Channel estimation methods, apparatus and equipment

Abstract

This invention provides a channel estimation method, apparatus, and device, relating to the field of communication technology. The method includes: determining initial channel estimates for each subcarrier based on an IAM-R type preamble and a least-squares algorithm; determining the signal-to-interference-plus-noise ratio (SINR) for each subcarrier based on its ICI power and noise power; and correcting the initial channel estimates for each subcarrier based on its SINR and a preset threshold to obtain a target channel estimate. The method of this application effectively solves the problems of large channel estimation deviation and weak anti-interference capability in traditional IAM-LS systems, significantly improving channel estimation accuracy and transmission reliability of FBMC / OQAM systems.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a channel estimation method, apparatus, and device. Background Technology

[0002] Filter Bank Multicarrier (FBMC) / Offset Quadrature Amplitude Modulation (OQAM) is a core modulation technology for next-generation wireless communication. Channel estimation is a key step in achieving accurate signal demodulation and recovery. Currently, the mainstream technical solution is the Interference Approximation Method (IAM) combined with the Least Squares (LS) estimation method. This method has the advantages of simple algorithm implementation and low computational complexity, but the channel estimation results deviate significantly from the actual channel transmission characteristics. Summary of the Invention

[0003] This invention provides a channel estimation method, apparatus, and device. By quantizing ICI power and calculating subcarrier-level signal-to-interference-plus-noise ratio (SINR) to correct the initial channel estimate, it incorporates the inherent pure imaginary interference caused by filter overlap, which is ignored by the traditional IAM-LS method, into the channel estimation process. This significantly improves the accuracy and reliability of the channel estimation results and effectively suppresses estimation biases introduced by interference and noise. Without changing the existing system architecture, reconstructing pilots, or significantly increasing computational complexity, it effectively solves the problems of large channel estimation bias and weak anti-interference capability in the traditional IAM-LS method, and significantly improves the channel estimation accuracy and transmission reliability of the FBMC / OQAM system.

[0004] This invention provides a channel estimation method, comprising the following steps: Based on the IAM-R type preamble and the least squares algorithm, the initial channel estimate value corresponding to each subcarrier is determined; The signal-to-interference-plus-noise ratio (SINR) of each subcarrier is determined based on the ICI power and noise power corresponding to each subcarrier. Based on the signal-to-interference-plus-noise ratio (SINR) and preset threshold of each subcarrier, the initial channel estimate corresponding to each subcarrier is corrected to obtain the target channel estimate.

[0005] According to a channel estimation method provided by the present invention, the step of correcting the initial channel estimation value corresponding to each subcarrier based on the signal-to-interference-plus-noise ratio (SINR) and a preset threshold of each subcarrier to obtain the target channel estimation value includes: If the signal-to-interference-plus-noise ratio of a subcarrier is greater than or equal to the preset threshold, the initial channel estimate corresponding to the subcarrier is used as the target channel estimate. If the signal-to-interference-plus-noise ratio of a subcarrier is less than the preset threshold, the initial channel estimate corresponding to the subcarrier is corrected based on the channel estimate of the adjacent subcarrier to obtain the current channel estimate. The target channel estimate is determined based on the current channel estimate.

[0006] According to a channel estimation method provided by the present invention, determining a target channel estimation value based on the current channel estimation value includes: If the relative change between the current channel estimate and the initial channel estimate is less than an iteration threshold, the current channel estimate is used as the target channel estimate. If the relative change between the current channel estimate and the initial channel estimate is greater than or equal to the iteration threshold, the current channel estimate is iteratively corrected to obtain the target channel estimate.

[0007] According to a channel estimation method provided by the present invention, the step of iteratively correcting the current channel estimate to obtain the target channel estimate includes: Step a: Based on the current channel estimate, redetermine the ICI power and signal-to-interference-plus-noise ratio; Step b: Based on the newly determined signal-to-interference-plus-noise ratio (SINR), correct the current channel estimate to obtain the iteratively corrected channel estimate. Step c: Determine the relative change between the iteratively corrected channel estimate and the current channel estimate; Step d: When the relative change is less than the iteration threshold, stop the iteration and use the iteratively corrected channel estimate as the target channel estimate. Step e: When the relative change is greater than or equal to the iteration threshold, the iteratively corrected channel estimate is used as the current channel estimate, and steps a-c are repeated until the relative change is less than the iteration threshold or the number of iterations reaches the preset maximum number of iterations.

[0008] According to a channel estimation method provided by the present invention, the step of correcting the initial channel estimation value corresponding to the subcarrier based on the channel estimation value of the adjacent subcarrier to obtain the current channel estimation value includes: The channel estimates of the subcarrier and its left and right adjacent subcarriers are processed by a signal-to-interference-plus-noise ratio (SINR) weighted average to obtain the current channel estimate.

[0009] According to a channel estimation method provided by the present invention, the method further includes: Based on the power coupling characteristics of the prototype filter in the FBMC / OQAM system, the inter-subcarrier interference (ICI) power corresponding to each subcarrier is determined.

[0010] According to a channel estimation method provided by the present invention, the method further includes: Based on the target channel estimate and the symbol field signal carrying ICI, the data transmitted by the transmitter is recovered.

[0011] The present invention also provides a channel estimation apparatus, comprising the following modules: The determination module is used to determine the initial channel estimate for each subcarrier based on the IAM-R type preamble and the least squares algorithm; The processing module is used to determine the signal-to-interference-plus-noise ratio of each subcarrier based on the ICI power and noise power corresponding to each subcarrier. The estimation module is used to correct the initial channel estimate value corresponding to each subcarrier based on the signal-to-interference-plus-noise ratio and preset threshold of each subcarrier, so as to obtain the target channel estimate value.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the channel estimation methods described above.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the channel estimation method as described above.

[0014] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the channel estimation method as described above.

[0015] The channel estimation method, apparatus, and device provided by this invention correct the initial channel estimation value by quantizing ICI power and calculating subcarrier-level signal-to-interference-plus-noise ratio. It incorporates the inherent pure imaginary interference caused by filter overlap, which is ignored by the traditional IAM-LS method, into the channel estimation process, which greatly improves the accuracy and reliability of the channel estimation results and effectively suppresses the estimation deviation introduced by interference and noise. Without changing the existing system architecture, reconstructing the pilot, or significantly increasing the computational complexity, it effectively solves the problems of large channel estimation deviation and weak anti-interference capability of the traditional IAM-LS, and significantly improves the channel estimation accuracy and transmission reliability of the FBMC / OQAM system. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the channel estimation method provided by the present invention.

[0018] Figure 2 This is one of the schematic diagrams illustrating the effect of the channel estimation method provided by the present invention.

[0019] Figure 3 This is the second schematic diagram illustrating the effect of the channel estimation method provided by the present invention.

[0020] Figure 4 This is a schematic diagram of the channel estimation device provided by the present invention.

[0021] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0023] The following is combined with Figures 1 to 5 The channel estimation method, apparatus, and device of the present invention are described.

[0024] To facilitate a clearer understanding of the technical solutions of the various embodiments of this application, some technical content related to the various embodiments of this application will be introduced first.

[0025] FBMC / OQAM is a core modulation technology for next-generation wireless communication. Channel estimation is a key step in achieving accurate signal demodulation and recovery. Currently, the mainstream technical solution is the Interference Approximation Method (IAM) combined with the least squares (LS) estimation method. This method designs a real-valued pilot sequence, combines it with the LS algorithm to solve for the channel frequency response, and uses a single-tap equalization structure. It features simple algorithm implementation, low computational complexity, and good compatibility with existing system architectures, making it a commonly used method for channel estimation in FBMC / OQAM systems at present.

[0026] It should be noted that existing channel estimation methods have the following drawbacks: First, they do not consider the inherent pure imaginary inter-carrier interference (ICI) caused by the overlap characteristics of the PHYDAYS prototype filter bank in FBMC / OQAM systems, resulting in a large deviation between the estimation results and the actual channel transmission characteristics. Second, they do not consider the different degrees to which different subcarriers are affected by ICI and noise, making it impossible to make targeted estimations for subcarriers with severe interference and low signal-to-interference-plus-noise ratio. Third, existing improvement schemes either increase computational complexity by adding complex iterative calculations and reconstructing pilot structures, thus increasing engineering implementation costs, or modify the filter bank architecture, changing the original transmission architecture of the system and reducing signal transmission efficiency.

[0027] Figure 1 This is a flowchart illustrating the channel estimation method provided by the present invention, as shown below. Figure 1 As shown, the method includes the following: Step 101: Determine the initial channel estimate for each subcarrier based on the IAM-R type preamble and the least squares algorithm.

[0028] Specifically, in the embodiments of this application, the standard IAM-R real-valued pilot structure of the FBMC / OQAM system can be adopted. At the pilot symbol position, based on the symbol domain signal output by the receiver analysis filter bank, the initial channel estimate of each subcarrier is obtained by the least squares algorithm.

[0029] For example, in the embodiments of this application, the initial channel frequency response in the FBMC / OQAM system is determined based on the following method: (1) Transmitting discrete-time baseband signals:

[0030] Where M is the number of subcarriers and N is the number of symbols. It is real data. It is the impulse response of the PHYDAYS prototype filter.

[0031] (2) Impulse response of the prototype filter:

[0032] in, For the m-th subcarrier and the n-th symbol at time... The corresponding PHYDAYS prototype filter impulse response; M represents the total number of system subcarriers; For the additional phase term, This is the filter length.

[0033] (3) Basic formula of prototype filter:

[0034] in, Indicates the filter's position at length. The amplitude at the point; M represents the total number of subcarriers in the system; Indicates the PHYDAYS filter number 1 One-sided coefficient.

[0035] (4) Receiving baseband signals:

[0036] in, It has zero mean and variance. Additive white Gaussian noise, It is the channel frequency response in the k-th row and n-th column.

[0037] (5) Analyze the output of the filter bank (AFB):

[0038] in, for( The virtual transmission symbol at ) , It is a purely imaginary interference factor.

[0039] (6) Derivation formula of pure imaginary interference factor:

[0040]

[0041]

[0042] The calculated fixed values ​​are: α=0.5644, β=0.2393, γ=0.2058.

[0043] (7) The preamble structure using the IAM-R method is a fixed multi-row, 3-column matrix.

[0044] (8) Solving for the channel frequency response (CFR) using the least squares (LS) method:

[0045] This application determines the initial channel estimate using the above method, which can effectively maintain compatibility with existing IAM systems without reconstructing the pilot structure or modifying the filter bank architecture, thereby reducing the implementation complexity of channel estimation.

[0046] Step 102: Determine the signal-to-interference-plus-noise ratio (SINR) of each subcarrier based on the ICI power and noise power corresponding to each subcarrier.

[0047] Specifically, after determining the initial channel estimate for each subcarrier based on the IAM-R type preamble and the least squares algorithm, this application can quantify the inter-subcarrier interference (ICI) power experienced by each subcarrier based on the power coupling characteristics of the prototype filter in the FBMC / OQAM system. Then, combined with the system noise power, the signal-to-interference-plus-noise ratio (SINR) of each subcarrier is calculated. This incorporates the inherent pure imaginary ICI interference of the filter into the channel estimation process, effectively compensating for the deficiency of the traditional IAM-LS method in ignoring ICI interference and improving the accuracy of channel estimation.

[0048] For example, in the embodiments of this application, at the pilot symbol position At this point, the virtual transmitted symbol is obtained from the real pilot and the IAM weighting coefficient. Get AFB output Then, the initial channel estimate is obtained using the LS algorithm: ; Optionally, the subcarriers are determined based on the power coupling characteristics of the prototype filter in the FBMC / OQAM system. Corresponding inter-carrier interference (ICI) power: ; Optionally, subcarriers that are out of range are set to 0. The value is the estimate for the λth iteration, and the estimate for the 0th iteration is the initial channel estimate. The filter one-sided coefficients... As shown in Table 1, Table 1

[0049] Current estimated average power:

[0050] Noise power conversion:

[0051] subcarrier Signal-to-interference-plus-noise ratio (SINR):

[0052] Step 103: Based on the signal-to-interference-plus-noise ratio and preset threshold of each subcarrier, correct the initial channel estimate corresponding to each subcarrier to obtain the target channel estimate.

[0053] Specifically, after determining the signal-to-interference-plus-noise ratio (SINR) of each subcarrier based on the ICI power and noise power corresponding to each subcarrier, this application corrects the initial channel estimate of each subcarrier based on the comparison result of the SINR and the preset threshold. The initial estimate is retained for subcarriers with high SINR, and the channel estimate of adjacent subcarriers is used to correct for subcarriers with low SINR. Finally, a high-precision target channel estimate is output, thereby significantly improving the accuracy of channel estimation and making the estimation result more consistent with the real channel characteristics. Without changing the system architecture or increasing the computational complexity, it effectively suppresses the estimation deviation caused by interference and noise and improves the system transmission reliability.

[0054] The method described above corrects the initial channel estimate by quantizing the ICI power and calculating the subcarrier-level signal-to-interference-plus-noise ratio. It incorporates the inherent pure imaginary interference caused by filter overlap, which is ignored by the traditional IAM-LS method, into the channel estimation process, significantly improving the accuracy and reliability of the channel estimation results. It effectively suppresses the estimation deviation introduced by interference and noise. Without changing the existing system architecture, reconstructing the pilot, or significantly increasing the computational complexity, it effectively solves the problems of large channel estimation deviation and weak anti-interference capability of the traditional IAM-LS, and significantly improves the channel estimation accuracy and transmission reliability of the FBMC / OQAM system.

[0055] In some embodiments, the initial channel estimate corresponding to each subcarrier is corrected based on the signal-to-interference-plus-noise ratio (SINR) of each subcarrier and a preset threshold to obtain the target channel estimate, including: If the signal-to-interference-plus-noise ratio of a subcarrier is greater than or equal to a preset threshold, the initial channel estimate corresponding to the subcarrier is used as the target channel estimate. If the signal-to-interference-plus-noise ratio of a subcarrier is less than a preset threshold, the initial channel estimate of the subcarrier is corrected based on the channel estimate of the adjacent subcarrier to obtain the current channel estimate. Determine the target channel estimate based on the current channel estimate.

[0056] Specifically, after quantizing the ICI power and calculating the subcarrier-level signal-to-interference-plus-noise ratio (SINR), this application can compare the SINR of the subcarrier with a preset threshold. When the SINR meets or exceeds the threshold, it is determined that the subcarrier is less affected by ICI interference and noise, and the initial channel estimate is directly retained as the final target channel estimate. This reduces the computational load, lowers the algorithm complexity, and improves the overall processing efficiency while ensuring the estimation accuracy.

[0057] Optionally, when the signal-to-interference-plus-noise ratio (SINR) of a subcarrier is less than a preset threshold, the initial channel estimate corresponding to the subcarrier is corrected based on the channel estimate of the adjacent subcarrier to obtain the current channel estimate. This fully utilizes the channel correlation of adjacent subcarriers, achieves accurate compensation for the channel estimate of subcarriers with low SINR and high interference, effectively suppresses the estimation deviation caused by ICI and noise, and significantly improves the reliability of channel estimation.

[0058] The method described above compares the signal-to-interference-plus-noise ratio (SINR) of a subcarrier with a preset threshold to differentiate the initial channel estimates for high-interference and low-interference subcarriers. This ensures efficient calculation of the channel estimate for high-SINR subcarriers and accurate correction of the channel estimate for low-SINR subcarriers, effectively improving the accuracy of channel estimation without changing the system architecture or increasing complexity.

[0059] In some embodiments, determining a target channel estimate based on the current channel estimate includes: If the relative change between the current channel estimate and the initial channel estimate is less than the iteration threshold, the current channel estimate is used as the target channel estimate. If the relative change between the current channel estimate and the initial channel estimate is greater than or equal to the iteration threshold, the current channel estimate is iteratively corrected to obtain the target channel estimate.

[0060] Specifically, when the signal-to-interference-plus-noise ratio (SINR) of a subcarrier is less than a preset threshold, this application corrects the initial channel estimate corresponding to the subcarrier based on the channel estimate of the adjacent subcarriers to obtain the current channel estimate. If the relative change between the current channel estimate and the initial channel estimate is less than the iteration threshold, it can be directly determined that the channel estimation has converged, and the current channel estimate can be used as the target channel estimate, avoiding unnecessary iterative calculations, reducing the amount of computation, and improving processing efficiency.

[0061] Optionally, if the relative change between the current channel estimate and the initial channel estimate is greater than or equal to the iteration threshold, and the current channel estimate result is determined to have a large deviation and has not reached the convergence condition, then the current channel estimate is iteratively corrected, and the ICI power, signal-to-interference-plus-noise ratio, and channel estimate are repeatedly updated until the convergence condition is met, and finally the target channel estimate is obtained. This significantly reduces the channel estimation error, significantly improves the accuracy of the channel estimation, and makes the channel estimation result more consistent with the actual transmission environment.

[0062] The method in the above embodiments achieves convergence determination of the estimation result by comparing the relative change between the current channel estimate and the initial channel estimate with an iteration threshold. If the channel estimation has converged, the current channel estimate is directly used as the target channel estimate; if the channel estimation has not converged, the channel estimate is iteratively corrected to make the target channel estimate closer to the actual transmission environment, thereby significantly improving the accuracy of the channel estimation.

[0063] In some embodiments, iteratively correcting the current channel estimate to obtain the target channel estimate includes: Step a: Based on the current channel estimate, redetermine the ICI power and signal-to-interference-plus-noise ratio; Step b: Based on the newly determined signal-to-interference-plus-noise ratio (SINR), correct the current channel estimate to obtain the iteratively corrected channel estimate. Step c: Determine the relative change between the iteratively corrected channel estimate and the current channel estimate; Step d: When the relative change is less than the iteration threshold, stop the iteration and use the channel estimate after iteration correction as the target channel estimate. Step e: When the relative change is greater than or equal to the iteration threshold, the iteratively corrected channel estimate is used as the current channel estimate, and steps a-c are repeated until the relative change is less than the iteration threshold or the number of iterations reaches the preset maximum number of iterations. For example, the iteration threshold is 10e-6, and the maximum number of iterations is 5.

[0064] Specifically, if the relative change between the current channel estimate and the initial channel estimate is greater than or equal to the iteration threshold, and the current channel estimate result is determined to have a large deviation and has not reached the convergence condition, then based on the current channel estimate, the inter-subcarrier interference (ICI) power of each subcarrier is recalculated, and combined with the noise power, the signal-to-interference-plus-noise ratio (SINR) of each subcarrier is recalculated. Based on the recalculated SINR and the preset threshold, the current channel estimate is further modified differentially, and the error is continuously suppressed in each iteration, thereby continuously reducing the estimation deviation.

[0065] For example, the iterative update rules of this application are as follows: like ≥1, retain the LS estimate: ; like If <1, perform SINR-weighted average correction: ; Where μ represents the weighting coefficient of adjacent subcarriers; The iteration stopping condition is as follows:

[0066] The optimized estimate is denoted as: .

[0067] The method described in the above embodiments requantizes ICI interference and signal-to-interference-plus-noise ratio based on the iteratively updated channel estimate, and combines the iteration threshold and the preset maximum number of iterations for convergence decision. This can effectively suppress estimation errors, make the estimation results gradually approach the real channel response, and significantly improve the accuracy of channel estimation.

[0068] In some embodiments, the initial channel estimate corresponding to a subcarrier is corrected based on the channel estimate of an adjacent subcarrier to obtain the current channel estimate, including: The channel estimates of the subcarrier, its left-hand adjacent subcarrier, and its right-hand adjacent subcarrier are then processed by a signal-to-interference-plus-noise ratio (SINR) weighted average to obtain the current channel estimate.

[0069] Specifically, in this embodiment, when correcting the channel estimate of a low signal-to-interference-plus-noise ratio (SINR) subcarrier, the SINR of the current subcarrier to be corrected, as well as the SINR of the left and right subcarriers adjacent to it, are selected as weights for weighted average calculation. The subcarrier with a higher SINR has a larger weight and contributes more to the final result, thereby obtaining the corrected channel estimate. This effectively utilizes the channel correlation between subcarriers in the FBMC / OQAM system and significantly improves the estimation accuracy of low SINR subcarriers.

[0070] The method described in the above embodiment utilizes the channel correlation between subcarriers in the FBMC / OQAM system to perform signal-to-interference-plus-noise ratio (SINR) weighted averaging on the channel estimates of the subcarriers and their left and right adjacent subcarriers to obtain the channel estimates. This effectively enhances the positive contribution of high SINR subcarriers to the channel estimation results while suppressing the adverse effects of ICI interference and noise on low SINR subcarriers. As a result, the corrected channel estimates are closer to the actual channel characteristics, significantly improving the estimation accuracy of low SINR subcarriers.

[0071] In some embodiments, the channel estimation method further includes: Based on the target channel estimate and the symbol field signal carrying ICI, the data transmitted by the transmitter is recovered.

[0072] Specifically, after determining the target channel estimate, this application can perform equalization processing on the received symbol domain signal carrying inter-carrier interference (ICI) based on the target channel estimate, to compensate for the channel fading and ICI interference suffered by the signal during transmission, thereby recovering the original data symbols initially sent by the transmitter, accurately canceling multipath effects and inherent ICI interference, effectively reducing the bit error rate, and improving the transmission reliability of the FBMC / OQAM system in high interference scenarios.

[0073] For example, this application can use the optimized estimate as the channel frequency response for equalization, solve for the equalized data symbols, and use the real part to hard-determine the transmitted real symbols:

[0074] For example, such as Figure 2 and Figure 3 As shown, this application is based on an FBMC / OQAM system, employing a PHYDAYS prototype filter, 512 subcarriers, a sampling rate of 20MHz, and 4QAM modulation. Simulations were conducted under both EPA and EVA wireless channel environments to compare the performance of the proposed channel estimation method with that of the traditional IAM-LS method. While maintaining the same preamble, AFB output, and single-tap equalization architecture as existing IAM systems, this application optimizes the initial estimation through ICI power and SINR-based adaptive updates. In contrast, the traditional IAM-LS algorithm does not quantize ICI, does not classify based on SINR, and exhibits large estimation bias for low-SINR subcarriers. Experimental comparisons show that, under different channel environments (EPA and EVA), the proposed method outperforms the traditional IAM-LS method in both estimation accuracy and transmission reliability. Optionally, the algorithm of this application can be abbreviated as the IAM-EM algorithm, while existing algorithms are simply referred to as the IAM algorithm. For example, Table 2 shows a comparison of the bit error rate (BER) of this application and the traditional IAM-LS method under the EPA channel environment; Table 3 shows a comparison of the BER of this application and the traditional IAM-LS method under the EVA channel Doppler frequency shift of 70Hz; Table 4 shows a comparison of the BER of this application and the traditional IAM-LS method under the EVA channel Doppler frequency shift of 170Hz; Table 5 shows a comparison of the BER of this application and the traditional IAM-LS method under the EVA channel Doppler frequency shift of 333Hz. Table 2

[0075] Table 3

[0076] Table 4

[0077] Table 5

[0078] The method described above performs equalization processing on the symbol domain signal carrying ICI interference using the target channel estimate, which can effectively compensate for the effects of channel fading and inherent interference, accurately restore the original data symbols transmitted by the transmitter, and reduce the system bit error rate.

[0079] The channel estimation apparatus provided by the present invention will be described below. The channel estimation apparatus described below can be referred to in correspondence with the channel estimation method described above. For example, Figure 4 As shown, the channel estimation device includes: The determination module 410 is used to determine the initial channel estimate value corresponding to each subcarrier based on the IAM-R type preamble and the least squares algorithm; The processing module 420 is used to determine the signal-to-interference-plus-noise ratio of each subcarrier based on the ICI power and noise power corresponding to each subcarrier; The estimation module 430 is used to correct the initial channel estimation value corresponding to each subcarrier based on the signal-to-interference-plus-noise ratio and a preset threshold, so as to obtain the target channel estimation value.

[0080] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5 As shown, the electronic device may include a processor 510, a communications interface 520, a memory 530, and a communication bus 540. The processor 510, communications interface 520, and memory 530 communicate with each other via the communication bus 540. The processor 510 can call logic instructions in the memory 530 to execute a channel estimation method. This method includes: determining the initial channel estimate value corresponding to each subcarrier based on the IAM-R type preamble and the least squares algorithm; determining the signal-to-interference-plus-noise ratio (SINR) of each subcarrier based on the ICI power and noise power corresponding to each subcarrier; and correcting the initial channel estimate value corresponding to each subcarrier based on the SINR and a preset threshold to obtain the target channel estimate value.

[0081] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0082] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the channel estimation method provided by the above methods. The method includes: determining the initial channel estimation value corresponding to each subcarrier based on the IAM-R type preamble and the least squares algorithm; determining the signal-to-interference-plus-noise ratio (SINR) of each subcarrier based on the ICI power and noise power corresponding to each subcarrier; and correcting the initial channel estimation value corresponding to each subcarrier based on the SINR and a preset threshold to obtain the target channel estimation value.

[0083] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program is implemented to perform the channel estimation methods provided by the above methods. The method includes: determining an initial channel estimate value corresponding to each subcarrier based on an IAM-R type preamble and a least squares algorithm; determining the signal-to-interference-plus-noise ratio (SINR) of each subcarrier based on the ICI power and noise power corresponding to each subcarrier; and correcting the initial channel estimate value corresponding to each subcarrier based on the SINR of each subcarrier and a preset threshold to obtain a target channel estimate value.

[0084] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0085] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A channel estimation method applied to an FBMC / OQAM system, characterized in that, include: Based on the IAM-R type preamble and the least squares algorithm, the initial channel estimate value corresponding to each subcarrier is determined; The signal-to-interference-plus-noise ratio (SINR) of each subcarrier is determined based on the ICI power and noise power corresponding to each subcarrier; the ICI power corresponding to each subcarrier is determined based on the power coupling characteristics of the prototype filter in the FBMC / OQAM system. Based on the signal-to-interference-plus-noise ratio and preset threshold of each subcarrier, the initial channel estimate value corresponding to each subcarrier is corrected to obtain the target channel estimate value; The step of correcting the initial channel estimate corresponding to each subcarrier based on the signal-to-interference-plus-noise ratio and a preset threshold to obtain the target channel estimate includes: If the signal-to-interference-plus-noise ratio of a subcarrier is greater than or equal to the preset threshold, the initial channel estimate corresponding to the subcarrier is used as the target channel estimate. If the signal-to-interference-plus-noise ratio of a subcarrier is less than the preset threshold, the initial channel estimate corresponding to the subcarrier is corrected based on the channel estimate of the adjacent subcarrier to obtain the current channel estimate. Based on the current channel estimate, determine the target channel estimate; Determining the target channel estimate based on the current channel estimate includes: If the relative change between the current channel estimate and the initial channel estimate is less than the iteration threshold, the current channel estimate is used as the target channel estimate. If the relative change between the current channel estimate and the initial channel estimate is greater than or equal to the iteration threshold, the current channel estimate is iteratively corrected to obtain the target channel estimate. The iterative correction of the current channel estimate to obtain the target channel estimate includes: Step a: Based on the current channel estimate, redetermine the ICI power and signal-to-interference-plus-noise ratio; Step b: Based on the newly determined signal-to-interference-plus-noise ratio (SINR), correct the current channel estimate to obtain the iteratively corrected channel estimate. Step c: Determine the relative change between the iteratively corrected channel estimate and the current channel estimate; Step d: When the relative change is less than the iteration threshold, stop the iteration and use the channel estimate after iteration correction as the target channel estimate. Step e: When the relative change is greater than or equal to the iteration threshold, the iteratively corrected channel estimate is used as the current channel estimate, and steps a-c are repeated until the relative change is less than the iteration threshold or the number of iterations reaches the preset maximum number of iterations.

2. The channel estimation method according to claim 1, characterized in that, The channel estimation value based on adjacent subcarriers is used to correct the initial channel estimation value corresponding to the subcarrier to obtain the current channel estimation value, including: The channel estimates of the subcarrier, its left-side adjacent subcarrier, and its right-side adjacent subcarrier are processed by a signal-to-interference-plus-noise ratio (SINR) weighted average to obtain the current channel estimate.

3. The channel estimation method according to claim 1 or 2, characterized in that, The method further includes: Based on the target channel estimate and the symbol field signal carrying ICI, the data transmitted by the transmitter is recovered.

4. A channel estimation apparatus for implementing the channel estimation method as described in any one of claims 1 to 3, characterized in that, include: The determination module is used to determine the initial channel estimate for each subcarrier based on the IAM-R type preamble and the least squares algorithm; The processing module is used to determine the signal-to-interference-plus-noise ratio of each subcarrier based on the ICI power and noise power corresponding to each subcarrier. The estimation module is used to correct the initial channel estimate value corresponding to each subcarrier based on the signal-to-interference-plus-noise ratio and preset threshold of each subcarrier, so as to obtain the target channel estimate value.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the channel estimation method as described in any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the channel estimation method as described in any one of claims 1 to 3.

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