are grouped and the feature information of each group is obtained, the first signals of respective REs in the group are combined based on the feature information of each group to obtain the second signal, and the first signals are associated with the plurality of antenna ports, so that the number of antenna ports associated with the second signal after combing the first signals may be less than the number of antenna ports associated with the first signals, thus not only the computational burden and processing delay during subsequent signal detection may be reduced and the signal detection efficiency may be improved, but also by allowing each RE in each group of REs to share the feature information of the group of REs (i.e., share consistent feature information), there is no need to calculate the feature information of each RE separately and then perform the combining of the first signals based on it, and thus the computational amount and complexity of performing signal combining before signal detection may be reduced, the overall computational burden of the communication device is effectively reduced, which facilitates realization of a lightweight and low-cost communication device and at the same time, objectively avoids inaccuracy of the feature information of an individual RE from adversely affecting the subsequent combining and detection, and facilitates the improvement of the performance stability of signal detection.

[0076] Hereinafter, contents involved in the above steps S410 to S440 are described in detail, respectively.

[0077] Referring to FIG. 4, at step S410, the first signals of respective resource elements (REs) are received, and the respective REs are grouped. According to an embodiment, the first signals are associated with a plurality of antenna ports, e.g., the first signals may be signals received based on the plurality of antenna ports. For example, the first signals may be either signals themself received through the plurality of antenna ports or signals obtained by performing processing on the signals received at the plurality of antenna ports. For example, the first signals may be received from a plurality of antenna ports of a base station in step S410. According to an embodiment, since the first signals are carried by the REs and the first signals are associated with the plurality of antenna ports, the REs carrying the first signals are also associated with the plurality of antenna ports.

[0078] In some examples, the first signals may be signals received through the antenna ports that carry data information transmitted by a transmitting terminal, and may include at least one of: an RF signal received by the antenna port of the base station; a digital-domain signal after digital-to-analog conversion of the foregoing signal; and a signal obtained after specific processing (e.g., amplification, denoising, filtering, whitening, fast Fourier transform, etc.) of the foregoing signal. In the present application, "at least one" may denote one, or may denote a combination of two or more. In some examples, the first signals includes a digital-domain wireless signal received at the antenna ports of the base station, or a denoised signal obtained by passing the digital-domain wireless signal through a bandpass filter. In other examples, the first signals include a signal obtained by multiplying the signal received by the antenna ports of the base station by a certain spatial basis matrix, such as an eigenvector matrix of channel or a spatial Discrete Fourier Transform (DFT) matrix. In other examples, the first signals include a signal obtained by whitening the signal received at the antenna ports of the base station, such as estimating an interference covariance matrix in a current environment based on a reference signal in the signal received at the antenna ports of the base station; performing a Cholesky decomposition or Principal Component Analysis (PCA) decomposition of the interference covariance matrix; and multiplying an inverse matrix of the decomposed matrix by the signal received at the antenna ports of the base station to obtain the first signals.

[0079] After receiving the first signals of the respective REs, the respective REs are grouped. According to an embodiment, each group includes at least one RE for carrying a reference signal.

[0080] As an example, the grouping of the respective REs includes grouping the respective REs by at least one of following grouping methods: grouping the respective REs based on frequency domain and / or time domain information of the respective REs; grouping the respective REs based on a predefined grouping table; selecting at least one RE, as a group of REs after grouping, based on a predefined RE selection rule. For each grouping method, REs between respective groups after the grouping are different, partially the same, or all the same.

[0081] In some examples, the respective REs may be grouped based on frequency domain and / or time domain information of the respective REs, e.g., the respective REs may be grouped in frequency domain and / or time domain dimensions, and each RE is classified into only one group. In some embodiments of the example, the respective REs are grouped into N1 groups based on a descending order of frequency domain numbers (i.e., subcarrier numbers) of the respective REs, with every a1 REs having consecutive subcarrier numbers being grouped into a group, e.g., the parameter of a1 may be set based on a bandwidth associated with a current environment, where N1*a1 is equal to the total number of the REs. For example, a total of 72 REs corresponding to subcarriers numbers 0-71 may be divided into 6 groups, each group containing 12 REs with consecutive subcarrier numbers, i.e., REs with subcarrier numbers 0-11 are divided into one group, REs with subcarrier numbers 12-23 are divided into one group, and so on. In other embodiments of the example, the respective REs are divided into N2 groups based on orders of frequency domain numbers (i.e., subcarrier numbers) and time domain numbers (i.e., the OFDM symbol numbers) of the REs, with every a2 neighboring REs being divided into groups (wherein N2*a2 is equal to the total number of REs). For example, there are 144 REs corresponding to subcarrier numbers 0-35 and OFDM symbol numbers 0-3, which may be divided into 12 groups according to a time-frequency grid, with each group containing 12 REs with 2 consecutive OFDM symbols and consecutive subcarrier numbers, i.e., REs with subcarrier numbers 0-11 on OFDM symbols whose numbers are 0-1 are divided into one group, REs with subcarrier numbers 12-23 on the OFDM symbols whose numbers are 0-1 are divided into one group, REs with subcarrier numbers 0-11 on OFDM symbols whose numbers are 2-3 are divided into one group, REs with subcarrier numbers 12-23 on the OFDM symbols whose numbers are 2-3 are divided into one group, and so on.

[0082] In some examples, the respective REs may be grouped based on a predefined grouping table. The grouping table may be predefined based on the REs, e.g., which group each RE is assigned to may be defined in the grouping table.

[0083] In some examples, the grouping of the REs may be performed based on a predefined RE selection rule. In some embodiments of the example, the predefined RE selection rule may be that REs are divided into groups every interval of a3 subcarrier numbers based on subcarrier numbers of the REs, e.g., REs with odd / even subcarrier numbers are divided into groups (i.e., divided into groups every interval of 2 REs). In other embodiments of the example, the predefined RE selection rule may be selecting at least one RE from the plurality of REs and divide the at least one RE into a group. For example, certain REs may be directly selected to be divided into a group. For example, OFDM symbols whose numbers are 1, 4, and 9 are divided into one group, OFDM symbols whose numbers are 2, 5, and 8 are divided into another group, and so on.

[0084] In some examples, the grouping of REs is done in such a way that there is an overlapping division, i.e., each RE may be divided into one or more groups, and the REs contained in each group being partially the same, or all the same. In some embodiments of the example, the REs are sequentially divided into N groups, each group including M REs, with m overlapping REs in each group, where N (M-m) is equal to the total number of REs occupied by the first signals. For example, it is also possible to determine only one group which includes all REs.

[0085] It should be noted that the above grouping method is only exemplary and the present disclosure is not limited thereto.

[0086] After grouping the respective REs, next, at step S420, the feature information of each group is obtained based on the reference signals on at least one of the REs for carrying the reference signal in each group. According to an embodiment, RE(s) may be selected from each group as sample(s) and the feature information of the group may be obtained based on the sample(s), wherein the sample(s) include at least a part of REs in the at least one RE for carrying the reference signal. In actual communication, there may exist some REs that carry signals of poor quality due to the influence of the communication environment and so on, affecting the performance stability of subsequent signal combining, and thus according to an embodiment of the present disclosure, the sample(s) may be selected from each group of REs and the feature information of the group of REs may be obtained based on the selected sample(s), thereby facilitating the acquisition of stable feature information, and performing signal detection after combining the signals in the antenna port dimension based on the stable feature information, whereby the signal interference noise ratio of the signal at the time of entry into the signal detection process may be improved, the quality of the signal detection may be improved, and the degradation of the performance due to interference and noise may be avoided.

[0087] According to an embodiment, the RE(s) may be selected as the sample(s) from each group using one of following sample selection methods: selecting all of REs in the group as the sample(s); selecting at least a part of the REs in the group as the sample(s) based on frequency domain and / or time domain information of the REs in the group; selecting at least a part of the REs from the group as the sample(s) based on a predefined selection table; selecting at least one RE from the group as the sample(s) based on a predefined RE selection rule.. However, the manner of selecting the sample(s) is not limited to the above methods, and at least a part of the REs that satisfy a predefined condition may be selected as the sample(s) based on the communication needs.

[0088] In some examples, all of REs within one group of REs may be directly selected as samples.

[0089] In some examples, at least a part of REs within one group of REs may be selected as samples based on frequency domain and / or time domain information of the group of REs. As an example, the frequency domain and / or time domain information of the REs may include positional information of the REs in the frequency domain and / or time domain dimensions, e.g., an absolute position and a relative position. For example, all or a part of the REs within the group may be selected as samples based on the relative positions of the REs in the frequency domain and / or time domain dimensions. In some embodiments of the example, all of the REs within the group are selected as samples. In other embodiments of the example, a part / all of REs located at the edge of the frequency domain and / or time domain dimensions contained within the group are selected as samples. For example, the group contains 12 consecutive REs with subcarrier numbers 12-23, and the RE with subcarrier number 12 and / or the RE with subcarrier number 23 are selected as samples. In other embodiments of the example, a part / all of REs located at the center of the frequency domain and / or time domain contained within the group are selected as samples. For example, there are a total of 48 REs corresponding to subcarriers numbers 0-12 and corresponding to OFDM symbol numbers 0-3, and REs with subcarrier numbers 5 and 6 on an OFDM symbol whose number is 1 may be selected as samples, REs with subcarrier numbers 5 and 6 on an OFDM symbol whose number is 2 may be selected as samples, and all of REs on OFDM symbols whose number are 1and / or 2 may be selected as samples.

[0090] In some examples, the selection of the sample may be based on a predefined selection table. For example, those REs to be selected may be predefined in the selection table.

[0091] In some examples, the selection of the sample may be based on a predefined selection rule. In some embodiments of the example, the selection rule for the sample may be selecting one RE to be added to the samples based on subcarrier numbers of the REs every interval of a4 REs, e.g., selecting REs with subcarrier numbers that are multiples of 3 as samples. In other embodiments of the example, the selection rule for the sample may be selecting at least one RE from each group and using the at least one RE as a sample. For example, certain REs may be directly selected as samples. For example, an RE with an odd subcarrier number for an OFDM symbol whose number is 2 in the group is selected as a sample. As another example, an RE for carrying a reference signal in the group is selected as a sample. For example, the reference signal may be a reference signal extracted from the first signal, or may be a pre-stored reference signal corresponding to the first signal.

[0092] It should be noted that the above sample selection method is only exemplary, and the present disclosure is not limited thereto.

[0093] After performing the sample selection, the feature information of each group of REs may be obtained based on the sample(s). Specifically, for example, for each group, obtaining the feature information of the group based on the sample(s) may include: obtaining channel state information corresponding to the sample(s); obtaining the feature information of the group based on the channel state information corresponding to the sample(s). The feature information of the group is feature information shared by all REs in the group.

[0094] For example, the channel state information may include direct effects (e.g., amplitude and phase effects) and / or indirect effects (e.g., signal-to-noise ratio) of the wireless communication environment on the signal carried by the sample. Without loss of generality, the channel state information described in the present disclosure may be a comprehensive effect of an entire wireless communication link on an amplitude, a phase and / or a signal-to-noise ratio of the signal over all REs occupied by its transmission. Optionally, the channel state information may also include information obtained after the channel state information described above has been subjected to specific processing (e.g., interpolation, denoising, filtering, etc.). In some embodiments, the channel state information is filtered by a Minimum Mean Square Error (MMSE) algorithm to obtain information that becomes new channel state information. In some embodiments, the channel state information is subjected to eigenvalue decomposition to obtain a Pre-coding Matrix Indication (PMI) matrix and eigenvalues, and the PMI matrix may be used as new channel state information. In some embodiments, sliding average is performed on the channel state information in the time domain (over multiple OFDM symbols), and the averaged result is used as the channel state information, i.e., , where is a previously stored channel, is a newly acquired channel, and is an averaging factor.

[0095] According to an embodiment, the obtaining of the channel state information corresponding to the sample(s) may include: obtaining the channel state information corresponding to the sample(s) based on a reference signal corresponding to the sample(s); or, using pre-stored channel state information associated with the sample(s) as the channel state information corresponding to the sample(s).

[0096] The reference signal corresponding to the sample may be a reference signal contained in the first signal carried by the sample, which is extracted from the first signal , or may be a pre-stored reference signal corresponding to the first signal. In some examples, the reference signal (e.g., Sounding Reference Signal (SRS), Demodulation Reference Signal (DM-RS) in PUSCH or PUCCH) which is contained in the first signal carried by the sample may be extracted from the first signal, or the reference signal stored by the base station corresponding to the first signal may be directly read, and then the impact of the channel is calculated as channel state information based on the relevant configuration information. The reference signal may be a transmitting signal consisting of a generated sequence, the content of which and the RE for transmitting it are shared by the transmitter and the receiver. The reference signal may also be referred to as a pilot signal, a training signal, and the like. The reference signal includes, for example, a SRS for a terminal to estimate an uplink transmission channel and obtain uplink channel state information; a DM-RS in PUSCH or PUCCH for the terminal to demodulate a downlink shared channel; a Channel State Information Reference Signal (CSI-RS) for the terminal to estimate a downlink transmission channel and obtain downlink channel state information. The reference signal may also be a signal obtained based on the sounding reference signal and / or the demodulation reference signal. In some embodiments of the example, the base station extracts a DM-RS signal corresponding to each transmitted data stream from a particular RE carrying a first signal based on configuration information transmitted by a PUSCH, and then divides the received DM-RS signal by a transmitted DM-RS signal specified in the configuration information to obtain the channel state information. In other embodiments of the example, after obtaining channel state information corresponding to the RE at which the reference signal is located, the base station performs linear interpolation to obtain channel state information corresponding to all REs for each data stream.

[0097] In other examples, the channel state information corresponding to the sample may be pre-stored channel state information associated with the sample, e.g., the channel state information associated with the sample may, for example, include, but is not limited to: channel state information stored by the base station based on the last received signal and / or the reference signal and / or information obtained after processing thereof, or channel state information currently received and stored in the base station and / or information obtained after processing thereof; channel state information obtained by the base station through a channel feedback process (e.g., a channel state information (CSI) measurement report) of the terminal and / or information obtained after processing thereof (e.g., channel state information corresponding to a first signal reported by the terminal); channel state information predicted by the base station based on stored channel state information (e.g., past channel information) and / or information obtained after processing thereof.

[0098] It should be noted that the above embodiments of obtaining the channel state information are only exemplary, and the present disclosure is not limited thereto.

[0099] For each group of REs, after obtaining the channel state information corresponding to the sample(s), the feature information of the group may be obtained based on the channel state information corresponding to the sample(s).

[0100] According to an embodiment, the feature information of each group includes statistical information obtained based on channel state information associated with the reference signal of the group; or, the feature information of each group includes feature information obtained by a neural network based on the channel state information associated with the reference signal of the group. For example, the statistical information may be higher-order statistics obtained based on the channel state information associated with the reference signal of the group.

[0101] According to an embodiment, the obtaining of the feature information of the group based on the channel state information corresponding to the sample(s) may include: obtaining the feature information of the group by performing a statistical operation and / or an algebraic operation on the channel state information corresponding to the sample(s); or, obtaining the feature information of the group using a first neural network based on the channel state information corresponding to the sample(s) and auxiliary information, wherein the auxiliary information includes at least one of: information related to time domain resource(s) corresponding to the sample(s), information related to frequency domain resource(s) corresponding to the sample(s), and information related to a channel environment of the sample(s).

[0102] In some examples, the feature information is obtained by performing a statistical operation and / or an algebraic operation on the channel state information. Specifically, for example, weighted averaging may be performed on channel state information corresponding to respective samples, a covariance matrix may be calculated, a DFT transform, a Singular Value Decomposition (SVD), a wavelet transform, a sparse projection, and the like may be performed, and representative information data therein may be extracted as the feature information. In some embodiments of the example, a result of weighted averaging channel state information corresponding to respective samples is used as the feature information. For example, channel state information corresponding to 2 samples is directly averaged. As another example, channel state information of samples at different time domain positions are each multiplied by time-dependent coefficients and then summed to obtain the feature information. In other embodiments of the example, a covariance matrix is first calculated based on channel state information of a plurality of samples, and then the covariance matrix is subjected to eigenvalue decomposition, and the decomposed eigenvectors are used as the feature information. In other embodiments of the example, channel state information corresponding to respective samples is first weighted averaged and subjected to a DFT transform, and then the transformed vector is sparsified (i.e., all elements of the vector having an energy less than a threshold T1 are set to zero), and finally, a transformed vector after a discrete Fourier inverse transform (IDFT) is used as the feature information.

[0103] In other examples, the feature information is obtained by inputting the channel state information corresponding to the sample(s) together with the auxiliary information into the first neural network, wherein the auxiliary information includes at least one of: information related to time domain resource(s) corresponding to the sample(s), information related to frequency domain resource(s) corresponding to the sample(s), and information related to a channel environment of the sample(s). In some embodiments of the example, the auxiliary information may be information related to time domain resource(s) and / or frequency domain resource(s) corresponding to the sample(s), for example, at least one of position information (including an absolute position and / or a relative position), structure information, and distribution information of the sample(s), such as an OFDM symbol position (relative position or absolute position) at which the sample in the group is located or distance or relative position information between a RE in the group that is not selected as a sample and respective samples. In other embodiments of the example, the auxiliary information may include information related to the channel environment of the sample(s). As an example, the information related to the channel environment of the sample(s) may include at least one of: a signal-to-noise ratio, multipath delay expansion information of a channel, and correlation time information of the channel. For example, the signal-to-noise ratio may include a SNR or a SINR. The multipath delay expansion information of the channel may be a distribution pattern of the multipath of the channel. As another example, the correlation time information of the channel may be a relationship between a correlation of channels on respective REs and their distances in time, e.g., a temporal correlation coefficient. FIG. 5 is a schematic diagram illustrating a neural network according to an embodiment of the present disclosure. The neural network as shown in FIG. 5 may be a first neural network as mentioned above or a second neural network as will be mentioned hereinafter, except that inputs and outputs are different for different neural networks.

[0104] The neural network may be realized jointly and / or independently by the same physical entity or by different physical units, and the physical entity may be composed of hardware, software, or a combination thereof, which may be configured by a person skilled in the art according to the actual needs.

[0105] As shown in FIG. 5, the neural network may include an information processing unit and a plurality of neural network layers. The information processing unit may convert input information (such as first information and auxiliary information in FIG. 5) into at least one of a data vector, a matrix, and a tensor that may be computed by the neural network layer, and the neural network layer may include at least one of an input layer, a hidden / intermediate layer, and an output layer shown in FIG. 5. In addition, each neural network layer may be composed of a plurality of neurons n and may utilize at least one activation function to activate the neurons, as an example only and not as a limitation, e.g., a tanh function, a ReLU function, an eLU function, a seLU function, a ceLU function, a preLU function, a geLU function, a LeakyReLU function, a Sigmoid function, a Softmax function, a Softplus function, etc. Each neural network layer may perform operations on its input data to realize functions such as matrix transformation, data dimensionality reduction, data feature extraction, data feature combination, and the like. By way of example only and not as a limitation, multiple neural network layers may be connected in series or in parallel, etc., to form different neural network structures, including, but not limited to, a multilayer perceptron machine (MLP), a multilayer perceptron mixer (MLP-mixer), a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a generative adversarial network (GAN), a transformer network (Transformer), etc.

[0106] For example, in a case where the neural network shown in FIG. 5 is the above first neural network, the first information may be channel state information corresponding to samples(s), and the information processing unit may convert the channel state information and the auxiliary information into at least one of a data vector, a matrix, and a tensor that may be computed by the neural network layer. In some embodiments of this example, the first neural network may employ a deep convolutional neural network, and channel state information corresponding to a plurality of samples is stacked and arranged in the form of a tensor according to the positions of their physical resources and is input into the first neural network, and the first neural network may output matrix data whose shape corresponds to transmitting and receiving antennas as the feature information. In other embodiments of the example, the first neural network may adopt a Transformer structure, and the channel state information corresponding to the plurality of samples is superimposed with a position Token corresponding to the positions of their physical resources, and then is input into the first neural network together with a pre-trained query Token, and the network outputs data of the channel in which the query Token is located as the feature information.

[0107] As an example and not a limitation, a set composed of channel state information is defined as . Firstly, the information processing unit processes known channel state information by a numerical mapping method (e.g., normalization, standardization, etc.) so that its numerical scale or distribution is suitable for computation by the neural network. More specifically, taking minimum-maximum (Min-Max) standardization as an example, a maximum value max( ) and a minimum value min( ) of all elements in may be computed separately, after which the following processing is performed for each element in :

[0108]

[0109] After the above numerical mapping has been performed, a set composed of the processed elements may be converted into at least one of a vector, a matrix, and a tensor corresponding to the input dimension of the neural network layer.

[0110] In other embodiments of the present disclosure, the information processing unit may also perform position encoding of the input information and convert the positional encoded information into at least one of a vector, a matrix, and a tensor for neural network computation. Optionally, the position encoding may be a process of adding and / or multiplying the first information with a code word in a predetermined codebook. By way of example only and not as a limitation, in the case where the first information is channel state information, the position encoding using the codebook may include the following processing:

[0111] Firstly, a set composed of known channel state information is processed according to a numerical mapping method to obtain a set , which contains a number of elements for which numerical mapping is performed; and subsequently, a codebook for position encoding is obtained according to the following equation.

[0112]

[0113] where n denotes a physical resource (e.g., time, frequency, and space domain) position where the channel state information is located, d denotes a physical resource dimension of the channel state information, N is any positive number greater than the number of elements contained in the set , and i denotes an index of the physical resource dimension. Subsequently, position encoding is performed on the known channel state information elements by an equation to get a set ; and finally the set composed by the processed elements is converted into at least one of a vector, a matrix, and a tensor corresponding to the input dimension of the neural network layer to be input to the neural network layer.

[0114] As shown in FIG. 5, the information processing unit may process (e.g., mapping, position encoding, etc., as described above) the input first information (e.g., channel state information) and auxiliary information, so that the neural network may obtain apriori information implied therein to assist the neural network in improving the accuracy and flexibility of obtaining the prediction result. Utilizing the neural network to obtain the feature information may make the obtained feature information more accurate. Optionally, the information processing unit may also input its input data directly to the neural network layer without any additional processing. Further, it is noted that the operations in the neural network unit described above are only exemplary and the present disclosure is not limited thereto.

[0115] After obtaining the feature information of each group, at step S430, the first signals may be combined based on the feature information of the respective groups to obtain the second signal. As mentioned above, the first signals of the respective REs are associated with a plurality of antenna ports, and thus the respective REs carrying the first signals are also associated with the plurality of antenna ports. For example, signals received at different antenna ports associated with each RE are combined to obtain the second signal such that the number of antenna ports associated with the second signal is less than the number of antenna ports associated with the first signals. Specifically, for example, based on the feature information of the respective group, received signals of antenna ports associated with each RE in the group are combined to obtain the second signal. As an example, a combining coefficient matrix corresponding to the group may be obtained based on the feature information of the group, wherein the combining coefficient matrix includes combining coefficients of respective antenna ports associated with each RE in the group; received signals of the antenna ports associated with each RE in the group are combined based on the combining coefficient matrix to obtain the second signal. For example, the combining coefficient matrix may include one or more sets of combining coefficients, each set of combining coefficients including complex numbers consistent with the number of antenna ports associated with each RE (the combining coefficients may also be 0). The received signals of the antenna ports associated with each RE in the group are then combined to obtain the second signal according to the above combining coefficient matrix, wherein each set of combining coefficients is considered as a new equivalent antenna port. According to an embodiment, for each RE in the same group, its associated antenna ports are the same. For REs in different groups, their associated antenna ports may be the same, partially the same or completely different.

[0116] Since all REs in each group share consistent feature information, all REs in each group share the same combining coefficients, and there is no need to repeat the calculation of the combining coefficients of each RE used in combining the first signals, thereby effectively reducing the computational burden of the communication device while ensuring a smooth and consistent antenna combining process.

[0117] According to an embodiment, an element included in the feature information of each group may have a correspondence with an antenna port associated with each RE in the group. In this case, the obtaining of the combining coefficient matrix corresponding to the group based on the feature information of the group may include: obtaining combining coefficients of respective antenna ports associated with each RE in the group based on elements included in the feature information of the group. The combining of the received signals of the antenna ports associated with each RE in the group based on the combining coefficient matrix to obtain the second signal may include: weighting the received signals of the respective antenna ports based on the combining coefficients of the respective antenna ports associated with each RE, and obtaining the second signal based on the weighted signals. For example, the combining coefficients of the respective antenna ports associated with each RE may be weights used for weighting, and the second signal may be obtained by summing the weighted signals, or the second signal may be obtained by averaging the weighted signals.

[0118] According to an embodiment, the obtaining of the combining coefficients of the respective antenna ports associated with each RE in the group based on the elements included in the feature information of the group may include: calculating norms of the elements corresponding to the respective antenna ports, and obtaining the combining coefficients of the respective antenna ports based on the calculated norms; calculating energy information of the respective antenna ports based on the elements corresponding to the respective antenna ports, and obtaining the combining coefficients of the respective antenna ports based on the energy information; or, obtaining the combining coefficients of the respective antenna ports based on a predefined mapping relationship between the elements and the combining coefficients. However, the method of obtaining the combining coefficients of the respective antenna ports is not limited to this.

[0119] The second signal obtained by combining the signals of the respective antenna ports based on the combining coefficients of the respective antenna ports associated with each RE may include: a received signal of an antenna port the element corresponding to which has the largest norm among all antenna ports associated with each RE; a received signal of an antenna port with the largest energy among all antenna ports associated with each RE; and / or a signal obtained by weighted averaging received signals of all antenna ports associated with each RE, wherein the weights used for weighted averaging are determined based on the feature information.

[0120] In some examples, the method of combining received signals of the antenna ports is calculating norms of elements corresponding to respective antenna ports in the feature information, and selecting a received signal of an antenna port corresponding to the largest norm as the second signal. In some embodiments of the example, there is a one-to-one correspondence between the elements contained in the feature information and the antenna ports, a L1 norm (i.e., an absolute value) of each element is calculated, the combining coefficient of the antenna port corresponding to the element having the largest L1 norm is set to one, the combining coefficients of the rest of the antenna ports are set to zero, and the signals after weighted summation is the second signal. In other embodiments of the example, the feature information may be in the form of a matrix with one row of elements corresponding to one antenna port, a L2 norm of each row of elements is calculated, the combining coefficient of the antenna port corresponding to the row of elements with the largest L2 norm is set to 1, and the combining coefficients of the rest of the antenna ports are set to zero, and finally the received signals of the respective antenna ports are weighted summed by using the obtained combining coefficients as the weights to obtain the second signal.

[0121] In some examples, the method of combining the received signals of the antenna ports may be calculating energy information (such as average energy and / or instantaneous energy) of respective antenna ports based on elements corresponding to the respective antenna ports in the feature information, and selecting a received signal of an antenna port with the largest energy among the antenna ports as the second signal. In some embodiments of the example, the feature information of each group of REs may contain a plurality of data on time as elements thereof, the energy of each antenna port is calculated based on the element corresponding to that antenna port, the combining coefficient of the antenna port with the largest energy is set to 1, the combining coefficients of the rest of the antenna ports are set to zero, and finally, the received signals of the respective antenna ports are weighted summed by using the obtained combining coefficients as the weights to obtain the second signal.

[0122] In some examples, the coefficients for combining the received signals of the antenna ports are obtained by calculating based on the feature information, and a weighted average of the signals on all the antenna ports may be used as the second signal. In some embodiments of the example, there is a one-to-one correspondence between the elements contained in the feature information and the antenna ports, and based on a predefined mapping relationship between the elements and the combining coefficients as a conjugate relationship, a conjugate of the element in the feature information may be directly used as the combining coefficient of the antenna port corresponding to the element, and then the received signals of the respective antenna ports are multiplied by the corresponding combining coefficients, and then the second signal is obtained by summation. In some embodiments of the example, each element in the feature information is quantized into a corresponding combining coefficient according to a table (which defines a correspondence between the elements and the combining coefficients), and a signal after weighted summation of the received signals of the antenna ports based on combining coefficients obtained from the quantization is the second signal. For example, a rounding operation is performed on the element in the feature information, so that if the element is located in a range of -1 to 1, the combining coefficient of its corresponding antenna port is 0, and if the element is located in a range of 1 to 3, the combining coefficient of its corresponding antenna port is 1, and so on, and finally, the signal after weighted summation of the received signals of the antenna ports using the combining coefficients is used as the second signal. In some embodiments of the example, the combining coefficients are derived from a certain fixed codebook (e.g., a spatial DFT oversampled codebook), a most suitable code word to be employed is found based on the feature information, and the second signal obtained by signal combining is a signal obtained after weighted averaging of signals on the antenna ports using the code word as the combining coefficients of the antennas. For example, a code word in a certain fixed codebook having the maximum correlation with an element in the feature information is selected as the combining coefficient (weight) of an antenna port corresponding to that element, and the weighted averaged signal is used as the second signal. In some embodiments of the example, the feature information may be subjected to eigenvalue decomposition, and then eigenvectors corresponding to the first b largest eigenvalues may be sequentially selected as the combining coefficients, and an inner product of the signals on all antenna ports and the combining coefficients may be used as the second signal.

[0123] It should be noted that the above way of combining the received signals of the antenna ports associated with each RE in each group is only an example, and the combining method is not limited thereto.

[0124] After the second signal is obtained by combining, at step S440, signal detection may be performed based on the second signal. According to an embodiment, step S440 may include: performing at least one of channel equalization, demodulation, and decoding of the second signal to obtain information contained in the first signal.

[0125] In some examples, the equalization process includes: obtaining a channel equalization matrix; and equalizing the second signal based on the channel equalization matrix. The channel equalization matrix used in equalizing the second signal may be obtained from channel state information corresponding to the first signal or channel state information corresponding to the second signal. The obtaining of the channel equalization matrix may include: obtaining a channel equalization matrix based on channel state information corresponding to the first signal; performing channel estimation on the second signal, obtaining channel state information corresponding to the second signal, and obtaining a channel equalization matrix based on the channel state information corresponding to the second signal.

[0126] In some embodiments, the channel equalization matrix may be obtained directly from channel state information corresponding to the first signal. For example, the first signal is , the channel state information matrix corresponding to the first signal is , and a conjugate transpose matrix thereof is used as a coefficient for performing the combination on the first signals at an antenna port dimension to produce the second signal , of which corresponding channel equalization matrix is , and the channel equalized signal is .

[0127] In other examples, the channel equalization matrix may be obtained from channel state information corresponding to the second signal. For example, the channel state information corresponding to the second signal is , and a second module may use a MMSE algorithm to calculate a corresponding channel equalization matrix , and the equalization matrix is multiplied by the second signal to obtain the equalized signal. In some embodiments of the example, a corresponding reference signal may be extracted from the second signal, and the channel state information corresponding to the second signal may be obtained based on the reference signal. In some other embodiments of the example, the channel state information corresponding to the second signal may be obtained by the channel state information corresponding to the first signal or the reference signal. Specifically, the channel state information corresponding to the first signal is subjected to an antenna port combining process based on an antenna combining coefficient used by that physical resource to obtain the channel state information corresponding to the second signal.

[0128] In some examples, the demodulation may include Quadrature Amplitude Modulation (QAM) demodulation, and the decoding may include, for example, Low Density Parity Check Code (LDPC) decoding, turbo decoding, polar decoding, but is not limited thereto. For example, a corresponding QAM modulation constellation diagram may be found based on the second signal, corresponding bit data may be obtained according to corresponding constellation symbols, and then the bit data may be input into a decoder to obtain information transmitted by a terminal communicating with a base station. For example, the decoder may be an LDPC decoder, but is not limited thereto.

[0129] In other examples, soft information (e.g., likelihood probability ratio) for each QAM modulated constellation symbol or bit information may be calculated based on the second signal, and then the soft information is input into an LDPC soft information decoder to obtain information transmitted by a terminal communicating with a base station.

[0130] The method performed by a communication device according to the above embodiment may be applicable to a receiver on the base station 101, 102, 103 as shown in FIG. 1. In some embodiments of the present disclosure, the base station 101, 102, 103 may be a macro base station, a micro base station, a pico base station, a fly base station for a radio access network, a backhaul base station for unlimited backhaul, a base station integrating both access and backhaul functions, and the like. According to the method of the above embodiment, repeated calculation of a large amount of similar data may be avoided, the overall complexity of the signal reception process may be significantly reduced, and at the same time, the signal-to-noise ratio of the signal at the time of entry into the signal detection process may be stably improved, and the performance stability of the signal detection may be improved.

[0131] Optionally, according to another embodiment of the present disclosure, the method shown in FIG. 4 may further include: updating the grouping based on at least one of the first signals, the second signal, a block error rate (BLER), a bit error rate (BER), and a signal-to-noise ratio.

[0132] In some examples, the RE grouping method may be updated to balance the signal-to-noise ratio of the second signal against the computational burden. In some embodiments, when the signal-to-noise ratio of the second signal is less than a threshold d1, the subsequent grouping method is changed to use a shorter grouping length m1, and when the signal-to-noise ratio of the second signal is greater than the threshold d1, otherwise, a longer grouping length m2 is used. In other embodiments, the base station may measure a signal-to-noise ratio in the first signal and a signal-to-noise ratio in the second signal. When a difference between them is less than a threshold d2, the grouping length is reduced by Δ. In other embodiments, the base station may measure an average value of the BER or BLER of received information during a period of time t1. When the average value is higher than d5 (e.g., d5 may be configured as a minimum BER requirement for a NR system i.e., 1e-3), the grouping length is reduced by Δ to improve the performance of the system. When the average value is consistently lower than d6 (e.g., 1e-5), the grouping length is increased by Δ to reduce the computational burden of the system. The grouping length may refer to the number of REs in each group, and updating the grouping is not limited to updating the grouping length.

[0133] Optionally, if RE(s) are selected as sample(s) from each group in step S420, and feature information of the group is obtained based on the samples, the method shown in FIG. 4 further includes at least one of: grouping respective REs based on at least one of the first signal, the second signal, the block error rate (BLER), the bit error rate (BER), and the signal-to-noise ratio; determining the number of sample(s) based on at least one of the first signal, the second signal and the result of performing the signal detection; determining a type of feature information based on at least one of the first signal, the second signal, and the result of performing the signal detection; and determining a sample selection method based on at least one of the first signal, the second signal, and the result of performing the signal detection. As an example, the result of performing the signal detection may include at least one of: the BLER, the BER, and the signal-to-noise ratio. Optionally, the grouping method, the number of samples, the type of feature information, and / or the sample selection method may be adaptively updated. When being updated, grouping the respective REs, determining the number of samples, determining the type of feature information, and / or determining the sample selection method may be performed in the above manner. By adaptively updating the grouping method, the number of samples, the type of feature information, and / or the sample selection method, the accuracy of the obtained feature information may be further improved, so that the signal-to-noise ratio of the signal at the time of entry into the signal detection process may be improved, the quality of the signal detection may be improved, and the performance degradation due to interference and noise may be avoided.

[0134] In some examples, the sample selection method may be updated to obtain more accurate feature information. In some embodiments, the base station may measure interference strength of respective REs. After grouping the REs, in each group, the REs are sorted according to the interference strength on the REs, and only signals of REs with lower interference strength are selected as samples. In other embodiments, after selecting the samples from each group of REs, the samples may be screened again based on the interference strength of the samples to eliminate samples with interference strength greater than d3.

[0135] In some examples, the type of feature information may be updated for better system performance. The type of feature information may include a calculation method of the feature information, and different calculation methods may obtain different types of feature information. In some embodiments, the base station may measure interference strength of the first signal. When the interference strength is lower than d4, a first type of feature information is obtained by using a method of directly averaging the channel state information corresponding to the samples. When the interference strength is higher than d4, a second type of feature information is obtained by using a method of IDFT sparsification. In some embodiments, when the result of signal detection for the second signal is poor (e.g., the false bit rate is higher than expected), a third type of feature information is obtained by using a feature information calculation method with better performance (higher computational complexity). For example, when the block error rate (BLER) is higher than expected, the third type of feature information is obtained by using a method of SVD decomposition.

[0136] In some examples, the second neural network may be utilized to perform an update of the grouping method, the number of samples, the sample selection method, and / or the type of feature information. According to an embodiment, the update of the grouping method, the number of samples, the sample selection method, and / or the type of feature information may be performed using the second neural network based on the first signal, the second signal, and / or the result of performing the signal detection. In some embodiments, the first signal may be input into the second neural network, which may output one of four category labels, wherein each category label corresponds to that the grouping length is updated to 2, 4, 8, 16, respectively. In other embodiments, the first signals carried by the grouped REs may be input into the second neural network, an output length thereof is consistent with the grouping length, which corresponds to whether or not each of the REs is selected to be a sample, e.g., when the output is 1, the corresponding RE is selected as a sample. In other embodiments, the result (e.g., an error block rate) of performing the signal detection may be input into the second neural network together with the first signal, and the second neural network may directly output all parameters involved in the process of obtaining the feature information, e.g., the grouping method, the number of samples, the sample selection method, and the type of feature information. As an example, the second neural network may be the neural network shown in FIG. 5 above, and details of the neural network have been described above and will not be repeated here. A related description may be found above, except that the second neural network has different inputs and outputs than the first neural network.

[0137] It should be noted that the above embodiments for updating the parameters in the process of acquiring feature information are only exemplary and may be adopted in part or in full, and the present disclosure is not limited thereto.

[0138] FIG. 6 is a block diagram illustrating a communication device according to an embodiment of the present disclosure. Referring to FIG. 6, the communication device 600 may include a transceiver 610 as well as a processor 620, wherein the processor 620 is coupled to the transceiver 610 and configured to perform the methods performed by the communication device as described above.

[0139] According to an embodiment, the communication device may be any communication entity, such as a base station device, a sidelink device, a user equipment, etc., or may also be a network functional entity.

[0140] In addition, according to an embodiment of the present disclosure, a computer readable storage medium storing instructions is also provided. The instructions, when executed by at least one processor, causes the at least one processor to perform the methods as mentioned above. Examples of computer-readable storage media herein include: Read Only Memory (ROM), Random Access Programmable Read Only Memory (RAPROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Random Access Memory (RAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blue-ray or optical disk storage, Hard Disk Drive (HDD), Solid State Drive (SSD), card storage (such as multimedia cards, secure digital (SD) cards or extremely fast digital (XD) cards), magnetic tapes, floppy disks, magneto-optical data storage devices, optical data storage devices, hard disks, solid state disks, and any other devices that are configured to store computer programs and any associated data, data files and data structures in a non-transitory manner and provide the computer programs and any associated data, data files and data structures to a processor or computer so that the processor or computer can execute the computer programs. The instructions or computer programs in the computer-readable storage medium described above may be executed in an environment deployed in a computer device, such as client, host, proxy device, server, etc. In addition, in one example, the computer programs and any associated data, data files, and data structures are distributed on a networked computer system, so that the computer programs and any associated data, data files, and data structures are stored, accessed and executed through one or more processors or computers in a distributed manner.

[0141] Other embodiments of the present disclosure will readily be conceived by those skill in the art after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variation, use, or adaptation of the present disclosure that follows the general principle of the present disclosure and includes commonly known or customary technical means in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the disclosure is limited by the claims.

Claims

1.A method performed by a communication device comprising:receiving first signals of respective resource elements (REs), and grouping the respective REs, wherein the first signals are associated with a plurality of antenna ports, each group comprises at least one RE for carrying a reference signal;obtaining feature information of each group based on the reference signal on the at least one RE for carrying the reference signal in the each group;combining the first signals to obtain a second signal based on the feature information of each group; andperforming signal detection based on the second signal.2.The method according to claim 1, wherein the feature information of each group comprises statistical information obtained based on channel state information associated with the reference signal of the group; orthe feature information of each group comprises feature information obtained by a neural network based on the channel state information associated with the reference signal of the group.3.The method according to claim 1, wherein the grouping of the respective REs comprises grouping the respective REs by at least one of following grouping methods:grouping the respective REs based on frequency domain or time domain information of the respective REs;grouping the respective REs based on a predefined grouping table;selecting at least one RE, as a group of REs after grouping, based on a predefined RE selection rule,wherein, for each grouping method, REs between respective groups after the grouping are different, partially the same, or all the same.4.The method according to claim 1, wherein the obtaining of the feature information of each group based on the reference signal on the at least one RE for carrying the reference signal in each group comprises:selecting at least one RE from the each group as at least one sample and obtaining the feature information of the group based on the at least one sample, wherein the at least one sample comprise at least a part of REs in the at least one RE for carrying the reference signal;wherein the at least one RE is selected as the at least one sample from each group using one of following sample selection methods:selecting all of REs in the group as the at least one sample;selecting at least a part of the REs in the group as the at least one sample based on frequency domain or time domain information of the REs in the group;selecting at least a part of the REs from the group as the at least one sample based on a predefined selection table;selecting at least one RE from the group as the at least one sample based on a predefined RE selection rule.5.The method according to claim 4, wherein the obtaining of the feature information of the group based on the at least one sample comprises:obtaining channel state information corresponding to the at least one sample;obtaining the feature information of the group based on the channel state information corresponding to the at least one sample.6.The method according to claim 5, wherein the obtaining of the channel state information corresponding to the at least one sample comprises:obtaining the channel state information corresponding to the at least one sample based on a reference signal corresponding to the at least one sample; orusing pre-stored channel state information associated with the at least one sample as the channel state information corresponding to the at least one sample.7.The method according to claim 5, wherein the obtaining of the feature information of the group based on the channel state information corresponding to the at least one sample comprises:obtaining the feature information of the group by performing a statistical operation or an algebraic operation on the channel state information corresponding to the at least one sample; orobtaining the feature information of the group using a first neural network based on the channel state information corresponding to the at least one sample and auxiliary information, wherein the auxiliary information comprises at least one of: information related to at least one time domain resource corresponding to the at least one sample, information related to at least one frequency domain resource corresponding to the at least one sample, and information related to a channel environment of the at least one sample.8.The method according to claim 7, wherein the information related to the channel environment of the at least one sample comprises at least one of: a signal-to-noise ratio, multipath delay expansion information of a channel, and correlation time information of the channel.9.The method according to claim 1, further comprising:updating the grouping based on at least one of the first signals, the second signal, a block error rate (BLER), a bit error rate (BER), and a signal-to-noise ratio.10.The method according to claim 4, further comprising at least one of:grouping the respective REs based on at least one of the first signals, the second signal, a block error rate (BLER), a bit error rate (BER), and a signal-to-noise ratio;determining a number of the at least one sample based on at least one of the first signals, the second signal, and a result of performing the signal detection;determining a type of the feature information based on at least one of the first signals, the second signal, and the result of performing the signal detection;determining a sample selection method based on at least one of the first signals, the second signal, and the result of performing the signal detection.11.The method according to claim 10, wherein the result of performing the signal detection comprises at least one of: the BLER, the BER, the signal-to-noise ratio.12.A communication device comprising:at least one transceiver;at least one processor communicatively coupled to the at least one transceiver; andat least one memory, communicatively coupled to the at least one processor, storing instructions executable by the at least one processor individually or in any combination to cause the communication device to:receive first signals of respective resource elements (REs), and group the respective REs, wherein the first signals are associated with a plurality of antenna ports, each group comprises at least one RE for carrying a reference signal;obtain feature information of each group based on the reference signal on the at least one RE for carrying the reference signal in the each group;combine the first signals to obtain a second signal based on the feature information of each group; andperform signal detection based on the second signal.13.the communication device according to claim 12, wherein the information of each group comprises statistical information obtained based on channel state information associated with the reference signal of the group; orwherein the feature information of each group comprises feature information obtained by a neural network based on the channel state information associated with the reference signal of the group.14.the communication device according to claim 12, wherein the communication device is configured to;group the respective REs based on frequency domain or time domain information of the respective REsgroup the respective REs based on a predefined grouping table;select at least one RE, as a group of REs after grouping, based on a predefined RE selection rule,wherein, for each grouping method, REs between respective groups after the grouping are different, partially the same, or all the same.15.the communication device according to claim 12, wherein the communication device is configured to;selecting at least one RE from the each group as at least one sample and obtaining the feature information of the group based on the at least one sample, wherein the at least one sample comprise at least a part of REs in the at least one RE for carrying the reference signal;wherein the at least one RE is selected as the at least one sample from each group using one of following sample selection methods:selecting all of REs in the group as the at least one sample;selecting at least a part of the REs in the group as the at least one sample based on frequency domain or time domain information of the REs in the group;selecting at least a part of the REs from the group as the at least one sample based on a predefined selection table;selecting at least one RE from the group as the at least one sample based on a predefined RE selection rule.

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