MIMO-AFDM-IM all-sensing integrated signal transmission method and system based on multi-layer encryption

Abstract

The application discloses a MIMO-AFDM-IM communication integrated signal transmission method and system based on multi-layer encryption. The method comprises the following steps: an AFDM-IM communication transmitting end transmits a plurality of probe symbols to an AFDM-IM communication receiving end to obtain a plurality of echo signals; a communication parameter combination is calculated according to the plurality of echo signals; a to-be-sent signal is obtained; the to-be-sent signal is encrypted and modulated to obtain an encrypted sending signal and a key group; the encrypted sending signal is sent to the AFDM-IM communication receiving end through the AFDM-IM communication transmitting end according to the communication parameter combination; a received signal received by the AFDM-IM communication receiving end is obtained; the received signal is decrypted and demodulated according to the communication parameter combination and the key group to obtain decrypted information. The method has the characteristics of low calculation complexity, high reliability, fast transmission speed and strong confidentiality.

Description

Technical Field

[0001] This invention relates to the field of radio frequency multiplexing, and more specifically, to a method and system for integrated MIMO-AFDM-IM sensing signal transmission based on multi-layer encryption. Background Technology

[0002] With the research and evolution of 6G mobile communication technology, the contradiction between scarce spectrum resources and increased hardware power consumption has become increasingly prominent. Traditional communication systems and radar sensing systems are usually deployed independently, resulting in serious resource waste. Therefore, integrated sensing and communication (ISAC) technology has become a trend. By sharing spectrum and hardware architecture, it can realize the perception of environmental targets while transmitting data, providing basic support for future applications such as intelligent transportation and low-altitude economy. In dynamic scenarios, traditional waveforms face many limitations. With the time-varying channel characteristics brought about by mobility, especially the significant impact of the Doppler frequency shift effect, traditional orthogonal frequency division multiplexing (OFDM) technology is very sensitive to frequency deviation. The orthogonality between subcarriers is easily destroyed in dynamic environments, leading to increased bit error rate and reduced sensing accuracy, failing to meet the requirements of high-reliability transmission. To address the challenges of dynamic channels, affine radio frequency division multiplexing (AFDM) has been proposed as a new type of multi-carrier waveform. AFDM uses discrete affine Fourier transform (DAFT) and chirp signals as carriers to provide a full-delay-Doppler representation of the channel. Compared to the structurally complex OTFS technology, AFDM not only achieves full diversity gain under dual-channel selection, but also has more flexible parameter configuration capabilities, making it a potential waveform for realizing integrated communication and sensing.

[0003] The broadcast nature of wireless channels makes MIMO communication links highly vulnerable to eavesdropping. Traditional upper-layer encryption mechanisms face challenges in key distribution and computational overhead when dealing with massive connections and low latency requirements. While utilizing physical layer features for secure transmission is a current research hotspot, few schemes currently combine AFDM waveform parameters (such as...) The mapping mechanism between multiple input multiple output (MIMO) and indexed modulation (IM) constructs a multi-layered intrinsic encryption protection system. To further improve system reliability, combining multiple input multiple output (MIMO) and indexed modulation (IM) technologies has become an effective approach. However, in MIMO-AFDM-IM systems, the receiver not only needs to perform equalization and detection of multiple antenna signals but also needs to synchronously recover indexed modulation information and related encryption parameters, resulting in highly coupled and high-dimensional signal processing. Traditional maximum likelihood (ML) detection algorithms have excessively high computational complexity, making them difficult to apply directly to practical systems; while relying solely on low-complexity detection methods makes it difficult to balance the reliability of index decryption and symbol detection under strong interference and encryption conditions. In summary, MIMO-AFDM-IM systems suffer from high computational complexity, low reliability, slow transmission speed, and poor confidentiality.

[0004] Existing technology discloses a signal transmission method and apparatus based on orthogonal matrix precoding. The method includes: generating four chaotic sequences X, Y, Z, and W using a four-dimensional chaotic model based on an initial key value; bit-interleaving the original data using X and W to obtain first data; then performing serial-to-parallel conversion and QPSK mapping to obtain a mapped signal; scrambling the subcarriers and symbols of the mapped signal using Y and Z to obtain encrypted data; performing IFFT, adding a cyclic prefix, and parallel-to-serial conversion on the encrypted data to obtain an encrypted data stream; performing binary conversion and serial-to-parallel conversion on the initial key value to obtain a square matrix K, and shaping the square matrix K using a public key to obtain a key matrix; performing serial-to-parallel conversion, QPSK mapping, IFFT, adding a cyclic prefix, and parallel-to-serial conversion on the key matrix to obtain a key stream; and linearly superimposing the encrypted data stream and key stream through power allocation to obtain a power-division multiplexed orthogonal frequency division multiplexed signal, which is then transmitted. This method is based on traditional orthogonal frequency division multiplexing and is not suitable for the field of simulated radio frequency division multiplexing. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies, such as high computational complexity, low reliability, slow transmission speed, and poor confidentiality, by providing a MIMO-AFDM-IM integrated sensing signal transmission method and system based on multi-layer encryption. This method features low computational complexity, high reliability, fast transmission speed, and strong confidentiality.

[0006] The primary objective of this invention is to solve the aforementioned technical problems. The technical solution of this invention is as follows: A multi-layer encrypted MIMO-AFDM-IM integrated sensing signal transmission method, characterized by comprising: S1: The AFDM-IM communication transmitter sends multiple probe symbols to the AFDM-IM communication receiver, and obtains multiple echo signals; S2: Calculate the combination of communication parameters based on the multiple echo signals; S3: Obtain the signal to be sent; S4: Encrypt and modulate the signal to be transmitted to obtain an encrypted transmission signal and a key group; S5: Based on the combination of communication parameters, the encrypted transmission signal is sent to the AFDM-IM communication receiver through the AFDM-IM communication transmitter; S6: Obtain the received signal from the AFDM-IM communication receiver; S7: Decrypt and demodulate the received signal according to the communication parameter combination and key group to obtain decrypted information.

[0007] Further, in step S2, the communication parameter combination is calculated, including: S201: Construct a sensing fast lift matrix based on the multiple echo signals; S202: Calculate the communication parameter combination based on the sensing fast-up matrix.

[0008] Furthermore, in step S201, the formula for constructing the sensing fast-lift matrix is ​​as follows:

[0009]

[0010] Indicates the response of a noiseless system. Indicates the position of environmental reflectors during transmission. This represents path-based channel gain. Indicates user status. This represents the total number of paths, where 'l' represents the path number. This represents the path complex gain of the l-th path. , This represents the ULA steering vector of the uniform array. This represents the angle of arrival for the l-th path. Let represent the emission angle of the l-th path, j represent the complex field, and m represent the AFDM symbol number. This represents the Doppler angular frequency of the l-th path. Indicates the symbol period, This represents the sensing symbol matrix corresponding to the m-th AFDM sensing symbol. Represents the time delay phase rotation matrix. This represents the delay of the l-th path. Represents the perceptual fast lift matrix, This represents the additive noise matrix.

[0011] Further, in step S4, the encryption and modulation include: S401: Calculate a key group using a chaotic sequence; the key group includes a first key and a second key; S402: Encrypt the index bits of the signal to be transmitted using the first key to obtain the set of active positions; S403: Associate and concatenate the information bits in the signal to be sent with the set of active positions to obtain the first encrypted signal; S404: Construct the encrypted AFDM transformation matrix based on the second key; S405: Perform IDAFT operation based on the AFDM transform matrix and the first encrypted signal to obtain the second encrypted signal; S406: Add a prefix to the second encrypted signal to form an encrypted transmission signal.

[0012] Further, in step S401, the key group is calculated using a chaotic sequence, including:

[0013]

[0014] This represents the first key in the key group. This represents the second key in the key group. This indicates modulo operation, and floor indicates rounding down. Represents a chaotic sequence. Indicates the AFDM-IM sub-block number. , This represents the offset, and m represents the AFDM symbol number. Indicates the quantization length. Indicates the number of index mapping rules. This indicates the size of the pre-chirped parameter codebook.

[0015] Further, in step S404, the formula for constructing the encrypted AFDM transformation matrix based on the second key is as follows:

[0016]

[0017]

[0018] Represents the N-point discrete Fourier transform matrix. Represents the post-chirp matrix. Indicates via second key Encrypted pre-chirped diagonal matrix, Represents the encrypted AFDM transformation matrix. Indicates the second key The mapping, Represents a pre-chirped diagonal matrix. Let j denote a diagonal function, and j denote the complex field. This represents the k-th simulated RF multiplexing parameter, where k represents the simulated RF multiplexing parameter number.

[0019] Furthermore, in step S7, decryption and demodulation include: S701: Remove the prefix from the received signal to obtain the first received signal; S702: Calculate the time-domain equivalent channel matrix based on the communication parameter combination; obtain the AFDM transform matrix based on the key group; S703: Calculate the second received signal based on the time-domain equivalent channel matrix and the AFDM transform matrix; S704: Decrypt the second received signal according to the key group to obtain decrypted information.

[0020] Furthermore, in step S702, the formula for calculating the time-domain equivalent channel matrix is ​​as follows:

[0021] This represents the equivalent path gain, including the spatial beam gain. This represents the cyclic prefix phase compensation matrix. Represents the normalized Doppler parameters. Indicates a delay index. Indicates the path sequence number. Indicates the total number of paths. This represents the forward cyclic shift matrix.

[0022] Furthermore, in step S703, the formula for calculating the second received signal is as follows:

[0023]

[0024] Indicates the second received signal. Represents the DAFT-domain equivalent channel matrix. Let I represent noise power, and let I represent a unit diagonal matrix. Indicates the first received signal. Represents the AFDM transformation matrix. This represents the time-domain equivalent channel matrix.

[0025] A multi-layered encrypted MIMO-AFDM-IM integrated sensing signal transmission system includes: Sensing and transmitting module: The AFDM-IM communication transmitter transmits multiple probe symbols to the AFDM-IM communication receiver, and obtains multiple echo signals; Parameter combination calculation module: Calculates the communication parameter combination based on multiple echo signals; Signal acquisition module: Acquires the signal to be sent; Encryption module: encrypts and modulates the signal to be transmitted to obtain an encrypted transmission signal and a key group; Communication sending module: Based on the combination of communication parameters, the encrypted sending signal is sent to the AFDM-IM communication receiving end through the AFDM-IM communication sending end; Receiver module: Acquires the received signals from the AFDM-IM communication receiver; Decryption module: Decrypts and demodulates the received signal according to the communication parameter combination and key group to obtain decrypted information.

[0026] Compared with the prior art, the beneficial effects of the present invention are: This invention obtains multiple echo signals by sensing, and obtains a combination of communication parameters based on the echo signals. Then, it performs communication based on this combination of communication parameters, realizing a deep integration of communication and sensing functions, which makes the overall solution have the characteristics of high transmission speed.

[0027] This invention encrypts the signal to be transmitted and decrypts the received signal using a key set, introducing physical layer security protection, thereby constructing an efficient multi-layered encrypted transmission mechanism, making the overall solution highly confidential.

[0028] This invention decrypts and demodulates the received signal based on a combination of communication parameters and a key set. While reliably recovering index information and modulation symbols, it effectively alleviates the increased computational complexity caused by the introduction of MIMO and indexed modulation. It provides excellent multi-dimensional sensing capabilities while significantly enhancing the reliability and security of communication. Attached Figure Description

[0029] Figure 1 The flowchart is for the MIMO-AFDM-IM integrated sensing signal transmission method based on multi-layer encryption provided in Example 1.

[0030] Figure 2 This is a flowchart illustrating the multi-layer encrypted MIMO-AFDM-IM integrated sensing signal transmission method provided in Example 1.

[0031] Figure 3 The graph shows a comparison of the BER performance of the multi-layer encryption scheme and the single-layer encryption strategy provided in Example 1 at the legitimate receiving end and the eavesdropping end.

[0032] Figure 4 The graph shows a comparison of the BER performance of encrypted AFDM and OFDM waveforms under ideal channel conditions and channel estimation conditions, as provided in Example 1. Detailed Implementation

[0033] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent. To better illustrate this embodiment, some parts in the accompanying drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions; It will be understood by those skilled in the art that certain well-known structures and their descriptions may be omitted in the accompanying drawings.

[0034] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0035] Example 1 like Figure 1 As shown, the MIMO-AFDM-IM integrated sensing signal transmission method based on multi-layer encryption is characterized by comprising: S1: The AFDM-IM communication transmitter sends multiple probe symbols to the AFDM-IM communication receiver, and obtains multiple echo signals; S2: Calculate the combination of communication parameters based on the multiple echo signals; S3: Obtain the signal to be sent; S4: Encrypt and modulate the signal to be transmitted to obtain an encrypted transmission signal and a key group; S5: Based on the combination of communication parameters, the encrypted transmission signal is sent to the AFDM-IM communication receiver through the AFDM-IM communication transmitter; S6: Obtain the received signal from the AFDM-IM communication receiver; S7: Decrypt and demodulate the received signal according to the communication parameter combination and key group to obtain decrypted information.

[0036] It should be noted that this invention utilizes the transformation characteristics of AFDM waveforms to achieve multi-symbol joint sensing based on the spatiotemporal signal characteristics, and uses the sensing results as prior information for AFDM parameter design, MIMO beamforming and channel equalization in the communication stage, thereby improving link gain and anti-interference capability; at the same time, it introduces chaotic sequence keys to achieve multi-layer security enhancement of index modulation mapping encryption and waveform parameter c2 encryption.

[0037] The system adopts a bistatic multi-antenna architecture, with multiple antenna arrays configured at both the transmitting and receiving ends. The system operation is divided into a sensing phase and a communication phase: In the sensing phase, the transmitting end transmits multi-symbol AFDM probe signals, and the receiving end uses the echo signals to estimate the multi-dimensional parameters of environmental targets; In the communication phase, all antennas at the transmitting end collaboratively transmit the same encrypted data stream, and implement AFDM parameter design and transmit beamforming based on the sensing results, while the receiving end performs beamforming based on the sensing results, thereby obtaining array gain at both the transmitting and receiving ends and compensating for high-frequency transmission path loss, improving the signal-to-noise ratio and anti-interference capability of the communication link.

[0038] The general steps of this invention are as follows: Step 1: Transmission of multi-symbol AFDM sensing signal: The transmitter continuously transmits multiple AFDM probe symbols. Utilizing the transformation characteristics of the AFDM waveform and combining multi-antenna spatial observation information, a probe signal for joint space-time-frequency estimation is constructed, providing observation data for subsequent joint estimation of multi-dimensional parameters.

[0039] Step 2, Multidimensional Parameter Cascade Estimation: Based on the AFDM multi-symbol echo signal received by the multi-antenna array, the receiver sequentially completes the following three-stage environmental parameter estimation: First, the angle of arrival (AOA) of the propagation path is estimated to obtain the target receiving direction; second, the propagation delay is estimated based on this to obtain the target distance information; finally, the departure angle, Doppler frequency shift, and channel gain are jointly estimated by combining the angle of arrival and delay information to reconstruct the multidimensional propagation parameters.

[0040] Step 3, Perception-Assisted Beamforming and Prior Information Acquisition: The transmitting end applies directional beamforming weights to the transmitted signal based on the perceived target departure angle information; the receiving end performs beamforming processing on the received signal based on the perceived target arrival angle information, thereby obtaining array gain at both the transmitting and receiving ends, improving anti-interference capability and enhancing communication link reliability.

[0041] Step 4: Synchronous generation of chaotic sequence keys: The transmitting and receiving parties synchronously run the chaotic mapping model to generate a pseudo-random chaotic sequence based on the pre-shared initial values ​​and control parameters of the chaotic system; the chaotic sequence is divided into at least two parts, which are used for index modulation mapping encryption and waveform parameter encryption c2, respectively, to achieve multi-layer security enhancement.

[0042] Step 5, Index Modulation Mapping Encryption and Symbol Generation: Divide the bit stream of information to be transmitted into index bits and modulation bits; generate a dynamic index mapping relationship based on the chaotic sequence; select the active subcarrier position in each sub-block according to the index bits, and the activation mode changes in real time with the chaotic sequence to realize dynamic encryption of the index field; then map the modulation bits to the modulation symbols on the predetermined constellation diagram and load them into the corresponding active subcarrier positions, while the inactive subcarriers remain at zero.

[0043] Step 6, Waveform Construction and DAFT Parameter Encryption: Another part of the chaotic sequence is used to dynamically perturb and encrypt the core parameters of the Discrete Affine Fourier Transform (DAFT) to enhance system security. Based on the encrypted parameters, an inverse discrete affine Fourier transform is performed on the chirp domain symbol vector to generate the corresponding AFDM time-domain signal.

[0044] Step 7: Adding a cyclic prefix and directional transmission with multiple antennas: The transmitter adds a cyclic prefix to the AFDM time-domain signal to suppress multipath interference, and applies transmit beamforming weights based on the angle information obtained from sensing. High-gain transmission is achieved through directional transmission with multiple antenna arrays, enabling coordinated sensing and communication.

[0045] Step 8, Receive Preprocessing and DAFT Domain Recovery: After receiving the multi-antenna signal, the receiver performs cyclic prefix removal processing and beamforming on the received signal based on the sensed angle of arrival information; then, it uses the locally synchronized chaotic sequence to recover the encrypted transform parameters, performs discrete affine Fourier transform on the received signal, and transforms the signal to the DAFT domain.

[0046] Step 9, Signal Detection and Decryption Recovery: After completing the DAFT domain transformation and recovering the dynamic index mapping relationship under chaotic constraints, the receiving end performs a joint decryption-minimum mean square error maximum likelihood (MMSE-ML) detection algorithm on the received signal to recover the original information bits. This algorithm, based on chaotic sequence constraints, ensures highly reliable signal detection and achieves high-precision data recovery during communication.

[0047] This invention considers a downlink ISAC system for 6G mobile scenarios, at a carrier frequency The following uses a MIMO-AFDM system to achieve integrated communication and sensing. For example... Figure 2 As shown, the system includes a base station (BS) and a vehicle-mounted user terminal (UE). Both the base station and the user terminal are configured with a uniform linear array (ULA), wherein the base station has... Each transmitting antenna, the user terminal has There are 1 receiving antenna, with the spacing between adjacent array elements fixed at half the carrier wavelength. The sampling period is... It should be noted that this invention employs different spatial transmission methods in the sensing and communication phases: In the sensing phase, the base station... Each transmitting antenna can send independent sensing data streams / sensing sequences to enhance the dimensionality of spatial observation; during the communication phase, the base station transmits information data using a single data stream beamforming method, which, under the combined effect of transmitting beam and receiving beam merging, can be equivalent to a single-input single-output (SISO) communication link.

[0048] In this invention, the sensing part adopts AFDM sensing symbols, each symbol consisting of It consists of 1 subcarrier, of which A pre-defined positive integer is used to characterize the time observation length of the sensing processing in the symbol domain, supporting channel estimation and multi-dimensional parameter extraction in the integration of sensing and communication. A single AFDM sensing symbol is represented in discrete baseband by... It consists of 10 sampling points, therefore the symbol period is set to 1. .

[0049] in The sampling period is defined as follows. In one embodiment, if the system inserts a cyclic prefix (CP), the CP is not included in the sensing observation window, and the receiver, after removing the CP, performs a sampling period of length [missing information]. The valid symbols are processed.

[0050] Establish a two-dimensional plane coordinate system, with the base station array normal and array extension direction corresponding to the coordinate axes respectively, and the base station array orientation set to 0°. Base station location. The two-dimensional position of the user terminal is known. Two-dimensional velocity and the attitude angle of the user terminal array relative to the coordinate axes Unknown. For a unified representation, user state is defined as...

[0051] It is the core state variable that both the sensing and communication stages of the integrated sensing system rely on.

[0052] This invention considers mobile scenarios involving multipath propagation. The system contains... There are distinguishable propagation paths, among which Indicates a direct route. This represents a single reflection path caused by an environmental reflector, with the corresponding reflector position denoted as . To balance physical accuracy and complexity, only a single reflection path is considered.

[0053] For the propagation paths (including) (direct path), defining its propagation delay Angle of Arrival (AOA) (Relative to the receiver array normal), transmit angle (AOD) (Relative to the transmitter array normal). From geometric relationships, the time delays of each path satisfy...

[0054] in It is the speed of light.

[0055] Similarly, the angle parameters of each path are related to position / attitude. To ensure unified modeling of communication and sensing, this invention shares the angle parameters as a set of physical channel parameters in both types of processing links. Furthermore, in a two-dimensional planar geometric model, if both the transmitting and receiving arrays use their respective array normals as the angle reference direction, and the influence of the height dimension is ignored, then the departure angle and arrival angle of the direct path satisfy the following:

[0056] Due to the movement of the user terminal, in the... A Doppler frequency shift occurs along this path. Let the first path be... The Doppler angular frequency of the path is The corresponding Doppler frequency shift (Hz) is Its radial velocity along the path. Decision, satisfaction:

[0057] The radial velocity is determined by the user's two-dimensional velocity vector:

[0058] Under spatial domain MIMO conditions, the array response matrices of the receiver and transmitter are defined as follows:

[0059] The guiding vector of the uniform linear array (ULA) can be expressed as:

[0060] Based on a unified set of path parameters During the perception stage, for the first One AFDM sensing symbol, subcarrier index is In this case, the corresponding baseband MIMO frequency domain channel matrix can be expressed as:

[0061] For the first The model characterizes the channel at the subcarrier index. With the index of perceptual symbols The two-dimensional variation characteristics unify time delay, Doppler, and spatial angle parameters into the same frequency domain MIMO channel matrix. In one embodiment, the complex gain of each path... To characterize Rayleigh fading channel features.

[0062] Furthermore, let Indicates the first Within the AFDM symbol period, the th The received vectors at each subcarrier. To jointly utilize the spatial, frequency, and symbol domain structures, all... The received vectors at each subcarrier are stacked to form a sensing snapshot matrix.

[0063] Then the received snapshot satisfies:

[0064] in Represents the additive noise matrix; For the first The perceptual symbol matrix corresponding to each AFDM perceptual symbol satisfies:

[0065] For the first The AFDM symbol, the first The sensing symbol vectors of each subcarrier; wherein, to enhance identifiability and spatial observation dimension, in one embodiment, Different rows (corresponding to different transmit antennas) can carry different sensing sequences / different sensing data streams (which can be mutually orthogonal or low-correlation designs), thereby realizing multi-stream MIMO transmission in the sensing phase. For the time-delay phase rotation matrix:

[0066] Further, in step S2, the communication parameter combination is calculated, including: S201: Construct a sensing fast lift matrix based on the multiple echo signals; S202: Calculate the communication parameter combination based on the sensing fast-up matrix.

[0067] Furthermore, in step S201, the formula for constructing the sensing fast-lift matrix is ​​as follows:

[0068]

[0069] Indicates the response of a noiseless system. Indicates the position of environmental reflectors during transmission. This represents path-based channel gain. Indicates user status. This represents the total number of paths, where 'l' represents the path number. This represents the path complex gain of the l-th path. , This represents the ULA steering vector of the uniform array. This represents the angle of arrival for the l-th path. Let represent the emission angle of the l-th path, j represent the complex field, and m represent the AFDM symbol number. This represents the Doppler angular frequency of the l-th path. Indicates the symbol period, This represents the sensing symbol matrix corresponding to the m-th AFDM sensing symbol. Represents the time delay phase rotation matrix. This represents the delay of the l-th path. Represents the perceptual fast lift matrix, This represents the additive noise matrix.

[0070] Based on this unified model, the sensing side can estimate... This allows for the recovery of user location, two-dimensional velocity, and environmental reflector information; the communication side can complete channel estimation, equalization, and subsequent AFDM-IM data detection and multi-layer decryption processing under the same parameter set.

[0071] From the perspective of communication symbol transmission, in one embodiment, the base station uses a single-data-stream beamforming method for transmission. Let the communication transmission symbol be... (Scalar), the transmitted beam vector is Then the actual transmission vector of the base station is

[0072] The receiver uses a merged vector. After merging, an equivalent scalar received signal is obtained, thus transforming the multi-antenna link into an equivalent SISO form. The above propagation process can be equivalently modeled as a dual-selective fading channel under discrete baseband. Let...

[0073] Let represent the equivalent dual-selective fading channel matrix, and .in Indicates the number of distinguishable propagation paths. In the communication equivalent model, the first The discrete channel gain corresponding to each path is determined by the physical path gain in the sensing model. The mapping yields a one-to-one correspondence between the path index i from the communication perspective and the path index l from the perception perspective in the modeling. and These represent the corresponding discrete time delay and Doppler shift, respectively.

[0074] The forward cyclic shift matrix is ​​used to characterize the cyclic shift effect caused by discrete time delay taps, and is defined as follows:

[0075]

[0076] This is the Doppler modulation matrix, used to characterize the effect of path Doppler frequency shift on communication symbols; The cyclic prefix phase compensation matrix is ​​defined as follows:

[0077] In the above model, path channel gain Follows a complex Gaussian distribution

[0078] To characterize the Rayleigh fading channel properties. Discrete time delay parameters. ,in This represents the maximum resolvable time delay tap of the system. Doppler offset. The normalized representation is as follows:

[0079] in The discrete sampling time interval is taken as [value missing] in this embodiment. , This represents the maximum normalized Doppler shift. The normalized Doppler parameter is a continuous value and is not rounded, used to characterize the fractional-multiple Doppler effect in moving scenes. It should be noted that the "direct path" only indicates that the geometric propagation relationship is direct, without introducing a deterministic line-of-sight component; the complex gain of each path is modeled according to a zero-mean complex Gaussian distribution to characterize Rayleigh fading characteristics.

[0080] It should be noted that, and The meanings are basically the same, with H mainly described from a communication perspective. This is primarily described from a perception perspective. The baseband MIMO frequency domain channel model from a perception perspective and the dual-selective fading channel model from a communication perspective are essentially determined by the same set of physical propagation parameters, and are interconnected through the normalized mapping relationship between time delay and Doppler. Specifically, the continuous propagation delay in the perception model... After bandwidth normalization and rounding, the corresponding discrete delay tap in the communication model The Doppler angular frequency in the perception model After time normalization, the normalized Doppler parameters in the corresponding communication model Furthermore, this parameter remains a continuous value without rounding. Therefore, the dual-selective fading channel can be uniformly represented as a superposition of cyclic shift, Doppler modulation, and phase compensation matrices corresponding to multiple paths. This discrete channel model from a communication perspective, while maintaining the integer delay tap structure, introduces continuously normalized Doppler parameters, thus maintaining consistency with the delay-Doppler parameter modeling from a sensing perspective. This provides a unified channel model foundation for subsequent equalization, detection, and joint decryption processing at the AFDM-IM communication receiver.

[0081] Further, in step S4, the encryption and modulation include: S401: Calculate a key group using a chaotic sequence; the key group includes a first key and a second key; S402: Encrypt the index bits of the signal to be transmitted using the first key to obtain the set of active positions; S403: Associate and concatenate the information bits in the signal to be sent with the set of active positions to obtain the first encrypted signal; S404: Construct the encrypted AFDM transformation matrix based on the second key; S405: Perform IDAFT operation based on the AFDM transform matrix and the first encrypted signal to obtain the second encrypted signal; S406: Add a prefix to the second encrypted signal to form an encrypted transmission signal.

[0082] To simultaneously improve the concealment of the index position of index modulation (IM) and the modulation parameters of AFDM, this invention proposes a multi-layer joint encryption system model of AFDM-IM driven by homologous chaotic sequences. The sender and receiver share a chaotic key.

[0083] in As the initial value, These are the control parameters for chaotic mapping. To quantize length, This refers to the offset (or set of offset parameters). A sequence is generated from the chaotic mapping. Two key streams are derived using different offsets and quantization methods, which are used for dynamic switching of index mapping rules (index field encryption) and the AFDM pre-chirp parameter set, respectively. The dynamic parameter selection (parameter field encryption) forms a cascaded encryption structure of "index encryption first, AFDM transformation encryption second".

[0084] Chaotic sequences satisfy:

[0085] in This can be a chaotic function such as a Logistic map or a Tent map. Two keystreams are constructed based on the sequence: Further, in step S401, the key group is calculated using a chaotic sequence, including:

[0086]

[0087] This represents the first key in the key group. This represents the second key in the key group. This indicates modulo operation, and floor indicates rounding down. Represents a chaotic sequence. Indicates the AFDM-IM sub-block number. , This represents the offset, and m represents the AFDM symbol number. Indicates the quantization length. Indicates the number of index mapping rules. This indicates the size of the pre-chirped parameter codebook.

[0088] In index field encryption, for the first... Each AFDM-IM sub-block has its index bits. Instead of using fixed mapping rules, it uses a key stream. In the rule base

[0089] Select a mapping function to generate a set of activation locations. The constellation symbols are then written into the corresponding activation positions and concatenated to obtain the indexed encrypted affine Fourier domain symbol vector. .

[0090] In AFDM parameter domain encryption, the DAFT transformation matrix is ​​defined. and its inverse matrix ,in

[0091] for Point DFT matrix, and These are the diagonal matrices for post-chirping and pre-chirping, respectively. The pre-chirping matrix... From the parameter set:

[0092] The only certainty.

[0093] In traditional AFDM systems, the prechirp parameter is typically subcarrier independent (and index-independent). A constant that is irrelevant to all have ,thereby

[0094] Pre-chirp matrix From a single constant The only certainty.

[0095] Further, in step S404, the formula for constructing the encrypted AFDM transformation matrix based on the second key is as follows:

[0096]

[0097]

[0098] Represents the N-point discrete Fourier transform matrix. Represents the post-chirp matrix. Indicates via second key Encrypted pre-chirped diagonal matrix, Represents the encrypted AFDM transformation matrix. Indicates the second key The mapping, Represents a pre-chirped diagonal matrix. Let j denote a diagonal function, and j denote the complex field. This represents the k-th simulated RF multiplexing parameter, where k represents the simulated RF multiplexing parameter number.

[0099] Modulation is then performed using encrypted IDAFT operations:

[0100] Legitimate receivers, sharing the same chaotic key, can synchronously reconstruct the parameter set and perform matching DAFT operations, while unauthorized receivers will have difficulty recovering the DAF field symbols due to pre-chirped parameter mismatch.

[0101] Based on the AFDM sensing channel model established by the above method, this invention proposes a multi-dimensional parameter joint estimation method based on AFDM and its communication assistance method. In MIMO scenarios, this method utilizes received observations obtained by the base station within multiple AFDM sensing symbol periods to jointly estimate the angle of arrival (AOA), propagation delay, Doppler / radial velocity, angle of departure (AOD), and path complex gain of the propagation path. The sensing results are further used for channel equalization and transmit / receive beamforming of the communication link.

[0102] In one embodiment, the base station is continuously Acquiring a receive snapshot within one AFDM sensing symbol period Let the number of resolvable propagation paths be... , No. Path ( Corresponding parameter set

[0103] in For the angle of arrival, To delay the transmission time, The angle of departure. This represents the radial velocity (corresponding to Doppler). This is the path complex gain.

[0104] To reduce the complexity of joint estimation of multi-dimensional parameters, this invention first utilizes the subspace structure of the AFDM received signal in the receiver array dimension to extract path angle of arrival information. Specifically, a spatial correlation matrix is ​​constructed based on the received snapshots within multiple AFDM sensing symbol periods.

[0105] Then, eigenvalue decomposition is performed, and the effective signal subspace and noise subspace are determined based on the relationship between eigenvalues ​​and noise variance. The array steering vector is used... Parallelism with the signal subspace and orthogonality with the noise subspace are used to construct the angle discriminant function.

[0106] The angle of arrival estimates for each propagation path are obtained through spectral peak search. .

[0107] After obtaining the angle of arrival estimate, the present invention further introduces a receiving beam to achieve path-by-path estimation of propagation delay. For the first... A path is constructed that satisfies:

[0108] The received beam vector is used to align the spatial components of the corresponding path, and the remaining paths and noise are treated as interference terms. By incorporating time-delay-independent unknown parameters (including the exit angle, Doppler phase term, and path gain) as intermediate variables, the original coupled multi-parameter estimation problem can be transformed into a linear least squares problem under given time delay conditions. Substituting the closed-form optimal solution of this intermediate variable back, an equivalent one-dimensional search criterion with respect to propagation time delay can be obtained.

[0109] in This is a weighted matrix jointly determined by AFDM receiver observations, sensing symbol structure, and candidate delay parameters. By performing a spectral search on this criterion, the propagation delay estimates for each path can be obtained sequentially.

[0110] After obtaining the angle of arrival and time delay estimates, this invention utilizes the harmonic correlation structure of the AFDM received signal in the transmit array dimension and symbol time dimension to jointly reconstruct the remaining parameters. Specifically, a path-related intermediate matrix is ​​constructed based on the aforementioned estimation results:

[0111] Under ideal conditions, it approximately satisfies the following outer product model:

[0112] in The guide vector for the transmission array. Let be the symbolic domain phase rotation vector caused by Doppler.

[0113] Based on the aforementioned space-time outer product structure, by... By performing rearrangement, constructing the correlation matrix, and extracting the principal eigenvectors, the exit angle of the path can be recovered using the phase rotation relationship between adjacent element directions and adjacent symbol directions, respectively. With radial velocity Based on this, the path complex gain estimate can be obtained by combining least squares or projection operations. This yields the set of multidimensional parameter estimates for all distinguishable paths:

[0114] This invention further applies the aforementioned multidimensional parameter estimation results to communication-aided processing. On one hand, an equivalent biselective channel model can be constructed based on the estimated propagation delay, Doppler, and path gain, and equalizers such as zero-forcing or minimum mean square error equalizers can be designed accordingly to suppress inter-symbol interference under high-speed movement conditions. On the other hand, beamforming can be performed at the receiving and transmitting ends based on the estimated angle of arrival and angle of departure, thereby enhancing the main path gain and suppressing interference paths. In one embodiment, the multidimensional parameter joint estimation and communication-aided process can be executed cyclically within each frame or sliding window to achieve stable operation of the integrated sensing system in high-speed movement and strong multipath environments.

[0115] To ensure the identifiability and estimation stability of the above-mentioned multidimensional parameter joint estimation process, in one embodiment, the system configuration and operating conditions satisfy one or a combination of the following constraints: First, the effective observation dimensions in the spatial domain, frequency domain, and time domain are not less than the number of resolvable propagation paths, i.e., satisfying:

[0116] Secondly, the user terminal's moving speed is jointly constrained by the system carrier frequency, symbol period, and observation length to ensure that a sufficient number of AFDM sensing symbols can be obtained within the coherence time for stable estimation. Thirdly, given the moving speed and coherence time, the number of stable estimable effective propagation paths is affected by the number of observed symbols. The upper limit constraint reflects the trade-off between speed range, number of detectable paths, and estimation accuracy.

[0117] The present invention further uses the multidimensional parameter estimation results obtained in the above-mentioned sensing stage for communication-aided processing to improve communication performance in high-speed mobile and strong multipath environments.

[0118] In one embodiment, based on the estimated propagation delay, Doppler, and path gain parameters, the multipath channel can be mapped to the delay-Doppler or equivalent transform domain of AFDM, constructing an equivalent communication channel model with sparse or structured characteristics. On this basis, equalizers such as zero-forcing or minimum mean square error equalizers are designed to suppress interference caused by high-speed Doppler spread and fractional delay. Compared to traditional methods that rely solely on pilot interpolation, this communication-assisted approach utilizes explicit physical parameters as prior information, exhibiting higher stability under rapidly changing channel conditions.

[0119] On the other hand, the multidimensional parameter estimation results can also be directly used for beamforming at the transceiver end. The receiver can construct or modify the combining vector based on the estimated angle of arrival, and the transmitter can form a precoding vector based on the estimated angle of departure. Multiple propagation paths can be weighted and synthesized by combining the path gain or the estimated signal-to-interference-plus-noise ratio to achieve main path enhancement and interference suppression. In scenarios with multiple available reflection paths, the subset of paths with the best communication performance can be selected to further improve link robustness.

[0120] In one embodiment, the aforementioned multidimensional parameter joint estimation and communication-aided processing can be performed cyclically within each frame, subframe, or sliding window. That is, the path parameters are dynamically corrected by continuously updating the AFDM sensing observations, and the equalizer and beam vector are adjusted synchronously, thereby maintaining the stable operation of the integrated sensing link under conditions of high-speed movement, strong multipath, or occlusion.

[0121] It should be understood that the aforementioned multidimensional parameter estimation method and communication-aided processing flow can be flexibly combined with other modules of the present invention; the related correlation matrix construction method, threshold selection strategy, spectral search resolution, and equalization and beamforming criteria can all be equivalently replaced or engineered without departing from the spirit of the present invention.

[0122] Furthermore, in step S7, decryption and demodulation include: S701: Remove the prefix from the received signal to obtain the first received signal; S702: Calculate the time-domain equivalent channel matrix based on the communication parameter combination; obtain the AFDM transform matrix based on the key group; S703: Calculate the second received signal based on the time-domain equivalent channel matrix and the AFDM transform matrix; S704: Decrypt the second received signal according to the key group to obtain decrypted information.

[0123] This embodiment provides a joint decryption, equalization, and detection method for an AFDM-IM communication receiver, applicable to a MIMO-AFDM-IM sensing integrated system based on multi-layer encryption. This method utilizes the multi-dimensional sensing parameters obtained in Embodiment 3 to perform equivalent modeling of the communication link, and introduces key constraints into the equalization and detection process at the receiver, achieving secure communication reception with sensing assistance.

[0124] The receiving end performs cyclic prefix removal and parallel-to-serial conversion on the received signal to obtain the received signal vector. or multi-antenna receiving signal matrix , of which Listed as the number At each discrete moment The received vectors at each receiving antenna. The received noise is modeled as... .

[0125] In one embodiment, the receiving end calls the multipath parameter estimation results output by the integrated sensing module to obtain the angle of arrival for each path. , angle of departure Delay Index Normalized Doppler and path gain Estimate the beamforming and equivalent channel construction used on the communication side.

[0126] To match the single-data-stream beamforming transmission during the communication phase, the receiver combines the signals from multiple antennas to obtain an equivalent single-stream received signal. To reduce the dimensionality complexity of subsequent equalization and detection, this invention employs an equal-weight beamforming method based on multipath steering vector superposition to equivalence the multi-antenna link. The beam vectors at the receiver and transmitter are constructed as follows:

[0127] The multi-antenna received signals are combined into an equivalent single-stream signal. Among them, the The guide vectors of the receiver array and the transmitter array for each path are estimated by the angle of arrival. With the estimation of the angle of departure Construction, i.e.

[0128] After beamforming, the first The equivalent complex gain of the path is

[0129] Meanwhile, the delay index Estimated by propagation delay By sampling period Discretization yields, i.e.

[0130] Normalized Doppler parameters Estimation by radial velocity The mapping is obtained, and it is related to the carrier frequency. speed of light and subcarrier spacing The following conditions must be met:

[0131] Thus, a one-to-one correspondence is formed between the physical parameters estimated in the sensing phase and the equivalent biselective channel matrix constructed on the communication side.

[0132] After beamforming, the multipath MIMO dual-selectivity channel is equivalent to a single-input single-output form. Furthermore, in step S702, the formula for calculating the time-domain equivalent channel matrix is ​​as follows:

[0133] This represents the equivalent path gain, including the spatial beam gain. This represents the cyclic prefix phase compensation matrix. Represents the normalized Doppler parameters. Indicates a delay index. Indicates the path sequence number. Indicates the total number of paths. This represents the forward cyclic shift matrix.

[0134] After obtaining the equivalent single-stream received signal and equivalent time-domain channel matrix Subsequently, to maintain consistency with the AFDM modulation process at the transmitting end, the receiving end uses a parameter layer key. Reconstructing the pre-chirped parameter set under constraints Thus, the matching pre-chirp matrix is ​​determined. And construct the matching DAFT transformation matrix.

[0135] The receiving end to Performing a DAFT transform yields the DAFT domain receive vector. It satisfies:

[0136] in For the DAFT field symbol vector of the transmitter AFDM-IM, This is the noise vector.

[0137] In one embodiment, the receiver pairs the data in the DAFT domain. Perform minimum mean square error (MMSE) equalization to obtain a preliminary estimate of the transmitted symbol vector. Furthermore, in step S703, the formula for calculating the second received signal is as follows:

[0138]

[0139] Indicates the second received signal. Represents the DAFT-domain equivalent channel matrix. Let I represent noise power, and let I represent a unit diagonal matrix. Indicates the first received signal. Represents the AFDM transformation matrix. This represents the time-domain equivalent channel matrix.

[0140] Then The AFDM-IM structure is divided into multiple sub-blocks for subsequent detection and processing.

[0141] This invention employs a two-layer encryption mechanism, wherein the index layer key... Index mapping rules used to constrain AFDM-IM; parameter layer key Pre-chirped parameter set used for reconstructing AFDM This allows for the determination of the pre-chirp matrix required for the DAFT / IDAFT transformation at the receiver. Under the condition that legitimate receivers share the same key, a matching key can be generated. Demodulation is completed; however, the unlicensed receiver suffers from equivalent transformation mismatch due to parameter mismatch, making it difficult to recover the DAFT field symbol and complete subsequent detection.

[0142] The receiving end to the first Each sub-block performs a key-constrained maximum likelihood joint decision:

[0143] in For the key A dynamically selected set of candidate indices. For in the index set The set of candidate symbols whose values ​​are derived from a preset constellation (such as QAM / PSK). Parameter layer key. This has already been reflected in the aforementioned DAFT transform matching process.

[0144] After completing the maximum likelihood decision, the receiver performs an index layer inverse mapping to recover the index bits:

[0145] The constellation symbols obtained from the decision are then demapped to obtain information bits. Among them, the parameter layer key The corresponding decryption process has been matched. The DAFT / IDAFT transform implementation.

[0146] Compared with traditional receiving schemes that separate equalization, detection, and decryption, this invention introduces joint maximum likelihood detection with key constraints after the MMSE equalization output, which highly couples the decryption / matching process of the index layer and parameter layer with the detection process. At the same time, it reduces the detection dimensionality by using perception-assisted equivalent channel modeling, thereby improving the detection reliability under complex channel conditions while ensuring security.

[0147] Through the above-mentioned joint decryption, equalization, and detection methods, this invention enables the direct empowerment of the AFDM-IM secure communication receiver by sensing information, thereby improving both the physical layer security and communication reliability of the system in high-speed mobile and strong multipath scenarios.

[0148] A multi-layered encrypted MIMO-AFDM-IM integrated sensing signal transmission system includes: Sensing and transmitting module: The AFDM-IM communication transmitter transmits multiple probe symbols to the AFDM-IM communication receiver, and obtains multiple echo signals; Parameter combination calculation module: Calculates the communication parameter combination based on multiple echo signals; Signal acquisition module: Acquires the signal to be sent; Encryption module: encrypts and modulates the signal to be transmitted to obtain an encrypted transmission signal and a key group; Communication sending module: Based on the combination of communication parameters, the encrypted sending signal is sent to the AFDM-IM communication receiving end through the AFDM-IM communication sending end; Receiver module: Acquires the received signals from the AFDM-IM communication receiver; Decryption module: Decrypts and demodulates the received signal according to the communication parameter combination and key group to obtain decrypted information.

[0149] Figure 3 The BER performance of AFDM and its security enhancement schemes in the LTV channel was compared under MIMO configuration. The system carrier frequency was 60 GHz, and the bandwidth was 100 MHz; the frame parameters were 64 subcarriers, 32 symbols, and QPSK modulation. Both the transceiver and receiver used 16-antenna arrays (16 transmit antennas and 16 receive antennas). The comparison schemes included AFDM, AFDM-IM encryption, AFDM-c_2 encryption, AFDM-IM-c_2 joint encryption, and the OFDM baseline scheme. The BER curves of each scheme were statistically analyzed under the same SNR scanning conditions.

[0150] The simulation compares the bit error rate performance of "no encryption baseline - single encryption - joint encryption" under the same LTV channel and the same 16×16 MIMO configuration, and further presents the security differences under different eavesdropper prior information. Figure 3 The horizontal axis represents the signal-to-noise ratio (SNR) in dB, and the vertical axis represents the BER (logarithmic scale).

[0151] Specifically, legitimate receivers employ AFDM, AFDM-IM, AFDM-c_2, and AFDM-IM-c_2 schemes for transmission recovery, respectively, and compare their performance with the OFDM baseline scheme. Three eavesdropper scenarios are also constructed: eavesdroppers with only unknown IM activation index (IM only), eavesdroppers with only unknown c_2 perturbation parameters (c_2 only), and eavesdroppers with unknown IM and c_2 parameters (Nokey). By comparing the BER difference between legitimate receivers and eavesdroppers on the same channel, the suppression effect of introducing IM and c_2 individually and jointly on unauthorized reception is demonstrated, and the joint encryption scheme is verified to have stronger anti-eavesdropping capabilities while ensuring legitimate reliability.

[0152] like Figure 4 As shown, the BER performance under a 16×16 MIMO configuration was compared between Estimated CSI and Ideal CSI conditions. The test channel was an LTV channel. The system carrier frequency was 60 GHz, and the bandwidth was 100 MHz. The frame parameters were 64 subcarriers, 32 symbols, and QPSK modulation. The comparison schemes were AFDM-IM-c_2 and OFDM-IM, and BER curves were statistically analyzed under both Estimated CSI and Ideal CSI conditions. The Estimated CSI was obtained through periodic updates and was used to characterize the performance changes when sensed information participates in communication reception processing.

[0153] The simulation aims to compare the bit error rate differences between AFDM-IM-c_2 and OFDM-IM under two conditions: "perception-assisted estimation CSI" and "ideal CSI", in order to evaluate the impact of perception-assisted channel estimation on communication reliability. Figure 4 The horizontal axis represents the signal-to-noise ratio (SNR) (dB), and the vertical axis represents the BER (logarithmic scale). The curves of the same scheme under two CSI conditions are used to illustrate the impact of estimation error on receiver performance and to provide a comparison with OFDM-IM.

[0154] also, Figure 4 The eavesdropper (Eve) BER performance of AFDM-IM-c_2 under ideal CSI conditions is also presented to illustrate that when the eavesdropper lacks key information, even with ideal channel conditions, information recovery is still significantly degraded, thus further verifying that the proposed secure communication scheme still has anti-eavesdropping capabilities under high-quality channel information conditions.

[0155] The same or similar labels correspond to the same or similar parts; The terms used to describe positional relationships in the accompanying drawings are for illustrative purposes only and should not be construed as limiting this patent. Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A multi-layer encrypted MIMO-AFDM-IM integrated sensing signal transmission method, characterized in that, include: S1: The AFDM-IM communication transmitter sends multiple probe symbols to the AFDM-IM communication receiver, and obtains multiple echo signals; S2: Calculate the combination of communication parameters based on the multiple echo signals; S3: Obtain the signal to be sent; S4: Encrypt and modulate the signal to be transmitted to obtain an encrypted transmission signal and a key group; S5: Based on the combination of communication parameters, the encrypted transmission signal is sent to the AFDM-IM communication receiver through the AFDM-IM communication transmitter; S6: Obtain the received signal from the AFDM-IM communication receiver; S7: Decrypt and demodulate the received signal according to the communication parameter combination and key group to obtain decrypted information.

2. The MIMO-AFDM-IM integrated sensing signal transmission method based on multi-layer encryption according to claim 1, characterized in that, In step S2, the communication parameter combination is calculated, including: S201: Construct a sensing fast lift matrix based on the multiple echo signals; S202: Calculate the communication parameter combination based on the sensing fast-up matrix.

3. The MIMO-AFDM-IM integrated sensing signal transmission method based on multi-layer encryption according to claim 2, characterized in that, In step S201, the formula for constructing the sensing fast lift matrix is ​​as follows: Indicates the response of a noiseless system. Indicates the position of environmental reflectors during transmission. This represents path-based channel gain. Indicates user status. This represents the total number of paths, where 'l' represents the path number. This represents the path complex gain of the l-th path. , This represents the ULA steering vector of the uniform array. This represents the angle of arrival for the l-th path. Let represent the emission angle of the l-th path, j represent the complex field, and m represent the AFDM symbol number. This represents the Doppler angular frequency of the l-th path. Indicates the symbol period, This represents the sensing symbol matrix corresponding to the m-th AFDM sensing symbol. Represents the time delay phase rotation matrix. This represents the delay of the l-th path. Represents the perceptual fast lift matrix, This represents the additive noise matrix.

4. The MIMO-AFDM-IM integrated sensing signal transmission method based on multi-layer encryption according to claim 1, characterized in that, In step S4, the encryption and modulation include: S401: Calculate a key group using a chaotic sequence; the key group includes a first key and a second key; S402: Encrypt the index bits of the signal to be transmitted using the first key to obtain the set of active positions; S403: Associate and concatenate the information bits in the signal to be sent with the set of active positions to obtain the first encrypted signal; S404: Construct the encrypted AFDM transformation matrix based on the second key; S405: Perform IDAFT operation based on the AFDM transform matrix and the first encrypted signal to obtain the second encrypted signal; S406: Add a prefix to the second encrypted signal to form an encrypted transmission signal.

5. The MIMO-AFDM-IM integrated sensing signal transmission method based on multi-layer encryption according to claim 4, characterized in that, In step S401, the key group is calculated using a chaotic sequence, including: This represents the first key in the key group. This represents the second key in the key group. This indicates modulo operation, and floor indicates rounding down. Represents a chaotic sequence. Indicates the AFDM-IM sub-block number. , This represents the offset, and m represents the AFDM symbol number. Indicates the quantization length. Indicates the number of index mapping rules. This indicates the size of the pre-chirped parameter codebook.

6. The MIMO-AFDM-IM integrated sensing signal transmission method based on multi-layer encryption according to claim 4, characterized in that, In step S404, the formula for constructing the encrypted AFDM transformation matrix based on the second key is as follows: Represents the N-point discrete Fourier transform matrix. Represents the post-chirp matrix. Indicates via second key Encrypted pre-chirped diagonal matrix, Represents the encrypted AFDM transformation matrix. Indicates the second key The mapping, Represents a pre-chirped diagonal matrix. Let j denote a diagonal function, and j denote the complex field. This represents the k-th simulated RF multiplexing parameter, where k represents the simulated RF multiplexing parameter number.

7. The MIMO-AFDM-IM integrated sensing signal transmission method based on multi-layer encryption according to claim 1, characterized in that, Step S7, decryption and demodulation, includes: S701: Remove the prefix from the received signal to obtain the first received signal; S702: Calculate the time-domain equivalent channel matrix based on the communication parameter combination; obtain the AFDM transform matrix based on the key group; S703: Calculate the second received signal based on the time-domain equivalent channel matrix and the AFDM transform matrix; S704: Decrypt the second received signal according to the key group to obtain decrypted information.

8. The MIMO-AFDM-IM integrated sensing signal transmission method based on multi-layer encryption according to claim 7, characterized in that, In step S702, the formula for calculating the time-domain equivalent channel matrix is ​​as follows: This represents the equivalent path gain, including the spatial beam gain. This represents the cyclic prefix phase compensation matrix. Represents the normalized Doppler parameters. Indicates a delay index. Indicates the path sequence number. Indicates the total number of paths. This represents the forward cyclic shift matrix.

9. The MIMO-AFDM-IM integrated sensing signal transmission method based on multi-layer encryption according to claim 7 or 8, characterized in that, In step S703, the formula for calculating the second received signal is as follows: Indicates the second received signal. Represents the DAFT-domain equivalent channel matrix. Let I represent noise power, and let I represent a unit diagonal matrix. Indicates the first received signal. Represents the AFDM transformation matrix. This represents the time-domain equivalent channel matrix.

10. A multi-layer encrypted MIMO-AFDM-IM integrated sensing signal transmission system, applied to the transmission method described in any one of claims 1 to 9, characterized in that, include: Sensing and transmitting module: The AFDM-IM communication transmitter transmits multiple probe symbols to the AFDM-IM communication receiver, and obtains multiple echo signals; Parameter combination calculation module: Calculates the communication parameter combination based on multiple echo signals; Signal acquisition module: Acquires the signal to be sent; Encryption module: encrypts and modulates the signal to be transmitted to obtain an encrypted transmission signal and a key group; Communication sending module: Based on the combination of communication parameters, the encrypted sending signal is sent to the AFDM-IM communication receiving end through the AFDM-IM communication sending end; Receiver module: Acquires the received signals from the AFDM-IM communication receiver; Decryption module: Decrypts and demodulates the received signal according to the communication parameter combination and key group to obtain decrypted information.

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