Authentication Method, Device, Electronic Device and Storage Medium Based on Compressed Sensing

Abstract

The present application provides an authentication method, apparatus, electronic device, and storage medium based on compressive sensing. The method includes: determining channel state information based on the received transmission signal from the transmitting end according to channel estimation technology; extracting an original authentication vector according to the channel state information; performing compressive processing on the original authentication vector by using a sparse matrix and an observation matrix to determine the authentication vector at the current moment; constructing a test statistic by using the previously obtained authentication vector at the previous moment and the authentication vector at the current moment; determining whether the test statistic is greater than a preset decision threshold; and in response to the test statistic being greater than the decision threshold, determining that the transmission signal is a legal signal. The present application meets the requirements for authentication security in the 5G network, reduces the complexity of authentication, speeds up the authentication process, improves the security and reliability of the Internet of Things, utilizes the characteristics of the physical channel, can resist attacks from the physical layer, and reduces the power consumption of the terminal device.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to an authentication method, apparatus, electronic device, and storage medium based on compressive sensing. Background Art

[0002] In the related art, authentication is performed using the channel characteristics of two-way continuous data packets. The time difference between two-way continuous data packets is much smaller than the channel coherence time, which solves the defect that the current physical layer authentication scheme is not applicable to high-speed mobile wireless network environments and enables one-way authentication and two-way authentication in both mobile and stationary wireless network environments. However, in a large-scale multi-antenna system, when extracting the channel characteristics of data packets, the dimension of the obtained channel matrix is too large, which makes the time complexity of the subsequent authentication process too high. Therefore, there is an authentication that is not applicable to large-scale multi-antenna systems. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose an authentication method, apparatus, electronic device, and storage medium based on compressive sensing.

[0004] Based on the above purpose, in a first aspect, this application provides an authentication method based on compressive sensing, including:

[0005] Determine channel state information based on channel estimation technology according to the received transmission signal from the transmitter;

[0006] Extract the original authentication vector according to the channel state information;

[0007] Perform compression processing on the original authentication vector using a sparse matrix and an observation matrix to determine the authentication vector at the current moment;

[0008] Construct a test statistic using the previously obtained authentication vector at the previous moment and the authentication vector at the current moment;

[0009] Determine whether the test statistic is greater than a preset decision threshold;

[0010] If the test statistic is greater than the decision threshold, then determine that the transmission signal is a legitimate signal.

[0011] In a possible implementation, the determining channel state information based on channel estimation technology according to the received transmission signal from the transmitter further includes:

[0012] Perform channel estimation on the transmission signal using pilot-based channel estimation, semi-blind channel estimation, and blind channel estimation to determine the channel state information.

[0013] In a possible implementation, extracting the original authentication vector according to the channel state information further includes:

[0014] Determine the total channel matrix between the receiving end and the transmitting end according to the channel state information; wherein, the total channel matrix is expressed as

[0015]

[0016] Determine the total channel matrix as the original authentication vector.

[0017] In a possible implementation, compressing the original authentication vector by using the sparse matrix and the observation matrix to determine the authentication vector at the current moment further includes:

[0018] Determine the SIMO channel matrix according to the original authentication vector; wherein, the SIMO channel matrix is expressed as

[0019]

[0020] wherein, R RX represents the column correlation matrix of the channel matrix of the receiving end, and tr() represents the matrix;

[0021] Convert the SIMO channel matrix into an NL-dimensional column vector; wherein, the column vector is expressed as

[0022]

[0023] wherein, represents an L-dimensional column vector, N represents the number of antennas, and L represents the number of paths of each channel;

[0024] Determine the column vector estimate of the k-th data packet according to the column vector of the k-th data packet; the column vector estimate of the k-th data packet is expressed as

[0025]

[0026] wherein, N(k) represents the error of channel estimation and satisfies the complex Gaussian distribution;

[0027] Determine the compressible sparse signal according to the column vector estimate of the k-th data packet.

[0028] In a possible implementation, after determining the compressible sparse signal according to the column vector estimate of the k-th data packet, it further includes:

[0029] Define the observation matrix; wherein, the observation matrix is expressed as

[0030] Compress the compressible sparse signal according to the observation matrix to determine the authentication vector at the current moment; wherein, the authentication vector at the current moment is expressed as

[0031]

[0032] In a possible implementation manner, the constructing a test statistic by using the authentication vector at the previous moment and the authentication vector at the current moment obtained in advance further includes:

[0033] Define H0 to indicate that the transmitted signal comes from a legitimate terminal, and H1 to indicate that the received signal comes from an illegal terminal;

[0034] According to the authentication vector at the previous moment and the authentication vector at the current moment obtained in advance, let Let the estimated value of the channel state information of the (k + 1)-th data packet be Then H0 and H1 respectively indicate that the estimated value of the channel state information of the (k + 1)-th data packet is Then H0 and H1 respectively indicate

[0035]

[0036] Construct a test statistic according to H0 and H1; wherein, the test statistic is expressed as

[0037]

[0038] In a possible implementation manner, before determining whether the test statistic is greater than a preset decision threshold, it further includes:

[0039] Determine a binary hypothesis test according to the preset decision threshold, and the binary hypothesis test is expressed as

[0040]

[0041] In a second aspect, the present application provides an authentication device based on compressive sensing, including:

[0042] A first determination module, configured to determine channel state information based on channel estimation technology according to a received transmission signal from a transmitting end;

[0043] An extraction module, configured to extract an original authentication vector according to the channel state information;

[0044] A second determination module, configured to compress the original authentication vector by using a sparse matrix and an observation matrix to determine an authentication vector at the current moment;

[0045] A construction module, configured to construct a test statistic by using a pre-acquired authentication vector at a previous moment and an authentication vector at the current moment;

[0046] A decision module, configured to determine whether the test statistic is greater than a pre-set decision threshold;

[0047] A third determination module, configured to determine that the transmitted signal is a legal signal in response to the test statistic being greater than the decision threshold.

[0048] In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the authentication method based on compressive sensing as described in the first aspect.

[0049] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium, which stores computer instructions for causing a computer to execute the authentication method based on compressive sensing as described in the first aspect.

[0050] As can be seen from the above, an authentication method, device, electronic device, and storage medium based on compressive sensing provided by the present application determine channel state information based on channel estimation technology according to a received transmitted signal from a transmitting end; extract an original authentication vector according to the channel state information; perform compressive processing on the original authentication vector by using a sparse matrix and an observation matrix to determine an authentication vector at the current moment; construct a test statistic by using a pre-acquired authentication vector at a previous moment and an authentication vector at the current moment; determine whether the test statistic is greater than a pre-set decision threshold; and determine that the transmitted signal is a legal signal in response to the test statistic being greater than the decision threshold. It meets the requirements for authentication security in the 5G network, reduces the complexity of authentication, speeds up the authentication process, improves the security and reliability of the Internet of Things, and utilizes the characteristics of the physical channel to resist attacks from the physical layer and reduce the power consumption of terminal devices. Description of the Drawings

[0051] To more clearly illustrate the technical solutions in the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0052] Figure 1 Shows an exemplary flowchart of an authentication method based on compressive sensing provided by an embodiment of the present application.

[0053] Figure 2 A schematic diagram of an application scenario according to an embodiment of the present application is shown.

[0054] Figure 3 An exemplary structural schematic diagram of an authentication device based on compressive sensing provided by an embodiment of the present application is shown.

[0055] Figure 4 An exemplary structural schematic diagram of an electronic device provided by an embodiment of the present application is shown. Detailed implementation manners

[0056] To make the objectives, technical solutions, and advantages of the present application clearer and more understandable, the following further describes the present application in detail with reference to specific embodiments and the accompanying drawings.

[0057] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the ordinary meanings understood by those of ordinary skill in the art to which the present application belongs. The terms "first", "second", and similar terms used in the embodiments of the present application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The terms "including" or "comprising" and the like mean that the elements or items appearing before the term cover the elements or items listed after the term and their equivalents, without excluding other elements or items. The terms "connected" or "coupled" and the like are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0058] Authentication technology provides the first line of defense for 5G network security. It can ensure the identity legitimacy of access terminals, control access and data transmission security, and is the key to ensuring communication security. The most widely used existing authentication mechanism is the upper-layer authentication mechanism based on key encryption, such as asymmetric encryption algorithms like RSA (Ron Rivest, Adi Shamir, Leonard Adleman) and symmetric encryption algorithms like the Advanced Encryption Standard (AES), which can effectively ensure the security of traditional networks. However, in future network scenarios, there will be a large number of terminals such as sensors with low power, which brings many disadvantages to the authentication of these energy-constrained terminals. At the same time, the upper-layer authentication protocol does not consider the vulnerability and openness of the wireless channel and is easily vulnerable to physical-layer attacks.

[0059] The comparison of channel responses in physical layer authentication only involves hardware operations. Therefore, physical layer authentication features low latency and low computational complexity, making it very suitable for authentication of terminals with limited energy and computational capabilities in mMTC networks. Wireless channels are diverse and private in space, meaning that the wireless link established between any two communication entities is unique and non-replicable. They are time-varying and short-term mutually different in time, that is, wireless channels are constantly changing, but can be considered invariant within the channel coherence time. At this time, the communication parties can extract the same channel characteristics. It uses these characteristics of the wireless channel to authenticate terminals. Physical layer authentication has the characteristics of low overhead and being lightweight, and can resist attacks from the physical layer. It can be applied to the Internet of Things, smart grid, and industrial Internet of Things.

[0060] As described in the background art section, in the related art, authentication is performed using the channel characteristics of two-way continuous data packets. The time difference between two-way continuous data packets is much smaller than the channel coherence time, which solves the defect that the current physical layer authentication scheme is not applicable to high-speed mobile wireless network environments and enables one-way authentication and two-way authentication in both mobile and stationary wireless network environments.

[0061] However, the applicant found through research that in a large-scale multiple-input multiple-output (MIMO) system, when extracting the channel characteristics of data packets, the dimension of the obtained channel matrix is too large, which makes the time complexity of the subsequent authentication process too high. Therefore, there is a problem that it cannot be applied to the authentication of large-scale MIMO systems.

[0062] Therefore, a compression-sensing-based authentication method, device, electronic device, and storage medium provided in this application determine channel state information based on channel estimation technology according to the received transmission signal from the transmitter; extract the original authentication vector according to the channel state information; perform compression processing on the original authentication vector using a sparse matrix and an observation matrix to determine the authentication vector at the current moment; construct a test statistic using the previously obtained authentication vector at the previous moment and the authentication vector at the current moment; determine whether the test statistic is greater than a preset decision threshold; and if the test statistic is greater than the decision threshold, determine that the transmission signal is a legitimate signal. This meets the requirements for authentication security in 5G networks, reduces the complexity of authentication, speeds up the authentication process, improves the security and reliability of the Internet of Things, and utilizes the characteristics of the physical channel to resist attacks from the physical layer and reduce the power consumption of terminal devices.

[0063] The following specifically describes the compression-sensing-based authentication method provided in the embodiments of this application through specific embodiments.

[0064] Figure 1 Fig. shows an exemplary flowchart of a compression-sensing-based authentication method provided in the embodiments of this application.

[0065] Reference Figure 1 A authentication method based on compressive sensing provided by an embodiment of the present application specifically includes the following steps:

[0066] S102: Determine the channel state information based on the received transmission signal from the transmitter according to the channel estimation technology.

[0067] S104: Extract the original authentication vector according to the channel state information.

[0068] S106: Compress the original authentication vector by using a sparse matrix and an observation matrix to determine the authentication vector at the current moment.

[0069] S108: Construct a test statistic by using the authentication vector at the previous moment and the authentication vector at the current moment obtained in advance.

[0070] S110: Determine whether the test statistic is greater than a preset decision threshold.

[0071] S112: In response to the test statistic being greater than the decision threshold, determine that the transmission signal is a legal signal.

[0072] Figure 2 Shows a schematic diagram of an application scenario according to an embodiment of the present application.

[0073] Reference Figure 2 This application considers a large-scale MIMO system, where Bob is the base station, Alice is the mobile terminal, and Eve is the attacker. The current legitimate terminals for communication are Alice and Bob, while Eve attempts to simulate the channel state information of Alice for a simulation attack. Therefore, the message received by Bob may come from the legitimate transmitter Alice or the attacker Eve. To cope with the simulation attack from Eve, Bob needs to authenticate each data packet to ensure the source identity and true integrity of the communication message between Alice and Bob. The channel estimation of the legitimate link can be obtained through the initial transmission between Alice and Bob. Within the channel coherence time, Bob receives the next data packet, and Bob needs to use physical layer authentication to determine the source of the received message.

[0074] In some alternative embodiments, for step S102, after the receiver Bob receives the signal sent by Alice, the channel state information can be determined based on the received transmission signal from the transmitter according to the channel estimation technology. Specifically, methods based on pilot-based channel estimation, semi-blind channel estimation, and blind channel estimation can be used to perform channel estimation on the transmission signal to determine the channel state information.

[0075] In some alternative embodiments, for step S104, after determining the channel state information (also referred to as Channel State Information, CSI), the original authentication vector can be extracted from the channel state information. The original authentication vector may be any relevant channel information, such as the channel frequency domain response, channel impulse response, etc.

[0076] Furthermore, the total channel matrix between the receiving end and the transmitting end can be determined according to the channel state information; wherein, the total channel matrix is expressed as

[0077]

[0078] Determine the total channel matrix as the original authentication vector.

[0079] Specifically, taking the channel frequency domain response as an example in this application, assuming that the channel between each antenna of Bob and Alice has been sampled L times, and this application uses channel taps to represent the multipath channel, then each independent channel between Alice and the nth antenna of Bob can be expressed as:

[0080]

[0081] Define the total channel matrix of N antennas between Alice and Bob as:

[0082]

[0083] In some alternative embodiments, for step S106, the original authentication vector can be compressed using a sparse matrix and an observation matrix to determine the authentication vector at the current moment. Specifically, determine the SIMO channel matrix according to the original authentication vector; wherein, the SIMO channel matrix is expressed as

[0084]

[0085] wherein, R RX represents the column correlation matrix of the channel matrix at the receiving end, and tr() represents the matrix;

[0086] Convert the SIMO channel matrix into an NL-dimensional column vector; wherein, the column vector is expressed as

[0087]

[0088] wherein, represents an L-dimensional column vector, N represents the number of antennas, and L represents the number of paths of each channel; [[ID=…]] [[ID=…]]

[0089] Determine the column vector estimate of the k-th data packet according to the column vector of the k-th data packet; the column vector estimate of the k-th data packet is expressed as

[0090]

[0091] where N(k) represents the error of channel estimation and satisfies complex Gaussian distribution;

[0092] Determine the compressible sparse signal according to the column vector estimate of the k-th data packet.

[0093] It should be noted that the original authentication vector can be sparsified by using a sparse matrix. In a multi-antenna system, because the distances between different receiving antennas are relatively close, there is correlation between their received signals. We can utilize the correlation between them to make it a compressible sparse signal. Generally, each column of is modeled as an independent and identically distributed Gaussian vector. However, in the multi-antenna system of this paper, the channel state information of different receiving antennas is strongly correlated. The SIMO channel matrix H with spatial correlation AB can be modeled as:

[0094]

[0095] where R RX is the column correlation matrix of the channel matrix at the receiving side (i.e., Bob side), and its value can be obtained from the Jakes scattering model.

[0096] H AB can be written as

[0097]

[0098] is an L-dimensional column vector. For the convenience of authentication, H AB is transformed into a column vector:

[0099]

[0100] Z AB is an NL-dimensional column vector, N is the number of antennas, and L is the number of paths of each channel. Assume that the k-th data packet has passed the upper-layer authentication and has been determined to be legal. Then the Z of the k-th data packet AB is

[0101]

[0102] where is an L-dimensional column vector. Let be the estimate of Z of the k-th data packet AB (k), and Z AB(k) There is an error N(k) that satisfies the complex Gaussian distribution, N(k) ~ CN(0, Σ * ), which can be expressed as:

[0103]

[0104] Because has a large dimension, directly using it for authentication has a high complexity. Since the channel state information of different receiving antennas is strongly correlated, according to the theory of compressive sensing, the channel information can be sparsely represented in the space-frequency domain, and most of the information of the original channel is contained in the sparse signal. Therefore, this application considers using the compressed information for authentication to reduce the authentication complexity by reducing the dimension of the channel matrix.

[0105] In some alternative embodiments, after determining the compressible sparse signal based on the estimated value of the column vector of the k-th data packet, the observation matrix can be defined; wherein, the observation matrix is expressed as

[0106] Compress the compressible sparse signal according to the observation matrix to determine the authentication vector at the current moment; wherein, the authentication vector at the current moment is expressed as

[0107]

[0108] Furthermore, the original authentication vector can be randomly sampled using the observation matrix to obtain the authentication vector. In a multi-antenna system, since the dimension of the original authentication vector is large, directly using it for authentication has a high complexity. After being processed by the observation matrix, the dimension of the authentication vector is greatly reduced. The observation matrix here can be a random Gaussian matrix, a random Bernoulli matrix, a partial Hadamard matrix, or other matrices.

[0109] It should be noted that the original authentication vector can be randomly sampled and compressed using the observation matrix. Define as the measurement matrix of compressive sensing. Since the measurement matrix in this article plays a compression role, it will be referred to as the compression matrix in the following text. Using the compression matrix to after compression, we get

[0110]

[0111] Φ can be a random Gaussian matrix, a random Bernoulli matrix, a partial Hadamard matrix, or other matrices.

[0112] In some alternative embodiments, for step S108, a test statistic is constructed by using the authentication vectors at the previous moment and the current moment obtained in advance. Specifically, it can be defined that H0 represents that the transmitted signal comes from a legitimate terminal, and H1 represents that the received signal comes from an illegal terminal;

[0113] According to the authentication vectors at the previous moment and the current moment obtained in advance, let Let the estimated value of the channel state information of the (k + 1)-th data packet be Then H0 and H1 respectively represent that the estimated value of the channel state information of the (k + 1)-th data packet is Then H0 and H1 respectively represent

[0114]

[0115] A test statistic is constructed according to H0 and H1; wherein, the test statistic is expressed as

[0116]

[0117] It should be noted that a test statistic can be constructed based on the normalized Euclidean distance between the current authentication vector and the authentication vector at the previous moment, a decision threshold is set, and the test statistic is compared with the decision threshold value.

[0118] Specifically, after receiving the (k + 1)-th data packet, by calculating the distance between its channel state information and the channel state information of the k-th data packet, it is determined whether the received signal comes from Alice or Eve. Define H0 as the received signal coming from a legitimate terminal, and H1 as the received signal coming from an illegal terminal, and let Let the estimated value of the channel state information of the (k + 1)-th data packet be Then H0 and H1 are respectively represented as follows

[0119]

[0120] Based on Equation 9, a test statistic based on the standardized Euclidean distance is constructed as follows:

[0121]

[0122] Furthermore, before determining whether the test statistic is greater than the pre-set decision threshold, it also includes:

[0123] A binary hypothesis test is determined according to the pre-set decision threshold, and the binary hypothesis test is expressed as

[0124]

[0125] It should be noted that △ is set as the decision threshold. The binary hypothesis test of this application is as follows:

[0126]

[0127] By comparing with the value of the decision threshold, the final decision result is obtained. That is, if it is greater than the decision threshold, it is determined that the received signal is a legal signal; otherwise, it is an illegal signal. It can be understood that subsequent signals can continue to be received for authentication.

[0128] As can be seen from the above, an authentication method, device, electronic device, and storage medium based on compressive sensing provided by this application determine the channel state information based on the channel estimation technology according to the received transmission signal from the transmitting end; extract the original authentication vector according to the channel state information; use the sparse matrix and the observation matrix to perform compression processing on the original authentication vector to determine the authentication vector at the current moment; use the authentication vector at the previous moment and the authentication vector at the current moment obtained in advance to construct a test statistic; determine whether the test statistic is greater than the pre-set decision threshold; in response to the test statistic being greater than the decision threshold, determine that the transmission signal is a legal signal. It meets the requirements for authentication security in the 5G network, reduces the complexity of authentication, speeds up the authentication process, improves the security and reliability of the Internet of Things, and utilizes the characteristics of the physical channel, can resist attacks from the physical layer, and reduces the power consumption of the terminal device.

[0129] This application first utilizes the correlation between the signals received by different antennas in the multi-antenna system, samples and compresses the received signals using a Gaussian random matrix, then constructs a test statistic based on the compressed channel matrices in the two time slots before and after within the coherence time, and analyzes the computational complexity of this scheme under different receiving antennas. The simulation shows that this scheme can significantly reduce the authentication complexity in the multi-antenna scenario, and provides a solution to the problem of too high authentication computational complexity caused by the too large dimension of the channel feature matrix in the large-scale multi-antenna system.

[0130] It should be noted that the method of this application embodiment can be executed by a single device, such as a computer or a server, etc. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In this case of a distributed scenario, one of these multiple devices can only execute one or more steps in the method of this application embodiment, and these multiple devices will interact with each other to complete the described method.

[0131] Note that some embodiments of the present application have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0132] Figure 3 FIG. shows an exemplary structural schematic diagram of an authentication device based on compressive sensing provided by an embodiment of the present application.

[0133] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application further provides an authentication device based on compressive sensing.

[0134] Referring to Figure 3 , the authentication device based on compressive sensing includes: a first determination module, an extraction module, a second determination module, a construction module, a decision module, and a third determination module; wherein,

[0135] The first determination module is configured to determine channel state information based on channel estimation technology according to the received transmission signal from the transmitting end;

[0136] The extraction module is configured to extract an original authentication vector according to the channel state information;

[0137] The second determination module is configured to perform compression processing on the original authentication vector by using a sparse matrix and an observation matrix to determine the authentication vector at the current moment;

[0138] The construction module is configured to construct a test statistic by using the authentication vector at the previous moment and the authentication vector at the current moment obtained in advance;

[0139] The decision module is configured to determine whether the test statistic is greater than a preset decision threshold;

[0140] The third determination module is configured to, in response to the test statistic being greater than the decision threshold, determine that the transmission signal is a legal signal.

[0141] In a possible implementation manner, the first determination module is further configured to:

[0142] Perform channel estimation on the transmission signal by using pilot-based channel estimation, semi-blind channel estimation, and blind channel estimation to determine the channel state information.

[0143] In a possible implementation manner, the extraction module is further configured to:

[0144] Determine the total channel matrix between the receiving end and the transmitting end according to the channel state information; wherein, the total channel matrix is expressed as

[0145]

[0146] Determine the total channel matrix as the original authentication vector.

[0147] In a possible implementation manner, the second determination module is further configured to:

[0148] Determine the SIMO channel matrix according to the original authentication vector; wherein, the SIMO channel matrix is expressed as

[0149]

[0150] wherein, R RX represents the column correlation matrix of the channel matrix of the receiving end, and tr() represents the matrix;

[0151] Convert the SIMO channel matrix into an NL-dimensional column vector; wherein, the column vector is expressed as

[0152]

[0153] wherein, represents an L-dimensional column vector, N represents the number of antennas, and L represents the number of paths of each channel;

[0154] Determine the column vector estimate of the k-th data packet according to the column vector of the k-th data packet; the column vector estimate of the k-th data packet is expressed as

[0155]

[0156] wherein, N(k) represents the error of channel estimation and satisfies the complex Gaussian distribution;

[0157] Determine the compressible sparse signal according to the column vector estimate of the k-th data packet.

[0158] In a possible implementation manner, the second determination module is further configured to:

[0159] Define the observation matrix; wherein, the observation matrix is expressed as

[0160] Perform compression processing on the compressible sparse signal according to the observation matrix to determine the authentication vector at the current moment; wherein, the authentication vector at the current moment is expressed as

[0161]

[0162] In a possible implementation, the building module is further configured to:

[0163] Define H0 to indicate that the transmitted signal comes from a legitimate terminal, and H1 to indicate that the received signal comes from an illegal terminal;

[0164] According to the authentication vectors obtained in advance at the previous moment and the current moment, let Let the estimated value of the channel state information of the (k + 1)-th data packet be Then H0 and H1 respectively indicate that the estimated value of the channel state information of the (k + 1)-th data packet is Then H0 and H1 respectively indicate

[0165]

[0166] Construct a test statistic according to H0 and H1; wherein, the test statistic is expressed as

[0167]

[0168] In a possible implementation, the decision module is further configured to:

[0169] Determine a binary hypothesis test according to the preset decision threshold, and the binary hypothesis test is expressed as

[0170]

[0171] For the convenience of description, when describing the above device, various modules are described separately according to their functions. Of course, when implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0172] The device of the embodiment is used to implement the corresponding compressed-sensing-based authentication method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated herein.

[0173] Figure 4 Shows an exemplary structural schematic diagram of an electronic device provided by an embodiment of the present application.

[0174] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the compressed-sensing-based authentication method described in any of the above embodiments. Figure 4FIG. 0 shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 410, a memory 420, an input / output interface 430, a communication interface 440, and a bus 450. Among them, the processor 410, the memory 420, the input / output interface 430, and the communication interface 440 are communicatively connected to each other inside the device through the bus 450.

[0175] The processor 410 may be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0176] The memory 420 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 420 may store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 420 and are called and executed by the processor 410.

[0177] The input / output interface 430 is used to connect to an input / output module to implement information input and output. The input / output module may be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0178] The communication interface 440 is used to connect to a communication module (not shown in the figure) to implement communication interaction between this device and other devices. Among them, the communication module may implement communication in a wired manner (such as USB, network cable, etc.) or in a wireless manner (such as mobile network, WIFI, Bluetooth, etc.).

[0179] The bus 450 includes a path for transmitting information between various components of the device (such as the processor 410, the memory 420, the input / output interface 430, and the communication interface 440).

[0180] Note that although the device only shows the processor 410, the memory 420, the input / output interface 430, the communication interface 440, and the bus 450, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the device may also only include the components necessary to implement the solution of the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0181] The electronic device of the embodiment is used to implement the corresponding authentication method based on compressive sensing in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0182] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application also provides a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the authentication method based on compressive sensing as described in any of the foregoing embodiments.

[0183] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0184] The computer instructions stored in the storage medium of the embodiment are used to cause the computer to execute the authentication method based on compressive sensing as described in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0185] Those of ordinary skill in the art should understand that: the discussion of any of the above embodiments is only exemplary, and is not intended to imply that the scope of the present application (including the claims) is limited to these examples; under the idea of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present application as described above, and they are not provided in detail for the sake of brevity.

[0186] In addition, for simplicity of explanation and discussion, and so as not to make the embodiments of the present application difficult to understand, well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Further, the devices may be shown in block diagram form in order to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that details regarding the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present application are to be implemented (i.e., these details should be fully within the understanding of those skilled in the art). In cases where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application may be practiced without these specific details or with variations of these specific details. Accordingly, these descriptions should be considered illustrative rather than restrictive.

[0187] Although the present application has been described in connection with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0188] Embodiments of the present application are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of the present application shall be included within the protection scope of the present application.

Claims

1. An authentication method based on compressive sensing, characterized in that including: determining channel state information based on channel estimation technology according to the received transmission signal from the transmitter; extracting an original authentication vector according to the channel state information; determining a SIMO channel matrix according to the original authentication vector; wherein, the SIMO channel matrix is expressed as where R RX represents the column correlation matrix of the channel matrix of the receiving end, and tr() represents a matrix; converting the SIMO channel matrix into an NL-dimensional column vector; wherein, the column vector is expressed as Among them, represents an L-dimensional column vector, N represents the number of antennas, and L represents the number of paths of each channel; determining an estimated value of the column vector of the k-th data packet according to the column vector of the k-th data packet; the estimated value of the column vector of the k-th data packet is expressed as Determine a compressible sparse signal based on the column vector estimation value of the k-th data packet; define an observation matrix; wherein, the observation matrix is represented as Φ ∈ R M*NL (M << NL); wherein, N(k) represents the error of channel estimation and satisfies a complex Gaussian distribution; compressing the compressible sparse signal according to the observation matrix to determine the authentication vector at the current moment; wherein, the authentication vector at the current moment is expressed as constructing a test statistic by using the previously obtained authentication vector at the previous moment and the authentication vector at the current moment; determining whether the test statistic is greater than a preset decision threshold; 2. The method according to claim 1, wherein in response to the test statistic being greater than the decision threshold, determining that the transmission signal is a legal signal. The determining of the channel state information based on channel estimation technology according to the received transmission signal from the transmitter further includes:

3. The method according to claim 1, wherein performing channel estimation on the transmission signal by using pilot-based channel estimation, semi-blind channel estimation and blind channel estimation to determine the channel state information. [[ID=,13]]The extracting of the original authentication vector according to the channel state information further includes: determining a total channel matrix between the receiver and the transmitter according to the channel state information; wherein, the total channel matrix is expressed as 4. The method according to claim 1, wherein determining the total channel matrix as the original authentication vector. The constructing of the test statistic by using the previously obtained authentication vector at the previous moment and the authentication vector at the current moment further includes: Based on the pre-acquired authentication vector at the previous moment and the authentication vector at the current moment, let Let the estimated value of the channel state information of the (k + 1)-th data packet be Then H0 and H1 respectively represent that the estimated value of the channel state information of the (k + 1)-th data packet is Then H0 and H1 respectively represent defining H0 to represent that the transmission signal comes from a legal terminal and H1 to represent that the received signal comes from an illegal terminal; 5. The method according to claim 1, wherein constructing a test statistic according to H0 and H1; wherein, the test statistic is expressed as Before the determining of whether the test statistic is greater than a preset decision threshold, it further includes:

6. An authentication device based on compressive sensing, characterized in that, determining a binary hypothesis test according to the preset decision threshold, and the binary hypothesis test is expressed as including: a first determining module configured to determine channel state information based on channel estimation technology according to the received transmission signal from the transmitter; an extraction module configured to extract an original authentication vector according to the channel state information; where R RX represents the column correlation matrix of the channel matrix of the receiving end, and tr() represents a matrix; a second determining module configured to determine a SIMO channel matrix according to the original authentication vector; wherein, the SIMO channel matrix is expressed as Among them, represents an L-dimensional column vector, N represents the number of antennas, and L represents the number of paths of each channel; converting the SIMO channel matrix into an NL-dimensional column vector; wherein, the column vector is expressed as determining an estimated value of the column vector of the k-th data packet according to the column vector of the k-th data packet; the estimated value of the column vector of the k-th data packet is expressed as Determine a compressible sparse signal based on the column vector estimation value of the k-th data packet; define an observation matrix; wherein, the observation matrix is expressed as Φ ∈ R M*NL (M << NL); wherein, N(k) represents the error of channel estimation and satisfies a complex Gaussian distribution; compressing the compressible sparse signal according to the observation matrix to determine the authentication vector at the current moment; wherein, the authentication vector at the current moment is expressed as A construction module, configured to construct a test statistic by using a pre-acquired authentication vector at a previous moment and an authentication vector at the current moment; A decision module, configured to determine whether the test statistic is greater than a pre-set decision threshold; A third determination module, configured to determine that the transmitted signal is a legal signal in response to the test statistic being greater than the decision threshold.

7. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the method described in any one of claims 1 to 5 is implemented.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to implement the method described in any one of claims 1 to 5.

Images