An adaptive RS channel coding optimization method based on fuzzy reasoning

Abstract

This invention discloses an adaptive RS channel coding optimization method based on fuzzy inference, aiming to solve the problems of high bit error rate and long setup time in ammunition setting communication. This invention dynamically optimizes the codeword length of RS coding by real-time acquisition of channel state parameters, including bit error rate, error concentration, and response sensitivity, combined with a fuzzy control algorithm. First, setting information messages and test messages are designed to detect channel state and obtain dynamic environmental parameters. Second, the channel state parameters are fused using fuzzy inference to generate the optimal coding strategy. Finally, the optimized RS coding parameters are applied to ammunition setting communication, significantly shortening the encoding and decoding time. Compared with traditional RS coding methods, this invention reduces encoding and decoding time by more than 40% without reducing the maximum error correction capability, effectively adapting to complex dynamic channel environments. This method is suitable for ammunition setting communication scenarios with strict requirements for high reliability and high efficiency.

Description

Technical Field

[0001] This invention belongs to the field of communications, specifically relating to an adaptive RS channel coding optimization method based on fuzzy reasoning. Background Technology

[0002] Fuzzy control and RS coding are two technologies widely used in engineering and communication fields, each with important theoretical value and practical significance.

[0003] Fuzzy control originated from the fuzzy set theory proposed by Zadeh in 1965. As a rule-based control method, fuzzy control is particularly suitable for complex, nonlinear, and uncertain systems due to its advantage of not requiring precise mathematical models. In recent years, with the rise of artificial intelligence, the combination of fuzzy control with intelligent algorithms such as neural networks and genetic algorithms has further improved its performance. However, traditional fuzzy control relies on empirical knowledge in rule design and parameter optimization, resulting in high computational complexity when dealing with high-dimensional multivariable systems. The lack of automated design methods has become a major bottleneck in its development. To address this issue, current research focuses on data-driven fuzzy modeling, online adaptive fuzzy control, and hardware implementation of fuzzy controllers to achieve more efficient and intelligent control performance.

[0004] Proposed by Reed and Solomon in 1960, RS coding, as a linear block code based on finite fields, plays a crucial role in digital communication and storage due to its powerful error correction capabilities. RS coding is particularly suitable for handling burst errors and is therefore widely used in optical communication, satellite communication, deep space exploration, and storage devices such as optical discs and flash memory. Currently, although RS coding exhibits excellent error correction performance, existing microcontroller-based RS coding and decoding speeds, such as those on FPGAs, are slow. Furthermore, traditional RS coding decoding algorithms, such as the Berlekamp-Massey and Euclidean algorithms, have limitations in real-time performance and computational efficiency under complex channel environments, especially as the code length and check code length increase, leading to a rapid rise in computational complexity. In addition, traditional RS coding relies on fixed parameters, making it difficult to dynamically adapt to complex channel environments, and it faces implementation difficulties when combined with emerging technologies such as deep learning and quantum communication, as well as in scenarios with limited hardware resources. To address these challenges, researchers have proposed improved algorithms based on parallel processing and hardware acceleration, while also exploring the possibility of combining them with emerging technologies such as quantum error correction codes and deep learning to further enhance their efficiency and adaptability.

[0005] With the increasing demand for system intelligence and reliability in the fields of communication and control, applying fuzzy control to optimize RS coding parameters, designing channel state parameters, and using fuzzy logic to process complex dynamic channel states can effectively improve the encoder's adaptive capability and error correction performance. Summary of the Invention

[0006] This invention proposes an adaptive RS channel coding optimization method based on fuzzy inference, which solves the problems of high bit error rate and long setting time in ammunition setting communication, and significantly improves the speed and stability of encoding and decoding.

[0007] The technical solution for implementing this invention is: an adaptive RS channel coding optimization method based on fuzzy inference, characterized by the following steps:

[0008] Step 1: Before the host computer detects the channel status parameters, design and configure the information message and channel test message. After sending them, they are used to evaluate the channel transmission quality during the ammunition firing interval. Proceed to Step 2.

[0009] Step 2: During the interval between ammunition firings, the host computer detects the channel status through setting information messages and channel test messages, and collects channel status parameters, including the bit error rate ε. e Error concentration C e and response sensitivity η e The above channel state parameters are precise values; proceed to step 3.

[0010] Step 3: Let the codeword length of the RS coding algorithm be n, and design the bit error rate ε. e Error concentration C e Response sensitivity η e The membership function corresponding to the codeword length n is used, and the bit error rate ε is determined based on the precise values ​​of the collected channel state parameters. e Error concentration C e Response sensitivity η e The corresponding fuzzy subset is then used to proceed to step 4.

[0011] Step 4: Formulate the fuzzy control rule "IF-AND-AND-THEN" and set the bit error rate ε e Error concentration C e and response sensitivity η e After substituting the corresponding fuzzy subset into the fuzzy control rules for judgment, the fuzzy subset with the optimal codeword length n* is output, and the process proceeds to step 5.

[0012] Step 5: Use the centroid method to defuzzify the fuzzy subset of the optimal codeword length n* to obtain the precise value of the optimal codeword length n*.

[0013] Compared with the prior art, the significant advantages of this invention are:

[0014] (1) Compared with traditional RS encoding, the encoding algorithm proposed in this invention reduces the encoding and decoding time of RS encoding in FPGA by about 40% without reducing the maximum stable error correction capability, thus greatly shortening the setup time.

[0015] (2) Compared with traditional RS coding, the coding algorithm proposed in this invention effectively improves the system's adaptability to sudden channel fluctuations by dynamically adjusting the coding strategy and implementing a real-time error detection mechanism. Its robustness and fault tolerance are significantly enhanced, enabling it to maintain communication stability in complex environments. Attached Figure Description

[0016] Figure 1 This is a flowchart of the adaptive RS channel coding optimization method based on fuzzy inference of the present invention.

[0017] Figure 2 For the bit error rate ε e The membership function graph.

[0018] Figure 3 Error concentration C e The membership function graph.

[0019] Figure 4 For the response sensitivity η e The membership function graph.

[0020] Figure 5 This is a membership function graph for codeword length n. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not 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 effort are within the scope of protection of the present invention.

[0022] The following section will further introduce the specific implementation method, as well as the technical difficulties and inventive points of this invention, using this design example as an example.

[0023] Combination Figure 1 The adaptive RS channel coding optimization method based on fuzzy inference described in this invention comprises the following steps:

[0024] Step 1: Before the host computer detects the channel status parameters, design and configure information messages and channel test messages. After sending, these messages will be used to evaluate the channel transmission quality during the ammunition firing interval. The specific steps are as follows:

[0025] Step 1-1: This invention designs an 8-byte configuration information message, as follows:

[0026] Bytes 0-1 are the start bits, with a fixed value of 0xaa 0xbb, which indicate the start of the message;

[0027] Two bytes are the length bits, with a fixed value of 0x08, which indicates the message length;

[0028] The 3 bytes are the function code, with a value of 0xa0, which indicates the ammunition's working mechanism, including but not limited to proximity (unit: m) and delay (unit: ms);

[0029] Bytes 4-5 are data bits, representing ammunition detonation data, including but not limited to detonation height (unit: m) and delay time (unit: ms);

[0030] Bytes 6 and 7 are the end bits, with a fixed value of 0xbb 0xaa, which indicates the end of the message.

[0031] Steps 1-2: To adapt to the dynamic channel environment inside the gun barrel, this invention designs a channel test message with diverse bit combinations including high and low bit alternation, boundary value padding, and symmetric bits, thereby meeting the bit error detection requirements under different dynamic channel environments, as detailed below:

[0032] Byte 0 is 0xff (11111111), indicating the start of the test encoding;

[0033] One byte is 0xf0 (11110000), which represents a combination where the high-order bits are 1;

[0034] Two bytes are 0x0f (00001111), representing a combination where the lower bits are 1;

[0035] The 3 bytes are 0x7e (01111110), which represents a combination that remains 1 for a long time;

[0036] The 4 bytes are 0x00 (00000000), representing an all-zero combination;

[0037] The 5-byte value is 0x33 (00110011), representing a combination of continuous transmission and switching of 0 and 1.

[0038] The 6 bytes are 0x55 (01010101), representing a combination of 0 and 1 fast switching;

[0039] The 7-byte value is 0xff (11111111), indicating the end of the test encoding.

[0040] Proceed to step 2.

[0041] Step 2: During the interval between ammunition firings, the host computer detects the channel state through setting information messages and channel test messages to obtain the channel state parameters, namely the bit error rate ε. e Error concentration C e and response sensitivity η e The channel state parameters mentioned above are precise values.

[0042] This invention addresses the communication characteristics between the ammunition setter and the fuze. The host computer sends setting information messages and channel test messages, and by detecting the channel state, the bit error rate ε is obtained. e Error concentration C e and response sensitivity η e The precise value is as follows:

[0043] Bit error rate ε e This refers to the method used to evaluate the reliability of channel transmission, defined as the ratio of erroneous bits in transmission to the total number of transmitted bits. Where N se Represented as the number of error bits, N s This represents the total number of bits transmitted.

[0044] Error Concentration C e Error concentration C refers to the characteristic used to describe the distribution of errors in a channel, measuring whether errors are concentrated in one area. e The expression is Where R e =max-min+1 represents the coverage range of the error distribution and the length of the range where the error bits are located, where max represents the most significant bit of the error bits and min represents the least significant bit of the error bits.

[0045] Error Concentration C e The value range of is [0, 1], when the error concentration C e The closer the value of C is to 1, the more concentrated the errors are in the channel; when the error concentration C... e The closer the value is to 0, the more dispersed the errors are in the channel.

[0046] Response sensitivity η e This refers to the sensitivity of the signal receiver to changes in the 0 and 1 signals of the channel test message, used to evaluate the channel's response speed to changes in the channel test message signal. Response sensitivity η e The expression is Where T s k represents the number of 01 and 10 transitions in the channel test message. s This represents the number of bits that the receiver perceives after a change in signal 01 or 10.

[0047] When the signal change is perceived perfectly, T s =k s Then η e =1.

[0048] Proceed to step 3.

[0049] Step 3: Let the codeword length of the traditional RS coding algorithm be n. Design the bit error rate ε.e Error concentration C e Response sensitivity η e The membership function corresponding to the codeword length n is used, and the precise values ​​of the collected channel state parameters are substituted into the membership function expression to determine the bit error rate ε. e Error concentration C e Response sensitivity η e The corresponding fuzzy subsets are defined in the following steps:

[0050] Step 3-1: Design the bit error rate ε e Error concentration C e Response sensitivity η e The membership function corresponding to the codeword length n. Details are as follows:

[0051] Bit error rate ε e The membership function adopts, for example Figure 2 The Gaussian curve shown is represented as:

[0052]

[0053] The universe of discourse is [0, 0.1], and the standard deviation is [0, 0.1]. μ L (ε e ) represents the membership degree of the fuzzy subset L, μ M (ε e ) represents the membership degree of the fuzzy subset M, μ H (ε e ε represents the membership degree of the fuzzy subset H, i.e., the bit error rate ε. e The membership degree is divided into three intervals from low to high: low (L), medium (M), and high (H).

[0054] Error Concentration C e The membership function adopts, for example Figure 3 The triangular form shown is represented as:

[0055] μ L (C e ) = 4-5C e 0.6≤C e ≤0.8

[0056]

[0057] μ H (C e ) = 5C e -4, 0.8≤C e ≤1

[0058] The universe of discourse is [0.6, 1], μ L (Ce ) represents the membership degree of the fuzzy subset L, μ M (C e ) represents the membership degree of the fuzzy subset M, μ H (C e ) represents the membership degree of the fuzzy subset H.

[0059] Response sensitivity η e The membership function adopts, for example Figure 4 The triangular form shown is represented as:

[0060] μ L (η e )=3-4η e , 0.5≤η e ≤0.75

[0061]

[0062] μ H (η e )=4η e -3, 0.75≤η e ≤1

[0063] Where the universe of discourse is [0.5, 1], μ L (η e ) represents the membership degree of the fuzzy subset L, μ M (η e ) represents the membership degree of the fuzzy subset M, μ H (η e ) represents the membership degree of the fuzzy subset H.

[0064] The membership function for codeword length n is as follows: Figure 5 The Gaussian curve shown is represented as:

[0065]

[0066] The universe of discourse is [8, 55], and the standard deviation σ is... n =17.48, μ x (n) represents the membership degree of the fuzzy subset x, x∈{VL, L, ML, M, MH, H, VH}, which means dividing the fuzzy subset x into seven intervals from low to high: extremely low (VL), low (L), medium-low (ML), medium (M), medium-high (MH), high (H), and extremely high (VH).

[0067] Step 3-2: Calculate the collected bit error rate ε e Error concentration C e Response sensitivity η e The precise values ​​are mapped to the corresponding fuzzy subsets according to the membership function.

[0068] Proceed to step 4.

[0069] Step 4: Based on the bit error rate ε e Fuzzy subsets, error concentration C e Fuzzy subsets and response sensitivity η e Different combinations of fuzzy subsets are used to formulate fuzzy control rules "IF-AND-AND-THEN", as shown in Table 1:

[0070] Table 1 Fuzzy Control Rules

[0071] Rule No. Rule 1 <![CDATA[IF ε e is L AND C e is H AND η e is H,THEN n*is VL]]> 2 <![CDATA[IF ε e is L AND C e is M AND η e is H,THEN n*is VL]]> 3 <![CDATA[IF ε e is L AND C e is L AND η e is H,THEN n*is VL]]> 4 <![CDATA[IF ε e is L AND C e is M AND η e is M,THEN n*is L]]> 5 <![CDATA[IF ε e is L AND C e is L AND η e is M,THEN n*is ML]]> 6 <![CDATA[IF ε e is M AND C e is H AND η e is L,THEN n*is L]]> 7 <![CDATA[IF ε e is M AND C e is M AND η e is L,THEN n*is ML]]> 8 <![CDATA[IF ε e is M AND C e is L AND η e is L,THEN n*is M]]> 9 <![CDATA[IF ε e is H AND C e is H AND η e is H,THEN n*is VH]]> 10 <![CDATA[IF ε e is H AND C e is M AND η e is H,THEN n*is VH]]> 11 <![CDATA[IF ε e is H AND C e is L AND η e is H,THEN n*is VH]]> 12 <![CDATA[IF ε e is H AND C e is M AND η e is M,THEN n*is VH]]> 13 <![CDATA[IF ε e is H AND C e is L AND η e is M,THEN n*is VH]]>

[0072] Bit error rate ε e Error concentration C e and response sensitivity η e After substituting the corresponding fuzzy subset into the fuzzy control rules for judgment, the membership degree of the fuzzy subset with the optimal codeword length n* is output.

[0073] Proceed to step 5.

[0074] Step 5: After defuzzifying the membership degrees of the fuzzy subsets of the optimal codeword length n* using the centroid method, the precise value of the optimal codeword length n* is obtained, expressed as follows:

[0075]

[0076] Where i is the index of the rule, m is the number of membership functions, and μ(U i U is the membership function value of the i-th rule. i Let be the discrete value of the fuzzy control output under the i-th rule.

[0077] Example 1:

[0078] Set the bit error rate ε for multiple different states. e Error concentration C e Response sensitivity η e The combination is used as the input to the fuzzy controller. At the bit error rate ε e During the process of increasing from 0 to 0.1, the adaptive RS channel coding optimization algorithm based on fuzzy inference and the traditional RS(55,8) coding algorithm were compared and tested. The test results are shown in Table 2.

[0079] Table 2 shows some comparative experimental results between the adaptive RS channel coding optimization algorithm based on fuzzy inference and the traditional RS(55,8) coding algorithm.

[0080] Test number Channel state parameters Code word length n Total compilation and decoding time 1 RS(55, 8) 55 13.46us 2 <![CDATA[C e =0.95、h e =0.95、e e =0.04]]> 28 3.44us 3 <![CDATA[C e =0.80、h e =0.60、e e =0.04]]> 30 3.965us 4 <![CDATA[C e =0.80、h e =0.70、e e =0.04]]> 33 4.55us 5 <![CDATA[C e =0.65、h e =0.55、e e =0.04]]> 37 5.81us

[0081] Experimental results show that the adaptive RS channel coding optimization algorithm based on fuzzy inference effectively shortens the codeword length n, and without reducing the maximum error correction capability, the optimization algorithm proposed in this invention reduces the encoding and decoding time by more than 40%, verifying the effectiveness of the optimization algorithm.

[0082] The working principle of this invention is as follows: The core idea is to combine RS coding technology with fuzzy control algorithms to dynamically optimize RS coding parameters to adapt to complex channel environment changes, thereby achieving efficient and reliable ammunition fuse setting communication. The entire system uses real-time acquisition of channel state parameters, utilizes fuzzy inference to calculate and generate the optimal RS coding strategy, and adjusts the RS coding parameters to balance error correction performance and transmission efficiency.

Claims

1. An adaptive RS channel coding optimization method based on fuzzy inference, characterized in that, The steps are as follows: Step 1: Before the host computer detects the channel status parameters, design and configure the information message and channel test message, then proceed to Step 2; Step 2: During the interval between ammunition firings, the host computer detects the channel status through setting information messages and channel test messages, and collects channel status parameters, including the bit error rate ε. e Error concentration C e and response sensitivity η e The above channel state parameters are precise values; proceed to step 3. Step 3: Let the codeword length of the RS coding algorithm be n, and design the bit error rate ε. e Error concentration C e Response sensitivity η e The membership function corresponding to the codeword length n is used, and the bit error rate ε is determined based on the precise values ​​of the collected channel state parameters. e Error concentration C e Response sensitivity η e Proceed to step 4 for the corresponding fuzzy subset; Step 4: Formulate the fuzzy control rule "IF-AND-AND-THEN" and set the bit error rate ε e Error concentration C e and response sensitivity η e After substituting the corresponding fuzzy subset into the fuzzy control rules for judgment, the fuzzy subset with the optimal codeword length n* is output, and then proceed to step 5. Step 5: Use the centroid method to defuzzify the fuzzy subset of the optimal codeword length n* to obtain the precise value of the optimal codeword length n*.

2. The adaptive RS channel coding optimization method based on fuzzy inference according to claim 1, characterized in that: In step 1, before the host computer detects the channel status parameters, the configuration information message and channel test message are designed and configured, as follows: Step 1-1: The configuration information message uses 8 bytes; Steps 1-2: The channel test message contains a variety of bit combinations including alternating high and low bits, boundary value padding, and symmetric bits, in order to meet the bit error detection requirements under different dynamic channel environments.

3. The adaptive RS channel coding optimization method based on fuzzy inference according to claim 2, characterized in that, In step 1-1, the setup information message is set to 8 bytes, as follows: Bytes 0-1 are the start bits, with a fixed value of 0xaa 0xbb, which indicate the start of the message; Two bytes are the length bits, with a fixed value of 0x08, which indicates the message length; The 3 bytes are the function code, with a value of 0xa0, which indicates the ammunition's working mechanism; Bytes 4-5 are data bits, representing ammunition detonation data; Bytes 6 and 7 are the end bits, with a fixed value of 0xbb 0xaa, which indicates the end of the message.

4. The adaptive RS channel coding optimization method based on fuzzy inference according to claim 3, characterized in that, In steps 1-2, the channel test message contains a variety of bit combinations, including alternating high and low bits, boundary value padding, and symmetric bits, to meet the bit error detection requirements under different dynamic channel environments, as detailed below: Byte 0 is 0xff (11111111), indicating the start of the test encoding; One byte is 0xf0 (11110000), which represents a combination where the high-order bits are 1; Two bytes are 0x0f (00001111), representing a combination where the lower bits are 1; The 3 bytes are 0x7e (01111110), which represents a combination that remains 1 for a long time; The 4 bytes are 0x00 (00000000), representing an all-zero combination; The 5-byte value is 0x33 (00110011), representing a combination of continuous transmission and switching of 0 and 1. The 6 bytes are 0x55 (01010101), representing a combination of 0 and 1 fast switching; The 7-byte value is 0xff (11111111), indicating the end of the test encoding.

5. The adaptive RS channel coding optimization method based on fuzzy inference according to claim 1, characterized in that, In step 2, during the interval between ammunition firings, the host computer detects the channel status and collects channel status parameters, including the bit error rate ε, through setting information messages and channel test messages. e Error concentration C e and response sensitivity η e The details are as follows: Bit error rate ε e Defined as the ratio of erroneous bits in transmission to the total number of bits transmitted, i.e. Where, N se Represented as the number of error bits, N s Represented as the total number of bits transmitted; Error Concentration C e The expression is The coverage area of ​​the error distribution and the length R of the range where the error bits are located. e =max - min + 1, where max represents the most significant bit of the error bit and min represents the least significant bit of the error bit; Response sensitivity η e The expression is Where T s k represents the number of 01 and 10 transitions in the channel test message. s This represents the number of bits that the receiver perceives after a change in signal 01 or 10.

6. The adaptive RS channel coding optimization method based on fuzzy inference according to claim 1, characterized in that, In step 3, let the codeword length of the RS coding algorithm be n, and design the bit error rate ε. e Error concentration C e Response sensitivity η e The membership function corresponding to the codeword length n is used, and the bit error rate ε is determined based on the precise values ​​of the collected channel state parameters. e Error concentration C e Response sensitivity η e The corresponding fuzzy subsets are as follows: In step 3-1, the bit error rate ε is designed. e Error concentration C e Response sensitivity η e The membership function corresponding to the codeword length n is as follows: Bit error rate ε e The membership function is expressed in Gaussian curve form as follows: The universe of discourse is [0, 0.1], and the standard deviation is [0, 0.1]. μ L (ε e ) represents the membership degree of the fuzzy subset L, μ M (ε e ) represents the membership degree of the fuzzy subset M, μ H (ε e ) represents the membership degree of the fuzzy subset H; Error Concentration C e The membership function is in triangular form and is expressed as: μ L (C e )=4-5C e ,0.6≤C e ≤0.8 μ H (C e )=5C e -4,0.8≤C e ≤1 The universe of discourse is [0.6, 1], μ L (C e ) represents the membership degree of the fuzzy subset L, μ M (C e ) represents the membership degree of the fuzzy subset M, μ H (C e ) represents the membership degree of the fuzzy subset H; Response sensitivity η e The membership function is in triangular form and is expressed as: m L (or e )=3-4th e ,0.5≤η e ≤0.75 m H (or e )=4th e -3,0.75≤η e ≤1 Where the universe of discourse is [0.5, 1], μ L (η e ) represents the membership degree of the fuzzy subset L, μ M (η e ) represents the membership degree of the fuzzy subset M, μ H (η e ) represents the membership degree of the fuzzy subset H; The membership function of codeword length n is expressed in Gaussian curve form as follows: The universe of discourse is [8, 55], and the standard deviation σ is... n =17.48, μ x (n) represents the membership degree of the fuzzy subset x, x∈{VL, L, ML, M, MH, H, VH}; c x The central peak of the fuzzy subset x; Step 3-2: Calculate the bit error rate ε e Error concentration C e Response sensitivity η e The precise values ​​are mapped to the corresponding fuzzy subsets according to the membership function.

7. The adaptive RS channel coding optimization method based on fuzzy inference according to claim 1, characterized in that, In step 4, the fuzzy control rule "IF-AND-AND-THEN" is defined as follows: Bit error rate ε e Error concentration C e and response sensitivity η e After substituting the corresponding fuzzy subset into the fuzzy control rules for judgment, the fuzzy subset with the optimal codeword length n* is output.

8. The adaptive RS channel coding optimization method based on fuzzy inference according to claim 1, characterized in that, In step 5, the centroid method is used to defuzzify the fuzzy subset of the optimal codeword length n* to obtain the precise value of the optimal codeword length n*, as follows: Where i is the index of the rule, m is the number of membership functions, and μ(U i U is the membership function value of the i-th rule. i Let be the discrete value of the fuzzy control output under the i-th rule.

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