Physical layer key generation method based on multipath perception and spatial pseudospectrum estimation
By using antenna array covariance matrix decomposition and spatial pseudospectral fitting, the problem of inconsistent key generation under low signal-to-noise ratio was solved, the key matching rate and efficiency were improved, and the security of wireless communication was enhanced.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TSINGHUA UNIVERSITY
- Filing Date
- 2024-01-16
- Publication Date
- 2026-07-31
AI Technical Summary
In low signal-to-noise ratio scenarios, inconsistent quantization results during physical layer key generation lead to low key matching rates and low generation efficiency between communication devices.
By decomposing the covariance matrix of the received signal through the antenna array, the signal subspace basis and noise subspace basis are obtained, a spatial pseudospectrum is generated and a fitting function is performed, and the key generation efficiency is improved by using orthogonal polynomials and the least squares method.
It improves the matching rate and generation efficiency of physical layer keys, and enhances the security between communication devices.
Smart Images
Figure CN117879812B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of wireless communication technology, and in particular to a physical layer key generation method, system, device, electronic device, and computer-readable medium based on multipath sensing and spatial pseudospectral estimation. Background Technology
[0002] With the widespread use of wireless communication, the misuse of wireless communication devices for information theft, malicious attacks, eavesdropping, and other behaviors will lead to a lot of losses. Therefore, ensuring the security of communication between various communication devices has become an urgent need. In order to ensure secure wireless communication, physical layer key generation is usually used to generate random physical layer keys by taking advantage of the dissimilarity of wireless channels, and to use the characteristics of wireless channels to ensure the security of physical layer keys.
[0003] However, during the generation of physical layer keys, especially under low signal-to-noise ratio conditions, inconsistencies in the quantization results of the quantization of the feature values of the wireless channel can easily occur, resulting in a low matching rate of physical layer keys generated between different communication devices, and ultimately reducing the efficiency of physical layer key generation. Summary of the Invention
[0004] In view of this, this application provides a physical layer key generation method, system, device, electronic device, and computer-readable medium based on multipath sensing and spatial pseudospectral estimation. The method involves a first communication device calculating the covariance matrix of a signal received through an antenna array, and performing eigenvalue decomposition on the covariance matrix to obtain a first signal subspace basis corresponding to the eigenvector corresponding to the largest eigenvalue and a first noise subspace basis corresponding to the other eigenvectors besides the eigenvector corresponding to the largest eigenvalue. Then, the first communication device generates a first spatial pseudospectrum based on the first noise subspace basis, and represents the curves in the first spatial pseudospectrum using orthogonal polynomials. A first function representing the curves in the first spatial pseudospectrum is then fitted using the least squares method to obtain a first coefficient sequence of the first function. Finally, the first coefficient sequence is mapped to a physical layer key used for communication with a second communication device. This maximizes the noise subspace, preventing the coherent signal after multipath propagation from being impaired by background noise, thus improving the matching rate of the physical layer key. Furthermore, the use of orthogonal polynomial fitting to quantize the coefficients of the spatial pseudospectrum further improves the generation efficiency of the physical layer key.
[0005] In a first aspect, this application provides a physical layer key generation method based on multipath sensing and spatial pseudospectral estimation, the method comprising:
[0006] The first communication device calculates the covariance matrix of the signal received through the antenna array and performs eigenvalue decomposition on the covariance matrix to obtain a first signal subspace basis and a first noise subspace basis. The first signal subspace basis is the eigenvector corresponding to the largest eigenvalue, and the first noise subspace basis includes other eigenvectors besides the eigenvector corresponding to the largest eigenvalue.
[0007] The first communication device generates a first spatial pseudospectrum based on the first noise subspace basis;
[0008] The first communication device characterizes the curves in the first spatial pseudospectrum using orthogonal polynomials and fits the first function characterizing the curves in the first spatial pseudospectrum using the least squares method to obtain the first coefficient sequence of the first function.
[0009] The first communication device maps the first coefficient sequence to a physical layer key for communicating with the second communication device.
[0010] In one embodiment, the first communication device calculates the covariance matrix of the signal received through the antenna array and performs eigenvalue decomposition on the covariance matrix to obtain a first signal subspace basis and a first noise subspace basis, including:
[0011] The first communication device calculates the covariance matrix of the signal received through the antenna array. Where U1 is the eigenvector of R1, Λ1 is the diagonal matrix of R1, and the diagonal elements of Λ1 are the eigenvalues of R1.
[0012] The first communication device will Decompose into R1 = U 1s Λ 1s U 1s H +U 1N Λ 1N U 1N H , where Rank(Λ 1s ) = 1, Rank(Λ 1N ) = M1-1, where, U 1s U is the eigenvector corresponding to the largest eigenvalue. 1s H For U 1s The conjugate vector, Λ 1s For U 1s The diagonal array, Λ 1s The diagonal element is U 1s eigenvalues, U 1N For R1, excluding U 1s Other feature vectors besides Λ 1N For U 1NThe diagonal matrix, U 1N H For U 1N The conjugate vector, Λ 1N The diagonal element is U 1N The eigenvalues of R1 are given by M1, where M1 is the number of eigenvectors of R1.
[0013] In one implementation, the first communication device generates a first spatial pseudospectrum based on the first noise subspace basis, including:
[0014] The first communication device calculates the first spatial pseudospectrum based on the first noise subspace basis. Where a(θ) is the steering vector in the MUSIC algorithm, a H (θ) is the conjugate vector of a(θ).
[0015] In one embodiment, the first communication device characterizes the curves in the first spatial pseudospectrum using orthogonal polynomials and fits a first function characterizing the curves in the first spatial pseudospectrum using the least squares method to obtain a first coefficient sequence of the first function, including:
[0016] The first communication device uses Legendre polynomials. The curve representing the first spatial pseudospectrum, wherein,
[0017] The first communication device uses the least squares method to analyze the... By performing fitting, the first coefficient sequence of the first function is obtained.
[0018] Where x represents the signal received by the antenna array calculated by the first communication device, and K is the number of fitting bits, which is determined based on the spatial environment complexity, the computing power of the first communication device, and the computing power of the second communication device.
[0019] In one implementation, the first communication device maps the first coefficient sequence to a physical layer key for communicating with the second communication device, including:
[0020] The first communication device for the first coefficient sequence The coefficients are quantized into a sequence of 0 / 1 coefficients, wherein the number of quantization bits for each coefficient in the sequence decreases as k1 increases.
[0021] The bit sequence in the 0 / 1 coefficient sequence is mapped to the physical layer key using a hash function.
[0022] In one implementation, before mapping the bit sequence in the 0 / 1 coefficient sequence to a physical layer key using a hash function, the method further includes:
[0023] Error correction codes are used to adjust the mismatched bits in the quantized bit sequences of the first and second communication devices, respectively.
[0024] The bit sequence in the 0 / 1 coefficient sequence is mapped to a physical layer key using a hash function, including:
[0025] The bit sequence in the adjusted 0 / 1 coefficient sequence is mapped to the physical layer key using a hash function.
[0026] A second aspect of this application provides a communication system, including:
[0027] The first communication device and the second communication device respectively execute the physical layer key generation method based on multipath sensing and spatial pseudospectral estimation as described in any one of claims 1-6;
[0028] The first communication device and the second communication device communicate based on their respective generated physical layer keys.
[0029] A third aspect of this application provides a physical layer key generation apparatus based on multipath sensing and spatial pseudospectral estimation, the apparatus comprising:
[0030] The decomposition module is used by the first communication device to calculate the covariance matrix of the signal received through the antenna array, and to perform eigenvalue decomposition on the covariance matrix to obtain a first signal subspace basis and a first noise subspace basis. The first signal subspace basis is the eigenvector corresponding to the largest eigenvalue, and the first noise subspace basis includes other eigenvectors besides the eigenvector corresponding to the largest eigenvalue.
[0031] A spatial pseudospectral generation module is used by the first communication device to generate a first spatial pseudospectral based on the first noise subspace basis;
[0032] The fitting module is used by the first communication device to characterize the curves in the first spatial pseudospectrum using orthogonal polynomials and to fit the first function characterizing the curves in the first spatial pseudospectrum using the least squares method to obtain the first coefficient sequence of the first function.
[0033] A physical layer key mapping module is used by the first communication device to map the first coefficient sequence into a physical layer key for communicating with the second communication device.
[0034] A third aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described in the first aspect.
[0035] A fourth aspect of this application provides a computer-readable medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the method described in the first aspect.
[0036] The beneficial effects of this application are:
[0037] This application provides a physical layer key generation method, system, device, electronic device, and computer-readable medium based on multipath sensing and spatial pseudospectral estimation. The method involves a first communication device calculating the covariance matrix of a signal received through an antenna array, and performing eigenvalue decomposition on the covariance matrix to obtain a first signal subspace basis corresponding to the eigenvector corresponding to the largest eigenvalue and a first noise subspace basis corresponding to the other eigenvectors besides the eigenvector corresponding to the largest eigenvalue. Then, the first communication device generates a first spatial pseudospectrum based on the first noise subspace basis, and represents the curves in the first spatial pseudospectrum using orthogonal polynomials. A first function representing the curves in the first spatial pseudospectrum is then fitted using the least squares method to obtain a first coefficient sequence of the first function. Finally, the first coefficient sequence is mapped to a physical layer key for communication with a second communication device. By selecting the eigenvector corresponding to the largest eigenvalue as the basis of the first signal subspace, other eigenvectors correspond to the basis of the first noise subspace, thereby maximizing the noise subspace. This ensures that the coherent signal after multipath propagation is not damaged by background noise, improving the matching rate of the physical layer key. Furthermore, by using orthogonal polynomial fitting to quantize the coefficients of the spatial pseudospectrum, the generation efficiency of the physical layer key is further improved. Attached Figure Description
[0038] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.
[0039] To more clearly illustrate the technical solution of this application, the drawings used in the description of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0040] Figure 1 This is a flowchart of a physical layer key generation method based on multipath sensing and spatial pseudospectral estimation provided in an embodiment of this application;
[0041] Figure 2 This is a schematic diagram of the framework of a physical layer key generation device based on multipath sensing and spatial pseudospectral estimation provided in an embodiment of this application. Detailed Implementation
[0042] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.
[0043] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0044] Physical layer key generation (PJK) refers to the crucial role of secure communication between devices in an IoT environment. PJK technology ensures secure communication by leveraging the physical layer characteristics of wireless signals to generate and encrypt PJK keys. Because the propagation characteristics of wireless signals are affected by environmental factors (such as obstacles, distance, and signal attenuation), each communication link can generate a unique PJK key, thereby enhancing security.
[0045] Channel reciprocity, in wireless communication, refers to the consistent transmission characteristics of a wireless channel in both directions at the same frequency and time. Internet of Things (IoT) devices typically need to communicate in complex and changing environments. The concept of channel reciprocity is crucial for optimizing signal transmission and reception in these devices. This property is often used for physical layer security and channel estimation because channel characteristics measured at one end can be inferred in the opposite direction.
[0046] MUSIC (Multiple Signal Classification) is a popular signal processing technique used to estimate the angles of signals received from multiple directions. Particularly important in array signal processing, MUSIC determines the direction of a signal source by analyzing the spatial pseudospectrum of the received signal and is commonly used in radar, sonar, and wireless communication systems. In IoT scenarios, MUSIC can improve positioning accuracy and environmental awareness. For example, in smart home systems, MUSIC can accurately locate the positions of different devices, enabling smarter environmental control and monitoring.
[0047] Spatial pseudospectral analysis, in the fields of signal processing and wireless communication, refers to the analysis and description of how signals vary at different spatial points. For example, in antenna array processing, spatial pseudospectral analysis can be used to determine the direction of different signal sources or to analyze and design the beamforming characteristics of antenna arrays. Spatial pseudospectral analysis is a key tool for understanding and designing signal propagation in complex communication systems, radar systems, and acoustic systems.
[0048] Coherent signals refer to two or more signals that have a fixed phase relationship. In signal processing and communications, coherence generally refers to the relationship between the phase and frequency of two signals. This phase relationship may be because they originate from the same signal source or are synchronized in some way. Coherent signals are very important in radar systems, wireless communications, and many other electronic systems because they allow for the enhancement of signal characteristics or the extraction of more information through signal combination.
[0049] In practical applications, low-power devices using narrowband signals for communication lack sufficient physical layer information in the frequency dimension for rapid physical layer key generation. Furthermore, IoT scenarios contain abundant spatial multipath information. Under low bandwidth conditions, the resolution of coherent signals propagating along different paths is low, making it difficult to correctly extract spatial awareness information in the presence of background noise and use it for physical layer key exchange. RSSI-based methods, due to the low sensitivity of RSSI (compared to finer-grained information like phase) to environmental sniffing and its small variation, require a considerable communication window to generate a sufficiently long key (commonly 128 bits). CSI-based methods require large signal bandwidth; for common IoT devices (such as those communicating via Bluetooth Low Energy), the number of distinguishable carrier frequencies is limited, making it difficult to extract sufficient entropy for key generation. Angle estimation-based methods require high-precision estimation of the signal angle to generate a high-match-rate key. These methods either require high bandwidth or high computing power, which is impractical for low-power devices using narrowband signals. Based on this, this application provides a physical layer key generation method based on multipath sensing and spatial pseudospectral estimation. By maximizing the noise subspace, the coherent signal after multipath propagation is not damaged by background noise, thus improving the matching rate of the physical layer key. Furthermore, the method uses orthogonal polynomial fitting to quantize the coefficients of the spatial pseudospectrum, further improving the generation efficiency of the physical layer key.
[0050] Figure 1 This is a flowchart of a physical layer key generation method based on multipath sensing and spatial pseudospectral estimation provided in an embodiment of this application, as follows: Figure 1 As shown in the figure, this embodiment provides a physical layer key generation method based on multipath sensing and spatial pseudospectral estimation. The method includes:
[0051] In step S101, the first communication device calculates the covariance matrix of the signal received through the antenna array and performs eigenvalue decomposition on the covariance matrix to obtain a first signal subspace basis and a first noise subspace basis. The first signal subspace basis is the eigenvector corresponding to the largest eigenvalue, and the first noise subspace basis includes other eigenvectors besides the eigenvector corresponding to the largest eigenvalue.
[0052] In step S102, the first communication device generates a first spatial pseudospectrum based on the first noise subspace basis;
[0053] In step S103, the first communication device characterizes the curves in the first spatial pseudospectrum using orthogonal polynomials and fits the first function characterizing the curves in the first spatial pseudospectrum using the least squares method to obtain the first coefficient sequence of the first function.
[0054] In step S104, the first communication device maps the first coefficient sequence to a physical layer key for communicating with the second communication device.
[0055] First, in step S101, the first communication device calculates the covariance matrix of the signal received through the antenna array and performs eigenvalue decomposition on the covariance matrix to obtain a first signal subspace basis and a first noise subspace basis. The first signal subspace basis is the eigenvector corresponding to the largest eigenvalue, and the first noise subspace basis includes other eigenvectors besides the eigenvector corresponding to the largest eigenvalue.
[0056] In the embodiments of this application, different communication devices need to have a physical layer key for communication during the communication process. Therefore, the following embodiments will be described in detail using the generation process of the physical layer key between the first communication device and the second communication device as an example.
[0057] First, an antenna array with multiple elements is constructed. The signal x(t) is received through the antenna array. The first communication device samples and collects the received signal x(t) over a certain period of time at a preset sampling rate (e.g., 2 MHz / Hz), and constructs the covariance matrix of the signal x(t).
[0058]
[0059] Where R1 is the covariance matrix, N is the number of signal samples, and x(t) is a 2*N matrix of the signal. HLet be the conjugate matrix of x(t). Furthermore, since the eigenvectors corresponding to the eigenvalues of R1 simultaneously correspond to multiple signal and noise features, it is necessary to first transform the covariance matrix R1 into a form used for decomposing the matrix eigenvalues. Then, eigenvalue decomposition is performed on R1, and the resulting eigenvectors of R1 are used as the first signal subspace basis and the first noise subspace basis. The first signal subspace basis is the eigenvector corresponding to the largest eigenvalue, representing the signal features of the acquired signal. The first noise subspace basis includes all eigenvectors except the one corresponding to the largest eigenvalue, representing the noise features that interfere with the signal during acquisition. This means that the eigenvector corresponding to the unique largest eigenvalue is used as the basis of the first signal subspace, which is equivalent to maximizing the noise subspace. Since only the eigenvector corresponding to the largest eigenvalue is considered, the number of eigenvectors corresponding to the first signal subspace basis is (the number of signal sources). All other eigenvectors except the eigenvector corresponding to the largest eigenvalue are regarded as noise features. That is, the first noise subspace basis corresponds to the subspace composed of eigenvectors (the number of antenna arrays - the number of signal sources). This ensures that the coherent signal after multipath propagation will not be damaged by background noise, improves the matching rate of the physical layer key, and is robust to possible side-channel attacks.
[0060] Similarly, the second communication device samples and collects the signal x(t) and constructs the covariance matrix of the signal x(t):
[0061]
[0062] Where R² is the covariance matrix, N is the number of signal samples, is a 2*N matrix of the signal, and is the conjugate matrix of . Then, eigenvalue decomposition is performed on R² to obtain the second signal subspace basis and the second noise subspace basis. The second signal subspace basis is the eigenvector corresponding to the largest eigenvalue, representing the signal characteristics of the acquired signal. The second noise subspace basis includes all eigenvectors except the eigenvector corresponding to the largest eigenvalue, representing the noise characteristics of the noise that interferes with the signal during acquisition.
[0063] Further, in step S102, the first communication device generates a first spatial pseudospectrum based on the first noise subspace basis. In this embodiment, the first noise subspace basis corresponds to eigenvectors other than the eigenvector corresponding to the largest eigenvalue. Ideally, the steering vector and the first noise subspace basis are orthogonal. However, due to the presence of noise, the steering vector and the first noise subspace basis are not completely orthogonal, but rather a very small value. This very small value, presented in reciprocal form, is the peak value in the spatial pseudospectrum. Therefore, in this embodiment, the first spatial pseudospectrum is generated by maximizing the first noise subspace basis corresponding to the noise portion and combining the first noise subspace basis with the reciprocal form.
[0064] It should be noted that the technical objectives and details of this application differ from the MUSIC algorithm. This method does not determine the signal subspace dimension based on the number of signal paths reaching the receiver, nor does it use smoothing algorithms to process coherent signal groups. Instead, it directly uses the eigenvector corresponding to the largest eigenvalue as the basis of the signal subspace and uses all remaining eigenvectors as the basis of the noise subspace. The fundamental reason for this is that this method does not aim to accurately calculate the arrival direction of the signal along different paths, but only needs to eliminate noise interference in both devices to generate a stable and reciprocal spatial spectrum.
[0065] Similarly, the second noise subspace basis obtained by decomposing the covariance matrix by the second communication device is used to obtain the second spatial pseudospectrum in the same way.
[0066] Furthermore, in step S103, the first communication device characterizes the curves in the first spatial pseudospectrum using orthogonal polynomials and fits the first function characterizing the curves in the first spatial pseudospectrum using the least squares method to obtain the first coefficient sequence of the first function. In this embodiment, for the first spatial pseudospectrum obtained in the above steps, the curves in this first spatial pseudospectrum can be characterized using orthogonal polynomials, and then the first function characterizing the first spatial pseudospectrum can be fitted using the least squares method to obtain the first coefficient sequence of the first function. Using orthogonal polynomial fitting to quantize the coefficients of the entire spatial spectrum (rather than the angular peaks) improves the generation efficiency of the physical layer key.
[0067] Similarly, the second communication device also fits the second spatial pseudospectrum in the same way to obtain the second coefficient sequence of the second function corresponding to the curve on the second spatial pseudospectrum.
[0068] Finally, in step S104, the first communication device maps the first coefficient sequence to a physical layer key for communicating with the second communication device. In this embodiment, after obtaining the first coefficient sequence, the first communication device can map it to a physical layer key for communicating with the second communication device in various ways, such as using hash functions, including SHA256 and SHA512. The length of the physical layer key can be set according to actual needs.
[0069] Similarly, the second communication device maps the second coefficient sequence to a physical layer key for communicating with the first communication device.
[0070] In the above embodiments, the first communication device calculates the covariance matrix of the signal received through the antenna array, and performs eigenvalue decomposition on the covariance matrix to obtain the first signal subspace basis corresponding to the eigenvector corresponding to the largest eigenvalue and the first noise subspace basis corresponding to the other eigenvectors besides the eigenvector corresponding to the largest eigenvalue. Then, the first communication device generates a first spatial pseudospectrum based on the first noise subspace basis, and represents the curves in the first spatial pseudospectrum using orthogonal polynomials. Next, it fits the first function representing the curves in the first spatial pseudospectrum using the least squares method to obtain the first coefficient sequence of the first function. Finally, the first coefficient sequence is mapped to a physical layer key used for communication with the second communication device. This maximizes the noise subspace, preventing the coherent signal after multipath propagation from being damaged by background noise, thus improving the matching rate of the physical layer key. Furthermore, the use of orthogonal polynomial fitting to quantize the coefficients of the spatial pseudospectrum further improves the generation efficiency of the physical layer key.
[0071] Optionally, step S101 includes:
[0072] The first communication device calculates the covariance matrix of the signal received through the antenna array. Where U1 is the eigenvector of R1, Λ1 is the diagonal matrix of R1, and the diagonal elements of Λ1 are the eigenvalues of R1.
[0073] The first communication device will Decompose into R1 = U 1s Λ 1s U 1s H +U 1N Λ 1N U 1N H , where Rank(Λ 1s ) = 1, Rank(Λ 1N ) = M1-1, where, U 1s U is the eigenvector corresponding to the largest eigenvalue. 1sH For U 1s The conjugate vector, Λ 1s For U 1s The diagonal array, Λ 1s The diagonal element is U 1s eigenvalues, U 1N For R1, excluding U 1s Other feature vectors besides Λ 1N For U 1N The diagonal matrix, U 1N H For U 1N The conjugate vector, Λ 1N The diagonal element is U 1N The eigenvalues of R1 are given by M1, where M1 is the number of eigenvectors of R1.
[0074] Specifically, in this embodiment, the first communication device constructs the covariance matrix of signal x(t) based on the signal x(t) received through the antenna array. The form of the covariance matrix R1 is then transformed to obtain the covariance matrix. Where U1 is the eigenvector of R1, Λ1 is the diagonal matrix of R1, and the diagonal elements of Λ1 are the eigenvalues of R1. Furthermore, for the covariance matrix... Decomposition yields R1 = U 1s Λ 1s U 1s H +U 1N Λ 1N U 1N H , among which, U 1s U is the eigenvector corresponding to the largest eigenvalue. 1s H For U 1s The conjugate vector, Λ 1s For U 1s The diagonal array, Λ 1s The diagonal element is U 1s eigenvalues, U 1N For R1, excluding U 1s Other feature vectors besides Λ 1N For U 1N The diagonal matrix, U 1N H For U 1N The conjugate vector, Λ 1N The diagonal element is U 1N The eigenvalues, M1 is the number of eigenvectors of R1, U 1s Λ 1s U 1s H U represents the basis of the first signal subspace.1N Λ 1N U 1N H Let represent the basis of the first noise subspace.
[0075] Similarly, the second communication device also constructs the covariance matrix of the signal x(t) received through the antenna array. The form of the covariance matrix R2 is then transformed to obtain the covariance matrix. Decompose R² into R² = U 2s Λ 2s U 2s H +U 2N Λ 2N U 2N H Where U2 is the eigenvector of R2, Λ2 is the diagonal matrix of R2, and the diagonal elements of Λ2 are the eigenvalues of R2. 2s U is the second eigenvector of R2. 2s H Λ is the conjugate vector of the second eigenvector of R2. 2s For U 2s The diagonal array, Λ 2s The diagonal element is U 2s The corresponding eigenvalue, U 2N For R2, excluding U 2s Other feature vectors besides Λ 2N For U 2N The diagonal matrix, U 2N H For U 2N The conjugate vector of R2, M2 is the number of eigenvalues of R2, U 2s Λ 2s U 2s H U is used to represent the basis of the second signal subspace. 2N Λ 2N U 2N H Used to represent the basis of the second noise subspace.
[0076] Optionally, the first communication device generates a first spatial pseudospectrum based on the first noise subspace basis, including:
[0077] The first communication device calculates the first spatial pseudospectrum based on the first noise subspace basis. Where a(θ) is the steering vector in the MUSIC algorithm, a H (θ) is the conjugate vector of a(θ).
[0078] Specifically, the first noise subspace basis includes eigenvectors other than the eigenvector corresponding to the largest eigenvalue. Therefore, a first spatial pseudospectrum is generated for the first noise subspace basis, and the location of the signal source is solved using the eigenvectors of the first noise subspace basis. The first spatial pseudospectrum is thus obtained. Where a(θ) is the steering vector in the MUSIC algorithm, a H (θ) is the conjugate vector of a(θ).
[0079] Similarly, the second communication device also generates a second spatial pseudospectrum through the second noise subspace basis. Where a(θ) is the steering vector in the MUSIC algorithm, a H (θ) is the conjugate vector of a(θ).
[0080] Optionally, the first communication device characterizes the curves in the first spatial pseudospectrum using orthogonal polynomials and fits a first function characterizing the curves in the first spatial pseudospectrum using the least squares method to obtain a first coefficient sequence of the first function, including:
[0081] The first communication device uses Legendre polynomials. The curve representing the first spatial pseudospectrum, wherein,
[0082]
[0083] The first communication device uses the least squares method to analyze the... By performing fitting, the first coefficient sequence of the first function is obtained.
[0084] Where x represents the signal received by the antenna array calculated by the first communication device, and K is the number of fitting bits, which is determined based on the spatial environment complexity, the computing power of the first communication device, and the computing power of the second communication device.
[0085] Specifically, in this embodiment, after generating the first spatial pseudospectrum, the curves in the first spatial pseudospectrum can be represented by orthogonal polynomials. Legendre polynomials are a series of orthogonal polynomials, commonly found in solving differential equations in physics and engineering problems. A key property of Legendre polynomials is that they form an orthogonal basis within their domain, meaning that each polynomial is orthogonal to all other polynomials (i.e., their integrals are zero). These polynomials have wide applications in quantum mechanics, electromagnetism, signal processing, and other fields. By using Legendre polynomials... To characterize the curve in the pseudospectrum of the first space, k1 represents the k1-th polynomial, and K represents the number of fitting bits, obtained through recursion. Furthermore, the first communication device then uses the least squares method to refine the above recursive relationship. By performing fitting, the first coefficient sequence of the first function is obtained. Similarly, the curve of the pseudospectrum in the second space can also be represented using Legendre polynomials, yielding... Furthermore, the second communication device then uses the least squares method to refine the above recursive relationship. By fitting the data, we obtain the second coefficient sequence of the second function.
[0086] It should be noted that the fitting process in this application embodiment can also use other orthogonal polynomials other than Legendre polynomials to fit the curves in the spatial pseudospectrum. The number of fitting bits K is determined according to the complexity of the spatial environment, the computing power of the first communication device and the computing power of the second communication device.
[0087] Optionally, step S104 includes:
[0088] The first communication device for the first coefficient sequence The coefficients are quantized into a sequence of 0 / 1 coefficients, wherein the number of quantization bits for each coefficient in the sequence decreases as k1 increases.
[0089] The bit sequence in the 0 / 1 coefficient sequence is mapped to the physical layer key using a hash function.
[0090] Specifically, in the embodiments of this application, the first coefficient sequence obtained in the above steps is... Quantization is performed to obtain a binary sequence of 0 / 1 coefficients. When fitting the curve in the first spatial pseudospectrum, the number of quantization bits for each coefficient in the resulting polynomial within the 0 / 1 coefficient sequence decreases as k1 increases. Furthermore, as the number of polynomials increases, the amount of information represented by each polynomial decreases. Therefore, later polynomials contain less information than earlier ones and require fewer quantization bits. Thus, the number of quantization bits for each coefficient can decrease as k1 increases.
[0091] Optionally, before step S104, the method further includes:
[0092] Error correction codes are used to adjust the mismatched bits in the quantized bit sequences of the first and second communication devices, respectively.
[0093] The bit sequence in the 0 / 1 coefficient sequence is mapped to a physical layer key using a hash function, including:
[0094] The bit sequence in the adjusted 0 / 1 coefficient sequence is mapped to the physical layer key using a hash function.
[0095] Specifically, in this embodiment, after the first communication device fits the first spatial pseudospectrum and obtains a quantized 0 / 1 coefficient sequence, and the second communication device fits the first spatial pseudospectrum and obtains a quantized 0 / 1 coefficient sequence, the first communication device needs to perform a function mapping on its own 0 / 1 coefficient sequence and send it to the second communication device so that the second communication device can verify its own 0 / 1 coefficient sequence to determine whether there are mismatched coefficient bits. Similarly, the second communication device also needs to perform a function mapping on its own 0 / 1 coefficient sequence and send it to the first communication device so that the second communication device can verify its own 0 / 1 coefficient sequence to determine whether there are mismatched coefficient bits, thus realizing a mutual verification process. When mismatched coefficient bits occur, error correction codes are used to adjust the mismatched bits in the quantized bit sequences of the first and second communication devices. For example, error correction codes such as BCH code, extended BCH code (EBCH), double cyclic redundancy check BCH code (BCH-BCH code), and RS-BCH code can be used to adjust the mismatched bits in the quantized bit sequences of the first and second communication devices.
[0096] Furthermore, after adjusting the mismatched bits in the quantized bit sequences of the first and second communication devices, the bit sequence in the adjusted 0 / 1 coefficient sequence is mapped to a physical layer key using a hash function. For example, various hash functions, including sha256 and sha512, can be used to generate keys of different lengths.
[0097] Based on the same inventive concept, this application also provides a communication system, including:
[0098] The first communication device and the second communication device respectively execute the physical layer key generation method based on multipath sensing and spatial pseudospectral estimation described above.
[0099] The first communication device and the second communication device communicate based on their respective generated physical layer keys.
[0100] Figure 2 This is a schematic diagram of the framework of a physical layer key generation device based on multipath sensing and spatial pseudospectral estimation, provided in one embodiment of this application. Figure 2 As shown in the figure, this embodiment provides a physical layer key generation device based on multipath sensing and spatial pseudospectral estimation. The device includes:
[0101] The decomposition module 11 is used for the first communication device to calculate the covariance matrix of the signal received through the antenna array, and to perform eigenvalue decomposition on the covariance matrix to obtain a first signal subspace basis and a first noise subspace basis. The first signal subspace basis is the eigenvector corresponding to the largest eigenvalue, and the first noise subspace basis includes other eigenvectors besides the eigenvector corresponding to the largest eigenvalue.
[0102] Spatial pseudospectral generation module 12 is used by the first communication device to generate a first spatial pseudospectral based on the first noise subspace basis;
[0103] Fitting module 13 is used by the first communication device to characterize the curve in the first spatial pseudospectrum using an orthogonal polynomial, and to fit the first function characterizing the curve in the first spatial pseudospectrum using the least squares method to obtain the first coefficient sequence of the first function.
[0104] The physical layer key mapping module 14 is used by the first communication device to map the first coefficient sequence into a physical layer key for communicating with the second communication device.
[0105] Optionally, the decomposition module 11 includes:
[0106] The covariance matrix calculation unit is used by the first communication device to calculate the covariance matrix of the signal received through the antenna array. Where U1 is the eigenvector of R1, Λ1 is the diagonal matrix of R1, and the diagonal elements of Λ1 are the eigenvalues of R1.
[0107] Decomposition unit, used by the first communication device to decompose the Decompose into R1 = U 1s Λ 1s U 1s H +U 1N Λ 1N U 1N H , where Rank(Λ 1s ) = 1, Rank(Λ 1N ) = M1-1, where, U 1s U is the eigenvector corresponding to the largest eigenvalue. 1s H For U 1s The conjugate vector, Λ 1s For U 1s The diagonal array, Λ 1s The diagonal element is U 1s eigenvalues, U 1N For R1, excluding U 1s Other feature vectors besides Λ 1N For U 1NThe diagonal matrix, U 1N H For U 1N The conjugate vector, Λ 1N The diagonal element is U 1N The eigenvalues of R1 are given by M1, where M1 is the number of eigenvectors of R1.
[0108] Optionally, the spatial pseudospectral generation module 12 includes:
[0109] The first spatial pseudospectral calculation unit is used by the first communication device to calculate the first spatial pseudospectral based on the first noise subspace basis.
[0110] Where a(θ) is the steering vector in the MUSIC algorithm, a H (θ) is the conjugate vector of a(θ).
[0111] Optionally, the fitting module 13 includes:
[0112] Characterization unit, used by the first communication device via Legendre polynomials The curve representing the first spatial pseudospectrum, wherein,
[0113] A fitting unit is used by the first communication device to fit the data using the least squares method. By performing fitting, the first coefficient sequence of the first function is obtained.
[0114] Where x represents the signal received by the antenna array calculated by the first communication device, and K is the number of fitting bits, which is determined based on the spatial environment complexity, the computing power of the first communication device, and the computing power of the second communication device.
[0115] Optionally, the physical layer key mapping module 14 includes:
[0116] Quantization unit, used by the first communication device to quantize the first coefficient sequence The coefficients are quantized into a sequence of 0 / 1 coefficients, wherein the number of quantization bits for each coefficient in the sequence decreases as k1 increases.
[0117] The mapping unit is used to map the bit sequence in the 0 / 1 coefficient sequence to a physical layer key using a hash function.
[0118] Optionally, the device further includes:
[0119] An adjustment unit is used to adjust mismatched bits in the quantized bit sequences of the first communication device and the second communication device using error correction codes before mapping the bit sequence in the 0 / 1 coefficient sequence to the physical layer key using a hash function.
[0120] The adjustment unit includes:
[0121] The mapping unit is used to map the bit sequence in the adjusted 0 / 1 coefficient sequence to the physical layer key using a hash function.
[0122] Based on the same inventive concept, another embodiment of this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the physical layer key generation method based on multipath sensing and spatial pseudospectral estimation as described in any of the above embodiments.
[0123] Based on the same inventive concept, another embodiment of this application provides a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, it implements the physical layer key generation method based on multipath sensing and spatial pseudospectral estimation as described in any of the above embodiments.
[0124] For systems or devices, since they are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be found in the description of the method embodiments.
[0125] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.
[0126] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0127] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0128] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0130] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.
[0131] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes the element.
[0132] The foregoing has provided a detailed description of a physical layer key generation method, system, apparatus, electronic device, and computer-readable medium based on multipath sensing and spatial pseudospectral estimation provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A physical layer key generation method based on multipath perception and spatial pseudospectrum estimation, characterized in that, The method includes: The first communication device calculates the covariance matrix of the signal received through the antenna array and performs eigenvalue decomposition on the covariance matrix to obtain a first signal subspace basis and a first noise subspace basis. The first signal subspace basis is the eigenvector corresponding to the largest eigenvalue, and the first noise subspace basis includes other eigenvectors besides the eigenvector corresponding to the largest eigenvalue. The first communication device generates a first spatial pseudospectrum based on the first noise subspace basis; The first communication device characterizes the curves in the first spatial pseudospectrum using orthogonal polynomials and fits the first function characterizing the curves in the first spatial pseudospectrum using the least squares method to obtain the first coefficient sequence of the first function. The first communication device maps the first coefficient sequence to a physical layer key for communicating with the second communication device.
2. The physical layer key generation method based on multipath sensing and spatial pseudospectral estimation according to claim 1, characterized in that, The first communication device calculates the covariance matrix of the signal received through the antenna array, and performs eigenvalue decomposition on the covariance matrix to obtain a first signal subspace basis and a first noise subspace basis, including: The first communication device calculates the covariance matrix of the signal received through the antenna array. Where U1 is the eigenvector of R1, Λ1 is the diagonal matrix of R1, and the diagonal elements of Λ1 are the eigenvalues of R1. The first communication device will Decompose into R1 = U 1s Λ 1s U 1s H +U 1N Λ 1N U 1N H , where Rank(Λ 1s ) = 1, Rank(Λ 1N ) = M1-1, where, U 1s U is the eigenvector corresponding to the largest eigenvalue. 1s H For U 1s The conjugate vector, Λ 1s For U 1s The diagonal array, Λ 1s The diagonal element is U 1s eigenvalues, U 1N For R1, excluding U 1s Other feature vectors besides Λ 1N For U 1N The diagonal matrix, U 1N H For U 1N The conjugate vector, Λ 1N The diagonal element is U 1N The eigenvalues of R1 are given by M1, where M1 is the number of eigenvectors of R1.
3. The physical layer key generation method based on multipath sensing and spatial pseudospectral estimation according to claim 2, characterized in that, The first communication device generates a first spatial pseudospectrum based on the first noise subspace basis, including: The first communication device calculates the first spatial pseudospectrum based on the first noise subspace basis. Where a(θ) is the steering vector in the MUSIC algorithm, a H (θ) is the conjugate vector of a(θ).
4. The physical layer key generation method based on multipath sensing and spatial pseudospectral estimation according to claim 3, characterized in that, The first communication device characterizes the curves in the first spatial pseudospectrum using orthogonal polynomials and fits the first function characterizing the curves in the first spatial pseudospectrum using the least squares method to obtain the first coefficient sequence of the first function, including: The first communication device uses Legendre polynomials. The curve representing the first spatial pseudospectrum, wherein, The first communication device uses the least squares method to analyze the... By performing fitting, the first coefficient sequence of the first function is obtained. Where x represents the signal received by the antenna array calculated by the first communication device, and K is the number of fitting bits, which is determined based on the spatial environment complexity, the computing power of the first communication device, and the computing power of the second communication device.
5. The physical layer key generation method based on multipath sensing and spatial pseudospectral estimation according to claim 4, characterized in that, The first communication device maps the first coefficient sequence into a physical layer key for communicating with the second communication device, including: The first communication device for the first coefficient sequence The coefficients are quantized into a sequence of 0 / 1 coefficients, wherein the number of quantization bits for each coefficient in the sequence decreases as k1 increases. The bit sequence in the 0 / 1 coefficient sequence is mapped to the physical layer key using a hash function.
6. The physical layer key generation method based on multipath sensing and spatial pseudospectral estimation according to claim 5, characterized in that, Before mapping the bit sequence in the 0 / 1 coefficient sequence to the physical layer key using a hash function, the method further includes: Error correction codes are used to adjust the mismatched bits in the quantized bit sequences of the first and second communication devices, respectively. The bit sequence in the 0 / 1 coefficient sequence is mapped to a physical layer key using a hash function, including: The bit sequence in the adjusted 0 / 1 coefficient sequence is mapped to the physical layer key using a hash function.
7. A communication system, characterized in that, include: The first communication device and the second communication device respectively execute the physical layer key generation method based on multipath sensing and spatial pseudospectral estimation as described in any one of claims 1-6; The first communication device and the second communication device communicate based on their respective generated physical layer keys.
8. A physical layer key generation device based on multipath sensing and spatial pseudospectral estimation, characterized in that, The device includes: The decomposition module is used by the first communication device to calculate the covariance matrix of the signal received through the antenna array, and to perform eigenvalue decomposition on the covariance matrix to obtain a first signal subspace basis and a first noise subspace basis. The first signal subspace basis is the eigenvector corresponding to the largest eigenvalue, and the first noise subspace basis includes other eigenvectors besides the eigenvector corresponding to the largest eigenvalue. A spatial pseudospectral generation module is used by the first communication device to generate a first spatial pseudospectral based on the first noise subspace basis; The fitting module is used by the first communication device to characterize the curves in the first spatial pseudospectrum using orthogonal polynomials and to fit the first function characterizing the curves in the first spatial pseudospectrum using the least squares method to obtain the first coefficient sequence of the first function. A physical layer key mapping module is used by the first communication device to map the first coefficient sequence into a physical layer key for communicating with the second communication device.
9. An electronic device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the physical layer key generation method based on multipath sensing and spatial pseudospectral estimation as described in any one of claims 1-6.
10. A computer-readable medium, characterized in that, It stores a computer program, wherein when the program is executed by a processor, it implements the physical layer key generation method based on multipath sensing and spatial pseudospectral estimation as described in any one of claims 1-6.