A method and system for suppressing channel response nonstationarity based on OFDM signals

Abstract

A method and system for suppressing channel response unevenness based on OFDM signals, including: channel response unevenness estimation, compensation, the channel response unevenness estimation including: converting a signal into an amplitude-phase method representation, channel estimation, IIR filtering, updating a signal estimate; the method and system for suppressing channel response unevenness based on OFDM signals, through filtering operation, reduces the risk of performance sharp decline when an individual carrier is suddenly affected by fading or noise, reduces the burr phenomenon from the flatness result, makes the flatness result closer to the power amplifier frequency domain property of the DUT, and suppresses the performance calculation deterioration caused by carrier mutation, which helps to improve the decoding ability to a certain extent.

Description

TECHNICAL FIELD

[0001] The present application relates to signal analysis and testing technology of wireless system, in particular to a method and system for suppressing channel response unevenness based on OFDM signal. BACKGROUND

[0002] IEEE802.11 series (a / g / n / ac / ax / be) standard uses orthogonal frequency division multiplexing (OFDM) as the modulation technology, which has the advantages of realizing parallel transmission of high-speed serial data through frequency division multiplexing to improve transmission capacity, effectively resisting frequency selective fading, interference, and supporting multi-user access. The OFDM system estimates the channel through the training sequence and the pilot, and then applies the channel time-invariant characteristics to equalize the received data to analyze the signal sent by the sender. However, in the actual environment, the channel frequency selective fading will cause the channel response to be abnormal, and if an individual sub-channel is interfered, the amplitude response and frequency response of the channel estimation will be inaccurate, and the equalized data part will introduce greater errors.

[0003] The spectrum analyzer is a device for analyzing the radio frequency performance of the vector signal of the device under test (DUT), and is a high-precision test device. The power, spectrum, frequency offset, etc. of the DUT signal can be analyzed using conventional means, but the influence of the environment such as frequency selective fading and shadow fading on the channel cannot be predicted and compensated, especially when an individual sub-channel response is abnormal, which becomes a receiving short board, so the spectrum analyzer is required to have the ability to suppress the unevenness of the channel response of the individual sub-channel.

[0004] The existing method for correcting channel response unevenness is to interpolate the pilot within the data symbol for amplitude tracking, and the principle is to use the difference between the received symbol pilot amplitude and the ideal pilot amplitude to correct the amplitude of the entire symbol data. However, this tracking method can only statistically adjust the amplitude of all sub-channels, and is affected by the accuracy of the amplitude response of the pilot sub-channel, and cannot effectively compensate for individual sub-channels that are greatly affected. SUMMARY

[0005] Therefore, it is necessary to provide a method for suppressing channel response unevenness based on OFDM signal to improve performance.

[0006] At the same time, a system for suppressing channel response unevenness based on OFDM signal to improve performance is provided.

[0007] A method for suppressing channel response unevenness based on OFDM signal, comprising: channel response unevenness estimation, compensation, the channel response unevenness estimation comprising: signal into amplitude-phase method representation, channel estimation, IIR filtering, updating signal estimation.

[0008] In the preferred embodiment, the signal into amplitude-phase method representation comprises: representing the training sequence in the signal in frequency domain with Y=[y0, y1, …, yK-1], representing the ideal training sequence in frequency domain with R=[r0, r1, …, rK-1], wherein the subscript denotes the subcarrier number, K is the total number of subcarriers, calculating the complex signal x=x K-1 +j*x K-1 in amplitude method, and calculating the complex signal x=x I +j*x Q in phase method. The phase method is Both Y and R are converted into polar coordinate form, i.e. into amplitude-phase method representation, A is the amplitude representation of Y, A is the phase representation of Y, A is the amplitude representation of R, A is the phase representation of R, and there is a relationship

[0009] In the preferred embodiment, the channel estimation comprises: representing the channel estimation of the training sequence with H=[h0, h1, …, hK-1], K-1 A is the amplitude representation of H, A is the phase representation of H, and

[0010] In the preferred embodiment, the IIR filtering comprises: constructing a moving weighted IIR filter, using a difference form, the IIR filter structure of the difference form is as follows, wherein x(m) is the input, y(m) is the filtered output, z i (m) is the state of the i-th register, a(n) and b(n) are filter coefficients, and the operation process is

[0011] y(m)=b(1)x(m)+z1(m-1)

[0012] z1(m)=b(2)x(m)+z2(m-1)-a(2)y(m)

[0013]

[0014] z n-2 (m)=b(n-1)x(m)+zn-1(m-1)-a(n-1)y(m)

[0015] z n-2 ​(m) = b(n)x(m) - a(n)y(m)

[0016] The difference equation coefficients are set as a(i) = [1, 0, 0, 0, 0], b(i) = [1 / 9, 2 / 9, 3 / 9, 2 / 9, 1 / 9], initial state z i (m) = 0, m = 5, the signal estimation As the x(m) input, the output x(m) is expressed as

[0017] In the preferred embodiment, the updating channel estimation comprises: the amplitude response value after filtering according to the IIR filtering step The phase response value obtained in combination with the channel estimation step The updated channel estimation is Wherein j is the imaginary unit.

[0018] In the preferred embodiment, it further comprises: symbol energy phase flatness estimation and compensation, which comprises: symbol channel estimation, amplitude response filtering, phase response filtering, and updating data symbol information, the symbol signal estimation comprises: when the receiving end processes each symbol, a pilot with a known sending sequence is inserted in the data symbol, at this time, the channel estimation is performed on the pilot subcarrier, and the response condition of all subcarriers is fitted, and then the data is corrected, Y(m) = [y 0，m , y 1，m , …, y K-1，m ] represents the frequency domain of the mth symbol, the pilot is Y P (m) = [y p0，m , y p1，m , …, y pL，m ], R P (m) = [r p0，m , r p1，m , …, r pL,m ] represents the ideal value corresponding to the pilot of the mth symbol, the channel estimation is performed on the pilot part, and is expressed as H P (m) = [h p0 , h p1 , …, h pL ], then h pk = y pk，m / r pk，m , k ∈ [0, L], and then h pk is converted into polar coordinate form, and is expressed as Wherein L is the total pilot quantity minus 1.

[0019] In the preferred embodiment, the amplitude response filtering comprises: the amplitude response value obtained in the symbol signal estimation step As input x(m), an IIR filter is applied, and the coefficients of the difference equation are set as a(i) = [1, 0, 0] and b(i) = [1 / 4, 1 / 2, 1 / 4]. The initial state z i When m = 0 and m = 3, the filtered output x(m) is expressed as:

[0020] In a preferred embodiment, the phase response filtering includes: using the signal obtained from the symbol signal estimation step... As input to x(m), an IIR filter is applied, and the coefficients of the difference equation are set to a(i) = [1,0,0] and b(i) = [1 / 3,1 / 3,1 / 3]. The initial state z i When m = 0 and m = 3, the filtered output x(m) is expressed as:

[0021] In a preferred embodiment, updating the data symbol information includes: first recovering the filtered pilot channel estimate by taking the information from the amplitude response filtering step and the phase response filtering step, denoted as... in Then, based on the carrier sequence number of the pilot and the carrier sequence number of the data, using... The corrected channel estimate H(m) is obtained by linear interpolation = [h 0,m ,h 1,m ,…,h K-1,m ], correct the received signal Y(m) = [y 0,m ,y 1,m ,…,y K-1,m The method is

[0022]

[0023] The corrected form is as follows

[0024] A system for analyzing OFDM signals and calculating and compensating for inter-carrier interference includes: channel response unevenness estimation, compensation module, and symbol energy phase unevenness estimation, compensation module.

[0025] The channel response unevenness estimation and compensation module includes:

[0026] The signal is represented by the amplitude and phase method: using Y = [y0, y1, ..., y2] as the unit. K-1 The frequency domain of the training sequence in the signal is represented by R = [r0, r1, ..., r]. K-1 [] represents the frequency domain of an ideal training sequence, where the subscripts indicate the subcarrier indices, and K is the total number of subcarriers. Find the complex signal x = x I +j*x Q Amplitude method The method for determining the phase is as follows Convert both Y and R to polar coordinates, i.e., represent them using the amplitude-phase method. Let Y represent the amplitude. The phase representation of Y, Let R represent the magnitude. Let R be the phase representation, then the following relationship exists.

[0027] Channel estimation unit: using H = [h0, h1, ..., h K-1 ] represents the channel estimation of the training sequence. Let H represent the amplitude. Let H be the phase representation, then

[0028] IIR Filtering Unit: Constructs a moving weighted IIR filter using a differential form. The differential IIR filter structure is as follows, where x(m) is the input, y(m) is the filtered output, and z... i (m) represents the state of the i-th register, and a(n) and b(n) are the filter coefficients. The calculation process is as follows:

[0029] y(m)=b(1)x(m)+z1(m-1)

[0030] z1(m)=b(2)x(m)+z2(m-1)-a(2)y(m)

[0031]

[0032] z n-2 (m)=b(n-1)x(m)+z n-1 (m-1)-a(n-1)y(m)

[0033] z n-2 (m)=b(n)x(m)-a(n)y(m)

[0034] Set the coefficients of the difference equation as a(i) = [1, 0, 0, 0, 0], b(i) = [1 / 9, 2 / 9, 3 / 9, 2 / 9, 1 / 9], and the initial state z i (m) = 0, m = 5, in the signal estimation unit As input to x(m), the output x(m) is expressed as

[0035] Update the channel estimation unit: based on the amplitude response value obtained after filtering by the IIR filtering unit. Combined with the phase response value in the channel estimation unit Update the channel estimate as follows in j is the imaginary unit;

[0036] The symbol energy phase unevenness estimation and compensation module includes:

[0037] Symbol signal estimation unit: When the receiver processes each symbol, a pilot signal with a known transmission sequence is inserted onto the data symbol. At this time, channel estimation is performed on the pilot subcarrier, and the response of all subcarriers is fitted. Then, the data is corrected using Y(m) = [y 0，m y 1，m , ..., y K-1，m ] represents the frequency domain of the m-th symbol, with pilot frequency Y. P (m)=[y p0，m y p1，m , ..., y pL，m ], using R P (m)=[r p0，m r p1，m , ..., r pL，m [] represents the ideal value corresponding to the m-th symbol pilot. Channel estimation is performed on the pilot portion, denoted as H. p (m)=[h p0 h p1 , ..., h pL ], then h pk =y pk，m / r pk，m , k∈[0,L], then h pk Converted to polar coordinates, it is represented as Where L is the total number of pilots minus 1;

[0038] Amplitude response filtering unit: This unit filters the signals from the symbol signal estimation unit. As input x(m), an IIR filter is applied, and the coefficients of the difference equation are set as a(i) = [1, 0, 0] and b(i) = [1 / 4, 1 / 2, 1 / 4]. The initial state z i When m = 0 and m = 3, the filtered output x(m) is expressed as:

[0039] The phase response filtering unit includes: [the function] in the symbol signal estimation unit. As input to x(m), an IIR filter is applied, and the coefficients of the difference equation are set to a(i) = [1, 0, 0] and b(i) = [1 / 3, 1 / 3, 1 / 3]. The initial state z i When m = 0 and m = 3, the filtered output x(m) is expressed as:

[0040] Update data symbol information unit: First, recover the filtered pilot channel estimate by taking the information from the amplitude response filtering unit and the phase response filtering unit, denoted as... in

[0041] Then, based on the carrier sequence number of the pilot and the carrier sequence number of the data, using... The corrected channel estimate H(m) is obtained by linear interpolation = [h 0,m h 1，m , ..., h K-1，m ], correct the received signal Y(m) = [y 0，m y 1，m , ..., y K-1，m The method is

[0042]

[0043] The corrected form is as follows

[0044] The aforementioned method and system for suppressing channel response instability based on OFDM signals reduces the risk of drastic performance degradation when individual carriers are suddenly affected by fading or noise through filtering operations. From a flatness perspective, it reduces glitches, making the flatness results closer to the frequency domain characteristics of the power amplifier in the DUT. Simultaneously, it suppresses performance degradation caused by carrier abrupt changes, which helps improve decoding capability to some extent. Analysis shows a significant improvement in spectral flatness, better constellation convergence, and optimized performance metrics, including EVM(all). Attached Figure Description

[0045] Figure 1 The channel conditions for each symbol of the VHT signal and the power response diagram of the LS channel are shown.

[0046] Figure 2 This is a flowchart of the signal analysis process of the comprehensive measuring instrument of the present invention;

[0047] Figure 3 This is a flowchart of the channel response unevenness estimation and compensation method of the present invention;

[0048] Figure 4 This is a flowchart of the symbol energy phase unevenness estimation and compensation method of the present invention;

[0049] Figure 5 This is a schematic diagram of the working principle of an IIR filter.

[0050] Figure 6 The graph shows the signal quality analysis results of the OFDM-based method and system for suppressing channel response instability without using the present invention.

[0051] Figure 7 The diagram shows the signal quality analysis results of the method and system for suppressing channel response instability based on OFDM signals according to the present invention. Detailed Implementation

[0052] The following examples are provided to help better understand the present invention, but are not intended to limit the invention.

[0053] Signals using OFDM technology generally have a large bandwidth, and it is difficult for the components of the DUT to guarantee linearity over a large bandwidth. The power amplification of the amplifier will vary at different frequency points. If the power amplification at a certain frequency is reduced or amplified, it will introduce noise and interference into the signal. In addition, the frequency selective fading of the channel will also affect the signal reception.

[0054] Channel estimation is a technique in OFDM signal processing. Based on the amplitude response of the channel estimation, the frequency fading characteristics of each sub-channel can be determined. Based on the phase response of the channel estimation, the effects of sampling frequency deviation or overall noise on each sub-channel can be determined. For example... Figure 1 As shown, the amplitude response of a training sequence and data symbols of a real signal packet is analyzed. It can be seen that the amplitude response of its sub-channels varies whether analyzed in terms of time or frequency.

[0055] However, through theory and analysis of actual signals, it can be determined that the channel still has the characteristics of being relatively smooth in short time and short distance. Therefore, by taking advantage of the relatively smooth amplitude and phase responses of adjacent sub-channels, the channel response of sub-channels that may be abnormal can be corrected by combining the channel conditions of multiple sub-channels.

[0056] When analyzing OFDM-based signals using a comprehensive test instrument, to support the signal quality testing of the device under test (DUT), this invention provides a method and system for suppressing channel response instability based on OFDM signals. This system comprises two subsystems: a channel response unevenness estimation and compensation system, and a symbol energy phase unevenness estimation and compensation system. This effectively suppresses unexpected noise and interference that could degrade the performance of individual subcarriers, leading to inaccurate DUT performance measurements. This invention provides a method and system for estimating and optimizing channel response instability in the field of DUT RF performance testing. It assists in chip development by adjusting the linearity of broadband frequency amplifiers and provides a method and system for optimizing RF performance testing with channel response instability.

[0057] like Figures 3 to 4 As shown, an embodiment of the present invention provides a method for suppressing channel response instability based on OFDM signals, which includes: channel response instability estimation, compensation, symbol energy phase instability estimation, and compensation.

[0058] The channel response unevenness estimation and compensation of this invention mainly filters out the impact of burst noise on channel estimation, and only considers the amplitude part.

[0059] In a preferred embodiment of the present invention, the channel response unevenness estimation and compensation includes:

[0060] The signal is represented using the amplitude-phase method: Y = [y0, y1, ..., y2] K-1 The frequency domain of the training sequence in the signal is represented by R = [r0, r1, ..., r]. K-1 [] represents the frequency domain of an ideal training sequence, where the subscripts indicate the subcarrier indices, and K is the total number of subcarriers. Find the complex signal x = x I +j*x Q Amplitude method The method for determining the phase is as follows Convert both Y and R to polar coordinates, i.e., represent them using the amplitude-phase method. Let Y represent the amplitude. The phase representation of Y, Let R represent the magnitude. Let R be the phase representation, then the following relationship exists.

[0061] Channel estimation: using H = [h0, h1, ..., h K-1 ] represents the channel estimation of the training sequence. Let H represent the amplitude. Let H be the phase representation, then

[0062] IIR (Infinite Impulse Response) Filtering: To eliminate the adverse effects of noise on individual carriers, a motion-weighted IIR (Infinite Impulse Response) filter is constructed using a differential form. The differential IIR filter structure is as follows, where x(m) is the input, y(m) is the filtered output, and z... i (m) represents the state of the i-th register, and a(n) and b(n) are the filter coefficients. The working principle is as follows: Figure 5 As shown,

[0063] The calculation process is as follows

[0064] y(m)=b(1)x(m)+z1(m-1)

[0065] z1(m)=b(2)x(m)+z2(m-1)-a(2)y(m)

[0066]

[0067] z n-2 (m)=b(n-1)x(m)+z n-1 (m-1)-a(n-1)y(m)

[0068] z n-2(m)==b(n)x(m)-a(n)y(m)

[0069] Specifically, in this invention, the coefficients of the difference equation are set as a(i) = [1, 0, 0, 0, 0], b(i) = [1 / 9, 2 / 9, 3 / 9, 2 / 9, 1 / 9], and the initial state z i (m) = 0, m = 5, in the channel estimation step As input to x(m), the output x(m) is expressed as like Figure 5 As shown, a(i) and b(i) are weighting coefficients, and the calculated z i (m) will move in the register as input occurs;

[0070] Updated channel estimation: The amplitude response value obtained after filtering by the IIR filtering step. Phase response value obtained by combining channel estimation steps Update the channel estimate as follows in

[0071]

[0072] j is the imaginary unit;

[0073] The aforementioned signal conversion is represented by the amplitude-phase method. Channel estimation, IIR filtering, and channel estimation updates are all performed during channel estimation in the DTU analysis process, and the operation is only performed on the training sequence.

[0074] The core of the IIR (Infinite Impulse Response) filter of this invention is a recursive function, which is adjusted by changing the recursive coefficients to achieve different effects.

[0075] The symbol energy phase unevenness estimation and compensation module in this embodiment mainly filters out interference between subcarriers, which needs to consider both amplitude and phase, and different filtering coefficients are used for amplitude and phase.

[0076] Symbol energy phase unevenness estimation and compensation include:

[0077] Data symbol channel estimation: When the receiver processes each symbol according to the procedure, a pilot signal with a known transmission sequence is inserted onto the data symbol. At this time, channel estimation is performed on the pilot subcarrier, and the response of all subcarriers is fitted. Then, the data is corrected, using Y(m) = [y 0，m y 1，m , ..., y K-1，m ] represents the frequency domain of the m-th symbol, with pilot frequency Y. P (m)=[y p0，m y p1，m , ..., y pL，m], using R P (m)=[r p0，m r p1，m , ..., r pL，m [] represents the ideal value corresponding to the m-th symbol pilot. Channel estimation is performed on the pilot portion, denoted as H. P (m)=[h p0 h p1 , ..., h pL ], then h pk =y pk，m / r pk，m , k∈[0,L], then h pk Converting the form in the amplitude-phase representation step of the signal conversion method to polar coordinates, it is expressed as follows: Where L is the total number of pilots minus 1;

[0078] Amplitude response filtering: In data symbol channel estimation... As input to x(m), the IIR filter in the IIR filtering step is applied. Due to the large pilot spacing, the coefficients of the difference equation are set as a(i) = [1, 0, 0] and b(i) = [1 / 4, 1 / 2, 1 / 4]. The initial state z i When m = 0 and m = 3, the filtered output x(m) is expressed as:

[0079] Phase response filtering: In data symbol channel estimation... As input to x(m), the IIR filter in the IIR filtering step is applied. Due to the large pilot spacing, the coefficients of the difference equation are set as a(i) = [1, 0, 0] and b(i) = [1 / 3, 1 / 3, 1 / 3]. The initial state z i When m = 0 and m = 3, the filtered output x(m) is expressed as:

[0080] Update data symbol information: Using the information from the amplitude response filtering step and the phase response filtering step, first recover the filtered pilot channel estimate, denoted as... in Then, based on the carrier sequence number of the pilot and the carrier sequence number of the data, using... The corrected channel estimate H(m) is obtained by linear interpolation = [h 0，m h 1，m , ..., h K-1，m ], correct the received signal Y(m) = [y 0，m y 1，m , ..., y K-1，m The method is The corrected form is as follows This data will be used in subsequent calculations and analyses.

[0081] This invention is applied within a comprehensive test instrument. To support the evaluation of the impact of DUT phase noise, the method and system designed in this invention for suppressing channel response instability consists of two subsystems: a channel response instability estimation and compensation system, and a symbol energy phase instability estimation and compensation system. This effectively suppresses the performance degradation of individual subcarriers caused by unexpected noise and interference, leading to inaccurate DUT performance measurements. It can also assist in chip development by adjusting the linearity of broadband frequency amplifiers and provides a method and system for optimizing RF performance testing of channel response instability.

[0082] This invention is applied to a comprehensive test instrument as a testing device for performance testing of a DUT. It can also be directly used for other signals based on OFDM communication. The complete process of the comprehensive test instrument analyzing DUT signals is as follows: Figure 2 As shown, the channel response unevenness estimation and compensation module, and the symbol energy phase unevenness estimation and compensation module can be newly introduced into the conventional process. This invention is only illustrated using the 11a signal as an example, and can be easily extended to 11g / n / ac / ax / be, as well as multiple-input multiple-output (MIMO) modes.

[0083] An embodiment of the present invention provides a system for suppressing channel response instability based on OFDM signals, comprising: a channel response instability estimation module, a compensation module, and a symbol energy phase instability estimation module, and a compensation module.

[0084] The channel response unevenness estimation and compensation module of this invention mainly filters out the impact of burst noise on channel estimation, and only considers the amplitude part.

[0085] In a preferred embodiment of the present invention, the channel response unevenness estimation and compensation module includes:

[0086] Convert the signal to an amplitude-phase representation unit: using Y = [y0, y1, ..., y2] K-1 The frequency domain of the training sequence in the signal is represented by R = [r0, r1, ..., r]. K-1 [] represents the frequency domain of an ideal training sequence, where the subscripts indicate the subcarrier indices, and K is the total number of subcarriers. Find the complex signal x = x i +j*x Q Amplitude method The method for determining the phase is as follows Convert both Y and R to polar coordinates, i.e., represent them using the amplitude-phase method. Let Y represent the amplitude. The phase representation of Y, Let R represent the magnitude. Let R be the phase representation, then the following relationship exists.

[0087] Channel estimation unit: using H = [h0, h1, ..., h K [-1] represents the channel estimation of the training sequence. Let H represent the amplitude. Let H be the phase representation, then

[0088] IIR Filtering Unit: To eliminate the adverse effects of noise on individual carriers, a moving-weighted IIR filter is constructed using a differential form. The differential IIR filter structure is as follows, where x(m) is the input, y(m) is the filtered output, and z... i (m) represents the state of the i-th register, and a(n) and b(n) are the filter coefficients. The working principle is as follows: Figure 5 As shown,

[0089] The calculation process is as follows

[0090] y(m)=b(1)x(m)+z1(m-1)

[0091] z1(m)=b(2)x(m)+z2(m-1)-a(2)y(m)

[0092]

[0093] z n-2 (m)=b(n-1)x(m)+z n-1 (m-1)-a(n-1)y(m)

[0094] z n-2 (m)=b(n)x(m)-a(n)y(m)

[0095] This invention sets the coefficients of the difference equation as a(i) = [1, 0, 0, 0, 0], b(i) = [1 / 9, 2 / 9, 3 / 9, 2 / 9, 1 / 9], and the initial state z i (m) = 0, m = 5, in the channel estimation unit As input to x(m), the output x(m) is expressed as

[0096] Update channel estimation unit: The amplitude response value obtained after filtering by the IIR filtering unit. Phase response value obtained by combining the channel estimation unit Update the channel estimate as follows in j is the imaginary unit.

[0097] The aforementioned signal conversion is represented by the amplitude-phase method. Channel estimation, IIR filtering, and channel estimation updates are all performed during channel estimation in the DTU analysis process, and the operation is only performed on the training sequence.

[0098] The symbol energy phase unevenness estimation and compensation module in this embodiment mainly filters out interference between subcarriers, which needs to consider both amplitude and phase, and different filtering coefficients are used for amplitude and phase.

[0099] The symbol energy phase unevenness estimation and compensation module includes:

[0100] Data Symbol Channel Estimation Unit: When the receiver processes each symbol according to the procedure, a pilot signal with a known transmission sequence is inserted onto the data symbol. At this time, channel estimation is performed on the pilot subcarrier, and the response of all subcarriers is fitted. Then, the data is corrected, using Y(m) = [y 0，m y 1，m , ..., y K-1，m ] represents the frequency domain of the m-th symbol, with pilot frequency Y. P (m)=[y p0，m y p1,m , ..., y pL,m ], using R P (m)=[r p0，m r p1,m , ..., r pL,m [] represents the ideal value corresponding to the m-th symbol pilot. Channel estimation is performed on the pilot portion, denoted as H. P (m)=[h p0 h p1 , ..., h pL ], then hpk = y pk，m / r pk，m , k∈[0,L], then h pk Converting the form of the signal in the amplitude-phase method to polar coordinates, it is represented as follows: Where L is the total number of pilots minus 1;

[0101] Amplitude response filtering unit: This unit processes the data symbol channel estimation data. As input to x(m), the IIR filter in the IIR filter unit is applied. Due to the large pilot spacing, the coefficients of the difference equation are set as a(i) = [1, 0, 0] and b(i) = [1 / 4, 1 / 2, 1 / 4]. The initial state z i When m = 0 and m = 3, the filtered output x(m) is expressed as:

[0102] Phase response filtering unit: Filters the data symbol channel estimation unit's output. As input to x(m), the IIR filter in the IIR filter unit is applied. Due to the large pilot spacing, the coefficients of the difference equation are set as a(i) = [1, 0, 0] and b(i) = [1 / 3, 1 / 3, 1 / 3]. The initial state z iWhen m = 0 and m = 3, the filtered output x(m) is expressed as:

[0103] Update data symbol information unit: First, use the information from the amplitude response filtering unit and the phase response filtering unit to recover the filtered pilot channel estimate, denoted as... in Then, based on the carrier sequence number of the pilot and the carrier sequence number of the data, using... The corrected channel estimate H(m) is obtained by linear interpolation = [h 0，m h 1，m , ..., h K-1，m ], correct the received signal Y(m) = [y 0，m y 1，m , ..., y K-1，m The method is The corrected form is as follows This data will be used in subsequent calculations and analyses.

[0104] The method and system for suppressing channel response instability of the present invention reduces the risk of a sharp performance drop when individual carriers are suddenly affected by fading or noise through a series of smoothing filtering operations. From the perspective of flatness results, it reduces the glitches and makes the flatness results closer to the frequency domain properties of the power amplifier of the DUT. At the same time, it suppresses the performance degradation caused by carrier mutation. This suppression helps to improve the decoding capability to a certain extent.

[0105] This invention is applied to the WT series comprehensive test instrument of Shenzhen Jizhi Huiyi Technology Co., Ltd. According to the method and system for suppressing channel response instability of this invention, a preamble average option is set in the analysis settings interface of the Meter analysis software. The comprehensive test instrument is used to analyze models based on 802.11a / g / n / ac / ax / be types, such as... Figures 6 to 7 As shown in one example, Figure 6 It is unoptimized signal quality, such as Figure 7 As shown, when the preamble average option is set to ON to use optimized signal quality, the signal analysis results show the following changes: Spectral Flatness is significantly improved, constellation convergence is better, and the performance metric EVM(all) - Error Vector Magnitude, the error vector magnitude is also optimized from -25.24dB to -27.83dB.

[0106] Based on the above-described preferred embodiments according to this application, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the technical concept of this application. The technical scope of this application is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for suppressing channel response instability based on OFDM signals, characterized in that, include: Channel response unevenness estimation and compensation, wherein the channel response unevenness estimation includes: signal conversion to amplitude-phase representation, channel estimation, IIR filtering, and updated signal estimation; the signal conversion to amplitude-phase representation includes: using Y = [y0, y1, ..., y K-1 The frequency domain of the training sequence in the signal is represented by R = [r0, r1, ..., r]. K-1 [] represents the frequency domain of an ideal training sequence, where the subscripts indicate the subcarrier indices, and K is the total number of subcarriers. Find the complex signal x = x I +j*x Q Amplitude method The method for determining the phase is as follows Convert both Y and R to polar coordinates, i.e., represent them using the amplitude-phase method. Let Y represent the amplitude. The phase representation of Y, Let R represent the magnitude. Let R be the phase representation, then the following relationship exists. The channel estimation includes: using H = [h0, h1, ..., h K-1 ] represents the channel estimation of the training sequence. Let H represent the amplitude. Let H be the phase representation, then The IIR filtering includes: constructing a moving-weighted IIR filter using a differential form, the differential form of which is as follows, where x(m) is the input, y(m) is the filtered output, and z... i (m) represents the state of the i-th register, and a(n) and b(n) are the filter coefficients. The calculation process is as follows: Set the coefficients of the difference equation as a(i)===[1,0,0,0,0], b(i)==[1 / 9,2 / 9,3 / 9,2 / 9,1 / 9], and the initial state z i (m) = 0, m = 5, in signal estimation As input to x(m), the output x(m) is expressed as The updated signal estimation includes: the amplitude response value obtained after filtering according to the IIR filtering step. Phase response value obtained by combining channel estimation steps The updated signal is estimated to be in j is the imaginary unit; It also includes: symbol energy phase unevenness estimation and compensation, wherein the symbol energy phase unevenness estimation and compensation includes: symbol channel estimation, amplitude response filtering, phase response filtering, and updating data symbol information. The symbol channel estimation includes: when the receiver processes each symbol, a pilot with a known transmission sequence is inserted on the data symbol. At this time, channel estimation is performed on the pilot subcarrier, and the response of all subcarriers is fitted. Then the data is corrected. Y(m)=[y 0,m ,y 1,m ,…,y K-1,m ] represents the frequency domain of the m-th symbol, with pilot frequency Y. P (m)=[y p0,m ,y p1,m ,…,y pL,m ], using R P (m)=[r p0,m ,r p1,m ,…,r pL,m ] represents the ideal value corresponding to the m-th symbol pilot. Channel estimation is performed on the pilot portion, denoted as: H P (m)=[h p0 ,h p1 ,…,h pL ], So h pk =y pk,m / r pk,m ,k∈[0,L], then h pk Converted to polar coordinates, it is represented as Where L is the total number of pilots minus 1; the amplitude response filtering includes: applying the values ​​obtained from the symbol channel estimation step... As input x(m), construct an IIR filter, setting the coefficients of the difference equation as a(i) = [1,0,0], b(i) = [1 / 4,1 / 2,1 / 4], and the initial state z i When m = 0 and m = 3, the filtered output x(m) is expressed as: The phase response filtering includes: applying the phase response obtained in the symbol channel estimation step... As input to x(m), Construct an IIR filter, setting the coefficients of the difference equation as a(i) = [1, 0, 0], b(i) = [1 / 3, 1 / 3, 1 / 3], and the initial state z i When m = 0 and m = 3, the filtered output x(m) is expressed as: The updated data symbol information includes: first recovering the filtered pilot channel estimate by taking the information from the amplitude response filtering step and the phase response filtering step, denoted as... in 2. The method for suppressing channel response instability based on OFDM signals according to claim 1, characterized in that, The updated data symbol information includes: based on the pilot channel estimation after recovery filtering, and then according to the carrier sequence number of the pilot and the carrier sequence number of the data, using... The corrected channel estimate H(m) is obtained by linear interpolation = [h 0,m ,h 1,m ,…,h K-1,m ], Corrected received signal Y(m)=[y 0,m ,y 1,m ,…,y K-1,m The method is The corrected form is as follows 3. A system for suppressing channel response instability based on OFDM signals, characterized in that, include: The channel response unevenness estimation and compensation module and the symbol energy phase unevenness estimation and compensation module, wherein the channel response unevenness estimation and compensation module includes: The signal is represented by the amplitude and phase method: Using Y = [y0, y1, ..., y K-1 The frequency domain of the training sequence in the signal is represented by R = [r0, r1, ..., r]. K-1 [] represents the frequency domain of an ideal training sequence, where the subscripts indicate the subcarrier indices, and K is the total number of subcarriers. Find the complex signal x = x I +j*x Q Amplitude method The method for determining the phase is as follows Convert both Y and R to polar coordinates, i.e., represent them using the amplitude-phase method. Let Y represent the amplitude. The phase representation of Y, Let R represent the magnitude. Let R be the phase representation, then the following relationship exists. Channel estimation unit: using H = [h0, h1, ..., h K-1 ] represents the channel estimation of the training sequence. Let H represent the amplitude. Let H be the phase representation, then IIR Filtering Unit: Constructs a moving weighted IIR filter using differential form. The differential IIR filter is shown below, where x(m) is the input, y(m) is the filtered output, and z... i (m) represents the state of the i-th register, and a(n) and b(n) are the filter coefficients. The calculation process is as follows Set the coefficients of the difference equation as a(i) = [1,0,0,0,0] and b(i) = [1 / 9,2 / 9,3 / 9,2 / 9,1 / 9], and the initial state z i (m) = 0, m = 5, in the signal estimation unit As input to x(m), the output x(m) is expressed as Update the channel estimation unit: based on the amplitude response value obtained after filtering by the IIR filtering unit. Phase response value obtained by combining the channel estimation unit Update the channel estimate as follows in j is the imaginary unit; The symbol energy phase unevenness estimation and compensation module includes: Symbol signal estimation unit: When the receiver processes each symbol, a pilot signal with a known transmission sequence is inserted onto the data symbol. At this time, channel estimation is performed on the pilot subcarrier, and the response of all subcarriers is fitted. Then, the data is corrected using Y(m) = [y 0,m ,y 1,m ,…,y K-1,m ] represents the frequency domain of the m-th symbol, with pilot frequency Y. P (m)=[y p0,m ,y p1 , m ,…,y pL,m ],use R P (m)=[r p0,m ,r p1,m ,…,r pL,m [] represents the ideal value corresponding to the m-th symbol pilot. Channel estimation is performed on the pilot portion, denoted as H. P (m)=[h p0 ,h p1 ,…,h pL ],So h pk =y pk,m / r pk,m ,k∈[0,L], Then h pk Converted to polar coordinates, it is represented as Where L is the total number of pilots minus 1; Amplitude response filtering unit: This unit filters the signals from the symbol signal estimation unit. As input to x(m), an IIR filter is applied, and the coefficients of the difference equation are set to a(i) = [1,0,0] and b(i) = [1 / 4,1 / 2,1 / 4]. Initial state... z i When m = 0 and m = 3, the filtered output x(m) is expressed as: The phase response filtering unit includes: [the function] in the symbol signal estimation unit. As input to x(m), an IIR filter is applied, and the coefficients of the difference equation are set to a(i) = [1,0,0] and b(i) = [1 / 3,1 / 3,1 / 3]. The initial state z i When m = 0 and m = 3, the filtered output x(m) is expressed as: Update data symbol information unit: First, use the information from the amplitude response filtering unit and the phase response filtering unit to recover the filtered pilot channel estimate, denoted as... in Then, based on the carrier sequence number of the pilot and the carrier sequence number of the data, using... The corrected channel estimate H(m) is obtained by linear interpolation = [h 0,m ,h 1,m ,…,h K-1,m ], Corrected received signal Y(m)=[y 0,m ,y 1,m ,…,y K-1,m The method is The corrected form is as follows

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