Signal Processing Method, Apparatus, and Storage Medium

Abstract

The present application provides a method and apparatus for processing signals and a storage medium. The method includes: a first node obtains channel parameters respectively corresponding to P second nodes, where the channel parameters include channel information and a first parameter, and the first parameter includes noise-related information or noise information. The noise-related information is used to indicate the noise correlation between M c antennas corresponding to the second node, and the noise information is used to indicate the noise of the first signal received by the second node. P is an integer greater than 1, and M c is an integer greater than 0. The first node determines equalization information according to the channel parameters respectively corresponding to the P second nodes, and the equalization information is used to perform channel equalization on a second signal, where the second signal is determined according to the first signals respectively received by the P second nodes. Distributed equalization processing based on colored noise is achieved, reducing the amount of data exchanged between nodes and the computational complexity of the first node.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and in particular, to a signal processing method, apparatus, and storage medium. Background Art

[0002] In some communication systems, such as the 5th generation wireless system (5G), multi-in-multi-out (MIMO) technology and extreme large multiple-input and multiple-output (XL-MIMO) technology, as the core technologies of future mobile communication systems, can significantly improve the spectral efficiency and energy efficiency of mobile communication systems. In this scenario, channel equalization technologies, such as maximal ratio combining (MRC), zero forcing (ZF), minimum mean square error (MMSE), etc., can reduce the crosstalk between user data streams in the uplink transmission link to ensure the receiving performance of the base station, and are the key technologies to achieve high spectral efficiency and energy efficiency of XL-MIMO technology.

[0003] However, with the increase in the number of antennas of network devices, the network device performs centralized channel equalization on each antenna or antenna subarray, resulting in a large amount of interactive data and high computational complexity. The existing distributed equalization technology uses each distributed baseband processing unit in the network device, based on the MMSE equalization algorithm under white noise, and realizes multiple rounds of iteration through data interaction between each distributed baseband processing unit to achieve the equalization effect of the centralized equalization algorithm. It can be seen that this distributed equalization technology still has the problems of a large amount of interactive data and high computational complexity. Summary of the Invention

[0004] A signal processing method, apparatus, and storage medium provided in an embodiment of this application can reduce the processing complexity of baseband signals and improve information transmission efficiency.

[0005] In a first aspect, an embodiment of this application provides a signal processing method, including: a first node obtains channel parameters respectively corresponding to P second nodes, where the channel parameters include channel information and a first parameter, and the first parameter includes noise-related information or noise information The noise-related information is used to indicate the noise correlation between M c antennas corresponding to the second node, and the noise information For indicating the noise of the first signal received by the second node, P is an integer greater than 1, and M c is an integer greater than 0; the first node determines equalization information according to the channel parameters respectively corresponding to the P second nodes, and the equalization information is used for channel equalization of the second signal, and the second signal is determined according to the first signals respectively received by the P second nodes.

[0006] Through the signal processing method provided by the first aspect, the channel parameters respectively corresponding to the P second nodes obtained by the first node carry the noise correlation information between M c antennas. The first node determines equalization information based on the channel parameters and performs channel equalization according to the equalization information, realizing distributed equalization processing based on colored noise, reducing the amount of data exchanged between nodes and the computational complexity of the first node.

[0007] In a possible implementation manner, the first node determines equalization information according to the channel parameters respectively corresponding to the P second nodes, including: the first node performs parameter fusion on the channel parameters respectively corresponding to the P second nodes to obtain equivalent parameters; the first node determines equalization information according to the equivalent parameters.

[0008] Through the signal processing method provided by this implementation manner, the first node can determine equalization information according to the fused equivalent parameters, reducing the computational complexity and improving the processing efficiency of the first node.

[0009] In a possible implementation manner, the first node performs parameter fusion on the channel parameters respectively corresponding to the P second nodes to obtain equivalent parameters, including: the first node performs parameter fusion on the channel information respectively corresponding to the P second nodes to obtain equivalent channel information and, the first node performs parameter fusion on the noise information respectively corresponding to the P second nodes to obtain equivalent noise information Or, the first node performs parameter fusion on the noise correlation information respectively corresponding to the P second nodes to obtain equivalent noise correlation information

[0010] Through the signal processing method provided by this implementation manner, the first node performs parameter fusion on each channel parameter respectively to obtain equivalent parameters of each channel parameter, avoiding determining equalization information based on each channel parameter, reducing the computational complexity, and improving the processing efficiency of the first node.

[0011] In a possible implementation manner, the method further includes: the first node determines equivalent noise correlation information according to the equivalent noise information ​

[0012] In a possible implementation, the first node performs parameter fusion on the channel information corresponding to each of the P second nodes to obtain equivalent channel information including: the first node performs summation or matrix concatenation on the channel information corresponding to each of the P second nodes to obtain the equivalent channel information

[0013] In a possible implementation, the first node performs parameter fusion on the noise information corresponding to each of the P second nodes to obtain equivalent noise information including: the first node performs summation or matrix concatenation on the noise information corresponding to each of the P second nodes to obtain the equivalent noise information

[0014] In a possible implementation, the first node performs parameter fusion on the noise correlation information corresponding to each of the P second nodes to obtain equivalent noise correlation information including: the first node performs summation or matrix concatenation on the noise correlation information corresponding to each of the P second nodes to obtain the equivalent noise correlation information

[0015] In a possible implementation, the equalization information W H satisfies the following formula:

[0016] In a possible implementation, the channel parameter is obtained by weighting with a compression matrix and the compression matrix is determined according to the channel parameter.

[0017] In a possible implementation, the compression matrix is an L*M c dimensional matrix, where L is the number of columns of H c column number.

[0018] Through the signal processing method provided by this implementation, when L is greater than or equal to the number of columns of the channel information H c column number, the channel parameter and / or the first signal can be losslessly compressed. When L is less than the number of columns of the channel information H c column number, signal loss is likely to occur. Therefore, when L is equal to the number of columns of the channel information H cWhen the number of columns is such that the first node can achieve maximum lossless compression of the channel parameters and / or the first signal.

[0019] In a possible implementation, the compression matrix satisfies the following formula: where H c is the channel information before compression, and R cc is the noise correlation information before compression

[0020] In a possible implementation, the method further includes: the first node fuses the first signals respectively received by the P second nodes to obtain the second signal.

[0021] In a possible implementation, the first node fuses the first signals respectively received by the P second nodes to obtain the second signal, including: the first node sums or matrix splices the first signals respectively received by the P second nodes to obtain the second signal.

[0022] Through the signal processing method provided by the above embodiments related to parameter fusion, the channel parameters obtained by the first node through the summation strategy have a lower complexity in the subsequent process of determining the equalization information compared to those obtained through the matrix splicing strategy, while the channel parameters obtained by the first node through the matrix splicing strategy have higher performance of the subsequent determined equalization information compared to those obtained through the summation strategy.

[0023] In a possible implementation, the first signal is weighted by a compression matrix and the compression matrix is determined according to the channel parameters.

[0024] Through the signal processing method provided by the above embodiments related to the compression matrix, the second node sends the channel parameters and / or the first signal compressed by the compression matrix to the first node, further reducing the amount of data to be interacted.

[0025] In a possible implementation, the P second nodes include the first node and P - 1 third nodes, or the P second nodes include P third nodes, and the third node is a child node of the first node.

[0026] In a second aspect, an embodiment of the present application provides a signal processing method, including: a second node sends channel parameters to a first node, where the channel parameters include channel information and a first parameter, and the first parameter includes noise correlation information or noise information The noise correlation information is used to indicate the M corresponding to the second node cThe noise correlation between antennas, and this noise information is used to indicate the noise of the first signal received by the second node, P is an integer greater than 1, and M c is an integer greater than 0.

[0027] In a possible implementation, the channel parameter is obtained by weighting with a compression matrix , and this compression matrix is determined according to the channel parameter.

[0028] In a possible implementation, this compression matrix is an L*M c dimensional matrix, where L is the number of columns of H c .

[0029] In a possible implementation, this compression matrix satisfies the following formula: where H c is the channel information before compression, and R cc is the noise correlation information before compression.

[0030] In a possible implementation, the first signal is obtained by weighting with a compression matrix , and this compression matrix is determined according to the channel parameter.

[0031] For the signal processing method provided by the second aspect and each possible implementation of the second aspect, the beneficial effects can be referred to the beneficial effects brought by the first aspect and each possible implementation of the first aspect, which will not be elaborated here.

[0032] In a third aspect, an embodiment of the present application provides a communication device, including a transceiver unit, configured to obtain channel parameters respectively corresponding to P second nodes, where the channel parameter includes channel information and a first parameter, and the first parameter includes noise correlation information or noise information This noise correlation information is used to indicate the noise correlation between M c antennas corresponding to the second node, and this noise information is used to indicate the noise of the first signal received by the second node, P is an integer greater than 1, and M c is an integer greater than 0; a processing unit, configured to determine equalization information according to the channel parameters respectively corresponding to the P second nodes, where the equalization information is used to perform channel equalization on a second signal, and the second signal is determined according to the first signals respectively received by the P second nodes.

[0033] In a possible implementation, the processing unit is specifically configured to: perform parameter fusion on the channel parameters respectively corresponding to the P second nodes to obtain equivalent parameters; and determine equalization information according to the equivalent parameters.

[0034] In a possible implementation, the processing unit is specifically configured to: perform parameter fusion on the channel information respectively corresponding to the P second nodes to obtain equivalent channel information and perform parameter fusion on the noise information respectively corresponding to the P second nodes to obtain equivalent noise information Alternatively, perform parameter fusion on the noise-related information respectively corresponding to the P second nodes to obtain equivalent noise-related information

[0035] In a possible implementation, the processing unit is further configured to: determine equivalent noise-related information according to the equivalent noise information

[0036] In a possible implementation, the processing unit is specifically configured to: perform summation or matrix concatenation on the channel information respectively corresponding to the P second nodes to obtain the equivalent channel information

[0037] In a possible implementation, the processing unit is specifically configured to: perform summation or matrix concatenation on the noise information respectively corresponding to the P second nodes to obtain the equivalent noise information

[0038] In a possible implementation, the processing unit is specifically configured to: perform summation or matrix concatenation on the noise-related information respectively corresponding to the P second nodes to obtain the equivalent noise-related information

[0039] In a possible implementation, the equalization information W H satisfies the following formula:

[0040] In a possible implementation, the channel parameter is obtained by weighting with a compression matrix and the compression matrix is determined according to the channel parameter.

[0041] In a possible implementation, the compression matrix is an L*M c dimensional matrix, where L is Hc The number of columns.

[0042] In a possible implementation, the compression matrix satisfies the following formula: where H c is the channel information before compression, and R cc is the noise correlation information before compression.

[0043] In a possible implementation, the processing unit is further configured to: perform signal fusion on the first signals respectively received by the P second nodes to obtain the second signal.

[0044] In a possible implementation, the processing unit is specifically configured to: perform summation or matrix splicing on the first signals respectively received by the P second nodes to obtain the second signal.

[0045] In a possible implementation, the first signal is weighted by a compression matrix and the compression matrix is determined according to the channel parameter.

[0046] In a possible implementation, the P second nodes include the communication device and P - 1 third nodes, or the P second nodes include P third nodes, and the third node is a child node of the communication device.

[0047] For the signal processing method provided by the foregoing third aspect and each possible implementation of the foregoing third aspect, the beneficial effects can be referred to the beneficial effects brought by the foregoing first aspect and each possible implementation of the first aspect, and will not be elaborated here.

[0048] Fourth aspect, an embodiment of the present application provides a communication device, including: a transceiver unit, configured to send a channel parameter to a first node, where the channel parameter includes channel information and a first parameter, and the first parameter includes noise correlation information or noise information The noise correlation information is used to indicate the noise correlation between M c antennas corresponding to the communication device, and the noise information is used to indicate the noise of the first signal received by the communication device. P is an integer greater than 1, and M c is an integer greater than 0.

[0049] In a possible implementation, the channel parameter is weighted by a compression matrix and the compression matrix is determined according to the channel parameter.

[0050] In a possible implementation, the compression matrix is an L*M c dimensional matrix, where L is the number of columns of H c .

[0051] In a possible implementation, the compression matrix satisfies the following formula: where H c is the channel information before compression, and R cc is the noise correlation information before compression.

[0052] In a possible implementation, the first signal is obtained by weighting with the compression matrix , and the compression matrix is determined according to the channel parameters.

[0053] For the signal processing method provided by the fourth aspect and each possible implementation of the fourth aspect, the beneficial effects can be referred to the beneficial effects brought by the first aspect and each possible implementation of the first aspect, which will not be elaborated here.

[0054] Fifth aspect, an embodiment of the present application provides a communication device, including: a processor and a memory, the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method in the first aspect, the second aspect or each possible implementation.

[0055] Sixth aspect, an embodiment of the present application provides a chip, including: a processor, used to call and run computer instructions from a memory, so that a device installed with the chip executes the method in the first aspect, the second aspect or each possible implementation.

[0056] Seventh aspect, an embodiment of the present application provides a computer-readable storage medium, used to store computer program instructions, and the computer program enables a computer to execute the method in the first aspect, the second aspect or each possible implementation.

[0057] Eighth aspect, an embodiment of the present application provides a computer program product, including computer program instructions, and the computer program instructions enable a computer to execute the method in the first aspect, the second aspect or each possible implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a schematic diagram of the architecture of the communication system to which the embodiment of the present application is applied.

[0059] Figure 2 is a schematic diagram of the XL-MIMO scenario to which the embodiment of the present application is applied.

[0060] Figure 3 Schematic diagram of a distributed baseband processing architecture provided for this application.

[0061] Figure 4a Schematic diagram of a star - shaped distributed baseband processing architecture provided for an embodiment of this application.

[0062] Figure 4b Another schematic diagram of a star - shaped distributed baseband processing architecture provided for an embodiment of this application.

[0063] Figure 5 Schematic flowchart of a signal processing method 500 provided for an embodiment of this application.

[0064] Figure 6 Schematic flowchart of a method for determining equalization information provided for an embodiment of this application.

[0065] Figure 7 Schematic flowchart of another method for determining equalization information provided for an embodiment of this application.

[0066] Figure 8 Schematic diagram of a communication device 600 provided for an embodiment of this application.

[0067] Figure 9 Another schematic block diagram of a communication device 700 provided for an embodiment of this application. Detailed implementation manners

[0068] Next, the technical solutions in this application will be described in conjunction with the accompanying drawings.

[0069] The antenna detection method provided by this application can be applied to various communication systems, such as: Long Term Evolution (LTE) system, LTE Frequency Division Duplex (FDD) system, LTE Time Division Duplex (TDD), Universal Mobile Telecommunication System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX) communication system, future 5th Generation (5G) mobile communication system or New Radio Access Technology (NR), and the three application scenarios of 5G mobile communication system: enhanced mobile broadband (eMBB), ultra-reliable low latency communications (uRLLC), and massive machine type communications (mMTC), device-to-device (D2D) communication system, satellite communication system, Internet of Things (IoT), narrow band Internet of Things (NB-IoT) system, Global System for Mobile Communications (GSM), Enhanced Data Rate for GSM Evolution (EDGE), Wideband Code Division Multiple Access (WCDMA) system, Code Division Multiple Access 2000 (CDMA2000) system, Time Division-Synchronization Code Division Multiple Access (TD-SCDMA) system. Among them, the 5G mobile communication system can include non-standalone (NSA) and / or standalone (SA).

[0070] The antenna detection method provided by this application can also be applied to future communication systems, such as the sixth-generation mobile communication system, etc. This application does not make any limitations in this regard.

[0071] Figure 1 It is a schematic diagram of the architecture of the communication system to which the embodiments of this application are applied. As Figure 1 shown, the mobile communication system includes a core network device 110, a network device 120, and at least one terminal device (such as Figure 1 the terminal device 130 and the terminal device 140 in). The terminal device is connected to the network device in a wireless manner, and the network device is connected to the core network device in a wireless or wired manner. The core network device and the network device can be independent different physical devices, or the functions of the core network device and the logical functions of the network device can be integrated on the same physical device, or the functions of part of the core network device and part of the network device can be integrated on a physical device. The terminal device can be fixed in position or movable. Figure 1 This is just a schematic diagram. The communication system may also include other network devices, such as wireless relay devices and wireless backhaul devices, which are not drawn in Figure 1 . The embodiments of this application do not limit the number of core network devices, network devices, and terminal devices included in the mobile communication system.

[0072] The network device is an access device through which the terminal device accesses the mobile communication system in a wireless manner. It can be a base station NodeB, an evolved base station eNodeB, a base station in an NR mobile communication system, a base station in a future mobile communication system, or an access node in a WiFi system, etc. The embodiments of this application do not limit the specific technologies and specific device forms adopted by the network device.

[0073] The terminal device can also be referred to as a terminal, user equipment (UE), mobile station (MS), mobile terminal (MT), etc. The terminal device can be a mobile phone, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical surgery, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, and so on.

[0074] The network device and the terminal device can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and can also be deployed on airplanes, balloons and satellites in the air. The embodiments of the present application do not limit the application scenarios of the network device and the terminal device.

[0075] The network device and the terminal device can communicate with each other through licensed spectrum, or through unlicensed spectrum, or through both licensed spectrum and unlicensed spectrum at the same time. The network device and the terminal device can communicate through spectrum below 6G, or through spectrum above 6G, or use both spectrum below 6G and spectrum above 6G at the same time. The embodiments of the present application do not limit the spectrum resources used between the network device and the terminal device.

[0076] It should be understood that the present application does not limit the specific forms of the network device and the terminal device.

[0077] In the above communication system, multi-in-multi-out (MIMO) technology and extreme large multiple-input and multiple-output (XL-MIMO) technology, as the core technologies of future mobile communication systems, can significantly improve the spectrum efficiency and energy efficiency of mobile communication systems. CombiningFigure 2 As shown, network device 210 (which can be, for example, the network device 120 in Figure 1 ) transmits uplink information and / or downlink information to / from multiple terminal devices 220 (which can include, for example, the terminal devices 130, 140 in Figure 1 ) via multiple antennas or antenna arrays. Any one of the terminal devices can be equipped with multiple antennas or antenna arrays (not shown in the figure) for sending uplink information to network device 210 or receiving downlink information sent by network device 210. Channel equalization techniques, such as maximal ratio combining (MRC), zero forcing (ZF), minimum mean square error (MMSE), etc., can reduce the interference between user data streams in the uplink transmission link (simply referred to as inter-stream interference) to ensure the receiving performance of the base station, and are the key technologies for achieving high spectral efficiency and energy efficiency of XL-MIMO technology.

[0078] In the MIMO or XL-MIMO scenario, considering multiple terminal devices sending signals to a network device equipped with M antennas or antenna sub-arrays, in the uplink transmission link, the channel matrix between the network device and the multiple terminal devices is denotes an M-by-K dimensional matrix, and each element in the matrix is a complex number. denotes the user data stream signal. denotes the interference and noise of the network device. Here, K is the number of user data streams of the multiple terminal devices, and the received signal of the network device satisfies the following equation (1):

[0079] y = Hs + n (1)

[0080] Based on the above uplink transmission link model, the MMSE equalization algorithm can be implemented under the white noise assumption or under the colored noise assumption. For example, the MMSE equalization algorithm under independent Gaussian white noise is implemented by the following formula (2):

[0081] W H = (H H H + σ 2 I) -1 H H (2)

[0082] where σ 2 is the average power of white noise, I is the identity matrix, and the superscript H is the conjugate transpose symbol. For example, H H is the conjugate transpose of H.

[0083] There is a large difference between the actual noise of the signal received by the network device and the white noise. The above formula (2) does not consider the obvious correlation between different antenna noises. Therefore, in practical applications, the improvement of the above formula (2) is not obvious compared with the ZF equalization. The equalization algorithm considers the information of the antenna reception noise correlation to perform more effective interference cancellation, that is, to consider the colored noise hypothesis. For example, the MMSE equalization algorithm under colored noise is implemented by the following formula (3):

[0084] W H =(H H R -1 H + I) -1 H H R -1 (3)

[0085] Among them, R is the noise correlation information, which is used to indicate the noise correlation among multiple antenna brackets. The MMSE equalization algorithm under colored noise can effectively reduce the inter-stream crosstalk in the uplink. However, as the number of antennas of the network device increases, the network device performs centralized channel equalization on each antenna or antenna subarray, resulting in a large amount of interactive data and high computational complexity.

[0086] Figure 3 This is a schematic diagram of a distributed baseband processing architecture provided by this application. In Figure 3 each distributed baseband processing unit, such as distributed baseband processing units 311, 312, and 313, respectively receive the signaling and / or data sent by the terminal device through the corresponding antenna clusters (such as one of antenna clusters 1 to antenna clusters P), for example, connected to the radiofrequency (RF) units in the antenna clusters. Each distributed baseband processing unit determines its own equalization matrix according to the received signaling and / or data, and sends the obtained equalization matrix to the next distributed baseband processing unit. The next distributed baseband processing unit corrects its own generated equalization matrix according to the received equalization matrix, and then sends the corrected equalization matrix to the next distributed baseband processing unit, and so on. After multiple rounds of iteration, a converged equalization matrix is obtained, and then channel equalization processing is performed. It can be seen that the existing distributed equalization technology uses each distributed baseband processing unit in the network device to perform multiple rounds of iteration through data interaction between the distributed baseband processing units based on the MMSE equalization algorithm under the above white noise to achieve the equalization effect of the centralized equalization algorithm. This distributed equalization technology still has the problems of a large amount of interactive data and high computational complexity.

[0087] In view of the above technical problems, in the process of the network device in the embodiment of the present application performing distributed channel equalization, on the one hand, a "central node (the same as the first node in the following text)" is introduced based on multiple distributed nodes (such as the above-mentioned distributed baseband processing units), so that each of the multiple nodes sends the interaction data to the central node, and the central node determines the equalization information by combining the interaction data of each node, and then performs channel equalization; on the other hand, the noise-related information or noise information is included in the interaction data, and the antenna reception noise correlation information of the colored noise is reflected through the noise-related information or noise information. The equalization algorithm based on colored noise can more effectively achieve interference cancellation. Therefore, the embodiment of the present application can implement channel equalization based on the equalization algorithm of colored noise with a star-shaped distributed baseband processing architecture, and can achieve the equalization effect of the centralized equalization algorithm without iteration, reducing the amount of interaction data and the computational complexity.

[0088] To facilitate the understanding of the embodiment of the present application, the relevant terms in the embodiment of the present application will be described first.

[0089] 1. Channel equalization: It refers to the equalization of the channel characteristics, that is, the equalizer at the receiving end generates characteristics opposite to the channel to cancel the inter-stream interference introduced by the wireless channel.

[0090] 2. Dimensionality reduction (DR): In a very large-scale communication system, linear or non-linear transformation is used to reduce (compress) the dimension of high-dimensional data to reduce the communication volume and computational complexity, and at the same time, try to make the compression not cause excessive information loss.

[0091] Next, the signal processing method provided by the embodiment of the present application will be described with reference to the accompanying drawings.

[0092] The embodiment of the present application can be applied to the uplink transmission process, and the execution subject is the receiving end in the uplink transmission process, that is, the network device. For example, it can be Figure 1 the network device 120 in. It should be understood that for the convenience of understanding and description below, the method provided by the embodiment of the present application will be mainly described by taking the interaction between the first node of the network device and P (P is an integer greater than 1) second nodes as an example. In one scenario, the first node serves as the central node. For example, it can be Figure 4a the node 401 in, and the second node is Figure 4aNodes respectively connected to each antenna cluster. Each second node can obtain the signals received by its corresponding antenna cluster and process the received signals. For example, the second node includes a first node 401 and other non-central nodes 402. The non-central nodes other than the first node in each second node are connected to the first node in a wired or wireless manner. In this case, each second node including the first node can be the above-mentioned distributed baseband processing unit, deployed in the baseband lower (BBL). The first node and the baseband higher (BBH) can be connected in a wired or wireless manner. In another scenario, the first node as a central node can be, for example, Figure 4b the node 411 in Figure 4b , and the second node is a non-central node, for example, it can be

[0093] the node 412 in. Each second node is connected to the first node in a wired or wireless manner. In this case, the first node can be a BBH or a processing unit deployed in the BBH, and the second node can be deployed in the BBL. The second node can be, for example, the above-mentioned distributed baseband processing unit.

[0094] It should be understood that each second node corresponds to an antenna cluster, and is used to perform signal processing on the uplink signals (including signaling and / or data) received on the antenna cluster. Figure 4a Figure 4b It should be noted that the sub-nodes of the first node in the following text are also called third nodes, that is, P second nodes include a first node and P - 1 third nodes (see

[0095] Figure 5 ), or P second nodes include P third nodes (see Figure 5 ).

[0096] S510, the second node sends channel parameters to the first node, and the channel parameters include channel information and a first parameter, and the first parameter includes noise-related information or noise information c This noise-related information is used to indicate the noise correlation between the M c antennas corresponding to the second node, and this noise information is used to indicate the noise of the first signal received by the second node. P is an integer greater than 1, and M c is an integer greater than 0.

[0097] Correspondingly, the first node obtains the channel parameters corresponding to the P second nodes respectively. It should be noted that, in Figure 4a the scenario shown, the first node receives the channel parameters respectively sent by P - 1 third nodes among the P second nodes, and reads the channel parameters obtained by the first node itself; in Figure 4b the scenario shown, the first node receives the channel parameters respectively sent by the P second nodes. It is understandable for those skilled in the art that the first node, as one of the second nodes, obtains its own channel parameters, which will not be elaborated in this embodiment. For the sake of simplicity in the following description, only the example where the first node receives the channel parameters respectively sent by the P second nodes will be used for illustration.

[0098] S520. The first node determines equalization information according to the channel parameters corresponding to the P second nodes respectively. This equalization information is used for channel equalization of the second signal, and the second signal is determined according to the first signals respectively received by the P second nodes.

[0099] It should be noted that the channel parameters sent by the second node at least include channel information and noise - related information or at least include channel information and noise information

[0100] Optionally, is an L - by - L matrix, is an L - by - L matrix, is an L - by - N matrix. Wherein, N is related to the number of pilot signals and the sub - carrier granularity in the time unit. For example, in a time slot, it is calculated once every 48 sub - carriers and each time it is calculated based on 2 pilot symbols, then N = 48 * 2.

[0101] In some embodiments, the channel parameters sent by the second node further include the first signal. It should be understood that the first signal may be an uplink signal received by the second node through its corresponding antenna cluster c (this antenna cluster c may include the above - mentioned M c antennas).

[0102] When the above - mentioned first parameter includes noise information the first node can determine the noise - related information according to the respective noise information of the P second nodes

[0103] noise information can reflect the noise correlation among the M c antennas.

[0104] ​In the above S520, since the channel parameters respectively corresponding to the P second nodes all carry information capable of reflecting the noise correlation between the M c antennas, the equalization information determined by the first node based on the channel parameters respectively corresponding to the P second nodes implements an equalization algorithm based on colored noise.

[0105] The equalization information can be, for example, an equalization matrix, such as the equalization matrix determined by the foregoing formula (3).

[0106] Exemplarily, the channel information and the first parameter can both be measured by the second node through the M c antennas corresponding to it for receiving the pilot signal sent by the terminal device. Optionally, the second node can send the channel information and the first parameter to the first node in each time unit, and / or the first node can obtain its own channel information and the first parameter in each time unit. It can be understood that the channel information and the first parameter of the first node itself are measured by the first node through the M c antennas corresponding to it for receiving the pilot signal sent by the terminal device. Among them, the time unit can be a time slot, a subframe, etc.

[0107] It should be understood that the number of antennas or antenna sub-arrays corresponding to each second node can be the same or different, and the present application does not make any limitation in this regard. It should also be understood that the M c antennas are taken as an example of being divided by antenna granularity, but it does not represent any limitation to the present application. For example, it can also be divided by antenna sub-array granularity, that is, the M c antennas can be replaced by M c antenna sub-arrays.

[0108] The following will describe the above S520 in conjunction with Figure 6 and Figure 7 .

[0109] Combined with Figure 6 shown, the above S520 may specifically include the following S521 and S522:

[0110] S521, the first node performs parameter fusion on the channel parameters respectively corresponding to the P second nodes to obtain equivalent parameters;

[0111] S522, the first node determines equalization information according to the equivalent parameters.

[0112] In the above S521, the first node can perform parameter fusion on each channel parameter respectively to obtain the equivalent parameter of each channel parameter. For example, combined with Figure 7As shown, the first node can perform parameter fusion on the channel information corresponding to each of the P second nodes to obtain equivalent channel information Moreover, the first node can perform parameter fusion on the first parameters corresponding to each of the P second nodes. For example Figure 7 in S5211-1, the first node performs parameter fusion on the noise information corresponding to each of the P second nodes to obtain equivalent noise information Or, as in Figure 7 S5211-2, the first node performs parameter fusion on the noise-related information corresponding to each of the P second nodes to obtain equivalent noise-related information

[0113] Regarding the above S5211-1 and S5211-2, the first node performs parameter fusion on the channel information corresponding to each of the P second nodes to obtain equivalent channel information Specifically, it can be achieved by the first node summing or matrix concatenating the channel information corresponding to each of the P second nodes to obtain the equivalent channel information For example, in the summation strategy, the equivalent channel information In the matrix concatenation strategy, the equivalent information

[0114] Regarding the above S5211-1, the first node performs parameter fusion on the noise information corresponding to each of the P second nodes to obtain equivalent noise information Specifically, it can be achieved by the first node summing or matrix concatenating the noise information corresponding to each of the P second nodes to obtain equivalent noise information For example, in the summation strategy, the equivalent noise information In the matrix concatenation strategy, the equivalent noise information

[0115] Regarding the above S5211-2, the first node performs parameter fusion on the noise-related information corresponding to each of the P second nodes to obtain equivalent noise-related information Specifically, it can be achieved by the first node summing or matrix concatenating the noise-related information corresponding to each of the P second nodes to obtain equivalent noise-related information For example, in the summation strategy, the equivalent noise-related information In the matrix concatenation strategy, it is possible to perform block diagonal matrix concatenation on to obtain

[0116] It can be understood that the channel parameters obtained by the first node through the summation strategy have a lower complexity in the subsequent process of determining the equalization information compared to the channel parameters obtained through the matrix splicing strategy, while the channel parameters obtained by the first node through the matrix splicing strategy have higher performance of the determined equalization information compared to the channel parameters obtained through the summation strategy.

[0117] If the first node executes S5211-1 as shown in Figure 7 , it is necessary to further determine the equivalent noise related information according to the equivalent noise information, that is, Figure 7 S5212 in

[0118] In the above S522, the first node determines the equalization information W H according to the equivalent parameters, for example, it can be implemented through the following formula:

[0119]

[0120] In some embodiments, in order to further reduce the amount of data to be interacted, the channel parameters and / or the first signal sent by the second node to the first node are obtained by weighting with a compression matrix , that is, the compressed channel parameters.

[0121] Optionally, the compression matrix is determined according to the channel parameters. The compression matrix can be an L*M c -dimensional matrix, where L≥rank(H c ), it should be noted that rank(H c ) is the number of columns of the channel information H c , that is, the number of user data streams received by the second node. When L is greater than or equal to the number of columns of the channel information H c , the channel parameters and / or the first signal can be losslessly compressed. When L is less than the number of columns of the channel information H c , signal loss is likely to occur. Therefore, when L is equal to the number of columns of the channel information H c , it is the maximum lossless compression; M c is the number of antennas corresponding to any second node.

[0122] For example, the compression matrix satisfies the following formula:

[0123]

[0124] Among them, H c is the channel information before compression, Hc is an M c by L-dimensional matrix, and R cc is the noise correlation information before compression, and R cc is an M c by M c dimensional matrix.

[0125] Exemplarily, the second node can perform weighting on the channel parameters and / or the first signal as follows: weighting the channel information H to obtain the compressed channel information c weighting the noise correlation information R to obtain the compressed noise correlation information cc weighting the noise information n to obtain the compressed noise information c weighting the first signal y to obtain the compressed first signal c It can be understood that when the compression matrix

[0126] is the identity matrix, the compressed channel parameters are the same as the channel parameters before compression, that is the same as H and c the same, the same as R cc and the same as n c In other words, the channel information, the noise correlation information the noise information can all be compressed, or at least one of the three can be uncompressed.

[0127] Exemplarily, before S520, it can further include: the first node determines the second signal according to the first signals respectively received by P second nodes. The first signal can be, for example, the first signal compressed by the above compression matrix. For example, the first node can perform signal fusion on the first signals respectively received by P second nodes to obtain the second signal. The signal fusion process can be, for example, a process in which the first node sums or concatenates the first signals respectively received by P second nodes. For example, in the summation strategy, the second signal in the matrix concatenation strategy, the second signal

[0128] Furthermore, the first node can perform equalization processing on the second signal according to the equalization matrix to obtain an estimate of the signal sent by the terminal device. For example, the estimation result of the signal sent by the terminal device can satisfy the formula: that is

[0129] In this embodiment, the transmitted channel parameters are all channel parameters after dimension reduction DR. On this basis, the algorithm adopted in the equalization process provided in this embodiment can be called the DRMMSE equalization algorithm, but this does not impose any limitation on this application, and this embodiment does not limit the naming of the algorithm adopted in the equalization process.

[0130] In a possible implementation manner, based on a distributed star-shaped baseband processing architecture, a practical embodiment uses a typical M c = 128 antenna base station. The base station antennas are divided into P = 8 antenna clusters. The base station serves at most L = 16 single-antenna users at the same time, and a 16-QAM coding and modulation scheme is considered.

[0131] Therefore, among the channel parameters corresponding to the P second nodes obtained by the first node in the embodiment of the present application, the noise correlation information between Mc antennas is carried. The first node determines the equalization information based on the channel parameters and performs channel equalization according to the equalization information, realizing distributed equalization processing based on colored noise, reducing the amount of data exchanged between nodes and the computational complexity of the first node.

[0132] Above, the method provided in the embodiment of the present application has been described in detail. Next, in combination with Figures 5 to 7 The device provided in the embodiment of the present application will be described in detail below. Figure 8 and Figure 9 The device provided in the embodiment of the present application will be described in detail.

[0133] Figure 8 It is a schematic structural diagram of a communication device 600 provided in an embodiment of the present application. As Figure 8 shown, the communication device 600 may include a transceiver unit 610 and a processing unit 620.

[0134] Optionally, the communication device 600 can be applied to the first node in the above method embodiment, and can be, for example, a component in a network device (such as a chip or a chip system, etc.).

[0135] Among them, when the communication device 600 is applied to the first node, the transceiver unit 610 can be used to obtain the channel parameters corresponding to the P second nodes respectively, and the channel parameters include channel information and a first parameter, and the first parameter includes noise correlation information or noise information The noise correlation information is used to indicate the noise correlation between the M c antennas corresponding to the second node, and the noise information is used to indicate the noise of the first signal received by the second node. P is an integer greater than 1, and M cP is an integer greater than 0; the processing unit 620 can be used to determine equalization information according to the channel parameters respectively corresponding to the P second nodes, and the equalization information is used to perform channel equalization on the second signal, where the second signal is determined according to the first signals respectively received by the P second nodes.

[0136] In some embodiments, the processing unit 620 is specifically configured to:

[0137] Perform parameter fusion on the channel parameters respectively corresponding to the P second nodes to obtain equivalent parameters;

[0138] Determine equalization information according to the equivalent parameters.

[0139] In some embodiments, the processing unit 620 is specifically configured to:

[0140] Perform parameter fusion on the channel information respectively corresponding to the P second nodes to obtain equivalent channel information and,

[0141] Perform parameter fusion on the noise information respectively corresponding to the P second nodes to obtain equivalent noise information Alternatively, the first node performs parameter fusion on the noise-related information respectively corresponding to the P second nodes to obtain equivalent noise-related information

[0142] In some embodiments, the processing unit 620 is further configured to:

[0143] Determine equivalent noise-related information according to the equivalent noise information

[0144] In some embodiments, the processing unit 620 is specifically configured to:

[0145] Sum or perform matrix splicing on the channel information respectively corresponding to the P second nodes to obtain the equivalent channel information

[0146] In some embodiments, the processing unit 620 is specifically configured to:

[0147] Sum or perform matrix splicing on the noise information respectively corresponding to the P second nodes to obtain the equivalent noise information

[0148] In some embodiments, the processing unit 620 is specifically configured to:

[0149] The noise-related information corresponding to each of the P second nodes is summed or matrix-concatenated to obtain the equivalent noise-related information

[0150] In some embodiments, the equalization information W H satisfies the following formula:

[0151]

[0152] In some embodiments, the channel parameter is obtained by weighting with a compression matrix and the compression matrix is determined according to the channel parameter.

[0153] In some embodiments, the compression matrix is an L*M c dimensional matrix, where L is the number of columns of H c of.

[0154] In some embodiments, the compression matrix satisfies the following formula:

[0155]

[0156] In some embodiments, the processing unit 620 is further configured to:

[0157] perform signal fusion on the first signals respectively received by the P second nodes to obtain the second signal.

[0158] In some embodiments, the processing unit 620 is specifically configured to:

[0159] sum or matrix-concatenate the first signals respectively received by the P second nodes to obtain the second signal.

[0160] In some embodiments, the first signal is obtained by weighting with a compression matrix and the compression matrix is determined according to the channel parameter.

[0161] In some embodiments, the P second nodes include a first node and P-1 third nodes or the P second nodes include P third nodes, and the third node is a child node of the first node.

[0162] It should be understood that the specific processes of the respective units performing the above corresponding steps have been described in detail in the above method embodiments, and for the sake of brevity, they will not be repeated here.

[0163] When the communication device 600 is the first node, the processing unit 620 in the communication device 600 can be implemented by a processor, for example, it can correspond to the processor 710 in the communication device 700 shown in Figure 9 The transceiver unit 610 can be implemented by a transceiver, for example, it can correspond to the transceiver 720 in the communication device 700 shown in Figure 9 .

[0164] When the communication device 600 is a chip or a chip system configured in a network device, both the processing unit 620 and the transceiver unit 610 in the communication device 600 can be implemented by an input / output interface, a circuit, etc.

[0165] Optionally, the communication device 600 can be applied to the second node in the above method embodiments, for example, it can be a component (such as a chip or a chip system, etc.) configured in a network device.

[0166] Wherein, when the communication device 600 is applied to the second node, the transceiver unit 610 can be used to send channel parameters to the first node, and the channel parameters include channel information and a first parameter, and the first parameter includes noise-related information or noise information The noise-related information is used to indicate the noise correlation between the M c antennas corresponding to the second node, and the noise information is used to indicate the noise of the first signal received by the second node, P is an integer greater than 1, and M c is an integer greater than 0.

[0167] In some embodiments, the channel parameter is obtained by weighting with a compression matrix , and the compression matrix is determined according to the channel parameter.

[0168] In some embodiments, the compression matrix is an L*M c dimensional matrix, where L is the number of columns of H c .

[0169] In some embodiments, the compression matrix satisfies the following formula:

[0170]

[0171] In some embodiments, the first signal is obtained by weighting with a compression matrix , and the compression matrix is determined according to the channel parameter.

[0172] In some embodiments, the equalization information W H satisfies the following formula:

[0173]

[0174] It should be understood that the specific processes of each unit executing the above corresponding steps have been described in detail in the above method embodiments. For the sake of brevity, they will not be repeated here.

[0175] When the communication device 600 is the second node, the processing unit 620 in the communication device 600 can be implemented by a processor, for example, it can correspond to Figure 9 the processor 710 in the communication device 700 shown in Figure 9 The transceiver unit 610 can be implemented by a transceiver, for example, it can correspond to

[0176] the transceiver 720 in the communication device 700 shown in

[0177] Figure 9 FIG. Another schematic block diagram of the communication device 700 provided by the embodiments of the present application. As Figure 7 shown, the device 700 may include: a processor 710, a transceiver 720, and a memory 730. Among them, the processor 710, the transceiver 720, and the memory 730 communicate with each other through an internal connection path. The memory 730 is used to store instructions, and the processor 710 is used to execute the instructions stored in the memory 730 to control the transceiver 720 to send signals and / or receive signals.

[0178] It should be understood that the communication device 700 may correspond to the first node in the above method embodiments, and may be used to execute each step and / or process executed by the first node in the above method embodiments. Optionally, the memory 730 may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include a non-volatile random access memory. The memory 730 may be a separate device or integrated in the processor 710. The processor 710 may be used to execute the instructions stored in the memory 730, and when the processor 710 executes the instructions stored in the memory, the processor 710 is used to execute each step and / or process of the above method embodiment corresponding to the first node.

[0179] Optionally, the communication device 700 is the first node in the foregoing embodiments.

[0180] Optionally, the communication device 700 is the second node in the foregoing embodiments.

[0181] Among them, the transceiver 720 may include a transmitter and a receiver. The transceiver 720 may further include an antenna, and the number of antennas may be one or more. The processor 710 and the memory 730 and the transceiver 720 may be devices integrated on different chips. For example, the processor 710 and the memory 730 may be integrated in a baseband chip, and the transceiver 720 may be integrated in a radio frequency chip. The processor 710 and the memory 730 and the transceiver 720 may also be devices integrated on the same chip. This application does not limit this.

[0182] Optionally, the communication device 700 is a component configured in a network device, such as a chip, a chip system, etc.

[0183] Among them, the transceiver 720 may also be a communication interface, such as an input / output interface, a circuit, etc. The transceiver 720, the processor 710, and the memory 730 may all be integrated in the same chip, such as integrated in a baseband chip.

[0184] This application also provides a processing device, including at least one processor, and the at least one processor is configured to execute a computer program stored in a memory, so that the processing device executes the method performed by the first node or the second node in the foregoing method embodiments.

[0185] An embodiment of this application also provides a processing device, including a processor and an input / output interface. The input / output interface is coupled to the processor. The input / output interface is configured to input and / or output information. The information includes at least one of instructions and data. The processor is configured to execute a computer program, so that the processing device executes the method performed by the first node or the second node in the foregoing method embodiments.

[0186] An embodiment of this application also provides a processing device, including a processor and a memory. The memory is configured to store a computer program, and the processor is configured to call and run the computer program from the memory, so that the processing device executes the method performed by the first node or the second node in the foregoing method embodiments.

[0187] It should be understood that the above processing device can be one or more chips. For example, the processing device can be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a micro controller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0188] In the implementation process, each step of the above method can be completed by the integrated logic circuit in the hardware of the processor or the instructions in the form of software. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by the hardware processor, or executed and completed by the combination of the hardware and software modules in the processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read only memory, a programmable read only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0189] It can be understood that the memory in the embodiments of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0190] According to the method provided by the embodiments of the present application, the present application also provides a computer program product, which includes: computer program code, when the computer program code runs on a computer, enabling the computer to execute the method executed by the first node or the second node in the foregoing method embodiments.

[0191] According to the method provided by the embodiments of the present application, the present application also provides a computer-readable storage medium, which stores program code, when the program code runs on a computer, enabling the computer to execute the methods executed by the first node and the second node in the foregoing method embodiments.

[0192] According to the method provided by the embodiments of the present application, the present application also provides a communication system, which can include the foregoing terminal device or network device.

[0193] In addition, in each embodiment of the present application, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0194] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0195] As described above, the above are only specific implementation manners of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, and all of them should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

Claims

1. A method for processing a signal, characterized in that, Including: The first node obtains channel parameters respectively corresponding to P second nodes, and the channel parameters include channel information and a first parameter, and the first parameter includes noise-related information or noise information The noise-related information is used to indicate the noise correlation between M c antennas corresponding to the second node, and the noise information is used to indicate the noise of the first signal received by the second node. P is an integer greater than 1, and M c is an integer greater than 0; The first node determines equalization information according to the channel parameters respectively corresponding to the P second nodes, and the equalization information is used for channel equalization of a second signal, where the second signal is determined according to the first signals respectively received by the P second nodes; The first node determines equalization information according to the channel parameters respectively corresponding to the P second nodes, including: The first node performs parameter fusion on the channel parameters respectively corresponding to the P second nodes to obtain an equivalent parameter; The first node determines equalization information according to the equivalent parameter.

2. The method according to claim 1, characterized in that, The channel parameters are obtained by weighting with a compression matrix and the compression matrix is determined according to the channel parameters.

3. The method according to claim 2, characterized in that, The compression matrix is an L*M c dimensional matrix, where L is the number of columns of H c , and H c is the channel information before compression.

4. The method according to claim 3, characterized in that, The compression matrix satisfies the following formula: Among them, R cc is the noise-related information before compression.

5. The method according to claim 1, characterized in that, The first node performs parameter fusion on the channel parameters respectively corresponding to the P second nodes to obtain an equivalent parameter, including: The first node performs parameter fusion on the channel information corresponding to each of the P second nodes to obtain equivalent channel information and and The first node performs parameter fusion on the noise information corresponding to each of the P second nodes to obtain equivalent noise information Alternatively, the first node performs parameter fusion on the noise-related information corresponding to each of the P second nodes to obtain equivalent noise-related information 6. The method according to claim 5, characterized in that, The method further includes: The first node determines equivalent-noise related information according to the equivalent noise information to determine equivalent-noise related information 7. The method according to claim 5 or 6, characterized in that, The first node performs parameter fusion on the channel information corresponding to the P second nodes respectively to obtain equivalent channel information (The parameter fusion includes:) including: The first node sums or concatenates matrices for the channel information respectively corresponding to the P second nodes to obtain the equivalent channel information ​ 8. The method according to claim 5 or 6, characterized in that, The first node performs parameter fusion on the noise information corresponding to each of the P second nodes to obtain equivalent noise information including: The first node sums or performs matrix concatenation on the noise information corresponding to each of the P second nodes respectively to obtain the equivalent noise information to obtain the equivalent noise information 9. The method according to claim 5 or 6, characterized in that, The first node performs parameter fusion on the noise-related information corresponding to each of the P second nodes to obtain equivalent noise-related information including: ​ The first node sums or performs matrix concatenation on the noise correlation information respectively corresponding to the P second nodes to obtain the equivalent noise correlation information ​ 10. The method according to claim 5 or 6, characterized in that, The balance information W H satisfies the following formula: where I is an identity matrix.

11. The method according to any one of claims 1 to 3, 5, 6, characterized in that, The method further includes: The first node performs signal fusion on the first signals respectively received by the P second nodes to obtain the second signal.

12. The method according to claim 11, characterized in that, The first node performs signal fusion on the first signals respectively received by the P second nodes to obtain the second signal, including: The first node sums or performs matrix splicing on the first signals respectively received by the P second nodes to obtain the second signal.

13. The method according to any one of claims 1 to 4, 5, 6, and 12, characterized in that The first signal is obtained by weighting with a compression matrix and the compression matrix is determined according to the channel parameters.

14. The method according to any one of claims 1 to 3, 5, 6, and 12, characterized in that The P second nodes include a first node and P - 1 third nodes, or the P second nodes include P third nodes, and the third nodes are child nodes of the first node.

15. A method for processing a signal, characterized in that Including: The second node sends channel parameters to the first node, and the channel parameters include channel information and a first parameter, and the first parameter includes noise-related information or noise information The noise-related information is used to indicate the noise correlation between the M c antennas corresponding to the second node, and the noise information is used to indicate the noise of the first signal received by the second node, P is an integer greater than 1, and M c is an integer greater than 0. The channel parameters are used for the first node to perform parameter fusion on the channel parameters corresponding to P second nodes respectively to obtain equivalent parameters; and determine equalization information according to the equivalent parameters; The equalization information is used for channel equalization of a second signal, where the second signal is determined according to the first signals respectively received by the P second nodes.

16. The method according to claim 15, characterized in that The channel parameter is obtained by weighting with a compression matrix and the compression matrix is determined according to the channel parameter.

17. The method according to claim 16, characterized in that The compression matrix is an L*M c dimensional matrix, where L is the number of columns of H c , and H c is the channel information before compression.

18. The method according to claim 17, characterized in that The compression matrix satisfies the following formula: Among them, R cc is the noise-related information before compression.

19. The method according to any one of claims 15 to 17, characterized in that The first signal is obtained by weighting with a compression matrix and the compression matrix is determined according to the channel parameters.

20. A communication device, characterized in that Including: A transceiver unit, configured to obtain channel parameters respectively corresponding to P second nodes, where the channel parameters include channel information and a first parameter, where the first parameter includes noise-related information or noise information The noise-related information is used to indicate the noise correlation between M c antennas corresponding to the second node, and the noise information is used to indicate the noise of a first signal received by the second node. P is an integer greater than 1, and M c is an integer greater than 0; A processing unit is configured to determine equalization information according to the channel parameters respectively corresponding to the P second nodes, and the equalization information is used for channel equalization of a second signal, where the second signal is determined according to the first signals respectively received by the P second nodes; The processing unit is specifically configured to: Perform parameter fusion on the channel parameters respectively corresponding to the P second nodes to obtain an equivalent parameter; Determine equalization information according to the equivalent parameter.

21. The device according to claim 20, characterized in that The channel parameters are obtained by weighting with a compression matrix and the compression matrix is determined according to the channel parameters.

22. The device according to claim 21, characterized in that The compression matrix is an L*M c dimensional matrix, where L is the number of columns of H c , and H c is the channel information before compression.

23. The device according to claim 22, characterized in that The compression matrix satisfies the following formula: Among them, R cc is the noise-related information before compression.

24. The device according to claim 20, characterized in that The processing unit is specifically configured to: Perform parameter fusion on the channel information corresponding to each of the P second nodes to obtain equivalent channel information and Perform parameter fusion on the noise information corresponding to each of the P second nodes to obtain equivalent noise information Alternatively, perform parameter fusion on the noise-related information corresponding to each of the P second nodes to obtain equivalent noise-related information 25. The device according to claim 24, characterized in that The processing unit is further configured to: According to the equivalent noise information Determine the equivalent noise related information 26. The device according to claim 24 or 25, characterized in that, The processing unit is specifically configured to: The channel information corresponding to each of the P second nodes is summed or matrix concatenated to obtain the equivalent channel information 27. The device according to claim 24 or 25, characterized in that, The processing unit is specifically configured to: The noise information corresponding to each of the P second nodes is summed or matrix-concatenated to obtain the equivalent noise information 28. The device according to claim 24 or 25, characterized in that, The processing unit is specifically configured to: The noise-related information corresponding to each of the P second nodes is summed or matrix-concatenated to obtain the equivalent noise-related information 29. The device according to claim 24 or 25, characterized in that, The equalization information W H satisfies the following formula: where I is an identity matrix.

30. The device according to any one of claims 20 to 22, 24, 25, characterized in that, The processing unit is further configured to: Perform signal fusion on the first signals respectively received by the P second nodes to obtain the second signal.

31. The device according to claim 30, characterized in that, The processing unit is specifically configured to: Sum or perform matrix splicing on the first signals respectively received by the P second nodes to obtain the second signal.

32. The device according to any one of claims 20 to 22, 24, 25, 31, characterized in that, The first signal is obtained by weighting with a compression matrix and the compression matrix is determined according to the channel parameters.

33. The device according to any one of claims 20 to 22, 24, 25, 31, characterized in that, The P second nodes include the communication device and P - 1 third nodes, or the P second nodes include P third nodes, and the third nodes are child nodes of the communication device.

34. A communication device, characterized in that, Including: A transceiver unit, configured to send channel parameters to a first node, where the channel parameters include channel information and a first parameter, where the first parameter includes noise-related information or noise information The noise-related information is used to indicate the noise correlation between M c antennas corresponding to the communication device, and the noise information is used to indicate the noise of a first signal received by the communication device. P is an integer greater than 1, and M c is an integer greater than 0. The channel parameters are used for the first node to perform parameter fusion on the channel parameters respectively corresponding to P second nodes to obtain equivalent parameters; and determine equalization information according to the equivalent parameters; The equalization information is used for channel equalization of a second signal, where the second signal is determined according to the first signals respectively received by the P second nodes.

35. The device according to claim 34, characterized in that, The channel parameter is obtained by weighting with a compression matrix and the compression matrix is determined according to the channel parameter.

36. The device according to claim 35, characterized in that, The compression matrix is an L*M c dimensional matrix, where L is the number of columns of H c , and H c is the channel information before compression.

37. The device according to claim 36, characterized in that, The compression matrix satisfies the following formula: Among them, R cc is the noise-related information before compression.

38. The device according to any one of claims 34 to 36, characterized in that, The first signal is obtained by weighting with a compression matrix and the compression matrix is determined according to the channel parameters.

39. A communication device, characterized in that, It includes a logic circuit and an input / output interface. Among them, the input / output interface is used to receive signals from other communication devices outside the device and transmit them to the logic circuit, or send signals from the logic circuit to other communication devices outside the device. The logic circuit is used to execute code instructions to implement the method described in any one of claims 1 to 19.

40. A chip, characterized in that, It includes: A processor, configured to call and run computer instructions from a memory, so that a device installed with the chip executes the method described in any one of claims 1 to 19.

41. A computer-readable storage medium, characterized in that, For storing computer program instructions, the computer program causes a computer to execute the method described in any one of claims 1 to 19.

42. A computer program product, characterized in that, It includes computer program instructions, and the computer program instructions cause a computer to execute the method described in any one of claims 1 to 19.

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