Channel estimation method, device, electronic device and storage medium in RedCap

Abstract

The present application relates to a channel estimation method, device, electronic device and storage medium in RedCap, which are applied to the field of communication technology, wherein the method includes: obtaining an initial channel matrix and an initial receiving matrix; obtaining a modulation order, and reconstructing the initial channel matrix and the initial receiving matrix into a target channel matrix and a target receiving matrix respectively based on the modulation order; performing QR decomposition on the target channel matrix to obtain a unitary matrix Q and an upper triangular matrix R; performing a maximum likelihood operation based on the target receiving matrix, the unitary matrix Q and the upper triangular matrix R to obtain a channel estimation value. This is to solve the problem in the prior art that RedCap directly uses the original 5G channel estimation solution, which will cause a large performance waste.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a channel estimation method, device, electronic device and storage medium in RedCap. Background Art

[0002] The development of mobile communication systems from the initial 1G to the now highly-anticipated 5G is inseparable from the continuous research of all workers. At present, 5G technology is constantly developing and has been deeply applied to various aspects of social production and management, becoming an important technology closely related to people's daily work and life.

[0003] With the widespread use of Multiple Input Multiple Output (MIMO) technology and Orthogonal Frequency Division Multiplexing (OFDM) technology in wireless communication systems, the computational complexity of baseband signal processing algorithms has increased exponentially. In order to communicate reliably with multiple users in the same frequency band at the same time, multi-user MIMO (MU-MIMO) systems have become the preferred solution for various future wireless communication systems. These new technologies have placed extremely high demands on baseband signal processors, especially MIMO technology and MU-MIMO technology, which require baseband signal processors to complete real-time operations on a large number of matrices for the first time.

[0004] MIMO signal detection is a very challenging task for the receiver of baseband signal processing in MU-MIMO wireless communication systems. MIMO signal detection is used to eliminate interference from other antennas and obtain useful signals from each antenna. Similar to precoding algorithms, MIMO signal detection is usually divided into nonlinear detection algorithms represented by maximum likelihood detection (MLD) detection. Nonlinear MLD signal detection requires calculating the Euclidean distance between the received signal and all possible transmitted signal vectors and searching for the minimum value. MLD signal detection is currently the best detection algorithm.

[0005] 5G Lightweight (RedCap) refers to the 5G standard proposed by 3GPP, which is more streamlined than the previously released 5G standard. The emergence of 5G RedCap defines a new, less complex NR device for IoT applications, which can provide faster data transmission speeds than LTE Cat-M and NB-IoT, but is much simpler and cheaper than the traditional 5G NR devices currently deployed.

[0006] In order to achieve rapid product iteration, RedCap's products will basically absorb the advantages of the original user equipment (UE) products, iterate from traditional 5G, and thus realize RedCap support quickly and with high quality.

[0007] However, since the traditional 5G requires the support of four downlink antennas, the corresponding MIMO layers are also 2 to 4, and the modulation order is generally 256QAM, while the downlink branch in RedCap is two antennas, if the original 5G channel estimation solution is directly used, it will cause a large performance waste. Summary of the invention

[0008] The present application provides a channel estimation method, device, electronic device and storage medium in RedCap, so as to solve the problem in the prior art that RedCap directly uses the original 5G channel estimation solution, which will cause a large performance waste.

[0009] In a first aspect, an embodiment of the present application provides a channel estimation method in RedCap, comprising:

[0010] Get the initial channel matrix and the initial receiving matrix ;

[0011] Acquire a modulation order, and convert the initial channel matrix into and the initial receiving matrix Reconstruct the target channel matrix and the target receiving matrix :

[0012] Wherein, when the modulation order is less than or equal to 6, , or, , ;

[0013] When the modulation order is greater than 6,

[0014] , ,or, , ;

[0015] For the target channel matrix Perform QR decomposition to obtain the unitary matrix Q and the upper triangular matrix R;

[0016] Based on the target receiving matrix , the unitary matrix Q and the upper triangular matrix R perform maximum likelihood operation to obtain a channel estimation value.

[0017] In a second aspect, an embodiment of the present application provides a channel estimation device in RedCap, comprising:

[0018] The first acquisition module is used to obtain the initial channel matrix and the initial receiving matrix ;

[0019] The second acquisition module is used to acquire a modulation order, and based on the modulation order, the initial channel matrix and the initial receiving matrix Reconstruct the target channel matrix and the target receiving matrix ;

[0020] Wherein, when the modulation order is less than or equal to 6, , or, , ;

[0021] When the modulation order is greater than 6,

[0022] , ,or, , ;

[0023] A decomposition module is used to decompose the target channel matrix Perform QR decomposition to obtain the unitary matrix Q and the upper triangular matrix R;

[0024] An estimation module is used to receive a matrix based on the target , the unitary matrix Q and the upper triangular matrix R perform maximum likelihood operation to obtain a channel estimation value.

[0025] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0026] The memory is used to store computer programs;

[0027] The processor is used to execute the program stored in the memory to implement the channel estimation method in RedCap described in the first aspect.

[0028] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the channel estimation method in RedCap described in the first aspect.

[0029] The above technical solution provided by the embodiment of the present application has the following advantages over the prior art: the method provided by the embodiment of the present application obtains an initial channel matrix and an initial receiving matrix; obtains a modulation order, and converts the initial channel matrix into a receiving matrix based on the modulation order. and the initial receiving matrix Reconstruct the target channel matrix and the target receiving matrix ; For the target channel matrix Perform QR decomposition to obtain a unitary matrix Q and an upper triangular matrix R; based on the target receiving matrix , the unitary matrix Q and the upper triangular matrix R are subjected to maximum likelihood operation to obtain channel estimation values. According to the elements in the reconstructed target channel matrix and the target receiving matrix, the reconstructed target channel matrix and the target receiving matrix reuse the architecture of 5G channel estimation, and the target channel matrix and the target receiving matrix are configured with the parameters in addition to the initial channel matrix and the initial receiving matrix, which are padded with 0, so that in the iterative operation process of maximum likelihood, a 2x2 matrix is ​​supported for QR decomposition, reducing the occupied hardware area and power consumption, so that the original 5G structure can be reused, and the channel estimation function of RedCap can be realized under the premise of maintaining the relationship between data and clock. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0032] Figure 1 An application scenario diagram of a channel estimation method in RedCap provided in an embodiment of the present application;

[0033] Figure 2 A flow chart of a channel estimation method in RedCap provided in an embodiment of the present application;

[0034] Figure 3 A constellation diagram of 64QAM provided in an embodiment of the present application;

[0035] Figure 4 A constellation diagram of 256QAM provided in an embodiment of the present application;

[0036] Figure 5A flow chart of a channel estimation method in RedCap provided by another embodiment of the present application;

[0037] Figure 6 A simulation diagram of BLER under 16QAM provided in an embodiment of the present application;

[0038] Figure 7 A simulation diagram of BLER under 256QAM provided in an embodiment of the present application;

[0039] Figure 8 A structural diagram of a channel estimation device in RedCap provided in an embodiment of the present application;

[0040] Fig. 9 A structural diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0042] According to an embodiment of the present application, a channel estimation method in RedCap is provided. Optionally, in the embodiment of the present application, the channel estimation method in RedCap can be applied to Figure 1 In the hardware environment composed of the terminal 101 and the server 102 shown in FIG. Figure 1 As shown, the server 102 is connected to the terminal 101 via a network, and can be used to provide services (such as communication services, etc.) for the terminal or a client installed on the terminal. A database can be set up on the server or independently of the server to provide data storage services for the server 102. The above-mentioned network includes but is not limited to: a wide area network, a metropolitan area network or a local area network, and the terminal 101 is not limited to a PC, a mobile phone, a tablet computer, etc.

[0043] The channel estimation method in RedCap of the embodiment of the present application may be executed by the server 102, or by the terminal 101, or by both the server 102 and the terminal 101. The terminal 101 executes the channel estimation method in RedCap of the embodiment of the present application, or it may be executed by a client installed thereon.

[0044] Taking the terminal executing the channel estimation method in RedCap of the embodiment of the present application as an example, Figure 2is a flow chart of an optional channel estimation method in RedCap according to an embodiment of the present application, such as Figure 2 As shown, the process of the method may include the following steps:

[0045] Step 201: Obtain an initial channel matrix and the initial receiving matrix .

[0046] In some embodiments, the channel matrix refers to a matrix that describes the relationship between each transmitting antenna and receiving antenna during signal transmission in a MIMO communication system. It reflects the propagation of signals on different channels, including factors such as signal attenuation, phase change, and interference. The channel matrix can be used for the design and performance evaluation of key technologies such as signal detection, beamforming, and space-time coding.

[0047] Initial channel matrix A multi-antenna system can be used for measurement to construct a channel matrix by measuring the signal transmission between different transmit and receive antennas.

[0048] Initial receiving matrix is the matrix received by the receiving antenna.

[0049] In an optional embodiment, obtaining an initial receiving matrix includes:

[0050] Get the original receiving matrix;

[0051] Obtain channel decoding information;

[0052] In a case where the channel decoding information includes a precoding matrix, multiplying the original receiving matrix by the precoding matrix to obtain the initial receiving matrix;

[0053] In a case where the channel decoding information does not include a precoding matrix, the original receiving matrix is ​​determined to be the initial receiving matrix.

[0054] In some embodiments, the UE obtains control information by decoding information in a physical broadcast channel (PBCH) and a physical downlink control channel (PDCCH). The control information includes channel decoding information. The channel decoding information can be used to obtain whether the current physical downlink shared channel (PDSCH) has been precoded. If so, the corresponding precoding matrix is ​​obtained. If not, it indicates that the currently obtained matrix does not need to be precoded.

[0055] It is understandable that when the number of transmitting and receiving antennas is different, precoding is required, and when the number of transmitting and receiving antennas is the same, a precoding matrix is ​​also required in some cases, depending on the working environment. For the UE, it depends on the parameter information obtained by decoding the PBCH / PDCCH.

[0056] Exemplarily, when the original receiving matrix is ​​a 4×2 matrix, the precoding matrix can be obtained from the channel decoding information, thereby converting it into a 2×2 matrix for subsequent RedCap channel estimation.

[0057] Step 202: Obtain a modulation order, and convert the initial channel matrix and the initial receiving matrix Reconstruct the target channel matrix and the target receiving matrix .

[0058] Wherein, when the modulation order is less than or equal to 6, , Formula (1) or, , Formula (2);

[0059] When the modulation order is greater than 6,

[0060] , Formula (3), or , Formula (4).

[0061] In some embodiments, based on different application scenarios, the modulation order in the communication protocol may also be different. Generally, the modulation order is less than or equal to 6, that is, the modulation order of 64-phase quadrature amplitude modulation (Quadrature Amplitude Modulation, referred to as QAM) and below. In special scenarios, the modulation order may be 8, that is, the modulation order of 256QAM.

[0062] In some embodiments, in the traditional 5G mode, the orthogonal triangular (QR) decomposition part needs to implement 4×4 matrix decomposition and fill the matrix for different receiving ends. Under RedCap, 2×2 matrix decomposition needs to be completed. Assuming that the input of the QR decomposition module is the target channel matrix H and the target receiving matrix y, the data format in the traditional 5G working mode is expressed as:

[0063] ,

[0064] .

[0065] In order to maintain the boundary, signal and clock relationship, the original boundary is modified to the above target channel matrix and the target receiving matrix in the form of.

[0066] The target channel matrix reconstructed by the above method and the target receiving matrix , the QR decomposition becomes a matrix decomposition of at most two diagonal 2×2 matrices. Because the QR decomposition algorithm is generally based on the Cordic algorithm for iteration, such an improvement can optimize the iterative row-column relationship from 4 to 2. Based on the algorithm relationship, the hardware requirements can be improved to:

[0067] ;

[0068] Among them, 2 and 4 are the row and column relationships mentioned above, and 3 is the hardware execution step module of the Cordic operation. Under the premise of reuse, the hardware relationship will be maintained, but because of the reduction in rows and columns and the influence of the iterative relationship, the hardware requirement is 12.5% ​​of the traditional 5G.

[0069] Step 203: Calculate the target channel matrix Perform QR decomposition to obtain the unitary matrix Q and the upper triangular matrix R.

[0070] In some embodiments, in a MIMO scenario, when performing signal detection, matrix decomposition is first performed through QR decomposition to decompose the channel matrix H into the product of an orthogonal unitary matrix Q and an upper triangular matrix R, and then channel estimation is performed to obtain decoding information for final decoding.

[0071] There are differences in the calculation of channel estimation values ​​due to the difference in modulation orders.

[0072] Step 204: Based on the target receiving matrix , the unitary matrix Q and the upper triangular matrix R perform maximum likelihood operation to obtain a channel estimation value.

[0073] In an optional embodiment, based on the target receiving matrix , performing a maximum likelihood operation on the unitary matrix Q and the upper triangular matrix R to obtain a channel estimation value, including:

[0074] The target receiving matrix is ​​calculated based on the noise minimum function , the unitary matrix Q and the upper triangular matrix R, to obtain an initial estimate;

[0075] Determining a target estimated value in the initial estimated value based on the modulation order;

[0076] Based on the modulation order, constellation mapping is performed on the target estimation value to obtain a channel estimation value.

[0077] In some embodiments, during signal transmission, the following relationship often exists:

[0078] ;

[0079] Among them, y represents the receiving matrix, x represents the signal at the transmitting end, H represents the channel matrix, and n represents the noise.

[0080] Based on this, the noise minimum function can be used to substitute the target channel matrix and the target receiving matrix to determine the estimated value of the transmitted signal obtained when the noise is minimized, that is, the initial estimated value.

[0081] The noise minimum function is: ;

[0082] In the formula, represents the initial estimate, represents the target channel matrix, Represents the target receiving matrix.

[0083] Based on the above-mentioned related embodiments, matrix decomposition is performed through QR decomposition, and the channel matrix H is decomposed into the product of the orthogonal matrix Q and the initial upper triangular matrix R, which is substituted into the above-mentioned noise minimum function to obtain:

[0084] ;

[0085] QR decomposition is performed, and the obtained Q is a unitary matrix, so in the calculation of argmin, it can be calculated by multiplying it by its transposed matrix, and R is an upper triangular matrix, so the obtained The operation can be performed row by row, and we get The value of .

[0086] In an optional embodiment, when the modulation order is greater than 6, the number of the initial estimation values ​​is two; when the modulation order is less than or equal to 6, the number of the initial estimation values ​​is one;

[0087] The step of determining a target estimated value in the initial estimated value based on the modulation order includes:

[0088] When the modulation order is greater than 6, the sum of the absolute values ​​of the sub-estimated values ​​in each of the initial estimated values ​​is calculated; and the initial estimated value having the larger sum of the absolute values ​​is determined as the target estimated value;

[0089] When the modulation order is less than or equal to 6, the initial estimated value is determined to be the target estimated value.

[0090] In some embodiments, when the modulation order is greater than 6, as in Formula 3 and Formula 4 above, the elements in the target channel matrix are the matrices obtained by sequentially permuting the initial channel matrix and the column elements in the initial channel matrix, and are respectively set at the upper left corner and the lower right corner of the target channel matrix. Therefore, when performing QR decomposition, the sequentially permuted matrix and the original initial channel matrix are subjected to QR decomposition in parallel, thereby obtaining two initial estimates. By calculating the sum of the absolute values ​​of each sub-estimate in the two initial estimates; comparing the sums of the two absolute values, the initial estimate with the larger sum of the absolute values ​​is taken as the target initial estimate, and the layer with the larger sum of the absolute values ​​is considered to be the more robust layer at the moment, and participates in the subsequent channel estimation process.

[0091] Exemplarily, when the modulation order is 8, the initial estimated values ​​are (0.9, 1.1, -0.5, 1.3, -0.1, -0.8, 1.5, 0.7) and (0.8, 1.3, -0.5, 1.2, -0.4, -0.7, 1.1, 0.7), respectively. The sum of the absolute values ​​of the first initial estimated values ​​is 0.9+1.1+|-0.5|+1.3+|-0.1|+|-0.8|+1.5+0.7, i.e. 6.9, and the sum of the absolute values ​​of the second initial estimated values ​​is 0.8+1.3+|-0.5|+1.2+|-0.4|+|-0.7|+1.1+0.7, i.e. 6.7. Since 6.9>6.7, the above-mentioned first initial estimated value is taken as the target estimated value.

[0092] When the modulation order is less than or equal to 6, as shown in Formula 1 and Formula 2 above, since only one element in the initial channel matrix participates in the operation, the number of initial estimated values ​​obtained is 1, which is directly used as the target estimated value.

[0093] In an optional embodiment, performing constellation mapping on the target estimated value based on the modulation order to obtain a channel estimated value includes:

[0094] When the modulation order is less than or equal to 6, the confidence between the target estimated value and each constellation point in the target constellation diagram is determined, and the first preset constellation points with the highest confidence and their confidence are determined as the channel estimated value.

[0095] When the modulation order is greater than 6, a hard decision is performed on the upper 2-bit estimated value in the target estimated value to obtain a decision result; the confidence between other estimated values ​​and each constellation point in the target constellation diagram is calculated, and the decision result and a preset value of constellation points with the highest confidence and their confidence are determined as the channel estimated value, and the other estimated values ​​are the values ​​​​excluding the upper 2-bit estimated value in the initial estimated value.

[0096] In some embodiments, the traditional 5G mainly works in the range of 256QAM. RedCap knows that most of the supported scenarios only need 64QAM, but in some application scenarios, it is necessary to expand to 256QAM. Under this premise, if the performance and area of ​​MLD with 256QAM are retained, the loss will be greater than the performance requirement. Therefore, MLD is downgraded to 64QAM, and when the modulation order is less than or equal to 6, the operation is performed according to 64QAM. When the modulation is equal to or greater than 6, the operation is expanded from 64QAM to 256QAM.

[0097] The target constellation diagram is a constellation diagram of 64QAM. The preset value can be set based on the actual situation. For example, the preset value can be 3 or 5. For the constellation diagram of 64QAM, which includes 64 constellation points, the confidences can be arranged in descending order, and the first three constellation points with the highest confidences and the corresponding confidences are taken as channel estimation values.

[0098] The confidence level can be calculated by calculating the probability that the target estimated value or other estimated values ​​are the possibility of each constellation point in the target constellation diagram, and using the probability as the confidence level.

[0099] See also Figure 3 The constellation diagram of 64QAM is shown in Figure 1. It can be seen that under 64QAM, each data contains 6 bits, and the transmission of four phases can represent a total of 64 different data. Figure 4 From the constellation diagram of 256QAM shown in the figure, we can see that compared with 64QAM, 256QAM can express a total of 256 data combinations, each data contains 8 bits. Compared with the mapping of 64QAM, its high 2 bits have a total of 00, 01, 10, and 11. Therefore, 256QAM can be regarded as setting a 64QAM in each of the four quadrants, and each 64QAM is supplemented with the high 2 bits to obtain the mapping of 256QAM. Furthermore, in the channel estimation process, for 256QAM, the high 2 bits can be extracted separately to form 4 different 64QAM phase limits, which are handed over to MLD for decoding, and then the high 2 bits are supplemented to complete the 256QAM MLD work using 64QAM.

[0100] Based on this, in order to avoid the situation where the performance and area loss of the MLD of 256QAM will be greater than the performance requirements, when the modulation order is greater than 6, the high 2-bit estimation value in the target estimation value can be calculated separately, and the final channel estimation value of the high 2-bit estimation value is obtained by hard decision (that is, when it is greater than 0, it is judged as 1, and when it is less than or equal to 0, it is judged as 0). For the remaining estimation values ​​in the target estimation value, the calculation can be carried out according to the constellation mapping method of 64QAM to determine the top 6 constellation points with the highest confidence and their confidence, and then, the decision result of the high 2 bits is supplemented to complete the channel estimation of 256QAM.

[0101] The channel estimation method in the RedCap of this application is based on the characteristics of RedCap's two downlink antennas, two MIMO layers, and the modulation order defined as 64QAM or 256QAM based on the scenario. A multiplexing design logic is designed to ensure the port information definition and bit width between each module while retaining the original design path and data relationship, and to improve the implementation method of the QR decomposition (QR-decomposition, QRD) algorithm, which does not destroy the data and clock relationship between hardware modules, does not require firmware intervention, and thus improves efficiency through hardware work, and achieves 75% area optimization of QRD.

[0102] In a specific embodiment, see Figure 5 , under the requirements of RedCap, taking the case where the number of receiving antennas is 2 and the number of transmitting antennas is 4 as an example, the received signal is multiplied by the precoding matrix, and the corresponding MIMO is implemented as 2×2. Then, when the modulation order is less than or equal to 6, QR decomposition is performed according to the improved formula (1) or formula (2). When the modulation order is greater than 6, QR decomposition is performed according to the improved formula (3) or formula (4). The maximum number of QR decompositions required is 2×2, and the modulation parameters support 64QAM in most scenarios. Under 256QAM, the strength of the QR decomposition result is first judged, that is, the layer with a higher absolute value in the QR decomposition result is considered to have a stronger signal strength, and the higher 2-bit estimated value is hard-determined, and the other estimated values ​​are used for channel estimation according to 64QAM, and the hard decision result is added to the estimated result, and finally input into the decoder for decoding.

[0103] Among them, when making a robustness judgment, the absolute value of the obtained LLR value is judged. For example, the two LLR values ​​solved are -1 and -0.1, respectively, then the absolute values ​​of the two are 1 and 0.1, respectively, because 1>0.1, so -1 is considered to be more robust. After completing the robustness judgment, the corresponding LLR value will be obtained, and the hard bit information of 00, 01, 10, and 11 will be obtained, because the MLD decoding will obtain 6 bits of 64QAM information, plus 2 bits of hard judgment, combined into 8 bits, which can represent the constellation mapping information of 256QAM.

[0104] The number of transmitting antennas is determined by the base station, and the number can be 1, 6, 8, 10, etc.

[0105] The channel estimation method in RedCap of the present application is a more practical and efficient solution based on the scenario in the implementation of 5G. Therefore, based on the architecture and solution of the original 5G product, the iterative design of RedCap compared to 5G is carried out to achieve faster implementation, more stable quality, and more optimized cost. The present invention proposes an improved solution under the traditional 5G architecture for the needs of RedCap, and the hardware area of ​​the solution can reach 12.5% ​​of the original traditional 5G UE solution.

[0106] In addition, the complexity of MLD is reduced to meet special needs, and a solution through firmware and phase limit selection is proposed to support the needs in different scenarios.

[0107] In order to understand the performance changes, the block error rate (BLER) was simulated under 16QAM and 256QAM. The performance is as follows Figure 6 and Figure 7 as shown.

[0108] Figure 6 and Figure 7 The solid line in the figure is the traditional solution, and the dotted line is the solution of the present invention. Figure 6 and Figure 7 It can be seen that under 16QAM, BLER converges quickly, and the difference in BLER data between traditional 5G and the present invention can be attributed to simulated jitter.

[0109] Under 256QAM, the BLER deteriorates slightly due to the introduction of the phase limit judgment scheme, but the difference is below 0.01. Even if it decreases, it does not affect network access and actual performance.

[0110] Therefore, this application can ensure chip stability under the premise of ensuring the signals and clocks of traditional 5G chip channel estimation, improve the design according to the changes in RedCap's requirements, achieve 75% optimization of area, and support different modes.

[0111] Based on the same concept, the present application provides a channel estimation device in RedCap. The specific implementation of the device can refer to the description of the method embodiment part, and the repeated parts will not be repeated. Figure 8 As shown, the device mainly includes:

[0112] The first acquisition module 801 is used to acquire an initial channel matrix and the initial receiving matrix ;

[0113] The second acquisition module 802 is used to acquire a modulation order, and based on the modulation order, convert the initial channel matrix and the initial receiving matrix Reconstruct the target channel matrix and the target receiving matrix ;

[0114] Wherein, when the modulation order is less than or equal to 6, , or, , ;

[0115] When the modulation order is greater than 6,

[0116] , ,or, , ;

[0117] Decomposition module 803, used for the target channel matrix Perform QR decomposition to obtain the unitary matrix Q and the upper triangular matrix R;

[0118] The estimation module 804 is used to estimate the target receiving matrix based on the target receiving matrix. , the unitary matrix Q and the upper triangular matrix R perform maximum likelihood operation to obtain a channel estimation value.

[0119] Wherein, the estimation module comprises:

[0120] A calculation unit, used to calculate the target receiving matrix based on the noise minimum function , the unitary matrix Q and the upper triangular matrix R, to obtain an initial estimate;

[0121] A determination unit, configured to determine a target estimated value in the initial estimated value based on the modulation order;

[0122] The estimation unit is used to perform constellation mapping on the target estimation value based on the modulation order to obtain a channel estimation value.

[0123] Based on the same concept, an electronic device is also provided in the embodiment of the present application, such as Fig. 9 As shown, the electronic device mainly includes: a processor 901, a memory 902 and a communication bus 903, wherein the processor 901 and the memory 902 communicate with each other through the communication bus 903. The memory 902 stores a program executable by the processor 901, and the processor 901 executes the program stored in the memory 902 to implement the following steps:

[0124] Get the initial channel matrix and the initial receiving matrix ;

[0125] Acquire a modulation order, and convert the initial channel matrix into and the initial receiving matrix Reconstruct the target channel matrix and the target receiving matrix :

[0126] Wherein, when the modulation order is less than or equal to 6, , or, , ;

[0127] When the modulation order is greater than 6,

[0128] , ,or, , ;

[0129] For the target channel matrix Perform QR decomposition to obtain the unitary matrix Q and the upper triangular matrix R;

[0130] Based on the target receiving matrix , the unitary matrix Q and the upper triangular matrix R perform maximum likelihood operation to obtain a channel estimation value.

[0131] The communication bus 903 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 903 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0132] The memory 902 may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the processor 901 .

[0133] The above-mentioned processor 901 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., and can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0134] The electronic device provided in the embodiment of the present application may specifically be a module capable of realizing a communication function or a terminal device including the module, etc. The terminal device may be a mobile terminal or a smart terminal. The mobile terminal may specifically be at least one of a mobile phone, a tablet computer, a laptop computer, etc.; the smart terminal may specifically be a terminal including a wireless communication module such as a smart car, a smart watch, a shared bicycle, a smart cabinet, etc.; the module may specifically be a wireless communication module, such as any one of a 2G communication module, a 3G communication module, a 4G communication module, a 5G communication module, and a NB-IOT communication module.

[0135] In another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed on a computer, the computer executes the channel estimation method in RedCap described in the above embodiment.

[0136] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instruction is loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instruction may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instruction is transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape, etc.), an optical medium (e.g., a DVD) or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0137] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0138] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A channel estimation method in RedCap, characterized in that: include: Get the initial channel matrix and the initial receiving matrix ; Acquire a modulation order, and convert the initial channel matrix into and the initial receiving matrix Reconstruct the target channel matrix and the target receiving matrix ; Wherein, when the modulation order is less than or equal to 6, , or, , ; When the modulation order is greater than 6, , ,or, , ; For the target channel matrix Perform QR decomposition to obtain the unitary matrix Q and the upper triangular matrix R; Based on the target receiving matrix , the unitary matrix Q and the upper triangular matrix R perform maximum likelihood operation to obtain a channel estimation value.

2. The channel estimation method in RedCap according to claim 1, characterized in that: The target receiving matrix , performing a maximum likelihood operation on the unitary matrix Q and the upper triangular matrix R to obtain a channel estimation value, including: The target receiving matrix is ​​calculated based on the noise minimum function , the unitary matrix Q and the upper triangular matrix R, to obtain an initial estimate; Determining a target estimated value in the initial estimated value based on the modulation order; Based on the modulation order, constellation mapping is performed on the target estimation value to obtain a channel estimation value.

3. The channel estimation method in RedCap according to claim 2, characterized in that: When the modulation order is greater than 6, the number of the initial estimated values ​​is two; when the modulation order is less than or equal to 6, the number of the initial estimated values ​​is one; The step of determining a target estimated value in the initial estimated value based on the modulation order includes: When the modulation order is greater than 6, calculating the sum of the absolute values ​​of the sub-estimated values ​​in each of the initial estimated values; Determine the initial estimate of the sum of the absolute values ​​as the target estimate; When the modulation order is less than or equal to 6, the initial estimated value is determined to be the target estimated value.

4. The channel estimation method in RedCap according to claim 2, characterized in that: The performing constellation mapping on the target estimated value based on the modulation order to obtain a channel estimated value includes: When the modulation order is less than or equal to 6, the confidence between the target estimated value and each constellation point in the target constellation diagram is determined, and the first preset constellation points with the highest confidence and their confidence are determined as the channel estimated value.

5. The channel estimation method in RedCap according to claim 2, characterized in that: The performing constellation mapping on the target estimated value based on the modulation order to obtain a channel estimated value includes: When the modulation order is greater than 6, a hard decision is performed on the upper 2-bit estimated value in the target estimated value to obtain a decision result; the confidence between other estimated values ​​and each constellation point in the target constellation diagram is calculated, and the decision result and a preset value of constellation points with the highest confidence and their confidence are determined as the channel estimated value, and the other estimated values ​​are the values ​​​​excluding the upper 2-bit estimated value in the initial estimated value.

6. The channel estimation method in RedCap according to claim 1, characterized in that: Get the initial receiving matrix, including: Get the original receiving matrix; Obtain channel decoding information; In a case where the channel decoding information includes a precoding matrix, multiplying the original receiving matrix by the precoding matrix to obtain the initial receiving matrix; In a case where the channel decoding information does not include a precoding matrix, the original receiving matrix is ​​determined to be the initial receiving matrix.

7. The channel estimation method in RedCap according to claim 2, characterized in that: The target receiving matrix is ​​calculated based on the noise minimum function , the unitary matrix Q and the upper triangular matrix R, to obtain an initial estimate, including: In the formula, represents the initial estimate, represents the target channel matrix, represents the target receiving matrix, Q represents the unitary matrix, and R represents the upper triangular matrix.

8. A channel estimation device in RedCap, characterized in that: include: The first acquisition module is used to obtain the initial channel matrix and the initial receiving matrix ; The second acquisition module is used to acquire a modulation order, and based on the modulation order, the initial channel matrix and the initial receiving matrix Reconstruct the target channel matrix and the target receiving matrix ; Wherein, when the modulation order is less than or equal to 6, , or, , ; When the modulation order is greater than 6, , ,or, , ; A decomposition module is used to decompose the target channel matrix Perform QR decomposition to obtain the unitary matrix Q and the upper triangular matrix R; An estimation module is used to receive a matrix based on the target , the unitary matrix Q and the upper triangular matrix R perform maximum likelihood operation to obtain a channel estimation value.

9. An electronic device, characterized in that: include: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; The memory is used to store computer programs; The processor is used to execute the program stored in the memory to implement the channel estimation method in RedCap described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the channel estimation method in RedCap described in any one of claims 1-7 is implemented.

Images