A high-dimensional index modulation spread spectrum communication method

Abstract

In view of the deficiencies of the prior art, the application designs a high-dimensional index modulation spread spectrum communication method, first, according to the user demand, customizes the user security level code and the transmission efficiency requirement code, and then carries out dimension division, and then according to the user security level code, flexibly constructs the order index pool, the initial value index pool and the shift index pool; secondly, the converted second-dimensional data index is used to generate a Tent chaotic sequence, and the converted third-dimensional data index is used to generate a cyclic right shift amount, combined with a fixed pseudo code for spread spectrum processing, finally, the converted fourth-dimensional data index WFRFT order is used for WFRFT processing, and a high-dimensional index modulation spread spectrum signal is generated; the receiving end is processed through corresponding multi-channel correlation, peak value and peak value channel calculation, etc., to analyze the second-dimensional, third-dimensional, fourth-dimensional data and despread the first-dimensional data, and complete the high-dimensional index modulation spread spectrum signal reception; the application is applied to the spread spectrum communication field, and the purposes of improving the data rate and improving the frequency band utilization rate are achieved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication, and more specifically to a high-dimensional indexed modulation spread spectrum communication method. Background Technology

[0002] Direct sequence spread spectrum (DSSS) boasts numerous advantages, including wide spectral density, low signal-to-noise ratio, ease of code division multiple access (CDMA) implementation, strong security, and robust anti-interference capabilities, leading to its widespread application in various fields such as GPS, telemetry, tracking, and command (TT&C), and satellite communications. While traditional DSS offers many advantages, these advantages come at the cost of widening the signal spectrum. This makes it unsuitable for efficient transmission in channels with narrow bandwidth and high processing gain requirements. Consequently, indexed modulation DSS emerged. Although it addresses the trade-off between data transmission rate and bandwidth utilization in traditional DSS, it relies on increased pseudocode resources. While effectively improving spectral efficiency and increasing transmission rate, it also consumes significant index code resources and reduces bit error rate performance, severely limiting the rate of increase in transmission rate. Furthermore, with ever-increasing communication demands, the need for multi-level, multi-application, and more secure communication is urgent. Therefore, research on more secure, high-dimensional data indexed transmission methods has become a key focus at present. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention designs a high-dimensional indexed modulation spread spectrum communication method. First, user security level codes and transmission efficiency requirement codes are customized according to user needs, followed by dimensional division. Then, based on the user security level codes, order index pools, initial value index pools, and shift index pools are flexibly constructed. Second, a Tent chaotic sequence is generated using the converted two-dimensional data index, and the cyclic right shift of the converted three-dimensional data index, combined with a fixed pseudocode, is used for spread spectrum processing. Finally, WFRFT processing is performed using the converted four-dimensional data index WFRFT order to generate a high-dimensional indexed modulation spread spectrum signal. At the receiving end, multi-channel correlation, peak value, and peak channel calculations are performed to parse the multi-dimensional data and despread the one-dimensional data, completing the reception of the high-dimensional indexed modulation spread spectrum signal. This invention is applied in the field of spread spectrum communication, achieving the goal of improving data rate and bandwidth utilization.

[0004] A high-dimensional indexed modulation spread spectrum communication method, specifically comprising two parts: data communication between the transmitting end and the receiving end;

[0005] The specific processing steps at the transmitting end include:

[0006] Step 1: Let the total amount of data to be transmitted by the user be D(n), and the total data length of each cycle be L, where n∈[1,L]. Based on the user's requirements for security level and transmission efficiency, we define a three-digit user security level code A and a one-digit transmission efficiency requirement code R.

[0007] User security level requirements are divided into 8 levels, with level 0 being the lowest and level 7 being the highest. The higher the level, the more secure data needs to be transmitted. The user security level is determined by the ratio of the secure data rate that the user needs to transmit to the basic data rate.

[0008] The specific correspondence between the user security level requirements and the user security level codes is as follows: When the user security level is 0, A = 000; when the user security level is 1, A = 001; when the user security level is 2, A = 010; when the user security level is 3, A = 011; when the user security level is 4, A = 100; when the user security level is 5, A = 101; when the user security level is 6, A = 110; when the user security level is 7, A = 111.

[0009] The specific correspondence between the user's transmission efficiency requirement and the transmission efficiency requirement code is as follows: when the user does not need fast transmission, R = 0; when the user needs fast transmission, R = 1.

[0010] Step 2: Based on the user security level code A and the transmission efficiency requirement code R, define the second dimension data length L2 and the third dimension data length L3 as shown in formulas (1) and (2), and then calculate the first dimension data length L1 as shown in formula (3). Considering that the user security level code A is 3 digits, the fourth dimension data length is defined as 3.

[0011]

[0012] L3=bin2dec[A] (2)

[0013]

[0014] Where N is the length of a one-cycle spreading pseudocode, Bin2dec[·] is a function that rounds down to the nearest integer and to the power of 2, while bin2dec[·] is a function for converting binary to decimal.

[0015] Step 3: Using the first dimension data length L1, the second dimension data length L2 and the third dimension data length L3, perform dimension partitioning on the total data D(n) of length L as shown in formula (4). At the same time, the user security level code A is the fourth dimension data D4(n), and the partitioned dimension is 4.

[0016]

[0017] Step 4: After dividing the data dimensions, perform data transformation processing on the second-dimensional data D2(n), the third-dimensional data D3(n), and the fourth-dimensional data D4(n) as shown in formula (5); since the data lengths of the second-dimensional data D2(n), the third-dimensional data D3(n), and the fourth-dimensional data D4(n) are L2, L3, and 3 respectively, the calculation range of γ is 0 to 7, and the calculation range of η is... The calculation range of f is bin2dec[·] is a function to convert binary to decimal;

[0018]

[0019] Step 5: Calculate the index pool for data indexing using the transformed f, η, and γ;

[0020] Step 5.1: First, establish the order index pool of WFRFT. Since γ is 0 to 7, the size of the order index pool is 8. Since the anti-scanning interval of WFRFT is greater than 0.01, and considering that the anti-interception interval of WFRFT is the order interval of 1.92 to 2, the order index pool is established as shown in formula (6). The order index pool is public during the communication process.

[0021] α γ =1.92+0.011γ (6)

[0022] Step 5.2: Then, calculate the initial value index pool for the pseudocode sequence, since the range of η is... Therefore, the initial index pool size is... To enhance the three-dimensional resistance capabilities of data communication, the pseudocode sequence is selected as a Tent mapping chaotic sequence. Since the initial value range of the Tent mapping is [0 1), the initial value index pool is established as follows:

[0023] β η =0.0078η (7)

[0024] Step 5.3: Finally, combining the good correlation properties of chaotic sequences in the Tent mapping, a shift index pool for the chaotic sequence is established, since the range of f is... Its shift index pool size is The shifted index pool is:

[0025] κ f =1000f (8)

[0026] Step 6: Based on the established initial value index pool, use the η index Tent to map the initial value β of the chaotic sequence. η ,Right now: Introducing Tent map iterative equations to generate chaotic sequences As shown in formula (9), due to the fractal parameter When the fractal parameter is 0.4997, the Kent chaotic sequence exhibits the best chaotic form, reaching a fully mapped state. Therefore, the fractal parameter... The initial value range of the Tent mapping is [0 1), therefore... The sequence value range is also [0 1), based on the generated Sequence, further The sequence is binarized to obtain As shown in formula (10); where ave(·) is the mean function;

[0027]

[0028]

[0029] Step 7: Based on the shift index pool established in Step 5, use f to index the shift parameters of the chaotic sequence, and use κ... f For the cycle offset, the production Perform a circular right shift κ f Position obtained in, Circular right shift κ f Bit manipulation functions;

[0030]

[0031] Step 8: Generate with all An orthogonal fixed pseudocode E(n), that is, E(n) and all They are also orthogonal; therefore, pseudocode sequence superposition is performed to obtain...

[0032]

[0033] Step 9: Utilize The baseband signal y(n) is obtained by spreading the first-dimensional data D1(n);

[0034]

[0035] Step 10: Based on the order index pool established in Step 5, use γ, which is the result of data conversion from the user security level code, to obtain α from the index order index pool. γ Furthermore, the baseband signal y(n) is subjected to an order of α. γ The WFRFT processing yields the baseband modulated signal S'(n); where Y(n), y(-n), and Y(-n) are the results of performing the 1st, 2nd, and 3rd Fourier transforms on y(n), respectively.

[0036]

[0037] The specific processing procedure at the receiving end includes:

[0038] Step 1: Perform front-end processing on the received signal S'(n) by down-conversion, sampling, and filtering. The processed baseband signal is denoted as S(n), where N0 is the weighted terms of various noises after processing.

[0039]

[0040] Step 2: For authorized users, the order index pool is public, so j is traversed from 0 to 7 to generate a local order index pool as shown in formula (16), and then the processed baseband signal S(n) is processed to have 8 channels with orders ε. j The inverse WFRFT processing is shown in Equation (17);

[0041] ε j =1.92+0.011j (16)

[0042]

[0043] Step 3: Generate the local fixed pseudocode E(n), and apply it to the 8-channel S j Each of the n values ​​is correlated with E(n) to obtain the correlation results X for 8 channels. j (n); j iterates from 0 to 7, and * represents the relevant operator;

[0044] X j (n)=S j (n)*E(n) (18)

[0045] Step 4: Analyze the relevant results X j (n) Peak processing is performed. First, the maximum peak-to-average power ratio (PAPR) is calculated, i.e., 8 sets of X are calculated. j The ratio of the maximum peak value to the average peak value in each group (n), where PRA[·] is the maximum peak-to-average ratio calculation function; further, 8 P values ​​are selected. j The maximum value in is obtained as P. J Where max[·] is the maximum value calculation function; J is the index of the maximum value channel, that is, J equals P. j The maximum value P J j, where max[·] channel Calculate the function for the maximum channel; then, calculate X. J The peak position H of (n) is given by channel[·], where channel[·] is the function for calculating the position of the maximum value.

[0046] P j =PRA[X j(n)] (19)

[0047] P J =max[P j (20)

[0048] J = max[P] j ] channel -1 (21)

[0049] H = channel[X] J (n)] (22)

[0050] Step 5: Analyze the fourth dimension data D'4(n) using the maximum value channel J;

[0051] D'4(n)=dec2bin[J] (23)

[0052] Step 6: Circularly shift the fixed pseudocode E(n) right by H bits, and simultaneously substitute j = J into S. j In (n), we obtain S J (n), and then S J Multiplying E(n) by a sequence of signals yields the first-level despread baseband signal R(n), where shift[·] H This is the function for calculating a circular right shift of H bits;

[0053] R(n) = S J (n)shift[E(n)] H (twenty four)

[0054] Step 7: Calculate the number of secondary despreading channels using J, which is 2. J Therefore, i ranges from 0 to (2 J -1) Traverse the initial value index pool and, in conjunction with formula (25), calculate the initial value parameter set β′. i Furthermore, combining formula (26), with β′ i Replace with initial value Construction 2 J Group local Tent mapping chaotic sequence And by combining the binarization of formula (27), we obtain 2 J Local chaotic sequence

[0055] β′ i =0.0078i (25)

[0056]

[0057]

[0058] Step 8: Compare the baseband signal R(n) after the first-stage despreading with 2 J Each channel Perform parallel correlation operations to obtain 2 J 1 relevant result

[0059]

[0060] Step 9: Analyze the relevant results To perform peak processing, firstly, the maximum peak-to-average power ratio (PAPR) is calculated, and then a value of 2 is selected. J indivual The index of the maximum value channel in the data, i.e., I equals The maximum value of β i '; Then, calculate The peak position H';

[0061]

[0062]

[0063]

[0064] Step 10: Using I, H, and H′, perform data parsing as follows to obtain the parsed second-dimensional and third-dimensional data;

[0065] Step 10.1: Perform data conversion on the index I of the maximum value channel to obtain the parsed third-dimensional data D′3(n), where dec2bin[·] is the decimal to binary conversion function;

[0066] D'3(n)=dec2bin[I] (32)

[0067] Step 10.2: Using the peak positions H and H', perform data parsing to obtain the parsed second-dimensional data D'2(n), where round[·] is the rounding function;

[0068]

[0069] Step 11: Substitute I into β′ i That is, I = β′ i Select the local chaotic sequence C I (n), and perform a circular right shift of H' bits, and perform a sequence multiplication operation with R(n) to obtain the first dimension data D′1(n) after second-level despreading; complete the data communication;

[0070] D′1(n)=R(n)shift[C I (n)] H' (34)

[0071] Beneficial technical effects of the present invention:

[0072] This invention establishes a high-dimensional index modulation spread spectrum communication method that can improve the data rate without sacrificing bandwidth utilization, and the related complexity can be flexibly controlled by the user security level code, which greatly improves the limitation of the high and fixed complexity of traditional index modulation and can provide a theoretical basis for the development of a new generation of spread spectrum communication systems. Attached Figure Description

[0073] Figure 1 Schematic diagram of the high-dimensional index modulation spread spectrum communication method of this invention;

[0074] Figure 2 The graph shows the change in gain as the total data rate increases according to the embodiment of the present invention.

[0075] Figure 3 The graph shows the change in the data transmission rate multiplier as the complexity of the relevant channels increases, according to an embodiment of the present invention. Detailed Implementation

[0076] The present invention will be further described below with reference to the accompanying drawings and embodiments;

[0077] A high-dimensional indexed modulation spread spectrum communication method, as shown in the appendix. Figure 1 As shown, the specific content includes two parts: data communication between the transmitting end and the receiving end;

[0078] The specific processing steps at the transmitting end include:

[0079] Step 1: Let the total amount of data to be transmitted by the user be D(n), and the total data length of each cycle be L, where n∈[1,L]. Based on the user's requirements for security level and transmission efficiency, we define a three-digit user security level code A and a one-digit transmission efficiency requirement code R; the specific explanation is shown in Table 1.

[0080] User security level requirements are divided into 8 levels, with level 0 being the lowest and level 7 being the highest. The higher the level, the more secure data transmission is required. The user security level is determined by the ratio of the secure data rate that the user needs to transmit to the basic data rate. For example, if the secure data rate is twice the basic data rate, then the user security level is 2.

[0081] Table 1 Explanation of User Security Level Code A and Transmission Efficiency Requirement Code R

[0082]

[0083]

[0084] Step 2: Based on the user security level code A and the transmission efficiency requirement code R, define the second dimension data length L2 and the third dimension data length L3 as shown in formulas (1) and (2), and then calculate the first dimension data length L1 as shown in formula (3). Considering that the user security level code A is 3 digits, the fourth dimension data length is defined as 3.

[0085]

[0086] L3=bin2dec[A] (2)

[0087] L1 = L - L2 - L3 (3)

[0088] Where N is the length of a one-cycle spreading pseudocode, Bin2dec[·] is a function that rounds down to the nearest integer and to the power of 2, while bin2dec[·] is a function for converting binary to decimal.

[0089] Step 3: Using the first dimension data length L1, the second dimension data length L2 and the third dimension data length L3, perform dimension partitioning on the total data D(n) of length L as shown in formula (4). At the same time, the user security level code A is the fourth dimension data D4(n), and the partitioned dimension is 4.

[0090]

[0091] Step 4: After dividing the data dimensions, perform data transformation processing on the second-dimensional data D2(n), the third-dimensional data D3(n), and the fourth-dimensional data D4(n) as shown in formula (5); since the data lengths of the second-dimensional data D2(n), the third-dimensional data D3(n), and the fourth-dimensional data D4(n) are L2, L3, and 3 respectively, the calculation range of γ is 0 to 7, and the calculation range of η is... The calculation range of f is bin2dec[·] is a function to convert binary to decimal;

[0092]

[0093] Step 5: Calculate the index pool for data indexing using the transformed f, η, and γ;

[0094] Step 5.1: First, establish the order index pool of WFRFT. Since γ is 0 to 7, the size of the order index pool is 8. Since the anti-scanning interval of WFRFT is greater than 0.01, and considering that the anti-interception interval of WFRFT is the order interval of 1.92 to 2, the order index pool is established as shown in formula (6). The order index pool is public during the communication process.

[0095] α γ=1.92+0.011γ (6)

[0096] Step 5.2: Then, calculate the initial value index pool for the pseudocode sequence, since the range of η is... Therefore, the initial index pool size is... To enhance the three-dimensional resistance capabilities of data communication, the pseudocode sequence is selected as a Tent mapping chaotic sequence. Since the initial value range of the Tent mapping is [0 1), the initial value index pool is established as follows:

[0097] β η =0.0078η (7)

[0098] Step 5.3: Finally, combining the good correlation properties of chaotic sequences in the Tent mapping, a shift index pool for the chaotic sequence is established, since the range of f is... Its shift index pool size is The shifted index pool is:

[0099] κ f =1000f (8)

[0100] Step 6: Based on the established initial value index pool, use the η index Tent to map the initial value β of the chaotic sequence. η ,Right now: Introducing Tent map iterative equations to generate chaotic sequences As shown in formula (9), due to the fractal parameter When the fractal parameter is 0.4997, the Kent chaotic sequence exhibits the best chaotic form, reaching a fully mapped state. Therefore, the fractal parameter... The initial value range of the Tent mapping is [0 1), therefore... The sequence value range is also [0 1), based on the generated Sequence, further The sequence is binarized to obtain As shown in formula (10); where ave(·) is the mean function;

[0101]

[0102]

[0103] Step 7: Based on the shift index pool established in Step 5, use f to index the shift parameters of the chaotic sequence, and use κ... f For the cycle offset, the production Perform a circular right shift κ f Position obtained in, Circular right shift κ f Bit manipulation functions;

[0104]

[0105] Step 8: Generate with all An orthogonal fixed pseudocode E(n), that is, E(n) and all They are also orthogonal; therefore, pseudocode sequence superposition is performed to obtain...

[0106]

[0107] Step 9: Utilize The baseband signal y(n) is obtained by spreading the first-dimensional data D1(n);

[0108]

[0109] Step 10: Based on the order index pool established in Step 5, use γ, which is the result of data conversion from the user security level code, to obtain α from the index order index pool. γ Furthermore, the baseband signal y(n) is subjected to an order of α. γ The WFRFT processing yields the baseband modulated signal S'(n); where Y(n), y(-n), and Y(-n) are the results of performing the 1st, 2nd, and 3rd Fourier transforms on y(n), respectively.

[0110]

[0111] The specific processing procedure at the receiving end includes:

[0112] Step 1: Perform front-end processing on the received signal S'(n) by down-conversion, sampling, and filtering. The processed baseband signal is denoted as S(n), where N0 is the weighted terms of various noises after processing.

[0113]

[0114] Step 2: For authorized users, the order index pool is public, so j is traversed from 0 to 7 to generate a local order index pool as shown in formula (16), and then the processed baseband signal S(n) is processed to have 8 channels with orders ε. j The inverse WFRFT processing is shown in Equation (17);

[0115] ε j =1.92+0.011j (16)

[0116]

[0117] Step 3: Generate the local fixed pseudocode E(n), and apply it to the 8-channel S jEach of the n values ​​is correlated with E(n) to obtain the correlation results X for 8 channels. j (n); j iterates from 0 to 7, and * represents the relevant operator;

[0118] X j (n)=S j (n)*E(n) (18)

[0119] Step 4: Analyze the relevant results X j (n) Peak processing is performed. First, the maximum peak-to-average power ratio (PAPR) is calculated, i.e., 8 sets of X are calculated. j The ratio of the maximum peak value to the average peak value in each group (n), where PRA[·] is the maximum peak-to-average ratio calculation function; further, 8 P values ​​are selected. j The maximum value in is obtained as P. J Where max[·] is the maximum value calculation function; J is the index of the maximum value channel, that is, J equals P. j The maximum value P J j, where max[·] channel Calculate the function for the maximum channel; then, calculate X. J The peak position H of (n) is given by channel[·], where channel[·] is the function for calculating the position of the maximum value.

[0120] P j =PRA[X j (n)] (19)

[0121] P J =max[P j (20)

[0122] H = max[P] j ] channel -1 (21)

[0123] H = channel[X] J (n)] (22)

[0124] Step 5: Analyze the fourth dimension data D'4(n) using the maximum value channel J;

[0125] D'4(n)=dec2bin[J] (23)

[0126] Step 6: Circularly shift the fixed pseudocode E(n) right by H bits, and simultaneously substitute j = J into S. j In (n), we obtain S J (n), and then S J Multiplying E(n) by a sequence of signals yields the first-level despread baseband signal R(n), where shift[·] HThis is the function for calculating a circular right shift of H bits;

[0127] R(n) = S J (n)shift[E(n)] H (twenty four)

[0128] Step 7: Calculate the number of secondary despreading channels using J, which is 2. J Therefore, i ranges from 0 to (2 J -1) Traverse the initial value index pool and, in conjunction with formula (25), calculate the initial value parameter set β′. i Furthermore, combining formula (26), with β′ i Replace with initial value Construction 2 J Group local Tent mapping chaotic sequence And by combining the binarization of formula (27), we obtain 2 J Local chaotic sequence

[0129] β′ i =0.0078i (25)

[0130]

[0131]

[0132] Step 8: Compare the baseband signal R(n) after the first-stage despreading with 2 J Each channel Perform parallel correlation operations to obtain 2 J 1 relevant result

[0133]

[0134] Step 9: Analyze the relevant results To perform peak processing, firstly, the maximum peak-to-average power ratio (PAPR) is calculated, and then a value of 2 is selected. J indivual The index of the maximum value channel in the data, i.e., I equals The maximum value of β′ i Then, calculate The peak position H';

[0135]

[0136]

[0137]

[0138] Step 10: Using I, H, and H′, perform data parsing as follows to obtain the parsed second-dimensional and third-dimensional data;

[0139] Step 10.1: Perform data conversion on the index I of the maximum value channel to obtain the parsed third-dimensional data D′3(n), where dec2bin[·] is the decimal to binary conversion function;

[0140] D'3(n)=dec2bin[I] (32)

[0141] Step 10.2: Using the peak positions H and H', perform data parsing to obtain the parsed second-dimensional data D'2(n), where round[·] is the rounding function;

[0142]

[0143] Step 11: Substitute I into β i ', that is, I = β i Select the local chaotic sequence C. I (n), and perform a circular right shift of H' bits, and perform a sequence multiplication operation with R(n) to obtain the first dimension data D1'(n) after second-level despreading; complete the data communication;

[0144] D1'(n)=R(n)shift[C I (n)] H' (34)

[0145] To verify the technical effectiveness of the present invention, the above method was tested and verified as follows:

[0146] To verify the innovativeness of the method of this invention, it is compared with the existing code index modulation (CIM) method; the parameters are set as follows: base rate of 100 bit / s and signal-to-noise ratio of -12dB.

[0147] Test 1: With a fixed complexity of 128 channels, and the spread spectrum sequence rate and fundamental rate multiples B being 1000, 5000, and 9000 respectively, the gain changes as the total data rate multiple (the multiple of the total data rate and the fundamental rate) increases, as shown in the following figures. Figure 2 As shown, under the same total data rate multiple and the same B, the gain obtained by the method of the present invention is 3dB-5dB greater than that of the CIM method, thus achieving better anti-interference capability. Simultaneously, under the same gain and the same B, the total data rate that the method of the present invention can transmit is far greater than that of the CIM method, indicating that the method of the present invention has stronger transmission capability.

[0148] Test 2: With a fixed gain of 50dB, and the spread spectrum sequence rate and fundamental rate multiples B being 1000, 5000, and 9000 respectively, the results of comparing the present invention method and the CIM method with the changes in the transmittable data rate multiples (the ratio of the total data rate to the fundamental rate) as the complexity of the relevant channels increases are as follows: Figure 3 As shown, under the same correlation channel complexity, the data transmission rate of the method of the present invention is much higher than that of the CIM method, meaning the method of the present invention has stronger transmission capabilities. Simultaneously, under the same data transmission rate, the correlation channel complexity of the method of the present invention is much lower than that of the CIM method, meaning the method of the present invention has lower processing complexity and greater practical value.

Claims

1. A high-dimensional indexed modulation spread spectrum communication method, characterized in that, The specific content includes data communication between the transmitting and receiving ends; The specific processing steps at the transmitting end include: Step 1: Let the total amount of data to be transmitted by the user be... The total data length for each period is ,at this time Based on the analysis of users' requirements for security level and transmission efficiency, a three-digit user security level code is defined. And a one-bit transmission efficiency requirement code ; Step 2: Based on the user's security level code and transmission efficiency requirement code Define the second dimension data length and the length of the third dimension data As shown in formulas (1) and (2), the length of the first dimension data is then calculated. As shown in formula (3), user security level codes are also considered. Since there are 3 digits, the length of the fourth dimension is defined as 3. (1) (2) (3) in, The length of one period of spreading pseudocode, This is a function that rounds down to the floor and then to the power of 2. This is a function for converting binary to decimal. Step 3: Utilize the first dimension data length Second dimension data length and the length of the third dimension data For length of Total data The dimensional partitioning process is performed as shown in formula (4). Meanwhile, the user security level code A is the fourth dimension. The resulting dimension is 4; (4) Step 4: After dividing the data into dimensions, process the second dimension of the data. Third-dimensional data Fourth dimension data The data transformation process is shown in formula (5); due to the second dimension data Third-dimensional data Fourth dimension data The data lengths are respectively , And 3, therefore, The calculation range is 0~7. The calculation range is 0~ , The calculation range is 0~ ; Function to convert binary to decimal; (5) Step 5: Utilize the transformed... , , Perform index pool calculation for data indexing; Step 6: Based on the established initial value index pool, utilize... Initial values ​​of the index Tent mapping chaotic sequence ,Right now: Introducing the Tent map iterative equation to generate chaotic sequences As shown in formula (9), due to the fractal parameter When the fractal parameter is 0.4997, the Kent chaotic sequence exhibits the best chaotic form, i.e., it reaches a fully mapped state. Therefore, the fractal parameter... =0.4997; the initial value range of the Tent mapping is [0 1), therefore The sequence value range is also [0 1), based on the generated Sequence, further The sequence is binarized to obtain As shown in formula (10); where, To calculate the mean function; (9) (10) Step 7: Based on the shift index pool established in Step 5, utilize... The shift parameters of the indexed chaotic sequence, and in For the cycle offset, the production Perform a circular right shift Position obtained ,in, For circular right shift Bit manipulation functions; (11) Step 8: Generate with all Orthogonal fixed pseudocode ,Right now With all They are also orthogonal; therefore, pseudocode sequence superposition is performed to obtain... ; (12) Step 9: Utilize For the first dimension data The baseband signal is obtained by spreading the spectrum. ; (13) Step 10: Based on the order index pool established in Step 5, utilize the data transformation of the user security level code. , to obtain the index order index pool Furthermore, regarding the baseband signal The order of the process is WFRFT processing yields the baseband modulated signal. ;in, , , They are respectively for The results of performing the 1st, 2nd, and 3rd Fourier transforms; (14)。 2. The high-dimensional indexed modulation spread spectrum communication method according to claim 1, characterized in that, The user security level requirements described in step 1 are divided into 8 levels, with the lowest level being level 0 and the highest level being level 7. The higher the level, the more secure data is required for transmission. The user security level is determined by the ratio of the secure data rate that the user needs to transmit to the basic data rate; The specific correspondence between the user security level requirements and the user security level code is as follows: when the user security level is 0, =000; When the user's security level is level 1. =001; When the user's security level is level 2. =010; When the user's security level is level 3. =011; When the user's security level is 4. =100; When the user's security level is 5. =101; When the user's security level is 6. =110; When the user's security level is 7. =111; The specific correspondence between the user's transmission efficiency requirements and the transmission efficiency requirement code is as follows: when the user does not require fast transmission, =0; when the user needs fast transmission. =1.

3. The high-dimensional indexed modulation spread spectrum communication method according to claim 1, characterized in that, Step 5 specifically involves: Step 5.1: First, establish the order index pool for WFRFT, since... The order index pool size is 8 because the anti-scanning interval of WFRFT is greater than 0.01, and considering that the anti-interception interval of WFRFT is the order interval of 1.92 to 2, the order index pool is established as shown in formula (6). The order index pool is public during the communication process. (6) Step 5.2: Then, calculate the initial value index pool for the pseudocode sequence, since... The range is 0~ Therefore, the initial index pool size is To enhance the three-dimensional resistance capabilities of data communication, the pseudocode sequence selected is the Tent mapping chaotic sequence. Since the initial value range of the Tent mapping is [0 1), the initial value index pool is established as follows: (7) Step 5.3: Finally, combining the good correlation properties of chaotic sequences in the Tent mapping, a shift index pool for chaotic sequences is established. Because... The range is 0~ Its shift index pool size is The shifted index pool is: (8)。 4. The high-dimensional indexed modulation spread spectrum communication method according to claim 1, characterized in that, The specific processing procedure at the receiving end includes: S1: For the received signal The front-end processing involves down-conversion, sampling, and filtering. The processed baseband signal is denoted as... ,in, Weighting terms for various noise types after processing; (15) S2: For authorized users, the order index pool is public, therefore Traverse from 0 to 7 to generate a local order index pool as shown in formula (16), and then process the baseband signal. The order of the 8 channels is as follows The inverse WFRFT processing is shown in Equation (17); (16) (17) S3: Generate local fixed pseudocode and for 8 channels respectively with Correlation calculations were performed to obtain correlation results for 8 channels. ; Iterate through the numbers from 0 to 7. For the relevant operators; (18) S4: Relevant Results To perform peak value processing, firstly, the maximum peak-to-average ratio (PAPR) is calculated, which involves calculating 8 sets of PAPR values. The ratio of the maximum peak value to the average peak value in each group, where, The maximum peak-to-average ratio (PAPR) calculation function is used; eight further samples are selected. The maximum value in is obtained ,in, This is a function for calculating the maximum value. The index of the maximum value channel, i.e. equal maximum value of ,in, Calculate the function for the maximum value channel; then, calculate... peak position ,in The function is used to calculate the position corresponding to the maximum value; (19) (20) (21) (22) S5: Utilizing the maximum value channel Analyzing the fourth dimension data ; (23) S6: For fixed pseudocode Perform a circular right shift Position, at the same time Substitution In the middle, we get ,and then and Perform sequence multiplication to obtain the baseband signal after first-stage despreading. ,in, For circular right shift Bit calculation functions; (24) S7: Utilize The number of secondary despreading channels is calculated as follows: ,and then From 0 to ( -1) Traverse the initial value index pool and, in conjunction with formula (25), calculate the initial value parameter set. Furthermore, combining formula (26), respectively using Replace with initial value ,structure Group local Tent mapping chaotic sequence And by combining the binarization of formula (27), we can obtain Local chaotic sequence ; (25) (26) (27) S8: For the baseband signal after primary despreading and Each channel Perform parallel correlation operations to obtain 1 relevant result ; (28) S9: Regarding the relevant results To perform peak processing, firstly, the maximum peak-to-average ratio (PAR) is calculated, and then further selection is performed. indivual The index of the maximum value channel in the data, i.e. equal The maximum value Then, calculate peak position ; (29) (30) (31) S10: Utilize and and The data is parsed and processed as follows to obtain the parsed second-dimensional data and third-dimensional data. S11: Use Substitute ,Right now Select the local chaotic sequence And perform a circular right shift. Position, and with Performing sequence multiplication yields the first dimension of the data after second-order despreading. Complete data communication; (34)。 5. The high-dimensional indexed modulation spread spectrum communication method according to claim 4, characterized in that, S10 specifically refers to: S10.1: Index to the maximum value channel The data is transformed to obtain the parsed third-dimensional data. ,in, This is a function for converting decimal to binary. (32) S10.2: Utilizing peak position and Data parsing is performed to obtain the parsed second-dimensional data. ,in This is a rounding function; (33)。

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