Channel estimation method and related apparatus

Abstract

The application discloses a channel estimation method and related device, wherein the method comprises: performing minimum mean square error (MMSE) channel estimation based on the product of the first amplitude factor and the first noise power of the first dimension, the first channel autocorrelation matrix of the first dimension, and the first diagonal matrix to obtain the channel estimation value matrix of the first dimension; wherein the channel estimation value matrix of the first dimension is obtained by performing MMSE channel estimation in the non-last step of the multi-dimensional step-by-step MMSE channel estimation; the first amplitude factor is a positive number less than or equal to 1; and performing MMSE channel estimation based on the channel estimation value matrix of the first dimension, the second channel autocorrelation matrix of the last dimension, the second noise power and the second diagonal matrix to obtain the multi-dimensional channel estimation value matrix. The method can improve the performance of the multi-dimensional step-by-step MMSE channel estimation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, in particular to a channel estimation method and related device. BACKGROUND

[0002] The minimum mean squared error (MMSE) channel estimation method can be applied in multiple dimensions such as time domain, frequency domain, space domain and code domain. The principle thereof is to utilize the correlation of the channel in the time domain, frequency domain, space domain or code domain, minimize the mean squared error between the actual value and the estimated value of the channel matrix through filtering, and achieve the effect of reducing noise and improving channel estimation performance. For multi-dimensional MMSE channel estimation, an optional way is to use a multi-dimensional joint MMSE channel estimation method. However, the multi-dimensional joint MMSE channel estimation method has high complexity although it has good performance. Another optional way is to use a multi-dimensional step-by-step MMSE channel estimation method. However, compared with the multi-dimensional joint MMSE channel estimation method, the multi-dimensional step-by-step MMSE channel estimation method can reduce complexity, but there is a certain performance loss. Therefore, how to improve the performance of the multi-dimensional step-by-step MMSE channel estimation has become a problem to be solved. SUMMARY

[0003] Embodiments of the present application provide a channel estimation method and related device, which can improve the performance of multi-dimensional step-by-step MMSE channel estimation.

[0004] In a first aspect, the embodiments of the present application provide a channel estimation method, which comprises:

[0005] performing minimum mean squared error (MMSE) channel estimation based on the product of the first noise power and the first amplitude factor, the first channel autocorrelation matrix of the first dimension and the first diagonal matrix, to obtain a channel estimation value matrix of the first dimension;

[0006] The channel estimation value matrix of the first dimension is obtained by performing MMSE channel estimation in the non-last step of the multi-dimensional step-by-step MMSE channel estimation;

[0007] The first noise power is the noise power corresponding to the least square (LS) channel estimation value, or the noise power corresponding to the channel estimation value matrix obtained by the last step of MMSE channel estimation; and the first amplitude factor is a positive number less than or equal to 1;

[0008] performing MMSE channel estimation based on the channel estimation value matrix of the first dimension and the second channel autocorrelation matrix, the second noise power and the second diagonal matrix of the last dimension, to obtain a multi-dimensional channel estimation value matrix; wherein the second noise power is the noise power corresponding to the channel estimation value matrix of the first dimension.

[0009] It can be seen that, in the embodiment of the present application, the first noise power used in the non-last step of the multi-dimensional step-by-step MMSE channel estimation is multiplied by a first amplitude factor, wherein the first amplitude factor is a positive number less than or equal to 1, so that in the process of multi-dimensional step-by-step MMSE channel estimation, different first amplitude factors can be used to obtain the channel estimation value matrix after multi-dimensional step-by-step MMSE channel estimation with optimal channel estimation performance, thereby improving the performance of multi-dimensional step-by-step MMSE channel estimation.

[0010] In an optional embodiment, the first amplitude factor is determined from an amplitude factor table, and the amplitude factor table stores the amplitude factors corresponding to the signal-to-noise ratio (SNR) of each LS channel estimation value, the channel autocorrelation matrix, and the number of taps used for channel estimation.

[0011] In an optional embodiment, the amplitude factor corresponding to the signal-to-noise ratio (SNR) of each LS channel estimation value, the channel autocorrelation matrix, and the number of taps used for channel estimation in the amplitude factor table is obtained by the following steps:

[0012] In the case where the signal-to-noise ratio (SNR) of each LS channel estimation value, the channel autocorrelation matrix, and the number of taps used for channel estimation remain unchanged, a plurality of different amplitude factors are used for simulation to obtain simulation results corresponding to each amplitude factor, respectively.

[0013] From the simulation results corresponding to each amplitude factor, respectively, a channel estimation value matrix with optimal simulation performance is selected.

[0014] The amplitude factor corresponding to the selected channel estimation value matrix is determined as the amplitude factor corresponding to the signal-to-noise ratio (SNR) of each LS channel estimation value, the channel autocorrelation matrix, and the number of taps used for channel estimation.

[0015] In an optional embodiment, the amplitude factor corresponding to the signal-to-noise ratio (SNR) of each LS channel estimation value, the channel autocorrelation matrix, and the number of taps used for channel estimation in the amplitude factor table is obtained by the following steps:

[0016] In the case where the signal-to-noise ratio (SNR) of each LS channel estimation value, the channel autocorrelation matrix, and the number of taps used for channel estimation remain unchanged, a plurality of different amplitude factors are used to determine a multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor, respectively; the multi-dimensional step-by-step MMSE channel estimation coefficient matrix is used to determine a multi-dimensional channel estimation value matrix in combination with the LS channel estimation value matrix.

[0017] selecting a multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to the amplitude factor from the multi-dimensional step-by-step MMSE channel estimation coefficient matrices corresponding to each amplitude factor respectively, the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to the amplitude factor being the multi-dimensional step-by-step MMSE channel estimation coefficient matrix having the largest normalized correlation with the multi-dimensional joint MMSE channel estimation coefficient matrix;

[0018] The amplitude factor corresponding to the selected multi-dimensional step-by-step MMSE channel estimation coefficient matrix is determined as the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of the LS channel estimation value.

[0019] In an optional embodiment, the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of the LS channel estimation value in the amplitude factor table is obtained by the following steps:

[0020] In the case that the SNR, the channel autocorrelation matrix and the tap number of the LS channel estimation value are unchanged, multi-dimensional step-by-step MMSE channel estimation coefficient matrices corresponding to each amplitude factor are determined by using a plurality of different amplitude factors, the multi-dimensional step-by-step MMSE channel estimation coefficient matrices being used to determine a multi-dimensional channel estimation value matrix in combination with an LS channel estimation value matrix;

[0021] A multi-dimensional step-by-step MMSE channel estimation coefficient matrix having the smallest first value with the multi-dimensional joint MMSE channel estimation coefficient matrix is selected from the multi-dimensional step-by-step MMSE channel estimation coefficient matrices corresponding to each amplitude factor respectively.

[0022] The first value is obtained by summing the modulus of the difference between each element in the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor respectively and the element at the corresponding position in the multi-dimensional joint MMSE channel estimation coefficient matrix, or the first value is obtained by summing the square of the modulus of the difference between each element in the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor respectively and the element at the corresponding position in the multi-dimensional joint MMSE channel estimation coefficient matrix.

[0023] The amplitude factor corresponding to the selected multi-dimensional step-by-step MMSE channel estimation coefficient matrix is determined as the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of the LS channel estimation value. In an optional embodiment, in the case that the SNR, the channel autocorrelation matrix and the tap number of the LS channel estimation value are unchanged, multi-dimensional step-by-step MMSE channel estimation coefficient matrices corresponding to each amplitude factor are determined by using a plurality of different amplitude factors, including:

[0024] In the case that the signal-to-noise ratio (SNR) of the LS channel estimation value, the channel autocorrelation matrix, and the number of taps used in the channel estimation remain unchanged, a plurality of different amplitude factors are used to determine a first-dimension channel estimation coefficient matrix corresponding to each amplitude factor;

[0025] Based on the first-dimension channel estimation coefficient matrix corresponding to each amplitude factor, a last-dimension channel estimation coefficient matrix corresponding to each amplitude factor is determined.

[0026] Based on the first-dimension channel estimation coefficient matrix and the last-dimension channel estimation coefficient matrix corresponding to each amplitude factor, a multi-dimension multi-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor is obtained.

[0027] In an optional embodiment, the amplitude factor corresponding to the signal-to-noise ratio (SNR) of the LS channel estimation value, the channel autocorrelation matrix, and the number of taps used in the channel estimation in the amplitude factor table is obtained by the following steps:

[0028] In the case that the signal-to-noise ratio (SNR) of the LS channel estimation value, the channel autocorrelation matrix, and the number of taps used in the channel estimation remain unchanged, a plurality of different amplitude factors are used to determine a multi-dimension channel estimation value matrix corresponding to each amplitude factor.

[0029] Based on the multi-dimension channel estimation value matrix corresponding to each amplitude factor and the channel matrix, an average mean square error (MSE) corresponding to each amplitude factor is determined.

[0030] From the average mean square error (MSE) corresponding to each amplitude factor, the amplitude factor corresponding to the smallest MSE is selected as the amplitude factor corresponding to the signal-to-noise ratio (SNR) of the LS channel estimation value, the channel autocorrelation matrix, and the number of taps used in the channel estimation.

[0031] In a second aspect, an embodiment of the present application provides a channel estimation device, which comprises:

[0032] The determination unit is configured to perform minimum mean square error (MMSE) channel estimation based on a product of a first noise power of the first dimension and a first amplitude factor, a first channel autocorrelation matrix of the first dimension, and a first diagonal matrix, to obtain a channel estimation value matrix of the first dimension.

[0033] The channel estimation value matrix of the first dimension is obtained by performing MMSE channel estimation in a non-last step of multi-dimension multi-step MMSE channel estimation.

[0034] The first noise power is a noise power corresponding to a least square channel estimation value, or a noise power corresponding to a channel estimation value matrix obtained by performing MMSE channel estimation in a previous step, and the first amplitude factor is a positive number less than or equal to 1.

[0035] The acquisition unit is used to perform MMSE channel estimation based on the channel estimation matrix of the first dimension and the second channel autocorrelation matrix, the second noise power and the second diagonal matrix of the last dimension to obtain a multidimensional channel estimation matrix; wherein, the second noise power is the noise power corresponding to the channel estimation matrix of the first dimension.

[0036] Optionally, the channel estimation device may implement optional methods and its beneficial effects, as can be found in the relevant content of the first aspect above, and will not be described in detail here.

[0037] Thirdly, embodiments of this application provide an electronic device comprising: a processor and a memory, the processor and the memory being interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is used to invoke the program instructions to implement the method involved in any of the optional embodiments of the first aspect described above. Optionally, the electronic device may be a terminal device or a chip or chip module in a terminal device. Optionally, the electronic device may also be a network device or a chip or chip module in a network device.

[0038] Fourthly, embodiments of this application provide a chip, the chip including a processor, wherein the processor executes the method involved in any of the optional embodiments of the first aspect described above. Optionally, the chip may further include a memory and a computer program or instructions stored in the memory, the processor executing the computer program or instructions to implement the method involved in any of the optional embodiments of the first aspect described above.

[0039] Fifthly, embodiments of this application provide a chip module, including a transceiver component and a chip. The chip includes a processor, wherein the processor executes the method involved in any of the optional embodiments of the first aspect described above. Optionally, the chip may further include a memory and a computer program or instructions stored in the memory, and the processor executes the computer program or instructions to implement the method involved in any of the optional embodiments of the first aspect described above.

[0040] In a sixth aspect, embodiments of this application provide a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a computer, implement the method involved in any of the optional embodiments of the first aspect described above.

[0041] In a seventh aspect, embodiments of this application provide a computer program product, including a computer program or program instructions, which, when executed, implement the method involved in any of the optional implementations of the first aspect described above. Attached Figure Description

[0042] Figure 1 is a flowchart of a channel estimation method provided by an embodiment of the present application;

[0043] Figure 2a is a comparison result diagram of time-frequency two-dimensional weight amplitudes obtained by different channel estimation methods provided by an embodiment of the present application;

[0044] Figure 2b is another comparison result diagram of time-frequency two-dimensional weight amplitudes obtained by different channel estimation methods provided by an embodiment of the present application;

[0045] Figure 3a is a simulation result diagram of MSE obtained by different channel estimation methods provided by an embodiment of the present application;

[0046] Figure 3b is another simulation result diagram of MSE obtained by different channel estimation methods provided by an embodiment of the present application;

[0047] Figure 3c is still another simulation result diagram of MSE obtained by different channel estimation methods provided by an embodiment of the present application;

[0048] Figure 4 is a structural diagram of a channel estimation device provided by an embodiment of the present application;

[0049] Figure 5 is a structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0050] Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase "in one embodiment" or "an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily all referring to a common set of embodiments. It is expressly understood that the embodiments described in this specification are intended to be combined with each other in any combination.

[0051] It should be noted that "first", "second", "third", etc. in the present application are used to distinguish similar objects, and do not necessarily mean a specific order or sequence. In addition, the term "comprise" and any variation thereof is intended to cover non-exclusive inclusion. For example, a process, method, software, product or device comprising a series of steps or units is not limited to the listed steps or units, but also includes steps or units not listed, or includes other steps or units inherent to the process, method, product or device.

[0052] In the present application, "equal to" can be used with "less than" or "greater than", but not both. When "equal to" is used with "less than", it applies to the technical solution adopted by "less than". When "equal to" is used with "greater than", it applies to the technical solution adopted by "greater than".

[0053] In the present application, "and / or" is only used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent three cases: A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in this paper represents an "or" relationship between the associated objects before and after it.

[0054] "Any of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can mean a, b, c, a and b, a and c, b and c, or a, b and c, where each of a, b and c can be an element or a set containing one or more elements.

[0055] In the present application, "one or more" means one or more. "Multiple" means two or more. The first, second, etc. appearing in the embodiments of the present application are only used for illustration and distinction of the described objects, and there is no order difference, nor does it represent a special limitation on the number in the embodiments of the present application. For example, the first identifier and the second identifier are only used to distinguish the identifiers corresponding to different vehicles, and do not mean that the two identifiers are the same or different.

[0056] In the present application, "example", "in some embodiments", "in other embodiments" and the like are used to represent an example, illustration or explanation. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the use of the word example is intended to present the concept in a specific way.

[0057] In the present application, "of", "corresponding", "corresponding", "associated" can be used interchangeably at times. It should be pointed out that when the distinction is not emphasized, the meanings expressed are consistent. In the embodiments of the present application, communication and transmission can be used interchangeably at times. It should be pointed out that when the distinction is not emphasized, the meanings expressed are consistent. For example, transmission can include sending and / or receiving, and can be a noun or a verb.

[0058] In the present application, the electronic device can be a terminal device or a network device.

[0059] The terminal device is a device with wireless communication function, and can also be referred to as a terminal, a user equipment (UE), a mobile station (MS), a mobile terminal (MT), an access terminal device, a vehicle-mounted terminal device, an industrial control terminal device, a UE unit, a UE station, a mobile station, a mobile station, a remote station, a remote terminal device, a mobile device, a UE terminal device, a wireless communication device, a smart terminal device, a UE agent or a UE apparatus, etc. The terminal device can be fixed or mobile.

[0060] Optionally, the terminal device can be deployed on land, including indoor or outdoor, handheld, wearable or vehicle-mounted; can be deployed on the water surface (such as a ship, etc.); and can also be deployed in the air (such as an airplane, a balloon and a satellite, etc.).

[0061] It should be noted that the terminal device can support at least one wireless communication technology, such as a long-term evolution (LTE) system, a new radio (NR) system, a 6G or a next-generation wireless communication technology, etc. For example, the terminal device can be a mobile phone, a pad, a desktop computer, a notebook computer, an all-in-one machine, a vehicle-mounted terminal, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical surgery, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, a cellular phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), a handheld device with wireless communication functions, a computing device or other processing device connected to a wireless modem, a wearable device, a terminal device in a next-generation communication system such as an NR network, a terminal device in a future mobile communication network, or a terminal device in a future evolved public land mobile network (PLMN), etc.

[0062] Further, the terminal device can also include an apparatus with transceiver functions, such as a chip system. The chip system can include a chip and can also include other discrete devices.

[0063] The network device can be a device for communicating with the terminal device, responsible for radio resource management (RRM), quality of service (QoS) management, data compression and encryption, data transmission, etc. on the air interface side. The network device can be a base station (BS) in a communication system or a device deployed in a radio access network (RAN) for providing wireless communication functions. For example, a base transceiver station (BTS) in a Global System for Mobile Communication (GSM) or Code Division Multiple Access (CDMA) communication system, a node B (NB) in a Wideband Code Division Multiple Access (WCDMA) communication system, an evolved node B (eNB or eNodeB) in an LTE communication system, a next generation evolved node B (ng-eNB) in an NR communication system, a next generation node B (gNB) in an NR communication system, a master node (MN) in a dual-link architecture, a secondary node (SN) in a dual-link architecture, etc., without specific limitation.

[0064] Optionally, the network device can also be other devices in the core network (CN), such as an access and mobility management function (AMF), a user plane function (UPF), etc.; it can also be an access point (AP) in a wireless local area network (WLAN), a relay station, a communication device in a future evolved PLMN network, a communication device in a non-terrestrial network (NTN) network, etc.

[0065] Optionally, the network device can include a device with wireless communication function for the terminal device, such as a chip system. For example, the chip system can include a chip and other discrete devices.

[0066] It should be noted that in some network deployments, the network device can be a standalone node to implement all the functions of the above base station, which can include a centralized unit (CU) and a distributed unit (DU), such as gNB-CU and gNB-DU; it can also include an active antenna unit (AAU). Among them, the CU can implement part of the functions of the network device, and the DU can also implement part of the functions of the network device. For example, the CU is responsible for processing non-real-time protocols and services, implementing the functions of the radio resource control (RRC) layer, the service data adaptation protocol (SDAP) layer, and the packet data convergence protocol (PDCP) layer. The DU is responsible for processing the physical layer protocol and real-time service, and implementing the functions of the radio link control (RLC) layer, the medium access control (MAC) layer, and the physical (PHY) layer. In addition, the AAU can implement part of the physical layer processing function, the radio frequency processing function, and the related function of the active antenna. Since the information of the RRC layer will eventually become the information of the PHY layer, or be converted from the information of the PHY layer, in this network deployment, the high-layer signaling (such as the RRC layer signaling) can be considered as being sent by the DU, or being sent by the DU and the AAU together. It can be understood that the network device can include at least one of the CU, the DU, and the AAU. In addition, the CU can be divided into a network device in the radio access network (RAN), or can be divided into a network device in the core network, which is not limited here.

[0067] Optionally, the network device can have a mobile characteristic, for example, the network device can be a mobile device. Optionally, the network device can be a satellite, a balloon station. For example, the satellite can be a low earth orbit (LEO) satellite, a medium earth orbit (MEO) satellite, a geostationary earth orbit (GEO) satellite, a high elliptical orbit (HEO) satellite, etc. Optionally, the network device can also be a base station arranged at a position on land, water, etc. The network device includes the network device 102.

[0068] First, some concepts related to embodiments of the present application are briefly described.

[0069] 1. MMSE channel estimation

[0070] MMSE channel estimation refers to using the correlation of the channel in the time domain, frequency domain, spatial domain, or code domain to calculate the minimum mean square error between the actual value and the estimated value of the channel matrix, so as to reduce noise and improve channel estimation performance.

[0071] The formula of MMSE channel estimation is as follows:

[0072]

[0073] In formula (1), denotes the MMSE channel estimation value matrix; G denotes the MMSE channel estimation coefficient matrix, and G = R hh (R hh + σ 2 I) -1 ; h LS denotes the least square (LS) channel estimation value matrix; R hh denotes the channel autocorrelation matrix; σ 2 denotes the noise power of the LS channel estimation value, and if R hh is a normalized matrix, σ 2 = 1 / SNR, wherein SNR denotes the signal-to-noise ratio of the LS channel estimation value; I denotes a diagonal matrix, and G is the MMSE channel estimation coefficient matrix; (·) -1 denotes the inverse operation of the matrix.

[0074] 2. Multi-dimensional joint MMSE channel estimation

[0075] Multi-dimensional joint MMSE channel estimation refers to joint MMSE channel estimation of multiple different dimensions (for example, time domain and frequency domain).

[0076] For multi-dimensional joint MMSE channel estimation, in the aforementioned formula (1), h LS represents a multi-dimensional joint LS channel estimation value matrix; R hh represents a multi-dimensional joint channel autocorrelation matrix; G represents a multi-dimensional joint MMSE channel estimation coefficient matrix, which has a large dimension.

[0077] The following describes the multi-dimensional joint MMSE channel estimation by taking the time-frequency two-dimensional joint MMSE channel estimation as an example.

[0078] Suppose N tap,t represents the tap number of the time-domain MMSE channel estimation; R hh,t represents a time-domain channel autocorrelation matrix, R hh,t has a dimension of N tap,t ×N tap,t ; N tap,f represents the tap number of the frequency-domain MMSE channel estimation; R hh,f represents a frequency-domain channel autocorrelation matrix, R hh,f has a dimension of N tap,f ×N tap,f , and then the tap number N tap of the time-frequency two-dimensional joint MMSE channel estimation is N tap,t ×N tap,f . The formula of the time-frequency two-dimensional joint MMSE channel estimation is as follows:

[0079]

[0080] In the formula (2), Kr represents a Kronecker product; R hh represents a time-frequency two-dimensional channel autocorrelation matrix, R hh has a dimension of N tap ×N tap ; h LS represents a time-frequency two-dimensional LS channel estimation value matrix, h LS has a dimension of N tap ×1; G represents a time-frequency two-dimensional joint MMSE channel estimation coefficient matrix, G has a dimension of N tap ×N tap ; represents a time-frequency two-dimensional MMSE channel estimation value matrix; σ 2 represents the noise power of the LS channel estimation value; I represents a diagonal matrix; (·) -1 represents the inverse operation of a matrix.

[0081] That is, for the joint MMSE channel estimation in time-frequency two dimensions, the multi-dimensional joint MMSE channel estimation coefficient matrix G (denoted as G unite ) can be expressed as the following formula (3).

[0082]

[0083] In the formula (3), R hh , σ 2 , I, R hh,t and R hh,f have the same physical meanings as those described in the aforementioned formula (2), which will not be repeated here.

[0084] It can be seen that the multi-dimensional joint MMSE channel estimation has high complexity in both the generation of the coefficient matrix G and the operation of the channel estimation process.

[0085] 3. Multi-dimensional step-by-step MMSE channel estimation

[0086] The multi-dimensional step-by-step MMSE channel estimation refers to performing MMSE channel estimation in a certain dimension first, then performing MMSE channel estimation in the next dimension, and so on until MMSE channel estimation in all dimensions is completed.

[0087] The following describes the multi-dimensional step-by-step MMSE channel estimation by taking the time-frequency two-dimensional step-by-step MMSE channel estimation as an example.

[0088] Suppose that the frequency-domain MMSE channel estimation is performed first, and then the time-domain MMSE channel estimation is performed, the calculation formula of the frequency-domain MMSE channel estimation is as follows:

[0089]

[0090] In the formula (4), R hh,f represents the frequency-domain channel autocorrelation matrix, and the dimension of R hh,f is N tap,f × N tap,f , where N tap,f is the tap number of the frequency-domain MMSE channel estimation; G f represents the frequency-domain MMSE channel estimation coefficient matrix, and the dimension of G f is N tap,f × N tap,f ; h LS represents the LS channel estimation value matrix, and the dimension of h LS is N tap,f × N tap,t , where N tap,t is the tap number of the time-domain MMSE channel estimation, and the row of h LS is the frequency-domain dimension, and the column is the time-domain dimension; σ 2represents the noise power of the frequency-domain LS channel estimation value; I f represents the diagonal matrix in the frequency-domain MMSE channel estimation; (·) -1 represents the inverse operation of the matrix; (·) represents the channel estimation value matrix after the frequency-domain MMSE channel estimation, has a dimension of N tap,f ×N tap,t .

[0091] After the channel estimation value matrix after the frequency-domain MMSE channel estimation is calculated, time-domain MMSE channel estimation can be further performed, as shown in the following formula (5):

[0092]

[0093] In formula (5), R hh,t represents the time-domain channel autocorrelation matrix, R hh,t has a dimension of N tap,t ×N tap,t , wherein N tap,t is the tap number of the time-domain MMSE channel estimation; G t represents the time-domain MMSE channel estimation coefficient matrix, G t has a dimension of N tap,t ×N tap,t . represents the channel estimation value matrix after the frequency-domain MMSE channel estimation; σ ′2 is the residual noise power after the frequency-domain MMSE channel estimation, and generally has σ ′2 < σ 2 ; I t represents the diagonal matrix in the time-domain MMSE channel estimation; (·) -1 represents the inverse operation of the matrix; (·) T represents the transposition operation of the matrix.

[0094] The calculated by formula (5) is the channel estimation value matrix after the time-frequency two-dimensional step-by-step MMSE channel estimation, and has a dimension of N tap,f ×N tap,t .

[0095] Optionally, according to the above formula (4) and formula (5), the calculation formula of the time-frequency two-dimensional step-by-step MMSE channel estimation is as follows:

[0096]

[0097] In formula (6), R G f , h LS , and GThe physical meaning of G t may refer to the relevant description in the foregoing formula (4) and formula (5), and will not be described here again.

[0098] Since the dimension of the two-dimensional step-by-step MMSE channel estimation coefficient G f and G t is lower than the dimension of the two-dimensional joint MMSE channel estimation coefficient G, the multi-dimensional step-by-step MMSE channel estimation can reduce the complexity of calculation compared with the multi-dimensional joint MMSE channel estimation method. However, since the multi-dimensional step-by-step MMSE channel estimation is not the optimal channel estimation in multiple dimensions, the multi-dimensional step-by-step MMSE channel estimation will have a certain performance loss compared with the multi-dimensional joint MMSE channel estimation method. Therefore, how to improve the performance of the multi-dimensional step-by-step MMSE channel estimation has become a problem to be solved.

[0099] The channel estimation method provided by the embodiment of the application is described in detail below.

[0100] Please refer to Figure 1 , Figure 1 is a flowchart of a channel estimation method provided by an embodiment of the application. For ease of description, a two-dimensional step-by-step MMSE channel estimation is taken as an example for description. As shown in Figure 1 , the channel estimation method can include but is not limited to the following steps:

[0101] S101, performing minimum mean square error (MMSE) channel estimation based on the product of the first noise power and the first amplitude factor in the first dimension, the first channel autocorrelation matrix in the first dimension, and the first diagonal matrix, to obtain a channel estimation value matrix in the first dimension; wherein the channel estimation value matrix in the first dimension is obtained by performing MMSE channel estimation in the non-last step of the multi-dimensional step-by-step MMSE channel estimation; and the first amplitude factor is a positive number less than or equal to 1.

[0102] , wherein the first noise power is the noise power corresponding to the least square (LS) channel estimation value, or the noise power corresponding to the channel estimation value matrix obtained by the last step of MMSE channel estimation.

[0103] Optionally, if the first dimension is the first step of the multi-dimensional step-by-step MMSE channel estimation, the first noise power is the noise power corresponding to the least square (LS) channel estimation value; if the first dimension is the non-first step of the multi-dimensional step-by-step MMSE channel estimation, the first noise power is the noise power corresponding to the channel estimation value matrix obtained by the last step of MMSE channel estimation.

[0104] In an alternative embodiment, the electronic device performs a minimum mean square error (MMSE) channel estimation based on the product of the first noise power and the first amplitude factor, the first channel autocorrelation matrix of the first dimension, and the first diagonal matrix to obtain a channel estimation value matrix of the first dimension, which can be performed in the following manner: determining a channel estimation coefficient matrix of the first dimension based on the product of the first noise power and the first amplitude factor, the first channel autocorrelation matrix of the first dimension, and the first diagonal matrix; if the first dimension is the first step of the MMSE channel estimation in the multi-dimensional step-by-step MMSE channel estimation, performing a multiplication operation on the channel estimation coefficient matrix of the first dimension and a least square (LS) channel estimation value to obtain the channel estimation value matrix of the first dimension; if the first dimension is a non-first step of the MMSE channel estimation in the multi-dimensional step-by-step MMSE channel estimation, performing a multiplication operation on the channel estimation coefficient matrix of the first dimension and a channel estimation value matrix output by a previous step of the MMSE channel estimation to obtain the channel estimation value matrix of the first dimension.

[0105] In this embodiment, the electronic device determines a channel estimation coefficient matrix of the first dimension based on the product of the first noise power and the first amplitude factor, the first channel autocorrelation matrix of the first dimension, and the first diagonal matrix, which can be performed in the following manner: performing a multiplication operation on the product of the first noise power and the first amplitude factor and the first diagonal matrix of the first dimension to obtain a first matrix; performing an addition operation on the first matrix and the first channel autocorrelation matrix of the first dimension to obtain a second matrix; performing an inverse operation on the second matrix to obtain a third matrix; and performing a multiplication operation on the third matrix and the first channel autocorrelation matrix to obtain the channel estimation coefficient matrix of the first dimension.

[0106] Taking the time-domain and frequency-domain two-dimensional step-by-step MMSE channel estimation as an example, assuming that the electronic device first performs the frequency-domain MMSE channel estimation and then performs the time-domain MMSE channel estimation, the channel estimation value matrix of the first dimension described above is a channel estimation value matrix after the frequency-domain MMSE channel estimation.

[0107] The electronic device can first determine a frequency-domain MMSE channel estimation coefficient matrix G according to the following formula (7) when determining the channel estimation value matrix after the frequency-domain MMSE channel estimation. f .

[0108] G f = R hh,f (R hh,f + scale * σ 2 I f ) -1 (7)

[0109] In formula (7), scale represents an amplitude factor, and scale≤1, where Ntap,t is the number of taps for time-domain MMSE channel estimation; σ 2 denotes the noise power corresponding to the frequency-domain LS channel estimation value; I f denotes the frequency-domain diagonal matrix (corresponding to the aforementioned first diagonal matrix); R hh,f denotes the frequency-domain channel autocorrelation matrix (corresponding to the aforementioned first channel autocorrelation matrix).

[0110] After determining the frequency-domain MMSE channel estimation coefficient matrix G f , the electronic device can determine the channel estimation value matrix after frequency-domain MMSE channel estimation by using the following formula (8)

[0111]

[0112] In formula (6), h LS denotes the frequency-domain LS channel estimation value; wherein G f , R hh, , scale, σ 2 , I f The physical meanings of and can be referred to the descriptions in the aforementioned formula (7), which will not be described herein.

[0113] S102, based on the channel estimation value matrix of the first dimension and the second channel autocorrelation matrix, the second noise power and the second diagonal matrix of the last dimension, MMSE channel estimation is performed to obtain a multi-dimensional channel estimation value matrix.

[0114] The second noise power is the noise power corresponding to the channel estimation value matrix of the first dimension.

[0115] In an optional implementation, the electronic device performs MMSE channel estimation based on the channel estimation value matrix of the first dimension and the second channel autocorrelation matrix, the second noise power and the second diagonal matrix of the last dimension to obtain a multi-dimensional channel estimation value matrix, which can be performed in the following manner: determining the second noise power; determining the channel estimation coefficient matrix of the last dimension based on the second channel autocorrelation matrix, the second noise power and the second diagonal matrix of the last dimension; and obtaining the multi-dimensional channel estimation value matrix based on the channel estimation coefficient matrix of the last dimension and the channel estimation value matrix of the first dimension.

[0116] Optionally, the electronic device determines the second noise power in the following manner: determining a channel estimation coefficient matrix in the first dimension; determining a channel estimation coefficient in the first position in the first dimension based on the channel estimation coefficient matrix in the first dimension; and determining the second noise power based on the channel estimation coefficient in the first position in the first dimension and the first noise power. Optionally, the second noise power can also be referred to as the filtered noise power in the first position in the first dimension. For example, the first position in the first dimension can be a subcarrier index in the frequency domain dimension, or can be an index of a symbol or a time slot in the time domain dimension, or can be a codebook index in the code domain, or an antenna index in the spatial domain, and the like, which are not limited in the present application.

[0117] Taking the time domain and frequency domain two-dimensional step MMSE channel estimation as an example, assuming that the electronic device first performs frequency domain MMSE channel estimation, and then performs time domain MMSE channel estimation, the first dimension is the frequency domain dimension, and the last dimension is the time domain dimension.

[0118] Wherein, when the electronic device performs MMSE channel estimation based on the channel estimation value matrix in the first dimension, the second channel autocorrelation matrix in the last dimension, the second noise power and the second diagonal matrix, the electronic device can first determine the frequency domain dimension MMSE channel estimation coefficient matrix G f using the above formula (7); and then convert G f into the form of a plurality of row vectors, that is, Wherein, g f, represents the channel estimation coefficient of the first position in the frequency domain dimension, such as the frequency domain subcarrier i, and g f, is a row vector of 1xN tap,f . Taking the frequency domain subcarrier i as the first position in the frequency domain dimension as an example, the determination manner of the second noise power (or referred to as the filtered noise power of the frequency domain subcarrier i) and the determination manner of the multi-dimensional channel estimation value matrix are described below.

[0119] Optionally, the electronic device can use the following formula (9) to determine the filtered noise power σ ′2 of the frequency domain subcarrier i based on the channel estimation coefficient g f, of the frequency domain subcarrier i and the first noise power σ 2 used in the frequency domain MMSE channel estimation.

[0120]

[0121] In formula (9), ‖·‖2 represents the two-norm of a vector.

[0122] After the electronic device determines the filtered noise power σ ′2 of the frequency domain subcarrier i, the electronic device can determine the multi-dimensional channel estimation value matrix based on σ′2 , a time domain diagonal matrix I t (corresponding to the aforementioned second diagonal matrix) and a time domain channel autocorrelation matrix R hh, (corresponding to the aforementioned second channel autocorrelation matrix), to determine the time domain MMSE channel estimation coefficient matrix of the frequency domain subcarrier i. For example, the electronic device can determine the time domain MMSE channel estimation coefficient matrix of the frequency domain subcarrier i by using the following formula (10) t .

[0123]

[0124] Optionally, the electronic device obtains a multi-dimensional channel estimation value matrix based on the channel estimation coefficient matrix of the last dimension and the channel estimation value matrix of the first dimension At this time, the following formula (11) can be used.

[0125]

[0126] In formula (11), denotes the channel estimation value matrix after frequency domain MMSE channel estimation, which can be determined by formula (8); G t denotes the time domain MMSE channel estimation coefficient matrix of the frequency domain subcarrier i, which can be determined by formula (10); (·) T denotes the transpose operation of the matrix; (·) -1 denotes the inverse operation of the matrix.

[0127] That is, for the time-frequency two-dimensional step-by-step MMSE channel estimation, the electronic device can determine the channel estimation value matrix after the multi-dimensional step-by-step MMSE channel estimation by using formula (7) to formula (11)

[0128] In an optional embodiment, taking the time domain, frequency domain, and spatial domain three-dimensional MMSE channel estimation as an example, it is assumed that the electronic device first performs frequency domain MMSE channel estimation, then performs time domain MMSE channel estimation, and finally performs spatial domain MMSE channel estimation, the first dimension is the frequency domain dimension or the time domain dimension, and the last dimension is the spatial domain dimension. Correspondingly, the electronic device determines the three-dimensional MMSE channel estimation value matrix in the following manner: based on the product of the first amplitude factor and the noise power corresponding to the least square (LS) channel estimation value, the channel autocorrelation matrix of the frequency domain dimension, and the diagonal matrix of the frequency domain dimension, the channel estimation value matrix of the frequency domain dimension is determined; based on the product of the first amplitude factor and the noise power corresponding to the channel estimation value matrix of the frequency domain dimension, the channel autocorrelation matrix of the time domain dimension, and the diagonal matrix of the time domain dimension, the channel estimation value matrix of the time domain dimension is determined; based on the channel estimation value matrix of the time domain dimension and the second channel autocorrelation matrix, the second noise power, and the second diagonal matrix of the spatial domain dimension, MMSE channel estimation is performed to obtain the multi-dimensional channel estimation value matrix. Optionally, the first amplitude factor used when determining the channel estimation value matrix of the frequency domain dimension and the first amplitude factor used when determining the channel estimation value matrix of the time domain dimension can be different values.

[0129] It can be seen that, in the embodiments of the present application, the first noise power used in the non-last step of the multi-dimensional step-by-step MMSE channel estimation is multiplied by an amplitude factor, wherein the amplitude factor is a positive number less than or equal to 1, so that, in the process of multi-dimensional step-by-step MMSE channel estimation, different first amplitude factors can be used to obtain the channel estimation value matrix after multi-dimensional step-by-step MMSE channel estimation, so as to improve the performance of multi-dimensional step-by-step MMSE channel estimation.

[0130] In an optional embodiment, the electronic device can also first determine a multi-dimensional step-by-step MMSE channel estimation coefficient matrix, which is used to determine the multi-dimensional channel estimation value matrix in combination with the LS channel estimation value. That is, the electronic device can determine the channel estimation value matrix after multi-dimensional step-by-step MMSE channel estimation based on the multi-dimensional step-by-step MMSE channel estimation coefficient matrix and the multi-dimensional LS channel estimation value.

[0131] Optionally, taking the time domain and frequency domain two-dimensional step-by-step MMSE channel estimation as an example, the electronic device can determine the multi-dimensional step-by-step MMSE channel estimation coefficient matrix G (denoted as G step ) in the following formula (12).

[0132]

[0133] In formula (10), G fdenotes a frequency-domain MMSE channel estimation coefficient matrix, which can be obtained by the aforementioned formula (7); G t denotes a time-domain MMSE channel estimation coefficient matrix, which can be obtained by the aforementioned formula (10).

[0134] In combination with formula (12), the electronic device determines the channel estimation value matrix after multi-dimensional step-by-step MMSE channel estimation based on the multi-dimensional step-by-step MMSE channel estimation coefficient matrix and the multi-dimensional LS channel estimation value matrix , which can adopt the following formula (13).

[0135]

[0136] In formula (13), h LS denotes a time-frequency two-dimensional LS channel estimation value matrix, h LS has a dimension of N tap × 1, where N tap = N tap,t × N tap,f , N tap,t is a tap number of time-domain MMSE channel estimation, and N tap,f is a tap number of frequency-domain MMSE channel estimation.

[0137] In an optional implementation, Figure 1 In the channel estimation method shown in the figure, the first amplitude factor is determined from an amplitude factor table (or referred to as a scale factor table), which stores amplitude factors corresponding to the signal-to-noise ratio SNR of each LS channel estimation value, the channel autocorrelation matrix of each dimension, and the tap number used for channel estimation. That is, the amplitude factor scale is related to the signal-to-noise ratio SNR of the LS channel estimation value, the channel autocorrelation matrix of each dimension, and the tap number used for MMSE channel estimation of each dimension. Optionally, the tap number used for channel estimation of each dimension can be the same or different, which is not limited here. Optionally, the amplitude factors corresponding to the signal-to-noise ratio SNR of each LS channel estimation value, the channel autocorrelation matrix, and the tap number used for channel estimation in the amplitude factor table can include but are not limited to the following implementation 1.1 to implementation 1.3.

[0138] Implementation 1.1, the amplitude factors corresponding to the signal-to-noise ratio SNR of each LS channel estimation value, the channel autocorrelation matrix, and the tap number used for channel estimation in the amplitude factor table are obtained by using a simulation traversal method.

[0139] In the embodiment, the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of each LS channel estimation value in the amplitude factor table is obtained by the following steps: in the case that the SNR, the channel autocorrelation matrix and the tap number of the channel estimation of the LS channel estimation value are unchanged, simulation is performed by using a plurality of different amplitude factors to obtain simulation results corresponding to each amplitude factor respectively; from the simulation results corresponding to each amplitude factor respectively, a channel estimation value matrix with the optimal simulation performance is selected; and the amplitude factor corresponding to the selected channel estimation value matrix is determined as the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of the channel estimation of the LS channel estimation value.

[0140] That is, the electronic device can fix a certain SNR, a certain channel autocorrelation matrix and a certain tap number, traverse a plurality of different amplitude factors (or scale factors) to perform simulation, and the amplitude factor with the best simulation performance is the amplitude factor corresponding to the fixed SNR, the channel autocorrelation matrix and the tap number.

[0141] Optionally, the simulation result corresponding to each amplitude factor can be a BLER result, a system throughput result, or a mean squared error (MSE), and the like, which is not limited here.

[0142] Optionally, the amplitude factor table can be obtained by the electronic device traversing a plurality of different SNRs, channel correlations of each dimension, tap numbers of channel estimation of each dimension, and obtaining the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of the channel estimation of each LS channel estimation value by using the embodiment 1.1.

[0143] In the embodiment 1.2, the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of each LS channel estimation value in the amplitude factor table is obtained by using a weight correlation method.

[0144] In the amplitude factor table, the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of each LS channel estimation value is obtained by the following steps: under the condition that the SNR, the channel autocorrelation matrix and the tap number of the LS channel estimation value are fixed, a plurality of different amplitude factors are used to determine a plurality of multi-dimensional step-by-step MMSE channel estimation coefficient matrices corresponding to each amplitude factor respectively; the multi-dimensional step-by-step MMSE channel estimation coefficient matrices are used to determine a multi-dimensional channel estimation value matrix in combination with the LS channel estimation value matrix; from the multi-dimensional step-by-step MMSE channel estimation coefficient matrices corresponding to each amplitude factor respectively, a multi-dimensional step-by-step MMSE channel estimation coefficient matrix having the maximum normalized correlation with the multi-dimensional joint MMSE channel estimation coefficient matrix is selected; and the amplitude factor corresponding to the selected multi-dimensional step-by-step MMSE channel estimation coefficient matrix is determined as the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of the LS channel estimation value.

[0145] That is, the electronic device can fix a certain SNR, a certain channel autocorrelation matrix and a certain tap number, traverse a plurality of different amplitude factors, and select an amplitude factor having the maximum normalized correlation between the multi-dimensional step-by-step channel estimation coefficient matrix and the multi-dimensional joint channel estimation coefficient matrix as the amplitude factor corresponding to the fixed SNR, the channel autocorrelation matrix and the tap number.

[0146] In the embodiment, the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of each LS channel estimation value in the amplitude factor table is obtained by the following steps: when the SNR, the channel autocorrelation matrix and the tap number of the LS channel estimation value are fixed, a plurality of different amplitude factors are used to determine a plurality of multi-dimensional step-by-step MMSE channel estimation coefficient matrices corresponding to each amplitude factor respectively; the multi-dimensional step-by-step MMSE channel estimation coefficient matrix is used to determine a multi-dimensional channel estimation value matrix in combination with the LS channel estimation value matrix; a multi-dimensional step-by-step MMSE channel estimation coefficient matrix with the minimum first value in the multi-dimensional joint MMSE channel estimation coefficient matrix is selected from the multi-dimensional step-by-step MMSE channel estimation coefficient matrices corresponding to each amplitude factor respectively; wherein the first value is obtained by summing the modulus of the difference between each element in the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor respectively and the element at the corresponding position in the multi-dimensional joint MMSE channel estimation coefficient matrix, or the first value is obtained by summing the square of the modulus of the difference between each element in the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor respectively and the element at the corresponding position in the multi-dimensional joint MMSE channel estimation coefficient matrix; the amplitude factor corresponding to the selected multi-dimensional step-by-step MMSE channel estimation coefficient matrix is determined as the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of the LS channel estimation value.

[0147] That is, the electronic device can fix a certain SNR, a certain channel autocorrelation matrix and a certain tap number, and traverse a plurality of different amplitude factors, wherein the amplitude factor that makes the sum of the modulus of the difference between each element in the multi-dimensional step-by-step channel estimation coefficient matrix and the element at the corresponding position in the multi-dimensional joint channel estimation coefficient matrix minimum, or the amplitude factor that makes the sum of the square of the modulus of the difference between each element in the multi-dimensional step-by-step channel estimation coefficient matrix and the element at the corresponding position in the multi-dimensional joint channel estimation coefficient matrix minimum, is the amplitude factor corresponding to the fixed SNR, the channel autocorrelation matrix and the tap number.

[0148] Optionally, the electronic device can determine the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor in the following manner: determining the channel estimation coefficient matrix of the first dimension corresponding to each amplitude factor, based on the SNR, the channel autocorrelation matrix, and the number of taps used for channel estimation of the LS channel estimation value; determining the channel estimation coefficient matrix of the last dimension corresponding to each amplitude factor, based on the channel estimation coefficient matrix of the first dimension corresponding to each amplitude factor; and obtaining the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor, based on the channel estimation coefficient matrix of the first dimension and the channel estimation coefficient matrix of the last dimension corresponding to each amplitude factor.

[0149] For example, the electronic device can determine the channel estimation coefficient matrix of the first dimension corresponding to each amplitude factor by using the aforementioned formula (7). Optionally, the electronic device can determine the channel estimation coefficient matrix of the last dimension corresponding to each amplitude factor by using the aforementioned formulas (9) and (10). Optionally, the electronic device can determine the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor by using the aforementioned formula (12).

[0150] Optionally, the electronic device can determine the multi-dimensional joint channel estimation coefficient matrix by using the aforementioned formula (3).

[0151] Optionally, the amplitude factor table can be obtained by the electronic device traversing a plurality of different SNRs, channel correlations of each dimension, and numbers of taps used for channel estimation of each dimension, and then obtaining the amplitude factor corresponding to the SNR, the channel autocorrelation matrix, and the number of taps used for channel estimation of each LS channel estimation value by using the implementation 1.2. In the implementation 1.3, the amplitude factor corresponding to the SNR, the channel autocorrelation matrix, and the number of taps used for channel estimation of each LS channel estimation value in the amplitude factor table is obtained by using the mean squared error (MSE) minimization method.

[0152] In the embodiment, the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of each LS channel estimation value in the amplitude factor table is obtained by the following steps: when the SNR, the channel autocorrelation matrix and the tap number of the channel estimation of the LS channel estimation value are fixed, a plurality of different amplitude factors are used to determine a plurality of multi-dimensional channel estimation value matrices respectively corresponding to each amplitude factor; based on the plurality of multi-dimensional channel estimation value matrices respectively corresponding to each amplitude factor and the channel matrix, the average mean square error (MSE) respectively corresponding to each amplitude factor is determined; and the amplitude factor corresponding to the minimum MSE is selected from the average mean square error (MSE) respectively corresponding to each amplitude factor as the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of the LS channel estimation value.

[0153] That is, the electronic device can fix a certain SNR, a certain channel autocorrelation matrix and a certain tap number, traverse a plurality of different amplitude factors, and select the amplitude factor corresponding to the minimum channel estimation statistical MSE value as the amplitude factor corresponding to the fixed SNR, channel autocorrelation matrix and tap number.

[0154] Optionally, the electronic device can determine the channel estimation statistical MSE value by using the following formula (14).

[0155]

[0156] In formula (14), h represents the channel matrix; represents the multi-dimensional channel estimation value matrix. Optionally, It can be obtained by the foregoing formula (11) or the foregoing formula (13), which is not limited here.

[0157] In the following, the electronic device obtains the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of the LS channel estimation value by using the foregoing formula (13). That is, The formula (14) is expanded, and the following formula (15) can be obtained.

[0158]

[0159] Let Then Wherein, n represents noise, E{nn H}=σ 2 I. In this case, the formula (15) can be written as the following formula (16).

[0160]

[0161] In formula (16), real(·) represents the real part operation.

[0162] That is, the electronic device can fix a certain SNR, a certain channel autocorrelation matrix, and a certain tap number, traverse a plurality of different amplitude factors, and obtain a plurality of G step Then, the channel estimation statistical MSE value is calculated based on the above formula (16), wherein the amplitude factor that makes the value of formula (16) minimum is the amplitude factor corresponding to the fixed SNR, channel autocorrelation matrix, and tap number.

[0163] Further, the electronic device can eliminate the term in formula (16) that is irrelevant to G step In formula (16), the first term on the right side of the equation, tr[R hh ] is irrelevant to G step R hh H + G step (R hh + σ 2 I)G step H ] is irrelevant to G The amplitude factor that makes the value of formula (16) minimum is the amplitude factor corresponding to the fixed SNR, channel autocorrelation matrix, and tap number.

[0164] Optionally, the amplitude factor table can be obtained by the electronic device traversing a plurality of different SNRs, channel correlations in each dimension, tap numbers used for channel estimation in each dimension, and obtaining the amplitude factor corresponding to the SNR, channel autocorrelation matrix, and tap number used for channel estimation of each LS channel estimation value by using the embodiment 1.3.

[0165] To verify the reliability of the channel estimation method provided in the embodiments of the present application, the following comparative experiments are performed by using the channel estimation method provided in the embodiments of the present application.

[0166] Please refer to Figure 2a , Figure 2a FIG. 1 is a comparison result diagram of time-frequency two-dimensional weight amplitudes obtained by using different channel estimation methods. In the process of obtaining the step-by-step weight by using the channel estimation method provided in the embodiments of the present application, the amplitude factor (scale value) used is 0.25. As shown in FIG. 1, in the case that the tap number used in the frequency domain MMSE channel estimation is 24, the tap number used in the time domain MMSE channel estimation is 4, the SNR is 10 dB, the frequency fd is 10 Hz, and the channel model of the link level simulation is a Tapped Delay Line-C (TDL-C) channel model, the step-by-step weight after using the multi-dimensional step-by-step channel estimation method provided in the embodiments of the present application is more consistent with the two-dimensional joint weight. Figure 2a Please refer toFigure 2b , Figure 2b is another comparison result diagram of time-frequency two-dimensional weight amplitude obtained by different channel estimation methods provided by the embodiment of the present application. In the process of obtaining the step-by-step weight by using the channel estimation method provided by the present application, the scale value is 0.25. As shown in Figure 2b , in the case of 24 taps for frequency domain MMSE channel estimation, 4 taps for time domain MMSE channel estimation, SNR of 20 dB, frequency fd of 10 Hz, and TDL-C channel model, the step-by-step weight and the two-dimensional joint weight obtained by using the multi-dimensional step-by-step channel estimation method provided by the present application are more consistent.

[0168] Please refer to Figure 3a , Figure 3a is a simulation result diagram of MSE obtained by different channel estimation methods provided by the embodiment of the present application. As shown in Figure 3a , in the case of 24 taps for frequency domain MMSE channel estimation, 4 taps for time domain MMSE channel estimation, frequency of 10 Hz, and TDL-A channel model, the MSE value obtained by using the channel estimation method provided by the present application is smaller than the MSE value obtained by using the original multi-dimensional step-by-step MMSE channel estimation, and the MSE value obtained by using the channel estimation method provided by the present application is closer to the MSE value obtained by using the two-dimensional joint MMSE channel estimation method.

[0169] Please refer to Figure 3b , Figure 3b is another simulation result diagram of MSE obtained by different channel estimation methods provided by the embodiment of the present application. As shown in Figure 3b , in the case of 24 taps for frequency domain MMSE channel estimation, 4 taps for time domain MMSE channel estimation, frequency of 10 Hz, and TDL-B channel model, the MSE value obtained by using the channel estimation method provided by the present application is smaller than the MSE value obtained by using the original multi-dimensional step-by-step MMSE channel estimation, and the MSE value obtained by using the channel estimation method provided by the present application is closer to the MSE value obtained by using the two-dimensional joint MMSE channel estimation method.

[0170] Please refer to Figure 3c , Figure 3c is still another simulation result diagram of MSE obtained by different channel estimation methods provided by the embodiment of the present application. As shown in Figure 3cAs shown in the table, in the case that the number of taps adopted by the frequency domain MMSE channel estimation is 24, the number of taps adopted by the time domain MMSE channel estimation is 4, the frequency is 10 Hz, and the channel model is the TDL-C channel model, the value of the MSE obtained by using the channel estimation method provided in the present application is smaller than the value of the MSE obtained by using the original multi-dimensional step-by-step MMSE channel estimation, and the value of the MSE obtained by using the channel estimation method provided in the present application is closer to the value of the MSE obtained by using the two-dimensional joint MMSE channel estimation method.

[0171] In summary, the channel estimation method provided in the embodiments of the present application can improve the performance of the multi-dimensional step-by-step MMSE channel estimation and reduce the performance loss thereof relative to the multi-dimensional joint MMSE channel estimation.

[0172] Please refer to Figure 4 , Figure 4 is a structural schematic diagram of a channel estimation device provided in an embodiment of the present application. As shown in the figure, the channel estimation device can include a determination unit 401 and an acquisition unit 402. Figure 4

[0173] The determination unit 401 is configured to perform minimum mean square error (MMSE) channel estimation based on the product of the first noise power and the first amplitude factor of the first dimension, the first channel autocorrelation matrix of the first dimension, and the first diagonal matrix, to obtain a channel estimation value matrix of the first dimension.

[0174] The channel estimation value matrix of the first dimension is obtained by performing MMSE channel estimation in a non-last step in the multi-dimensional step-by-step MMSE channel estimation.

[0175] The first noise power is the noise power corresponding to the least square (LS) channel estimation value, or the noise power corresponding to the channel estimation value matrix obtained by the last step of MMSE channel estimation, and the first amplitude factor is a positive number less than or equal to 1.

[0176] The acquisition unit 402 is configured to perform MMSE channel estimation based on the channel estimation value matrix of the first dimension and the second channel autocorrelation matrix of the last dimension, the second noise power, and the second diagonal matrix, to obtain a multi-dimensional channel estimation value matrix; the second noise power is the noise power corresponding to the channel estimation value matrix of the first dimension.

[0177] In an optional implementation, the first amplitude factor is determined from an amplitude factor table, and the amplitude factor table stores the amplitude factor corresponding to the signal-to-noise ratio (SNR) of each LS channel estimation value, the channel autocorrelation matrix, and the number of taps adopted for channel estimation.

[0178] ​In an optional implementation, the determining unit 401 is further configured to determine the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the number of taps of each LS channel estimation value in the amplitude factor table.

[0179] In this implementation, when determining the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the number of taps of each LS channel estimation value in the amplitude factor table, the determining unit 401 is specifically configured to: perform simulation by using multiple different amplitude factors under the condition that the SNR, the channel autocorrelation matrix and the number of taps of the LS channel estimation value remain unchanged, to obtain simulation results corresponding to each amplitude factor respectively; select a channel estimation value matrix with optimal simulation performance from the simulation results corresponding to each amplitude factor respectively; and determine the amplitude factor corresponding to the selected channel estimation value matrix as the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the number of taps of the LS channel estimation value.

[0180] In this implementation, when determining the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the number of taps of each LS channel estimation value in the amplitude factor table, the determining unit 401 is specifically configured to: determine a multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor by using multiple different amplitude factors under the condition that the SNR, the channel autocorrelation matrix and the number of taps of the LS channel estimation value remain unchanged; the multi-dimensional step-by-step MMSE channel estimation coefficient matrix is used to determine a multi-dimensional channel estimation value matrix in combination with a LS channel estimation value matrix; select a multi-dimensional step-by-step MMSE channel estimation coefficient matrix with maximum normalized correlation with the multi-dimensional joint MMSE channel estimation coefficient matrix from the multi-dimensional step-by-step MMSE channel estimation coefficient matrices corresponding to each amplitude factor respectively; and determine the amplitude factor corresponding to the selected multi-dimensional step-by-step MMSE channel estimation coefficient matrix as the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the number of taps of the LS channel estimation value.

[0181] In this embodiment, the determining unit 401 is specifically configured to: in the case that the SNR of the LS channel estimation value, the channel autocorrelation matrix, and the tap number of the channel estimation are unchanged, determine a multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor by using a plurality of different amplitude factors; the multi-dimensional step-by-step MMSE channel estimation coefficient matrix is used to determine a multi-dimensional channel estimation value matrix in combination with the LS channel estimation value matrix; select a multi-dimensional step-by-step MMSE channel estimation coefficient matrix with the minimum first value of the multi-dimensional joint MMSE channel estimation coefficient matrix from the multi-dimensional step-by-step MMSE channel estimation coefficient matrices corresponding to each amplitude factor; the first value is obtained by summing the modulus of the difference between each element in the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor and the element at the corresponding position in the multi-dimensional joint MMSE channel estimation coefficient matrix, or the first value is obtained by summing the square of the modulus of the difference between each element in the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor and the element at the corresponding position in the multi-dimensional joint MMSE channel estimation coefficient matrix; and determine the amplitude factor corresponding to the selected multi-dimensional step-by-step MMSE channel estimation coefficient matrix as the amplitude factor corresponding to the SNR of the LS channel estimation value, the channel autocorrelation matrix, and the tap number of the channel estimation.

[0182] Optionally, in the case that the SNR of the LS channel estimation value, the channel autocorrelation matrix, and the tap number of the channel estimation are unchanged, determining a multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor by using a plurality of different amplitude factors includes: in the case that the SNR of the LS channel estimation value, the channel autocorrelation matrix, and the tap number of the channel estimation are unchanged, determining a first-dimension channel estimation coefficient matrix corresponding to each amplitude factor by using a plurality of different amplitude factors; determining a last-dimension channel estimation coefficient matrix corresponding to each amplitude factor based on the first-dimension channel estimation coefficient matrix corresponding to each amplitude factor; and obtaining a multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor based on the first-dimension channel estimation coefficient matrix and the last-dimension channel estimation coefficient matrix corresponding to each amplitude factor.

[0183] In this embodiment, the determining unit 401 is specifically configured to: when the signal-to-noise ratio SNR of the LS channel estimation value, the channel autocorrelation matrix, and the number of taps used for channel estimation are constant, determine, by using a plurality of different amplitude factors, a plurality of multi-dimensional channel estimation value matrices respectively corresponding to each amplitude factor; determine, based on the plurality of multi-dimensional channel estimation value matrices respectively corresponding to each amplitude factor and the channel matrix, an average mean square error MSE respectively corresponding to each amplitude factor; and select, from the average mean square errors MSE respectively corresponding to each amplitude factor, an amplitude factor corresponding to a minimum MSE as the amplitude factor corresponding to the signal-to-noise ratio SNR of the LS channel estimation value, the channel autocorrelation matrix, and the number of taps used for channel estimation.

[0184] It can be understood that the specific implementation of each unit in the channel estimation device provided by the embodiments of the present application and the beneficial effects that can be achieved can refer to the description of the foregoing related channel estimation method embodiments, which will not be repeated here.

[0185] Please refer to Figure 5 , Figure 5 is a structural schematic diagram of an electronic device provided by the embodiments of the present application. It includes a processor 501, a memory 502, and a communication bus for connecting the processor 501 and the memory 502.

[0186] The channel estimation device can further include a communication interface, which can be used to receive and send data.

[0187] The memory 502 includes, but is not limited to, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), or a compact disc read-only memory (CD-ROM). The memory 502 is used to store the executed program code and the transmitted data.

[0188] The processor 501 can be one or more central processing units (CPUs), which can be a single core processor or a multiple core processor in the case of the processor 501 being a CPU. The processor can also be other general purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, etc. The general purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0189] In an optional embodiment, the processor 501 can be configured to execute the computer programs or instructions 503 stored in the memory 502 to perform the operations of the electronic device in the channel estimation method described above, for example:

[0190] performing minimum mean square error (MMSE) channel estimation based on the product of the first noise power and the first amplitude factor, the first channel autocorrelation matrix of the first dimension, and the first diagonal matrix to obtain a channel estimation value matrix of the first dimension;

[0191] The channel estimation value matrix of the first dimension is obtained by performing MMSE channel estimation in the non-last step of the multi-dimensional step-by-step MMSE channel estimation;

[0192] The first noise power is the noise power corresponding to the least square (LS) channel estimation value, or the noise power corresponding to the channel estimation value matrix obtained by the last step of MMSE channel estimation, and the first amplitude factor is a positive number less than or equal to 1.

[0193] performing MMSE channel estimation based on the channel estimation value matrix of the first dimension and the second channel autocorrelation matrix of the last dimension, the second noise power and the second diagonal matrix to obtain a multi-dimensional channel estimation value matrix; the second noise power is the noise power corresponding to the channel estimation value matrix of the first dimension.

[0194] In an optional embodiment, the first amplitude factor is determined from an amplitude factor table, and the amplitude factor table stores the amplitude factors corresponding to the signal-to-noise ratio (SNR) of each LS channel estimation value, the channel autocorrelation matrix and the number of taps used for channel estimation.

[0195] In an alternative embodiment, the processor 501 further performs: determining the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the number of taps of each LS channel estimation value in the amplitude factor table.

[0196] In this embodiment, when performing the determining of the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the number of taps of each LS channel estimation value in the amplitude factor table, the processor 501 specifically performs: simulating with a plurality of different amplitude factors to obtain a simulation result corresponding to each amplitude factor, under the condition that the SNR, the channel autocorrelation matrix and the number of taps of the LS channel estimation value are unchanged; selecting a channel estimation value matrix with the optimal simulation performance from the simulation result corresponding to each amplitude factor; and determining the amplitude factor corresponding to the selected channel estimation value matrix as the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the number of taps of the LS channel estimation value.

[0197] In this embodiment, when performing the determining of the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the number of taps of each LS channel estimation value in the amplitude factor table, the processor 501 specifically performs: determining a multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor with a plurality of different amplitude factors, under the condition that the SNR, the channel autocorrelation matrix and the number of taps of the LS channel estimation value are unchanged; the multi-dimensional step-by-step MMSE channel estimation coefficient matrix being used to determine a multi-dimensional channel estimation value matrix in combination with a LS channel estimation value matrix; selecting a multi-dimensional step-by-step MMSE channel estimation coefficient matrix with the largest normalized correlation with the multi-dimensional joint MMSE channel estimation coefficient matrix from the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor; and determining the amplitude factor corresponding to the selected multi-dimensional step-by-step MMSE channel estimation coefficient matrix as the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the number of taps of the LS channel estimation value.

[0198] In the embodiment, when the processor 501 determines the amplitude factor corresponding to the SNR of the LS channel estimation value, the channel autocorrelation matrix and the tap number of the channel estimation, the processor 501 specifically performs: determining, by using a plurality of different amplitude factors, a multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor, under the condition that the SNR of the LS channel estimation value, the channel autocorrelation matrix and the tap number of the channel estimation are unchanged; using the multi-dimensional step-by-step MMSE channel estimation coefficient matrix to determine a multi-dimensional channel estimation value matrix in combination with the LS channel estimation value matrix; selecting, from the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor, a multi-dimensional step-by-step MMSE channel estimation coefficient matrix with the minimum first value of the multi-dimensional joint MMSE channel estimation coefficient matrix; wherein the first value is obtained by summing the modulus of the difference between each element in the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor and the element at the corresponding position in the multi-dimensional joint MMSE channel estimation coefficient matrix, or the first value is obtained by summing the square of the modulus of the difference between each element in the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor and the element at the corresponding position in the multi-dimensional joint MMSE channel estimation coefficient matrix; and determining the amplitude factor corresponding to the selected multi-dimensional step-by-step MMSE channel estimation coefficient matrix as the amplitude factor corresponding to the SNR of the LS channel estimation value, the channel autocorrelation matrix and the tap number of the channel estimation.

[0199] Optionally, the determining, by using a plurality of different amplitude factors, a multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor under the condition that the SNR of the LS channel estimation value, the channel autocorrelation matrix and the tap number of the channel estimation are unchanged, includes: determining, by using a plurality of different amplitude factors, a first-dimensional channel estimation coefficient matrix corresponding to each amplitude factor under the condition that the SNR of the LS channel estimation value, the channel autocorrelation matrix and the tap number of the channel estimation are unchanged; determining a last-dimensional channel estimation coefficient matrix corresponding to each amplitude factor based on the first-dimensional channel estimation coefficient matrix corresponding to each amplitude factor; and obtaining the multi-dimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor based on the first-dimensional channel estimation coefficient matrix and the last-dimensional channel estimation coefficient matrix corresponding to each amplitude factor.

[0200] In this embodiment, the processor 501 determines the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of the LS channel estimation value in the following manner. When the SNR, the channel autocorrelation matrix and the tap number of the LS channel estimation value remain unchanged, the processor 501 determines a multi-dimensional channel estimation value matrix corresponding to each amplitude factor by using multiple different amplitude factors. The processor 501 determines an average mean square error (MSE) corresponding to each amplitude factor based on the multi-dimensional channel estimation value matrix corresponding to each amplitude factor and the channel matrix. The processor 501 selects the amplitude factor corresponding to the minimum MSE from the average MSEs corresponding to each amplitude factor as the amplitude factor corresponding to the SNR, the channel autocorrelation matrix and the tap number of the LS channel estimation value.

[0201] It can be understood that the specific implementation of the processor 501 and the beneficial effects that can be achieved can refer to the description of the foregoing related channel estimation method embodiments, which will not be described here.

[0202] The embodiments of the present application also provide a chip, which comprises a processor, a memory and a computer program or instructions stored in the memory, wherein the processor executes the computer program or instructions to implement the steps described in the foregoing method embodiments.

[0203] The embodiments of the present application also provide a chip module, which comprises a transceiver assembly and a chip, and the chip comprises a processor, a memory and a computer program or instructions stored in the memory, wherein the processor executes the computer program or instructions to implement the steps described in the foregoing method embodiments.

[0204] The embodiments of the present application also provide a computer readable storage medium, wherein the computer storage medium stores a computer program or instructions for signal processing, and the computer program or instructions are executed to cause the computer to implement some or all of the steps described in any of the foregoing method embodiments.

[0205] The embodiments of the present application also provide a computer program product, wherein the computer program product comprises a non-transitory computer readable storage medium storing a computer program or instructions, and the computer program or instructions are executed to implement some or all of the steps described in any of the foregoing method embodiments. The computer program product or instructions can be a software installation package.

[0206] It should be noted that, for the foregoing method embodiments, the sequences of the described actions are not necessarily required to achieve the objects of the application, and certain acts can be performed in other sequences, or even at the same time. Additionally, well known steps that are not specifically described can be omitted, or can be performed by methods known or preferred by one of ordinary skill in the art. Further, the described embodiments are not intended to be limited to the specific embodiments described, but rather, are meant to be limited only by the claims.

[0207] In the above embodiments, the description of each embodiment of the present application has its own focus, and any number of embodiments can be used in combination. Portions not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0208] In several embodiments provided by the present application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely schematic. For example, the division of the above units is merely a logical function division. In actual implementation, another division manner can be adopted. For example, a plurality of units or components can be combined or integrated into another system, or some features can be omitted or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical or other forms.

[0209] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. can be located in one place, or can be distributed on a plurality of network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiments of the present application.

[0210] In addition, each of the function units in each of the embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware or in the form of a software function unit. That is, each of the devices, products containing units / modules described in the above embodiments can be a software unit / module, or a hardware unit / module, or part of a software unit / module and part of a hardware unit / module. For example, for each device, product containing units / modules of an application or integrated chip, each unit / module can be implemented in the form of a circuit or other hardware, or at least part of the units / modules can be implemented in the form of a software program, which runs in an integrated processor inside the chip, and the remaining part of the units / modules can be implemented in the form of a circuit or other hardware; for each device, product containing units / modules of an application or integrated chip module, each unit / module can be implemented in the form of a circuit or other hardware, and different units / modules can be located in the same component (such as a chip, a circuit unit, etc.) or in different components of the chip module, at least part of the units / modules can be implemented in the form of a software program, which runs in an integrated processor inside the chip module, and the remaining part of the units / modules can be implemented in the form of a circuit or other hardware; for each device, product containing units / modules of an application or integrated terminal, each unit / module can be implemented in the form of a circuit or other hardware, and different units / modules can be located in the same component (such as a chip, a circuit unit, etc.) or in different components of the terminal, or at least part of the units / modules can be implemented in the form of a software program, which runs in an integrated processor inside the terminal, and the remaining part of the units / modules can be implemented in the form of a circuit or other hardware.

[0211] The steps of the methods or algorithms described in the embodiments of the present application can be implemented in hardware, or be implemented by a processor executing software instructions. The software instructions can be composed of corresponding software elements, and the software elements can be stored in a U disk, a random access memory (RAM), a flash memory, a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically EPROM (EEPROM), a register, a hard disk, a mobile hard disk, a magnetic disk, a compact disk read-only memory (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to a processor, so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a network device. Of course, the processor and the storage medium can also exist as discrete components in the terminal device or the network device.

[0212] When the above-mentioned integrated units are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application, essentially or say the part that contributes to the prior art, or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a TRP, etc.) to execute all or part of the steps of the embodiments of the present application.

[0213] Those skilled in the art should understand that, in one or more examples described above, the functions described in the embodiments of the present application can be implemented entirely or partially by software, hardware, firmware or any combination thereof. When the integrated units described above are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or the whole or part of the technical solutions can be implemented in the form of computer software products. The computer software product is stored in a memory and includes one or more computer instructions for causing a computer device (which can be a personal computer, a server or a TRP, etc.) to perform all or part of the steps of the method of each embodiment of the present application. The computer device can also be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. 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, data center, etc. integrated with one or more available media. The available media can be magnetic media (such as floppy disk, hard disk, magnetic tape), optical media (such as digital video disc (DVD)) or semiconductor media (such as solid state disk (SSD)) and the like.

[0214] The embodiments of the present application are described in detail above, and the principles and implementation manners of the present application are described by applying specific examples. The above description of the embodiments is only used to help understand the method of the present application and its core idea, and does not limit the protection scope of the embodiments of the present application. Meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range can be changed, and the above description of the present application should not be understood as a limitation. That is, the above description is only a specific implementation manner of the embodiments of the present application, and does not limit the protection scope of the embodiments of the present application. Any modification, equivalent replacement, improvement, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims

1. A channel estimation method, characterized in that, The method includes: Based on the product between the first noise power and the first amplitude factor in the first dimension, the first channel autocorrelation matrix in the first dimension, and the first diagonal matrix, minimum mean square error (MMSE) channel estimation is performed to obtain the channel estimation matrix in the first dimension. The channel estimation matrix of the first dimension is obtained by performing MMSE channel estimation in a multi-dimensional step-by-step process, which is not the last step. The first noise power is the noise power corresponding to the least squares LS channel estimate, or the noise power corresponding to the channel estimate matrix obtained in the previous MMSE channel estimation step, and the first amplitude factor is a positive number less than 1. Based on the channel estimation matrix of the first dimension and the second channel autocorrelation matrix, the second noise power and the second diagonal matrix of the last dimension, MMSE channel estimation is performed to obtain a multidimensional channel estimation matrix. The second noise power is the noise power corresponding to the channel estimation matrix of the first dimension.

2. The method according to claim 1, characterized in that, The first amplitude factor is determined from the amplitude factor table, which stores the signal-to-noise ratio (SNR), channel autocorrelation matrix, and amplitude factor corresponding to the number of taps used in the channel estimation for each LS channel estimate.

3. The method according to claim 2, characterized in that, In the amplitude factor table, the signal-to-noise ratio (SNR), channel autocorrelation matrix, and number of taps used in the channel estimation for each LS channel estimate are obtained through the following steps: With the signal-to-noise ratio (SNR), channel autocorrelation matrix, and number of taps used in the LS channel estimation unchanged, simulations were performed using multiple different amplitude factors to obtain simulation results for each amplitude factor. Select the channel estimation matrix with the best simulation performance from the simulation results corresponding to each amplitude factor; The amplitude factor corresponding to the selected channel estimation matrix is ​​determined as the amplitude factor corresponding to the signal-to-noise ratio (SNR), channel autocorrelation matrix, and number of taps used in the channel estimation of the LS channel estimation.

4. The method according to claim 2, characterized in that, In the amplitude factor table, the signal-to-noise ratio (SNR), channel autocorrelation matrix, and number of taps used in the channel estimation for each LS channel estimate are obtained through the following steps: With the signal-to-noise ratio (SNR), channel autocorrelation matrix, and number of taps used in the LS channel estimate unchanged, the multidimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor is determined by using multiple different amplitude factors. The multidimensional step-by-step MMSE channel estimation coefficient matrix is ​​used in conjunction with the LS channel estimation matrix to determine the multidimensional channel estimation matrix; From the multidimensional step-by-step MMSE channel estimation coefficient matrices corresponding to each amplitude factor, select the multidimensional step-by-step MMSE channel estimation coefficient matrix with the highest normalization correlation to the multidimensional joint MMSE channel estimation coefficient matrix. The amplitude factor corresponding to the selected multidimensional stepwise MMSE channel estimation coefficient matrix is ​​determined as the amplitude factor corresponding to the signal-to-noise ratio (SNR), channel autocorrelation matrix, and number of taps used in the channel estimation of the LS channel estimate.

5. The method according to claim 2, characterized in that, In the amplitude factor table, the signal-to-noise ratio (SNR), channel autocorrelation matrix, and number of taps used in the channel estimation for each LS channel estimate are obtained through the following steps: With the signal-to-noise ratio (SNR), channel autocorrelation matrix, and number of taps used in the LS channel estimate unchanged, the multidimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor is determined by using multiple different amplitude factors. The multidimensional step-by-step MMSE channel estimation coefficient matrix is ​​used in conjunction with the LS channel estimation matrix to determine the multidimensional channel estimation matrix; From the multidimensional step-by-step MMSE channel estimation coefficient matrices corresponding to each amplitude factor, select the multidimensional step-by-step MMSE channel estimation coefficient matrix with the smallest first value of the multidimensional joint MMSE channel estimation coefficient matrix. Wherein, the first value is obtained by summing the magnitudes of the differences between each element in the multidimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor and the corresponding element in the multidimensional joint MMSE channel estimation coefficient matrix; or, the first value is obtained by summing the squares of the magnitudes of the differences between each element in the multidimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor and the corresponding element in the multidimensional joint MMSE channel estimation coefficient matrix. The amplitude factor corresponding to the selected multidimensional stepwise MMSE channel estimation coefficient matrix is ​​determined as the amplitude factor corresponding to the signal-to-noise ratio (SNR), channel autocorrelation matrix, and number of taps used in the channel estimation of the LS channel estimate.

6. The method according to claim 4 or 5, characterized in that, With the signal-to-noise ratio (SNR), channel autocorrelation matrix, and number of taps used in the LS channel estimate remaining constant, the multidimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor is determined using multiple different amplitude factors, including: With the signal-to-noise ratio (SNR), channel autocorrelation matrix, and number of taps used in the LS channel estimation unchanged, the channel estimation coefficient matrix for the first dimension of each amplitude factor is determined using multiple different amplitude factors. Based on the channel estimation coefficient matrix of the first dimension corresponding to each amplitude factor, determine the channel estimation coefficient matrix of the last dimension corresponding to each amplitude factor. Based on the channel estimation coefficient matrix of the first dimension and the channel estimation coefficient matrix of the last dimension corresponding to each amplitude factor, the multidimensional step-by-step MMSE channel estimation coefficient matrix corresponding to each amplitude factor is obtained.

7. The method according to claim 2, characterized in that, In the amplitude factor table, the signal-to-noise ratio (SNR), channel autocorrelation matrix, and number of taps used in the channel estimation for each LS channel estimate are obtained through the following steps: With the signal-to-noise ratio (SNR), channel autocorrelation matrix, and number of taps used in the LS channel estimation unchanged, a multidimensional channel estimation matrix corresponding to each amplitude factor is determined using multiple different amplitude factors. Based on the multidimensional channel estimation matrix corresponding to each amplitude factor and the channel matrix, the mean square error (MSE) corresponding to each amplitude factor is determined. From the mean square error (MSE) corresponding to each amplitude factor, the amplitude factor corresponding to the smallest MSE is selected as the signal-to-noise ratio (SNR), channel autocorrelation matrix, and amplitude factor corresponding to the tap used in the channel estimation of the LS channel estimate.

8. A channel estimation device, characterized in that, The device includes: The determining unit is used to perform minimum mean square error (MMSE) channel estimation based on the product between the first noise power and the first amplitude factor in the first dimension, the first channel autocorrelation matrix in the first dimension, and the first diagonal matrix, to obtain the channel estimation matrix in the first dimension. The channel estimation matrix of the first dimension is obtained by performing MMSE channel estimation in a multi-dimensional step-by-step process, which is not the last step. The first noise power is the noise power corresponding to the least squares LS channel estimate, or the noise power corresponding to the channel estimate matrix obtained in the previous MMSE channel estimation step, and the first amplitude factor is a positive number less than 1. The acquisition unit is used to perform MMSE channel estimation based on the channel estimation matrix of the first dimension and the second channel autocorrelation matrix, the second noise power and the second diagonal matrix of the last dimension, to obtain a multidimensional channel estimation matrix. The second noise power is the noise power corresponding to the channel estimation matrix of the first dimension.

9. An electronic device, characterized in that, The device includes a processor and a memory interconnected thereto, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is used to invoke the program instructions to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions, which, when executed by a computer, perform the method as described in any one of claims 1 to 7.

11. A chip, characterized in that, The chip includes a processor that performs the method as described in any one of claims 1 to 7.

12. A chip module, characterized in that, The chip module includes a transceiver component and a chip, the chip including a processor, the processor performing the method as described in any one of claims 1 to 7.

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