CN106330806A - Fine Frequency Offset Estimation Algorithm and System Based on Cyclic Prefix and Long Training Sequence Field - Google Patents

Abstract

The present invention provides a fine frequency offset estimation algorithm and system based on a cyclic prefix and a long training sequence field. The method performs timing synchronization on received frame data and locates the 17th data sampling point in the long training sequence field; 64 data sampling points, starting from the 17th sampling point, calculate the autocorrelation value of 80 sampling points in sequence; accumulate the calculated autocorrelation values of 80 sampling points; use the accumulated sum to estimate the frequency offset value ; It overcomes the problem of low accuracy of fine frequency offset estimation using only cyclic prefix, and the problem of being easily interfered by other symbols due to multipath time delay, and reduces the calculation amount of DFT on the training sequence, and also reduces to some extent The error is reduced, and the estimation accuracy is further improved.

Description

Fine Frequency Offset Estimation Algorithm and System Based on Cyclic Prefix and Long Training Sequence Field

technical field

The present invention relates to the field of wireless mobile communication transmission, more specifically, to a fine frequency offset estimation algorithm and system based on cyclic prefix and long training sequence field.

Background technique

Since the 1980s, Orthogonal Frequency Division Multiplexing (OFDM, Orthogonal Frequency Division Multiplexing) technology has developed more and more maturely, and it must be widely used in many fields. For example, it is widely used in asymmetric digital subscriber line, wireless local loop, digital audio broadcasting, high-definition television, and wireless local area network. With the enhancement of people's demand for communication digitization, broadbandization, personalization and mobility, more researchers are concentrating more and more energy on the research and development of the application of OFDM technology in mobile communication systems.

The reason why OFDM can be widely used in various information transmission fields is mainly based on its high spectral efficiency, strong bandwidth expansion, anti-multipath fading, flexible allocation of spectrum resources and easy implementation of multi-antenna technology, but one OFDM symbol has multiple Orthogonal sub-carriers, spectrum deviation has a great influence on the orthogonality of its sub-carriers, this sensitivity to frequency deviation is a big shortcoming of OFDM technology, so how to eliminate the impact of frequency deviation on the system is very important important.

The OFDM multi-carrier system has many advantages such as: it can reduce the inter-symbol interference caused by the time dispersion of the wireless channel, and can maximize the use of spectrum resources. This is a great advantage in communication systems with very tight spectrum resources. Support Realize different transmission rates in different sub-channels, and can be combined with multiple access methods to form an orthogonal frequency division multiple access system and so on. But the disadvantages are also obvious. Since an OFDM symbol is composed of multiple orthogonal subcarriers, it is also very sensitive to the impact of frequency deviation, and often has a high peak-to-average power ratio, which will cause changes in the signal spectrum. Destroying the orthogonality of subcarriers will greatly affect the performance of the system.

Frequency deviation is generally caused by the following three reasons:

In practice, the crystal frequency of the carrier generated by the transmitter and the receiver cannot be exactly the same, which will lead to a certain deviation in the carrier frequency used for modulation and demodulation of the transmitter and receiver, breaking the subcarrier orthogonality, And the impact on the phase is also cumulative, causing serious impact on the system.

The transmitter and receiver are often not stationary, but have relative speed, which leads to Doppler frequency shift and frequency offset.

The crystal oscillators of the DAC of the transmitter and the A/D converter of the receiver cannot have the same sampling frequency, which will lead to a deviation in the sampling interval of the signal, and the accumulation of the deviation to a certain extent will have a serious impact on the system.

Subcarrier frequency offset frequency deviation is divided into integer multiple frequency offset and fractional multiple frequency offset. Integer multiple frequency offset means that the value of frequency offset generated by the system is greater than the subcarrier interval. Integer frequency offset value estimation algorithm is used for estimation. Fractional frequency offset means that the value of frequency offset is relatively small, generally within the range of a subcarrier interval. Fractional frequency offset algorithm has a smaller estimation range but higher accuracy. High, in the frequency offset estimation and compensation of the system, the two are often combined for frequency offset estimation. Fractional frequency offsets will lead to orthogonality between subcarriers, while integer frequency offsets will cause cyclic shift and phase rotation of the symbol sequence of the received OFDM signal. Frequency offset estimation can be divided into frequency offset estimation algorithms in the time domain and frequency offset estimation algorithms in the frequency domain. The carrier frequency offset will seriously affect the communication performance of the wireless communication system, resulting in service interruption and abnormal signal transmission. The frequency offset whose frequency offset value is smaller than the subcarrier spacing is called fractional frequency offset, and the estimation range of fractional frequency offset algorithm is small but the accuracy is higher. Considering that the frequency offset value is not large under normal circumstances, the present invention mainly discusses fine frequency deviation.

Moreover, frequency offset estimation using only cyclic prefixes is susceptible to the influence of multipath and the accuracy is low. The combination of cyclic prefixes and long training sequences can overcome the problem of cyclic prefix contamination and has a higher accuracy than traditional estimation algorithms that only use long training sequence fields. accuracy. As shown in Figure 1, the data frame structure of 802.11a includes a preamble field, a signaling field, a service field, a data field, and tails and padding. The preamble includes a short training sequence (STF, Short Train Field), a long training sequence (LTF, Long Train Field) and a signaling field. The short training sequence is a ten-segment repeated symbol, and the long training sequence includes a cyclic prefix, long training sequence field 1 and long training sequence field 2. Long training sequence 1 and sequence 2 are repeated sequence fields. The 802.11a protocol stipulates that one OFDM symbol has 64 data sampling points, the long training sequence has 160 data sampling points, the cyclic prefix has 32, and the long training field 1 and field 2 have 64 respectively. The present invention utilizes the cyclic prefix of the long training sequence and the interrelationship between the training sequence field 1 and the sequence field 2 to complete the fine frequency offset synchronization; the data frame structure of 802.11n includes the traditional preamble with the same structure of the 802.11a data frame .

Contents of the invention

The invention provides a fine frequency offset estimation algorithm based on cyclic prefix and long training sequence field, which can improve the frequency synchronization precision of the existing wireless communication system.

Another object of the present invention is to provide a fine frequency offset estimation system based on cyclic prefix and long training sequence fields.

In order to achieve the above-mentioned technical effect, the technical scheme of the present invention is as follows:

A fine frequency offset estimation algorithm based on cyclic prefix and long training sequence field, comprising the following steps:

S1: Perform timing synchronization on the received frame data, and locate the 17th data sampling point in the long training sequence field;

S2: Delay 64 data sampling points, and calculate the autocorrelation value of 80 sampling points sequentially from the 17th sampling point;

S3: Accumulate the calculated autocorrelation values of the 80 sampling points;

S4: Estimate the frequency offset value by using the accumulated sum.

Further, the specific process of the steps S1-S2 is as follows:

Position the symbol start time point of the preamble long training sequence field of the received frame data through symbol timing, the delay between the cyclic prefix and the repeated symbol corresponding to the long training sequence 1 is 64 sampling points and the long training sequence 1 The delay between the repeated symbols corresponding to the long training sequence 2 is also 64 data sampling points in length, and the delay autocorrelation is performed from the 17th data sampling point of the long training sequence with the data points after the delay of 64 sampling points , the 80-point time-delayed autocorrelation calculations are performed sequentially.

Preferably, the algorithm for estimating the frequency offset value in step S4 adopts a coordinate rotation digital calculation algorithm.

A fine frequency offset estimation system based on cyclic prefix and long training sequence field, comprising:

The delay autocorrelation calculation module is composed of a FIFO and a multiplier. The FIFO is composed of a static random access memory unit and is used as a data buffer. The depth is set to 64 to complete the delay operation of 64 points of data; first, initialize the FIFO to 0 , the current data enters the FIFO with a depth of 64, and after delaying the data of 64 points, the current data and the delayed data are synchronously output to the multiplier for complex multiplication, ensuring the synchronous output of the same position of the long training sequence and realizing the delayed autocorrelation computing functions;

The autocorrelation value accumulation module is composed of an adder and a register. First, the register is initialized with a value of 0. The autocorrelation value output by the delayed autocorrelation module and the current value temporarily stored in the register are simultaneously input to the adder for addition. Temporarily store the added result in the register until the accumulation of 80 delayed autocorrelation values is completed, and input the accumulated result to the frequency offset estimation module;

The frequency offset estimation module uses the coordinate rotation digital calculation algorithm to calculate the arctangent function of the accumulated results, and then completes the estimation and calculation of the frequency offset value.

Further, the autocorrelation module adopts a first-in-first-out queue to realize synchronous output of corresponding positions of the long training sequence, and then perform autocorrelation calculation of corresponding sampling points.

Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

The present invention performs timing synchronization on the received frame data, locates the 17th data sampling point in the long training sequence field; delays 64 data sampling points, and performs autocorrelation of 80 sampling points sequentially from the 17th adoption point Value calculation; accumulate the calculated autocorrelation values of 80 sampling points; use the accumulated sum to estimate the frequency offset value; overcome the low precision of fine frequency offset estimation using only cyclic prefixes, because multipath delay is easily The interference of other symbols reduces the calculation amount of DFT on the training sequence, and at the same time reduces the error to a certain extent, further improving the estimation accuracy.

Description of drawings

Fig. 1 is a structural diagram of a frame of data of 802.11a;

Fig. 2 is the algorithm flowchart of the present invention;

Fig. 3 is a system structure diagram of the present invention;

Fig. 4 is a schematic diagram of signal sending and receiving.

detailed description

The accompanying drawings are for illustrative purposes only and cannot be construed as limiting the patent;

In order to better illustrate this embodiment, some parts in the drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product;

For those skilled in the art, it is understandable that some well-known structures and descriptions thereof may be omitted in the drawings.

The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

Example 1

Suppose the source signal undergoes a series of processing at the transmitter to form a signal x(t). After the signal x(t) is up-converted, the complex baseband signal transmitted by the transmitter is:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&pi;f</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

ft represents the sending carrier frequency. Since there is a difference in the sampling period between the transmitter and the receiver, the receiving carrier frequency of the receiver is assumed to be fr, and the complex baseband signal received after the signal is down-converted is:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

w(t) represents Gaussian noise, because each module of the communication system can only process digital signals, and the down-converted signal is then subjected to analog-to-digital conversion to obtain the discrete expression of the received signal:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&pi;f</span>}}_{\mathrm{<span\; class="notranslate">\; \&Delta;</span>}}{\mathrm{<span\; class="notranslate">\; n</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

fΔ represents the frequency offset value generated by the signal after analog-to-digital conversion relative to the transmitted signal after the signal passes through the channel, and the estimated value of the frequency offset in formula (3) is normalized:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;\&epsiv;\&Delta;fn</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

fΔ=ε×Δf, ε is the normalized frequency offset, Δf is the subcarrier spacing, according to the IEEE 802.11n WLAN PHY layer standard, set Δf=312.5KHz, Ts=0.05us is the sampling period, N=64, the formula ( 4) The following formula can be further simplified:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\frac{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

The steps of the algorithm of the present invention are as follows:

Position the symbol start time point of the preamble long training sequence field through the symbol timing, assuming that the 17th point of the cyclic prefix corresponds to the time d, and the delay between the cyclic prefix and the repetition symbol corresponding to the long training sequence 1 is D= There are 64 sampling points and the delay between the repeated symbols corresponding to the long training sequence 1 and the long training sequence 2 is also 64 data sampling points in length. Therefore, from the 17th data sampling point of the long training sequence, the delay autocorrelation is performed with the data points after 64 sampling points, and the 80-point delay autocorrelation calculation is performed in sequence.

${\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; D.</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

Calculate the delayed autocorrelation sum of these 80 points:

$\mathrm{<span\; class="notranslate">\; Y</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; d</span>}}{\overset{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 79</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

Substitute formula (5) into formula (7) to get:

$\begin{array}{c}\mathrm{<span\; class="notranslate">\; Y</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; d</span>}}{\overset{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 79</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; N</span>}}\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; *</span>}\\ <span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; d</span>}}{\overset{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 79</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; x</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; *</span>}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; w</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; *</span>}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}<span\; class="notranslate">\; +</span>\\ \mathrm{<span\; class="notranslate">\; x</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; *</span>}\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; N</span>}}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; w</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; *</span>}\end{array}\right]\end{array}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

Because the signal and Gaussian noise are uncorrelated, and Gaussian noise signals are also uncorrelated, so the cross-correlation function between signal and noise is equal to zero, and the autocorrelation function between Gaussian noise is also zero, so Y can be written as:

$\mathrm{<span\; class="notranslate">\; Y</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; d</span>}}{\overset{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 79</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; x</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; *</span>}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span>$

Use equation (10) to estimate the fine frequency offset:

$\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; Im</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; Y</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; Re</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; Y</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

Accompanying drawing 4 is the structural diagram of the system of the present invention, and this hardware structure is made up of three modules, is delay autocorrelation calculation module, autocorrelation value accumulation module and frequency offset estimation module respectively. The delay autocorrelation calculation module consists of a FIFO and a multiplier. The FIFO is composed of static random access memory units, used as a data buffer, and the depth is set to 64, which can complete the delay operation of 64 points of data. First, initialize the FIFO to 0. The current data enters the FIFO with a depth of 64. After delaying the data at 64 points, the current data and the delayed data are synchronously output to the multiplier for complex multiplication, so that the same position of the long training sequence can be guaranteed. The synchronous output realizes the calculation function of delay autocorrelation.

The autocorrelation value accumulation module is composed of an adder and a register. First, the register is initialized to 0. The autocorrelation value output by the delayed autocorrelation module and the current value temporarily stored in the register are simultaneously input to the adder for addition. The result of the addition is temporarily stored in the register until the accumulation of 80 delayed autocorrelation values is completed, and the final accumulation result is input to the frequency offset estimation module.

It can be seen from formula (10) that the essence of estimating the frequency offset value is to calculate the arctangent function. The frequency offset estimation module uses the CORDIC algorithm to calculate the arctangent function, and then estimates the frequency offset value. The CORDIC algorithm, also known as the coordinate rotation digital calculation algorithm, uses the idea of dichotomy to change the ordinate value of the coordinate point to obtain the final accumulated angle value, which is the estimated arctangent value. The specific principles are as follows:

The autocorrelation cumulative sum Y of 80 points is obtained from formula (9). Y is a complex number. The imaginary part value of Y is taken as the ordinate point of the Cartesian coordinate, and the real part value is taken as the abscissa point. Note that the estimated arctangent angle is φ. Rotate the vector D (Im(Y), Re(Y)) clockwise by θ(k=0)=45 degrees, check the ordinate value of the new coordinate after rotation, if the value of the ordinate is greater than zero, it means that φ is greater than 45 degrees , continue to rotate the vector D by θ(k) degrees clockwise. If the value of the ordinate is less than zero, it means that φ is less than 45 degrees, and continue to rotate the vector D by θ(k) degrees counterclockwise. The angle of each subsequent rotation follows |tan[θ(k)]|=2-k, where k=1, 2,.... The angle value corresponding to 2-k can be realized through a lookup table. Adding the absolute value means that the angle can be positive or negative, which corresponds to clockwise or counterclockwise rotation. For the kth rotation, the calculation method is as follows:

(a+bi)(cos[θ(k)]+sin[θ(k)]i)=cos[θ(k)]×[a tan[θ(k)]b+i×(tan[θ( k)]a+b)]

Among them, cos[θ(k)] can also be saved by the lookup table method. The above rotation makes the value of the ordinate continuously close to 0, in practice as long as the value of the ordinate is smaller than a certain precision value. The angle values of multiple rotations are accumulated, and the accumulated result is the angle value of the arctangent function to be calculated. Obviously, the CORDIC algorithm can calculate the arctangent value by shifting and adding and subtracting, avoiding complex multiplication operations.

The same or similar reference numerals correspond to the same or similar components;

The positional relationship described in the drawings is only for illustrative purposes and cannot be construed as a limitation to this patent;

Apparently, the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, rather than limiting the implementation of the present invention. For those of ordinary skill in the art, other changes or changes in different forms can be made on the basis of the above description. It is not necessary and impossible to exhaustively list all the implementation manners here. All modifications, equivalent replacements and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the claims of the present invention.

Claims (5)

1. one kind based on Cyclic Prefix and the thin frequency excursion algorithm of long training sequence field, it is characterised in that include following step Rapid:

S1: the frame data received are timed synchronization, navigates to the 17th data sampled point of long training sequence field；

64 data sampled points of S2: time delay, calculate from the 17th autocorrelation value using point to start to carry out successively 80 sampled points；

S3: the autocorrelation value of calculate 80 sampled points is added up；

S4: utilize cumulative sum, estimate frequency deviation value.

The most according to claim 1 based on Cyclic Prefix with the thin frequency excursion algorithm of long training sequence field, its feature Being, the detailed process of described step S1-S2 is as follows:

The symbol initial time point of the lead code long training sequence field of the frame data received is navigated to, circulation by Symbol Timing Time delay between the replicator that prefix is corresponding with long training sequence 1 is 64 sampled points and long training sequence 1 and long training sequence Time delay between the replicator of row 2 correspondence is also 64 data sampled point length, from the 17th data sampling of long training sequence Data point after some beginning and 64 sampled points of time delay carries out time delay auto-correlation, carries out the time delay auto-correlation of 80 the most successively Calculate.

The most according to claim 3 based on Cyclic Prefix with the thin frequency excursion algorithm of long training sequence field, its feature It is, described step S4 being estimated, the algorithm of frequency deviation value uses Coordinate Rotation Digital computational algorithm.

4. one kind utilizes thin frequency excursion algorithm based on Cyclic Prefix and long training sequence field as claimed in claim 3 System, it is characterised in that including:

Time delay autocorrelation calculation module, is made up of a FIFO and multiplier, and FIFO is made up of static random access memory cell, makees Using for data buffer, the degree of depth is set to 64, completes the delay operation of 64 point data；First FIFO is initialized as 0, when It is the FIFO of 64 that front data enter the degree of depth, after time delay 64 point data, by current data and delay data synchronism output to multiplier Carry out complex multiplication operation, it is ensured that the synchronism output of long training sequence same position, it is achieved that the autocorrelative computing function of time delay；

Autocorrelation value accumulator module, is made up of an adder and depositor, and first depositor carries out 0 value initialization, time delay The autocorrelation value of auto-correlation module output is input simultaneously in adder be added, by phase with currency temporary in depositor The result added is kept in depositor, until 80 time delay autocorrelation value have added up, accumulation result is input to frequency deviation and estimates Module；

Frequency deviation estimating modules, uses Coordinate Rotation Digital computational algorithm that accumulation result is carried out arctan function calculating, and then complete Paired frequency deviation value carries out estimation and calculates.

The most according to claim 4 based on Cyclic Prefix with the thin frequency deviation estimation system of long training sequence field, its feature Being, described auto-correlation module uses fifo queue, it is achieved the synchronism output of long training sequence correspondence position, and then carries out The autocorrelation calculation of corresponding sampled point.