CN103248591A - Coarse frequency offset estimation method based on frequency spectrum barycenter - Google Patents
Coarse frequency offset estimation method based on frequency spectrum barycenter Download PDFInfo
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- CN103248591A CN103248591A CN2013102016940A CN201310201694A CN103248591A CN 103248591 A CN103248591 A CN 103248591A CN 2013102016940 A CN2013102016940 A CN 2013102016940A CN 201310201694 A CN201310201694 A CN 201310201694A CN 103248591 A CN103248591 A CN 103248591A
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Abstract
The invention provides a coarse frequency offset estimation method based on a frequency spectrum barycenter. The method comprises the steps of S1, receiving time domain signals and dividing the time domain signals into n groups, and performing fast N-point Fourier transform on the time domain signals in each group to obtain n groups of frequency domain data, wherein n is a positive integer and N is a fast Fourier transform point number; S2, processing each group of frequency domain data of the n groups of frequency domain data to obtain an average value vector containing n frequency domain data logarithm power; S3, setting the threshold value of a noise background and removing the noise in the average value vector containing n frequency domain data logarithm power according to the threshold value of the noise background; and S4, performing circulating translation on the denoised data and computing the frequency domain barycenter to obtain the frequency domain offset. The coarse frequency offset estimation method based on the frequency spectrum barycenter disclosed by the invention has the advantages of reducing data processing amount and algorithm complexity due to no need of adding a guide frequency signal and has good estimating property under a preset signal to noise ratio by estimating the offset amount.
Description
Technical field
The present invention relates to wireless communication technology field, particularly a kind of thick frequency deviation estimating method based on the frequency spectrum center of gravity.
Background technology
In wireless communication system, because the frequency difference between transmitting apparatus and receiving equipment, and client device moves the influences such as Doppler frequency-shift that bring, and makes to exist frequency shift (FS) between the frequency of carrier frequency and this locality.Frequency shift (FS) brings very big influence to wireless communication system, for example at OFDM (Orthogonal Frequency Division Multiplexing, OFDM) in the system, in the frequency domain channel is divided into many orthogonal sub-channels, the intercarrier of each subchannel keeps quadrature, and frequency spectrum is overlapped, in this case, fractional part of frequency offset just might destroy the orthogonality between subcarrier, produces inter-carrier interference (ICI) to cause the serious decline of systematic function.Though integer frequency offset can not cause the interference between subcarrier, can cause cyclic shift and the phase place rotation of the frequency domain data sequence of symhols of receiving terminal recovery, make the bit error rate of system improve greatly.So in radio communication, in order to guarantee the reliable transmission of data, need estimate and compensate that frequency deviation is estimated just to utilize the signal that receives to determine the frequency deviation of signal the unknown as observation sample to frequency shift (FS).The frequency deviation estimation approach has a lot, and different frequency deviation estimating methods is arranged in different systems.Traditional frequency deviation estimating method need add pilot signal at transmitting terminal, and receiving terminal utilizes known pilot signal to carry out the frequency deviation estimation.Therefore this method has increased amount of calculation and the algorithm complex of data owing to need to add pilot frequency information.
Summary of the invention
Purpose of the present invention is intended to solve at least one of above-mentioned technological deficiency.
For this reason, the objective of the invention is to propose a kind of thick frequency deviation estimating method based on the frequency spectrum center of gravity.
For achieving the above object, embodiments of the invention propose a kind of thick frequency deviation estimating method based on the frequency spectrum center of gravity, may further comprise the steps: S1: receive time-domain signal and described time-domain signal is divided into the n group, and the time-domain signal of each group carried out fast Fourier transform that N orders to obtain n group frequency domain data, wherein, described n is positive integer, and N is that fast Fourier transform is counted, and is 2 positive integer time power; S2: described n group frequency domain data is handled, to obtain containing the average value vector of n frequency domain data logarithm power; S3: the threshold value of Noise Background is set, and removes noise in the described average value vector that contains n frequency domain data logarithm power according to the threshold value of described Noise Background; And S4: will remove the translation that circulates of data behind the noise, and calculate the frequency domain center of gravity, to obtain the frequency domain skew.
In one embodiment of the present of invention, described step S2 specifically comprises:
According to described n group frequency domain data, to obtain the logarithm power of each element in each group; And
According to the logarithm power of each element in described each group, to obtain containing the average value vector of n frequency domain data logarithm power.
In one embodiment of the present of invention, described step S3 further comprises: when the value in the described average value vector during less than the threshold value of described Noise Background, the value in the described average value vector is set to 0; And when the value in the described average value vector is not less than the threshold value of described Noise Background, do not change the value in the described average value vector.
In one embodiment of the present of invention, described step S4 further comprises: with the translation that circulates of the data behind the described removal noise, to obtain new vector; Described new vector is handled, to obtain described frequency domain center of gravity; And handle according to described frequency domain center of gravity, to obtain described frequency domain skew.
In one embodiment of the present of invention, described translational movement is the N/2 point, and wherein, N is that fast Fourier transform is counted, and is 2 positive integer time power.
In one embodiment of the present of invention, described frequency domain center of gravity obtains by following formula, and described formula is,
Wherein, f is the frequency domain center of gravity, q
iBe i element in the new vector, q
kBe k element in the new vector, i, k are positive integer, and N is that fast Fourier transform is counted, and are 2 positive integer time power.
In one embodiment of the present of invention, the skew of described frequency domain obtains by following formula, and described formula is, Δ f=| (N+1)/2-f|, and wherein, Δ f is the frequency domain skew, and N is that fast Fourier transform is counted, and is 2 positive integer time power, and f is the frequency domain center of gravity.
According to the method for the embodiment of the invention, owing to do not need to add pilot signal, reduced data processing amount and algorithm complex, and by frequency shift (FS) is estimated, made under predetermined signal to noise ratio to have good estimation performance.
The aspect that the present invention adds and advantage part in the following description provide, and part will become obviously from the following description, or recognize by practice of the present invention.
Description of drawings
Above-mentioned and/or the additional aspect of the present invention and advantage are from obviously and easily understanding becoming the description of embodiment below in conjunction with accompanying drawing, wherein:
Fig. 1 is according to an embodiment of the invention based on the flow chart of the thick frequency deviation estimating method of frequency spectrum center of gravity; And
Fig. 2 is for utilizing the frequency spectrum center of gravity to TD-LTE uplink frequency skew estimation performance curve chart according to an embodiment of the invention.
Embodiment
Describe embodiments of the invention below in detail, the example of embodiment is shown in the drawings, and wherein identical or similar label is represented identical or similar elements or the element with identical or similar functions from start to finish.Be exemplary below by the embodiment that is described with reference to the drawings, only be used for explaining the present invention, and can not be interpreted as limitation of the present invention.
Fig. 1 is according to an embodiment of the invention based on the flow chart of the thick frequency deviation estimating method of frequency spectrum center of gravity.As shown in Figure 1, the thick frequency deviation estimating method based on the frequency spectrum center of gravity according to the embodiment of the invention may further comprise the steps:
Step 101 receives time-domain signal and time-domain signal is divided into the n group, and the time-domain signal of each group is carried out fast Fourier transform that N order to obtain n group frequency domain data, and wherein, n is positive integer, and N is that fast Fourier transform is counted, and is 2 the inferior power of positive integer.
Particularly, receive time-domain signal and the time-domain signal data that receive are divided into the n group, and respectively each group data is carried out the fast Fourier transform that N is ordered, to obtain n group frequency domain data.
Step 102 is handled each group frequency domain data of n group frequency domain data, to obtain containing the average value vector of n frequency domain data logarithm power.
Particularly, for n group frequency domain data A=(A
1, A
2..., A
n), wherein, A
i=(a
I1, a
I2..., a
IN) (i=1,2 ..., n) for containing the column vector of N data.At first obtain each group A
iIn the power B of each element
i, B
i=(b
I1, b
I2..., b
IN) (i=1,2 ..., n).Then, according to power B
iIn each element utilize formula c
Ij=10log10 (b
Ij) (j=1,2 ..., N) calculate its logarithm power, transforming into dB is the logarithm power C of unit
i, C
i=(c
I1, c
I2..., c
IN) (i=1,2 ..., n).Afterwards, according to logarithm power C
i(i=1,2 ..., n) utilize formula
Calculate, to obtain the average value vector P of frequency domain data logarithm power, P=(p
1, p
2... p
N).
Step 103 arranges the threshold value of Noise Background, and removes noise in the average value vector that contains n frequency domain data logarithm power according to the threshold value of Noise Background.
Particularly, the threshold value of Noise Background is set, wherein, this threshold value can be decided as the case may be.When the value in the average value vector during less than the threshold value of Noise Background, the value in the average value vector is set to 0, otherwise does not change the value in the average value vector then, thereby generate vectorial R after the denoising, R=(r
1, r
2..., r
N).
Step 104 will be removed the translation that circulates of data behind the noise, and calculated the frequency domain center of gravity, to obtain the frequency domain skew.
Particularly, be the vectorial R translation N/2 point that circulates with removing data behind the noise, to obtain new vectorial Q, Q=(q
1, q
2..., q
N).Then, according to vectorial Q, utilize formula
Calculate, to obtain frequency spectrum center of gravity f, wherein, q
iBe i element in the vector, q
kBe k element in the vector, i, k are positive integer, and N is that fast Fourier transform is counted, and are 2 positive integer time power.
At last,, calculate by formula Δ f=| (N+1)/2-f| according to frequency domain center of gravity f, to obtain Frequency offset estimation Δ f, wherein, Δ f is the frequency domain skew, and N is that fast Fourier transform is counted, and is 2 positive integer time power, and f is the frequency domain center of gravity.
According to the method for the embodiment of the invention, owing to do not need to add pilot signal, reduced data processing amount and computation complexity, and by frequency shift (FS) is estimated, made under predetermined signal to noise ratio to have good estimation performance.
In order to verify estimation effect of the present invention, carried out following test.Be example with the TD-LTE up link among the present invention, its basic parameter arranges as shown in table 1.
| Parameter | Parameter value |
| Bandwidth | 20MHz |
| Packet count | 45groups |
| Number of users | 1 |
| Channel type | PedA |
| Resource is distributed | 100RBs |
| Launching Model | 1x1 |
| Frequency shift (FS) | 15×12KHz |
| Emulation length | 20000subframes |
Table 1
Fig. 2 is for utilizing the frequency spectrum center of gravity to TD-LTE uplink frequency skew estimation performance curve chart according to an embodiment of the invention.As shown in Figure 2, as signal to noise ratio snr=0:2:30dB, as can be seen from Figure 2, when signal to noise ratio was low, the root-mean-square error that frequency deviation is estimated was bigger, but when signal to noise ratio surpassed 8dB, root-mean-square error all in 0.3 unit, had estimated performance preferably.
Although illustrated and described embodiments of the invention above, be understandable that, above-described embodiment is exemplary, can not be interpreted as limitation of the present invention, those of ordinary skill in the art can change above-described embodiment under the situation that does not break away from principle of the present invention and aim within the scope of the invention, modification, replacement and modification.
Claims (7)
1. the thick frequency deviation estimating method based on the frequency spectrum center of gravity is characterized in that, may further comprise the steps:
S1: receive time-domain signal and described time-domain signal is divided into the n group, and the time-domain signal of each group is carried out fast Fourier transform that N order to obtain n group frequency domain data, wherein, described n is positive integer, and N is that fast Fourier transform is counted, and is 2 the inferior power of positive integer;
S2: described n group frequency domain data is handled, to obtain containing the average value vector of n frequency domain data logarithm power;
S3: the threshold value of Noise Background is set, and removes noise in the described average value vector that contains n frequency domain data logarithm power according to the threshold value of described Noise Background; And
S4: will remove the translation that circulates of data behind the noise, and calculate the frequency domain center of gravity, to obtain the frequency domain skew.
2. the thick frequency deviation estimating method based on the frequency spectrum center of gravity as claimed in claim 1 is characterized in that described step S2 specifically comprises:
According to described n group frequency domain data, to obtain the logarithm power of each element in each group; And
According to the logarithm power of each element in described each group, to obtain containing the average value vector of n frequency domain data logarithm power.
3. the thick frequency deviation estimating method based on the frequency spectrum center of gravity as claimed in claim 1 is characterized in that described step S3 further comprises:
When the value in the described average value vector during less than the threshold value of described Noise Background, the value in the described average value vector is set to 0; And
When the value in the described average value vector is not less than the threshold value of described Noise Background, do not change the value in the described average value vector.
4. the thick frequency deviation estimating method based on the frequency spectrum center of gravity as claimed in claim 1 is characterized in that described step S4 specifically comprises:
With the translation that circulates of the data behind the described removal noise, to obtain new vector;
Described new vector is handled, to obtain described frequency domain center of gravity; And
Handle according to described frequency domain center of gravity, to obtain described frequency domain skew.
5. the thick frequency deviation estimating method based on the frequency spectrum center of gravity as claimed in claim 4 is characterized in that, described translational movement is the N/2 point, and wherein, N is that fast Fourier transform is counted, and is 2 positive integer time power.
6. the thick frequency deviation estimating method based on the frequency spectrum center of gravity as claimed in claim 4 is characterized in that, described frequency domain center of gravity obtains by following formula, and described formula is,
Wherein, f is the frequency domain center of gravity, q
iBe i element in the new vector, q
kBe k element in the new vector, i, k are positive integer, and N is that fast Fourier transform is counted, and are 2 positive integer time power.
7. the thick frequency deviation estimating method based on the frequency spectrum center of gravity as claimed in claim 4 is characterized in that, the skew of described frequency domain obtains by following formula, and described formula is,
Δf=|(N+1)/2-f|，
Wherein, Δ f is the frequency domain skew, and N is that fast Fourier transform is counted, and is 2 positive integer time power, and f is the frequency domain center of gravity.
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Cited By (3)
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| CN103743403A (en) * | 2014-01-22 | 2014-04-23 | 中国船舶重工集团公司第七〇五研究所 | Calculating method of single-frequency signal reverberation center |
| CN104639191A (en) * | 2014-12-25 | 2015-05-20 | 大唐半导体设计有限公司 | Receiver frequency domain noise reduction method and device |
| CN111654308A (en) * | 2020-04-30 | 2020-09-11 | 中国科学院上海微系统与信息技术研究所 | Precision frequency offset estimation method for burst spread spectrum weak signal |
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| US8064553B2 (en) * | 2008-04-23 | 2011-11-22 | Newport Media, Inc. | Coarse frequency offset estimation in ISDB receivers |
| CN102694763A (en) * | 2012-05-31 | 2012-09-26 | 重庆邮电大学 | Method for assessing integer frequency offset of TD-LTE system |
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2013
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| US8064553B2 (en) * | 2008-04-23 | 2011-11-22 | Newport Media, Inc. | Coarse frequency offset estimation in ISDB receivers |
| CN102694763A (en) * | 2012-05-31 | 2012-09-26 | 重庆邮电大学 | Method for assessing integer frequency offset of TD-LTE system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103743403A (en) * | 2014-01-22 | 2014-04-23 | 中国船舶重工集团公司第七〇五研究所 | Calculating method of single-frequency signal reverberation center |
| CN104639191A (en) * | 2014-12-25 | 2015-05-20 | 大唐半导体设计有限公司 | Receiver frequency domain noise reduction method and device |
| CN104639191B (en) * | 2014-12-25 | 2017-02-01 | 大唐半导体设计有限公司 | Receiver frequency domain noise reduction method and device |
| CN111654308A (en) * | 2020-04-30 | 2020-09-11 | 中国科学院上海微系统与信息技术研究所 | Precision frequency offset estimation method for burst spread spectrum weak signal |
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