Method performed by base station in communication system and base station performing the method

Abstract

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. The present disclosure provides 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, and relates to the field of artificial intelligence. The method performed by the base station in the communication system comprises: receiving a first signal through an antenna port of the base station; determining information related to combining of the first signal based on the first signal; performing combining on the first signal in an antenna port dimension based on the determined information, to obtain a second signal, wherein a number of antenna ports related to the second signal is less than a number of antenna ports related to the first signal; and performing a signal detection based on the second signal. The present disclosure solves the problems that the number of antenna ports of signals to be detected is too high and the amount of data is too large, which not only reduces the data transmission load between the modules in the base station, but also significantly reduces the computational burden of the signal detection.

Description

METHOD PERFORMED BY BASE STATION IN COMMUNICATION SYSTEM AND BASE STATION PERFORMING THE METHOD

[0001] Embodiments of the present disclosure generally relate to the field of communication technology, and more specifically 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.

[0002] Fifth generation (5G) mobile communication technologies define broad frequency bands such that high transmission rates and new services are possible, and can be implemented not only in "sub 6 gigahertz (GHz)" bands such as 3.5GHz, but also in "above 6GHz" bands referred to as millimeter wave (mmWave) including 28GHz and 39GHz. In addition, implementing sixth generation (6G) mobile communication technologies (referred to as Beyond 5G systems) in terahertz (THz) bands (for example, 95GHz to 3THz bands) in order to accomplish transmission rates fifty times faster than 5G mobile communication technologies and ultra-low latencies one-tenth of 5G mobile communication technologies is being considered.

[0003] At the beginning of the development of 5G mobile communication technologies, in order to support services and to satisfy performance requirements in connection with enhanced mobile broadband (eMBB), ultra reliable low latency communications (URLLC), and massive machine-type communications (mMTC), there has been ongoing standardization regarding beamforming and massive multi input multi output (MIMO) for mitigating radio-wave path loss and increasing radio-wave transmission distances in mmWave. In addition, supporting numerologies (for example, operating multiple subcarrier spacings) for efficiently utilizing mmWave resources and dynamic operation of slot formats, initial access technologies for supporting multi-beam transmission and broadbands, definition and operation of bandwidth part (BWP), new channel coding methods such as a low density parity check (LDPC) code for large amounts of data transmission and a polar code for highly reliable transmission of control information, layer two (L2) pre-processing, and network slicing for providing a dedicated network specialized to a specific service are also being used to support services and to satisfy performance requirements.

[0004] Currently, there are ongoing discussions regarding improvement and performance enhancement of initial 5G mobile communication technologies in view of services to be supported by 5G mobile communication technologies, and there has been physical layer standardization regarding technologies such as vehicle-to-everything (V2X) technologies for aiding driving determination by autonomous vehicles based on information regarding positions and states of vehicles transmitted by the vehicles and for enhancing user convenience, new radio unlicensed (NR-U) technologies aimed at system operations conforming to various regulation-related requirements in unlicensed bands, new radio (NR) user equipment (UE) power saving technologies, non-terrestrial network (NTN) technologies, which are UE-satellite direct communication technologies for providing coverage in an area in which communication with terrestrial networks is unavailable, and positioning technologies.

[0005] Moreover, there has been ongoing standardization in air interface architecture / protocol regarding technologies such as industrial Internet of things (IIoT) for supporting new services through interworking and convergence with other industries, integrated access and backhaul (IAB) for providing a node for network service area expansion by supporting a wireless backhaul link and an access link in an integrated manner, mobility enhancement including conditional handover and dual active protocol stack (DAPS) handover, and two-step random access for simplifying random access procedures (for example, 2-step random access channel (RACH) for NR). There also has been ongoing standardization in system architecture / service regarding a 5G baseline architecture (for example, service based architecture or service based interface) for combining network functions virtualization (NFV) and software-defined networking (SDN) technologies, and mobile edge computing (MEC) for receiving services based on UE positions.

[0006] As 5G mobile communication systems are commercialized, connected devices will be connected to communication networks, and it is expected that enhanced functions and performances of 5G mobile communication systems and integrated operations of connected devices will be necessary. To this end, new research is scheduled in connection with extended Reality (XR) for efficiently supporting augmented reality (AR), virtual reality (VR), and mixed reality (MR). 5G performance improvement and complexity reduction may be accomplished by utilizing artificial intelligence (AI) and machine learning (ML), AI service support, metaverse service support, and drone communication.

[0007] Furthermore, such development of 5G mobile communication systems will serve as a basis for developing new waveforms for providing coverage in THz bands of 6G mobile communication technologies, multi-antenna transmission technologies such as full dimensional multiple input multiple output (FD-MIMO), array and large-scale antennas, metamaterial-based lenses and antennas for improving coverage of THz band signals, high-dimensional space multiplexing technology using orbital angular momentum (OAM), reconfigurable intelligent surface (RIS) technology, full-duplex technology for increasing frequency efficiency of 6G mobile communication technologies and improving system networks, AI-based communication technology for implementing system optimization by utilizing satellites and AI from the design stage and internalizing end-to-end AI support functions, and next-generation distributed computing technology for implementing services at levels of complexity exceeding the limit of UE operation capability by utilizing ultra-high-performance communication and computing resources.

[0008] 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 called "Beyond 4G networks" or "Post-LTE systems".

[0009] In order to achieve a higher data rate, 5G communication systems are implemented in higher frequency (millimeter, mmWave) bands, e.g., 60 GHz bands. In order to reduce propagation loss of radio waves and increase a transmission distance, technologies such as beamforming, massive multiple-input multiple-output (MIMO), full-dimensional MIMO (FD-MIMO), array antenna, analog beamforming and large-scale antenna are discussed in 5G communication systems.

[0010] In addition, in 5G communication systems, developments of system network improvement are underway based on advanced small cell, cloud radio access network (RAN), ultra-dense network, device-to-device (D2D) communication, wireless backhaul, mobile network, cooperative communication, coordinated multi-points (CoMP), reception-end interference cancellation, etc.

[0011] In 5G systems, hybrid FSK and QAM modulation (FQAM) and sliding window superposition 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.

[0012] Since the rate of the data 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 processing delay of the base station to increase rapidly. What is more serious is that when the buffer queue of the RU unit is filled, the data to be subsequently received by the antenna will be naturally lost, causing severe system performance loss or even a system crash. Therefore, as the demands of the users for the transmission rate continuously increase and the amount of data continues continuously increases, the structural design of the traditional base station for wireless communication is challenged. It is required to enhance the data transmission (e.g., compress data) to match the increasing amount of data.

[0013] The above problem can be solved by performing compression and dimension reduction on the received signal. However, the current mainstream data compression method requires a large amount of calculation, and a large transmission delay is even more introduced. Furthermore, lossy compression will significantly reduce the signal-to-noise ratio of the received data, resulting in impaired system performance. In addition, the problem of an exponential increase in the amount of data due to the increase in the scale of the antenna cannot be solved.

[0014] The antenna port reduction technique effectively circumvents the above problems. The antenna port reduction technique reduces the number of antenna ports receiving data, by combining signals on the reception antennas. Considering that the number of uplink transmission streams of the current NR system is limited (8 layers at most), the number of layers of the combined signal needs to be only greater than the number of the uplink transmission streams of the current system. The antenna port reduction does not necessarily reduce the signal-to-noise ratio of the processed data, but may generate a signal-to-noise ratio gain, causing a system performance improvement.

[0015] However, the antenna combining scheme used is highly sensitive to the channel environment where the system is, which results in that a certain antenna signal combining scheme, although excellent in performance in some scenarios, will cause serious performance degradation in some other scenarios, and therefore, the technique is in lack of performance stability.

[0016] In a first aspect of the present disclosure, a method performed by a base station in a communication system is provided. The method comprises: receiving a first signal through an antenna port of the base station; determining information related to combining of the first signal based on the first signal; performing combining on the first signal in an antenna port dimension based on the determined information, to obtain a second signal, wherein a number of antenna ports related to the second signal is less than a number of antenna ports related to the first signal; and performing a signal detection based on the second signal.

[0017] In some embodiments, the first signal may comprise at least one of: a radio signal received by the antenna port of the base station; a digital domain wireless signal received by the antenna port of the base station; or a signal obtained based on the radio signal and / or the digital domain wireless signal.

[0018] In some embodiments, the determining information related to combining of the first signal based on the first signal may comprise at least one of: determining, based on the first signal, the information related to the combining of the first signal through a first neural network; determining, based on a reference signal corresponding to the first signal, the information related to the combining of the first signal through a second neural network; determining, based on channel state information corresponding to the first signal, the information related to the combining of the first signal through a third neural network; or determining, based on channel feature information corresponding to the first signal, the information related to the combining of the first signal through a fourth neural network.

[0019] In some embodiments, the reference signal may be obtained based on a detecting reference signal and / or a demodulation reference signal.

[0020] In some embodiments, the channel state information corresponding to the first signal may comprise at least one of: channel state information obtained by performing a channel estimation based on the first signal and / or the reference signal; channel state information stored in the base station; channel state information reported by a terminal and corresponding to the first signal; or channel state information predicted by the base station based on the stored channel state information.

[0021] In some embodiments, the method may further comprise: performing processing on the channel state information, wherein the processing comprises at least one of interpolating, denoising, filtering, averaging over a time domain, grouping and averaging in a subcarrier dimension, or averaging over an antenna domain.

[0022] In some embodiments, the channel feature information corresponding to the first signal may comprise at least one of: a 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; interference-related information related to the first signal; higher-order statistics of the channel for the first signal; and information related to a difference between the reference signal corresponding to the first signal and an expected received signal, wherein the expected received signal is obtained based on the reference signal and the channel state information corresponding to the first signal.

[0023] In some embodiments, the determining information related to combining of the first signal based on the first signal may comprise: determining the information related to the combining of the first signal based on a comparison result between the channel feature information corresponding to the first signal and a threshold value.

[0024] In some embodiments, the information related to the combining of the first signal may comprise at least one of: third information for indicating a grouping mode of an antenna port related to the first signal; or fourth information related to an output signal corresponding to an antenna port group.

[0025] In some embodiments, the third information may comprise at least one of: indices of antenna ports contained in each antenna port group; or information related to a number of groups.

[0026] In some embodiments, the grouping mode of the antenna port related to the first signal may comprise at least one of: performing grouping based on an order of antenna port indices; a first signal contained in each group being partly or entirely identical; performing grouping based on a predefined grouping table; and selecting at least one signal from the first signal based on a predefined signal selection rule, and dividing an antenna port related to the at least one signal into one group.

[0027] In some embodiments, the output signal corresponding to the antenna port group may comprise at least one of: a signal with maximum signal energy in the antenna port group; a signal with a maximum L2 norm corresponding to a channel gain in the antenna port group; a signal with maximum average energy in the antenna port group; and a signal obtained after weighted　averaging is performed on signals in the antenna port group, wherein a weight for the weighted　averaging is determined based on the first signal.

[0028] In some embodiments, the determining information related to combining of the first signal based on the first signal may further comprise: determining the information related to the combining of the first signal based on the first signal and auxiliary information, wherein the auxiliary information comprises information related to a resource configuration for the first signal and / or information related to a channel environment for the first signal.

[0029] In some embodiments, the method may further comprise: obtaining a channel equalization matrix; and equalizing the second signal based on the channel equalization matrix. The performing a signal detection based on the second signal comprises: performing the signal detection based on the equalized second signal.

[0030] In some embodiments, the obtaining a channel equalization matrix may comprise: obtaining the channel equalization matrix based on channel state information corresponding to the first signal; or performing a channel estimation on the second signal to obtain 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.

[0031] In some embodiments, the obtaining the channel equalization matrix based on channel state information corresponding to the first signal may comprise: 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.

[0032] In a second aspect of the present disclosure, a base station is provided. The base station comprises: a first module, configured to: receive a first signal through an antenna port of the base station; and determine information related to combining of the first signal based on the first signal; and perform combining on the first signal in an antenna port dimension based on the determined information, to obtain a second signal, wherein a number of antenna ports related to the second signal is less than a number of antenna ports related to the first signal; and a second module, configured to: perform a signal detection based on the second signal.

[0033] In some embodiments, the first module is further configured to: obtain a channel equalization matrix; and equalize the second signal based on the channel equalization matrix. The second module is configured to: perform the signal detection based on the equalized second signal.

[0034] In some embodiments, the second module is further configured to: obtain a channel equalization matrix; equalize the second signal based on the channel equalization matrix, and perform the signal detection based on the equalized second signal.

[0035] In a third aspect of the present disclosure, a base station is provided. The base station comprises: at least one processor; and at least one memory, storing instructions, wherein the instructions are executed by the at least one processor to implement the method according to the first aspect and / or the second aspect of the present disclosure.

[0036] In a fourth aspect of the present disclosure, a computer readable medium storing instructions is provided. The instructions, when executed by at least one processing unit, cause the at least one processing unit to be configured to perform the method according to the first aspect and / or the second aspect of the present disclosure.

[0037] It should be understood that the content described in this part is not intended to define key or important features of the embodiments of the present disclosure, and is not used to limit the scope of the present disclosure. Other features of the present disclosure will be easily understood through the following description.

[0038] The present disclosure provides a method performed by a base station in a communication system. According to the received signal, the information related to the combining of the signal can be determined, and then, the combining is performed on the signal in an antenna port dimension based on the determined information, thereby significantly reducing the number of antenna ports of signals to be detected and avoiding the performance degradation due to a channel environment mismatch that may occur in a fixed antenna combining scheme. The method not only avoids a large amount of data transmission among various functional modules in the base station, but also significantly reduces the computational complexity of subsequent signal detection algorithms, thereby facilitating the realization of lightweight and low cost of the base station.

[0039] In order to more clearly describe the technical solution in the embodiments of the present disclosure, the accompanying drawings required to be used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, other accompanying drawings can further be obtained based on these accompanying drawings without exceeding the scope of protection claimed in the present disclosure.

[0040] FIG. 1 is a block diagram of an example wireless network communication system in which an embodiment of the present disclosure may be implemented;

[0041] FIGs. 2A and 2B illustrate example wireless transmission and reception paths according to an embodiment of the present disclosure;

[0042] FIG. 3A illustrates an example user equipment according to an embodiment of the present disclosure;

[0043] FIG. 3B illustrates an example node according to an embodiment of the present disclosure;

[0044] FIG. 4 illustrates an example access network structure (network architecture) according to an embodiment of the present disclosure;

[0045] FIG. 5 illustrates a method performed by a base station in a communication system according to an embodiment of the present disclosure;

[0046] FIG. 6 illustrates an example structure of a neural network according to an embodiment of the present disclosure;

[0047] FIG. 7 illustrates a base station according to an embodiment of the present disclosure;

[0048] FIG. 8 is a simplified block diagram of an electronic device adapted to implement embodiments of the present disclosure; and

[0049] FIG. 9 is a schematic diagram of a computer readable medium adapted to implement embodiments of the present disclosure.

[0050] Throughout the accompanying drawings, the same or similar reference numerals denote the same or similar elements.

[0051] The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of various embodiments of the present disclosure as defined by the claims and their equivalents. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the various embodiments described herein can be made without departing from the scope and spirit of the present disclosure. In addition, descriptions of well-known functions and constructions may be omitted for clarity and conciseness.

[0052] The terms and words used in the following description and claims are not limited to the bibliographical meanings, but, are merely used by the inventor to enable a clear and consistent understanding of the present disclosure. Accordingly, it should be apparent to those skilled in the art that the following description of various embodiments of the present disclosure is provided for illustration purpose only and not for the purpose of limiting the present disclosure as defined by the appended claims and their equivalents.

[0053] It is to be understood that the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a component surface" includes reference to one or more of such surfaces.

[0054] The term "include" or "may include" refers to the existence of a corresponding disclosed function, operation or component which can be used in various embodiments of the present disclosure and does not limit one or more additional functions, operations, or components. The terms such as "include" and / or "have" may be construed to denote a certain characteristic, number, step, operation, constituent element, component or a combination thereof, but may not be construed to exclude the existence of or a possibility of addition of one or more other characteristics, numbers, steps, operations, constituent elements, components or combinations thereof.

[0055] The term "or" used in various embodiments of the present disclosure includes any or all of combinations of listed words. For example, the expression "A or B" may include A, may include B, or may include both A and B.

[0056] Unless defined differently, all terms used herein, which include technical terminologies or scientific terminologies, have the same meaning as that understood by a person skilled in the art to which the present disclosure belongs. Such terms as those defined in a generally used dictionary are to be interpreted to have the meanings equal to the contextual meanings in the relevant field of art, and are not to be interpreted to have ideal or excessively formal meanings unless clearly defined in the present disclosure.

[0057] A learning algorithm is a method of training a predetermined target apparatus (e.g., a robot) using a plurality of pieces of learning data to enable, allow or control the target apparatus to perform a determination or prediction. Examples of the learning algorithm include, but not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0058] The method provided by the present disclosure may relate to the field of data intelligence technology.

[0059] According to the present disclosure, in a method performed by a base station in a communication system (the method being performed 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 pre-processing operation on data to convert the data to a form suitable for being used as an input of the artificial intelligence model. The artificial intelligence model can be obtained through training. Here, "obtained through training" means that a predefined operation rule or artificial intelligence model configured to perform an expected feature (or purpose) is obtained by training, through a training algorithm, a basic artificial intelligence model using a plurality of pieces of training data. Inference and prediction refer to a technique of performing logical inference and prediction based on determined information, which includes, for example, knowledge-based inference, optimization prediction, and preference-based planning or recommendation.

[0060] The apparatus provided in the embodiments of the present disclosure may implement at least one of a plurality of modules through an AI model. The functions associated with the AI may be performed by a non-volatile memory, a volatile memory and a processor.

[0061] The processor may include one or more processors. At this time, the one or more processors may be general purpose processors, for example, a central processing unit (CPU) or an application processor (AP), or be a pure graphics processing unit, for example, a graphics processing unit (GPU), a visual processing unit (VPU), and / or an AI application specific processor, for example, a neural processing unit (NPU).

[0062] The one or more processors control the processing for inputted 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 by training or learning.

[0063] Here, providing by learning refers to obtaining a predefined operation rule or an AI model having an expected characteristic by applying the learning algorithm to a plurality of pieces of learning data. The learning may be performed in the apparatus in which the AI according to the embodiment is performed, and / or may be implemented by a separate server / system.

[0064] The AI model may include a plurality of neural network layers. Each layer has a plurality of weight values, and the calculation of one layer is performed through the calculation result of the previous layer and the plurality of weights of the current layer. Examples of a neural network include, but not limited to, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bi-directional recurrent deep neural network (BRDNN), a generative adversarial network (GAN), and a deep Q network.

[0065] A learning algorithm is a method of training a predetermined target apparatus (e.g., a robot) using a plurality of pieces of learning data to enable, allow or control the target apparatus to perform a determination or prediction. Examples of the learning algorithm include, but not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement　learning.

[0066] The technical solution of the embodiments of the present disclosure and the technical effects produced by the technical solution of the present disclosure are described below through the description for several alternative embodiments. It should be noted that reference or combination may be made between the following embodiments, and that the same terms, similar features and similar implementation steps, etc. in different embodiments are not repeatedly described.

[0067] FIG. 1 illustrates an example wireless network 100 according to various embodiments of the present disclosure. The embodiment of the wireless network 100 shown in FIG. 1 is for illustration only. Other embodiments of the wireless network 100 can be used without departing from the scope of the present disclosure.

[0068] The wireless network 100 includes a gNodeB (gNB) 101, a gNB 102, and a 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 private IP network, or other data networks.

[0069] Depending on a type of the network, other well-known terms such as "base station" or "access point" can be used instead of "gNodeB" or "gNB". For convenience, the terms "gNodeB" and "gNB" are used in this patent document to refer to network infrastructure components that provide wireless access for remote terminals. And, depending on the type of the network, other well-known terms such as "mobile station", "user station", "remote terminal", "wireless terminal" or "user apparatus" can be used instead of "user equipment" or "UE". For convenience, the terms "user equipment" and "UE" are used in this patent document to refer to remote wireless devices that wirelessly access the gNB, no matter whether the UE is a mobile device (such as a mobile phone or a smart phone) or a fixed device (such as a desktop computer or a vending machine).

[0070] gNB 102 provides wireless broadband access to the network 130 for a first plurality of user equipments (UEs) within a coverage area 120 of gNB 102. The first plurality of UEs include a UE 111, which may be located in a Small Business (SB); a UE 112, which may be located in an enterprise (E); a UE 113, which may be located in a WiFi Hotspot (HS); a UE 114, which may be located in a first residence (R); a UE 115, which may be located in a second residence (R); a UE 116, which may be a mobile device (M), such as a cellular phone, a wireless laptop computer, a wireless PDA, etc. GNB 103 provides wireless broadband access to network 130 for a second plurality of UEs within a coverage area 125 of gNB 103. The second plurality of UEs include a UE 115 and a 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-A, WiMAX or other advanced wireless communication technologies.

[0071] The dashed lines show approximate ranges of the coverage areas 120 and 125, and the ranges are shown as approximate circles merely for illustration and explanation purposes. It should be clearly understood that the coverage areas associated with the gNBs, such as the coverage areas 120 and 125, may have other shapes, including irregular shapes, depending on configurations of the gNBs and changes in the radio environment associated with natural obstacles and man-made obstacles.

[0072] As will be 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.

[0073] Although FIG. 1 illustrates an example of the wireless network 100, various changes can be made to FIG. 1. The wireless network 100 can include any number of gNBs and any number of UEs in any suitable arrangement, for example. Furthermore, gNB 101 can directly communicate with any number of UEs and provide wireless broadband access to the network 130 for those UEs. Similarly, each gNB 102-103 can directly communicate with the network 130 and provide direct wireless broadband access to the network 130 for the UEs. In addition, gNB 101, 102 and / or 103 can provide access to other or additional external networks, such as external telephone networks or other types of data networks.

[0074] FIGs. 2A and 2B illustrate example wireless transmission and reception paths according to the present disclosure. In the following description, the transmission path 200 can be described as being implemented in a gNB, such as gNB 102, and the reception path 250 can be described as being implemented in a UE, such as UE 116. However, it should be understood that the reception path 250 can be implemented in a gNB and the transmission path 200 can be implemented in a UE. In some embodiments, the reception path 250 is configured to support codebook designs and structures for systems with 2D antenna arrays as described in embodiments of the present disclosure.

[0075] The transmission path 200 includes a channel coding and modulation block 205, a serial-to-parallel (S-to-P) block 210, a size N 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 reception path 250 includes a down-converter (DC) 255, a cyclic prefix removal block 260, a serial-to-parallel (S-to-P) block 265, a size N fast Fourier transform (FFT) block 270, a parallel-to-serial (P-to-S) block 275, and a channel decoding and demodulation block 280.

[0076] In the transmission 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 modulated symbols. The serial-to-parallel (S-to-P) block 210 converts (such as demultiplexes) serial modulated symbols into parallel data to generate N parallel symbol streams, where N is a size of the IFFT / FFT used in gNB 102 and UE 116. The size N IFFT block 215 performs IFFT operations on the N parallel symbol streams to generate a time-domain output signal. The Parallel-to-Serial block 220 converts (such as multiplexes) parallel time-domain output symbols from the Size N 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 up-converter 230 modulates (such as up-converts) the output of the cyclic prefix addition block 225 to an RF frequency for transmission via a wireless channel. The signal can also be filtered at a baseband before switching to the RF frequency.

[0077] The RF signal transmitted from gNB 102 arrives at UE 116 after passing through the wireless channel, and operations in reverse to those at gNB 102 are performed at UE 116. The down-converter 255 down-converts the received signal to a 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 Size N 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 signal 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.

[0078] Each of gNBs 101-103 may implement a transmission path 200 similar to that for transmitting to UEs 111-116 in the downlink, and may implement a reception path 250 similar to that for receiving from UEs 111-116 in the uplink. Similarly, each of UEs 111-116 may implement a transmission path 200 for transmitting to gNBs 101-103 in the uplink, and may implement a reception path 250 for receiving from gNBs 101-103 in the downlink.

[0079] Each of the components in FIGs. 2A and 2B can be implemented using only hardware, or using a combination of hardware and software / firmware. As a specific example, at least some of the components in FIGs. 2A and 2B may be implemented in software, while other components may be implemented in configurable hardware or a combination of software and configurable hardware. For example, the FFT block 270 and IFFT block 215 may be implemented as configurable software algorithms, in which the value of the size N may be modified according to the implementation.

[0080] Furthermore, although described as using FFT and IFFT, this is only illustrative and should not be interpreted as limiting the scope of the present disclosure. Other types of transforms can be used, such as discrete fourier transform (DFT) and inverse discrete fourier transform (IDFT) functions. It should be understood that for DFT and IDFT functions, the value of variable N may be any integer (such as 1, 2, 3, 4, etc.), while for FFT and IFFT functions, the value of variable N may be any integer which is a power of 2 (such as 1, 2, 4, 8, 16, etc.).

[0081] Although FIGs. 2A and 2B illustrate examples of wireless transmission and reception paths, various changes may be made to FIGs. 2A and 2B. For example, various components in FIGs. 2A and 2B can be combined, further subdivided or omitted, and additional components can be added according to specific requirements. Furthermore, FIGs. 2A and 2B are intended to illustrate examples of types of transmission and reception paths that can be used in a wireless network. Any other suitable architecture can be used to support wireless communication in a wireless network.

[0082] FIG. 3A illustrates an example UE 116 according to the present disclosure. The embodiment of UE 116 shown in FIG. 3A is for illustration only, and UEs 111-115 of FIG. 1 can have the same or similar configuration. However, a UE has various configurations, and FIG. 3A does not limit the scope of the present disclosure to any specific implementation of the UE.

[0083] UE 116 includes an antenna 305, a radio frequency (RF) transceiver 310, a transmission (TX) processing circuit 315, a microphone 320, and a reception (RX) processing circuit 325. UE 116 also includes a speaker 330, a processor / controller 340, an input / output (I / O) interface 345, an input device(s) 350, a display 355, and a memory 360. The memory 360 includes an operating system (OS) 361 and one or more applications 362.

[0084] The RF transceiver 310 receives an incoming RF signal transmitted by a 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 transmitted to the RX processing circuit 325, where the RX processing circuit 325 generates a processed baseband signal by filtering, decoding and / or digitizing the baseband or IF signal. The RX processing circuit 325 transmits the processed baseband signal to speaker 330 (such as for voice data) or to processor / controller 340 for further processing (such as for web browsing data).

[0085] The TX processing circuit 315 receives analog or digital voice data from microphone 320 or other outgoing baseband data (such as network data, email or interactive video game data) from processor / controller 340. The TX processing circuit 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 circuit 315 and up-converts the baseband or IF signal into an RF signal transmitted via the antenna 305.

[0086] The processor / controller 340 can include one or more processors or other processing devices and execute an OS 361 stored in the memory 360 in order to control the overall operation of UE 116. For example, the processor / controller 340 can control the reception of forward channel signals and the transmission of backward channel signals through the RF transceiver 310, the RX processing circuit 325 and the TX processing circuit 315 according to well-known principles. In some embodiments, the processor / controller 340 includes at least one microprocessor or microcontroller.

[0087] The processor / controller 340 is also capable of executing other processes and programs residing in the memory 360, such as operations for channel quality measurement and reporting for systems with 2D antenna arrays as described in embodiments of the present disclosure. The processor / controller 340 can move data into or out of the memory 360 as required by an execution process. In some embodiments, the processor / controller 340 is configured to execute the application 362 based on the OS 361 or in response to signals received from the gNB or the operator. The processor / controller 340 is also coupled to an I / O interface 345, where the I / O interface 345 provides UE 116 with the ability to connect to other devices such as laptop computers and handheld computers. I / O interface 345 is a communication path between these accessories and the processor / controller 340.

[0088] The processor / controller 340 is also coupled to the input device(s) 350 and the display 355. An operator of UE 116 can input data into UE 116 using the input device(s) 350. The display 355 may 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 part of the memory 360 can include a random access memory (RAM), while another part of the memory 360 can include a flash memory or other read-only memory (ROM).

[0089] Although FIG. 3A illustrates an example of UE 116, various changes can be made to FIG. 3A. For example, various components in FIG. 3A can be combined, further subdivided or omitted, and additional components can be added according to specific requirements. As a specific example, the processor / controller 340 can be divided into a plurality of processors, such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs). Furthermore, although FIG. 3A illustrates that the UE 116 is configured as a mobile phone or a smart phone, UEs can be configured to operate as other types of mobile or fixed devices.

[0090] FIG. 3B illustrates an example gNB 102 according to the present disclosure. The embodiment of gNB 102 shown in FIG. 3B is for illustration only, and other gNBs of FIG. 1 can have the same or similar configuration. However, a gNB has various configurations, and FIG. 3B does not limit the scope of the present disclosure to any specific implementation of a gNB. It should be noted that gNB 101 and gNB 103 can include the same or similar structures as gNB 102.

[0091] As shown in FIG. 3B, gNB 102 includes a plurality of antennas 370a-370n, a plurality of RF transceivers 372a-372n, a transmission (TX) processing circuit 374, and a reception (RX) processing circuit 376. In certain embodiments, one or more of the plurality of antennas 370a-370n include a 2D antenna array. gNB 102 also includes a controller / processor 378, a memory 380, and a backhaul or network interface 382.

[0092] RF transceivers 372a-372n receive an incoming RF signal from antennas 370a-370n, such as a signal transmitted by UEs or other gNBs. RF transceivers 372a-372n down-convert the incoming RF signal to generate an IF or baseband signal. The IF or baseband signal is transmitted to the RX processing circuit 376, where the RX processing circuit 376 generates a processed baseband signal by filtering, decoding and / or digitizing the baseband or IF signal. RX processing circuit 376 transmits the processed baseband signal to controller / processor 378 for further processing.

[0093] The TX processing circuit 374 receives analog or digital data (such as voice data, network data, email or interactive video game data) from the controller / processor 378. TX processing circuit 374 encodes, multiplexes and / or digitizes outgoing baseband data to generate a processed baseband or IF signal. RF transceivers 372a-372n receive the outgoing processed baseband or IF signal from TX processing circuit 374 and up-convert the baseband or IF signal into an RF signal transmitted via antennas 370a-370n.

[0094] The controller / processor 378 can include one or more processors or other processing devices that control the overall operation of gNB 102. For example, the controller / processor 378 can control the reception of forward channel signals and the transmission of backward channel signals through the RF transceivers 372a-372n, the RX processing circuit 376 and the TX processing circuit 374 according to well-known principles. The controller / processor 378 can also support additional functions, such as higher-level wireless communication functions. For example, the controller / processor 378 can perform a Blind Interference Sensing (BIS) process such as that performed through a BIS algorithm, and decode a received signal from which an interference signal is subtracted. A controller / processor 378 may support any of a variety of other functions in gNB 102. In some embodiments, the controller / processor 378 includes at least one microprocessor or microcontroller.

[0095] The controller / processor 378 is also capable of executing programs and other processes residing in the memory 380, such as a basic OS. The controller / processor 378 can also support channel quality measurement and reporting for systems with 2D antenna arrays as described in embodiments of the present disclosure. In some embodiments, the controller / processor 378 supports communication between entities such as web RTCs. The controller / processor 378 can move data into or out of the memory 380 as required by an execution process.

[0096] The controller / processor 378 is also coupled to the backhaul or network interface 382. The backhaul or network interface 382 allows gNB 102 to communicate with other devices or systems through a backhaul connection or through a network. The backhaul or network interface 382 can support communication over any suitable wired or wireless connection(s). For example, when gNB 102 is implemented as a 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 can allow gNB 102 to communicate with other gNBs through wired or wireless backhaul connections. When gNB 102 is implemented as an access point, the backhaul or network interface 382 can allow gNB 102 to communicate with a larger network, such as the Internet, through a wired or wireless local area network or through a wired or wireless connection. The backhaul or network interface 382 includes any suitable structure that supports communication through a wired or wireless connection, such as an Ethernet or an RF transceiver.

[0097] The memory 380 is coupled to the controller / processor 378. A part of the memory 380 can include an RAM, while another part of the memory 380 can include a flash memory or other ROMs. In certain embodiments, a plurality of instructions, such as the BIS algorithm, are stored in the memory. The plurality of instructions are configured to cause the controller / processor 378 to execute the BIS process and decode the received signal after subtracting at least one interference signal determined by the BIS algorithm.

[0098] As will be described in more detail below, the transmission and reception paths of gNB 102 (implemented using RF transceivers 372a-372n, TX processing circuit 374 and / or RX processing circuit 376) support aggregated communication with FDD cells and TDD cells.

[0099] Although FIG. 3B illustrates an example of gNB 102, various changes may be made to FIG. 3B. For example, gNB 102 can include any number of each component shown in FIG. 3A. 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, gNB 102 can include multiple instances of each (such as one for each RF transceiver). In a wireless communication system, one of the main means for ensuring the communication rate and reliability is to process a signal or adjust a transmission / reception decision through channel state information (CSI) between two nodes (the two nodes respectively serve as a transmission node and a reception node, or both the nodes serve as transmission nodes and reception nodes) performing information transmission. For example, before transmission, a node (such as the UE described in FIG. 3A) pre-encodes a transmission signal according to its own known channel state information, adjusts the power and modulation and coding scheme, of the transmission signal. After receiving the signal, a node (such as the gNB 102 shown in FIG. 3B) performs channel equalization on the received signal according to its own known channel state information, and selects a different transmission / reception beam or the like, thereby eliminating the influence of the channel on the signal as much as possible.

[0100] In the wireless communication system, the transmission of information (e.g., the transmission of information on a physical downlink control channel (PDCCH), a physical downlink shared channel (PDSCH), a physical uplink shared channel (PUSCH), a physical uplink control channel (PUCCH), etc.) occurs on more than one physical resource (e.g., a plurality of time points, a plurality of frequency points, a plurality of antennas, and various combinations of time, frequency and antennas). The physical resource is a resource entity which may be used to perform a signal transmission in a communication system, and may be, for example, a time domain physical resource, a frequency domain physical resource and an antenna domain physical resource. The state of an occupied physical resource will have a comprehensive influence on the amplitude and phase of the signal transmitted throughout the wireless communication link.

[0101] As the wireless communication network is becoming more and more popular and is continuously evolved, operators hope that a 5G access network architecture can increase the structural flexibility while reducing deployment costs. In this regard, during the design of a 5G communication system, it is possible to, for example, split a conventional integrated base station into a radio unit (RU unit), a distributed unit (DU unit) and a centralized unit (CU unit).

[0102] FIG. 4 illustrates an example access network structure (network architecture) according to an embodiment of the present disclosure. Here, the RU unit contains a radio frequency transmitter (RF) and a physical layer (PHY) function with low computational requirements, which is responsible for implementing a signal transmission / reception related process with a delay of less than 1ms. 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 the high computational requirements, which is responsible for processing a real-time service and performing a round-trip data transmission with the RU unit over a fronthaul link. The CU unit is composed of a control plane (CP) and a user plane (UP), and is responsible for processing non-real-time protocols and services, and the CU unit interacts with the DU unit through a communication link and is connected to the core network through backhaul. The operators can flexibly deploy the RU unit, the DU unit and the CU unit at different locations as required, thereby meeting various costs and service requirements. As an example, for a network requiring a low edge delay, the RU unit, the DU unit and the CU unit may be deployed together at the edge, which will improve the performance of connecting a user application remotely to the greatest extent. As another example, one DU unit may provide services to a plurality of RU units, and a plurality of DU units may share one CU unit, thereby providing sufficient performance within an acceptable maximum delay range while minimizing the network deployment hardware costs. The operators may deploy different architectures for different markets and regions.

[0103] For cost reasons, the RU unit itself has limited processing capabilities, and cannot directly complete all physical layer related processes for processing a signal. According to the current system architecture, the RU unit is only capable of extracting channel state information from a reference signal, and cannot perform subsequent steps such as channel equalization, demodulation, and decoding under a specified delay requirement. Therefore, the RU unit needs to perform simple processing on the received data and then transmit the data to the DU unit through a fronthaul line for the DU unit to complete the subsequent physical layer process. However, as the demands of current users for the transmission rate continuously increase, the design for the RU-fronthaul-DU structure in this architecture faces severe challenges. In order to meet the demand for higher-rate wireless data transmission, the scale of antennas used by the base station is getting larger and larger, the bandwidth is getting wider and wider, and the modulation order is getting higher and higher. As a result, the scale of reception data that the base station needs to process is also getting larger and larger. In contrast, the amount of data required 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 the optical fiber (under limited costs). For example, in the LTE / NR system, the amount of fronthaul data is approximately . Here, is the number of reception antennas, is the number of subcarriers, is the number of OFDM symbols, and is the number of quantization bits. If the base station is upgraded from 4 antennas to 64 antennas, the amount of fronthaul data will increase to 16 times its previous value. Since the rate of the data 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 processing delay of the base station to increase rapidly. What is more serious is that when the buffer queue of the RU unit is filled, the data to be subsequently received by the antenna will be naturally lost, causing severe system performance loss or even a system crash. Therefore, as the demands of the users for the transmission rate continuously increase and the amount of data continues continuously increases, the structural design of the traditional base station for wireless communication is challenged. It is required to enhance the data transmission (e.g., compress data) to match the increasing amount of data.

[0104] The above problem can be solved by performing compression and dimension reduction on the received signal. However, the current mainstream data compression method requires a large amount of calculation, and a large transmission delay is even more introduced. Furthermore, lossy compression will significantly reduce the signal-to-noise ratio of the received data, resulting in impaired system performance. In addition, the problem of an exponential increase in the amount of data due to the increase in the scale of the antenna cannot be solved.

[0105] The antenna port reduction technique effectively circumvents the above problems. The antenna port reduction technique reduces the number of antenna ports receiving data, by combining signals on the reception antennas. Considering that the number of uplink transmission streams of the current NR system is limited (8 layers at most), the number of layers of the combined signal needs to be only greater than the number of the uplink transmission streams of the current system. The antenna port reduction does not necessarily reduce the signal-to-noise ratio of the processed data, but may generate a signal-to-noise ratio gain, causing a system performance improvement.

[0106] However, the antenna combining scheme used is highly sensitive to the channel environment where the system is, which results in that a certain antenna signal combining scheme, although excellent in performance in some scenarios, will cause serious performance degradation in some other scenarios, and therefore, the technique is in lack of performance stability.

[0107] The present disclosure provides a method performed by a base station in a communication system. According to the received signal, the information related to the combining of the signal can be determined, and then, the combining is performed on the signal in an antenna port dimension based on the determined information, thereby significantly reducing the number of antenna ports of signals to be detected and avoiding the performance degradation due to a channel environment mismatch that may occur in a fixed antenna combining scheme. The method not only avoids a large amount of data transmission among various functional modules in the base station, but also significantly reduces the computational complexity of subsequent signal detection algorithms, thereby facilitating the realization of lightweight and low cost of the base station.

[0108] FIG. 5 illustrates a method 50 performed by a base station in a communication system according to an embodiment of the present disclosure.

[0109] As shown in FIG. 5, the method 50 includes steps S501-S504. The base station may include a first module and a second module that are communicatively connected.

[0110] In step S501, a first signal is received through an antenna port of the base station.

[0111] In some examples, step S501 may be performed by the first module. The first module may receive the first signal through the antenna of the base station. Here, the first module may refer to an entity for implementing signal reception and performing a preliminary processing function in the receivers on the sides of the foregoing gNB 101, gNB 102 and gNB 103, the entity possessing a wireless signal reception capability and a certain processing capability. For example, the first module includes the down-converter (DC) 255, the cyclic prefix removal block 260, the serial-to-parallel (S-to-P) block 265, the size N fast Fourier transform (FFT) block 270 and the parallel-to-serial (P-to-S) block 275 in the foregoing reception path 250.

[0112] In some examples, the first module is the RU module containing an antenna and a physical layer function (PHY) in the foregoing base station structure.

[0113] In some examples, the first signal refers to a signal received by the antenna, the signal carrying the data information transmitted by a terminal. The first signal may include at least one of: a radio signal received by the antenna port of the base station; a digital domain signal obtained after a digital-to-analog conversion is performed on the forgoing signal; and a signal obtained after specific processing (e.g., amplifying, de-noising, filtering, whitening and FFT) is performed on the forgoing signal. In the present disclosure, "at least one" may represent one or a combination of two or more. In some examples, the first signal includes a digital domain wireless signal received by the antenna port of the base station, or a denoised signal obtained after the digital domain wireless signal passes through a band pass filter. In other examples, the first signal includes a signal obtained by multiplying the signal received by the antenna of the base station by a certain space basis matrix (e.g., an feature vector matrix or spatial DFT matrix of a channel). In other examples, the first signal includes a signal obtained by whitening the signal received by the antenna of the base station. For example, the first signal is obtained by: estimating an interference covariance matrix in the current environment based on a reference signal in the signal received by the antenna of the base station; performing a Cholesky decomposition or PCA decomposition on the interference covariance matrix; and multiplying the inverse matrix of the decomposed matrix by the signal received by the antenna of the base station.

[0114] In step S502, information related to combining of the first signal is determined based on the first signal.

[0115] In some examples, since the first signal itself may contain the information related to the channel corresponding to a physical resource transmitting the first signal, the information related to the combining of the first signal may be determined based on the first signal itself. The information related to the combining of the first signal may include third information for indicating the grouping of an antenna port related to the first signal and / or fourth information related to an output signal corresponding to an antenna port group.

[0116] In some examples, the information (hereinafter also referred to as "second information") related to the combining of the first signal may be obtained based on the first information corresponding to the first signal. The first information may include the information related to the physical resource transmitting the first signal. A wireless environment may have a direct influence (e.g., on an amplitude and a phase) and / or an indirect influence (e.g., on a signal-to-noise ratio) on the first signal carried on the physical resource transmitting the first signal. Here, the physical resource transmitting the first signal includes, for example, one or more of: a time domain physical resource, a frequency domain physical resource and / or an antenna domain physical resource. Here, the time domain physical resource refers to a resource available in the time dimension. Specifically, the time domain physical resource may refer to a time unit such as a radio frame, a subframe, a time slot or a symbol. For example, in some systems, a time point may be represented as a time slot consisting of 14 OFDM symbols. The frequency domain physical resource refers to a resource available in the dimension of frequency. Specifically, the frequency domain physical resource may refer to a frequency unit such as a sub-band, a partial bandwidth, a resource block or a subcarrier. For example, in LTE and NR, one frequency point may refer to a frequency range consisting of 12 15kHz-spaced subcarriers. The antenna domain physical resource represent the space domain resource occupied for signal transmission. The antenna domain physical resource may include a physical antenna unit, an antenna array, a beam, an antenna port, a precoding matrix of a transmission end, or the like. Antennas are a broad sense and represent the spatial resources occupied for the signal transmission. The second information may include the third information for indicating the grouping of the antenna port related to the first signal and / or the fourth information related to the output signal corresponding to the antenna port group.

[0117] In some embodiments, the first signal may include, but not limited to, a reference signal (e.g., the reference signal is a transmission signal composed of a generation sequence) corresponding to the first signal, the content and the physical resource where the transmission is performed are shared by two nodes. The reference signal may also be referred to as a pilot signal, a training signal, or the like. The reference signal includes, for example, a detecting reference signal (SRS), which is used by a terminal to estimate an uplink transmission channel to acquire uplink channel state information; a demodulation reference signal (DM-RS) in an uplink physical channel (PUSCH) or an uplink physical control channel (PUCCH), which is used by the terminal to demodulate a downlink shared channel; and a channel state information reference signal (CSI-RS), which is used by the terminal to estimate a downlink transmission channel to acquire downlink channel state information. The reference signal may alternatively be a signal obtained based on the detecting reference signal and / or the demodulation reference signal.

[0118] In some embodiments, the first module may extract, from the first signal, the reference signal (e.g., the SRS signal, the DM-RS signal in the PUSCH or PUCCH) contained in from the first signal, or may directly read the received reference signal stored in the base station and corresponding to the first signal, and then calculate the influence of the channel as the first information according to the related configuration information. Specifically, the first module extracts the DM-RS signal corresponding to each transmission data stream from the specified physical resource transmitting the first signal according to the configuration information transmitted by the PUSCH, and then divides the received DM-RS signal by the transmission DM-RS signal specified in the configuration information, thereby obtaining the first information.

[0119] In some embodiments, after acquiring, from the first signal, the channel state information of the physical resource where the reference signal is, the first module acquires the corresponding channel state information on all physical resources for each data stream by performing linear interpolation, to use the channel state information as the first information.

[0120] In some embodiments, the first information may include the channel state information corresponding to the first signal. The channel state information includes, but not limited to, the channel state information acquired from the first signal or the channel state information corresponding to the first signal stored in the base station (without loss of generality, the channel state information described in the embodiment of the present disclosure is a comprehensive influence of the entire wireless communication link on the amplitude, phase and / or signal-to-noise ratio of the signal and the like on all physical resources occupied for the transmission of the signal, and may be obtained according to the reference signal received by the base station); and the information obtained by performing specific processing (e.g., interpolating, denoising and filtering) on the channel state information acquired from the first signal or stored in the base station (e.g., the PMI matrix and the eigenvalue obtained by performing feature value decomposition on the intermediate signal obtained through an MMSE algorithm). For example, the channel state information corresponding to the first signal includes channel state information obtained by performing a channel estimation based on the first signal and / or the reference signal.

[0121] In other examples, the channel state information corresponding to the first signal is from the information stored in the base station itself. The channel state information includes, but not limited to, channel state information acquired based on a signal and / or reference signal received last time and stored in the base station and / or information obtained after the channel state information is processed, or channel state information currently received and stored in the base station and / or information obtained after the channel state information is processed; channel state information obtained by the base station through a channel feedback process (e.g., a channel state information (CSI) measurement report) of a terminal and / or information (e.g., channel state information corresponding to a first signal reported by the terminal) obtained after the channel state information is processed; and channel state information predicted by the base station based on the stored channel state information (e.g., previous channel information).

[0122] In some embodiments, the first module extracts the signals received by the antennas numbered 0, a, 2a ... from the first signal according to a preset antenna spacing parameter a, and acquires the channel state information from the signals as the first information.

[0123] In some embodiments, the first module may perform a moving average on a channel over the time domain (over a plurality of OFDM symbols) to use the averaged time-domain channel state information as the first information, that is, . Here, is a previously stored channel, is a channel obtained from the first signal, and is an average coefficient.

[0124] In some embodiments, the first module may group the obtained channel state information in the subcarrier dimension, every b consecutive subcarriers being divided into one group (the parameter b may be set according to the relevant bandwidth in the current environment), and then average each group in the subcarrier dimension and use the averaged frequency domain channel state information as the first information.

[0125] In some embodiments, the first module may average the obtained channel state information on the reception antenna and / or the transmission antenna, that is, average the channel state information of a plurality of different transmission and reception antenna pairs occupying the same physical resource, to acquire the averaged channel state information for the reception antenna and / or the transmission antenna as the first information.

[0126] In some embodiments, the first information may include the channel feature information corresponding to the first information. The channel feature information may be obtained based on the first information, or obtained by the base station to be stored in the base station. The channel feature information may represent a channel-related feature including, but not limited to, a signal to interference plus noise ratio of the first signal, a correlation matrix of the channel for the first signal or a correlation matrix of the first signal, interference-related information (e.g., a neighbor cell interference strength) related to the first signal, high order-statistics of the channel (e.g., a statistical expectation, variance, autocorrelation matrix and multipath delay spread parameter of the channel) for the first signal, and the like.

[0127] In some embodiments, the first module may use the information related to the difference between the reference signal corresponding to the first signal and an expected received signal as the channel feature information corresponding to the first information. For example, it is possible to reconstruct the expected received signal corresponding to the physical resource according to the first signal and the corresponding channel state information, and then to use the difference (i.e., a composite signal of interference and noise) between the first signal and the expected received signal as the channel feature information corresponding to the first information.

[0128] 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 thereof is as follows. First, for the physical resource corresponding to each reference signal, the composite signal vector of interference and noise on all antennas corresponding to the physical resource is calculated. Then, the exterior product of the signal vector and the signal itself is calculated to obtain an inter-antenna cross-correlation matrix. Finally, the inter-antenna cross-correlation matrices on all physical resources are averaged to be used approximately as the covariance matrix of the interference in the current environment, that is, the first information. The above embodiment in which the first information is obtained is merely exemplary, and the present disclosure is not limited thereto.

[0129] In some examples, the first module inputs the first information into a neural network, thereby obtaining the second information through the neural network.

[0130] In some examples, it is possible to determine, based on the first information, the second information (i.e., the information related to the combining of the first signal) through the neural network. For example, the first module may determine, based on the first signal, the information related to the combining of the first signal through a first neural network; determine, based on the reference signal corresponding to the first signal, the information related to the combining of the first signal through a second neural network; determine, based on the channel state information corresponding to the first signal, the information related to the combining of the first signal through a third neural network; or determine, based on the channel feature information corresponding to the first signal, the information related to the combining of the first signal through a fourth neural network.

[0131] As an example, a neural network (e.g., the first fourth neural networks) may output one of 4 category tags, and the 4 category tags respectively correspond to: dividing sequentially the first information into 2, 4, 8 and 16 groups. The antenna port grouping mode corresponding to the one category tag outputted by the neural network is the second information. As another example, the output of the neural network is one of 3 category tags, and each category tag corresponds to a known intra-group signal combining mode. When the neural network outputs the tag 0, the fourth information refers to selecting an antenna with maximum reception energy in a group. When the neural network outputs the tag 1, the fourth information refers to selecting an average of all signals in a group. When the neural network outputs the tag 2, the fourth information refers to performing weighted　averaging on signals in a group according to the channel gain of each antenna.

[0132] In some examples, determining the information related to the combining of the first signal based on the first signal may include: determining the information related to the combining of the first signal based on the first signal and auxiliary information. Here, the auxiliary information includes information related to a resource configuration for the first signal and / or information related to a channel environment for the first signal. As an exemplary embodiment, the auxiliary information may be at least one of the position information, structure information and distribution information of the physical resource, and the relative position of the physical resource. The auxiliary information may alternatively be some feature quantity constraint of the channel in the current scenario, for example, the variance, delay spread and relevant time of the channel. An information processing unit may also convert the relevant information into at least one of a vector, a matrix and a tensor that are used for the computation of a neural network layer.

[0133] In some examples, through the neural network, the information related to the combining of the first signal may be determined based on the first signal and the auxiliary information.

[0134] In some embodiments, the third information for indicating the grouping mode of the antenna port related to the first signal may include indices of antenna ports contained in each antenna port group, and / or information related to a number of groups.

[0135] In some examples, the first module may determine the second information (i.e., at least one of the third information and the fourth information) based on the comparison result of the first information and a preset threshold value. As an example, when the channel feature information (e.g., the signal to interference plus noise ratio) contained in the first information is greater than a threshold , the antenna port grouping mode refers to being sequentially divided into 8 groups, and when the channel feature information ratio is less than the threshold , the antenna port grouping mode refers to being sequentially divided into 16 groups. As another example, when the inter-antenna correlation in the channel state information in the first information is less than a threshold , the intra-group signal combining mode in the second information refers to selecting the signal with the maximum signal reception energy in the group as the output, and when the signal-to-noise ratio is greater than the threshold the intra-group signal combining mode in the second information refers to using the average of all the signals in the group as the output. As another example, the output of the first neural network is a complex coefficient used for the intra-group signal combining of each antenna group (the real part and imaginary part of each coefficient are respectively outputted, and the real part and the imaginary part of each coefficient are combined into the corresponding signal weight coefficient). During the intra-group signal combining, each signal is multiplied by the corresponding coefficient, and the obtained signals are summed, thereby obtaining the second signal.

[0136] In some examples, the first module inputs the first signal and the first information into the neural network, and performs directly antenna port combining on the first signal through the neural network to generate the second signal. In the examples, the operations of determining the information related to the combining of the first signal and combining the first signal based on the determined information are performed through the neural network. In some examples, it is also possible to determine the information related to the combining of the first signal through one neural network and combine the first signal through an other neural network.

[0137] In step S503, combining is performed on the first signal in an antenna port dimension based on the determined information (i.e., the second information), to obtain the second signal. Here, a number of antenna ports related to the second signal is less than a number of antenna ports related to the first signal. For example, the signals received by different antenna ports in the first signal are combined to obtain the second signal, such that the number of the antenna ports related to the second signal is less than the number of the antenna ports related to the first signal.

[0138] In some embodiments, after obtaining the second information, the first module first performs grouping according to the third information indicating the grouping mode of the antenna port related to the first signal in the second information, then sequentially obtains the output signal of each group 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 in parallel as the second signal. Here, each group is considered as a new equivalent reception antenna. When the second information contains only the third information, the intra-group signal combining may be performed in a fixed or preset way. When the second information contains only the fourth information, the grouping of the first signal may be performed in a fixed or preset way.

[0139] The grouping mode of the antenna port related to the first signal may include at least one of: performing grouping based on an order of antenna port indices; a first signal contained in each group being partly or entirely identical; performing grouping based on a predefined grouping table; and selecting at least one signal from the first signal based on a predefined signal selection rule, and dividing an antenna port related to the at least one signal into one group.

[0140] In the present disclosure, the grouping mode of the antenna port related to the first signal indicates the mode of grouping the antenna of the first signal, that is, the signals received by which antennas should be divided into one group. The grouping mode of the antenna port includes, but not limited to:

[0141] performing the grouping based on the order of the antenna port indices;

[0142] the first signal contained in each group being partly or entirely identical;

[0143] performing the grouping based on the predefined grouping table; and

[0144] selecting at least one signal from the first signal based on the predefined signal selection rule, and dividing the antenna port related to the at least one signal into one group.

[0145] In some embodiments, the grouping mode of the antenna port refers to a non-overlapping division, that is, the received signal of each antenna port is divided into only one group. For example, the first signal is sequentially divided into N groups based on the order of the antenna port indices, and each group corresponds to the received signals of M antenna ports. Here, NM is equal to the total number of antennas. For example, there are 9 antenna ports in total, antenna ports 1-3 are divided into a group, antenna ports 4-6 are divided into a group, and antenna ports 7-9 are divided into a group.

[0146] In some embodiments, the grouping mode of the antenna port may be determined based on the predefined signal selection rule. For example, the predefined signal selection rule may be that the signal is divided into a group at an interval of n according to the order of the antenna ports, for example, an antenna having an odd / even serial number is divided into one group (i.e., at an interval of 2). For example, the predefined signal selection rule may refer to selecting at least one signal from the first signal and dividing the antenna port related to the at least one signal into a group. For example, the received signals of some antenna ports may be directly selected to be divided into a group (e.g., the received signals of antenna ports 1, 4 and 9 are divided into a group, and the received signals of antenna ports 2, 5 and 8 are divided into an other group).

[0147] In some embodiments, the grouping mode of the antenna port refers to an overlapping division, that is, the received signal of each antenna is divided into one or more groups, and the first signal contained in each group is partly or entirely identical. 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 signals overlapping in each group. Here, N(M-m) is equal to the total number of antennas. For example, each group contains the received signals on all the antennas, i.e., each group contains the entire first signal.

[0148] In some embodiments, the grouping mode of the antenna port may be predefined and stored, for example, in the first module of the base station. Upon grouping the antenna port, the stored grouping mode is read and the antenna port is grouped based on the grouping mode.

[0149] In the present disclosure, the output signal corresponding to antenna port group includes, but not limited to:

[0150] a signal with maximum signal energy in the antenna port group;

[0151] a signal with a maximum L2 norm corresponding to a channel gain in the antenna port group;

[0152] a signal with maximum average energy in the antenna port group; and

[0153] a signal obtained after weighted averaging is performed on signals in the antenna port group, a weight for the weighted averaging being determined based on the first signal.

[0154] In some embodiments, the signal combining mode refers to calculating directly the average energy of the received signals on each antenna in the antenna port group and selecting the signal with the maximum signal energy in the antenna port group as the output signal of the group.

[0155] In some embodiments, the signal combining mode refers to calculating an L2 norm of a channel gain of each antenna and selecting the received signal of the antenna corresponding to the maximum L2 norm as the output signal of the group.

[0156] In some embodiments, the signal combining mode refers to calculating directly the average energy of the received signals on each antenna in the group and selecting the signal with the maximum average energy as the output signal of the group.

[0157] In some embodiments, the received signals on each antenna in the group have the same weight, and the output signal is an average of all the signals in the group.

[0158] In some embodiments, the weight for the weighted averaging may be determined based on the first signal. For example, the weight is from a certain fixed codebook (e.g., a spatial DFT over-sampled codebook). According to the adopted codeword indicated by the second information, the output signal is the signal obtained after the weighted averaging is performed on the first signal with the codeword as the weight. For example, the codeword having the maximum correlation with the channel state information in the first information in a certain fixed codebook is selected as the weight, and the signal after the weighted averaging is used as the output signal.

[0159] In some embodiments, it is required to perform an feature value decomposition on the channel state information, and then sequentially select the feature vectors corresponding to the first c maximum (c is the number of the antenna groups) feature values as the weight of each group, and use the inner product of the received signal and the feature vector as the output signal of this group.

[0160] In some embodiments, the channel state information is subjected to time domain and / or frequency domain and / or antenna domain averaging, the conjugate of the average gain (a complex number) of each antenna given in the averaged channel state information is directly used as the weight, and the received signals are multiplied by the weights of the antennas respectively and summed, thereby obtaining the output signal of this group.

[0161] In some embodiments, the first module may further perform an equalization operation on the combined second signal as described below, and transfer the equalized signal as the second signal to the second module.

[0162] In some embodiments, the method 50 may further include: obtaining a channel equalization matrix; and equalizing the second signal based on the channel equalization matrix.

[0163] Further, in the method 50, a signal detection is performed based on the equalized second signal.

[0164] In some examples, the procedure in this step is accomplished in the first module, the equalized second signal is to be transmitted from the first module to the second module via a communication connection, and the second module is configured to perform the 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 procedure in this step is accomplished in the second module. That is, the second module is configured to obtain the channel equalization matrix; equalize the second signal based on the channel equalization matrix; and perform the signal detection based on the equalized second signal. Here, the communication connection between the first module and the second module may use a wired mode (e.g., an optical fiber), or may use a wireless mode (e.g., microwave). The second module refers to an entity for implementing signal processing and information extraction functions in the receivers on the sides of the foregoing gNB 101, gNB 102 and gNB 103, and should include the channel decoding and demodulation block 280 in the foregoing reception path 250. In some examples, the second module is a DU module containing partial physical layer function (PHY), the MAC layer function and the RLC layer function in the foregoing base station structure.

[0165] In some examples, the obtaining a channel equalization matrix may include: obtaining the channel equalization matrix based on the channel state information corresponding to the first signal; or performing a 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.

[0166] The channel equalization matrix used to equalize the second signal may be obtained through the channel state information of the first signal or the channel state information of the second signal.

[0167] In some examples, the channel equalization matrix may be directly obtained through the channel state information corresponding to the first signal. For example, in some embodiments, the first signal is , the channel state information matrix corresponding to the first signal is , the conjugate transpose matrix of the channel state information matrix is used as a coefficient for performing the combining on the first signal in the antenna port dimension, the second signal is generated, the corresponding channel equalization matrix is , and the signal after the channel equalization is .

[0168] In other examples, the channel equalization matrix may be obtained through the channel state information corresponding to the second signal. Here, the channel state information corresponding to the second signal may be obtained through the channel state information or reference signal corresponding to the first signal. For example, the channel state information corresponding to the second signal is . The second module may calculate the corresponding channel equalization matrix using an MMSE algorithm, and 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 from the combination of the channel state information corresponding to the first signal and the second information. Specifically, the channel state information corresponding to the first signal is grouped and combined based on the third information and the fourth information in the second information, thereby obtaining the channel state information corresponding to the second signal.

[0169] In some embodiments, the corresponding reference signal may be extracted from the second signal to obtain the channel state information corresponding to the second signal according to the reference signal.

[0170] In step S504, the second module performs the signal detection based on the second signal. Here, the target of the detection is to obtain the information transmitted by the terminal in communication with the base station. Specifically, the detection includes demodulation (e.g., QAM demodulation) and decoding (e.g., LDPC decoding, turbo decoding and polar decoding).

[0171] In some embodiments, the second module searches for a corresponding QAM modulation constellation graph according to the second signal, obtains corresponding bit data according to a corresponding constellation symbol, and then inputs the bit data into an LDPC decoder to obtain the information transmitted by the terminal in communication with the base station.

[0172] In other embodiments, the second module calculates soft information (e.g., a likelihood probability ratio) for each QAM modulation constellation symbol or bit information according to the second signal, and then inputs the soft information into an LDPC soft information decoder to obtain the information transmitted by the terminal in communication with the base station.

[0173] The signal processing method 50 provided in the above embodiment of the present disclosure can be applicable to the receivers on the sides of the base stations 101, 102 and 103 as shown in FIG. 1. In some embodiments of the present disclosure, the base stations 101, 102 and 103 may refer to a macro base station, a micro base station, a pico base station and a femto base station that are used for a radio access network, a backhaul base station for infinite backhaul, a base station that integrates both access and backhaul functions, and the like.

[0174] According to the signal processing method 50 provided in the above embodiment of the present disclosure, it is possible to precisely acquire the information transmitted by the terminal while significantly reducing the amount of the data transmitted between the functional modules in the base station, thereby solving the problem that the data transmission load between the two constituent modules (e.g., the RU unit and the DU unit) of the base station is excessively high. At the same time, since the antenna dimension of the second signal is significantly reduced, the calculation burden of performing the signal detection algorithm on the second signal is also significantly reduced.

[0175] FIG. 6 illustrates an example structure of a neural network according to some embodiments of the present disclosure. The neural network may be implemented by one physical entity or implemented jointly and / or independently by different entity units. The physical entity may consist of hardware, software, or a combination thereof, and may be configured as desired by those skilled in the art.

[0176] As shown in FIG. 6, an information processing unit may perform processing (e.g., combining and position encoding which will be described below) on inputted information (e.g., the first information, the first signal and the auxiliary information), such that the neural network will acquire priori information implied in the relevant information to assist the neural network in improving the precision and flexibility in acquiring channel state information.

[0177] Continuing to refer to FIG. 6, one neural network unit may include an information processing unit and a plurality of neural network layers. The information processing unit may convert data (such as the inputted information and auxiliary information in FIG. 6) to be inputted into the neural network into at least one of a data vector, a matrix and a tensor that can be used by the neural network for computation. The neural network layers may refer to at least one of an input layer, a hidden layer / an intermediate layer and an output layer shown in FIG. 6. In addition, each neural network layer may consist of a plurality of neurons n, and the neurons may be activated using at least one activation function, by way of example only and not by way of limitation, e.g., a tanh function, a ReLU function, an eLU function, a seLU function, a ceLU function, a preLU function, a geLU function, a LeakyReLU function, a Sigmoid function, a Softmax function, and a Softplus function. Each neural network layer may operate on the data inputted thereto to implement a function such as matrix transformation, data dimension reduction, data feature extraction, and data feature combination. By way of example only and not by way of limitation, the plurality of neural network layers may constitute different neural network structures in series or in parallel, including, but not limited to, a multilayer perceptron (MLP), a multilayer perceptron mixer (MLP-mixer), a convolutional neural network (CNN), a deep neural network (DNN), a recursive neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recursive deep neural network (BRDNN), a generative adversarial network (GAN), a Transformer and the like.

[0178] In some embodiments of the present disclosure, the information processing unit may convert the inputted information into at least one of the vector, the matrix, and the tensor used for the computation of the neural network layers. By way of example only and not by way of limitation, a set consisting of known channel state information is defined as . First, the known channel state information is processed through a numerical mapping method (e.g., normalization and standardization), such that the numerical scale or distribution of the known channel state information is adapted to the computation of the neural network. More specifically, taking the Min-Max normalization as an example, the maximum value and the minimum value of all elements in may first be respectively calculated, and then each element in may be subjected to the following processing:

[0179]

[0180] After the above numerical mapping is performed, the set consisting of the processed element may be converted into at least one of the vector, the matrix and the tensor that correspond to the input size of the neural network layer.

[0181] In other embodiments of the present disclosure, the information processing unit may further perform position encoding on the inputted information and convert the information after the position encoding into at least one of the vector, the matrix and the tensor that are used by the neural network for computation. Alternatively, the position encoding may be a process of adding and / or multiplying the inputted information with a codeword in a predetermined codebook. By way of example only and not by way of limitation, taking the preprocessing unit outputting the known channel state information (i.e., the preprocessing unit outputting the channel state information on the first resource according to the inputted first signal) as an example, the position encoding using the codebook may include the following processing:

[0182] First, the set consisting of the known channel state information is processed according to a numerical mapping method to obtain the set , containing several numerically mapped elements , and then, a position-encoded codebook is obtained according to the following equation:

[0183]

[0184] Here, n denotes the position of a physical resource (such as a time domain, a frequency domain, and a space domain) where the channel state information is, d denotes a physical resource dimension of the channel state information, N is any positive number greater than the number of elements contained in the set , and i denotes the index of the physical resource dimension. Then, the position encoding is performed on the known channel state information element through the equation to obtain a set . Finally, the set consisting of the processed element is converted into at least one of the vector, the matrix and the tensor that correspond to the input size of the neural network layer. In addition, the information processing unit may also input its input directly into the neural network unit without additional processing.

[0185] The operation in the above neural network unit is exemplary only, and the present disclosure is not limited thereto.

[0186] According to some embodiments of the present disclosure, as shown in FIG. 7, the present disclosure further provides a base station 700. The base station 700 includes a first module 710 and a second module 720.

[0187] The first module 710 is configured to:

[0188] receive a first signal through an antenna port of the base station; and

[0189] perform combining on the first signal in an antenna port dimension to obtain a second signal, wherein a number of antenna ports related to the second signal is less than a number of antenna ports related to the first signal.

[0190] The second module 720 is configured to:

[0191] perform a signal detection based on the second signal.

[0192] 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.

[0193] In some embodiments, the first module 710 may be further configured to: obtain a channel equalization matrix; and equalize the second signal based on the channel equalization matrix. The second module is configured to perform the signal detection based on the equalized second signal.

[0194] In some embodiments, the second module 720 may be further configured to: obtain a channel equalization matrix; equalize the second signal based on the channel equalization matrix; and perform the signal detection based on the equalized second signal.

[0195] 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 thus will not be repeatedly described herein.

[0196] One or more embodiments of the present disclosure may be applied to various communication systems, for example, a global system for mobile communications (GSM), a code division multiple access (CDMA) system, a wideband code division multiple access (WCDMA) system, a general packet radio service (GPRS), a long term evolution (LTE) system, an LTE frequency division duplex (FDD) system, an LTE time division duplex (TDD), a universal mobile telecommunication system (UMTS), a worldwide interoperability for microwave access (WiMAX) communication system, a 5th generation (5G) system, a new radio (NR), or the like. In addition, the technical solution in the embodiments of the present disclosure can be applied to future-oriented communication technologies.

[0197] The embodiments of the present disclosure further provide an electronic device. The electronic device includes a processor, and alternatively, may further a transceiver and / or memory coupled to the processor. The processor is configured to perform the steps of the method provided in any alternative embodiment of the present disclosure. The electronic device may be the base station described above.

[0198] FIG. 8 is a schematic structural diagram of an electronic device applicable to the embodiments of the present disclosure. As shown in FIG. 8, the electronic device 800 shown in FIG. 8 includes a processor 801 and a memory 803. The processor 801 is connected with the memory 803, for example, via a bus 802. Alternatively, the electronic device 800 may further include a transceiver 804, and the transceiver 804 may be used for the data interaction (e.g., transmission of data and / or reception of data) between the electronic device and an other electronic device. It should be noted that, in practical application, 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. Alternatively, the electronic device may be a first network node, a second network node, or a third network node.

[0199] The processor 801 may be a central processing unit (CPU), a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), an field programmable gate array (FPGA) or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. The processor 801 may implement or perform various exemplary logical blocks, modules and circuits described in combination with the present disclosure. The processor 801 may alternatively be a combination that implements a computing function, including, for example, one or more microprocessor combinations, a combination of a DSP and a microprocessor, and the like.

[0200] The bus 802 may include a path for transferring information between the above components. The bus 802 may be a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus or the like. The bus 802 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is used to represent the bus in FIG. 8, but it does not mean that there is only one bus or one type of bus.

[0201] The memory 803 may be a read only memory (ROM) or an other type of static storage device that can store static information and instructions, or a random access memory (RAM) or an other type of dynamic storage device that can store information and instructions, or may be an electrically erasable programmable read only memory (EEPROM), a compact disc read only memory (CD-ROM) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium, an other magnetic storage device, or any other medium that can be used to carry or store computer programs and that can be read by a computer, which is not limited here.

[0202] The memory 803 is used to store a computer program executing the embodiment of the present disclosure, and the execution is controlled by the processor 801. The processor 801 is used to execute the computer program stored in the memory 803 to implement the steps shown in the foregoing method embodiment.

[0203] FIG. 9 illustrates an example of a computer readable medium 900 in the form of a CD or DVD. The computer readable medium stores a program.

[0204] In general, various embodiments of the present disclosure may be implemented by hardware or dedicated circuits, software, logics, or any combination thereof. The embodiments may be implemented by hardware in some aspects, and may be implemented by firmware or software in other aspects, and may be performed by a controller, a microprocessor, or an other computing device. Although various aspects of the embodiments of the disclosure are shown and described as block diagrams, flowcharts, or represented using some other illustrations, it should be understood that the blocks, apparatuses, systems, techniques or methods described herein may be implemented as, for example, non-limiting examples, hardware, software, firmware, special purpose circuits or logics, general purpose hardware or controllers or other computing devices, or some combination thereof.

[0205] The present disclosure further 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, for example, instructions included in a program module, and the instructions are executed in a device on a real or virtual processor of a target to perform the method described above with reference to FIG. 5. Generally, the program module includes 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 the program modules may be combined or segmented between the program modules as desired. The machine executable instructions for the program modules may be executed within a local or distributed device. In the distributed device, the program modules may be in local and remote storage media.

[0206] The computer program codes for implementing the method of the present disclosure may be written in one or more programming languages. These computer program codes may be provided to the processor of a general purpose computer, a special purpose computer, or an other programmable data processing apparatus, such that the program codes, when executed by the computer or the other programmable data processing apparatus, cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program codes may be executed entirely on a computer, partly on a computer, as a stand-alone software package, partly on a computer and partly on a remote computer or entirely on a remote computer or server.

[0207] In the context of the present disclosure, the computer program codes or related data may be carried by any suitable carrier to enable a device, apparatus or processor to perform the various processes and operations described above. Examples of the carriers include signals, computer readable media, and the like. Examples of the signals may include electrical, optical, radio, sound, or other forms of propagated signals such as carrier waves and infrared signals.

[0208] A computer-readable medium may be any tangible medium containing or storing a program used for or relating to an instruction execution system, apparatus or device. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable medium may include, but not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses or devices, or any suitable combination thereof. More detailed examples of the computer readable storage medium include an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read only memory (ROM), an erasable programmable read only memory (EPROM or flash memory), an optical storage device, a magnetic storage device, or any suitable combination thereof. As used herein, the term "non-transient" or "non-instantaneous" is a definition for the medium itself (i.e., tangible, but not a signal), rather than a definition for the persistence of data storage (e.g., RAM and ROM).

[0209] Furthermore, although the operations of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that these operations must be performed in that particular order, or that the desired result can only be achieved by performing all of the operations shown. Instead, the order in which the steps depicted in the flowchart are performed may be changed. Additionally or alternatively, certain steps may be omitted, a plurality of steps may be combined into one step to perform, and / or one step may be decomposed into a plurality of steps to perform. It should also be noted that the features and functions of two or more apparatuses according to the present disclosure may be embodied in one apparatus. Conversely, the features and functions of one apparatus described above may be further divided into a plurality of apparatuses to be embodied.

[0210] The terms "first", "second", "third", "fourth", "1", "2" and the like, if present, in the specification and claims of the present disclosure and the drawings, are used to distinguish similar objects, and not necessarily to describe a particular order or a sequence. It should be understood that the data so used are interchangeable, as appropriate, such that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described literally.

[0211] It should be understood that although the operation steps are indicated by arrows in the flowchart of the embodiment of the present disclosure, the order in which these steps are implemented 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 may be performed in an other order as needed In addition, some or all of the steps in each flowchart, based on an actual implementation scenario, may include a plurality of sub-steps or a plurality of stages. Some or all of these sub-steps or stages may be executed at the same moment, and each of these sub-steps or stages may alternatively be executed at different moments. In the scenario where the execution moments are different, the execution order of these sub-steps or stages can be flexibly configured as needed, which is not limited in the embodiments of the present disclosure.

[0212] The above text and drawings are provided as examples only to assist the reader in understanding the present disclosure. The text and drawings are not intended to and should not be construed as limiting the scope of the present disclosure in any way. Although some embodiments and examples are provided, based on the disclosure herein, it would be obvious to those skilled in the art that, without departing from the scope of the disclosure, changes may be made to the illustrated embodiments and examples and similar implementation means based on the technical idea of the present disclosure may be adopted, which also fall within the scope of protection of the embodiments of the present disclosure.

Claims

1.A method performed by a base station in a communication system, the method comprises:receiving a first signal through an antenna port of the base station;determining information related to a combining of the first signal based on the first signal;performing combining on the first signal in an antenna port dimension based on the determined information, to obtain a second signal, wherein a number of antenna ports related to the second signal is less than a number of antenna ports related to the first signal; andperforming a signal detection based on the second signal.2.The method of claim 1, wherein the first signal comprises at least one of:a radio signal received by the antenna port of the base station;a digital domain wireless signal received by the antenna port of the base station; anda signal obtained based on the radio signal or the digital domain wireless signal.3.The method of claim 1, wherein the determining information related to the combining of the first signal based on the first signal comprises at least one of:determining, based on the first signal, the information related to the combining of the first signal through a first neural network;determining, based on a reference signal corresponding to the first signal, the information related to the combining of the first signal through a second neural network;determining, based on channel state information corresponding to the first signal, the information related to the combining of the first signal through a third neural network; ordetermining, based on channel feature information corresponding to the first signal, the information related to the combining of the first signal through a fourth neural network, andwherein the reference signal is obtained based on detecting the reference signal or a demodulation reference signal.4.The method of claim 3, wherein the channel state information corresponding to the first signal comprises at least one of:channel state information obtained by performing a channel estimation based on the first signal or the reference signal;channel state information stored in the base station;channel state information reported by a terminal and corresponding to the first signal; orchannel state information predicted by the base station based on the stored channel state information.5.The method of claim 4, further comprising:performing processing on the channel state information, wherein the processing comprises at least one of interpolating, denoising, filtering, averaging over a time domain, grouping and averaging in a subcarrier dimension, or averaging over an antenna domain.6.The method of claim 3, wherein the channel feature information corresponding to the first signal comprises at least one of:a 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;interference-related information related to the first signal;high-order statistics of the channel for the first signal; andinformation related to a difference between the reference signal corresponding to the first signal and an expected received signal, wherein the expected received signal is obtained based on the reference signal and the channel state information corresponding to the first signal.7.The method of claim 1, wherein the determining information related to combining of the first signal based on the first signal comprises:determining the information related to the combining of the first signal based on a comparison result between channel feature information corresponding to the first signal and a threshold value.8.The method of claim 1, wherein the information related to the combining of the first signal comprises at least one of:third information for indicating a grouping mode of an antenna port related to the first signal, orfourth information related to an output signal corresponding to an antenna port group, andwherein the third information comprises at least one of:indices of antenna ports contained in each antenna port group, orinformation related to a number of groups.9.The method of claim 9, wherein the grouping mode of the antenna port related to the first signal comprises at least one of:performing grouping based on an order of antenna port indices;a first signal contained in each group being partly or entirely identical;performing grouping based on a predefined grouping table; andselecting at least one signal from the first signal based on a predefined signal selection rule, and dividing an antenna port related to the at least one signal into one group.10.The method of claim 9, wherein the output signal corresponding to the antenna port group comprises at least one of:a signal with maximum signal energy in the antenna port group;a signal with a maximum L2 norm corresponding to a channel gain in the antenna port group;a signal with maximum average energy in the antenna port group; anda signal obtained after weighted averaging is performed on signals in the antenna port group, wherein a weight for the weighted averaging is determined based on the first signal.11.The method of claim 1, wherein the determining information related to combining of the first signal based on the first signal comprises:determining the information related to the combining of the first signal based on the first signal and auxiliary information, wherein the auxiliary information comprises information related to a resource configuration for the first signal and / or information related to a channel environment for the first signal.12.The method of claim 1, further comprising:obtaining a channel equalization matrix; and equalizing the second signal based on the channel equalization matrix,wherein the performing a signal detection based on the second signal comprises: performing the signal detection based on the equalized second signal.13.The method of claim 12, wherein the obtaining a channel equalization matrix comprises:obtaining the channel equalization matrix based on channel state information corresponding to the first signal; orperforming a channel estimation on the second signal to obtain 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, andwherein the obtaining the channel equalization matrix based on channel state information corresponding to the first signal comprises:obtaining the channel state information corresponding to the second signal based on the channel state information corresponding to the first signal; andobtaining the channel equalization matrix based on the channel state information corresponding to the second signal.14.A base station, comprising:a first module, configured to:receive a first signal through an antenna port of the base station, determine information related to combining of the first signal based on the first signal, and perform combining on the first signal in an antenna port dimension based on the determined information, to obtain a second signal, wherein a number of antenna ports related to the second signal is less than a number of antenna ports related to the first signal; anda second module, configured to:perform a signal detection based on the second signal.15.The base station of claim 14, wherein the first module is further configured to:obtain a channel equalization matrix; and equalize the second signal based on the channel equalization matrix, andwherein the second module is further configured to:obtain a channel equalization matrix, equalize the second signal based on the channel equalization matrix, and perform the signal detection based on the equalized second signal.

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