A QPSK phase ambiguity identification method based on convolution code

Abstract

The application discloses a QPSK phase ambiguity identification method based on convolution code, and comprises the following steps: firstly, 8 sequences are spliced from 8 kinds of phase ambiguities existing in QPSK demodulation according to demodulated I and Q data; secondly, an equation is established according to the coding constraint relationship of the convolution code; finally, the probability of the equation being established is taken as a standard for measurement, and 8 sequences are screened to reduce the number of phase ambiguities. According to the method, one sub-checking element of one code rate of the convolution code only needs to be stored, and the number of 8 kinds of phase ambiguities is reduced to two, so that the consumption of resources is reduced compared with a traditional phase ambiguity elimination mode. Meanwhile, the application can be realized only by binary multiplication and accumulation, and the calculation complexity is low and the algorithm structure is simple compared with a traditional method.

Description

Technical Field

[0001] This invention belongs to the field of satellite communication technology, and particularly relates to a QPSK phase fuzzy recognition method based on convolutional codes. Background Technology

[0002] Convolutional codes have good error correction performance and low decoding complexity, and are widely used in satellite communication, deep space communication and other fields. The CCSDS standard provides convolutional codes with five code rates: 1 / 2, 2 / 3, 3 / 4, 5 / 6 and 7 / 8.

[0003] Any codeword v in a convolutional code must satisfy the parity check equation:

[0004] vH T =0

[0005] The CCSDS standard convolutional code can be represented as (n, n-1, 6), and its parity-check matrix H is a semi-infinite cyclic matrix.

[0006]

[0007] The sub-verification element is h:

[0008]

[0009] Any codeword v in a convolutional code must satisfy vh T =0.

[0010] Due to factors such as the mapping relationship of the baseband signal, the lead-lag of the modulation carrier phase, the nonlinear carrier recovery method of demodulation, and the inconsistent rotation direction of the modulation and demodulation signals, there are a total of 8 types of phase ambiguity at the receiver of QPSK signals.

[0011] Existing traditional methods for overcoming phase ambiguity using convolutional codes can be divided into two categories: one involves iterating through eight ambiguity cases, performing convolutional code decoding eight times for each, then comparing the decoded data with the original data, calculating the bit error rate, and selecting the sequence with the lowest bit error rate as the final decoded output. The other category involves calculating and storing eight variants of the sub-check element under the eight phase ambiguity conditions, and reducing the phase ambiguity to two based on check element detection. In engineering implementation, both methods suffer from low efficiency and high resource consumption. Summary of the Invention

[0012] The purpose of this invention is to overcome the problems of existing technologies and address the technical shortcomings of traditional methods, such as low efficiency and high resource consumption. By fully utilizing the coding constraints of convolutional codes, a convolutional code of a certain rate only needs to store one sub-check element, thus providing a method for eliminating QPSK phase ambiguity with low resource consumption and high real-time performance.

[0013] The objective of this invention is achieved through the following technical solution:

[0014] A convolutional code sequence v that can be correctly decoded must satisfy vh T =0, while the correctly phase-ambigued sequence can be correctly decoded, that is, the correctly phase-ambigued IQ sequence satisfies vh T =0. Since the fundamental parity-check matrix of a convolutional code is transparent and the number of 1s in the parity-check elements of each rate convolutional code is even, therefore... The sequence also satisfies vh T =0.

[0015] Considering the initial ambiguity of convolutional codes, the presence of noise in actual channels, and the randomness of data, we need to accumulate and calculate a continuous segment of data to eliminate these uncertainties.

[0016] This invention discloses a QPSK phase fuzzy recognition method based on convolutional codes. The QPSK phase fuzzy recognition method includes: first, concatenating the demodulated I and Q data into 8 sequences according to the 8 types of phase fuzziness present in QPSK demodulation; then, establishing an equation based on the coding constraint relationship of the convolutional code; finally, using the probability of the equation being true as a criterion, filtering the 8 sequences to reduce the number of phase fuzzinesses.

[0017] According to a preferred embodiment, the QPSK phase fuzzy recognition method includes: generating 8 phase fuzzy types and calculating the sub-check element h of the convolutional code;

[0018] The receiver receives a convolutional code sequence v1v2v3v4v5v6..., with v i Calculate vh for the starting position T The value is denoted as T i , where v is any codeword of the convolutional code;

[0019] For each of the eight phase blurs generated, a continuous T segment is added. i The two paths with the smallest cumulative value Tsum are selected to obtain the IQ sequence and its sum. sequence.

[0020] According to a preferred embodiment, the QPSK phase fuzzy recognition method includes:

[0021] S1: Construct 8 phase ambiguities and calculate the sub-check element h of the convolutional code;

[0022] S2: with v i Calculate vh for the starting position T The value is denoted as T i ;

[0023] S3: Continuously accumulate l T's i Worth it And calculate the Tsum values ​​corresponding to the eight phase blurs respectively;

[0024] S4: Select the two items with the smallest Tsum values, the IQ sequence and... The sequence reduces the eight phase blurs to two.

[0025] According to a preferred embodiment, the eight phase blurs generated are IQ, QI and

[0026] The aforementioned main solution of the present invention and its various further alternative solutions can be freely combined to form multiple solutions, all of which are solutions that can be adopted and are claimed by the present invention. Those skilled in the art, after understanding the solution of the present invention, will realize that there are many combinations based on existing technology and common knowledge, all of which are technical solutions to be protected by the present invention, and will not be exhaustively listed here.

[0027] The beneficial effects of this invention are:

[0028] 1. Low resource consumption. The convolutional code of this invention only needs to store one sub-parity element, reducing eight phase ambiguities to two, which reduces resource consumption compared to traditional phase ambiguity elimination methods.

[0029] 2. Simple implementation structure. This invention only requires binary multiplication and accumulation, which is computationally complex compared to traditional methods, and the algorithm structure is easy to implement in hardware. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of QPSK phase fuzzy recognition of the present invention;

[0031] Figure 2 This is a schematic diagram of the QPSK phase fuzzy recognition method of the present invention. Detailed Implementation

[0032] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0033] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0034] Furthermore, it should be noted that, unless otherwise specified, the structures, connections, positions, power sources, etc. involved in this invention are all things that a person skilled in the art can know without creative effort based on the prior art.

[0035] Example

[0036] refer to Figure 2 As shown in the figure, a QPSK phase fuzzy recognition method based on convolutional codes is illustrated. The QPSK phase fuzzy recognition method includes:

[0037] The receiver receives a convolutional code sequence v1v2v3v4v5v6..., with v i Calculate vh for the starting position T The value is denoted as T i , where v is any codeword of the convolutional code, and h is the sub-check element of the subconvolutional code;

[0038] For each of the eight phase blurs generated, a continuous T segment is added. i The two paths with the smallest cumulative value Tsum are selected to obtain the IQ sequence and its sum. sequence.

[0039] Assuming the information sequence x is 01000100011111101101, the encoded sequence v obtained after encoding with a 1 / 2 convolutional code according to the CCSDS standard is 0011101111111100111100010101000001001101. Using this as an example, the method of this invention will be analyzed and explained:

[0040] The sub-parity elements of the S1 and CCSDS standard 1 / 2 convolutional code encoding are:

[0041] h=(1 1 0 1 0 0 1 1 1 1 1 0 1 1)

[0042] The eight QPSK phase ambiguity sequences generated by sequence v are:

[0043]

[0044] S2, with v i Calculate vh for the starting position T The value is denoted as T i .

[0045]

[0046]

[0047] Because the fundamental parity-check matrix of the CCSDS standard convolutional code is transparent, the number of 1s in the parity-check elements of each code rate convolutional code is even. Therefore, the IQ sequence and T obtained from sequence calculation i The values ​​are the same.

[0048] If the odd-numbered positions v1, v3, v5... of the IQ sequence fuzzing are the correct starting fuzzing positions, then the corresponding T1, T3, T5... are 0. However, v2, v4, v6... are not the correct starting fuzzing positions. Since both data and noise are random, the values ​​of T2, T4, T6... may be 0 or 1.

[0049] For the remaining six phase ambiguities, there is no correct decoding start position, and the values ​​of T1, T3, T5 and T2, T4, T6... can all be either 0 or 1. Therefore, the IQ sequence is continuously accumulated by l T... i Worth it It is highly likely to be smaller than other ambiguous values.

[0050] S3, continuously accumulate l T's i Worth it

[0051]

[0052] S4. Select the two items with the smallest Tsum values: the IQ sequence and... The sequence reduces the number of phase blurs from eight to two.

[0053] This invention provides a convolutional code with a specific code rate that requires only one sub-parity element to be stored, reducing eight types of phase ambiguity to two, thus reducing resource consumption compared to traditional phase ambiguity elimination methods. Furthermore, this invention only requires binary multiplication and accumulation, making its algorithm structure easier to implement in hardware compared to traditional methods in terms of computational complexity.

[0054] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A QPSK phase fuzzy recognition method based on convolutional codes, characterized in that, The QPSK phase fuzzy recognition method includes: First, the demodulated I and Q data are pieced together to form 8 sequences according to the 8 types of phase ambiguity present in QPSK demodulation; then, an equation is established based on the coding constraint relationship of the convolutional code; finally, the probability of the equation being true is used as a criterion to filter the 8 sequences and reduce the number of phase ambiguities. The QPSK phase fuzzy recognition method includes: Eight phase blurs are generated, and the sub-check element h is calculated. The convolutional code sequence received by the receiver ,by Calculate the starting position The value is denoted as ,in, Any codeword of the convolutional code; Add a continuous segment to each of the eight phase blurs generated. Value, take the accumulated value. The two smallest paths, that is, the two paths that yield sequence sum sequence; The QPSK phase fuzzy recognition method includes: S1: Construct 8 phase ambiguities and calculate the sub-check element h of the convolutional code; S2: with Calculate the starting position The value is denoted as ; S3: Continuous accumulation l indivual Worth it And calculate the corresponding values ​​for the eight phase blurs. value; S4: Selection The two items with the smallest values, sequence sum The sequence reduces the eight phase blurs to two.

2. The QPSK phase fuzzy recognition method as described in claim 1, characterized in that, The eight phase blurs produced are as follows: , , , , , , and .

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