Method and device for determining channel correlation, storage medium and chip

Abstract

The invention provides a method and device for determining channel correlation, a storage medium and a chip, and relates to the technical field of communication, and the method comprises the steps: firstly obtaining channel quality parameters of an MIMO system, such as prior channel capacity, posterior channel capacity and other information; determining a relative relationship among the channel quality parameters; the channel correlation of the MIMO system is determined according to the relative relation between the channel quality parameters, the channel correlation is directly estimated according to the channel quality parameters, analysis and calculation of a large amount of data are avoided, and compared with the prior art, the technical scheme of the invention effectively reduces the calculation complexity of the channel correlation and improves the calculation efficiency of the MIMO system. According to the method, channel correlation estimation can be directly given, calculation time delay is greatly reduced, and channel correlation estimation of different channels and different MIMO structures can be dynamically supported.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technologies, and in particular, to a method, apparatus, storage medium, communication device, and chip for determining channel correlation. Background Art

[0002] Multiple-Input Multiple-Output (MIMO) technology uses multiple antennas or antenna array technology at the transceiver ends of a communication system. Using MIMO technology can increase the channel capacity, improve the channel reliability, and reduce the bit error rate. Summary of the Invention

[0003] The present disclosure provides a method, apparatus, storage medium, communication device, and chip for determining channel correlation, and the main purpose is to improve the technical problem that the calculation complexity of related technologies is relatively high when estimating the channel correlation of a MIMO system currently.

[0004] According to a first aspect of an embodiment of the present disclosure, a method for determining channel correlation is provided, including:

[0005] Obtaining channel quality parameters of a MIMO system;

[0006] Determining the relative relationship between the channel quality parameters;

[0007] Determining the channel correlation of the MIMO system according to the relative relationship.

[0008] In some embodiments of the present disclosure, the channel quality parameters include at least two of the following:

[0009] Prior channel capacity;

[0010] Posterior channel capacity;

[0011] Open-loop channel capacity.

[0012] In some embodiments of the present disclosure, determining the relative relationship between the channel quality parameters includes:

[0013] Determining the relative relationship based on the difference and / or ratio between the channel quality parameters.

[0014] In some embodiments of the present disclosure, determining the channel correlation of the MIMO system according to the relative relationship includes:

[0015] Inputting the relative relationship into a mapping model for calculation, and determining the channel correlation according to the output result of the mapping model, where the mapping model is used to determine the channel correlation corresponding to the relative relationship between channel quality parameters in different scenarios; or,

[0016] Based on the relative relationship, determine the channel correlation by querying the mapping relationship in the relationship mapping table, where the relationship mapping table stores the channel correlations corresponding to the relative relationships between channel quality parameters in different scenarios respectively.

[0017] In some embodiments of the present disclosure, the method further includes:

[0018] Construct a training set according to the channel correlations corresponding to the relative relationships between channel quality parameters in different scenarios respectively;

[0019] Train the mapping model based on the training set.

[0020] In some embodiments of the present disclosure, training the mapping model based on the training set includes:

[0021] Determine a mapping table based on the training set and store the mapping table in the mapping model, where the mapping table stores the mapping relationship between the relative relationships between channel quality parameters in different scenarios and the channel relativity; and / or,

[0022] Perform linear fitting calculation based on the training set and store the calculated expression in the mapping model, where the expression is used to determine the channel correlations corresponding to the relative relationships between channel quality parameters in different scenarios respectively; and / or,

[0023] Learn the mapping relationship between the relative relationships between channel quality parameters in different scenarios and the channel relativity through the neural network in the mapping model based on the training set.

[0024] In some embodiments of the present disclosure, the method further includes:

[0025] Determine the output result of the mapping model through the calculation of the mapping table and / or the expression and / or the neural network.

[0026] According to the second aspect of the embodiments of the present disclosure, there is provided a device for determining channel correlation, including:

[0027] An acquisition module, configured to acquire channel quality parameters of a MIMO system;

[0028] A determination module, configured to determine the relative relationship between the channel quality parameters; determine the channel correlation of the MIMO system according to the relative relationship.

[0029] According to the third aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0030] According to a fourth aspect of the embodiments of the present disclosure, a communication device is provided, including: a transceiver; a memory; and a processor, which is respectively connected to the transceiver and the memory, and is configured to control the wireless signal transceiver of the transceiver by executing computer-executable instructions on the memory, and can implement the method described in the first aspect.

[0031] According to a fifth aspect of the embodiments of the present disclosure, a computer program product is provided, on which a computer program is stored, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0032] According to a sixth aspect of the embodiments of the present disclosure, a chip is provided, including at least one processor and a communication interface; the communication interface is used to receive signals input to the chip or signals output from the chip, and the processor communicates with the communication interface and implements the method described in the first aspect through logic circuits or by executing code instructions.

[0033] By means of the above technical solutions, a method, device, storage medium, communication device, and chip for determining channel correlation provided by the present disclosure are equivalent to providing a low-complexity solution for channel correlation estimation. Specifically, first, obtain channel quality parameters of the MIMO system, such as prior channel capacity, posterior channel capacity, and other information; then determine the relative relationship between these channel quality parameters; and then determine the channel correlation of the MIMO system according to the relative relationship between these channel quality parameters, and further directly estimate the channel correlation according to these channel quality parameters, avoiding a large amount of data analysis and calculation. Compared with the current related technologies, the technical solution of the present disclosure effectively reduces the computational complexity of channel correlation, can directly give the channel correlation estimation, greatly reduces the computational time delay, and can dynamically support giving channel correlation estimations for different channels and different MIMO structures.

[0034] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings here are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.

[0036] Figure 1 A flowchart showing a method for determining channel correlation provided by an embodiment of the present disclosure is shown;

[0037] Figure 2 A flowchart showing another method for determining channel correlation provided by an embodiment of the present disclosure is shown;

[0038] Figure 3A schematic diagram of an example provided by an embodiment of the present disclosure is shown;

[0039] Figure 4 A schematic structural diagram of a device for determining channel correlation provided by an embodiment of the present disclosure is shown;

[0040] Figure 5 A schematic structural diagram of a communication device provided by an embodiment of the present disclosure is shown;

[0041] Figure 6 A schematic structural diagram of a chip provided by an embodiment of the present disclosure is shown. Detailed implementation manners

[0042] Some embodiments of the present disclosure will be described in detail here, and their examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. Various changes, modifications, and equivalents of the methods, devices, and / or systems described herein will become apparent after understanding the present disclosure. For example, the order of the operations described herein is merely an example and is not limited to those set forth herein, but may be changed as will be apparent after understanding the present disclosure, except for operations that must be performed in a specific order. Additionally, descriptions of features known in the art may be omitted for the sake of clarity and conciseness. It should be noted that, without conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0043] Some embodiments of the present disclosure are introduced below. It should be noted that the implementation manners described in some embodiments of the present disclosure below do not represent all implementation manners consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0044] In order to improve the technical problem that the computational complexity of the related art is relatively high when estimating the channel correlation of a MIMO system at present. An embodiment of the present disclosure provides a method for determining channel correlation, as Figure 1 shown Figure 1 is a flowchart of a method for determining channel correlation shown according to some embodiments of the present disclosure, including the following steps.

[0045] Step 101, obtain the channel quality parameters of the MIMO system.

[0046] For the execution subject of this embodiment, it may be a device or equipment for determining channel correlation, such as a communication device or a chip, etc., and may be configured on the terminal or network device side.

[0047] In some examples, a terminal may be referred to as a terminal device, user equipment (UE), mobile station (MS), mobile terminal (MT), etc. The terminal may also be an automobile with communication capabilities, a smart vehicle, a mobile phone, a wearable device, a tablet computer (Pad), a computer with wireless transceiver capabilities, a virtual reality (VR) device, an augmented reality (AR) device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, a wireless terminal device in a smart home, and so on. This embodiment does not limit the specific technologies and specific device forms adopted by the terminal.

[0048] In some examples, the network device may be a device such as a base station or a satellite, which is not specifically limited in this embodiment. The network device may be an entity on the network side for transmitting or receiving signals. For example, the network device may be a communication satellite, an evolved NodeB (eNB), a transmission reception point (TRP), a next generation NodeB (gNB) in an NR system, a base station in other future mobile communication systems, or an access node in a wireless fidelity (WiFi) system. The embodiments of the present disclosure do not limit the specific technologies and specific device forms adopted by the network device. The network device provided in this embodiment may be composed of a central unit (CU) and a distributed unit (DU). Among them, the CU may also be referred to as a control unit. Adopting the CU-DU structure can split the protocol layer of the network device, such as a base station. The functions of some protocol layers are centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers are distributed in the DU, and the DU is centrally controlled by the CU.

[0049] Embodiments of the present disclosure can be applied to satellite communication, Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, the 4th generation mobile communication system (4G), the 5th generation mobile communication system (5G), 5G NR, Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) network, Device-to-Device (D2D) system, Machine to Machine (M2M) system, Internet of Things (IoT) system, Vehicle-to-Everything (V2X), systems using other communication methods, next-generation systems extended based on them, etc. In addition, multiple systems can be combined (for example, a combination of LTE or LTE-A and 5G, etc.) and applied.

[0050] In some embodiments, the channel quality parameters of the MIMO system are crucial for evaluating and optimizing the performance of wireless communication systems. These parameters can help understand the behavior of signals in space, time, and frequency, and provide a basis for designing effective transmission strategies. Channel quality parameters may include signal-to-noise ratio, channel capacity, Doppler shift, and bit error rate, etc.

[0051] In some examples, the channel quality parameters may include at least two of the following:

[0052] A priori channel capacity; posteriori channel capacity; open-loop channel capacity.

[0053] Step 102, determine the relative relationship between the channel quality parameters.

[0054] In some embodiments, the relative relationship between the channel quality parameters may be determined based on the difference and / or ratio between the channel quality parameters, etc.

[0055] For example, determine the relative relationship between the a priori channel capacity and the posteriori channel capacity based on the ratio between the a priori channel capacity and the posteriori channel capacity. Or, determine the relative relationship among the a priori channel capacity, the posteriori channel capacity, and the open-loop channel capacity based on the differences among the three.

[0056] Step 103, determine the channel correlation of the MIMO system according to the relative relationship between the channel quality parameters.

[0057] Channel correlation describes the correlation degree of signals between different antennas in the MIMO system. Utilizing the channel correlation can significantly improve the throughput and spectrum utilization rate of the communication system; therefore, the accuracy of algorithms such as data processing and channel estimation in MIMO highly depends on the estimation accuracy of the channel correlation.

[0058] In some embodiments, low channel correlation helps to improve the performance of the MIMO system because it allows for more effective spatial diversity and multiplexing gain, while high channel correlation may lead to performance degradation because multiple antennas receive similar information, reducing the number of independent information streams. In some examples, by analyzing the relative relationship between channel quality parameters such as the a priori and posteriori channel capacities, the change in channel correlation can be indirectly reflected. For example, under the condition of high channel correlation, the a priori estimate may deviate significantly from the actual situation, resulting in a large difference between the a priori and posteriori channel capacities. Therefore, for the embodiments of the present disclosure, after obtaining the channel quality parameters of the MIMO system, the relative relationship between these channel quality parameters may be first determined, and then the channel correlation of the MIMO system may be determined through this relative relationship, such as determining the channel correlation corresponding to this relative relationship through mapping calculation and other methods.

[0059] In some embodiments, based on the relative relationship between channel quality parameters, the channel correlation of the MIMO system is determined by querying the mapping relationship in the relationship mapping table. The relationship mapping table can be pre-constructed, and the relationship mapping table stores the channel correlations corresponding to the relative relationships between channel quality parameters in different scenarios. In this way, by querying the mapping relationship in the relationship mapping table, the channel correlation corresponding to the relative relationship between channel quality parameters is determined, and then used as the channel correlation of the MIMO system.

[0060] Compared with the current existing technologies, the embodiments of the present disclosure effectively reduce the computational complexity of channel correlation, can directly give the channel correlation estimation, greatly reduce the computational time delay, and can dynamically support giving the channel correlation estimations of different channels and different MIMO structures.

[0061] Further, to illustrate the specific implementation process of the method in this embodiment, as an optional way, this embodiment provides a specific method as shown in Figure 2 The specific method includes:

[0062] Step 201, obtain the channel quality parameters of the MIMO system.

[0063] In this embodiment, the channel correlation can be directly estimated through the channel quality parameters, including but not limited to the prior channel capacity, the posterior channel capacity, and the open-loop channel capacity, etc., through the pre-stored mapping model. This is because different channel correlations directly affect the prior, posterior, and open-loop channel capacities. Therefore, through the relative relationship between at least two of the three, a mapping model can be trained to achieve channel correlation estimation. The specific process can be performed as shown in steps 202 to 203.

[0064] Step 202, determine the relative relationship between the channel quality parameters according to the difference and / or ratio between the channel quality parameters.

[0065] Step 203, input the relative relationship between the channel quality parameters into the mapping model for calculation, and determine the channel correlation of the MIMO system according to the output result of the mapping model.

[0066] The mapping model can be used to determine the channel correlations corresponding to the relative relationships between channel quality parameters in different scenarios.

[0067] In some embodiments, first, a training set is constructed according to the channel correlations respectively corresponding to the relative relationships between channel quality parameters in different scenarios. For example, the relative relationships between channel quality parameters in different scenarios can be used as feature data, and their corresponding channel correlations can be used as label data. In this way, the training set stores these feature data and the label data respectively corresponding to these feature data; then, the mapping model is trained based on the constructed training set. Correspondingly, step 203 may specifically include: inputting the relative relationship between channel quality parameters into the trained mapping model for calculation, and determining the channel correlation of the MIMO system according to the output result of the mapping model.

[0068] There are various optional ways to train the mapping model based on the training set. In some examples, a mapping table can be determined based on the training set and stored in the mapping model to implement the training of the mapping model. The mapping table stores the mapping relationship between the relative relationships between channel quality parameters in different scenarios and the channel relativity; and / or, linear fitting calculation is performed based on the training set, and the calculated expression is stored in the mapping model to implement the training of the mapping model. The expression is used to determine the channel correlations respectively corresponding to the relative relationships between channel quality parameters in different scenarios; and / or, based on the training set, the mapping relationship between the relative relationships between channel quality parameters in different scenarios and the channel relativity is learned through the neural network in the mapping model.

[0069] Correspondingly, when the mapping model is applied, the relative relationship between the current channel quality parameters can be input into the mapping model for calculation, specifically through the calculation of the above mapping table and / or the above expression and / or the above neural network, and then the output result of the mapping model is determined according to the calculation result.

[0070] The following gives an example explanation, but is not limited to this:

[0071] The signal processing process involved in the embodiments of the present disclosure may be as Figure 3 shown. For channel capacity calculation, channel quality parameter calculation can be performed through classical communication algorithms, including but not limited to the prior channel capacity C pre , the posterior channel capacity C post , the open-loop channel capacity C open and so on.

[0072] In order to obtain the corresponding channel correlation through the mapping model, it is necessary to pre-train the mapping model, such as the mapping model training between the relative relationship between channel capacities and the channel correlation.

[0073] As an example, channel correlation can be estimated through the ratio relationship between the prior and posterior channel capacities. It should be noted that in addition to this method, various data processes of the prior, posterior, and open-loop channel capacities (such as the ratio, difference, etc. between the three) and the channel correlation relationship can also be fully considered.

[0074] This embodiment can comprehensively cover various channel parameters, including but not limited to: channel correlation and MIMO structure, etc., and collect data in each scenario of channel parameter combination, including but not limited to: signal-to-noise ratio (SNR), number of transmission data time slots (slot), channel capacity, etc. For example, assume the system channel scenario: the channel correlation is XP-high, the channel delay is m, the Doppler frequency shift is 5 Hz, the MIMO structure is 4x4, and SNR = -5:1. There are 46 kinds of SNR with 40 dB in total, and 10000 slot data are transmitted for each kind of SNR. Then, 460000 groups of channel capacities [prior and posterior channel capacities] are received at the receiving end.

[0075] Calculate the ratio of the prior and posterior channel capacities for 460000 groups, and record the result as CORR factor [460000], as shown in the following formula:

[0076]

[0077] CORR factor [i] represents the ratio of the prior and posterior channel capacities of the i-th group, C pre According to different values, it can be equally divided into 9 parts, that is: C pre _block[0] = [0, 5], C pre _block[1] = [5, 10],..., Cpre_block[8] = [35, 40]. Respectively count the CORR factor values corresponding to each part.

[0078] Calculate the cumulative distribution function (CDF) distribution of CORR factor for each part, and record the value CORR factor _block[i] corresponding to CDF = 90%, where i ∈ [0, 8]. Record the mapping relationship between C pre _block and CORR factor _block under the current channel correlation. This relationship can be stored through methods such as pre-stored tables, linear fitting, or neural networks to complete the mapping model training.

[0079] Traverse all correlation channel scenarios, pre-store the ratios of the prior and posterior channel capacities in various scenarios, and it can be recorded as CORRfactor [Various correlation scenarios], store the mapping model in the communication system for calculating channel correlation.

[0080] In specific applications, the receiving end calculates the ratio CORR of the prior and posterior channel capacities factor _calc and inputs it to the mapping model; the mapping model traverses and compares CORR factor _calc with the CORR of each pre-stored correlation scenario factor CORR factor _calc and CORR factor The closest scenario, that is, it is determined as the current channel correlation value. For example, according to the current prior channel capacity C pre , find the C pre _block to which it belongs and the corresponding CORR factor _block, and then according to the current CORR factor _calc, obtain the corresponding CORR factor , as the current channel correlation value.

[0081] The embodiments of the present disclosure provide a low-complexity scheme for channel correlation estimation, which is mainly reflected in the following aspects:

[0082] Low complexity: According to the relative relationship between channel quality parameters, such as prior, posterior, open-loop channel capacity, etc., the channel correlation can be directly estimated through the mapping model provided by this embodiment, avoiding a large amount of data analysis and calculation.

[0083] Low latency: Through the mapping calculation of the mapping model, the channel correlation estimation can be directly given, greatly reducing the calculation time delay.

[0084] High compatibility: Through a complete mapping model, it can dynamically support giving correlation estimations for different channels and different MIMO structures.

[0085] Figure 4 It is a block diagram of a device for determining channel correlation shown according to some embodiments of the present disclosure. Referring to Figure 4 , the device includes: an acquisition module 31 and a determination module 32.

[0086] The acquisition module 31 is configured to acquire channel quality parameters of the MIMO system;

[0087] The determination module 32 is configured to determine the relative relationship between the channel quality parameters; determine the channel correlation of the MIMO system according to the relative relationship.

[0088] In some examples of this embodiment, the channel quality parameters include at least two of the following:

[0089] Prior channel capacity;

[0090] Posterior channel capacity;

[0091] Open-loop channel capacity.

[0092] In some examples of this embodiment, the determining module 32 is specifically configured to determine the relative relationship based on the difference and / or ratio between the channel quality parameters.

[0093] In some examples of this embodiment, the determining module 32 is further specifically configured to input the relative relationship into a mapping model for calculation, and determine the channel correlation according to the output result of the mapping model, where the mapping model is used to determine the channel correlations corresponding to the relative relationships between channel quality parameters in different scenarios; or, based on the relative relationship, determine the channel correlation by querying the mapping relationship in the relationship mapping table, and the relationship mapping table stores the channel correlations corresponding to the relative relationships between channel quality parameters in different scenarios.

[0094] In some examples of this embodiment, the device further includes a training module;

[0095] The training module is configured to construct a training set according to the channel correlations corresponding to the relative relationships between channel quality parameters in different scenarios; and perform training of the mapping model based on the training set.

[0096] In some examples of this embodiment, the training module is specifically configured to determine a mapping table based on the training set and store the mapping table in the mapping model, and the mapping table stores the mapping relationship between the relative relationships between channel quality parameters in different scenarios and channel relativity; and / or, perform linear fitting calculation based on the training set and store the calculated expression in the mapping model, and the expression is used to determine the channel correlations corresponding to the relative relationships between channel quality parameters in different scenarios; and / or, learn the mapping relationship between the relative relationships between channel quality parameters in different scenarios and channel relativity through the neural network in the mapping model based on the training set.

[0097] In some examples of this embodiment, the determining module 32 is further specifically configured to determine the output result of the mapping model through the calculation of the mapping table and / or the expression and / or the neural network.

[0098] Regarding the device in the above embodiment, the specific manners in which each module performs operations have been described in detail in the embodiment related to the method, and will not be elaborated here.

[0099] Figure 5FIG. 0 is a schematic structural diagram of a communication device 1800 provided in this embodiment. The communication device 1800 may be a terminal device, a network device, a chip, a chip system, or a processor that supports the network device to implement the above method, or a chip, a chip system, or a processor that supports the user equipment to implement the above method. This device can be used to implement the method described in the above method embodiments, and for specific details, please refer to the description in the above method embodiments.

[0100] The communication device 1800 includes: a transceiver; a memory; and a processor, which are respectively connected to the transceiver and the memory, and are configured to control the wireless signal transceiver of the transceiver by executing computer-executable instructions on the memory, and can implement the functions of any of the above method embodiments.

[0101] The communication device 1800 may include one or more processors 1801. The processor 1801 may be a general-purpose processor or a special-purpose processor, etc. For example, it may be a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control a communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute a computer program, and process the data of the computer program.

[0102] Optionally, the communication device 1800 may further include one or more memories 1802, on which a computer program 1804 may be stored. The processor 1801 executes the computer program 1804 to enable the communication device 1800 to execute the method described in the above method embodiments. Optionally, data may also be stored in the memory 1802. The communication device 1800 and the memory 1802 may be provided separately or integrated together.

[0103] Optionally, the communication device 1800 may further include a transceiver 1805 and an antenna 1806. The transceiver 1805 may be referred to as a transceiver unit, a transceiver, or a transceiver circuit, etc., and is used to implement the transceiver function. The transceiver 1805 may include a receiver and a transmitter. The receiver may be referred to as a receiver or a receiving circuit, etc., and is used to implement the receiving function; the transmitter may be referred to as a transmitter or a transmitting circuit, etc., and is used to implement the transmitting function.

[0104] Optionally, the communication device 1800 may further include one or more interface circuits 1807. The interface circuit 1807 is used to receive code instructions and transmit them to the processor 1801. The processor 1801 runs the code instructions to enable the communication device 1800 to execute the method described in the above method embodiments.

[0105] In one implementation, the processor 1801 may include a transceiver for implementing the receiving and sending functions. For example, the transceiver may be a transceiver circuit, an interface, or an interface circuit. The transceiver circuit, interface, or interface circuit for implementing the receiving and sending functions may be separate or integrated. The above-mentioned transceiver circuit, interface, or interface circuit may be used for reading and writing code / data, or the above-mentioned transceiver circuit, interface, or interface circuit may be used for transmitting or delivering signals.

[0106] In one implementation, the processor 1801 may store a computer program 1803, which runs on the processor 1801 and enables the communication device 1800 to perform the method described in the above method embodiment. The computer program 1803 may be fixed in the processor 1801, in which case the processor 1801 may be implemented by hardware.

[0107] In one implementation, the communication device 1800 may include a circuit that can implement the functions of sending or receiving or communicating in the aforementioned method embodiments. The processor and transceiver described in the present disclosure may be implemented in an integrated circuit (IC), an analog IC, a radio frequency integrated circuit RFIC, a mixed signal IC, an application specific integrated circuit (ASIC), a printed circuit board (PCB), an electronic device, etc. The processor and transceiver may also be manufactured using various IC process technologies, such as complementary metal oxide semiconductor (CMOS), N-type metal oxide semiconductor (nMetal-oxide-semiconductor, NMOS), P-type metal oxide semiconductor (positive channelmetal oxide semiconductor, PMOS), bipolar junction transistor (bipolar junction transistor, BJT), bipolar CMOS (BiCMOS), silicon germanium (SiGe), gallium arsenide (GaAs), etc.

[0108] The communication device described in the above embodiments may be a network device or a user device, but the scope of the communication device described in the present disclosure is not limited thereto, and the structure of the communication device may not be limited thereto. Figure 5 The communication device may be an independent device or may be part of a larger device. For example, the communication device may be:

[0109] (1) An independent integrated circuit IC, or chip, or chip system or subsystem;

[0110] (2) A set having one or more ICs, optionally, the IC set may also include storage components for storing data and computer programs;

[0111] (3) ASIC, such as a modem;

[0112] (4) A module that can be embedded in other devices;

[0113] (5) A receiver, a terminal device, a smart terminal device, a cellular phone, a wireless device, a handset, a mobile unit, a vehicle-mounted device, a network device, a cloud device, an artificial intelligence device, etc.;

[0114] (6) Others, etc.

[0115] Based on the above embodiments, this embodiment also provides a chip, including at least one processor and a communication interface; the communication interface is used to receive signals input to the chip or signals output from the chip, and the processor communicates with the communication interface and implements the method shown above through a logic circuit or by executing code instructions.

[0116] Figure 6 is a schematic structural diagram of a chip 1000 for implementing the method for determining channel correlation provided in this embodiment. Refer to Figure 6 , the chip 1000 includes at least one communication interface 1001 and a processor 1002. The communication interface 1001 is used to receive signals input to the chip 1000 or signals output from the above chip 1000, and the processor 1002 communicates with the communication interface 1001 and implements the method for determining channel correlation described in the above embodiments of the present disclosure through a logic circuit or by executing code instructions.

[0117] Those skilled in the art can also understand that the various illustrative logical blocks and steps listed in the embodiments of the present disclosure can be implemented by electronic hardware, computer software, or a combination of both. Whether such a function is implemented by hardware or software depends on the specific application and the design requirements of the entire system. Those skilled in the art can use various methods to implement the described function for each specific application, but such implementation should not be construed as exceeding the scope protected by the embodiments of the present disclosure.

[0118] The present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor of a computer, it implements the functions of any of the above method embodiments.

[0119] The present disclosure also provides a computer program product. When the computer program product is executed by a computer, it implements the functions of any of the above method embodiments. That is, a computer program is stored thereon, and when the computer program product is executed by a processor of the computer, it implements the functions of any of the above method embodiments.

[0120] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs. When the computer program is loaded and executed on a computer, the processes or functions according to the embodiments of the present disclosure are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer program can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer program can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a high-density digital video disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0121] Those of ordinary skill in the art can understand that the various numerical numbers such as the first, second, etc. involved in the present disclosure are only for the convenience of description and are not used to limit the scope of the embodiments of the present disclosure, nor do they represent the order of precedence.

[0122] At least one in the present disclosure can also be described as one or more. The plurality can be two, three, four, or more, and the present disclosure does not make any limitations. In the embodiments of the present disclosure, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", "third", "A", "B", "C", and "D", etc. There is no order of precedence or size order among the technical features described by the "first", "second", "third", "A", "B", "C", and "D".

[0123] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) that provides machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal that provides machine instructions and / or data to a programmable processor.

[0124] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0125] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0126] It should be understood that the various forms of the flow shown above can be used, steps can be reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of this disclosure application can be achieved. This is not limited herein.

[0127] In addition, it should be understood that the various embodiments described in this disclosure can be implemented separately or, where the solution permits, in combination with other embodiments.

[0128] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this disclosure.

[0129] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0130] The above is only a specific implementation manner of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present disclosure and should be covered by the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. A method for determining channel correlation, characterized in that: include: Obtain channel quality parameters of a multiple-input multiple-output MIMO system; determining a relative relationship between the channel quality parameters; The channel correlation of the MIMO system is determined according to the relative relationship.

2. The method according to claim 1, characterized in that The channel quality parameters include at least two of the following: A priori channel capacity; A posteriori channel capacity; Open loop channel capacity.

3. The method according to claim 1, characterized in that Determining the relative relationship between the channel quality parameters includes: The relative relationship is determined according to a difference and / or a ratio between the channel quality parameters.

4. The method according to claim 1, characterized in that: Determining the channel correlation of the MIMO system according to the relative relationship includes: The relative relationship is input into a mapping model for calculation, and the channel correlation is determined according to an output result of the mapping model, wherein the mapping model is used to determine the channel correlations corresponding to the relative relationships between channel quality parameters in different scenarios; or, Based on the relative relationship, the channel correlation is determined by querying a mapping relationship in a relationship mapping table, wherein the relationship mapping table stores channel correlations corresponding to relative relationships between channel quality parameters in different scenarios.

5. The method according to claim 4, characterized in that The method further comprises: Constructing a training set based on the channel correlations corresponding to the relative relationships between channel quality parameters in different scenarios; The mapping model is trained based on the training set.

6. The method according to claim 5, characterized in that Training the mapping model based on the training set includes: Determine a mapping table based on the training set, and store the mapping table in the mapping model, wherein the mapping table stores a mapping relationship between relative relationships between channel quality parameters in different scenarios and channel relativity; and / or, Performing a linear fitting calculation based on the training set, and storing the calculated expression in the mapping model, wherein the expression is used to determine the channel correlations corresponding to the relative relationships between the channel quality parameters in different scenarios; and / or, Based on the training set, the neural network in the mapping model is used to learn the mapping relationship between the relative relationship between the channel quality parameters in different scenarios and the channel relativity.

7. The method according to claim 6, characterized in that The method further comprises: The output result of the mapping model is determined by calculation of the mapping table and / or the expression and / or the neural network.

8. A device for determining channel correlation, characterized in that: include: An acquisition module is configured to acquire a channel quality parameter of a multiple-input multiple-output MIMO system; A determination module, configured to determine a relative relationship between the channel quality parameters; The channel correlation of the MIMO system is determined according to the relative relationship.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A communication device, wherein: include: A transceiver, a memory and a processor, wherein the processor is connected to the transceiver and the memory respectively, and the processor is configured to control the wireless signal reception and transmission of the transceiver by executing computer executable instructions on the memory, and can implement the method described in any one of claims 1 to 7.

11. A chip, characterized in that: It includes at least one processor and a communication interface; the communication interface is used to receive a signal input to the chip or a signal output from the chip, and the processor communicates with the communication interface and implements the method as described in any one of claims 1 to 7 through a logic circuit or executing code instructions.

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