Channel quality estimation method for VDES satellite communication

Abstract

The invention provides a channel quality estimation method for VDES satellite communication, and belongs to the technical field of wireless communication. The method comprises the following steps of: performing rectification and integration on a baseband sampling signal output by a filter, and calculating a received signal strength index; compensating the phase of the baseband sampling signal of the CPM spread spectrum; the non-CPM spread spectrum phase is compensated; and according to the obtained baseband sampling synchronization word sequence, the calculated SNR passes through a mean filter, and a final SNR result is output. The MPART phase estimation compensation method is designed according to the maximum residual frequency offset, the chip rate, the chip length and the least square method, it is guaranteed that the input data phase of the ML algorithm is stable, the influence of the frequency offset on ML estimation is reduced, the dynamic range of SNR estimation is greatly widened, the robustness and accuracy of SNR detection are improved, and the method is suitable for popularization and application. And algebraic operation is simplified through the lookup table, so that the calculation amount is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication, and in particular, to a method for estimating channel quality for VDES satellite communication. Background Art

[0002] With the development of communication technology, the maritime network is developing towards a diverse and complex structure. At present, the International Maritime Organization has proposed to develop the third-generation maritime communication system VDES, which can meet the data interaction between ship-ship, ship-shore, and satellite-ground in the future. In particular, for the data exchange between satellite-ground, up to 12 transmission modes of links are proposed to cope with the stable, reliable, and effective transmission of data under different channel qualities. Therefore, there are relatively high requirements for the dynamic range, robustness, and accuracy of channel quality estimation.

[0003] Currently, the commonly used channel quality estimation algorithms are divided into two categories: (1) Data Aided (DA) represented by the Maximum-Likelihood (ML) estimation algorithm; (2) Non-Data-Aided (NDA) represented by the Second-Order Moment Fourth-Order Moment (M2M4) algorithm and the Split-Symbol Moments Estimator (SSME). The above algorithms have the following problems:

[0004] (1) The maximum likelihood algorithm is very sensitive to residual frequency offset. When the carrier synchronization error is large, there is a large phase difference between consecutive synchronization sequences, resulting in a large coordinate rotation error, which causes the accuracy and dynamic range of the maximum likelihood estimation algorithm based on DA to drop significantly.

[0005] (2) The traditional M2M4 algorithm requires a large number of sampling points under low signal-to-noise ratio conditions to ensure stable statistical characteristics through the ergodicity of Gaussian white noise. However, the data lengths of different LinkIDs in the uplink satellite link of VDES are different, making it difficult to ensure the robustness of channel estimation. Moreover, VDES includes 16QAM modulation, while the traditional M2M4 only supports MPSK.

[0006] (3) SSME divides the received signal into an even number of consecutive chips and calculates the signal-to-noise ratio through the cumulative average of the front and back groups of chips. However, the conventional received signal is not completely symmetric, and this method has high requirements for symbol synchronization and is not applicable to the constant envelope continuous phase (CPM) spread spectrum of VDES. Summary of the Invention

[0007] According to the technical problems mentioned in the above background art, a method for channel quality estimation for VDES satellite communication is provided. The present invention provides a method for channel quality estimation for VDES satellite communication with high dynamic range, strong robustness, and strong accuracy. As Figure 1 shown, this method mainly consists of an MPART phase compensation module and an MPART ML calculation module. First, the baseband sampling signals at 4 times the chip rate after coarse frequency offset correction are respectively sent to the CPM spread spectrum phase compensation module, the non-CPM spread spectrum phase compensation module, and the RSSI calculation module; secondly, the ML estimation is respectively performed on the phase-corrected synchronization word sampling sequence SEGn and the local sequence; finally, the SNR estimation value is obtained through mean filtering.

[0008] The technical means adopted by the present invention are as follows:

[0009] A method for channel quality estimation for VDES satellite communication, comprising the following steps:

[0010] Step 1: Calculate the received signal strength indicator by rectifying and integrating the baseband sampling signal output by the filter;

[0011] Step 2: Compensate the phase of the baseband sampling signal of CPM spread spectrum; first, generate a local synchronization word sequence through MATALB simulation according to LinkID, secondly, divide the synchronization word sequence into N segments, and respectively use the least squares method to calculate the initial phase and residual frequency offset of the synchronization word for segment nSEGn. The phase change is fitted to a linear function to reduce the influence of short pulse noise on phase estimation; finally, correct the baseband sampling synchronization word sequence with coarse frequency offset compensation according to the phase error.

[0012] Step 3: Compensate the non-CPM spread spectrum phase; first, generate a local synchronization word sequence through MATALB simulation; secondly, divide the synchronization word sequence into M segments, and respectively use the least squares method to calculate the initial phase and residual frequency offset of the synchronization word for segment nSEGn. The phase change is fitted to a linear function to reduce the influence of short pulse noise on phase estimation; finally, correct the baseband sampling synchronization word sequence with coarse frequency offset compensation according to the phase error.

[0013] Step 4: Calculate the SNR obtained from the baseband sampling synchronization word sequences obtained in Step 2 and Step 3 through a mean filter, and output the final SNR result.

[0014] Further, in Step 2, for CPM spread spectrum phase compensation, the baseband sampling rate is 134.4 KHz and the synchronization word chip length is 3072;

[0015] The MATALB simulation divides the local synchronization word sequence into N segments SEG, each SEG contains 3072 / N chips and calculates the phase difference through quadrant decision. The value of N is related to the chip length, the maximum residual frequency offset and the chip rate; the cumulative phase difference of the SEG is less than π / 2.

[0016] Further, in step 3, the non-CPM spreading phase is compensated; the baseband sampling rate is 33.6KHz, and the synchronization word chip length is 108.

[0017] The MATALB simulation generates a local synchronization word sequence, which is divided into M segments, each segment contains 108 / M chips and calculates the phase difference through quadrant decision. The value of M is related to the chip length, the maximum residual frequency offset and the chip rate. For the convenience of phase fitting, it is ensured that the cumulative phase difference of the SEG is less than Π / 2.

[0018] Further, the initial phase and the unit phase difference are calculated respectively by the least square method. The calculation formula is:

[0019]

[0020] where represents the phase difference of the sampling points, a i is the imaginary part of the correlation sequence, b i represents the real part of the correlation sequence, T is the length of the SEG, is the initial phase of each SEG; by judging the positive and negative polarities of a i and bi, and taking the symbol of a 1 +T(n - 1) as the reference, adjust the phase to the actual phase. Since the arctan function will map the second quadrant to the fourth quadrant, the phase is added by π, and the value corresponding to the third quadrant is mapped to the first quadrant, then the phase is subtracted by π. According to the real part a i and the imaginary part b i judge the quadrant, then:

[0021]

[0022] Further, the initial phase and the unit phase difference of SEGn are calculated respectively by the least square method T ∈ [1, sampling point length / N] The matrix formula is;

[0023]

[0024]

[0025]

[0026] Among them, the denominator and other parameters for calculating \(k_n\) are constant sequences with respect to \(n\). The parameter sequences of different SEGs are assigned addresses and written into the ROM, and read sequentially by address to simplify the calculation; \(x\) n,i \(\in[1,\text{sampling point length} / N]\), is the average value, \(T = \text{sampling point length} / N\);

[0027] \(x\) n，i \(=T(n - 1)+i\);

[0028]

[0029]

[0030] Finally, the phase compensation sequence of SEGn is fitted according to \(k_n\), and multiplied by the conjugate of SEGn respectively, so as to reduce the influence of residual carrier and phase deviation on ML estimation.

[0031] Furthermore, in the step 4, according to the ML estimation algorithm, the signal-to-noise ratio SNR\(_n\) of each segment \(n\text{SEG}_n\) after correction in steps 2 and 3 is calculated, and the average value is taken to obtain the final estimated SNR, and the SNR and RSSI are reported to the host computer. The specific calculation formula is as follows,

[0032] where, Re represents taking the real part; \(e\) i+T(n-1) represents the corrected baseband sampling sequence; represents the conjugate baseband local sequence generated by local MATLAB; \(S_n\) represents the estimated useful signal energy of a unit segment; \(N\) n represents the estimated noise energy of a unit segment; then the SNR represents the signal-to-noise ratio as:

[0033]

[0034] where, \(e\) n , \(i\) represents the corrected baseband received synchronization word sequence, \(c\) n , \(i\) represents the local synchronization word sequence.

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] The method of the present invention can be compatible with the channel quality estimation of CPM spread spectrum, 16QAM and MPSK modulation of VDES;

[0037] The present invention proposes an MPART phase estimation and compensation method designed according to the maximum residual frequency offset, chip rate, chip length, and least squares method, which ensures the phase stability of the input data of the ML algorithm, reduces the influence of frequency offset on ML estimation, thereby greatly improving the dynamic range of SNR estimation, improving the robustness and accuracy of SNR detection, and simplifying algebraic operations through a look-up table to reduce the calculation amount. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0039] Figure 1 It is the overall system flowchart provided by the embodiment of the present invention.

[0040] Figure 2 It is the design block diagram of the MPART phase compensation module provided by the embodiment of the present invention.

[0041] Figure 3 It is the result diagram of the sum of squares of the MPART channel quality estimation error provided by the embodiment of the present invention.

[0042] Figure 4 It is the result diagram of the mean value of the MPART channel quality estimation provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0044] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily need to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0045] As Figure 1 shown, the present invention provides a method for channel quality estimation for VDES satellite communication, including the following steps:

[0046] Step 1: Calculate the received signal strength indicator by rectifying and integrating the baseband sampled signal output from the filter.

[0047] Step 2: As Figure 2 shown, compensate the phase of the baseband sampled signal with CPM spread spectrum; first, generate a local synchronization word sequence through MATALB simulation according to LinkID, secondly, divide the synchronization word sequence into N segments, and respectively use the least squares method to calculate the initial phase and residual frequency offset of the synchronization word for segment nSEGn. The phase change is fitted as a linear function to reduce the influence of short pulse noise on phase estimation; finally, correct the baseband sampled synchronization word sequence with coarse frequency offset compensation according to the phase error.

[0048] In this application, for CPM spread spectrum phase compensation, the baseband sampling rate is 134.4KHz and the synchronization word chip length is 3072;

[0049] The MATALB simulation divides the local synchronization word sequence into N segments SEG, each SEG contains 3072 / N chips and quadrant decision is used to calculate the phase difference, where the value of N is related to the chip length, maximum residual frequency offset and chip rate; the cumulative phase difference of SEG is less than π / 2.

[0050] Further, Step 3: Compensate the non-CPM spread spectrum phase; first, generate a local synchronization word sequence through MATALB simulation; secondly, divide the synchronization word sequence into M segments, and respectively use the least squares method to calculate the initial phase and residual frequency offset of the synchronization word for segment nSEGn. The phase change is fitted as a linear function to reduce the influence of short pulse noise on phase estimation; finally, correct the baseband sampled synchronization word sequence with coarse frequency offset compensation according to the phase error; compensate the non-CPM spread spectrum phase; the baseband sampling rate is 33.6KHz and the synchronization word chip length is 108;

[0051] The MATALB simulation generates a local synchronization word sequence, divided into M segments, each segment has 108 / M chips and quadrant decision is used to calculate the phase difference, where the value of M is related to the chip length, maximum residual frequency offset and chip rate. For the convenience of phase fitting, ensure that the cumulative phase difference of SEG is less than Π / 2.

[0052] Step 4: According to the baseband sampled synchronization word sequence obtained in Step 2 and Step 3, calculate the SNR through a mean filter and output the final SNR result.

[0053] In this application, the initial phase and unit phase difference are calculated respectively by the least squares method, and the calculation formula is:

[0054]

[0055] where represents the phase difference of sampling points, ai is the imaginary part of the correlation sequence, b i represents the real part of the correlation sequence, T is the length of SEG, is the initial phase of each SEG; by judging the positive and negative polarities of a i and bi, and taking a 1 +T(n - 1) symbol as the reference, adjust the phase to the actual phase. Since the arctan function maps the second quadrant to the fourth quadrant, the phase is added by π. It maps the corresponding value in the third quadrant to the first quadrant, so the phase is subtracted by π. According to the real part a i and the imaginary part b i judge the quadrant, then:

[0056]

[0057] Preferably, the initial phase and the unit phase difference of SEGn are calculated separately by the least squares method T ∈ [1, sampling point length / N], the matrix formula is;

[0058]

[0059] wherein, the denominator and other parameters for calculating kn are constant sequences with respect to n. The parameter sequences of different SEGs are assigned addresses and written into the ROM, and read sequentially according to the addresses to simplify the calculation; x n,i ∈ [1, sampling point length / N], is the average value, T = sampling point length / N;

[0060] x n,i = T(n - 1) + i;

[0061]

[0062]

[0063] Finally, according to kn, the phase compensation sequence of SEGn is fitted and multiplied by the conjugate of SEGn respectively, so as to reduce the influence of residual carrier and phase offset on the ML estimation.

[0064] As a preferred implementation manner, in the present application, in step 4, according to the ML estimation algorithm, the signal-to-noise ratio SNR of each segment nSEGn after correction in steps 2 and 3 is calculated, and the average value is taken to obtain the final estimated SNR, and the SNR and RSSI are reported to the host computer. The specific calculation formula is as follows

[0065] wherein, Re represents taking the real part; e i+T(n-1) represents the corrected baseband sampling sequence; represents the conjugate baseband local sequence generated locally by MATLAB; Sn represents the estimated useful signal energy of the unit segment; Nn represents the noise energy of the estimated unit segment; then SNR represents the signal-to-noise ratio as:

[0066]

[0067] where e n,i represents the corrected baseband received synchronization word sequence, and c n,i represents the local synchronization word sequence.

[0068] The serial numbers of the embodiments of the present invention above are only for description and do not represent the superiority or inferiority of the embodiments.

[0069] In the above embodiments of the present invention, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0070] In the several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the units or modules can be in an electrical or other form.

[0071] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0072] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0073] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A channel quality estimation method for VDES satellite communication, characterized in that: The following steps are involved: Step 1: Calculate the received signal strength index by rectifying and integrating the baseband sampling signal output by the filter; Step 2: Compensate the phase of the baseband sampling signal of CPM spread spectrum; first, generate a local synchronization word sequence based on LinkID by combining MATALB simulation, then divide the synchronization word sequence into N segments, and use the least squares method to calculate the initial phase and residual frequency difference of the synchronization word for each segment nSEGn, and fit the phase change to a linear function to reduce the impact of short pulse noise on phase estimation; finally, correct the baseband sampling synchronization word sequence of coarse frequency offset compensation based on the phase error; Step 3: Compensate for the non-CPM spread spectrum phase; first, generate a local synchronization word sequence in combination with MATALB simulation; second, divide the synchronization word sequence into M segments, and use the least squares method to calculate the initial phase and residual frequency difference of the synchronization word for each segment nSEGn, and fit the phase change to a linear function to reduce the impact of short pulse noise on phase estimation; finally, correct the baseband sampling synchronization word sequence of coarse frequency offset compensation according to the phase error; Step 4: Based on the baseband sampling synchronization word sequence obtained in step 2 and step 3, the calculated SNR passes through a mean filter to output the final SNR result.

2. A channel quality estimation method for VDES satellite communication according to claim 1, characterized in that: In step 2, CPM spread spectrum phase compensation, baseband sampling rate is 134.4KHz, and synchronization word code length is 3072; The MATALB simulation divides the local synchronization word sequence into N segments SEG, each SEG contains 3072 / N code bits and quadrant decision to calculate the phase difference, where the value of N is related to the code bit length, the maximum residual frequency deviation and the code bit rate; the SEG cumulative phase difference is less than π / 2.

3. A channel quality estimation method for VDES satellite communication according to claim 1, characterized in that: In step 3, the non-CPM spread spectrum phase is compensated; the baseband sampling rate is 33.6KHz, and the synchronization word code length is 108; The MATALB simulation generates a local synchronization word sequence, which is divided into M segments, each segment has 108 / M code bits and quadrant decision to calculate the phase difference, where the value of M is related to the code bit length, the maximum residual frequency deviation and the code bit rate. To facilitate phase fitting, the SEG cumulative phase difference is guaranteed to be less than Π / 2.

4. A channel quality estimation method for VDES satellite communication according to claim 1, characterized in that: The least square method is used to calculate the initial phase and the unit phase difference respectively, and the calculation formula is: in, Indicates the sampling point difference, a i is the imaginary part of the correlation sequence, b i represents the real part of the correlation sequence, T is the SEG length, is the initial phase of each SEG; by judging a i and bi's positive and negative polarity, and adjust the sign of a1+T(n-1) based on the The phase is the actual phase. Since the arctan function will correspond the second quadrant to the fourth quadrant, the phase will be increased by π, and the third quadrant will be corresponded to the first quadrant, then the phase will be reduced by π. According to the real part a i and the imaginary part b i Decision quadrant, then:

5. A channel quality estimation method for VDES satellite communication according to claim 1, characterized in that: The least square method is used to calculate the initial phase and unit phase difference of SEGn respectively. The matrix formula of T∈[1, sampling point length / N] is; Among them, the denominator and other parameters of kn are constant sequences about n. The parameter sequences of different SEGs are assigned addresses and written into ROM, which are read in sequence according to the addresses to simplify the calculation; x n,i ∈[1, sampling point length / N], is the average value, T = sampling point length / N; x n,i =T(n-1)+i; Finally, the SEGn phase compensation sequence is fitted according to kn and conjugate multiplied with SEGn respectively, thereby reducing the influence of residual carrier and phase deviation on ML estimation.

6. A channel quality estimation method for VDES satellite communication according to claim 1, characterized in that: In step 4, the signal-to-noise ratio SNRn of each segment nSEGn corrected in steps 2 and 3 is calculated according to the ML estimation algorithm, and the average is taken to obtain the final estimated SNR, and the SNR and RSSI are reported to the upper computer. The specific calculation formula is as follows: Among them, Re represents the real part; e i+T(n-1) Represents the corrected baseband sampling sequence; represents the baseband local sequence generated by local MATLAB; Sn represents the estimated unit fragment useful signal energy; N n represents the estimated noise energy per unit segment; then SNR represents the signal-to-noise ratio: Among them, e n,i represents the corrected baseband receiving synchronization word sequence, c n,i Indicates the local synchronization word sequence.

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