A method, system, device and medium for secondary BEM channel estimation based on ICI elimination

Abstract

A secondary BEM channel estimation method, system, device and medium based on ICI elimination, the method comprising: performing windowing preprocessing on a received signal, calculating base coefficients based on the least squares (LS) method and obtaining a first channel tap coefficient estimation value, using a baseline tilt technology to reduce BEM modeling errors and obtain an ICI interference matrix, using an ICI self-elimination technology to perform ICI self-elimination on the received signal, solving base coefficients again on the signal after ICI elimination and constructing a channel frequency domain response matrix using the baseline tilt technology; the system, device and medium are used to implement a secondary BEM channel estimation method based on ICI elimination; the present invention reduces the influence of ICI interference and modeling errors on channel estimation, and at the same time improves the estimation accuracy and the bit error rate performance of the system.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital information transmission, and in particular relates to a secondary BEM channel estimation method, system, device and medium based on ICI elimination. Background Art

[0002] At present, my country is in a critical period of transformation from the traditional automobile industry to new automobile fields such as new energy, autonomous driving and intelligent interaction. In such application scenarios, vehicle to everything (V2X) communication is a key technology, and high-speed mobile scenarios are an important application scenario of vehicle to everything communication. Orthogonal frequency division multiplexing (OFDM) technology has the advantages of high frequency band utilization, high data transmission rate, and effective resistance to multipath fading. In the latest vehicle to everything communication standard LTE-V2X, OFDM technology is a major research direction, and OFDM technology is still one of the key physical layer technologies of communication systems in high-speed mobile environments.

[0003] In a high-speed mobile environment, multipath causes channel frequency selective fading, while the relative movement of the transmitter and receiver causes subcarrier frequency offset, making the OFDM subcarriers no longer orthogonal to each other. The channel undergoes drastic and nonlinear changes in a short period of time, resulting in serious time selective fading. The dual-selective fading characteristics of the channel have brought considerable challenges to the communication system. As an important part of the physical layer of the communication system, the performance of channel estimation technology directly affects the quality of the entire wireless communication system. Traditional OFDM channel estimation technology usually takes the receiver as the background of being stationary or in a low-speed operation state, and studies the situation where the channel tap remains approximately unchanged for the duration of one or more OFDM symbols. In the dual-selective channel scenario, the performance of traditional channel estimation technology is greatly reduced, resulting in the inability to provide reliable and stable communication services.

[0004] The basis expansion model channel estimation algorithm uses a linear combination of finite basis functions to approximate the changes of the dual-selection channel, and is currently a hot research direction in the channel estimation technology under dual-selection channels. In order to cope with different application scenarios, many scholars have proposed basis function models with low implementation complexity such as CE-BEM and Polynomial Basis Expansion Model (Polynomial BEM, P-BEM), but such basis function models usually have the disadvantages of large modeling errors and low estimation accuracy. Basis function models with high estimation accuracy, such as Discrete Prolate Spheroidal BEM (Discrete Prolate Spheroidal BEM, DPS-BEM) and Discrete Karhunen-Loeve BEM (Discrete Karhunen-Loeve BEM, DKL-BEM), usually have the disadvantages of high implementation complexity and the need for channel prior conditions. The channel estimation algorithm that combines BEM with Kalman filtering (Jellali Z, Atallah LN. Fast fading channel estimation by Kalman filtering and CIR support tracking [J]. IEEE Transactions on Broadcasting, 2017, 63 (4): 635-643.) has been a hot topic in recent years. This type of algorithm has good estimation accuracy under low Doppler frequency shift, but as the Doppler frequency shift increases, the estimation accuracy will decline. In addition, since the channel is tracked and estimated in the time domain, its application in OFDM systems is affected. In addition, under the condition of dual-selection channels, each subcarrier of the OFDM system is subject to greater inter-carrier interference, which seriously affects the accuracy of channel estimation. The literature (Liu TL, Chung WH, Yuan SY, et al. ICI self-cancellation with cosine windowing in OFDM transmitters over fast time-varying channels [J]. IEEE Transactions on Wireless Communications, 2015, 14 (7): 3559-3570.) suppresses ICI by adding a window at the receiving end. However, when the normalized Doppler frequency shift is large, the suppression effect on ICI is limited. Therefore, how to suppress the influence of ICI interference on channel estimation and reduce the modeling error of basis function modeling, and finally improve the estimation accuracy, is the main problem studied in the present invention.

[0005] Through the above analysis, the problems existing in the existing technology are: under the dual-selection channel, ICI interference affects the pilot subcarriers and thus affects the channel estimation accuracy, and there is a modeling error when selecting basis function modeling, which makes the final channel estimation accuracy fail to meet research expectations. Summary of the invention

[0006] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to propose a secondary BEM channel estimation method, system, device and medium based on ICI elimination, which performs ICI elimination by combining receiving end windowing, ICI self-elimination and other aspects on the basis of a base expansion model, and combines baseline tilt technology to reduce the impact of ICI interference and modeling errors on channel estimation, while improving the estimation accuracy and the bit error rate performance of the system.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] A secondary BEM channel estimation method based on ICI elimination comprises the following steps:

[0009] Step 1, performing windowing preprocessing on the received signal;

[0010] Step 2: using the least squares (LS) method on the estimation result of the receiving end signal-to-noise ratio, solving the basis coefficient corresponding to the basis expansion model, and fitting the channel tap coefficient;

[0011] Step 3: According to the channel tap coefficients calculated in step 2, the baseline tilt technology is used to suppress the BEM modeling error caused by the Gibbs effect, and the ICI interference matrix is ​​obtained, and the transmission signal estimation value is obtained by combining the equalization algorithm based on LDL decomposition;

[0012] Step 4, performing ICI self-cancellation on the received signal according to the ICI interference matrix calculated in step 3 and the estimated value of the transmitted signal;

[0013] Step 5: Repeat steps 2 and 3 for the received signal after ICI self-cancellation obtained in step 4 to obtain a channel frequency domain response matrix for secondary channel estimation.

[0014] The windowing preprocessing of the received signal in step 1 is specifically performed using the following formula:

[0015]

[0016] Among them, s(t) is a quadratic polynomial, that is, s(t) = a 0 +a 1 t+a 2 t 2 , based on the window function in the time domain and the optimal criterion of signal to interference plus noise ratio, a 0The value of a is 0.5, 1 The value of is -0.3, a 3 The value of is -0.2.

[0017] In step 2, the least square method (LS) is used for the estimation result of the receiving end signal-to-noise ratio to solve the basis coefficient corresponding to the basis expansion model and fit the channel tap coefficient. The specific process is:

[0018] Step 2.1: Calculate the frequency domain matrix Δ of the basis function q =Fdiag(b q (n))F H ; Where F is the Fourier transform matrix, F H is the inverse Fourier transform matrix;

[0019] Basis function b q (n) is expressed as follows:

[0020]

[0021] Where q is the oversampling factor, N is the total number of subcarriers in M ​​OFDM symbols, and Q is the base extension model order.

[0022] Step 2.2: Construct matrix B according to the corresponding transmission pilot cluster of the mth OFDM symbol m (P) ,make

[0023]

[0024] Among them, (F L ) m It is the matrix composed of the first L columns of the Fourier transform matrix and the corresponding row elements of the pilot cluster. m A pilot cluster is sent corresponding to the mth OFDM symbol;

[0025] Step 2.3: Use the least squares method (LS) to solve the basis coefficient, let A = ΔB, then the final basis coefficient calculation formula is:

[0026]

[0027] in, is the pseudo-inverse of A, Y P is the frequency domain observation value of the received pilot.

[0028] In step 3, the baseline tilt technology is specifically:

[0029] Step 3.1: Divide the channel taps into a linear part and a nonlinear part through baseline construction. The linear part is composed of M OFDM symbols connected end to end. Construct the linear part matrix The sub-diagonal elements are given by constitute;

[0030] Step 3.2: Calculate the nonlinear part of the received signal based on the linear part signal and the received signal:

[0031]

[0032] Calculate the base coefficient corresponding to the nonlinear part according to the base coefficient solution method in step 2

[0033] Step 3.3: For the frequency domain matrix Δ of the basis function in step 2 q , only retaining its main diagonal, B sub-diagonals and B super-diagonals to form a band matrix (Δ q ) B ;

[0034] Step 3.4: Calculate the channel frequency domain response matrix of the linear part and the nonlinear part:

[0035]

[0036]

[0037] Step 3.5: Combine the linear part and the nonlinear part to calculate the channel frequency domain response matrix H:

[0038]

[0039] The step 3 uses the baseline tilt technology to suppress the BEM modeling error caused by the Gibbs effect, which is to set the main diagonal of the frequency domain channel response matrix H to zero to form an ICI interference matrix.

[0040] In step 3, the equalization algorithm based on LDL decomposition is used to obtain the estimated value of the transmitted signal. The specific steps are:

[0041] Step 3.6: Solve the minimum mean square error MMSE coefficient denominator matrix: C = H B H H B +σ 2 I N ;

[0042] Step 3.7: Solve the intermediate variable matrices L and D:

[0043]

[0044]

[0045] Step 3.8: Solve x1, x2, and x3 in sequence to finally get the estimated value of the transmitted signal:

[0046]

[0047]

[0048] The step 4 performs ICI self-elimination on the received signal, specifically using the following formula:

[0049] Y′=YH ICI X

[0050] Y′ represents the received signal after ICI self-cancellation, Y represents the original received signal, X represents the estimated value of the transmitted signal, and H ICI Represents the ICI interference matrix.

[0051] A system for a secondary BEM channel estimation method based on ICI elimination, comprising:

[0052] A windowing module, used for performing windowing preprocessing on the received signal;

[0053] The base coefficient calculation module uses the least squares (LS) method to solve the base coefficients of the receiving end signal-to-noise ratio estimation result and fit the channel tap coefficients;

[0054] A baseline tilt module is used to suppress the BEM modeling error caused by the Gibbs effect through the baseline tilt technology, and obtain the ICI interference matrix and the estimated value of the transmitted signal;

[0055] ICI self-cancellation module, used for performing ICI self-cancellation on the received signal;

[0056] The loop module is used for repeatedly acquiring the base coefficients of the base expansion model and fitting channel tap coefficients, ICI interference matrix and transmission signal estimation value to obtain the channel frequency domain response matrix of the secondary channel estimation.

[0057] A device for implementing a secondary BEM channel estimation method based on ICI elimination, comprising:

[0058] Memory: used for storing a computer program for implementing a secondary BEM channel estimation method based on ICI elimination;

[0059] Processor: used to implement a secondary BEM channel estimation method based on ICI elimination when executing the computer program.

[0060] A computer-readable storage medium, comprising:

[0061] The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a secondary BEM channel estimation method based on ICI elimination can be implemented.

[0062] Compared with the prior art, the beneficial effects of the present invention are:

[0063] (1) Based on the base extension model, the present invention combines the receiving end windowing and ICI self-cancellation to eliminate ICI from multiple aspects, and combines the baseline tilt technology to reduce the impact of ICI interference and modeling errors on channel estimation, while improving the estimation accuracy and the bit error rate performance of the system.

[0064] (2) The present invention performs the first ICI elimination by adding a window at the receiving end, so that the BEM observation data is closer to the channel change, and then performs the second ICI elimination by the ICI self-elimination algorithm, which further suppresses the ICI of the observation data and effectively solves the problem of insufficient estimation accuracy caused by inter-carrier interference of the pilot subcarrier in the dual-selection channel.

[0065] (3) The present invention solves the limitation problem of using a single OFDM symbol for BEM modeling by using a larger BEM channel to fit the time-varying characteristics of multiple OFDM symbols, and is suitable for a multi-symbol OFDM system with grid pilot inserted.

[0066] (4) The present invention reduces the influence of the Gibbs effect on GCE-BEM modeling through the baseline tilt technology, reduces the modeling error of BEM modeling, has the advantage of high estimation accuracy, and is suitable for OFDM systems modeled using GCE-BEM. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a method flow chart of an embodiment of the present invention.

[0068] Figure 2 Schematic diagram of baseline construction of the baseline tilting technology according to an embodiment of the present invention.

[0069] Figure 3 It is a system structure block diagram of an embodiment of the present invention.

[0070] Figure 4 It is a block diagram of an OFDM system according to an embodiment of the present invention.

[0071] Figure 5 It is a schematic diagram of the data frame structure of an embodiment of the present invention.

[0072] Figure 6 It is a schematic diagram of the pilot cluster structure of an embodiment of the present invention.

[0073] Figure 7 It is a schematic diagram of a grid pilot insertion method according to an embodiment of the present invention.

[0074] Figure 8 It is a schematic diagram comparing the performance curves of the algorithm provided by the present invention and the existing BEM channel estimation algorithm under the modified EVA channel. DETAILED DESCRIPTION

[0075] The present invention is further described in detail below with reference to the accompanying drawings.

[0076] like Figure 1 As shown, a secondary BEM channel estimation method based on ICI elimination includes the following steps:

[0077] Step 1, performing windowing preprocessing on the received signal;

[0078] The received signal is preprocessed by adding a window at the receiving end. The time domain expression of the selected receiving end window function is:

[0079]

[0080] Among them, s(t) is a quadratic polynomial, that is, s(t) = a 0 +a 1 t+a 2 t 2 , This paper is based on the window function in the time domain and the optimal criterion of signal to interference plus noise ratio, a 0 The value of a is 0.5, 1 The value of is -0.3, a 3 The value of is -0.2.

[0081] Step 2: according to the estimation result of the signal-to-noise ratio at the receiving end, the least square method (LS) is used to solve the basis coefficient corresponding to the basis expansion model, and the channel tap coefficient is fitted;

[0082] Step 2.1: From length to N CP The vector composed of the CP sequence is represented as X CP , take a sequence of the same length at the end of the corresponding OFDM symbol, and the vector formed by it is represented as X D , calculate the signal power P D and noise power P N as follows:

[0083]

[0084]

[0085] The signal-to-noise ratio estimation result is P D / P N .

[0086] Step 2.2: Calculate the frequency domain matrix Δ of the basis function q =Fdiag(b q (n))F H ; Where F is the Fourier transform matrix, F H is the inverse Fourier transform matrix;

[0087] Basis function b q (n) is expressed as follows:

[0088]

[0089] Where P is the oversampling factor, N is the total number of subcarriers in M ​​OFDM symbols, and Q is the base extension model order;

[0090] Step 2.3, construct the matrix B according to the corresponding transmission pilot cluster of the mth OFDM symbol m (P) ,make

[0091]

[0092] Among them, (F L ) m It is the matrix composed of the first L columns of the Fourier transform matrix and the corresponding row elements of the pilot cluster. m A pilot cluster is sent corresponding to the mth OFDM symbol;

[0093] Step 2.4, use the least squares method (LS) to solve the basis coefficient, let A = ΔB, then the final basis coefficient calculation formula is:

[0094]

[0095] in, is the pseudo-inverse of A, Y P is the frequency domain observation value of the received pilot;

[0096] Step 3: Use the baseline tilt technique to reduce the BEM modeling error, set the main diagonal of the frequency domain channel response matrix to zero, form an ICI interference matrix, and use the low-complexity equalization algorithm based on LDL decomposition to obtain the estimated value of the transmitted signal;

[0097] Step 3.1, suppress the BEM modeling error caused by the Gibbs effect through baseline construction:

[0098] Through Figure 2 The baseline construction shown in the figure divides the channel taps into a linear part and a nonlinear part. The linear part is composed of M OFDM symbols connected end to end. The linear part matrix is ​​constructed. The sub-diagonal elements are given by constitute;

[0099] Step 3.2: Calculate the nonlinear part of the received signal based on the linear part signal and the received signal:

[0100]

[0101] Calculate the base coefficient corresponding to the nonlinear part according to the base coefficient solution method in step 2

[0102] Step 3.3: For the frequency domain matrix Δ of the basis function in step 2 q , only retaining its main diagonal, B sub-diagonals and B super-diagonals to form a band matrix (Δ q ) B ;

[0103] Step 3.4: Calculate the frequency domain channel response matrix of the linear part and the nonlinear part:

[0104]

[0105]

[0106] Step 3.5: Combine the linear part and the nonlinear part to calculate the frequency domain channel response H:

[0107]

[0108] Step 3.6: Solve for the MMSE coefficient denominator matrix: C = H B H H B +σ 2 I N

[0109] Solve the intermediate variable matrices L and D:

[0110]

[0111]

[0112] Solve x1, x2, and x3 in sequence, and finally get the estimated value of the transmitted signal:

[0113]

[0114]

[0115] Step 4: Perform ICI self-cancellation on the received signal according to the ICI interference matrix obtained in step 3 and the estimated value of the transmitted signal:

[0116] Y′=YH ICI X

[0117] Y′ represents the received signal after ICI self-cancellation, Y represents the original received signal, X represents the estimated value of the transmitted signal, and H ICI Represents the ICI interference matrix.

[0118] Step 5: Solve the base coefficients of the signal after ICI elimination again and reconstruct the channel frequency domain response matrix using the baseline tilt technology.

[0119] like Figure 3 As shown, a secondary BEM channel estimation system based on ICI elimination includes:

[0120] A windowing module is used to perform preliminary ICI suppression preprocessing on the received signal to implement step 1 in a secondary BEM channel estimation method based on ICI elimination;

[0121] A base coefficient calculation module calculates the base coefficient using the least squares (LS) method for the signal-to-noise ratio estimation result of the receiving end, thereby implementing step 2 in a quadratic BEM channel estimation method based on ICI elimination;

[0122] A baseline tilt module, used for suppressing the BEM modeling error caused by the Gibbs effect through baseline construction, and implementing step 3 in a secondary BEM channel estimation method based on ICI elimination;

[0123] An ICI self-cancellation module is used to perform ICI self-cancellation on the received signal according to the obtained ICI self-cancellation matrix and the estimated value of the transmitted signal, so as to implement step 4 in a secondary BEM channel estimation method based on ICI elimination;

[0124] The loop module repeatedly obtains the base coefficients of the base expansion model and the fitting channel tap coefficients, the ICI interference matrix and the estimated value of the transmitted signal to obtain the channel frequency domain response matrix of the secondary channel estimation, thereby realizing step 5 in a secondary BEM channel estimation method based on ICI elimination.

[0125] The present invention can be used in a vehicle networking communication system under high-speed movement. During the driving process of the vehicle, data can be exchanged with the outside world through on-board sensing equipment, terminals, etc., to obtain environmental information around the vehicle, etc., and services can be provided to the vehicle using advanced communication technologies, such as communication between the vehicle and the network, communication between vehicles, and communication between vehicles and infrastructure.

[0126] The embodiments of the present invention have achieved some positive effects during the development or use process, and indeed have great advantages over the prior art. The following content is described in conjunction with data, charts, etc. of the test process.

[0127] In the specific implementation, since all the elements of the A matrix in step 1 and step 2 are known, they can be calculated offline using software such as Matlab and stored in hardware ROM and other resources to avoid complex matrix operations in actual engineering and meet the real-time requirements of hardware processing; the remaining steps can theoretically be implemented through programmable logic.

[0128] The simulation system used in the present invention is the CP-OFDM system specified in the 3GPP standard. The system block diagram is as follows: Figure 4 As shown in the figure, the number of subcarriers in a single OFDM symbol is 2048, the number of OFDM blocks is 5, the subcarrier spacing is 15kHz, the normalized maximum Doppler shift is 0.2, the modulation method is QPSK, the coding method is Turbo coding, and the base extension model order is 3. The system frame structure is as follows Figure 5 As shown, the pilot insertion method is Figure 6 and Figure 7 The FDKD scattered pilot cluster form is shown.

[0129] The simulation channel is based on the extended vehicular channel (EVA) model of the 3GPP protocol. When using BEM for channel estimation, the delay of each path needs to be an integer multiple of the sampling period. In order to adapt to the BEM model, the tap delay of the EVA channel model is adjusted and normalized using the rounding method. The final adjusted PDP parameters are: normalized delay [0, 4, 12, 16, 28, 40] ns, and the average power is [-3.8515, -3.0972, -15.2762, -13.1762, -18.1762, -23.0762] dB.

[0130] The performance simulation mainly compares the performance of three algorithms:

[0131] (1) Existing multi-symbol BEM channel estimation methods.

[0132] (2) The method of the present invention removes the channel estimation method after ICI elimination.

[0133] (3) The method of the present invention is a secondary BEM channel estimation method based on ICI elimination.

[0134] The system bit error rate system simulation results are as follows Figure 8 The horizontal axis is the signal-to-noise ratio (SNR), and the vertical axis is the bit error probability (BER). The secondary BEM channel estimation method based on ICI elimination of the present invention has a greatly improved performance compared with the existing multi-symbol BEM channel estimation method and the BEM estimation method without ICI elimination. When the normalized Doppler shift is large, the BER of the existing multi-symbol BEM channel estimation method and the BEM estimation method without ICI elimination is 10 -4 The above examples all show a bit error floor phenomenon. When the signal-to-noise ratio is 16 dB, the bit error rate performance of the method proposed in the present invention is improved by two orders of magnitude compared to the BEM estimation method without ICI elimination.

[0135] A device for implementing a secondary BEM channel estimation method based on ICI elimination, comprising:

[0136] Memory: used to store computer programs;

[0137] A processor is used to implement the secondary BEM channel estimation method based on ICI elimination when executing the computer program.

[0138] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the device for implementing the secondary BEM channel estimation method based on ICI elimination, and uses various interfaces and lines to connect various parts of the device for implementing the secondary BEM channel estimation method based on ICI elimination.

[0139] When the processor executes the computer program, the steps of the above-mentioned secondary BEM channel estimation method based on ICI elimination are implemented, for example: windowing preprocessing is performed at the receiving end; a suitable criterion is selected through the signal-to-noise ratio estimation result to solve the base coefficient and obtain the first channel tap coefficient estimation value; the baseline tilt technology is used to suppress the Gibbs effect of CE-BEM to reduce the modeling error and obtain the ICI interference matrix; the ICI interference matrix and the equalization algorithm based on LDL decomposition are combined to perform ICI self-elimination on the received signal; the base coefficient of the signal after ICI elimination is solved again and the baseline tilt technology is used to obtain the final channel frequency domain response matrix, thereby realizing a secondary BEM channel estimation method based on ICI elimination.

[0140] Alternatively, when the processor executes the computer program, the functions of each module in the above system are implemented, for example: a windowing module, which is used to perform preliminary ICI suppression preprocessing on the received signal; a base coefficient calculation module, which uses the least squares (LS) method to calculate the base coefficient of the receiving end signal-to-noise ratio estimation result; a baseline tilt module, which is used to suppress the BEM modeling error caused by the Gibbs effect through baseline construction; an ICI self-elimination module, which performs ICI self-elimination on the received signal according to the obtained ICI self-elimination matrix and the estimated value of the transmitted signal, and a loop module, which is used to repeatedly obtain the base coefficients of the basis expansion model and the fitting channel tap coefficients, the ICI interference matrix and the estimated value of the transmitted signal, obtain the channel frequency domain response matrix of the secondary channel estimation, and output the result of the secondary BEM channel estimation based on ICI elimination.

[0141] Exemplarily, the computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments that can complete preset functions, and the instruction segments are used to describe the execution process of the computer program in the device for implementing the secondary BEM channel estimation method based on ICI elimination. For example, the computer program can be divided into a windowing module for performing preliminary ICI suppression preprocessing on the received signal; a base coefficient calculation module for calculating the base coefficient using the least squares method (LS) for the signal-to-noise ratio estimation result of the receiving end; a baseline tilt module for suppressing the BEM modeling error caused by the Gibbs effect through baseline construction; an ICI self-elimination module for performing ICI self-elimination on the received signal according to the obtained ICI self-elimination matrix and the estimated value of the transmitted signal, and a loop module for repeatedly obtaining the base coefficients of the base expansion model and the fitting channel tap coefficients, the ICI interference matrix and the estimated value of the transmitted signal, to obtain the channel frequency domain response matrix of the secondary channel estimation, and output the result of the secondary BEM channel estimation based on ICI elimination.

[0142] The device for implementing the secondary BEM channel estimation method based on ICI elimination can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The device for implementing the secondary BEM channel estimation method based on ICI elimination may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above is an example of a device for implementing the secondary BEM channel estimation method based on ICI elimination, and does not constitute a limitation on a device for implementing the secondary BEM channel estimation method based on ICI elimination, and may include more components than the above, or a combination of certain components, or different components. For example, the device for implementing the secondary BEM channel estimation method based on ICI elimination may also include input and output devices, network access devices, buses, etc.

[0143] The memory can be used to store the computer program and / or module, and the processor implements various functions of the device for implementing the secondary BEM channel estimation method based on ICI elimination by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory.

[0144] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0145] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the secondary BEM channel estimation method based on ICI elimination are implemented.

[0146] If the module / unit integrated in the signal detection system of the system of the secondary BEM channel estimation method based on ICI elimination is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0147] The present invention implements all or part of the processes in the above-mentioned method for secondary BEM channel estimation based on ICI elimination, and can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned method for secondary BEM channel estimation based on ICI elimination can be implemented. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or preset intermediate form, etc.

[0148] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0149] It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electrical carrier signals and telecommunication signals.

[0150] It should be noted that the embodiments of the present invention can be implemented by hardware, software or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware.

[0151] It will be appreciated by those skilled in the art that the above-mentioned devices and methods may be implemented using computer executable instructions and / or contained in processor control codes, such as providing such codes on a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., may also be implemented by software executed by various types of processors, or may be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

Claims

1. A secondary BEM channel estimation method based on ICI elimination, characterized in that: The following steps are involved: Step 1, performing windowing preprocessing on the received signal; Step 2: using the least squares (LS) method on the estimation result of the receiving end signal-to-noise ratio, solving the basis coefficient corresponding to the basis expansion model, and fitting the channel tap coefficient; In step 2, the least square method (LS) is used for the estimation result of the receiving end signal-to-noise ratio to solve the basis coefficient corresponding to the basis expansion model and fit the channel tap coefficient. The specific process is: Step 2.1: Calculate the frequency domain matrix Δ of the basis function q =Fdiag(b q (n))F H ; Where F is the Fourier transform matrix, F H is the inverse Fourier transform matrix; Basis function b q (n) is expressed as follows: Where q is the oversampling factor, N is the total number of subcarriers in M ​​OFDM symbols, and Q is the base extension model order; Step 2.2: Construct matrix B according to the corresponding transmission pilot cluster of the mth OFDM symbol m (P) ,make Among them, (F L ) m It is the matrix composed of the first L columns of the Fourier transform matrix and the corresponding row elements of the pilot cluster. m A pilot cluster is sent corresponding to the mth OFDM symbol; Step 2.3: Use the least squares method (LS) to solve the basis coefficient, let A = ΔB, then the final basis coefficient calculation formula is: in, is the pseudo-inverse of A, Y P is the frequency domain observation value of the received pilot; Step 3: According to the channel tap coefficients calculated in step 2, the baseline tilt technology is used to suppress the BEM modeling error caused by the Gibbs effect, and the ICI interference matrix is ​​obtained, and the transmission signal estimation value is obtained by combining the equalization algorithm based on LDL decomposition; The baseline tilting technique in step 3 is specifically as follows: Step 3.1: Divide the channel taps into a linear part and a nonlinear part through baseline construction. The linear part is composed of M OFDM symbols connected end to end. Construct the linear part matrix The sub-diagonal elements are given by constitute; Step 3.2: Calculate the nonlinear part of the received signal based on the linear part signal and the received signal: Calculate the base coefficient corresponding to the nonlinear part according to the base coefficient solution method in step 2 Step 3.3: For the frequency domain matrix Δ of the basis function in step 2 q , only retaining its main diagonal, B sub-diagonals and B super-diagonals to form a band matrix (Δ q ) B ; Step 3.4: Calculate the channel frequency domain response matrix of the linear part and the nonlinear part: Step 3.5: Combine the linear part and the nonlinear part to calculate the channel frequency domain response matrix H: The baseline tilt technique is used to suppress the BEM modeling error caused by the Gibbs effect, which is to set the main diagonal of the frequency domain channel response matrix H to zero to form an ICI interference matrix; Based on the equalization algorithm of LDL decomposition, the estimated value of the transmitted signal is obtained. The specific steps are: Step 3.6: Solve the minimum mean square error MMSE coefficient denominator matrix: C = H B H H B +σ 2 I N ; Step 3.7: Solve the intermediate variable matrices L and D: Step 3.8: Solve x1, x2, and x3 in sequence to finally get the estimated value of the transmitted signal: Step 4, performing ICI self-cancellation on the received signal according to the ICI interference matrix calculated in step 3 and the estimated value of the transmitted signal; Step 5: Repeat steps 2 and 3 for the received signal after ICI self-cancellation obtained in step 4 to obtain a channel frequency domain response matrix for secondary channel estimation.

2. The method for secondary BEM channel estimation based on ICI elimination according to claim 1, characterized in that: The windowing preprocessing of the received signal in step 1 is specifically performed using the following formula: Among them, s(t) is a quadratic polynomial, that is, s(t) = a0+a1t+a2t 2 , based on the window function in the time domain continuity and the optimal criterion of signal to interference plus noise ratio, the value of a0 is 0.5, the value of a1 is -0.3, and the value of a3 is -0.

2.

3. The method for secondary BEM channel estimation based on ICI elimination according to claim 1, characterized in that: In step 4, the received signal is subjected to ICI self-elimination, specifically using the following formula: Y′=Y-H ICI X Where Y′ represents the received signal after ICI self-cancellation, Y represents the original received signal, X represents the estimated value of the transmitted signal, and H ICI Represents the ICI interference matrix.

4. A system for a secondary BEM channel estimation method based on ICI elimination based on the method according to any one of claims 1 to 3, characterized in that: include: A windowing module, used for performing windowing preprocessing on the received signal; A base coefficient calculation module is used to solve the base coefficient and fit the channel tap coefficient using the least squares (LS) method for the signal-to-noise ratio estimation result of the receiving end; A baseline tilt module is used to suppress the BEM modeling error caused by the Gibbs effect through the baseline tilt technology, and obtain the ICI interference matrix and the estimated value of the transmitted signal; ICI self-cancellation module, used for performing ICI self-cancellation on the received signal; The loop module is used for repeatedly acquiring the base coefficients of the base expansion model and fitting channel tap coefficients, ICI interference matrix and transmission signal estimation value to obtain the channel frequency domain response matrix of the secondary channel estimation.

5. A device for implementing a secondary BEM channel estimation method based on ICI elimination, characterized in that: include: Memory: used for storing a computer program for implementing a secondary BEM channel estimation method based on ICI elimination according to claims 1-3; Processor: used to implement the secondary BEM channel estimation method based on ICI elimination of claims 1-3 when executing the computer program.

6. A computer-readable storage medium, characterized in that: include: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it can implement the secondary BEM channel estimation method based on ICI elimination of claims 1-3.

Images