Method performed by base station in communication system and base station performing same

Abstract

The invention provides a method executed by a base station in a communication system, the base station executing the method and a computer readable medium storing the method, and relates to the field of artificial intelligence. A method performed by a base station in a communication system includes: receiving a first signal through an antenna port of the base station; information related to combination of the first signals is determined based on the first signals, the first signals are combined in the antenna port dimension based on the determined information, second signals are obtained, and the number of antenna ports related to the second signals is smaller than that of the antenna ports related to the first signals; and performing signal detection based on the second signal. According to the invention, the problems that the number of to-be-detected signal antenna ports is too large and the data volume is too large are solved, the data transmission load between modules in the base station is reduced, and the calculation burden of signal detection is also significantly reduced.

Description

Technical Field

[0001] Embodiments of the present disclosure generally relate to the field of communication technologies, and more particularly to a method performed by a base station in a communication system, a base station performing the method, and a computer-readable medium storing the method. Background Art

[0002] In order to meet the increasing demand for wireless data communication services since the deployment of 4G communication systems, efforts have been made to develop improved 5G or pre-5G communication systems. Therefore, 5G or pre-5G communication systems are also referred to as "ultra 4G networks" or "post-LTE systems".

[0003] The 5G communication system is implemented in a higher frequency band (e.g., millimeter wave, mmWave), such as the 60 GHz band, to achieve higher data rates. In order to reduce the propagation loss of radio waves and increase the transmission distance, beamforming, massive multiple-input multiple-output (MIMO), full-dimensional MIMO (FD-MIMO), array antennas, analog beamforming, and massive antenna technologies are discussed in 5G communication systems.

[0004] In addition, in 5G communication systems, developments for improving system networks are ongoing based on advanced small cells, cloud radio access network (RAN), ultra-dense networks, device-to-device (D2D) communication, wireless backhaul, mobile networks, cooperative communication, coordinated multi-point (CoMP), receiver interference cancellation, etc.

[0005] In 5G systems, hybrid FSK and QAM modulation (FQAM) and sliding window superimposed coding (SWSC) as advanced coding modulation (ACM), and filter bank multicarrier (FBMC), non-orthogonal multiple access (NOMA), and sparse code multiple access (SCMA) as advanced access technologies have been developed. Summary of the Invention

[0006] In a first aspect of the present disclosure, a method performed by a base station in a communication system is provided. The method includes: receiving a first signal through an antenna port of the base station; determining information related to the combination of the first signal based on the first signal, and combining the first signal in the antenna port dimension based on the determined information to obtain a second signal, where the number of antenna ports related to the second signal is less than the number of antenna ports related to the first signal; and performing signal detection based on the second signal.

[0007] In some embodiments, the first signal may include at least one of the following: a radio frequency signal received by an antenna port of the base station; a digital domain wireless signal received by an antenna port of the base station; and a signal obtained based on the radio frequency signal and / or the digital domain wireless signal.

[0008] In some embodiments, determining information related to the combination of the first signal may include: determining information related to the combination of the first signal based on the first signal through a first neural network; or determining information related to the combination of the first signal based on a reference signal corresponding to the first signal through a second neural network; or determining information related to the combination of the first signal based on the channel state information corresponding to the first signal through a third neural network; or determining information related to the combination of the first signal based on the channel feature information corresponding to the first signal through a fourth neural network.

[0009] In some embodiments, the reference signal may be obtained based on the sounding reference signal and / or the demodulation reference signal.

[0010] In some embodiments, the channel state information corresponding to the first signal may include at least one of the following: channel state information obtained by performing channel estimation based on the first signal and / or the reference signal; channel state information stored in the base station; channel state information corresponding to the first signal reported by the terminal; and channel state information predicted by the base station based on the stored channel state information.

[0011] In some embodiments, the method may further include: processing the channel state information, where the processing includes one or more of interpolation, denoising, filtering, averaging in the time domain, grouping and averaging in the subcarrier dimension, and averaging in the antenna domain.

[0012] In some embodiments, the channel feature information corresponding to the first signal may include at least one of the following information: the signal-to-interference-plus-noise ratio of the first signal; the correlation matrix of the channel for the first signal or the correlation matrix of the first signal; information related to the interference related to the first signal; the high-order statistics of the channel for the first signal; and information related to the difference between the reference signal corresponding to the first signal and the expected received signal, where the expected received signal is obtained based on the reference signal and the channel state information corresponding to the first signal.

[0013] In some embodiments, determining information related to the combination of the first signal may include: determining information related to the combination of the first signal based on the comparison result between the channel feature information corresponding to the first signal and a threshold.

[0014] In some embodiments, the information related to the combination of the first signal may include at least one of the following: third information for indicating the way of grouping the antenna ports related to the first signal; and fourth information related to the output signal corresponding to the antenna port group.

[0015] In some embodiments, the third information may include at least one of the following: the index of the antenna ports included in each antenna port group; and information related to the number of groups.

[0016] In some embodiments, the ways of grouping the antenna ports related to the first signal may include at least one of the following: grouping based on the order of antenna port indices; the first signal parts included in each group being the same or all the same; grouping based on a predefined grouping table; and based on a predefined signal selection rule, selecting at least one signal from the first signal and dividing the antenna ports related to the at least one signal into a group.

[0017] In some embodiments, the output signal corresponding to the antenna port group may include at least one of the following: the signal with the maximum signal energy in the antenna port group; the signal with the maximum L2 norm of the corresponding channel gain in the antenna port group; the signal with the maximum average energy in the antenna port group; and the signal after weighted averaging of the signals in the antenna port group, where the weights for weighted averaging are determined based on the first signal.

[0018] In some embodiments, determining the information related to the combination of the first signal based on the first signal may further include: determining the information related to the combination of the first signal based on the first signal and auxiliary information, where the auxiliary information includes information related to resource allocation for the first signal and / or information related to the channel environment for the first signal.

[0019] In some embodiments, the method may further include: obtaining a channel equalization matrix; and equalizing the second signal based on the channel equalization matrix; performing signal detection based on the second signal includes: performing signal detection based on the equalized second signal.

[0020] In some embodiments, obtaining the channel equalization matrix may include: obtaining the channel equalization matrix based on the channel state information corresponding to the first signal; or performing channel estimation on the second signal to obtain the channel state information corresponding to the second signal, and obtaining the channel equalization matrix based on the channel state information corresponding to the second signal.

[0021] In some embodiments, obtaining the channel equalization matrix based on the state information of the channel corresponding to the first signal may include: obtaining the channel state information corresponding to the second signal based on the channel state information corresponding to the first signal; and obtaining the channel equalization matrix based on the channel state information corresponding to the second signal.

[0022] In a second aspect of the present disclosure, a base station is provided. The base station includes: a first module configured to: receive a first signal through the antenna ports of the base station; determine the information related to the combination of the first signal based on the first signal; combine the first signal in the antenna port dimension based on the determined information to obtain a second signal, where the number of antenna ports related to the second signal is less than the number of antenna ports related to the first signal; and a second module configured to: perform signal detection based on the second signal.

[0023] In some embodiments, the first module is further configured to: obtain a channel equalization matrix; and based on the channel equalization matrix, equalize the second signal; and the second module is configured to: perform signal detection based on the equalized second signal.

[0024] In some embodiments, the second module is further configured to: obtain a channel equalization matrix; and based on the channel equalization matrix, equalize the second signal; and perform signal detection based on the equalized second signal.

[0025] In a third aspect of the present disclosure, a base station is provided. The base station includes: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, implement the methods described in the first aspect and / or the second aspect of the present disclosure.

[0026] In a fourth aspect of the present disclosure, a computer-readable medium is provided. Instructions are stored on the computer-readable medium that, when executed by at least one processing unit, cause the at least one processing unit to be configured to execute the methods described in the first aspect and / or the second aspect of the present disclosure.

[0027] It should be understood that the content described in the Summary of the Invention section is not intended to limit the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without exceeding the scope claimed by the present application.

[0029] Figure 1 A block diagram of an exemplary wireless network communication system in which embodiments of the present disclosure can be implemented is shown;

[0030] Figure 2A and Figure 2B An exemplary wireless transmission path and an exemplary wireless reception path according to an embodiment of the present disclosure are shown;

[0031] Figure 3A An exemplary user equipment according to an embodiment of the present disclosure is shown;

[0032] Figure 3B An exemplary node according to an embodiment of the present disclosure is shown;

[0033] Figure 4Shows an example access network structure (network architecture) according to an embodiment of the present disclosure;

[0034] Figure 5 Shows a method performed by a base station in a communication system according to an embodiment of the present disclosure;

[0035] Figure 6 Shows an example structure of a neural network according to an embodiment of the present disclosure;

[0036] Figure 7 Shows a base station according to an embodiment of the present disclosure;

[0037] Figure 8 Shows a simplified block diagram of an electronic device suitable for implementing embodiments of the present disclosure;

[0038] Figure 9 Shows a schematic diagram of a computer-readable medium suitable for implementing embodiments of the present disclosure.

[0039] In all the figures, the same or similar reference numerals denote the same or similar elements. Detailed Description

[0040] The following description with reference to the accompanying drawings is provided to facilitate a thorough understanding of various embodiments of the present disclosure as defined by the claims and their equivalents. This description includes various specific details to facilitate understanding but should be considered merely exemplary. Thus, those of ordinary skill in the art will recognize that various changes and modifications can be made to the various embodiments described herein without departing from the scope and spirit of the present disclosure. In addition, descriptions of well-known functions and structures may be omitted for clarity and conciseness.

[0041] The terms and phrases used in the following specification and claims are not limited to their dictionary meanings but are used solely by the inventors to enable a clear and consistent understanding of the present disclosure. Thus, it should be apparent to those skilled in the art that the following description of the various embodiments of the present disclosure is provided for illustrative purposes only and not for the purpose of limiting the present disclosure as defined by the appended claims and their equivalents.

[0042] It should be understood that the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, a reference to "a component surface" includes a reference to one or more such surfaces.

[0043] The term "comprising" or "may comprise" refers to the presence of the corresponding disclosed function, operation, or component that can be used in various embodiments of the present disclosure, and does not limit the presence of one or more additional functions, operations, or features. In addition, the term "comprising" or "having" may be interpreted as indicating certain characteristics, numbers, steps, operations, components, components, or combinations thereof, but should not be construed as excluding the possibility of the presence of one or more other characteristics, numbers, steps, operations, components, components, or combinations thereof.

[0044] The term "or" used in various embodiments of the present disclosure includes any of the listed terms and all combinations thereof. For example, "A or B" may include A, may include B, or may include both A and B.

[0045] Unless otherwise defined, all terms (including technical terms or scientific terms) used in the present disclosure have the same meaning as understood by those skilled in the art described in the present disclosure. Commonly used terms defined in a dictionary are interpreted to have a meaning consistent with the context in the relevant technical field, and should not be interpreted idealistically or overly formally unless explicitly defined as such in the present disclosure.

[0046] A learning algorithm is a method of training a predetermined target device (e.g., a robot) using multiple learning data so as to enable, permit, or control the target device to make a determination or prediction. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0047] The method provided by the present disclosure may relate to the field of data intelligence technology.

[0048] According to the present disclosure, in the method performed by a base station in a communication system executed in an electronic device, the method for obtaining second information may be performed using an artificial intelligence model by using first information. The processor of the electronic device may perform a preprocessing operation on the data to convert it into a form suitable for use as an input to the artificial intelligence model. The artificial intelligence model may be obtained through training. Here, "obtained through training" means obtaining a predefined operation rule or artificial intelligence model configured to perform a desired feature (or purpose) by training a basic artificial intelligence model with multiple pieces of training data using a training algorithm. Inference prediction is a technology for making logical inferences and predictions by determining information, including, for example, knowledge-based reasoning, optimization prediction, preference-based planning, or recommendation.

[0049] The device provided in the embodiments of the present application may implement at least one of multiple modules through an AI model. Functions associated with AI may be executed through a non-volatile memory, a volatile memory, and a processor.

[0050] The processor may include one or more processors. At this time, the one or more processors may be general-purpose processors, such as a central processing unit (CPU), an application processor (AP), etc., or a pure graphics processing unit, such as a graphics processing unit (GPU), a vision processing unit (VPU), and / or an AI-specific processor, such as a neural processing unit (NPU).

[0051] The one or more processors control the processing of input data according to predefined operation rules or artificial intelligence (AI) models stored in the non-volatile memory and the volatile memory. The predefined operation rules or artificial intelligence models are provided through training or learning.

[0052] Here, providing through learning means obtaining a predefined operation rule or an AI model with desired characteristics by applying a learning algorithm to a plurality of learning data. The learning can be performed in the device itself that executes AI according to the embodiments, and / or can be implemented by a separate server / system.

[0053] The AI model may include multiple neural network layers. Each layer has multiple weight values, and the calculation of one layer is performed by the calculation results of the previous layer and the multiple weights of the current layer. Examples of neural networks include, but are not limited to, convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), generative adversarial networks (GANs), and deep Q networks.

[0054] A learning algorithm is a method of training a predetermined target device (e.g., a robot) using a plurality of learning data to enable, allow, or control the target device to make a determination or prediction. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0055] Next, through the description of several alternative embodiments, the technical solutions of the embodiments of the present disclosure and the technical effects produced by the technical solutions of the present disclosure will be described. It should be noted that the following embodiments can refer to, draw on, or combine with each other. For the same terms, similar features, and similar implementation steps in different embodiments, they will not be described repeatedly.

[0056] Figure 1 An example wireless network 100 according to various embodiments of the present disclosure is shown. Figure 1 The embodiments of the wireless network 100 shown are only for illustration. Other embodiments of the wireless network 100 can be used without departing from the scope of the present disclosure.

[0057] The wireless network 100 includes gNodeB (gNB) 101, gNB 102, and gNB 103. gNB 101 communicates with gNB 102 and gNB 103. gNB 101 also communicates with at least one Internet Protocol (IP) network 130, such as the Internet, a proprietary IP network, or other data networks.

[0058] Depending on the network type, other well-known terms such as "base station" or "access point" can be used in place of "gNodeB" or "gNB". For convenience, the terms "gNodeB" and "gNB" are used in this patent document to refer to the network infrastructure components that provide wireless access to remote terminals. Also, depending on the network type, other well-known terms such as "mobile station", "subscriber station", "remote terminal", "wireless terminal", or "user device" can be used in place of "user equipment" or "UE". For convenience, the terms "user equipment" and "UE" are used in this patent document to refer to the remote wireless devices that wirelessly access the gNB, whether the UE is a mobile device (such as a mobile phone or smartphone) or a device that is typically considered fixed (such as a desktop computer or vending machine).

[0059] gNB 102 provides wireless broadband access to the network 130 for a first plurality of user equipment (UE) within the coverage area 120 of gNB 102. The first plurality of UEs includes: UE 111, which can be located in a small business (SB); UE 112, which can be located in an enterprise (E); UE 113, which can be located in a WiFi hotspot (HS); UE 114, which can be located in a first residence (R); UE 115, which can be located in a second residence (R); UE 116, which can be a mobile device (M), such as a cellular phone, wireless laptop, wireless PDA, etc. gNB 103 provides wireless broadband access to the network 130 for a second plurality of UEs within the coverage area 125 of gNB 103. The second plurality of UEs includes UE 115 and UE 116. In some embodiments, one or more of gNBs 101 - 103 can communicate with each other and with UEs 111 - 116 using 5G, Long Term Evolution (LTE), LTE - Advanced (LTE - A), WiMAX, or other advanced wireless communication technologies.

[0060] The dashed lines illustrate the approximate extent of the coverage areas 120 and 125, which are shown as approximately circular merely for purposes of illustration and explanation. It should be clearly understood that the coverage areas associated with a gNB, such as coverage areas 120 and 125, can have other shapes, including irregular shapes, depending on the configuration of the gNB and changes in the radio environment associated with natural and man - made obstacles.

[0061] As described in more detail below, one or more of gNB 101, gNB 102, and gNB 103 include a 2D antenna array as described in embodiments of the present disclosure. In some embodiments, one or more of gNB 101, gNB 102, and gNB 103 support codebook designs and structures for systems with 2D antenna arrays.

[0062] Although Figure 1 an example of a wireless network 100 is shown, various changes can be made Figure 1 to it. For example, the wireless network 100 can include any number of gNBs and any number of UEs arranged in any suitable manner. Also, gNB 101 can communicate directly with any number of UEs and provide those UEs with wireless broadband access to the network 130. Similarly, each of gNBs 102 - 103 can communicate directly with the network 130 and provide UEs with direct wireless broadband access to the network 130. Additionally, gNBs 101, 102, and / or 103 can provide access to other or additional external networks (such as an external telephone network or other types of data networks).

[0063] Figure 2A and Figure 2B FIG. shows an example wireless transmit and receive path according to the present disclosure. In the following description, the transmit path 200 can be described as being implemented in a gNB (such as gNB 102), while the receive path 250 can be described as being implemented in a UE (such as UE 116). However, it should be understood that the receive path 250 can be implemented in a gNB, and the transmit path 200 can be implemented in a UE. In some embodiments, the receive path 250 is configured to support codebook designs and structures for systems with 2D antenna arrays as described in embodiments of the present disclosure.

[0064] The transmit path 200 includes a channel coding and modulation block 205, a serial - to - parallel (S - to - P) block 210, an N - point inverse fast Fourier transform (IFFT) block 215, a parallel - to - serial (P - to - S) block 220, a cyclic prefix addition block 225, and an up - converter (UC) 230. The receive path 250 includes a down - converter (DC) 255, a cyclic prefix removal block 260, a serial - to - parallel (S - to - P) block 265, an N - point fast Fourier transform (FFT) block 270, a parallel - to - serial (P - to - S) block 275, and a channel decoding and demodulation block 280.

[0065] In the transmit path 200, the channel coding and modulation block 205 receives a set of information bits, applies coding (such as low-density parity-check (LDPC) coding), and modulates the input bits (such as using quadrature phase shift keying (QPSK) or quadrature amplitude modulation (QAM)) to generate a sequence of frequency-domain modulation symbols. The serial-to-parallel (S-to-P) block 210 converts (such as demultiplexes) the serial modulation symbols into parallel data to generate N parallel symbol streams, where N is the number of IFFT / FFT points used in gNB 102 and UE 116. The N-point IFFT block 215 performs an IFFT operation on the N parallel symbol streams to generate a time-domain output signal. The parallel-to-serial block 220 converts (such as multiplexes) the parallel time-domain output symbols from the N-point IFFT block 215 to generate a serial time-domain signal. The cyclic prefix addition block 225 inserts a cyclic prefix into the time-domain signal. The upconverter 230 modulates (such as upconverts) the output of the cyclic prefix addition block 225 to an RF frequency for transmission via the wireless channel. Before upconverting to the RF frequency, the signal can also be filtered at baseband.

[0066] The RF signal transmitted from gNB 102 arrives at UE 116 after passing through the wireless channel, and operations opposite to those at gNB 102 are performed at UE 116. The downconverter 255 downconverts the received signal to baseband frequency, and the cyclic prefix removal block 260 removes the cyclic prefix to generate a serial time-domain baseband signal. The serial-to-parallel block 265 converts the time-domain baseband signal into a parallel time-domain signal. The N-point FFT block 270 performs an FFT algorithm to generate N parallel frequency-domain signals. The parallel-to-serial block 275 converts the parallel frequency-domain signals into a sequence of modulated data symbols. The channel decoding and demodulation block 280 demodulates and decodes the modulated symbols to recover the original input data stream.

[0067] Each of gNBs 101-103 can implement a transmit path 200 similar to that for transmitting to UEs 111-116 in the downlink, and can implement a receive path 250 similar to that for receiving from UEs 111-116 in the uplink. Similarly, each of UEs 111-116 can implement a transmit path 200 for transmitting to gNBs 101-103 in the uplink, and can implement a receive path 250 for receiving from gNBs 101-103 in the downlink.

[0068] Figure 2A and Figure 2B each of the components in can be implemented using only hardware, or using a combination of hardware and software / firmware. As a specific example, Figure 2A and Figure 2BAt least some of the components in [component name] can be implemented in software, while other components can be implemented by configurable hardware or a combination of software and configurable hardware. For example, the FFT block 270 and the IFFT block 215 can be implemented as configurable software algorithms, where the value of the number of points N can be modified according to the implementation.

[0069] In addition, although described as using FFT and IFFT, this is merely illustrative and should not be construed as limiting the scope of the present disclosure. Other types of transforms can be used, such as the discrete Fourier transform (DFT) and the inverse discrete Fourier transform (IDFT) functions. It should be understood that for the DFT and IDFT functions, the value of the variable N can be any integer (such as 1, 2, 3, 4, etc.), while for the FFT and IFFT functions, the value of the variable N can be any integer that is a power of 2 (such as 1, 2, 4, 8, 16, etc.).

[0070] Although Figure 2A and Figure 2B show an example of a wireless transmit and receive path, various changes can be made to Figure 2A and Figure 2B For example, Figure 2A and Figure 2B The various components in [component name] can be combined, further subdivided, or omitted, and additional components can be added according to specific needs. Moreover, Figure 2A and Figure 2B are intended to show examples of the types of transmit and receive paths that can be used in a wireless network. Any other suitable architecture can be used to support wireless communication in a wireless network.

[0071] Figure 3A Shows an example UE 116 according to the present disclosure. Figure 3A The embodiment of the UE 116 shown in [figure number] is for illustrative purposes only, and Figure 1 The UEs 111 - 115 can have the same or similar configurations. However, UEs have a wide variety of configurations, and Figure 3A The scope of the present disclosure is not limited to any specific implementation of the UE.

[0072] The UE 116 includes an antenna 305, a radio frequency (RF) transceiver 310, a transmit (TX) processing circuit 315, a microphone 320, and a receive (RX) processing circuit 325. The UE 116 also includes a speaker 330, a processor / controller 340, an input / output (I / O) interface 345, one or more input devices 350, a display 355, and a memory 360. The memory 360 includes an operating system (OS) 361 and one or more applications 362.

[0073] The RF transceiver 310 receives an incoming RF signal transmitted by the gNB of the wireless network 100 from the antenna 305. The RF transceiver 310 down-converts the incoming RF signal to generate an intermediate frequency (IF) or baseband signal. The IF or baseband signal is sent to the RX processing circuitry 325, where the RX processing circuitry 325 generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband or IF signal. The RX processing circuitry 325 sends the processed baseband signal to the speaker 330 (such as for voice data) or to the processor / controller 340 (such as for web browsing data) for further processing.

[0074] The TX processing circuitry 315 receives analog or digital voice data from the microphone 320, or other outgoing baseband data (such as network data, e-mail, or interactive video game data) from the processor / controller 340. The TX processing circuitry 315 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. The RF transceiver 310 receives the outgoing processed baseband or IF signal from the TX processing circuitry 315 and up-converts the baseband or IF signal to an RF signal transmitted via the antenna 305.

[0075] The processor / controller 340 can include one or more processors or other processing devices and execute the OS 361 stored in the memory 360 to control the overall operation of the UE 116. For example, the processor / controller 340 can control the reception of forward channel signals and the transmission of reverse channel signals through the RF transceiver 310, the RX processing circuitry 325, and the TX processing circuitry 315 according to well-known principles. In some embodiments, the processor / controller 340 includes at least one microprocessor or microcontroller.

[0076] The processor / controller 340 can also execute other processes and programs residing in the memory 360, such as operations for channel quality measurement and reporting for a system with a 2D antenna array as described in the embodiments of the present disclosure. The processor / controller 340 can move data into or out of the memory 360 as needed for executing processes. In some embodiments, the processor / controller 340 is configured to execute the application 362 based on the OS 361 or in response to a signal received from the gNB or the carrier. The processor / controller 340 is also coupled to the I / O interface 345, where the I / O interface 345 provides the UE 116 with the ability to connect to other devices such as laptop computers and handheld computers. The I / O interface 345 is a communication path between these accessories and the processor / controller 340.

[0077] The processor / controller 340 is also coupled to the input device(s) 350 and the display 355. An operator of the UE 116 can input data into the UE 116 using the input device(s) 350. The display 355 can be a liquid crystal display or other display capable of presenting text and / or at least limited graphics (such as from a website). The memory 360 is coupled to the processor / controller 340. A portion of the memory 360 can include random access memory (RAM), while another portion of the memory 360 can include flash memory or other read-only memory (ROM).

[0078] Although Figure 3A an example of the UE 116 is shown, various changes can be made to Figure 3A it. For example, Figure 3A the various components in can be combined, further subdivided, or omitted, and additional components can be added according to specific needs. As a specific example, the processor / controller 340 can be divided into multiple processors, such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs). Moreover, although Figure 3A the UE116 is shown configured as a mobile phone or smartphone, the UE can be configured to operate as other types of mobile or fixed devices.

[0079] Figure 3B An example gNB 102 according to the present disclosure is shown. Figure 3B The embodiment of the gNB 102 shown in is for illustration only, and Figure 1 other gNBs can have the same or similar configurations. However, gNBs have a wide variety of configurations, and Figure 3B the scope of the present disclosure is not limited to any particular implementation of the gNB. It should be noted that the gNB 101 and gNB 103 can include the same or similar structures as the gNB 102.

[0080] As Figure 3B shown, the gNB 102 includes a plurality of antennas 370a - 370n, a plurality of RF transceivers 372a - 372n, transmit (TX) processing circuitry 374, and receive (RX) processing circuitry 376. In certain embodiments, one or more of the plurality of antennas 370a - 370n include a 2D antenna array. The gNB 102 also includes a controller / processor 378, a memory 380, and a backhaul or network interface 382.

[0081] The RF transceivers 372a - 372n receive incoming RF signals from the antennas 370a - 370n, such as signals transmitted by a UE or other gNBs. The RF transceivers 372a - 372n down - convert the incoming RF signals to generate IF or baseband signals. The IF or baseband signals are sent to the RX processing circuitry 376, where the RX processing circuitry 376 generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband or IF signal. The RX processing circuitry 376 sends the processed baseband signal to the controller / processor 378 for further processing.

[0082] The TX processing circuitry 374 receives analog or digital data (such as voice data, network data, email, or interactive video game data) from the controller / processor 378. The TX processing circuitry 374 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. The RF transceivers 372a - 372n receive the outgoing processed baseband or IF signal from the TX processing circuitry 374 and up - convert the baseband or IF signal to an RF signal transmitted via the antennas 370a - 370n.

[0083] The controller / processor 378 can include one or more processors or other processing devices that control the overall operation of the gNB 102. For example, the controller / processor 378 can control the reception of forward - channel signals and the transmission of reverse - channel signals through the RF transceivers 372a - 372n, the RX processing circuitry 376, and the TX processing circuitry 374 according to well - known principles. The controller / processor 378 can also support additional functions, such as more advanced wireless communication functions. For example, the controller / processor 378 can perform a BIS process, such as that performed by a blind interference sensing (BIS) algorithm, and decode the received signal with the interference signal subtracted. The controller / processor 378 can support any one of a variety of other functions in the gNB 102. In some embodiments, the controller / processor 378 includes at least one microprocessor or microcontroller.

[0084] The controller / processor 378 can also execute programs and other processes resident in the memory 380, such as a basic OS. The controller / processor 378 can also support channel quality measurement and reporting for a system with a 2D antenna array as described in embodiments of the present disclosure. In some embodiments, the controller / processor 378 supports communication between entities such as web RTC. The controller / processor 378 can move data into or out of the memory 380 as needed for the execution of processes.

[0085] The controller / processor 378 is also coupled to a backhaul or network interface 382. The backhaul or network interface 382 allows the gNB 102 to communicate with other devices or systems via a backhaul connection or via a network. The backhaul or network interface 382 is capable of supporting communication via any suitable wired or wireless connection. For example, when the gNB 102 is implemented as part of a cellular communication system (such as a cellular communication system supporting 5G or new radio access technology or NR, LTE or LTE-A), the backhaul or network interface 382 is capable of allowing the gNB 102 to communicate with other gNBs via a wired or wireless backhaul connection. When the gNB 102 is implemented as an access point, the backhaul or network interface 382 is capable of allowing the gNB 102 to communicate with a larger network (such as the Internet) via a wired or wireless local area network or via a wired or wireless connection. The backhaul or network interface 382 includes any suitable structure that supports communication via a wired or wireless connection, such as an Ethernet or RF transceiver.

[0086] A memory 380 is coupled to the controller / processor 378. A portion of the memory 380 can include RAM, while another portion of the memory 380 can include flash memory or other ROM. In some embodiments, multiple instructions, such as BIS algorithms, are stored in the memory. The multiple instructions are configured to cause the controller / processor 378 to perform a BIS process and decode a received signal after subtracting at least one interference signal determined by the BIS algorithm.

[0087] As described in more detail below, the transmit and receive paths of the gNB 102 (implemented using the RF transceivers 372a - 372n, the TX processing circuitry 374, and / or the RX processing circuitry 376) support aggregated communication with FDD cells and TDD cells.

[0088] Although Figure 3B an example of the gNB 102 is shown, various changes can be made to Figure 3B it. For example, the gNB 102 can include any number of Figure 3AEach component shown in. As a specific example, the access point can include many backhaul or network interfaces 382, and the controller / processor 378 can support routing functions to route data between different network addresses. As another specific example, although shown as including a single instance of the TX processing circuit 374 and a single instance of the RX processing circuit 376, the gNB 102 can include multiple instances of each (such as one for each RF transceiver). One of the main means to ensure communication rate and reliability in a wireless communication system is to process signals or adjust transceiver decisions through channel state information (CSI) between two nodes for information transmission (the two nodes serve as a sending node and a receiving node respectively, or both nodes serve as a sending node and a receiving node). For example, a node (such as, Figure 3A the UE described in) precodes the transmitted signal according to its known channel state information before transmission, adjusts the power and modulation and coding scheme of the transmitted signal, and a node (such as Figure 3B the gNB 102 shown in) performs channel equalization on the received signal according to its known channel state information after receiving the signal, selects different transceiver beams (beams), etc., so as to eliminate the influence of the channel on the signal as much as possible.

[0089] In a wireless communication system, the transmission of information (for example, the information transmission of the physical downlink control channel (PDCCH), physical downlink shared channel (PDSCH), physical uplink shared channel (PUSCH), physical uplink control channel (PUCCH), etc.) occurs on more than one physical resource (for example, multiple time points, multiple frequency points, multiple antennas, and various combinations of time, frequency, and antennas). Physical resources are resource entities available for signal transmission in a communication system. For example, they can be time-domain physical resources, frequency-domain physical resources, and antenna-domain physical resources, etc. The state of the occupied physical resources will have a comprehensive impact on the amplitude and phase of the signals transmitted on the entire wireless communication link.

[0090] With the increasing popularity and continuous evolution of wireless communication networks, operators hope that the 5G access network structure can increase structural flexibility while reducing deployment costs. In this regard, when designing a 5G communication system, for example, the traditional monolithic base station can be split into a radio unit (RU unit), a distributed unit (DU unit), and a central unit (CU unit). Figure 4An example access network structure (network architecture) according to an embodiment of the present disclosure is shown. Among them, the RU unit includes a radio frequency transmitter (RF) and a physical layer (PHY) function with low computing requirements, and is responsible for implementing signal transceiver-related processing processes with a latency of less than 1 ms. The main components of the DU unit are a radio link controller (RLC), a media access controller (MAC), and the remaining physical layer functions (PHY) with high computing requirements, which are responsible for processing real-time services and performing round-trip data transmission with the RU unit through the fronthaul link. The CU unit consists of a control plane (CP) and a user plane (UP), and is responsible for processing non-real-time protocols and services. It interacts with the DU unit through a communication link and is connected to the core network through a backhaul. The operator can flexibly deploy the RU unit, DU unit, and CU unit in different locations according to needs, so as to meet various cost and service requirements. For example, a network that requires low edge latency can deploy the RU unit, CU unit, and DU unit together at the edge, which will maximize the performance of remote-connected user applications. Another example is that one DU unit can serve multiple RU units, and multiple DU units can share one CU unit, thus minimizing the hardware cost of network deployment while providing sufficient performance within an acceptable maximum latency range. For different markets and regions, the operator can deploy different architectures.

[0091] For cost considerations, the processing capacity of the RU unit itself is limited and it cannot directly complete all physical layer-related processes for signal processing. According to the current system architecture, the RU unit only has the ability to extract channel state information from reference signals, but cannot complete subsequent steps such as channel equalization, demodulation, and decoding under the specified latency requirements. Therefore, the RU unit needs to simply process the received data and then transmit it to the DU unit through the fronthaul line, and the DU unit completes the subsequent physical layer processes. However, with the continuous increase in the current user's demand for transmission rate, the RU-fronthaul-DU structure design in this architecture faces severe challenges. To meet the demand for higher-rate wireless data transmission, the scale of antennas used by the base station is getting larger, the bandwidth is getting wider, and the modulation order is getting higher, resulting in an increasingly large scale of received data that the base station needs to process. In contrast, the amount of data that needs to be transmitted from the RU unit to the DU unit through the fronthaul line has also increased rapidly, thus exceeding the carrying capacity of optical fiber (under limited costs). For example, in the LTE / NR system, the amount of fronthaul data is approximately N r *N f *N ofdm *N bit *2, where N r is the number of receive antennas, N f is the number of subcarriers, N ofdm is the number of OFDM symbols, N bitTo quantify the number of bits. If the base station is upgraded from 4 antennas to 64 antennas, the amount of fronthaul data will increase 16 times. Since the rate at which data is generated by the RU unit exceeds the transmission rate of the optical fiber, a large amount of data will accumulate at the RU unit waiting for transmission, causing the base station processing delay to rise rapidly. What's more serious is that when the cache queue of the RU unit is filled, the subsequent antenna received data will be naturally lost, causing serious system performance loss or even system crash. Therefore, as users' demand for transmission rates continues to increase and the amount of data continues to grow, the structural design of traditional base stations used for wireless communications is challenged. Data transmission needs to be enhanced to accommodate the increasing amount of data, such as compressing data.

[0092] The above problems can be solved by compressing and reducing the dimension of the received signal. However, the current mainstream data compression method requires a lot of calculations and will introduce huge transmission delays. In addition, lossy compression will significantly reduce the signal-to-noise ratio of the received data, resulting in impaired system performance. In addition, it cannot solve the problem of exponential growth in data volume caused by the increase in antenna scale.

[0093] Antenna port reduction technology effectively avoids the above problems. It reduces the number of antenna ports receiving data by combining the signals on the receiving antenna. Considering that the current NR system has a limited number of uplink transmission streams (up to 8 layers), the number of layers of the combined signal only needs to be greater than the number of uplink transmission streams of the current system. Antenna port compression does not necessarily reduce the signal-to-noise ratio of the processed data, but may produce a signal-to-noise ratio gain, resulting in improved system performance.

[0094] However, since the antenna combining scheme used is highly sensitive to the channel environment in which the system is located, a certain antenna signal combining scheme may have excellent performance in some scenarios, but may experience serious performance degradation in other scenarios. Therefore, this technology lacks performance stability.

[0095] The present disclosure provides a method executed by a base station in a communication system, which can determine information related to the merging of the signal according to the received signal, and then merge the signal in the antenna port dimension based on the determined information, thereby significantly reducing the number of antenna ports of the signal to be detected, and avoiding the performance degradation caused by the mismatch of the channel environment that may occur in the fixed antenna merging scheme. This method not only avoids a large amount of data transmission between various functional modules within the base station, but also significantly reduces the computational complexity of the subsequent signal detection algorithm, which is conducive to realizing the lightweight and low-cost base station.

[0096] Figure 5 A method 50 executed by a base station in a communication system according to an embodiment of the present disclosure is shown.

[0097] likeFigure 5 As shown, method 50 includes steps S501 to S504. The base station may include a first module and a second module that are communicatively connected. Among them:

[0098] In step S501, a first signal is received through the antenna port of the base station.

[0099] In some examples, step S501 may be executed by the first module. The first module may receive the first signal through the antenna of the base station. Among them, the first module may refer to the entity in the receivers on the sides of the aforementioned gNB 101, gNB 102, and gNB 103 that is used to implement the functions of receiving signals and performing preliminary processing, and it has the ability to receive wireless signals and perform certain processing. For example, it includes the downconverter (DC) 255, cyclic prefix removal block 260, serial-to-parallel (S-to-P) block 265, N-point fast Fourier transform (FFT) block 270, and parallel-to-serial (P-to-S) block 275 in the aforementioned receive path 250.

[0100] In some examples, the first module is the RU module in the aforementioned base station structure that includes an antenna and physical layer functions (PHY).

[0101] In some examples, the first signal refers to the signal received by the antenna, which carries the data information sent by the terminal, and may include at least one of the following: the radio frequency signal received by the antenna port of the base station; the digital domain signal after the aforementioned signal is subjected to digital-to-analog conversion; the signal obtained after the aforementioned signal is subjected to specific processing (such as amplification, denoising, filtering, whitening, FFT, etc.). In this application, "at least one" may represent one, or a combination of two or more. In some examples, the first signal includes the digital domain wireless signal received by the antenna port of the base station, or the denoised signal obtained by passing the digital domain wireless signal through a band-pass filter. In some other examples, the first signal includes the signal obtained by multiplying the signal received by the base station antenna by a certain spatial basis matrix (such as the eigenvector matrix of the channel or the spatial DFT matrix). In some other examples, the first signal includes the signal obtained after whitening the signal received by the base station antenna. For example: estimating the interference covariance matrix in the current environment based on the reference signal in the signal received by the base station antenna; performing Cholesky decomposition or PCA decomposition on the covariance matrix of the interference; and multiplying the inverse matrix of the decomposed matrix by the signal received by the base station antenna to obtain the first signal.

[0102] In step S502, based on the first signal, information related to the combination of the first signal is determined.

[0103] In some examples, since the first signal itself may include information related to the channel corresponding to the physical resources over which it is transmitted, information related to the combination of the first signal can be determined based on the first signal itself. The information related to the combination of the first signal may include third information for indicating the antenna port grouping related to the first signal and / or fourth information related to the output signal corresponding to the antenna port group.

[0104] In some examples, information related to the combination of the first signal (also referred to as "second information" hereinafter) can be obtained based on the first information corresponding to the first signal. The first information may include information related to the physical resources over which the first signal is transmitted. The wireless environment has a direct impact (such as amplitude and phase) and / or an indirect impact (such as signal-to-noise ratio) on the first signal carried on the physical resources over which the first signal is transmitted. The physical resources over which the first signal is transmitted include, for example, one or more of time-domain physical resources, frequency-domain physical resources, and / or antenna-domain physical resources. Among them, the time-domain physical resources refer to the resources available in the time dimension. Specifically, it can be time units such as radio frames, sub-frames, time slots, or symbols. For example, in some systems, a time point can be represented as a time slot composed of 14 OFDM symbols. The frequency-domain physical resources refer to the resources available in the frequency dimension. Specifically, it can be frequency units such as sub-bands, partial bandwidths, resource blocks, or sub-carriers. For example, in LTE and NR, a frequency point can be a frequency range composed of 12 sub-carriers spaced 15 kHz apart. The antenna-domain physical resources characterize the spatial resources occupied by signal transmission. It may include physical antenna units, antenna arrays, beams, antenna ports, or precoding matrices at the transmitting end. The antenna is in a broad sense and characterizes the spatial resources occupied by signal transmission. The second information may include third information for indicating the antenna port grouping related to the first signal and / or fourth information related to the output signal corresponding to the antenna port group.

[0105] In some embodiments, the first signal may include, but is not limited to, a reference signal corresponding to the first signal (for example, the reference signal is a transmitted signal composed of a generated sequence, and its content and the physical resources where it is transmitted are shared by two nodes. The reference signal may also be referred to as a pilot signal, a training signal, etc. The reference signal includes, for example, a sounding reference signal (SRS): used by the terminal to estimate the uplink transmission channel and obtain uplink channel state information; a demodulation reference signal (DM-RS) in the physical uplink shared channel (PUSCH) or the physical uplink control channel (PUCCH): used by the terminal to demodulate the downlink shared channel; a channel state information reference signal (CSI-RS): used by the terminal to estimate the 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.

[0106] In some embodiments, the first module may extract the reference signal contained in the first signal (e.g., the SRS signal, the DM-RS signal in PUSCH or PUCCH) or directly read the reference signal corresponding to the first signal stored in the base station, and then calculate the influence of the channel as the first information according to the relevant configuration information. Specifically, according to the configuration information of PUSCH transmission, the first module extracts the DM-RS signal corresponding to each transmitted data stream from the specific physical resource transmitting the first signal, and then divides the received DM-RS signal by the transmitted DM-RS signal specified in the configuration information, thereby obtaining the first information.

[0107] In some embodiments, after the first module obtains the channel state information of the physical resource where the reference signal is located from the first signal, it obtains the channel state information corresponding to all physical resources for each data stream through linear interpolation as the first information.

[0108] In some embodiments, the first information may include the channel state information corresponding to the first signal, including but not limited to: the channel state information corresponding to the first signal obtained from the first signal or stored in the base station (without loss of generality, the channel state information described in the embodiments of the present disclosure is the comprehensive influence of the entire wireless communication link on the amplitude, phase, and / or signal-to-noise ratio, etc. of the signal on all physical resources occupied by its transmission, which can be obtained according to the reference signal received by the base station); the information obtained after the channel state information obtained from the first signal or stored in the base station undergoes specific processing (e.g., interpolation, denoising, filtering, etc.) (e.g., after obtaining an intermediate signal through the MMSE algorithm, performing eigenvalue decomposition to obtain the PMI matrix and eigenvalues). For example, the channel state information corresponding to the first signal includes the channel state information obtained by channel estimation based on the first signal and / or the reference signal.

[0109] In some other examples, the channel state information corresponding to the first signal comes from the information stored in the base station itself, including but not limited to: the channel state information and / or the information obtained after its processing obtained from the signal and / or reference signal received by the base station last time; or the channel state information and / or the information obtained after its processing currently received and stored in the base station; the channel state information and / or the information obtained after its processing obtained by the base station through the channel feedback process of the terminal (e.g., the channel state information (CSI) measurement report) (e.g., the channel state information corresponding to the first signal reported by the terminal); the channel state information and / or the information obtained after its processing predicted by the base station based on the stored channel state information (e.g., the past channel information).

[0110] In some embodiments, the first module extracts, according to a preset antenna interval parameter a, signals received by antennas with antenna numbers 0, a, 2a, etc. from the first signal, and obtains channel state information therefrom as the first information.

[0111] In some embodiments, the first module may perform a moving average on the channel in the time domain (over multiple OFDM symbols), and use the averaged time-domain channel state information as the first information, i.e., H = (1 - α)H old + αH new , where H old is the previously stored channel, and H new is the channel obtained from the first signal, and α is the averaging coefficient.

[0112] In some embodiments, the first module may group the obtained channel state information in the subcarrier dimension. Every b consecutive subcarriers are divided into a group (the parameter b can be set according to the relevant bandwidth of the current environment), and then each group is averaged in the subcarrier dimension, and the averaged frequency-domain channel state information is used as the first information.

[0113] In some embodiments, the first module may average the obtained channel state information over the receiving antennas and / or transmitting antennas, that is, average the channel state information of different transceiver antenna pairs that occupy the same physical resources, and obtain the averaged channel state information for the receiving antennas and / or transmitting antennas as the first information.

[0114] In some embodiments, the first information may include channel characteristic information corresponding to the first information, which may be obtained based on the first information, or obtained by the base station and stored in the base station. The channel characteristic information may represent characteristics related to the channel, including but not limited to: the signal-to-interference-plus-noise ratio of the first signal, the correlation matrix of the channel used for the first signal or the correlation matrix of the first signal, information related to interference related to the first signal (e.g., the interference intensity of neighboring cells), the higher-order statistics of the channel of the first signal (e.g., the statistical expectation, variance, autocorrelation matrix, multipath delay spread parameter (delay spread)) of the channel, etc.

[0115] In some embodiments, the first module may use information related to the difference between the reference signal corresponding to the first signal and the expected received signal as the channel characteristic information corresponding to the first information. For example, the expected received signal corresponding to the corresponding physical resource may be reconstructed based on the first signal and the corresponding channel state information, and then the difference between the first signal and the expected received signal (i.e., the composite signal of interference and noise) is used as the channel characteristic information corresponding to the first information.

[0116] In some embodiments, the first module may use the covariance matrix of the interference in the current environment as the first information. The specific process is as follows: First, calculate the composite signal vector of the interference and noise on all antennas corresponding to each reference signal for each physical resource. Then, calculate the outer product of the signal vector with itself to obtain the antenna - to - antenna cross - correlation matrix. Finally, average the antenna - to - antenna cross - correlation matrices on all physical resources and approximate it as the covariance matrix of the interference in the current environment, that is, the first information. The above - mentioned embodiments for obtaining the first information are only exemplary, and the present disclosure is not limited thereto.

[0117] In some examples, the first module inputs the first information into a neural network to obtain the second information.

[0118] In some examples, based on the first information, through a neural network, the second information (i.e., information related to the combination of the first signal) can be determined. For example, the first module may: based on the first signal, through the first neural network, determine information related to the combination of the first signal; or based on the reference signal corresponding to the first signal, through the second neural network, determine information related to the combination of the first signal; or based on the channel state information corresponding to the first signal, through the third neural network, determine information related to the combination of the first signal; or based on the channel feature information corresponding to the first signal, through the fourth neural network, determine information related to the combination of the first signal.

[0119] For example, the neural network (e.g., the first neural network to the fourth neural network) may output one of 4 class labels, and each class label corresponds to dividing the first information into 2 groups, 4 groups, 8 groups, and 16 groups in sequence. The antenna port grouping method corresponding to a class label output by the neural network is the second information. Also, for example, the output of the neural network is one of 3 class labels, and each class label corresponds to a known in - group signal combination method. When the neural network outputs label 0, the fourth information is to select the antenna with the maximum received energy within the group; when the neural network outputs label 1, the fourth information is to select the average of all signals within the group; when the neural network outputs label 2, the fourth information is to perform weighted averaging on the in - group signals according to the channel gains of each antenna.

[0120] In some examples, determining information related to the combination of the first signal based on the first signal may include: determining information related to the combination of the first signal based on the first signal and auxiliary information, where the auxiliary information includes information related to resource configuration for the first signal and / or information related to the channel environment for the first signal. As an exemplary embodiment, the auxiliary information may be at least one of position information, structure information, distribution information of physical resources, and relative positions of physical resources. The auxiliary information may also be a constraint on a certain characteristic quantity of the channel in the current scenario, such as variance of the channel, delay spread, correlation time, etc. The information processing unit may also convert the relevant information into at least one of vectors, matrices, and tensors for neural network layer calculations.

[0121] In some examples, a neural network may be used to determine information related to the combination of the first signal based on the first signal and auxiliary information.

[0122] In some embodiments, the third information for indicating the manner of antenna port grouping related to the first signal may include indices of antenna ports included in each antenna port group and / or information related to the number of groups.

[0123] In some examples, the first module may determine the second information based on the comparison result of the first information with a preset threshold, that is, determine at least one of the third information and the fourth information. For example, when the channel characteristic information (e.g., signal-to-interference-plus-noise ratio) included in the first information is greater than the threshold β, the antenna port grouping manner is sequentially divided into 8 groups, and when the channel characteristic information is less than the threshold β, the antenna port grouping manner is sequentially divided into 16 groups. Another example is that when the inter-antenna correlation in the channel state information in the first information is less than the threshold γ, the in-group signal combination manner in the second information is to select the signal with the maximum received energy in the group as the output, and when the signal-to-noise ratio is greater than the threshold γ, the in-group signal combination manner in the second information is to use the average of all signals in the group as the output. Another example is that the output of the first neural network is the complex coefficients used for in-group signal combination of each antenna group (the real part and the imaginary part of each coefficient are output separately, and the real part and the imaginary part of the coefficient are combined into the corresponding signal weight coefficient), and when combining the in-group signals, each signal is multiplied by the corresponding coefficient and then summed to obtain the second signal.

[0124] In some examples, the first module inputs the first signal and the first information into a neural network, and directly performs antenna port combination on the first signal through the neural network, thereby generating the second signal. In this example, the operation of determining information related to the combination of the first signal and combining the first signal based on the determined information is performed through the neural network. In some examples, it is also possible to determine information related to the combination of the first signal through one neural network and combine the first signal through another neural network.

[0125] In step S503, based on the determined information (i.e., the second information), the first signal is combined in terms of antenna port dimension to obtain a second signal, where the number of antenna ports related to the second signal is less than the number of antenna ports related to the first signal. For example, signals received by different antenna ports in the first signal are combined to obtain the second signal, such that the number of antenna ports related to the second signal is less than the number of antenna ports related to the first signal.

[0126] In some embodiments, after obtaining the second information, the first module first groups according to the third information indicating the way of grouping the antenna ports related to the first signal in the second information, then obtains the output signal of each group in turn according to the fourth information related to the output signal corresponding to the antenna port group in the second information, and finally combines the output signals of all groups side by side as the second signal, where each group is regarded as a new equivalent receiving antenna. When the second information only contains the third information, the signals within the group can be combined in a fixed or preset combination manner. When the second information only contains the fourth information, the grouping of the first signal can be performed in a fixed or preset grouping manner.

[0127] The way of grouping the antenna ports related to the first signal may include at least one of the following: grouping based on the order of antenna port indexes; the first signal parts included in each group are the same or all the same; grouping based on a predefined grouping table; and based on a predefined signal selection rule, selecting at least one signal from the first signal and dividing the antenna ports related to the at least one signal into a group.

[0128] In the present disclosure, the way of grouping the antenna ports related to the first signal indicates the way of antenna grouping for the first signal, that is, which antenna received signals should be divided into a group. The way of antenna port grouping includes but is not limited to the following:

[0129] Grouping based on the order of antenna port indexes;

[0130] The first signal parts included in each group are the same or all the same;

[0131] Grouping based on a predefined grouping table; and

[0132] Based on a predefined signal selection rule, selecting at least one signal from the first signal and dividing the antenna ports related to the at least one signal into a group.

[0133] In some embodiments, the antenna port grouping is a non - overlapping division, that is, the received signal of each antenna port is only divided into one group. For example, the first signal is sequentially divided into N groups based on the order of antenna port indices, and each group corresponds to the received signals of M antenna ports, where NM is equal to the total number of antennas. For example, there are a total of 9 antenna ports. Among them, antenna ports numbered 1 - 3 are divided into one group, antenna ports numbered 4 - 6 are divided into one group, and antenna ports numbered 7 - 9 are divided into one group.

[0134] In some embodiments, the way of antenna port grouping can be determined by a predefined signal selection rule. For example, the predefined signal selection rule can be that the signal is divided into one group every n intervals according to the antenna port order. For example, the antennas numbered odd / even are divided into one group (that is, divided into one group every 2 intervals). For example, the predefined signal selection rule can be to select at least one signal from the first signal and divide the antenna ports related to the at least one signal into one group. For example, the received signals of certain antenna ports can be directly selected and divided into one group (for example, the received signals of antenna ports numbered 1, 4, and 9 are divided into one group, the received signals of antenna ports numbered 2, 5, and 8 are divided into another group, etc.).

[0135] In some embodiments, the antenna port grouping is an overlapping division, that is, the received signal of each antenna is divided into one or more groups, and the first signal parts included in each group are the same or all the same. For example, the received signal of the antenna is sequentially divided into N groups, each group corresponds to the received signals of M antennas, and there are m overlaps in the signals of each group, where N(M - m) is equal to the total number of antennas. For example, each group contains the received signals on all antennas, that is, each group contains all of the first signal.

[0136] In some embodiments, the way of antenna port grouping can be predefined and stored in the first module of the base station, for example. When grouping the antenna ports, the stored grouping method is read, and based on this grouping method, the antenna ports are grouped.

[0137] In the present disclosure, the output signals corresponding to the antenna port groups include but are not limited to:

[0138] The signal with the maximum signal energy in the antenna port group;

[0139] The signal with the maximum L2 norm of the corresponding channel gain in the antenna port group;

[0140] The signal with the maximum average energy in the antenna port group; and / or

[0141] The signal after weighted average of the signals in the antenna port group, where the weights for the weighted average are determined based on the first signal.

[0142] In some embodiments, the signal combining method is to directly calculate the average energy of the received signals on each antenna in the antenna port group, and select the signal with the maximum signal energy in the antenna port group as the output signal of the group.

[0143] In some embodiments, the signal combining method is to calculate the L2 norm of the channel gain for each antenna, and select the received signal of the antenna corresponding to the maximum L2 norm as the output signal of the group.

[0144] In some embodiments, the signal combining method is to directly calculate the average energy of the received signals on each antenna within the group, and select the signal with the maximum average energy as the output signal of the group.

[0145] In some embodiments, the received signals on each antenna within the group have the same weight, and the output signal is the average of all signals within the group.

[0146] In some embodiments, the weights for weighted averaging can be determined based on the first signal. For example, the weights are from a certain fixed codebook (e.g., a spatial DFT oversampled codebook), and according to the codeword indicated by the second information, the output signal is the signal obtained by weighted averaging the first signal using the codeword as the weight. For example, select the codeword in a certain fixed codebook that has the maximum correlation with the channel state information in the first information as the weight, and use the weighted averaged signal as the output signal.

[0147] In some embodiments, it is necessary to perform eigenvalue decomposition on the channel state information, and then sequentially select the eigenvectors corresponding to the first c (c is the number of antenna groups) largest eigenvalues as the weights for each group, and use the inner product of the received signal and the eigenvector as the output signal of this group.

[0148] In some embodiments, perform time-domain and / or frequency-domain and / or antenna-domain averaging processing on the channel state information, and directly use the conjugate of the average gain (a complex number) of each antenna given in the averaged channel state information as the weight, and multiply the received signal by the weight of the antenna and sum them as the output signal of this group.

[0149] In some embodiments, the first module may further perform an equalization operation (to be described below) on the combined second signal, and pass the equalized signal to the second module as the second signal.

[0150] In some embodiments, method 50 may further include: obtaining a channel equalization matrix; and equalizing the second signal based on the channel equalization matrix.

[0151] Further, in method 50, based on the equalized second signal, signal detection is performed.

[0152] In some examples, the process in this step is completed in the first module, and the equalized second signal will be transmitted from the first module to the second module via a communication connection. The second module is configured to perform signal detection based on the equalized second signal. In some examples, the second signal is transmitted from the first module to the second module via a communication connection, and the process in this step is completed in the second module, that is, the second module is configured to: obtain a channel equalization matrix; equalize the second signal based on the channel equalization matrix; and perform signal detection based on the equalized second signal. Among them, the communication connection between the first module and the second module can use a wired method (such as optical fiber, etc.) or a wireless method (such as microwave, etc.). The second module refers to the entity in the receivers on the gNB 101, gNB 102, and gNB 103 sides for implementing signal processing and information extraction functions, and it should include the channel decoding and demodulation block 280 in the aforementioned receiving path 250. In some examples, the second module is the DU module in the aforementioned base station structure that includes some physical layer functions (PHY), MAC layer, and RLC layer functions.

[0153] In some examples, obtaining the channel equalization matrix may include: obtaining the channel equalization matrix based on the channel state information corresponding to the first signal; or performing channel estimation on the second signal to obtain the channel state information corresponding to the second signal, and obtaining the channel equalization matrix based on the channel state information corresponding to the second signal.

[0154] The channel equalization matrix used when equalizing the second signal can be obtained through the channel state information of the first signal or the channel state information of the second signal.

[0155] In some examples, the channel equalization matrix can be directly obtained through the channel state information corresponding to the first signal. For example, in some embodiments, the first signal is y1, the channel state information matrix corresponding to the first signal is H1, and its conjugate transpose matrix As the coefficient for combining the antenna port dimensions of the first signal, the second signal is generated The corresponding channel equalization matrix is The signal after channel equalization is

[0156] In some other examples, the channel equalization matrix can be obtained through the channel state information corresponding to the second signal. Among them, the channel state information corresponding to the second signal can be obtained through the channel state information corresponding to the first signal or a reference signal. For example, the channel state information corresponding to the second signal is H2, and the second module can calculate the corresponding channel equalization matrix using the MMSE algorithm The equalized signal can be obtained by multiplying the equalization matrix by the second signal. In some embodiments, the channel state information of the second signal is derived from the combination of the channel state information corresponding to the first signal and the second information. Specifically, based on the third information and the fourth information in the second information, the channel state information corresponding to the first signal is grouped and combined to obtain the channel state information corresponding to the second signal.

[0157] In some embodiments, a corresponding reference signal can be extracted from the second signal, and the channel state information corresponding to the second signal can be obtained according to the reference signal.

[0158] In step S504, the second module performs signal detection based on the second signal, where the goal of the detection is to obtain the information sent by the terminal communicating with the base station. Specifically, it includes: demodulation (such as QAM demodulation) and decoding (such as LDPC decoding, turbo decoding, polar decoding).

[0159] In some embodiments, the second module will look up the corresponding QAM modulation constellation diagram according to the second signal, obtain the corresponding bit data according to the corresponding constellation symbol, and then input the bit data into the LDPC decoder to obtain the information sent by the terminal communicating with the base station.

[0160] In some other embodiments, the second module will calculate the soft information (e.g., likelihood probability ratio) of each QAM modulation constellation symbol or bit information according to the second signal, and then input the soft information into the LDPC soft information decoder to obtain the information sent by the terminal communicating with the base station.

[0161] The signal processing method 50 provided by the above embodiments of the present disclosure can be applied to the receivers on the side of base stations 101, 102, and 103 as shown in Figure 1 In some embodiments of the present disclosure, base stations 101, 102, and 103 can be macro base stations, micro base stations, pico base stations, femto base stations for radio access networks, backhaul base stations for wireless backhaul, and base stations that integrate both access and backhaul functions, etc.

[0162] The signal processing method 50 provided by the above embodiments of the present disclosure can still accurately obtain the information sent by the terminal while significantly reducing the amount of data transmitted between the internal functional modules of the base station, thus solving the problem of excessive data transmission load between two constituent modules of the base station (e.g., RU unit and DU unit). At the same time, since the antenna dimension of the second signal is significantly reduced, the computational burden of the signal detection algorithm for it is also significantly reduced.

[0163] Figure 6Shows an example structure of a neural network in some embodiments according to the present disclosure. The neural network can be jointly and / or independently implemented by the same physical entity or different entity units, and the physical entity can be composed of hardware, software, or a combination thereof, which can be configured by those skilled in the art according to actual needs.

[0164] As Figure 6 shown, the information processing unit can process the input information (e.g., the first information, the first signal, the auxiliary information) (such as merging, position encoding, etc. to be described below), enabling the neural network to obtain the prior information implicit in the relevant information to assist the neural network in improving the accuracy and flexibility of obtaining the channel state information.

[0165] Continuing to refer to Figure 6 , a neural network unit can include an information processing unit and multiple neural network layers. The information processing unit can convert the data to be input into the neural network (such as Figure 6 the input information and the auxiliary information therein) into at least one of a data vector, matrix, and tensor that the neural network can use for calculation. The neural network layer can be at least one of the input layer, hidden layer / intermediate layer, and output layer shown in Figure 6 . In addition, each neural network layer can be composed of multiple neurons n, and at least one activation function can be used to activate the neurons. By way of example only and not limitation, for example, the tanh function, ReLU function, eLU function, seLU function, ceLU function, preLU function, geLU function, LeakyReLU function, Sigmoid function, Softmax function, Softplus function, etc. Each neural network layer can perform operations on its input data to achieve functions such as matrix transformation, data dimensionality reduction, data feature extraction, data feature combination, etc. By way of example only and not limitation, multiple neural network layers can be combined into different neural network structures in series or in parallel, including but not limited to multi-layer perceptron (MLP), multi-layer perceptron mixer (MLP-mixer), convolutional neural network (CNN), deep neural network (DNN), recurrent neural network (RNN), restricted Boltzmann machine (RBM), deep belief network (DBN), bidirectional recurrent deep neural network (BRDNN), generative adversarial network (GAN), transform network (Transformer), etc.

[0166] In some embodiments of the present disclosure, the information processing unit can convert the input information into at least one of a vector, matrix, and tensor for the calculation of the neural network layer. By way of example only and not limitation, define the set composed of known channel state information as C RS. First, the known channel state information is processed by a numerical mapping method (e.g., normalization, standardization, etc.) to make its numerical scale or distribution suitable for neural network calculations. More specifically, taking Min-Max standardization as an example, the maximum value max(C RS ) and the minimum value min(C RS ) of all elements in RS can be calculated first, and then each element RS in C is processed as follows:

[0167]

[0168] After the above numerical mapping, the set composed of the processed elements can be converted into at least one of a vector, a matrix, and a tensor corresponding to the input size of the neural network layer.

[0169] In some other embodiments of the present disclosure, the information processing unit can also perform position encoding on the input information and convert the position-encoded information into at least one of a vector, a matrix, and a tensor for neural network calculations. Optionally, the position encoding can be a process of adding and / or multiplying the input information with the codewords in a predetermined codebook. Only by way of example and not limitation, taking the preprocessing unit as an example to output the known channel state information (i.e., the preprocessing unit outputs the channel state information on the first resource according to the input first signal), the position encoding using the codebook can include the following processing:

[0170] First, the set C RS composed of the known channel state information is processed according to a numerical mapping method to obtain a set containing several numerically mapped elements Subsequently, the codebook for position encoding is obtained according to the following equation.

[0171]

[0172] where n represents the position of the channel state information in the physical resource (such as time domain, frequency domain, and spatial domain), d represents the physical resource dimension of the channel state information, N is any positive number greater than the number of elements contained in the set , and i represents the index of the physical resource dimension. Subsequently, the known channel state information element is position-encoded according to the equation to obtain a set Finally, the set composed of the processed elements Converted into at least one of a vector, a matrix, and a tensor corresponding to the input size of the neural network layer. In addition, the information processing unit may also directly input its input to the neural network unit without additional processing.

[0173] The operations in the above neural network unit are only exemplary, and the present disclosure is not limited thereto.

[0174] According to some embodiments of the present disclosure, as Figure 7 shown, the present disclosure also provides a base station 700. The base station 700 includes: a first module 710 and a second module 720. Wherein:

[0175] The first module 710 is configured to:

[0176] Receive a first signal through the antenna port of the base station; and

[0177] Combine the first signal in the antenna port dimension to obtain a second signal, where the number of antenna ports related to the second signal is less than the number of antenna ports related to the first signal;

[0178] The second module 720 is configured to:

[0179] Perform signal detection based on the second signal.

[0180] In some embodiments, the first module 710 may be an RU unit in the base station, and the second module 720 may be a DU unit in the base station.

[0181] In some embodiments, the first module 710 may also be configured to: obtain a channel equalization matrix; and equalize the second signal based on the channel equalization matrix; and the second module is configured to: perform signal detection based on the equalized second signal.

[0182] In some embodiments, the second module 720 may also be configured to: obtain a channel equalization matrix; and equalize the second signal based on the channel equalization matrix; and perform signal detection based on the equalized second signal.

[0183] In some embodiments, the specific configurations of the first module 710 and / or the second module 720 are the same as or similar to those described in the foregoing method 50, and will not be elaborated herein one by one.

[0184] One or more of the above embodiments of the present disclosure can be applied to various communication systems, such as: Global System for Mobile Communications (GSM) system, Code Division Multiple Access (CDMA) system, Wideband Code Division Multiple Access (WCDMA) system, General Packet Radio Service (GPRS), Long Term Evolution (LTE) system, LTE Frequency Division Duplex (FDD) system, LTE Time Division Duplex (TDD), Universal Mobile Telecommunication System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX) communication system, 5th generation (5G) system, or New Radio (NR), etc. In addition, the technical solutions of the embodiments of the present application can be applied to future-oriented communication technologies.

[0185] An electronic device is also provided in the embodiments of the present disclosure. The electronic device includes a processor. Optionally, it may further include a transceiver and / or a memory coupled to the processor. The processor is configured to execute the steps of the method provided in any optional embodiment of the present disclosure. The electronic device may be the base station described above.

[0186] Figure 8 A schematic structural diagram of an electronic device to which the embodiments of the present disclosure are applicable is shown, as Figure 8 shown, Figure 8 As shown, the electronic device 800 includes: a processor 801 and a memory 803. Among them, the processor 801 and the memory 803 are connected, such as through a bus 802. Optionally, the electronic device 800 may further include a transceiver 804, and the transceiver 804 can be used for data interaction between the electronic device and other electronic devices, such as data sending and / or data receiving, etc. It should be noted that in practical applications, the transceiver 804 is not limited to one, and the structure of the electronic device 800 does not constitute a limitation to the embodiments of the present disclosure. Optionally, the electronic device may be a first network node, a second network node, or a third network node.

[0187] The processor 801 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in connection with the present disclosure. The processor 801 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0188] The bus 802 may include a path for transmitting information between the above components. The bus 802 may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 802 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 only a thick line is shown herein, but it does not mean that there is only one bus or one type of bus.

[0189] The memory 803 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory), or other type of dynamic storage device that can store information and instructions. It may also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium that can be used to carry or store a computer program and can be read by a computer, which is not limited herein.

[0190] The memory 803 is used to store the computer program for implementing the embodiments of the present disclosure and is controlled by the processor 801 to execute. The processor 801 is used to execute the computer program stored in the memory 803 to implement the steps shown in the foregoing method embodiments.

[0191] Figure 9 An example of a computer-readable medium 900 in the form of a CD or DVD is shown. A program is stored on the computer-readable medium.

[0192] Generally, the various embodiments of the present disclosure can be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects can be implemented in hardware, while other aspects can be implemented in firmware or software, which can be executed by a controller, a microprocessor, or other computing devices. Although the various aspects of the embodiments of the present disclosure are shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that the blocks, devices, systems, techniques, or methods described herein can be implemented as, by way of non-limiting example, hardware, software, firmware, dedicated circuits or logic, general-purpose hardware or a controller or other computing devices, or some combination thereof.

[0193] The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which are executed in a device on a target real or virtual processor to perform the method as referred to above Figure 5 The method. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided as needed. The machine-executable instructions for program modules can be executed within a local or distributed device. In a distributed device, program modules can be located in local and remote storage media.

[0194] The computer program code for implementing the method of the present disclosure can be written in one or more programming languages. These computer program codes can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program code is executed by the computer or other programmable data processing devices, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the computer, partially on the computer, as a stand-alone software package, partially on the computer and partially on a remote computer, or entirely on a remote computer or server.

[0195] In the context of the present disclosure, the computer program code or relevant data can be carried by any suitable carrier so that the device, apparatus or processor can execute the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals can include electrical, optical, radio, sound or other forms of propagated signals, such as carrier waves, infrared signals, etc.

[0196] A computer-readable medium can be any tangible medium that contains or stores a program for or relevant to an instruction execution system, apparatus or device. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, apparatuses or devices, or any suitable combination thereof. More detailed examples of computer-readable storage media include electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), optical storage devices, magnetic storage devices, or any suitable combination thereof. As used herein, the term "non-transitory" or "non-transient" is a limitation on the medium itself (i.e., tangible, rather than a signal), rather than a limitation on the persistence of data storage (e.g., RAM vs. ROM).

[0197] Furthermore, although the operations of the methods of the present disclosure are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the illustrated operations must be performed to achieve the desired result. On the contrary, the steps depicted in the flowcharts can be changed in the order of execution. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution. It should also be noted that the features and functions of two or more devices according to the present disclosure can be embodied in one device. Conversely, the features and functions of one device described above can be further divided and embodied by multiple devices.

[0198] The terms "first", "second", "third", "fourth", "1", "2", etc. (if any) in the description and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than that shown or described in words.

[0199] It should be understood that although the flowchart of the embodiments of the present disclosure indicates various operation steps by arrows, the execution order of these steps is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of the embodiments of the present disclosure, the implementation steps in each flowchart can be executed in other orders according to requirements. In addition, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on the actual implementation scenario. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage among these sub-steps or stages can also be executed at different times respectively. In the scenario where the execution times are different, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and the embodiments of the present disclosure do not limit this.

[0200] The above text and drawings are provided only as examples to assist the reader in understanding the present disclosure. They are not intended 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 the content disclosed herein, it will be apparent to those skilled in the art that the shown embodiments and examples can be changed without departing from the scope of the present disclosure, and other similar implementation means based on the technical idea of the present disclosure can be adopted, which also fall within the protection scope of the embodiments of the present disclosure.

Claims

1. A method performed by a base station in a communication system, characterized in that, The method includes: Receiving a first signal via an antenna port of the base station; Determining information related to the combination of the first signal based on the first signal; Combining the first signal in the dimension of antenna ports based on the determined information to obtain a second signal, where the number of antenna ports related to the second signal is less than the number of antenna ports related to the first signal; and Performing signal detection based on the second signal.

2. The method according to claim 1, wherein The first signal includes at least one of the following: A radio frequency signal received by an antenna port of the base station; A digital domain wireless signal received by the antenna port of the base station; And A signal obtained based on the radio frequency signal and / or the digital domain wireless signal.

3. The method according to claim 1, wherein Determining information related to the combination of the first signal based on the first signal includes: Determining information related to the combination of the first signal based on the first signal through a first neural network; or Determining information related to the combination of the first signal based on a reference signal corresponding to the first signal through a second neural network; or Determining information related to the combination of the first signal based on channel state information corresponding to the first signal through a third neural network; or Determining information related to the combination of the first signal based on channel feature information corresponding to the first signal through a fourth neural network.

4. The method according to claim 3, wherein The reference signal is obtained based on a sounding reference signal and / or a demodulation reference signal.

5. The method according to claim 3, wherein, The channel state information corresponding to the first signal includes at least one of the following: Channel state information obtained by performing channel estimation based on the first signal and / or the reference signal; Channel state information stored in the base station; Channel state information corresponding to the first signal reported by a terminal; And Channel state information predicted by the base station based on stored channel state information.

6. The method according to claim 5, further including: Processing the channel state information, where the processing includes one or more of interpolation, denoising, filtering, averaging in the time domain, grouping and averaging in the subcarrier dimension, and averaging in the antenna domain.

7. The method according to claim 3, wherein, The channel feature information corresponding to the first signal includes at least one of the following information: The signal-to-interference-plus-noise ratio of the first signal; A correlation matrix of a channel for the first signal or a correlation matrix of the first signal; Information related to interference related to the first signal; Higher-order statistics of a channel for the first signal; and Information related to the difference between a reference signal corresponding to the first signal and an expected received signal, where the expected received signal is obtained based on the reference signal and the channel state information corresponding to the first signal.

8. The method according to claim 1, wherein Determining information related to the combination of the first signal based on the first signal includes: Determining information related to the combination of the first signal based on a comparison result between the channel feature information corresponding to the first signal and a threshold.

9. The method according to any one of claims 1-8, wherein, The information related to the combination of the first signal includes at least one of the following: Third information for indicating a manner of grouping antenna ports related to the first signal; and Fourth information related to an output signal corresponding to an antenna port group.

10. The method according to claim 9, wherein The third information includes at least one of the following: The index of the antenna ports included in each antenna port group; and Information related to the number of groups.

11. The method according to claim 9, wherein The ways of grouping the antenna ports related to the first signal include at least one of the following: Grouping based on the order of the antenna port indices; The first signal parts included in each group are the same or all the same; Grouping based on a predefined grouping table; And Based on a predefined signal selection rule, select at least one signal from the first signal, and divide the antenna ports related to the at least one signal into a group.

12. The method according to claim 9, wherein, The output signal corresponding to the antenna port group includes at least one of the following: The signal with the maximum signal energy in the antenna port group; The signal with the maximum L2 norm of the corresponding channel gain in the antenna port group; The signal with the maximum average energy in the antenna port group; And The signal after weighted averaging of the signals in the antenna port group, where the weights for the weighted averaging are determined based on the first signal.

13. The method according to claim 1, wherein, Based on the first signal, determine information related to the combination of the first signal, including: Based on the first signal and auxiliary information, determine information related to the combination of the first signal, where the auxiliary information includes information related to the resource configuration for the first signal and / or information related to the channel environment for the first signal.

14. The method according to claim 1, further comprising: Obtain a channel equalization matrix; And based on the channel equalization matrix, equalize the second signal; Based on the second signal, performing signal detection includes: performing signal detection based on the equalized second signal.

15. The method according to claim 14, wherein, Obtaining a channel equalization matrix includes: Obtain a channel equalization matrix based on the channel state information corresponding to the first signal; or Perform channel estimation on the second signal to obtain the channel state information corresponding to the second signal, and obtain a channel equalization matrix based on the channel state information corresponding to the second signal.

16. The method according to claim 15, wherein, The obtaining of the channel equalization matrix based on the state information of the channel corresponding to the first signal includes: Based on the channel state information corresponding to the first signal, obtain the channel state information corresponding to the second signal; and Based on the channel state information corresponding to the second signal, obtain a channel equalization matrix.

17. A base station, comprising: A first module, configured to: Receive a first signal through the antenna ports of the base station; Based on the first signal, determine information related to the combination of the first signal; Based on the determined information, combine the first signal in the antenna port dimension to obtain a second signal, where the number of antenna ports related to the second signal is less than the number of antenna ports related to the first signal; And A second module, configured to: Based on the second signal, perform signal detection.

18. The base station according to claim 17, wherein, The first module is further configured to: Obtain a channel equalization matrix; and based on the channel equalization matrix, equalize the second signal; and The second module is configured to: perform signal detection based on the equalized second signal.

19. The base station according to claim 17, wherein, The second module is configured to: Obtain a channel equalization matrix; Based on the channel equalization matrix, equalize the second signal; And Perform signal detection based on the balanced second signal.

20. A base station, comprising: At least one processor; And At least one memory storing instructions that, when executed by the at least one processor, implement the method according to any one of claims 1-16.

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