A 5G communication module data transmission system for the Internet of Things

Abstract

This invention belongs to the field of wireless communication technology, specifically relating to a 5G communication module data transmission system for the Internet of Things (IoT). The system includes a communication decision module, a channel state prediction module, and a data transmission module, which are sequentially connected. The communication decision module employs a quantum genetic algorithm, encoding decision variables with qubits and generating a lightweight scheduling strategy through population iteration. This avoids the excessive computational demands of complex optimization algorithms on the device side and is compatible with low-power microprocessors. Simultaneously, it dynamically adjusts the acquisition frequency based on the needs of edge nodes, reducing invalid data acquisition and lowering device energy consumption. The channel state prediction module trains a model by maximizing the average reward value and uses an LSTM model with an attention mechanism to achieve accurate prediction. The data transmission module dynamically adjusts the compression and encryption strategy based on the channel state, ensuring security while reducing transmission energy consumption and avoiding energy inversion.

Description

Technical Field

[0001] This invention belongs to the field of 5G communication transmission technology, specifically relating to a 5G communication module data transmission system for the Internet of Things. Background Technology

[0002] Internet of Things (IoT) terminals are mostly deployed in unattended or mobile scenarios, relying on battery power and facing difficulties in recharging (such as environmental sensors and wearable devices). Although 5G technology provides IoT with high speed, large capacity, low latency, and high reliability through enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC), various cybersecurity issues arise as massive numbers of IoT devices continue to connect to 5G networks at high speed. IoT devices are inherently diverse and numerous; the rapid development of 5G networks, while increasing the number of connected IoT devices, also enhances the widespread impact and propagation of IoT security threats, presenting multiple problems and challenges:

[0003] While existing adaptive communication methods attempt to optimize energy consumption by dynamically adjusting transmission parameters (such as transmit power and modulation order), most devices in the system use low-power microprocessors with limited computing and storage capabilities. Complex optimization algorithms have high hardware requirements and are difficult to run directly on the device side, which restricts the practical application of subsequent adaptive communication methods. Moreover, when low-power MCUs run such algorithms, the computational power consumption can be several times that of the standby state, resulting in an energy consumption inversion phenomenon.

[0004] Therefore, there is a need for a method that can adaptively adjust the acquisition frequency of the acquisition end according to the data transmission acquisition requirements, and to simplify and lightweight the adaptive model to reduce the difficulty of model integration, thereby reducing the running speed of the device and reducing energy consumption. Summary of the Invention

[0005] This invention provides a 5G communication module data transmission system for the Internet of Things, implemented through the following technical solution:

[0006] A 5G communication module data transmission system for the Internet of Things is characterized by comprising a communication decision module, a channel state prediction module, and a data transmission module that are connected in sequence.

[0007] The communication decision module generates communication scheduling instructions using a quantum genetic algorithm based on the needs of the edge nodes. The quantum genetic algorithm uses qubits to encode decision variables. The communication scheduling instructions include communication time, acquisition frequency, data volume, and power. The acquisition end is controlled according to the communication scheduling instructions.

[0008] The channel state prediction module is used to train a channel state prediction model based on historical communication data and historical state data with the training objective of maximizing the average reward value, and to predict the channel state based on the real-time state of data transmission. The channel state indicators include channel redundancy, SINR, CSI and bit error rate.

[0009] The data transmission module includes:

[0010] The data compression and encryption unit performs compression and encryption operations on the data collected by IoT devices before transmission based on the predicted channel state and the improved LZ77 algorithm.

[0011] The data optimization unit performs secondary compression on the encrypted data frame using the Huffman algorithm before transmission.

[0012] Preferably, the execution steps of the communication decision module include:

[0013] S1: Obtain the demand instructions periodically sent by the edge node. The demand instructions include data priority, maximum allowable latency, data type identifier, and current load rate of the edge node.

[0014] S2: Quantum bit encoding: The four decision variables of communication time, acquisition frequency, data volume and power are encoded using quantum bits. Each variable corresponds to a set of quantum bits, forming a quantum chromosome.

[0015] S3: Generate the initial population: Randomly generate multiple quantum chromosomes and superimpose the qubits of each chromosome;

[0016] S4: Measure each quantum chromosome to obtain a definite binary solution and convert it into actual decision parameter values;

[0017] S5: Apply a rotation gate to the qubit to adjust its phase according to the adaptive degree function and perform a mutation operation on the qubit. The rotation angle is determined using the corresponding adjustment strategy.

[0018] S6: When the continuous fitness change rate is lower than the set fitness threshold, output the decision parameters corresponding to the optimal quantum chromosome, and generate communication scheduling instructions based on the decision parameters;

[0019] S7: Communication scheduling commands are sent to the IoT device acquisition terminal via the 5G control channel to control the device acquisition terminal.

[0020] Preferably, the fitness function is calculated using the following formula:

[0021] ;

[0022] Where E represents the energy consumption for the current decision-making process. P is the transmission power, and t is the transmission duration.

[0023] This represents the maximum energy consumption allowed for this edge node device. The maximum latency required by the edge point device. U represents the actual transmission delay for the current decision, and U represents the current channel utilization. For the target channel utilization, the weighting coefficients satisfy: .

[0024] Preferably, the channel state prediction model includes optimizing the output of the channel state prediction model using an average reward function, the formula for which the average reward function is calculated is:

[0025] ;

[0026] Where K is the total number of prediction rounds, and k is the k-th round. These are weighting coefficients used to adjust the importance of each indicator in the total reward, and .

[0027] Preferred, accuracy bonus The calculation formula is:

[0028] ;

[0029] in, Let be the predicted value of the i-th channel parameter at time step t in round k. This represents the actual value of the i-th channel parameter at time step t in the k-th round. and These are the maximum and minimum values ​​of the i-th channel parameter, respectively, used to normalize the deviation.

[0030] Preferably, the data compression and encryption unit uses an improved chaotic encryption algorithm combined with a permutation operation to generate a key stream, performs multiple chaotic iterations by XORing the key stream and the compressed data frame by bytes, and then transmits the ciphertext data stream.

[0031] Preferably, the specific steps of the data compression and encryption unit include:

[0032] Initialize the chaotic system, using a three-dimensional coupled tent map as the basic chaotic model, with the initial state... It is generated by performing a SHA-256 hash operation on a 256-bit initial key K;

[0033] Preheat the chaotic system by using the last iteration result of the chaotic system as a valid initial state;

[0034] Based on the preheated chaotic system, a key stream is generated, and a mitigated state value is obtained based on the effective initial state.

[0035] Construct a position permutation table P and a value permutation table Q to implement row permutation, XOR permutation, and position permutation, respectively;

[0036] Permutation of position and value;

[0037] By introducing a preceding byte feedback mechanism, the final ciphertext C is obtained by XORing the result of the feedback iteration with the reverse key stream.

[0038] Preferably, the XOR and position permutation includes: permuting each byte in the compressed data frame D. Corresponding bytes in the key stream Perform an XOR operation, then place the result in a new position according to the position permutation table P to obtain the intermediate result. Then, round keys were introduced. The round key is generated by shifting the initial key K left by 13 bits, and the round key is used to pair... Perform a second XOR operation to obtain the permutation result. .

[0039] Preferably, the data compression and encryption unit further includes configuring the new key according to set conditions. The update is performed using the following formula: ;

[0040] in, Here, t is the hash value of the previous frame's ciphertext, and t is the current timestamp. To update the previous key.

[0041] The beneficial effects of this invention are as follows:

[0042] The communication decision module of this invention employs a quantum genetic algorithm, encoding decision variables with qubits and generating a lightweight scheduling strategy through population iteration. This avoids the excessive computational demands of complex optimization algorithms on the device side and is compatible with low-power microprocessors. Simultaneously, it dynamically adjusts the acquisition frequency based on the needs of edge nodes, reducing invalid data acquisition and lowering device energy consumption. The channel state prediction module trains the model by maximizing the average reward value and combines an attention-based LSTM model to achieve accurate prediction. The data transmission module dynamically adjusts the compression and encryption strategy according to the channel state, ensuring security while reducing transmission energy consumption and avoiding energy inversion.

[0043] Furthermore, while ensuring the accuracy of state estimation, the channel monitoring power consumption is controlled within the power consumption range of the device to solve the energy consumption problem. This is achieved by binding the channel state and the chaotic system together, and by using CSI to restrict the generation of the key, thereby obtaining the corresponding key and reducing the need for additional energy consumption for the key.

[0044] By working together, the system improves data transmission security and efficiency while simplifying model integration, increasing device operating speed, and meeting the low energy consumption and high reliability requirements of IoT devices. Attached Figure Description

[0045] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0046] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0047] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0048] Please see Figure 1 This embodiment provides a 5G communication module data transmission system for the Internet of Things, including a communication decision module, a channel state prediction module and a data transmission module connected in sequence.

[0049] The communication decision module uses a quantum genetic algorithm to generate communication scheduling instructions based on the needs of edge nodes. The quantum genetic algorithm uses qubits to encode decision variables. The communication scheduling instructions include communication time, acquisition frequency, data volume and power. The acquisition end is controlled according to the communication scheduling instructions.

[0050] The communication decision-making module achieves precise scheduling of IoT device acquisition terminals through iterative optimization using a quantum genetic algorithm. The specific process includes:

[0051] S1: Obtain the demand instructions periodically sent by the IoT edge nodes. The demand instructions include data priority (high / medium / low levels), maximum allowable latency, data type identifier, and the current load rate of the edge nodes.

[0052] The instructions adopt a periodic push mechanism, and the push period is dynamically adjusted according to the device type;

[0053] High-priority devices correspond to low sampling frequency cycles, while medium- and low-priority devices correspond to high sampling cycles. Instructions are transmitted through a dedicated signaling format of the 5G control channel, with an accompanying CRC checksum to ensure instruction integrity.

[0054] S2: Encode the four decision variables—communication time, acquisition frequency, data volume, and power—using qubits. Each variable corresponds to a set of qubits, forming a quantum chromosome, specifically including:

[0055] Communication timing: Encoded with 16 qubits, covering a time range of 0-65535ms, quantum state. Probability distribution at corresponding time points;

[0056] Acquisition frequency: 8-bit qubit encoding is used, and the quantum measurement results are converted into continuous values ​​from 0.1Hz to 100Hz through linear mapping. The mapping formula here is: Where f is the sampling frequency, For quantum measurement results;

[0057] Data volume: 24-bit quantum bit encoding, supporting discrete values ​​from 0 to 4GB, using a segmented encoding method: the first 8 bits represent the number of data blocks (0-255 blocks), and the last 16 bits represent the size of a single block (0-65535KB).

[0058] Transmission power: Encoded with 16-bit qubits, mapped to power values ​​of 0.1W-5W;

[0059] The qubits of the four variables are connected in series to form a 64-bit quantum chromosome. The chromosome structure is [communication time (16 bits) + acquisition frequency (8 bits) + data volume (24 bits) + power (16 bits)].

[0060] S3: Randomly generate multiple quantum chromosomes and superimpose the qubits of each chromosome;

[0061] The population size is set at 50 quantum chromosomes, and a uniform distribution is used for initialization: each quantum bit... and The value is generated using a random function and satisfies , and These are used to represent the ratio of the two probability amplitudes of a quantum state, reflecting the quantum state in different ground states. probability distribution relationship

[0062] Quantum superposition control: For high-priority devices, the initial superposition variance is less than 0.2 (concentrated near the better solution).

[0063] The chromosome variance of low-priority devices is greater than or equal to 0.5 (expand the search range);

[0064] After the population is generated, its validity is verified, and chromosomes with power values ​​exceeding the device hardware limit are removed.

[0065] S4: Measure each quantum chromosome to obtain a definite binary solution and convert it into actual decision parameter values;

[0066] Perform a collapse measurement on each quantum chromosome: based on the qubits The classic state, with a probability of 0 or 1, generates a 64-bit binary string, which is then segmented and converted into actual parameters.

[0067] Communication time: The 16-bit binary value is directly converted to decimal milliseconds;

[0068] Acquisition frequency / power: calculated using the mapping formula in step S2;

[0069] Data volume: Total computation capacity = Number of blocks The block size is set to the maximum value of 4GB if it exceeds 4GB. The converted parameters are stored in the decision parameter matrix, which is set to 50×4.

[0070] S5: Apply a rotation gate to the qubit to adjust its phase according to the fitness function and perform a mutation operation on the qubit. The rotation angle is determined using the corresponding adjustment strategy.

[0071] The fitness function aims to minimize energy consumption while meeting latency requirements, and incorporates channel utilization and data integrity constraints. The specific formula is as follows:

[0072] ;

[0073] Where E is the energy consumption for the current decision-making process. P is the transmission power, and t is the transmission duration.

[0074] The maximum allowable energy consumption of this device. The maximum time delay required for the edge point. Let U be the actual transmission delay for the current decision, and U be the channel utilization rate, which is the ratio of the actual transmission rate to the maximum channel rate. For the target channel utilization, the weighting coefficients satisfy: The ratio of the coefficients is dynamically adjusted according to the data priority;

[0075] S6: When the continuous fitness change rate is lower than the set fitness threshold, output the decision parameters corresponding to the optimal quantum chromosome, and generate communication scheduling instructions based on the decision parameters;

[0076] S7: Communication scheduling commands are sent to the IoT device acquisition terminal through the 5G control channel to control the device acquisition terminal, including determining convergence when the fitness change rate is lower than the set threshold for 5 consecutive generations; Screening of the optimal quantum chromosome: Select the three individuals with the highest fitness in the last generation and determine the final decision parameters through secondary measurement;

[0077] Furthermore, all generated communication scheduling instructions contain parameter check codes, which are generated by encrypting the parameter combinations using the SHA-256 algorithm.

[0078] The channel state prediction module is used to train a channel state prediction model based on historical communication data and historical state data with the aim of maximizing the average reward value, and to predict the channel state based on the real-time state of data transmission. The channel state indicators include channel redundancy, SINR, CSI and bit error rate.

[0079] To enhance the feature extraction capability for key time nodes, the channel state prediction model adopts an LSTM model with an added attention mechanism.

[0080] The overall architecture of the channel prediction model is a five-level structure: input layer - embedding layer - LSTM coding layer - attention layer - fully connected output layer. The input layer receives preprocessed time-series data (including channel redundancy, SINR, CSI, and bit error rate). The output layer generates predicted channel state parameters for a certain time in the future using a softmax activation function (preferably set to 1 sampling point per second, for a total of 20 predicted values). The output format is a 4×5 matrix (4 parameters, 5 time steps).

[0081] Setting up an attention mechanism:

[0082] Step 1: LSTM Hidden State Extraction:

[0083] The LSTM encoding layer processes the input sequence (of length 100) to generate the hidden state at each time step t. , This forms the hidden state matrix H. ;

[0084] Step 2: Calculate attention weights:

[0085] Introducing a learnable weight matrix and bias vector This is used to perform a linear transformation on the hidden state: the result of the linear transformation is calculated as follows: ;

[0086] Through context vectors Calculate attention score , , The attention score used to represent time step t reflects the importance of the hidden state at that time step;

[0087] Then, the attention weights at time step t are obtained by normalization using the softmax function. , ;

[0088] Let be the attention weight at time step t, with a value ranging from 0 to 1, and the sum of the attention weights at all time steps is 1. This is achieved by calculating the attention weights of the hidden states at different time steps. This allows the model to automatically focus on information at time points that are more relevant to the current prediction task. In channel state prediction, the attention weight for critical time points where the channel state changes drastically is significantly increased, thereby enhancing the model's ability to extract key features and improving prediction accuracy.

[0089] Step 3: Sum the attention weights and the hidden state using a weighted average to generate the context vector c: where the calculation formula is: ;

[0090] Because the hidden states at different time steps have varying importance for prediction, the context vector c can integrate important information from different time steps, avoiding the information loss problem that may occur when traditional LSTM relies solely on the hidden state of the last time step. By adjusting the attention weights, the context vector can highlight information from key time nodes, providing more valuable input for subsequent predictions and further improving the model's predictive performance.

[0091] Step 4: For scenarios involving sudden changes in channel state (reference setting: SINR drops by more than 10dB), introduce a gating mechanism to correct the weights. Specifically: ;

[0092] in, The attention weights are dynamically adjusted for time step t. The gate control value is determined by the hidden state. It is calculated from the context vector c, and the calculation formula is: , This is the learnable weight matrix at this location. For the bias vector at this point, when the gate value is detected... When the value approaches 1, the attention weight at the moment of abrupt change is enhanced. By setting a gating mechanism, the channel state prediction model can dynamically adjust the attention weight according to the changes in the channel state. When the channel state changes abruptly, the attention weight at the moment of abrupt change can be enhanced in a timely manner, so that the model pays more attention to these key abrupt change information, thereby improving the model's adaptability and prediction accuracy in complex and variable channel environments.

[0093] Step 5: Model Output:

[0094] Therefore, the output layer combines the context vector c with the LSTM hidden state from the last time step. The input sequence is concatenated and the predicted value is output through a fully connected layer. The attention weights change dynamically with the input sequence, extracting key features at time points where the channel state fluctuates drastically.

[0095] Finally, the prediction results of the channel prediction model are sent to the data transmission module in JSON format as the basis for data compression and transmission optimization.

[0096] In constructing the channel prediction model, an average reward function is used to evaluate the model's long-term performance in multi-round prediction tasks. First, the reward value and penalty term for each round are calculated separately. Then, they are weighted and summed using weighting coefficients to obtain the total reward value for each round. Finally, the average of the total reward values ​​across multiple rounds is taken to obtain the average reward function, expressed as:

[0097] ;

[0098] Where K is the total number of prediction rounds, and k is the k-th round. These are weighting coefficients used to adjust the importance of each indicator in the total reward. The default values ​​are 0.4, 0.3, 0.2, and 0.1; specifically, they include:

[0099] This is a precision bonus, used to measure the deviation between the predicted value and the actual value in the k-th round.

[0100] ;

[0101] Let be the predicted value of the i-th channel parameter at time step t in round k. This represents the actual value of the i-th channel parameter at time step t in the k-th round. and These are the maximum and minimum values ​​of the i-th channel parameter, respectively, used to normalize the deviation; when the prediction is completely correct, The larger the error, the closer the reward value is to 0;

[0102] The trend reward is used to measure the consistency between the predicted trend and the actual trend. A trend sign function is defined for each parameter i and time step t.

[0103] ;

[0104] ;

[0105] ;

[0106] in, This is an indicator function that takes the value 1 when the condition in parentheses is true and 0 otherwise. By maximizing the trend reward, the model can better predict the direction of changes in the channel state.

[0107] This is a robustness reward term used to measure the model's resistance to noise;

[0108] ;

[0109] The predicted value after adding Gaussian noise (mean 0, variance 0.01) to the input. The closer this value is to 1, the stronger the model's robustness.

[0110] This is a penalty term used to constrain the occurrence of fatal errors. For key parameters such as bit error rate, when the deviation between the predicted value and the actual value exceeds a set threshold, the penalty term is triggered. The threshold is specifically defined based on the actual difference between the four output predicted values ​​and the true values.

[0111] Average reward value The value of is limited to 0-1. When the model has high prediction accuracy, accurate trend judgment, and strong robustness, the average reward value approaches 1. By maximizing the average reward value function, the channel state prediction model can balance various performance indicators in complex channel environments, especially ensuring its prediction capability in critical scenarios such as sudden changes in SINR and CSI fluctuations.

[0112] The data transmission module includes:

[0113] The data compression and encryption unit performs compression and encryption operations on the data collected by IoT devices before transmission based on the predicted channel state and the improved LZ77 algorithm.

[0114] During the compression phase, the data compression and encryption unit uses an improved LZ77 algorithm to process the raw data collected by IoT devices. Compared to the traditional LZ77 algorithm, the improved version has made targeted optimizations in the sliding window design and matching mechanism. The size of the sliding window is no longer fixed, but dynamically adjusted according to the predicted channel redundancy: that is, the window size is set with multiple levels.

[0115] When channel redundancy is sufficient, real-time performance is prioritized to reduce compression time, and the minimum window length is used. When channel redundancy is moderate, the window length is adjusted to a moderate level to achieve a balance between compression efficiency and time consumption. When channel redundancy is tight, the window length is expanded to the maximum level to improve the compression rate by matching a wider range of historical data.

[0116] During the matching process, the algorithm introduces a two-layer hash table mechanism: the first-layer hash table records the starting position of all 3-byte strings within the window, while the second-layer hash table establishes a separate index for frequently occurring long strings. This design can quickly locate duplicate data blocks, avoiding the inefficiency of byte-by-byte comparison in traditional algorithms. For example, in periodic temperature data collected by industrial sensors, the algorithm can directly match the continuously repeating sequence "25.3℃, 25.4℃" through the hash table, replacing it with a pointer marker "(offset: 120, length: 16)" to improve the compression ratio;

[0117] After compression, the data is encrypted.

[0118] The data compression and encryption unit further includes generating a key stream using an improved chaotic encryption algorithm combined with permutation operations, performing multiple chaotic iterations by XORing the key stream and the compressed data frame by bytes, and transmitting the ciphertext data stream. Specific steps include:

[0119] In the initialization phase of the chaotic system, the three-dimensional coupled tent mapping is selected as the basic chaotic model.

[0120] The three-dimensional coupled tent exhibits complex nonlinear dynamics through three mutually coupled state variables x, y, and z. The update of each state variable depends not only on its previous value but also on the influence of other state variables. This coupling significantly enhances the system's chaotic characteristics and resilience. (System control parameters) All values ​​are set within the range of 1-2, with an initial default value of 1.8, and the coupling coefficient λ is set to 0.05 to ensure that the system is in a completely chaotic state.

[0121] initial state It is generated from a 256-bit initial key K using a SHA-256 hash operation. The hash operation converts the key into a fixed-length hash value, which is then split into three 85-bit binary numbers. Then divide by each This yields three initial state values ​​within the 0-1 range.

[0122] To avoid potential periodicity issues in the initial state, the system performs 1000 iterations for warm-up. The results of the first 999 iterations are discarded, and only the result of the last iteration is retained as a valid initial state for subsequent encryption processes.

[0123] Generate a keystream N based on the preheated chaotic state, with the same length as the compressed data frame. For each byte in the keystream... Each of these processes requires three iterations of the chaotic system to obtain the state variables at that point. Add these three state variables together, multiply by 256, take the integer part, and then take the modulo 256 to obtain the mixed state value. .

[0124] To further enhance the randomness of the key stream, Perform nonlinear transformation, i.e. and The result of right-shifting by 4 bits is XORed, then modulo 256 is taken to finally obtain the bytes in the keystream. .

[0125] Simultaneously, a dynamic permutation table is constructed, including a position permutation table P and a value permutation table Q. The position permutation table P is based on the chaotic state. Generate a random permutation from 0 to L-1.

[0126] The output state values ​​of the chaotic system are the sequence elements generated by chaotic iteration. These elements serve as the random source for constructing the permutation table, reflecting chaotic characteristics. 0 to L-1 represents the index range of the permutation table, and j is the index variable. The process iterates from 0 to L-1.

[0127] And it satisfies: ; Ensure that the data is randomly shuffled in location without any discernible pattern. This is the formula for chaotic state transformation. This represents the 1000+jth state value in the chaotic sequence, meaning the first 1000 initial values ​​are skipped to avoid the influence of chaotic transients and ensure randomness.

[0128] ,right After sorting the sequence, take the "index position" of the element in the original sequence; if... After sorting, it becomes The corresponding index number is ;

[0129] The value permutation table Q is based on Generate byte mapping relationships, This is another set of state values ​​for a chaotic system, which needs to be distinguished from position permutation, and is used to achieve random replacement of data values.

[0130] Multi-round iterative encryption is the core step in ensuring encryption strength. The first round involves XOR and positional permutation, which applies this method to each byte in the compressed data frame D. Corresponding bytes in the key stream Perform an XOR operation, then place the result in a new position according to the position permutation table P to obtain the intermediate result. Then, the round key is introduced. It is generated by left-shifting the initial key K by 13 bits, using round key pairs. Perform a second XOR operation to obtain the permutation result. .

[0131] The second round involves value substitution and chaotic perturbation. Each byte in the result is permuted using the value permutation table Q, resulting in... Combined with chaotic state Perturb and generate .

[0132] The third round is feedback iteration and final encryption, which introduces a preceding byte feedback mechanism, that is, the encryption result of the current byte depends on the encryption result of the previous byte. Finally, the reverse key stream is XORed with the result of the feedback iteration to obtain the final ciphertext C.

[0133] In terms of ciphertext verification and key update, a CRC32 check value is calculated for the ciphertext data stream C and embedded into the ciphertext header to form the final transmitted data, so as to ensure that the data is not tampered with during transmission.

[0134] Meanwhile, to enhance the security of long-term encryption, the data compression and encryption unit also includes updating the initial key. The new key is generated by performing a SHA-256 hash operation on the old key, the hash value of the previous frame of ciphertext, the current timestamp, and the CSI update frequency, thus enabling dynamic key changes.

[0135] The updated formula is: ;

[0136] in, The hash value of the previous frame's ciphertext, t is the current timestamp; the new key. The update settings are: synchronize with the CSI update frequency, or automatically update the initial key after encrypting 1024 frames of data.

[0137] The data compression and encryption unit also employs an improved chaotic encryption algorithm combined with multiple permutation operations, significantly enhancing the encryption strength. The complex dynamics of the three-dimensional coupled tent map make the state of the chaotic system unpredictable. The generation of the keystream and the data encryption process are closely dependent on subtle changes in the chaotic state; even minute differences in the initial key can lead to drastically different encryption results, effectively resisting common attack methods such as brute-force and differential attacks. The introduction of a dynamic permutation table ensures that the data is thoroughly shuffled in both position and value dimensions, further enhancing the randomness of the ciphertext and making it difficult for attackers to extract patterns from the original data. The dynamic key update mechanism avoids the security risks associated with using the same key for extended periods. Even if the key is leaked at some point, it will not affect the security of subsequent data, providing continuous security for the system.

[0138] In terms of adaptability, the data compression and encryption unit integrates well into the entire data transmission system. Working in conjunction with the data compression unit, it encrypts compressed data, ensuring both data transmission efficiency and security. During encryption, the key stream length is equal to the compressed data frame length, avoiding excessive data additions and meeting the data transmission rate and bandwidth requirements of 5G communication environments. Furthermore, the encryption unit's computational complexity is moderate, enabling efficient operation within the hardware resource constraints of IoT devices without significantly impacting the real-time performance of the overall system.

[0139] From a reliability perspective, the ciphertext verification mechanism ensures the integrity of data during transmission. The CRC32 checksum can promptly detect errors caused by channel interference during data transmission, facilitating data retransmission or error correction at the receiving end and improving the overall reliability of the data transmission system. Furthermore, the stability of chaotic systems and the maturity of encryption algorithms guarantee the consistency and repeatability of the encryption process, enabling the receiving end to accurately decrypt the data and ensuring its proper reception and processing.

[0140] The data optimization unit performs secondary compression on the encrypted ciphertext data using the Huffman algorithm to further reduce the data volume and improve channel utilization. This process aims to maximize the compression ratio without compromising ciphertext security. Specifically:

[0141] The core of the Huffman algorithm is to assign short codes to frequently occurring bytes and long codes to infrequent bytes. However, the encrypted ciphertext exhibits an approximately random distribution, making the traditional Huffman algorithm ineffective. To address this, the data optimization unit employs a "block statistics + dynamic encoding table" strategy: the encrypted data frame is divided into 1KB blocks, and the frequency of bytes appearing in each block is independently counted.

[0142] For bytes whose frequency exceeds the set threshold, they are directly included in the encoding table; for low-frequency bytes, statistical efficiency is improved by merging similar items (such as treating three consecutive low-frequency bytes as a composite symbol).

[0143] The construction process of the encoding table is linked to the channel state: when the channel redundancy is high, a fixed encoding table (pre-generated based on historical ciphertext statistics) is used to reduce computation time; when the channel redundancy is low, a real-time encoding table is enabled. Although the computational load increases, it can more accurately match the current data distribution and improve the compression ratio. The encoding table itself is stored using a Huffman tree serialization method to reduce byte usage.

[0144] The secondary compression process consists of three steps: First, the encrypted data undergoes integrity verification to ensure the ciphertext has not been tampered with; then, frequency statistics are performed according to the block-based strategy to generate an encoding table; finally, each block of data is Huffman encoded, and an index of the encoding table for that block is appended to the end of the block. Before decryption, the receiving end must first use the index to call the corresponding encoding table to complete the decoding before performing the decryption operation.

[0145] It is worth noting that the secondary compression process strictly controls compression time, using hardware acceleration modules (such as dedicated Huffman coding IP cores) to keep the processing time of a single frame within 1ms, ensuring that the real-time performance of data transmission is not affected. Actual tests show that in typical IoT scenarios, secondary compression can further reduce the amount of data by 15% to 20%. Combined with the previous LZ77 compression, this improves the overall compression ratio and significantly reduces the demand for channel bandwidth.

[0146] This invention's technical solution utilizes a lightweight quantum genetic algorithm and dynamic acquisition strategy to enable low-power devices to achieve adaptive communication without running complex algorithms, reducing computational power consumption and solving the energy inversion problem. Furthermore, through the collaborative work of various modules, it improves data transmission security and efficiency while simplifying model integration and increasing device operating speed. Moreover, while ensuring state estimation accuracy, it controls channel monitoring power consumption within the device's power consumption range, addressing the energy consumption issue. By binding the channel state to the chaotic system and using CSI to restrict key generation, the invention further reduces the need for additional energy consumption for key generation.

[0147] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A 5G communication module data transmission system for the Internet of Things, characterized in that: It includes a communication decision module, a channel state prediction module, and a data transmission module that are connected in sequence. The communication decision module generates communication scheduling instructions using a quantum genetic algorithm based on the needs of the edge nodes. The quantum genetic algorithm uses qubits to encode decision variables. The communication scheduling instructions include communication time, acquisition frequency, data volume, and power. The acquisition end is controlled according to the communication scheduling instructions. The channel state prediction module is used to train a channel state prediction model based on historical communication data and historical state data with the training objective of maximizing the average reward value, and to predict the channel state based on the real-time state of data transmission. The channel state indicators include channel redundancy, SINR, CSI and bit error rate. The data transmission module includes: The data compression and encryption unit performs compression and encryption operations on the data collected by IoT devices before transmission based on the predicted channel state and the improved LZ77 algorithm. The data optimization unit performs secondary compression on the encrypted data frame using the Huffman algorithm before transmission.

2. The 5G communication module data transmission system for the Internet of Things according to claim 1, characterized in that: The execution steps of the communication decision module include: S1: Obtain the demand instructions periodically sent by the edge node. The demand instructions include data priority, maximum allowable latency, data type identifier, and current load rate of the edge node. S2: Quantum bit encoding: The four decision variables of communication time, acquisition frequency, data volume and power are encoded using quantum bits. Each variable corresponds to a set of quantum bits, forming a quantum chromosome. S3: Generate the initial population: Randomly generate multiple quantum chromosomes and superimpose the qubits of each chromosome; S4: Measure each quantum chromosome to obtain a definite binary solution and convert it into actual decision parameter values; S5: Apply a rotation gate to the qubit to adjust its phase according to the adaptive degree function and perform a mutation operation on the qubit. The rotation angle is determined using the corresponding adjustment strategy. S6: When the continuous fitness change rate is lower than the set fitness threshold, output the decision parameters corresponding to the optimal quantum chromosome, and generate communication scheduling instructions based on the decision parameters; S7: Communication scheduling commands are sent to the IoT device acquisition terminal via the 5G control channel to control the device acquisition terminal.

3. A 5G communication module data transmission system for the Internet of Things according to claim 2, characterized in that: The fitness function is calculated using the following formula: ； Where E represents the energy consumption for the current decision-making process. P is the transmission power, and t is the transmission duration. This represents the maximum energy consumption allowed for this edge node device. The maximum latency required by the edge point device. U represents the actual transmission delay for the current decision, and U represents the current channel utilization. For the target channel utilization, the weighting coefficients satisfy: .

4. A 5G communication module data transmission system for the Internet of Things according to claim 2, characterized in that: The channel state prediction model includes optimizing the output of the model using an average reward function, the formula for which the average reward function is calculated is: ； Where K is the total number of prediction rounds, and k is the k-th round. These are weighting coefficients used to adjust the importance of each indicator in the total reward, and , This is a trend reward metric used to measure the consistency between predicted and actual trends. This is a robustness reward term used to measure the model's resistance to noise. This is a penalty term used to constrain the occurrence of fatal errors. For key parameters such as bit error rate, the penalty term is triggered when the deviation between the predicted value and the actual value exceeds a set threshold.

5. A 5G communication module data transmission system for the Internet of Things according to claim 1, characterized in that: The formula for calculating the accuracy bonus is: ； in, The predicted value of the i-th channel parameter at time step t in round k. This represents the actual value of the i-th channel parameter at time step t in the k-th round. and These are the maximum and minimum values ​​of the i-th channel parameter, respectively, used to normalize the deviation.

6. A 5G communication module data transmission system for the Internet of Things according to claim 1, characterized in that: The data compression and encryption unit further includes generating a key stream using an improved chaotic encryption algorithm combined with a permutation operation, generating a ciphertext data stream by performing multiple chaotic iterations of byte-by-byte XOR operations on the key stream and the compressed data frame, and transmitting the ciphertext data stream.

7. A 5G communication module data transmission system for the Internet of Things according to claim 6, characterized in that: The specific steps of the data compression and encryption unit for encryption include: Initialize the chaotic system, using a three-dimensional coupled tent map as the basic chaotic model, with the initial state... It is generated by performing a SHA-256 hash operation on a 256-bit initial key K; Preheat the chaotic system by using the last iteration result of the chaotic system as a valid initial state; Based on the preheated chaotic system, a key stream is generated, and a mitigated state value is obtained based on the effective initial state. Construct a position permutation table P and a value permutation table Q to implement row permutation, XOR permutation, and position permutation, respectively; Permutation of position and value; By introducing a preceding byte feedback mechanism, the final ciphertext C is obtained by XORing the result of the feedback iteration with the reverse key stream.

8. A 5G communication module data transmission system for the Internet of Things according to claim 7, characterized in that: The XOR and position permutation includes: per byte in the compressed data frame D. Corresponding bytes in the key stream Perform an XOR operation, then place the result in a new position according to the position permutation table P to obtain the intermediate result. Then, round keys were introduced. The round key is generated by shifting the initial key K left by 13 bits. Perform a second XOR operation to obtain the permutation result. .

9. A 5G communication module data transmission system for the Internet of Things according to claim 8, characterized in that: The data compression and encryption unit further includes configuring the new key according to set conditions. The update is performed using the following formula: ; in, Here, t is the hash value of the previous frame's ciphertext, and t is the current timestamp. To update the previous key.

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