Chaotic interleaver and data interleaving method based on elementary cellular automata

Abstract

The present invention discloses a chaotic interleaver and data interleaving method based on elementary cellular automata. The method comprises the following specific steps: S1, determining the data packet length, interleaving depth, and chaotic interleaver system model of the chaotic interleaver; S2, setting the initial parameters and state of the chaotic interleaver system based on the elementary cellular automata; S3, iteratively generating data by the chaotic interleaver system according to the set parameters; S4, mapping the generated data into an interleaving sequence; S5, checking whether the interleaving sequence meets the interleaving depth requirement. If not, the method returns to step S3; if it meets, the method executes step S6; S6, obtaining the data to be interleaved, and then interleaving the data according to the interleaving sequence of step S5. The interleaver of the present invention has better performance than traditional interleavers such as block interleavers and chaotic interleavers. Moreover, the parameters are controllable, easy to optimize, and after the parameters are determined, a large number of interleavers can be generated quickly.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a chaotic interleaver based on elementary cellular automata and a data interleaving method. Background Art

[0002] Bit errors often occur in clusters over variable-parameter channels in land mobile communications. This is because long, deep fading dips can affect a continuous string of bits. However, channel coding is only effective at detecting and correcting single errors and relatively short error clusters. To address this issue, there is a desire to develop a method that disperses the successive bits within a message, sending them non-sequentially. This way, even if a cluster of errors occurs during transmission, when the message is restored as a continuous string of bits, the errors are minimal, or even reduced to single bit errors. Channel coding error correction can then be used to correct the errors and restore the original message. This method is known as interleaving.

[0003] The interleaver's interleaving process is a crucial step in turbo codec systems. It typically occurs after the channel encoder and before modulation, significantly impacting the performance of the entire codec system. Its primary function is to modify the code weight distribution and isolate consecutive burst errors caused by fading in the correlated channels during transmission. The interleaver's function is to reposition the elements in the data sequence to produce an interleaved sequence. Interleaver designs can be broadly categorized into two types: regular interleavers and random interleavers. Regular interleavers are generally easier to implement, with common examples including block interleavers, block interleavers, and block spiral interleavers. However, regular interleavers cannot modify the correlation and code weight of certain sequences, and their interleaving performance is suboptimal for long sequences. In this case, random interleavers can achieve better performance. They utilize a specific mapping rule to generate a pseudo-random sequence, which is used as an interleaving index to reorder the information sequence, thereby scrambling the information.

[0004] Chaotic systems are widely used in the communications field due to their inherent properties, including pseudo-randomness, sensitivity to initial values, determinism, and infinite periodicity. A cellular automaton is a grid dynamics model in which time, space, and state are discrete, and spatial interactions and temporal causality are localized. It is capable of simulating the spatiotemporal evolution of complex systems. An elementary cellular automaton is a one-dimensional cellular automaton, in which each cell has only two states, corresponding to 0 and 1 in binary. After each iteration, the state value of the current cell is determined by the previous state values ​​of the current cell and its neighbors.

[0005] Traditional chaotic systems suffer from poor uniformity and insufficiently complex dynamical characteristics. When directly applied to the mapping rules of an interleaver, uneven data distribution can result in incomplete scrambling of the interleaved sequence, with some data potentially fading and becoming unrecoverable. By introducing the perturbation results of elementary cellular automata into traditional spatiotemporal chaotic systems, the system achieves significantly better uniformity and dynamical complexity, making it well-suited for interleaver design. Summary of the Invention

[0006] In view of the above situation, the present invention introduces the iterative results of elementary cellular automata into chaotic mapping to design a new type of interleaver. The present invention improves on the traditional chaotic interleaver and uses chaotic mapping perturbed by elementary cellular automata instead of traditional chaotic mapping to generate interleaving sequences, thereby obtaining a chaotic interleaver and data interleaving method.

[0007] The present invention adopts the following technical solutions:

[0008] The data interleaving method of the chaotic interleaver based on elementary cellular automata has the following specific steps:

[0009] S1, determining the data packet length, interleaving depth and chaotic interleaver system model of the chaotic interleaver;

[0010] S2, setting the initial parameters and states of the chaotic interleaver system based on elementary cellular automata;

[0011] S3, the chaotic interleaver system iteratively generates data according to the set parameters;

[0012] S4, maps the generated data into an interleaved sequence;

[0013] S5, check whether the interleaving sequence meets the interleaving depth requirement, if not, jump back to step S3, if yes, continue to step S6;

[0014] S6, obtaining the data to be interleaved, and then interleaving the data according to the interleaving sequence generated in step S5.

[0015] Preferably, in step S1, L and R are determined based on user requirements. L is the packet length of the data, i.e., interleaving is performed once for every L bits of data; R is the interleaving depth, i.e., the length of a possible burst error in the channel. In particular, data originally spaced less than R apart should be spaced more than R apart after interleaving. Furthermore, the chaotic interleaver system model based on elementary cellular automaton perturbations is as follows:

[0016]

[0017] Where n = 0, 1, 2, ... N is the time dimension, i.e., the number of iterations; i = 0, 1, 2, ... L is the space dimension, i.e., the grid index; ε represents the coupling strength, ε∈(0,1); x n (i) represents the state value of the i-th grid point at time n; represents the state value of cell i at time n, There are two states, 0 or 1. The sum of the first and second terms in the above equation is the basic chaotic system, and the third term is the perturbation system based on the elementary cellular automaton. In particular, the cell index i and the grid point index i are numerically the same, and the value of the grid point i is determined by the iteration results of the basic chaotic system and the iteration results of the cell i. In addition, the boundary condition of the chaotic interleaver system is x n (L+1)=x n (1).

[0018] Furthermore, f in the basic chaotic system chooses a one-dimensional Logistic map, that is,

[0019] f(x n (i))=ux n (i)(1-x n (i))(2)

[0020] u is the basic chaotic system parameter, and its value range is (3.57,4]. In addition, x n (i) The value range is [0,1].

[0021] Furthermore, p(·) and δ(·) in formula (1) are the iterative results of elementary cellular automata. Their function is to optimize the generated chaotic data so that the dynamic complexity and uniformity of the entire system are better than those of the traditional spatiotemporal chaotic system. The specific method is as follows:

[0022]

[0023] Among them, S n is the binary sequence composed of all cells at the nth iteration, S n (b1b2…b 32 ) is a binary number composed of 32 cells randomly selected from all cells without repetition in the nth iteration. bin2dec is a function that converts binary data into decimal data. The binary number composed of cell states is converted into a decimal number and then used as the dividend to calculate p(S n ). Depend on The status is determined by .

[0024] Furthermore, the state update formula of the elementary cellular automaton is:

[0025]

[0026] g is a Boolean function and can be determined by the local mapping rules set by oneself. The state value of the current cell in each iteration is determined by the previous state values ​​of the current cell and its neighboring cells.

[0027] Furthermore, after the results of the basic chaotic system and the cellular automaton are added together, the sum is modulo 1, and after iteration, a set of chaotic data with a length of L and located in the interval [0,1] can be generated.

[0028] Preferably, in step S2, the initial parameters of the interleaver of the present invention include setting the parameters and initial state of the chaotic interleaver system, that is, the u value in the chaotic mapping logistic function f and the coupling coefficient ε of the chaotic interleaver system and the initial value x1(i) at the first iteration, i∈[1,L], and assigning the initial state to each cell in the system, that is, the initial value of each cell at the first iteration of formula (1) And set the number of iterations N required by the interleaver.

[0029] Preferably, step S3 includes the following steps:

[0030] S3.1, perform N iterations according to the parameter settings in S2 to generate L data. In particular, when returning to step S3 from S5, an additional iteration can obtain L data;

[0031] S3.2, write the generated L data into an array S, S = [x(1), x(2), ..., x(L)];

[0032] Preferably, step S4 includes the following steps:

[0033] S4.1, sort the data in the array S generated in step S3;

[0034] S4.2, write the position x′(i) of each sorted data x(i) in the original array S into S′ in sequence to obtain the interleaved sequence, that is, S′=[x′(1), x′(2),…, x′(L)].

[0035] Preferably, in step S5, each data in the interleaved sequence S′ is checked in turn to determine whether the distance between the R data to the right of the data and the data before interleaving is greater than R. If not satisfied, return to step S3; if satisfied, proceed to the next step S6.

[0036] Preferably, in step S6, the data to be interleaved seq(L) is obtained from the data interleaving module, and then seq(L) is interleaved with the interleaving sequence S′ that meets the requirements generated in step S5, and the interleaving rule is seq′(S′(i))=seq(i), i∈[1,L], and finally the interleaved data seq′(L) is obtained.

[0037] The present invention also discloses a chaotic interleaver based on elementary cellular automata, which includes the following modules:

[0038] Determining module: determining the data packet length, interleaving depth and chaotic interleaver system model of the chaotic interleaver;

[0039] Setting module: setting the initial parameters and states of the chaotic interleaver system based on elementary cellular automata;

[0040] Data generation module: The chaotic interleaver system iteratively generates data according to the set parameters;

[0041] Data mapping module: maps the generated data into an interleaved sequence;

[0042] Judgment module: checks whether the interleaving sequence meets the interleaving depth requirement. If not, the data generation module will execute it. If it meets the requirement, the data interleaving module will execute it.

[0043] Data interleaving module: obtains the data to be interleaved, and then interleaves the data according to the interleaving sequence of the judgment module.

[0044] The beneficial effects of the present invention are:

[0045] The interleaver of the present invention offers superior performance compared to conventional interleavers such as block interleavers and chaotic interleavers. Furthermore, its parameters are controllable, making optimization easy. Once the parameters are determined, interleavers can be rapidly and extensively generated. The present invention is not only applicable to the field of channel coding but also to other electronic communication systems that require scrambling of information sequences. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The present invention is a flow chart of a data interleaving method of a chaotic interleaver based on elementary cellular automata.

[0047] Figure 2 It is the interleaving sequence generated by the interleaver of the present invention. Figure 2 This is an interleaved sequence with a length of 1024 bits generated by a chaotic interleaver based on cellular automaton perturbation configured according to a preferred embodiment of the present invention. For ease of viewing, it is converted into a two-dimensional array. The actual sequence is a one-dimensional array with 1 row and 1024 columns. The two-dimensional array in the figure is read column by column to obtain the original sequence.

[0048] Figure 3It is a system block diagram of the chaotic interleaver based on elementary cellular automata of the present invention. DETAILED DESCRIPTION

[0049] The technical solutions of the preferred embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0050] Reference Figure 1 The data interleaving method of the chaotic interleaver based on elementary cellular automata in this embodiment is specifically implemented by the following steps:

[0051] S1, determining the data packet length, interleaving depth and chaotic interleaver system model of the chaotic interleaver;

[0052] S2, setting the initial parameters and states of the chaotic interleaver system based on elementary cellular automata;

[0053] S3, the chaotic interleaver system iteratively generates data according to the set parameters;

[0054] S4, maps the generated data into an interleaved sequence;

[0055] S5: Check whether the interleaving sequence meets the interleaving depth requirement. If not, jump back to S3; if yes, continue to execute below.

[0056] S6, obtaining the data to be interleaved from the data receiving module, and then interleaving the data according to the interleaving sequence generated in the previous step.

[0057] Each step is described in detail below.

[0058] In step S1, L and R are determined based on user requirements. L is the packet length of the data, meaning that every L bits of data are interleaved once. R is the interleaving depth, which is the length of the possible burst error in the channel. Specifically, data originally spaced less than R apart should be spaced more than R apart after interleaving. Figure 2 In the embodiment shown, L = 1024, R = 3. Further, the chaotic interleaver system model based on elementary cellular automaton perturbation is as follows:

[0059]

[0060] Where n = 0, 1, 2, ... N is the time dimension, i.e., the number of iterations; i = 0, 1, 2, ... L is the space dimension, i.e., the grid index; ε represents the coupling strength, ε∈(0,1); x n (i) represents the state value of the i-th grid point at time n; represents the state value of cell i at time n, There are two states, 0 or 1. The sum of the first and second terms in the above equation is the basic chaotic system, and the third term is the perturbation system based on the elementary cellular automaton. In particular, the cell index i and the grid point index i are numerically the same, and the value of the grid point i is determined by the iteration results of the chaotic system and the iteration results of the cell i. In addition, the boundary condition of the chaotic interleaver system is x n (L+1)=x n (1).

[0061] Furthermore, f in the basic chaotic system chooses a one-dimensional Logistic map, that is,

[0062] f(x n (i))=ux n (i)(1-x n (i))(2)

[0063] u is the basic chaotic system parameter, and its value range is (3.57,4]. In addition, x n The value range of (i) is [0,1].

[0064] Furthermore, p(·) and δ(·) in formula (1) are the iterative results of elementary cellular automata. Their function is to optimize the generated chaotic data so that the dynamic complexity and uniformity of the entire system are better than those of the traditional spatiotemporal chaotic system. The specific method is as follows:

[0065]

[0066] Among them, S n is the binary sequence composed of all cells at the nth iteration, S n (b1b2…b 32 ) is a binary data consisting of 32 cells randomly selected from all cells without repetition in the nth iteration. bin2dec is a function that converts binary data into decimal data. The binary number composed of the cell state is converted into a decimal number and then used as the dividend to calculate p(S n ). Depend on The status is determined by .

[0067] Furthermore, the elementary cell state update formula is:

[0068]

[0069] g is a Boolean function and can be determined by a custom local mapping rule. The state value of the current cell in each iteration is determined by the previous state values ​​of the current cell and its neighboring cells. The mapping rules of the elementary cellular automaton used in this embodiment are shown in Table 1.

[0070] Table 1 Mapping rules of elementary cellular automata

[0071]

[0072] Furthermore, after adding the results of the spatiotemporal chaotic system and the cellular automaton, the sum is modulo 1. After iteration, a set of chaotic data with a length of L and located in the interval [0, 1] is generated. For example, the data generated by the original chaotic system is located in the interval [0, 1], and the perturbation results of the cellular automaton are located in the interval [-0.5, 0.5]. To facilitate subsequent iterative calculations and ensure that each state value of the chaotic system is within the range [0, 1], the sum is modulo 1 after addition to ensure that the iterative result is within the interval [0, 1].

[0073] In step S2, setting the initial parameters of the chaotic system based on elementary cellular automata includes setting the parameters and initial state of the chaotic interleaver system, that is, the u value in the chaotic mapping logistic function f, the coupling coefficient ε of the chaotic interleaver system, the initial value x1(i) at the first iteration, i∈[1,L], and the number of iterations N of the system; and assigning the initial state to each cell in the system, that is, the initial value of each cell at the first iteration of formula (1) i∈[1,L]. Figure 2 In the embodiment shown, these system parameters are set to u=3.9, ε=0.625, x1(i)=0.4, and N=100 respectively; the state values ​​of cells numbered i=16, 32, 64, 128, 256, and 512 are set to 1, and the state values ​​of the remaining cells are set to 0.

[0074] In step S3, the chaotic interleaver system iteratively generates data according to the set parameters, including the following steps:

[0075] S3.1, perform N iterations according to the parameter settings in S2 to generate L data. In particular, when returning to step S3 from S5, an additional iteration can obtain L data;

[0076] S3.2, write the generated L data into an array S, S = [x(1), x(2),…, x(L)].

[0077] In step S4, the generated data is mapped into an interleaved sequence, including the following steps:

[0078] S4.1, sort the data in the array S generated in step S3;

[0079] S4.2, write the position x′(i) of each sorted data x(i) in the original array S into S′ in sequence to obtain the interleaved sequence, that is, S′=[x′(1), x′(2),…, x′(L)].

[0080] In step S5, the specific method for checking whether the interleaving sequence meets the interleaving depth requirement is: based on the interleaving depth R set in step S1 and the interleaving sequence S′ generated in step S4, each data in S′ is checked in turn to determine whether the distance between the R data to the right of the data and the data before interleaving is greater than R. If not, return to step S3; if the requirement is met, proceed to the next step.

[0081] In step S6, the data to be interleaved, seq(L), is obtained from the data receiving module. Then, seq(L) is interleaved using the interleaving sequence S′ generated in step S5. The interleaving result is seq′(S′(i)) = seq(i), i∈[1,L], and the interleaved data seq′(L) is finally obtained. For example, if S′ = [3,1,4,2] and seq(4) = [a,c,b,d], then seq′(4) = [c,d,a,b].

[0082] The interleaved sequence S′ generated according to the steps of the above embodiment is shown in FIG. Figure 2 .

[0083] like Figure 3 As shown, the present invention also discloses a chaotic interleaver based on elementary cellular automata, which includes the following modules:

[0084] Determining module: determining the data packet length, interleaving depth and chaotic interleaver system model of the chaotic interleaver;

[0085] Setting module: setting the initial parameters and states of the chaotic interleaver system based on elementary cellular automata;

[0086] Data generation module: The chaotic interleaver system iteratively generates data according to the set parameters;

[0087] Data mapping module: maps the generated data into an interleaved sequence;

[0088] Judgment module: checks whether the interleaving sequence meets the interleaving depth requirement. If not, the data generation module will execute it. If it meets the requirement, the data interleaving module will execute it.

[0089] Data interleaving module: obtains the data to be interleaved, and then interleaves the data according to the interleaving sequence of the judgment module.

[0090] For other contents of this embodiment, please refer to the above-mentioned data interleaving method embodiment.

[0091] The above are merely preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will appreciate that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions are possible for those skilled in the art without departing from the scope of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A data interleaving method based on a chaotic interleaver of elementary cellular automata, characterized in that: include: S1, determining the data packet length, interleaving depth and chaotic interleaver system model of the chaotic interleaver; S2, setting the initial parameters and states of the chaotic interleaver system based on elementary cellular automata; S3, the chaotic interleaver system iteratively generates data according to the set parameters; S4, maps the generated data into an interleaved sequence; S5, check whether the interleaving sequence meets the interleaving depth requirement, if not, jump back to step S3, if yes, go to step S6; S6, obtaining the data to be interleaved, and then interleaving the data according to the interleaving sequence of step S5; In step S1, L and R are determined, where L is the packet length of the data, i.e., interleaving is performed once every L bits of data; and R is the interleaving depth, i.e., the length of a possible burst error in the channel. The chaotic interleaver system model based on elementary cellular automaton perturbations is as follows: Where n = 0, 1, 2, ... N is the time dimension, i.e., the number of iterations; i = 0, 1, 2, ... L is the space dimension, i.e., the grid index; ε represents the coupling strength, ε∈(0,1); x n (i) represents the state value of the i-th grid point at time n; represents the state value of cell i at time n, There are two states, 0 or 1. The sum of the first and second terms in the above formula is the basic chaotic system, and the third term is the perturbation system based on the elementary cellular automaton. The cell index i and the grid point index i are numerically the same, and the value of the grid point i is determined by the iteration results of the basic chaotic system and the iteration results of the cell i. The boundary condition of the chaotic interleaver system is x n (L+1)=x n (1); The f in the basic chaotic system chooses a one-dimensional Logistic map, that is: f(x n (i))=ux n (i)(1-x n (i))(2) u is the basic chaotic system parameter, and its value range is (3.57,4], x n (i) The value range is [0,1]; The p(·) and δ(·) in formula (1) are the iterative results of the elementary cellular automaton, as follows: Among them, S n is the binary sequence composed of all cells at the nth iteration, S n (b1b2…b 32 ) is a binary number composed of 32 cells randomly selected from all cells without repetition in the nth iteration. bin2dec is a function that converts binary data into decimal data. The binary number composed of cell states is converted into a decimal number and then used as the dividend to calculate p(S n ); Depend on The status is determined by .

2. The data interleaving method of the chaotic interleaver based on elementary cellular automata according to claim 1, wherein: The state update formula of the elementary cellular automaton is: g is a Boolean function and is determined by the local mapping rules set by itself. The state value of the current cell in each iteration is determined by the previous state values ​​of the current cell and its neighboring cells.

3. The data interleaving method of the chaotic interleaver based on elementary cellular automata according to claim 1 or 2, characterized in that: After the results of the basic chaotic system and the cellular automaton are added together, the sum is modulo 1, and after iteration, a set of chaotic data with a length of L and located in the interval [0,1] is generated.

4. The data interleaving method of the chaotic interleaver based on elementary cellular automata as claimed in claim 3, wherein: In step S2, the initial parameters include setting the parameters and initial state of the chaotic system, including the u value in the chaotic mapping logistic function f and the coupling coefficient ε of the chaotic interleaver system and the initial value x1(i) at the first iteration, i∈[1,L], and assigning the initial state to each cell in the chaotic interleaver system, that is, the initial value of each cell at the first iteration of formula (1) i∈[1,L], and set the number of iterations N required for the chaotic interleaver.

5. The data interleaving method of the chaotic interleaver based on elementary cellular automata as claimed in claim 4, characterized in that: Step S3 includes the following steps: S3.1, perform N iterations according to the parameter settings in step S2 to generate L data; S3.2, write the generated L data into an array S, S = [x(1), x(2),…, x(L)].

6. The data interleaving method of the chaotic interleaver based on elementary cellular automata as claimed in claim 5, characterized in that: Step S4 The steps include: S4.1, sort the data in the array S generated in step S3; S4.2, write the position x′(i) of each sorted data x(i) in the original array S into S′ in sequence to obtain the interleaved sequence, that is, S′=[x′(1), x′(2),…, x′(L)].

7. The data interleaving method of the chaotic interleaver based on elementary cellular automata according to claim 6, characterized in that: In step S5, each data in the interleaved sequence S' is checked in turn to determine whether the distance between the R data to the right of the data and the data before interleaving is greater than R. If not, return to step S3; otherwise, go to step S6.

8. The data interleaving method of the chaotic interleaver based on elementary cellular automata as claimed in claim 7, characterized in that: In step S6, the data to be interleaved seq(L) is obtained from the data interleaving module, and then seq(L) is interleaved using the interleaving sequence S′ that meets the requirements in step S5. The interleaving rule is seq′(S′(i))=seq(i), i∈[1,L], and the interleaved data seq′(L) is obtained.

9. A chaotic interleaver based on elementary cellular automata, characterized in that: include: Determination module: Determine L and R, where L is the data packet length, i.e., interleaving is performed once every L bits of data; R is the interleaving depth, i.e., the length of possible burst errors in the channel. The chaotic interleaver system model based on elementary cellular automaton perturbations is as follows: Where n = 0, 1, 2, ... N is the time dimension, i.e., the number of iterations; i = 0, 1, 2, ... L is the space dimension, i.e., the grid index; ε represents the coupling strength, ε∈(0,1); x n (i) represents the state value of the i-th grid point at time n; represents the state value of cell i at time n, There are two states, 0 or 1. The sum of the first and second terms in the above formula is the basic chaotic system, and the third term is the perturbation system based on the elementary cellular automaton. The cell index i and the grid point index i are numerically the same, and the value of the grid point i is determined by the iteration results of the basic chaotic system and the iteration results of the cell i. The boundary condition of the chaotic interleaver system is x n (L+1)=x n (1); The f in the basic chaotic system chooses a one-dimensional Logistic map, that is: f(x n (i))=ux n (i)(1-x n (i))(2) u is the basic chaotic system parameter, and its value range is (3.57,4], x n (i) The value range is [0,1]; The p(·) and δ(·) in formula (1) are the iterative results of the elementary cellular automaton, as follows: Among them, S n is the binary sequence composed of all cells at the nth iteration, S n (b1b2…b 32 ) is a binary number composed of 32 cells randomly selected from all cells without repetition in the nth iteration. bin2dec is a function that converts binary data into decimal data. The binary number composed of cell states is converted into a decimal number and then used as the dividend to calculate p(S n ); Depend on The state of Setting module: setting the initial parameters and states of the chaotic interleaver system based on elementary cellular automata; Data generation module: The chaotic interleaver system iteratively generates data according to the set parameters; Data mapping module: maps the generated data into an interleaved sequence; Judgment module: checks whether the interleaving sequence meets the interleaving depth requirement. If not, the data generation module will execute it. If it meets the requirement, the data interleaving module will execute it. Data interleaving module: obtains the data to be interleaved, and then interleaves the data according to the interleaving sequence of the judgment module.

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