A Phase Noise Simulation Method Based on Power Spectrum Shaping

Abstract

The present invention relates to a phase noise simulation method based on power spectrum shaping, which belongs to the field of channel modeling technology. For some complete machine components or equipment, such as signal sources, communication transmitters, etc., their phase noise often does not conform to the power-law spectrum model, and their simulation results will have large errors. In view of the above problems, the present application proposes a phase noise simulation method based on power spectrum shaping, which can meet various forms of phase noise simulation needs.

Description

Technical Field

[0001] The invention belongs to the technical field of channel modeling, and in particular relates to a phase noise simulation method based on power spectrum shaping. Background Art

[0002] Phase noise is a term that can comprehensively reflect the offset of the carrier signal in the frequency domain, describing the change in timing. m At the frequency, the power of the sideband becomes 1 / f of the carrier power. m , f m is the offset. The power-law spectrum model is generally used for phase noise simulation. The power-law spectrum model simulation method can well simulate the phase noise of devices, such as oscillators and phase-locked loops. However, for some complete devices or equipment, such as signal sources and communication transmitters, their phase noise often does not conform to the power-law spectrum model, and the simulation results will have large errors. Summary of the invention

[0003] The purpose of the present invention is to provide a phase noise simulation method based on power spectrum shaping, which is used to solve the technical problems existing in the above-mentioned prior art. Generally, the simulation of phase noise mostly adopts a power-law spectrum model. The simulation method of the power-law spectrum model can well simulate the phase noise of devices, such as oscillators, phase-locked loop phase noise, etc. However, for some complete devices or equipment, such as signal sources, communication transmitters, etc., their phase noise often does not conform to the power-law spectrum model, and their simulation results will have large errors.

[0004] To achieve the above object, the technical solution of the present invention is:

[0005] A phase noise simulation method based on power spectrum shaping includes the following steps:

[0006] S1, generate a Gaussian white noise sequence n0(n) with a length of N, a mean of 0, and a variance of 1;

[0007] S2, perform a Fourier transform of length N on the sequence n0(n) and multiply it by Get its normalized spectrum sequence 0≤f i ≤F s ,0≤i≤N, where the frequency resolution Δf=F s / (N-1)=f i+1 -f i ;

[0008] S3, according to the power spectrum density sequence P′(f) input by the user, according to f i ,0≤i≤N / 2, where 0≤f i ≤F2 / 2, interpolation is performed to obtain 0≤f i ≤F2 / 2,0≤i≤N / 2, and for F2 / 2≤f j ≤F2, N / 2≤j≤N, so that it satisfies and The mirror symmetry of Where 0≤f i ≤F2 / 2,0≤i≤N / 2F2 / 2≤f j ≤F2, N / 2≤j≤N;

[0009] S4, the interpolated power spectral density sequence Convert to linear value to get the power spectrum sequence of phase noise

[0010] S5. According to the Rayleigh fading channel generation method, PSD and 0≤f i ≤F s ,0≤i≤N corresponding frequency points are multiplied to obtain the shaped frequency domain phase noise signal N F (f i ), 0≤f i ≤F s ,0≤i≤N;

[0011] S6, for N F (f i ) to perform inverse Fourier transform to obtain the shaped phase noise time domain sequence 1≤n≤N;

[0012] S7, take The N / 2 data in are used as the actual phase noise data, where N / 4+1≤n≤N / 4+N / 2;

[0013] S8, multiply the baseband IQ data by N / 4+1≤n≤N / 4+N / 2, thus achieving the addition of phase noise.

[0014] Further, in step S2,

[0015] For a power signal x(t), its power spectral density is defined as:

[0016]

[0017] Among them, X T (f) is the Fourier transform of x(t);

[0018] because

[0019]

[0020] then

[0021]

[0022] Therefore, the sum of the average powers of all frequency components is the total power of the signal;

[0023] For a time-domain white noise signal n0(n) with a mean of 0, a variance of 1, and a length of N, its N-point discrete Fourier transform is expressed as

[0024]

[0025] Furthermore, in step S2, Δf=1 Hz.

[0026] Furthermore, in step S4, the interpolated power spectrum density sequence Converted into linear values, the power spectrum sequence of phase noise is obtained according to formula (7):

[0027] Referring to formulas (3) and (4) and the Pasval theorem, the total frequency power of the white noise signal is expressed as:

[0028]

[0029] Writing formula (3) in discrete form, we have

[0030]

[0031] Where Δf = F s / (N-1) represents the frequency resolution, F s Indicates the baseband sampling rate;

[0032] Combining formula (5) and (6), and rearranging them, we get:

[0033]

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] One of the benefits of this solution is that for some complete devices or equipment, such as signal sources, communication transmitters, etc., their phase noise often does not conform to the power-law spectrum model, and their simulation results will have large errors. This solution proposes a phase noise simulation method based on power spectrum shaping that can meet various forms of phase noise simulation needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The figure is a block diagram of statistical channel modeling according to a specific implementation of the present invention.

[0037] Figure 2A schematic diagram of the frequency spectrum of phase noise in a specific implementation manner of the present invention.

[0038] Figure 3 The following is a block diagram of a phase noise generation principle according to a specific embodiment of the present invention.

[0039] Figure 4 The figure is a schematic diagram of a phase noise generation process flow of a specific implementation mode of the present invention.

[0040] Figure 5 A schematic diagram of the phase noise power spectrum density generated by a specific implementation of the present invention. (Left: Power law spectrum method, right: method of the present application)

[0041] Figure 6 A schematic diagram of the phase noise power spectrum density generated by SMW200A measured data in a specific embodiment of the present invention. (Left: Power law spectrum method, right: method of the present application) DETAILED DESCRIPTION

[0042] Below in conjunction with the appended Figure 1 -Attached Figure 6 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Example:

[0044] like Figure 1 As shown, a phase noise simulation method based on power spectrum shaping is provided, comprising the following steps:

[0045] S1, generate a Gaussian white noise sequence n0(n) with a length of N, a mean of 0, and a variance of 1;

[0046] S2, perform a Fourier transform of length N on the sequence n0(n) and multiply it by Get its normalized spectrum sequence 0≤f i ≤F s ,0≤i≤N, where the frequency resolution Δf=F s / (N-1)=f i+1 -f i ;

[0047] S3, according to the power spectrum density (dBc / Hz) sequence P′(f) input by the user, according to f i ,0≤i≤N / 2, where 0≤f i≤F2 / 2, interpolation is performed to obtain 0≤f i ≤F2 / 2,0≤i≤N / 2, and for F2 / 2≤f j ≤F2, N / 2≤j≤N, so that it satisfies and The mirror symmetry of Where 0≤f i ≤F2 / 2,0≤i≤N / 2F2 / 2≤f j ≤F2, N / 2≤j≤N;

[0048] S4, the interpolated power spectral density sequence Convert to linear value to get the power spectrum sequence of phase noise

[0049] S5. According to the Rayleigh fading channel generation method, PSD and 0≤f i ≤F s ,0≤i≤N corresponding frequency points are multiplied to obtain the shaped frequency domain phase noise signal N F (f i ), 0≤f i ≤F s ,0≤i≤N;

[0050] S6, for N F (f i ) to perform inverse Fourier transform to obtain the shaped phase noise time domain sequence 1≤n≤N;

[0051] S7, take The N / 2 data in are used as the actual phase noise data, where N / 4+1≤n≤N / 4+N / 2;

[0052] S8, multiply the baseband IQ data by N / 4+1≤n≤N / 4+N / 2, thus achieving the addition of phase noise.

[0053] In statistical channel modeling, there is a modeling method for the Rayleigh fading channel called the Gaussian white noise filtering method, which achieves different power spectrum characteristics by filtering white noise, such as Figure 1 If the control of power spectrum density can be converted into the control of power spectrum, the modeling of phase noise can be realized.

[0054] According to the definition of phase noise: the relative noise power of a single sideband at a certain frequency offset with the carrier amplitude as the reference. This value refers to the relative noise level at a bandwidth of 1Hz, and its unit is dBc / Hz, which can also be considered as power spectrum density, such as Figure 2shown.

[0055] If the frequency domain resolution is guaranteed to be 1Hz, the definition of phase noise is numerically equal to the relative power value, and the absolute power value of the phase noise that needs to be added can be converted according to the power value of the input signal. Through the above calculation method, the power spectrum density of phase noise can be converted into the power spectrum of noise.

[0056] For a power signal x(t), its power spectral density (PSD) is defined as:

[0057]

[0058] Among them, X T (f) is the Fourier transform of x(t).

[0059] The power spectrum density reflects the density of the single frequency signal with frequency f in the total power of the signal.

[0060]

[0061] then

[0062]

[0063] Therefore, the sum of the average powers of all frequency components is the total power of the signal.

[0064] For a time-domain white noise signal n0(n) with a mean of 0, a variance of 1, and a length of N, its N-point discrete Fourier transform (DFT) can be expressed as

[0065]

[0066] Referring to formulas (3) and (4) and the Pasval theorem, the total frequency power of the white noise signal can be expressed as:

[0067]

[0068] Writing formula (3) in discrete form, we have

[0069]

[0070] Where Δf = F s / (N-1) represents the frequency resolution, F s Indicates the baseband sampling rate.

[0071] Combining formula (5) and (6), and rearranging them, we get:

[0072]

[0073] Analyzing formula (7), we can get that corresponding to a certain frequency point fi ,0≤i≤N, when the generated white noise is determined, there is a simple linear relationship between its power spectrum and power spectrum density. According to the definition of phase noise, let Δf=1Hz and convert the power spectrum density into power spectrum after linear operation. The Gaussian white noise filtering method mentioned above can be used to generate phase noise that meets the specified phase noise power spectrum density. The generation principle block diagram is shown below Figure 3 shown.

[0074] Simulation analysis:

[0075] The simulation is performed by taking the measured data in the table below that conforms to the power-law spectrum model as an example (frequency is 4.832 MHz).

[0076] Frequency (Hz) 1 3 5 7 10 30 50 70 100 300 Spectral density (dBc / Hz) -90 -113 -120 -125 -128 -138 -142 -143 -145 -147 Frequency (Hz) 500 700 1000 3000 5000 7000 1e4 3e4 5e4 10e4 Spectral density (dBc / Hz) -148 -148 -148 -149 -149 -149 -149 -149 -149 -149

[0077] from Figure 5 It can be seen that if the measured data conforms to the power-law spectrum model, both the power-law spectrum method and the method in this article can simulate the phase noise.

[0078] Take the actual measured phase noise in Rohde SMW200A as an example (frequency is 6GHz) for simulation.

[0079] Frequency (Hz) 1 10 100 1k 10k 100k 1M 10M Spectral density (dBc / Hz) -66 -81 -95 -119 -128 -129 -135 -153

[0080] from Figure 6 It can be seen that if the measured data does not conform to the power-law spectrum model, the phase noise error obtained by the power-law spectrum method is large. However, the method in this article can accurately simulate the phase noise due to the use of power spectrum shaping.

[0081] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions do not exceed the scope of the technical solution of the present invention, belong to the protection scope of the present invention.

Claims

1. A phase noise simulation method based on power spectrum shaping, characterized in that: The following steps are involved: S1, generate a Gaussian white noise sequence n0(n) with a length of N, a mean of 0, and a variance of 1; S2, perform a Fourier transform of length N on the sequence n0(n) and multiply it by Get its normalized spectrum sequence Where the frequency resolution Δf = F s / (N-1)=f i+1 -f i ; S3, according to the power spectrum density sequence P′(f) input by the user, according to f i ,0≤i≤N / 2, where 0≤f i ≤F s / 2, interpolation is performed to obtain And for To satisfy and The mirror symmetry of S4, the interpolated power spectral density sequence Convert to linear value to get the power spectrum sequence of phase noise In step S4, the interpolated power spectrum density sequence Converted into linear values, the power spectrum sequence of phase noise is obtained according to formula (7): Referring to formulas (3) and (4) and the Pasval theorem, the total frequency power of the white noise signal is expressed as: Writing formula (3) in discrete form, we have Where Δf = F s / (N-1) represents the frequency resolution, F s Indicates the baseband sampling rate; Combining formula (5) and (6), and rearranging them, we get: S5. According to the Rayleigh fading channel generation method, PSD and The corresponding frequency points are multiplied to obtain the shaped frequency domain phase noise signal N F (f i ); S6, for N F (f i ) to perform inverse Fourier transform to obtain the shaped phase noise time domain sequence S7, take The N / 2 data in are used as the actual phase noise data, where N / 4+1≤n≤N / 4+N / 2; S8, multiply the baseband IQ data by Realize the addition of phase noise; In step S2, For a power signal x(t), its power spectral density is defined as: Among them, X T (f) is the Fourier transform of x(t); because then Therefore, the sum of the average powers of all frequency components is the total power of the signal; For a time-domain white noise signal n0(n) with a mean of 0, a variance of 1, and a length of N, its N-point discrete Fourier transform is expressed as 2. A phase noise simulation method based on power spectrum shaping as claimed in claim 1, characterized in that: In step S2, Δf=1 Hz.

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