Method, device, storage medium and chip for determining channel correlation
By acquiring the channel quality parameters of the MIMO system and determining their relative relationships, and using a mapping model to estimate channel correlation, the problem of high computational complexity in existing technologies is solved, and low-complexity and low-latency channel correlation estimation is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING X RING TECHNOLOGY CO LTD
- Filing Date
- 2025-03-26
- Publication Date
- 2026-06-12
AI Technical Summary
Existing techniques have high computational complexity when estimating channel correlation in MIMO systems, resulting in computational delays and low efficiency.
By obtaining the channel quality parameters of the MIMO system, such as a priori channel capacity, posterior channel capacity, and open-loop channel capacity, the relative relationships between these parameters are determined, and the channel correlation is directly estimated using a mapping model or relational mapping table, thus avoiding a large amount of data analysis and calculation.
It reduces the computational complexity of channel correlation, reduces computation time delay, and supports dynamic estimation of channel correlation for different channels and MIMO structures.
Smart Images
Figure CN120151144B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of communication technology, and in particular to a method, apparatus, storage medium, communication device, and chip for determining channel correlation. Background Technology
[0002] Multiple-input multi-output (MIMO) technology employs multiple antennas or antenna arrays at the transceiver end of a communication system. MIMO technology can increase channel capacity, improve channel reliability, and reduce the bit error rate. Summary of the Invention
[0003] This disclosure provides a method, apparatus, storage medium, communication device, and chip for determining channel correlation. The main purpose is to improve the technical problem of high computational complexity in current techniques for estimating channel correlation in MIMO systems.
[0004] According to a first aspect of the present disclosure, a method for determining channel correlation is provided, comprising:
[0005] Obtain the channel quality parameters of the MIMO system;
[0006] Determine the relative relationships between the channel quality parameters;
[0007] The channel correlation of the MIMO system is determined based on the aforementioned relative relationship.
[0008] In some embodiments of this 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 this disclosure, determining the relative relationships between the channel quality parameters includes:
[0013] The relative relationship is determined based on the differences and / or ratios between the channel quality parameters.
[0014] In some embodiments of this disclosure, determining the channel correlation of the MIMO system based on the relative relationship includes:
[0015] The relative relationship is input into the mapping model for calculation, and the channel correlation is determined based on the output of the mapping model. The mapping model is used to determine the channel correlation corresponding to the relative relationships between channel quality parameters under different scenarios; or...
[0016] Based on the relative relationship, the channel correlation is determined by querying the mapping relationship in the relationship mapping table, which stores the channel correlation corresponding to the relative relationships between channel quality parameters under different scenarios.
[0017] In some embodiments of this disclosure, the method further includes:
[0018] A training set is constructed based on the channel correlation corresponding to the relative relationships between channel quality parameters in different scenarios.
[0019] The mapping model is trained based on the training set.
[0020] In some embodiments of this disclosure, training the mapping model based on the training set includes:
[0021] A mapping table is determined based on the training set and stored in the mapping model. The mapping table stores the relative relationships between channel quality parameters and the mapping relationship between channel relativity under different scenarios; and / or,
[0022] Linear fitting calculations are performed based on the training set, and the calculated expressions are stored in the mapping model. These expressions are used to determine the channel correlation corresponding to the relative relationships between channel quality parameters under different scenarios; and / or,
[0023] Based on the training set, the relative relationships between channel quality parameters and the mapping relationship between channel relativity are learned through the neural network in the mapping model under different scenarios.
[0024] In some embodiments of this disclosure, the method further includes:
[0025] The output of the mapping model is determined by the calculation of the mapping table and / or the expression and / or the neural network.
[0026] According to a second aspect of the present disclosure, an apparatus for determining channel correlation is provided, comprising:
[0027] The acquisition module is configured to acquire the channel quality parameters of the MIMO system;
[0028] The determination module is configured to determine the relative relationships between the channel quality parameters and to determine the channel correlation of the MIMO system based on the relative relationships.
[0029] According to a third aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.
[0030] According to a fourth aspect of the present disclosure, a communication device is provided, comprising: a transceiver; a memory; and a processor connected to the transceiver and the memory respectively, configured to control the transmission and reception of wireless signals of the transceiver by executing computer-executable instructions on the memory, and capable of implementing the method as described in the first aspect.
[0031] According to a fifth aspect of the present disclosure, a computer program product is provided having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.
[0032] According to a sixth aspect of the present disclosure, a chip is provided, including at least one processor and a communication interface; the communication interface is configured 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 executing code instructions.
[0033] By employing the above technical solutions, this disclosure provides a method, apparatus, storage medium, communication device, and chip for determining channel correlation, which is equivalent to providing a low-complexity scheme for channel correlation estimation. Specifically, it first obtains the channel quality parameters of the MIMO system, such as prior channel capacity and posterior channel capacity; then, it determines the relative relationships between these channel quality parameters; then, it determines the channel correlation of the MIMO system based on these relative relationships, and further estimates the channel correlation directly based on these channel quality parameters. This avoids a large amount of data analysis and calculation. Compared with current related technologies, the technical solution of this disclosure effectively reduces the computational complexity of channel correlation, can directly provide channel correlation estimates, significantly reduces computation time delay, and can dynamically support channel correlation estimates for different channels and different MIMO structures.
[0034] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description
[0035] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.
[0036] Figure 1 A flowchart illustrating a method for determining channel correlation according to an embodiment of this disclosure is shown;
[0037] Figure 2 A flowchart illustrating another method for determining channel correlation provided in an embodiment of this disclosure is shown;
[0038] Figure 3A schematic diagram illustrating an example provided by an embodiment of this disclosure is shown;
[0039] Figure 4 A schematic diagram of the structure of an apparatus for determining channel correlation provided in an embodiment of this disclosure is shown;
[0040] Figure 5 A schematic diagram of the structure of a communication device provided in an embodiment of this disclosure is shown;
[0041] Figure 6 A schematic diagram of the structure of a chip provided in an embodiment of this disclosure is shown. Detailed Implementation
[0042] Some embodiments of this disclosure will be described in detail herein, examples of which are illustrated in the accompanying 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, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but can be changed as will become apparent upon understanding this disclosure, except for operations that must be performed in a particular order. Furthermore, for clarity and brevity, descriptions of features known in the art may be omitted. It should be noted that, without conflict, the embodiments and features in the embodiments of this disclosure can be combined with each other.
[0043] The following describes some embodiments of this disclosure. It should be noted that the implementation methods described in these embodiments do not represent all implementation methods consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.
[0044] To address the high computational complexity of current techniques for estimating channel correlation in MIMO systems, this disclosure provides a method for determining channel correlation, such as... Figure 1 As shown, Figure 1 This is a flowchart illustrating a method for determining channel correlation 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] The execution subject of this embodiment can be a device or equipment for determining channel correlation, such as a communication device or chip, and can 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 device (MT), etc. A terminal can also be a car with communication capabilities, a smart car, a mobile phone, a wearable device, a tablet computer, 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 technology or device form used in the terminal.
[0048] In some examples, the network device can be a base station, satellite, or other similar equipment. This embodiment does not impose specific limitations; the network device can be an entity on the network side used to transmit or receive signals. For example, the network device can 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 this disclosure do not limit the specific technology or device form used in the network device. The network device provided in this embodiment can be composed of a central unit (CU) and a distributed unit (DU). The CU can also be called a control unit. Using a CU-DU structure allows the protocol layer of a network device, such as a base station, to be separated. Some protocol layer functions are centrally controlled by the CU, while the remaining part or all protocol layer functions are distributed in the DU, which is centrally controlled by the CU.
[0049] The embodiments disclosed herein can be applied to satellite communications, Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 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), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X) systems, systems utilizing other communication methods, and next-generation systems built upon them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).
[0050] In some embodiments, channel quality parameters of a MIMO system are crucial for evaluating and optimizing the performance of the wireless communication system. These parameters help to understand the spatial, temporal, and frequency behavior of signals 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.
[0051] In some examples, the channel quality parameters may include at least two of the following:
[0052] Prior channel capacity; posterior channel capacity; open-loop channel capacity.
[0053] Step 102: Determine the relative relationships between channel quality parameters.
[0054] In some embodiments, the relative relationships between channel quality parameters can be determined based on the differences and / or ratios between them.
[0055] For example, the relative relationship between the prior channel capacity and the posterior channel capacity can be determined based on the ratio between the prior channel capacity and the posterior channel capacity. Alternatively, the relative relationship between the prior channel capacity, the posterior channel capacity, and the open-loop channel capacity can be determined based on the difference between them.
[0056] Step 103: Determine the channel correlation of the MIMO system based on the relative relationship between channel quality parameters.
[0057] Channel correlation describes the degree of correlation between signals from different antennas in a MIMO system. Utilizing channel correlation can significantly improve the throughput and spectral efficiency of communication systems; therefore, the accuracy of MIMO data processing, channel estimation, and other algorithms highly depends on the accuracy of channel correlation estimation.
[0058] In some embodiments, low channel correlation helps improve the performance of MIMO systems because it allows for more efficient spatial diversity and multiplexing gain, while high channel correlation can lead to performance degradation because multiple antennas receive similar information, reducing the number of independent information streams. In some examples, changes in channel correlation can be indirectly reflected by analyzing the relative relationships between channel quality parameters such as prior and posterior channel capacity. For instance, under high channel correlation conditions, prior estimates may deviate significantly from reality, resulting in a large difference between prior and posterior channel capacities. Therefore, for embodiments of this disclosure, after obtaining the channel quality parameters of the MIMO system, the relative relationships between these channel quality parameters can be determined first, and then the channel correlation of the MIMO system can be determined through these relative relationships, such as by determining the channel correlation corresponding to the relative relationship through mapping calculations.
[0059] In some embodiments, the channel correlation of the MIMO system is determined by querying the mapping relationship in the relationship mapping table based on the relative relationship between channel quality parameters. The relationship mapping table can be pre-constructed and stores the channel correlation corresponding to the relative relationship between channel quality parameters under different scenarios. In this way, the channel correlation corresponding to the relative relationship between channel quality parameters is determined by querying the mapping relationship in the relationship mapping table, and then used as the channel correlation of the MIMO system.
[0060] Compared with the existing technology, the embodiments of this disclosure effectively reduce the computational complexity of channel correlation, can directly provide channel correlation estimates, significantly reduce computation time delay, and can dynamically support providing channel correlation estimates for different channels and different MIMO structures.
[0061] Furthermore, to illustrate the specific implementation process of the method in this embodiment, as an optional approach, this embodiment provides the following... Figure 2 The specific method shown includes:
[0062] Step 201: Obtain the channel quality parameters of the MIMO system.
[0063] This embodiment can directly estimate channel correlation through channel quality parameters, including but not limited to prior channel capacity, posterior channel capacity, and open-loop channel capacity, using a pre-stored mapping model. This is because different channel correlations directly affect prior, posterior, and open-loop channel capacities. Therefore, a mapping model can be trained through the relative relationship between at least two of the three to achieve channel correlation estimation. Specifically, the process shown in steps 202 to 203 can be executed.
[0064] Step 202: Determine the relative relationships between channel quality parameters based on the differences and / or ratios between them.
[0065] Step 203: Input the relative relationship between channel quality parameters into the mapping model for calculation, and determine the channel correlation of the MIMO system based on the output of the mapping model.
[0066] Among them, the mapping model can be used to determine the channel correlation corresponding to the relative relationships between channel quality parameters under different scenarios.
[0067] In some embodiments, a training set is first constructed based on the channel correlations corresponding to the relative relationships between channel quality parameters under different scenarios. For example, the relative relationships between channel quality parameters under different scenarios can be used as feature data, and their corresponding channel correlations can be used as label data. Thus, the training set stores these feature data and the label data corresponding to these feature data. Then, the mapping model is trained based on the constructed training set. Accordingly, step 203 may specifically include: inputting the relative relationships between channel quality parameters into the trained mapping model for calculation, and determining the channel correlation of the MIMO system based on the output of the mapping model.
[0068] There are several possible methods for training a mapping model based on a training set. In some examples, a mapping table can be determined based on the training set and stored in the mapping model to train the model. This mapping table stores the mapping relationship between the relative relationships of channel quality parameters and channel relativity under different scenarios. And / or, linear fitting calculations can be performed based on the training set, and the calculated expressions can be stored in the mapping model to train the model. These expressions are used to determine the channel correlation corresponding to the relative relationships of channel quality parameters under different scenarios. And / or, based on the training set, the mapping relationship between the relative relationships of channel quality parameters and channel relativity under different scenarios can be learned through a neural network in the mapping model.
[0069] Accordingly, when applying the mapping model, the relative relationships between the current channel quality parameters can be input into the mapping model for calculation. Specifically, this can be done through the above mapping table and / or the above expression and / or the above neural network calculation, and then the output result of the mapping model can be determined based on the calculation result.
[0070] The following is one example explanation, but it is not limited to this:
[0071] The signal processing flow involved in the embodiments of this disclosure can be as follows: Figure 3 As shown, channel capacity calculation can be performed using classical communication algorithms to calculate channel quality parameters, including but not limited to the prior channel capacity C. pre posterior channel capacity C post Open-loop channel capacity C open wait.
[0072] In order to obtain the corresponding channel correlation through the mapping model, the mapping model needs to be trained in advance, such as the mapping model training of the relative relationship between channel capacity and channel correlation.
[0073] As an example, channel correlation can be estimated by the ratio of prior and posterior channel capacities. It should be noted that, in addition to this method, various data processing methods (such as ratios and differences between the three) and channel correlation relationships of prior, posterior, and open-loop channel capacities can also be fully considered.
[0074] This embodiment comprehensively covers various channel parameters, including but not limited to channel correlation and MIMO structure, and collects data for each combination of channel parameters, including but not limited to signal-to-noise ratio (SNR), number of data slots transmitted, channel capacity, etc. For example, assuming a system channel scenario with XP-high channel correlation, a channel delay of m, a Doppler shift of 5Hz, a 4x4 MIMO structure, and an SNR of -5:1, and 46 different SNRs (40dB), with each SNR transmitting 10,000 slots of data, the receiver will receive 460,000 sets of channel capacity [prior and posterior channel capacity].
[0075] Calculate the a priori and posterior channel capacity ratios for 460,000 groups and record the results 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 Based on different values, it can be divided into 9 equal parts, namely: C pre _block[0] = [0,5], C pre _block[1] = [5, 10], ..., Cpre_block[8] = [35, 40], calculate the CORR for each part respectively. factor value.
[0078] Calculate CORR for each part factor The distribution of the cumulative distribution function (CDF) is recorded, and the CORR value corresponding to CDF = 90% is recorded. factor _block[i], i∈[0,8]. Records the current channel correlation, C pre _block and CORR factor The mapping relationship of _block can be stored through pre-stored tables, linear fitting, or neural networks to complete the training of the mapping model.
[0079] By traversing all relevant channel scenarios and pre-storing the prior and posterior channel capacity ratios for each scenario, these ratios can be recorded as CORR.factor [Various correlation scenarios], the mapping model is stored in the communication system for calculating channel correlation.
[0080] In practical applications, the receiver calculates the a priori and a posteriori channel capacity ratio (CORR). factor _calc is then input into the mapping model; the mapping model iterates through and compares CORR values. factor CORR of _calc and pre-stored correlation scenarios factor CORR factor _calc and CORR factor The closest scenario is determined by the current channel correlation value, for example, based on the current prior channel capacity C. pre Find its corresponding C pre _block and its corresponding CORR factor _block, then based on the current CORR factor _calc, retrieve the corresponding CORR factor , which serves as the current channel correlation value.
[0081] This disclosure provides a low-complexity scheme for channel correlation estimation, mainly reflected in the following aspects:
[0082] Low complexity: Based on the relative relationships between channel quality parameters, such as prior, posterior, and open-loop channel capacity, the channel correlation can be directly estimated using the mapping model provided in this embodiment, avoiding a large amount of data analysis and calculation.
[0083] Low latency: Through the mapping calculation of the mapping model, channel correlation estimation can be directly given, which greatly reduces the calculation time delay.
[0084] High compatibility: Through a complete mapping model, it can dynamically support correlation estimation for different channels and different MIMO structures.
[0085] Figure 4 This is a block diagram illustrating an apparatus for determining channel correlation according to some embodiments of the present disclosure. (Refer to...) Figure 4 The device includes an acquisition module 31 and a determination module 32.
[0086] The acquisition module 31 is configured to acquire the channel quality parameters of the MIMO system;
[0087] The determination module 32 is configured to determine the relative relationship between the channel quality parameters and determine the channel correlation of the MIMO system based on 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 configured to input the relative relationship into the mapping model for calculation, and determine the channel correlation based on the output of the mapping model, wherein the mapping model is used to determine the channel correlation corresponding to the relative relationship between channel quality parameters under different scenarios; or, based on the relative relationship, the channel correlation is determined by querying the mapping relationship in the relationship mapping table, wherein the relationship mapping table stores the channel correlation corresponding to the relative relationship between channel quality parameters under different scenarios.
[0094] In some examples of this embodiment, the device also includes a training module;
[0095] The training module is configured to construct a training set based on the channel correlation corresponding to the relative relationship between channel quality parameters under different scenarios; and to train 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; the mapping table stores the mapping relationship between the relative relationships of channel quality parameters and channel relativity under different scenarios; and / or, perform linear fitting calculation based on the training set and store the calculated expression in the mapping model; the expression is used to determine the channel correlation corresponding to the relative relationships of channel quality parameters under different scenarios; and / or, learn the mapping relationship between the relative relationships of channel quality parameters and channel relativity under different scenarios through a neural network in the mapping model based on the training set.
[0097] In some examples of this embodiment, the determining module 32 is further 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 apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0099] Figure 5This is a schematic diagram of the structure of a communication device 1800 provided in this embodiment. The communication device 1800 can be a terminal device, a network device, a chip, chip system, or processor that supports the network device in implementing the above methods, or a chip, chip system, or processor that supports user equipment in implementing the above methods. This device can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions 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 transmission and reception of the transceiver by executing computer-executable instructions on the memory, and to realize 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 dedicated processor, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can be used to control the communication device (e.g., base station, baseband chip, terminal device, terminal device chip, DU or CU, etc.), execute computer programs, and process data from the computer programs.
[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 cause the communication device 1800 to perform the methods described in the above method embodiments. Optionally, the memory 1802 may also store data. The communication device 1800 and the memory 1802 may be provided separately or integrated together.
[0103] Optionally, the communication device 1800 may also include a transceiver 1805 and an antenna 1806. The transceiver 1805 may be referred to as a transceiver unit, transceiver, or transceiver circuit, etc., and is used to implement the transmission and reception functions. The transceiver 1805 may include a receiver and a transmitter. The receiver may be referred to as a receiver or receiving circuit, etc., and is used to implement the receiving function; the transmitter may be referred to as a transmitter or 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 circuits 1807 are used to receive code instructions and transmit them to the processor 1801. The processor 1801 executes the code instructions to cause the communication device 1800 to perform the methods described in the above method embodiments.
[0105] In one implementation, the processor 1801 may include a transceiver for implementing receive and transmit 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 receive and transmit functions may be separate or integrated. The aforementioned transceiver circuit, interface, or interface circuit can be used for reading and writing code / data, or it can be used for transmitting or relaying signals.
[0106] In one implementation, processor 1801 may store computer program 1803, which runs on processor 1801 and causes communication device 1800 to perform the methods described in the above method embodiments. Computer program 1803 may be embedded in processor 1801; in this case, processor 1801 may be implemented in hardware.
[0107] In one implementation, the communication device 1800 may include circuitry capable of performing the transmitting, receiving, or communication functions described in the foregoing method embodiments. The processor and transceiver described in this disclosure can be implemented on integrated circuits (ICs), analog ICs, radio frequency integrated circuits (RFICs), mixed-signal ICs, application-specific integrated circuits (ASICs), printed circuit boards (PCBs), electronic devices, etc. The processor and transceiver can also be manufactured using various IC process technologies, such as complementary metal oxide semiconductors (CMOS), n-metal-oxide-semiconductor (NMOS), positive-channel metal oxide semiconductors (PMOS), bipolar junction transistors (BJTs), 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 equipment, but the scope of the communication device described in this disclosure is not limited to this, and the structure of the communication device may vary. Figure 5 The communication device can be a standalone device or part of a larger device. For example, the communication device could be:
[0109] (1) Independent integrated circuit IC, or chip, or chip system or subsystem;
[0110] (2) A collection of one or more ICs, optionally including storage components for storing data and computer programs;
[0111] (3) ASIC, such as modem;
[0112] (4) Modules that can be embedded in other devices;
[0113] (5) Receivers, terminal equipment, smart terminal equipment, cellular phones, wireless equipment, handheld devices, mobile units, vehicle-mounted equipment, network equipment, cloud equipment, artificial intelligence equipment, 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 logic circuits or executing code instructions.
[0116] Figure 6 This is a schematic diagram of the structure of a chip 1000 provided in this embodiment for implementing the above-described method for determining channel correlation. (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 chip 1000. The processor 1002 communicates with the communication interface 1001 and implements the method for determining channel correlation described in the above embodiments of this disclosure through logic circuits or executing code instructions.
[0117] Those skilled in the art will also understand that the various illustrative logical blocks and steps listed in the embodiments of this disclosure can be implemented by electronic hardware, computer software, or a combination of both. Whether such functionality is implemented in hardware or software depends on the specific application and the overall system design requirements. Those skilled in the art can implement the described functionality using various methods for each specific application, but such implementation should not be construed as exceeding the scope of protection of the embodiments of this disclosure.
[0118] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer's processor, implements the functions of any of the above method embodiments.
[0119] This disclosure also provides a computer program product that, when executed by a computer, implements the functions of any of the above-described method embodiments. If a computer program is stored thereon, the computer program product, when executed by a computer's processor, implements the functions of any of the above-described method embodiments.
[0120] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer programs. When a computer program is loaded and executed on a computer, it generates, in whole or in part, the flow or function according to the embodiments of this disclosure. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, a computer program can be transferred from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).
[0121] Those skilled in the art will understand that the various numerical designations such as "first," "second," etc., used in this disclosure are merely for the convenience of description and are not intended to limit the scope of the embodiments of this disclosure, nor do they indicate the order of events.
[0122] At least one of the features described in this disclosure can also be described as one or more, and multiple features can be two, three, four or more, and this disclosure does not impose any limitations. In the embodiments of this disclosure, for a technical feature, the technical features in that technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no sequential order or size order among the technical features described by "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, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.
[0124] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.
[0125] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.
[0126] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.
[0127] Furthermore, it should be understood that the various embodiments described in this disclosure can be implemented individually or in combination with other embodiments, where the scheme allows.
[0128] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments claimed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.
[0129] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0130] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.
Claims
1. A method for determining channel correlation, characterized in that, include: Obtain the channel quality parameters of a multiple-input multiple-output (MIMO) system; Determining the relative relationship between the channel quality parameters includes: determining the relative relationship based on the difference and / or ratio between the channel quality parameters; Determining the channel correlation of a MIMO system based on the relative relationship includes: inputting the relative relationship into a mapping model for calculation, and determining the channel correlation based on the output of the mapping model, wherein the mapping model is used to determine the channel correlation corresponding to the relative relationship between channel quality parameters under different scenarios; The channel correlation is used to determine the spatial diversity and multiplexing gain of the MIMO system; The channel quality parameters include at least two of the following: Prior channel capacity; Posterior channel capacity; Open-loop channel capacity.
2. The method according to claim 1, characterized in that, The method further includes: A training set is constructed based on the channel correlation corresponding to the relative relationships between channel quality parameters in different scenarios. The mapping model is trained based on the training set.
3. The method according to claim 2, characterized in that, Training the mapping model based on the training set includes: A mapping table is determined based on the training set and stored in the mapping model. The mapping table stores the relative relationships between channel quality parameters and the mapping relationship between channel relativity under different scenarios; and / or, Linear fitting calculations are performed based on the training set, and the calculated expressions are stored in the mapping model. These expressions are used to determine the channel correlation corresponding to the relative relationships between channel quality parameters under different scenarios; and / or, Based on the training set, the relative relationships between channel quality parameters and the mapping relationship between channel relativity are learned through the neural network in the mapping model under different scenarios.
4. The method according to claim 3, characterized in that, The method further includes: The output of the mapping model is determined by the calculation of the mapping table and / or the expression and / or the neural network.
5. An apparatus for determining channel correlation, characterized in that, include: The acquisition module is configured to acquire channel quality parameters of a multiple-input multiple-output (MIMO) system. The determination module is configured to determine the relative relationship between the channel quality parameters, including: determining the relative relationship based on the difference and / or ratio between the channel quality parameters; and determining the channel correlation of the MIMO system based on the relative relationship, including: inputting the relative relationship into a mapping model for calculation, and determining the channel correlation based on the output of the mapping model, wherein the mapping model is used to determine the channel correlation corresponding to the relative relationship between the channel quality parameters under different scenarios. The channel correlation is used to determine the spatial diversity and multiplexing gain of the MIMO system; The channel quality parameters include at least two of the following: Prior channel capacity; Posterior channel capacity; Open-loop channel capacity.
6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 4.
7. A communication device, wherein, include: A transceiver, a memory, and a processor, the processor being connected to the transceiver and the memory respectively, the processor being configured to control the wireless signal transmission and reception of the transceiver by executing computer-executable instructions on the memory, and to implement the method of any one of claims 1 to 4.
8. A chip, characterized in that, It includes 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 as described in any one of claims 1 to 4 through logic circuits or executing code instructions.