Ad hoc network intelligent routing protocol conversion system and method

Abstract

The invention belongs to the technical field of intelligent routing, and discloses an ad hoc network intelligent routing protocol conversion system and method, and the algorithm specifically comprises a block synchronization algorithm, a frequency offset estimation algorithm and a frequency domain equalization algorithm. According to the block synchronization algorithm, a training symbol estimation algorithm is mainly used for carrying out sliding correlation on a signal by utilizing the symmetry characteristic of a training symbol; the frequency offset estimation algorithm estimates the frequency offset by using the symmetry between repeated small blocks A in a lead code; the frequency domain equalization algorithm is carried out on the frequency domain, and the basic idea of frequency domain equalization is that an amplitude equalizer and a phase equalizer are used for compensating unreasonable phases of amplitude-frequency characteristics and phase-frequency characteristics of a transmission system to achieve required reasonable phases to form waveforms. According to the invention, a synchronization and equalization algorithm, full-forward independent synchronization, no feedback and a phase-locked loop mechanism are adopted, and rapid synchronization can be realized.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent routing, and in particular relates to a self-organizing network intelligent routing protocol signal transfer system and method. Background Art

[0002] In recent years, satellite communication has been widely concerned and applied as one of the network access technologies. There are two typical transmission modes in satellite communication. One transmission mode is transparent transmission, also known as bent-pipe forwarding transmission, in which the signal only undergoes frequency conversion and signal amplification on the satellite; the other transmission mode is non-transparent transmission, also known as on-board regeneration mode, in which the satellite has some or all base station functions.

[0003] However, existing satellite communications have not brought into play their due advantages, and problems such as communication, decision-making consultation, professional command, intelligence information collection and summary, and service support frequently occur. Therefore, a self-organizing network intelligent routing protocol relay system and method are needed to solve the technical problems existing in these existing technologies. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a system and method for transferring information of an intelligent routing protocol in an ad hoc network.

[0005] The present invention is implemented as follows: a self-organizing network intelligent routing protocol signal transfer system and method, the algorithm specifically includes: a block synchronization algorithm, a frequency offset estimation algorithm and a frequency domain equalization algorithm;

[0006] The block synchronization algorithm adopts the estimation algorithm of the training symbol, which mainly utilizes the symmetric characteristics of the training symbol to perform sliding correlation on the signal;

[0007] The frequency offset estimation algorithm estimates the frequency offset by using the symmetry between repeated small blocks A in the preamble;

[0008] The frequency domain equalization algorithm is performed in the frequency domain. The basic idea of ​​frequency domain equalization is to use an amplitude equalizer and a phase equalizer to compensate for the irrationality of the amplitude-frequency characteristic and the phase-frequency characteristic of the transmission system to achieve the required irrationality to form a waveform.

[0009] Furthermore, in the block synchronization algorithm, the training symbol adopts the training symbol structure in the time offset estimation algorithm proposed by Schmidl and Cox, that is, the front and back parts are the same [+A, +A], and the data part excluding the CP has good autocorrelation, A still uses the Chu sequence format, and the entire symbol length is N, and the CP length is L;

[0010] For the received sequence r(n), the correlation between the front and back parts of the training symbol data is expressed as:

[0011]

[0012] Where d is the timing deviation to be estimated; R1 H represents the conjugate transpose of R1, and

[0013]

[0014] definition:

[0015]

[0016] The timing measure is defined as the signal energy of the second part of the received training symbol:

[0017]

[0018] Under multi-sampling conditions, if a code element has m sampling points, the length of A is L code elements, and the correction formula is:

[0019]

[0020] According to the relevant principles, the time corresponding to the maximum value of the timing measurement is the first sample value time of the OFDM symbol, indicating the start of transmission, that is:

[0021]

[0022] Further, the frequency offset estimation algorithm specifically includes:

[0023]

[0024] Δf=angle(P(d)) / (2πLT).

[0025] Furthermore, in the frequency offset estimation algorithm, the frequency offset estimation range and accuracy are related to L and L0. Generally, the smaller L is, the larger the estimation range is and the lower the estimation accuracy is. Sometimes, in order to increase the capture range without reducing the estimation accuracy, the influence of noise on the algorithm can be reduced by increasing L0. In practical applications, when the signal-to-noise ratio is not very small, L0=L is generally taken.

[0026] Due to the deviation of the crystal oscillator itself, a large frequency deviation will appear in the down-conversion process. For the OFDM broadband wireless communication system, the carrier is fc=2GHz, the deviation of the general crystal oscillator is d=25ppm, and the maximum frequency deviation of the system is 80kHz. To ensure that fast fading does not occur during the processing of a symbol (data block), the selected data block length will not be too long, so a larger frequency deviation can be estimated. Since the frequency deviation range is related to the length L of the UW word, a coarse frequency deviation estimation is first performed using a short UW word, and then a fine frequency deviation estimation is performed using a long UW word. The simulation results show that for the case of a small frequency deviation, it is actually only necessary to use a long UW to perform a frequency deviation estimation.

[0027] Furthermore, for the frequency offset estimation range relationship: the frequency offset estimation range in the formula is [-F / (2L) - F / (2L)], the actual maximum frequency offset is 2d*fc, and it is required to satisfy F / (2L) > 2d*fc, that is, fc / F < 1 / (4dL) = 10e4 / L, then L < F*10e4 / fc. When F / fc is too small, it will cause L to be too small and the frequency offset estimation accuracy to decrease; however, for coarse frequency offset estimation, in order to roughly capture the frequency offset, the improvement of accuracy is completed after block synchronization and timing synchronization.

[0028] Furthermore, the frequency domain equalization algorithm specifically includes:

[0029] A channel estimation module, which is used to perform initial channel estimation using the training sequence and output the frequency domain equalization coefficient W, the time domain feedback coefficient c, the feedback delay B, and the noise variance estimation σ 2 ;

[0030] The NPFDE module is used to perform adaptive frequency domain. Its time domain coefficient c is adaptively adjusted by symbol. After processing each block, it outputs the block error signal e to the channel tracking module for tracking;

[0031] A channel tracking module, which is used to adjust the equalization coefficient W using the initial channel estimation value W and the error signal e, and send the adjusted W to the NPFDE module as the equalization coefficient for the next block.

[0032] Furthermore, the channel tracking module uses the RLS algorithm for channel tracking.

[0033] Furthermore, the algorithm formula of the NPFDE module is specifically as follows:

[0034]

[0035] c = -V -1 v, c0 = 1

[0036]

[0037] F B is a set of B feedback tap delays corresponding to the B largest channel impulse response postcursors. (F B is a set of B feedback tap delays corresponding to the B largest channel impulse response postcursors.)

[0038] For F B = 1, the feedback coefficient is simplified to:

[0039]

[0040] B corresponds to the largest channel impulse response postcursors.

[0041] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the self-organizing network intelligent routing protocol signaling system and method.

[0042] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the self-organizing network intelligent routing protocol signaling system and method.

[0043] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0044] First, the present invention adopts synchronization and equalization algorithm, full forward independent synchronization, no feedback and phase-locked loop mechanism, and can achieve fast synchronization. The current synchronization error will not affect other burst receptions. Each burst can independently complete capture, timing synchronization, carrier recovery, demodulation, decoding and other processing; it supports multi-burst cascade pipeline transmission and pipeline reception, and can complete effective signal detection (PD), coarse frequency offset estimation (CFE), coarse frequency offset correction (CFEV), block synchronization (CTE), fine synchronization (FTE), fine frequency offset estimation (FFE), timing tracking (OM), fine frequency offset correction (FFEV) and timing correction (INTERP).

[0045] After a comprehensive comparison of various equalization algorithms, it was decided to use NoisePredictive (NP) FDE with feedback tap number FB = 1 for the following reasons:

[0046] Compared with the iterative equalization algorithm, the implementation is relatively simple, and its complexity is comparable to that of LE and DFE equalization. Theoretical and simulation results show that NP FDE has the same performance as DFE. The feedforward coefficient and feedback coefficient of NP FDE are optimized independently, and this structure has greater freedom in terms of implementation. It is more convenient in channel tracking.

[0047] Second, the present invention provides a self-organizing network intelligent routing protocol signal transfer system and method that combines a block synchronization algorithm, a frequency offset estimation algorithm, and a frequency domain equalization algorithm, aiming to improve the reliability and efficiency of self-organizing network communication. The working principle will be described in detail below:

[0048] First of all, the block synchronization algorithm is one of the key steps in the algorithm. It uses the symmetric characteristics of the training symbols for sliding correlation to achieve block synchronization of the signal. The training symbols adopt the structure of the time deviation estimation algorithm proposed by Schmidl and Cox, that is, the front and back parts are exactly the same [+A, +A]. This structure makes the data part without the cyclic prefix (CP) have good autocorrelation. At the receiving end, the timing deviation can be estimated by calculating the correlation between the front and back parts of the training symbol data. The time corresponding to the maximum value of the timing measure is the first sample value time of the OFDM symbol, marking the beginning of the transmission. This block synchronization algorithm can accurately determine the starting position of the signal, providing a basis for subsequent data processing.

[0049] Secondly, the frequency deviation estimation algorithm is used to estimate the frequency deviation of the signal. It uses the symmetry between the repeated small blocks A in the preamble to perform frequency deviation estimation. By measuring the changes in phase and amplitude at each moment, the frequency deviation of the signal can be inferred. This algorithm can effectively compensate for the frequency deviation caused by factors such as multipath effect and Doppler frequency shift, thereby improving the reception quality of the signal.

[0050] Finally, the frequency domain equalization algorithm is an equalization process performed in the frequency domain. It uses the amplitude equalizer and phase equalizer to compensate for the imperfect parts of the amplitude-frequency characteristics and phase-frequency characteristics of the transmission system to achieve ideal waveform formation. The frequency domain equalization algorithm can effectively eliminate signal distortion caused by channel distortion and interference and improve the transmission quality of the signal.

[0051] The system and method for transferring signals of the self-organizing network intelligent routing protocol provided by the present invention realizes accurate synchronization, frequency offset compensation and equalization processing of the self-organizing network communication signals by combining the block synchronization algorithm, the frequency offset estimation algorithm and the frequency domain equalization algorithm. These algorithms cooperate with each other to jointly improve the reliability and efficiency of the self-organizing network communication, so that the self-organizing network can stably transmit data in a complex and changeable environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is the training symbol format of the algorithm proposed by Schmidl and Cox provided in the embodiment of the present invention;

[0053] Figure 2 is a frequency offset estimation diagram provided by an embodiment of the present invention;

[0054] Figure 3 is a design diagram of a receiving equalization algorithm provided by an embodiment of the present invention;

[0055] Figure 4 is a channel estimation training sequence provided by an embodiment of the present invention;

[0056] Figure 5is a schematic diagram of a channel estimation principle provided by an embodiment of the present invention;

[0057] Figure 6 is a channel tracking performance diagram provided by an embodiment of the present invention;

[0058] Figure 7 is a noise prediction decision feedback equalization diagram provided by an embodiment of the present invention;

[0059] Figure 8 This is a SUI5 channel simulation diagram provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0061] Example 1: Application of block synchronization algorithm

[0062] In a specific communication system, assuming that OFDM modulation technology is adopted, the block synchronization algorithm is the key first step, which is used to accurately determine the starting point of each OFDM block. In this embodiment, the method of Schmidl and Cox is adopted, and the training symbol is designed as two identical parts [+A, +A], where A uses the Chu sequence to ensure that it has excellent autocorrelation properties. This structure helps to improve the synchronization accuracy and reduce the error in the synchronization process.

[0063] When the received signal is processed by the autocorrelation of the training symbol, the system can accurately estimate the timing deviation. In order to adapt to high-speed transmission and unstable wireless environment, the algorithm performs multi-sampling processing, so that the start time of the OFDM symbol can be determined more accurately at the receiving end to ensure correct demodulation of the data.

[0064] Embodiment 2: Combined application of frequency offset estimation algorithm and frequency domain equalization algorithm

[0065] In an ad hoc network environment, frequency offset estimation is one of the key steps to ensure accurate data transmission. In this embodiment, the frequency offset estimation algorithm uses the symmetry of the repeated small block A in the preamble code to effectively estimate the frequency offset from the received signal. This step is crucial to offset the drift of the carrier frequency, especially in a mobile environment.

[0066] Next, the system uses a frequency domain equalization algorithm to further process the signal, and compensates for the amplitude-frequency characteristics and phase-frequency characteristics of the signal through an amplitude equalizer and a phase equalizer. In an OFDM system, due to its frequency domain processing characteristics, frequency domain equalization can effectively compensate for the distortion caused by the channel, ensuring that clean data is restored at the receiving end.

[0067] By combining these two algorithms, communication devices in ad hoc networks can achieve efficient and stable data transmission in complex and changing network environments. Accurate frequency deviation correction combined with efficient frequency domain equalization enables the system to maintain communication reliability and stability in a dynamically changing network environment. In addition, this method also plays a positive role in improving the network's self-organizing ability and anti-destruction ability.

[0068] The estimation algorithm using training symbols mainly uses the symmetric characteristics of training symbols to perform sliding correlation on the signal. The training symbols use the training symbol structure in the time offset estimation algorithm proposed by Schmidl and Cox, that is, the front and back parts are the same [+A, +A], and the data part excluding the CP has good autocorrelation, and A still uses the Chu sequence format. Assume that the entire symbol length is N, and the CP length is L, such as Figure 1 shown.

[0069] For the received sequence r(n), the correlation between the front and back parts of the training symbol data is expressed as:

[0070]

[0071] Where d is the timing deviation to be estimated; R1 H represents the conjugate transpose of R1. And

[0072]

[0073] definition:

[0074]

[0075] The timing measure is defined as the signal energy of the second part of the received training symbol:

[0076]

[0077] Under multi-sampling conditions, if a code element has m sampling points, the length of A is L code elements, and the correction formula is:

[0078]

[0079] According to the relevant principles, the time corresponding to the maximum value of the timing measurement is the first sample value time of the OFDM symbol, indicating the start of transmission, that is:

[0080]

[0081] For Carrier Frequency Offset (CFO), since a simple carrier frequency offset will cause signal rotation and amplitude attenuation, for OFDM systems, when an OFDM block is transformed into the frequency domain through FFT, the frequency offset at this time will cause inter-carrier interference (ICI) on the signal at each frequency point, which cannot be offset by a single-tap frequency domain equalizer and will also introduce additional errors during equalization. After the incompletely compensated frequency domain signal passes through IFFT again, the obtained signal has not only the interference caused by the frequency offset, but also the interference caused by the estimation error. However, after the diffusion of this additional interference through the IFFT transformation, it has been averaged to each code element, and the impact caused is far less than that on OFDM. However, if the frequency offset is not compensated, it will have a great impact on the block synchronization algorithm and the timing algorithm. Therefore, frequency offset estimation and compensation are required when the signal is received.

[0082] Since the rough frequency offset estimation is done before block synchronization, the position of UW cannot be known at this time, so the DA algorithm cannot be used for estimation. However, the frequency offset can be estimated by using the symmetry between the repeated small blocks A in the preamble, such as Figure 2 As shown, the algorithm is as follows:

[0083]

[0084] Δf=angle(P(d)) / (2πLT)

[0085] The frequency offset estimation range and accuracy are related to L and L0. Generally, the smaller L is, the larger the estimation range is and the lower the estimation accuracy is. Sometimes, in order to increase the capture range without reducing the estimation accuracy, the influence of noise on the algorithm can be reduced by increasing L0. In practical applications, when the signal-to-noise ratio is not very small, L0 = L is generally used.

[0086] Due to the deviation of the crystal oscillator itself, a large frequency deviation will occur during the down-conversion process. For the OFDM broadband wireless communication system, the carrier is fc=2GHz, the deviation of the general crystal oscillator is d=25ppm, and the maximum frequency deviation of the system is 80kHz. The present invention ensures that no fast fading occurs during the processing of a symbol (data block), so the selected data block length will not be too long, so a larger frequency deviation can be estimated. Since the frequency deviation range is related to the length L of the UW word, a coarse frequency deviation estimation is first performed using a short UW word, and then a fine frequency deviation estimation is performed using a long UW word. The simulation results show that for the case of a small frequency deviation, it is actually only necessary to use a long UW to perform a frequency deviation estimation.

[0087] Frequency offset estimation range relationship: The frequency offset estimation range in the formula is [-F / (2L) - F / (2L)], and the actual maximum frequency offset is 2d*fc. It is necessary to satisfy F / (2L) > 2d*fc, that is, fc / F < 1 / (4dL) = 10e4 / L, then L < F*10e4 / fc. When F / fc is too small, it will cause L to be too small and the frequency offset estimation accuracy to decrease. However, for coarse frequency offset estimation, the main purpose is to roughly capture the frequency offset, and the improvement of accuracy is completed after block synchronization and timing synchronization.

[0088] The structural diagram of the frequency domain equalization algorithm is as Figure 3 shown;

[0089] The functions of each module are as follows:

[0090] 1. The "channel estimation" module uses the training sequence to perform initial channel estimation. The outputs include the frequency domain equalization coefficient W, the time domain feedback coefficient c, the feedback delay B, and the noise variance estimation σ2.

[0091] 2. The "NP FDE" module performs adaptive frequency domain. Its time domain coefficient c is adaptively adjusted symbol by symbol. After processing each block, the output block error signal e is given to the channel tracking module for tracking.

[0092] 3. The "channel tracking module" uses the initial channel estimation value W and the error signal e to adjust the equalization coefficient W, and sends the adjusted W to the NP FDE module as the equalization coefficient for the next block.

[0093] Select the following training sequence for channel estimation, as Figure 4 shown.

[0094] The process of channel estimation is as Figure 5 shown.

[0095] This scheme uses the RLS algorithm for channel tracking. Specifically, for the RLS algorithm:

[0096] RLS Algorithm: The L-input (L-weight) RLS algorithm, or Kalman algorithm, is defined by [3] as follows:

[0097] 1) Compute the Kalman gain vector (L×1): K = ΔR * / (λ + R T ΔR * );

[0098] 2)update inverse of correlation matrix (L×L): Δ←(1 / λ)(Δ-KR T Δ);

[0099] 3)update filter gains(L×1):G←G+KE.

[0100] Where: G is the frequency domain equalization coefficient

[0101] R is the FFT of the received data

[0102] Since the present invention adopts the NP FDE structure, it is necessary to track the feedforward coefficient G and the feedback coefficient c simultaneously. G is the frequency domain coefficient, which is updated in each demodulation block, that is, updated by block; c can be updated by symbol. Using the above tracking algorithm, the simulation results under the SUI5 channel are as follows Figure 6 shown.

[0103] The simulation parameters are: 256 symbols per block; 100 blocks per frame

[0104] Simulation results show that the performance of the channel tracking algorithm deteriorates by about 1dB at 5Hz.

[0105] After a comprehensive comparison of various equalization algorithms, we decided to use Noise Predictive (NP) FDE with feedback tap number FB = 1 for the following reasons:

[0106] Compared with the iterative equalization algorithm, it is relatively simple to implement, and its complexity is comparable to LE and DFE equalization.

[0107] Theoretical and simulation results show that NP FDE and DFE have the same performance.

[0108] The feedforward coefficient and feedback coefficient of NP FDE are optimized independently. This structure has greater freedom in implementation and is more convenient in channel tracking.

[0109] The present invention provides the NP FDE algorithm formula below:

[0110]

[0111] c=-V -1 v,c0=1

[0112]

[0113] F B is a set of B feedback tap delays corresponding to the B maximum channel impulse response post cursor. (F Bis a set of Bfeedback tap delays corresponding to the B largest channel impulse responsepostcursors.)

[0114] F B =1, the feedback coefficient is simplified to:

[0115]

[0116] B corresponds to the largest channel impulse response postcursors.

[0117] Figure 8 The performance of LE, DFE and NP FDE algorithms under ideal channel estimation under SUI5 channel. The simulation results show that NP FDE has the same performance as DFE, with a performance improvement of about 1dB compared to LE; 2x symbol sampling equalization is relatively insensitive to timing error, and there is no obvious deterioration in algorithm performance under 5% timing error and 100Hz carrier error.

[0118] The self-organizing network intelligent routing protocol signal transfer system and method provided by the application embodiment of the present invention is applied to a computer device, the computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the self-organizing network intelligent routing protocol signal transfer system and method.

[0119] The self-organizing network intelligent routing protocol signal transfer system and method provided by the application embodiment of the present invention is applied to a computer-readable storage medium, the computer-readable storage medium includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the self-organizing network intelligent routing protocol signal transfer system and method.

[0120] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0121] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A self-organizing network intelligent routing protocol signal transfer system and method, characterized in that: The algorithm specifically includes: a block synchronization algorithm, a frequency offset estimation algorithm, and a frequency domain equalization algorithm; The estimation algorithm of the block synchronization algorithm using training symbols mainly utilizes the symmetric characteristics of the training symbols to perform sliding correlation on the signals; The frequency offset estimation algorithm estimates the frequency offset by using the symmetry between the repeated small blocks A in the preamble; The frequency domain equalization algorithm is carried out in the frequency domain. The basic idea of frequency domain equalization is to use an amplitude equalizer and a phase equalizer to compensate for the unreasonable amplitude-frequency characteristics and phase-frequency characteristics of the transmission system to achieve the required ideal phase to form a waveform.

2. The system and method for transferring information via an intelligent routing protocol in an ad hoc network as claimed in claim 1, characterized in that: In the block synchronization algorithm, the training symbols adopt the training symbol structure in the time offset estimation algorithm proposed by Schmidl and Cox, that is, the front and back parts are the same [+A, +A], and the data part excluding the CP has good autocorrelation. A still uses the Chu sequence format. Let the entire symbol length be N and the CP length be L; For the received sequence r(n), the correlation between the front and back parts of the training symbol data is expressed as: Where d is the timing deviation to be estimated; R1 H represents the conjugate transpose of R1, and Define: is the signal energy of the second part of the received training symbol, and the timing measure is defined as: Under the condition of multi-sampling, if a symbol has m sampling points and the length of A is L symbols, the correction formula is: According to the correlation principle, the moment corresponding to the maximum value of the timing measure is the first sample value moment of the OFDM symbol, indicating the start of transmission, that is:

3. The system and method for transferring information via an intelligent routing protocol in an ad hoc network as claimed in claim 1, characterized in that: The frequency offset estimation algorithm specifically includes:

4. The system and method for transferring information via an intelligent routing protocol in a self-organizing network as claimed in claim 1, characterized in that: In the frequency offset estimation algorithm, the frequency offset estimation range and accuracy are related to L and L0. Generally, the smaller L is, the larger the estimation range is, and the lower the estimation accuracy is. Sometimes, in order to increase the capture range and not want to reduce the estimation accuracy, the influence of noise on the algorithm can be reduced by increasing L0. In practical applications, when the signal-to-noise ratio is not very small, generally L0 = L; Due to the deviation of the crystal oscillator itself, a large frequency offset will occur during the down-conversion process. For an OFDM broadband wireless communication system with a carrier frequency fc = 2 GHz, the deviation d of the general crystal oscillator is 25 ppm, and the maximum frequency offset of the system is 80 kHz. To ensure that fast fading does not occur during the processing of one symbol (data block), the selected data block length will not be too long, so a relatively large frequency offset can be estimated. Since the frequency offset range is related to the length L of the UW word, first use a short UW word to perform a rough frequency offset estimation, and then use a long UW word to perform a fine frequency offset estimation; the simulation results show that for the case of a small frequency offset, actually only one frequency offset estimation using the long UW is required.

5. The system and method for transferring information via an intelligent routing protocol in an ad hoc network as claimed in claim 4, characterized in that: The frequency offset estimation range relationship formula: The frequency offset estimation range in the formula is [-F / (2L) - F / (2L)], and the actual maximum frequency offset is 2d*fc. It is necessary to satisfy F / (2L) > 2d*fc, that is, fc / F < 1 / (4dL) = 10e4 / L, then L < F*10e4 / fc. When F / fc is too small, it will cause L to be too small and the frequency offset estimation accuracy to decrease; but for the rough frequency offset estimation, in order to roughly capture the frequency offset, the improvement of the accuracy is completed after block synchronization and timing synchronization.

6. The system and method for transferring information via an intelligent routing protocol in a self-organizing network as claimed in claim 4, characterized in that: The frequency domain equalization algorithm specifically includes: The channel estimation module is used to perform initial channel estimation using the training sequence. The output includes the frequency domain equalization coefficient W, the time domain feedback coefficient c and feedback delay B, and the noise variance estimate σ 2 ; NPFDE module is used for adaptive frequency domain. Its time domain coefficient c is adaptively adjusted according to the sign. After processing each block, it outputs the block error signal e to the channel tracking module for tracking; The channel tracking module is used to adjust the equalization coefficient W using the initial channel estimation value W and the error signal e, and send the adjusted W to the NPFDE module as the equalization coefficient of the next block.

7. The system and method for transferring information via an intelligent routing protocol in a self-organizing network as claimed in claim 6, characterized in that: The channel tracking module uses the RLS algorithm to perform channel tracking.

8. The system and method for transferring information via an intelligent routing protocol in a self-organizing network as claimed in claim 6, characterized in that: The NPFDE module algorithm formula is as follows: c=-V -1 V,c0=1 F B is a set of B feedback tap delays corresponding to the B maximum channel impulse response post cursor (F B is a set of Bfeedback tap delays corresponding to the B largest channel impulseresponsepostcursors); F B =1, the feedback coefficient is simplified to: B corresponds to the largest channel impulse response postcursors.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the self-organizing network intelligent routing protocol signal transfer system and method as described in any one of claims 1-8.

10. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the system and method for transferring signals via an intelligent routing protocol in a self-organizing network as claimed in any one of claims 1 to 8.

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