A method and device for intelligent metasurface-assisted key generation

Abstract

This invention discloses a method and device for intelligent metasurface-assisted key generation. First, the intelligent metasurface can modify channel characteristics, randomizing the channel within the coherence time to achieve multiple key extractions, thereby improving the key rate. Simultaneously, within the same coherence time, the results of previous mutual channel estimations are used to estimate the endogenous channel. Phase matching is then used to determine the coefficients of the intelligent metasurface for the final mutual channel estimation. This invention improves the key rate while reducing the key inconsistency rate.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication security, and in particular relates to a method and device for generating an intelligent metasurface-assisted key. Background Art

[0002] To achieve secure communications, current communication devices primarily rely on modern cryptography based on the intractability of mathematical problems, such as the Diffie-Hellman (DH) key exchange protocol. However, these algorithms are computationally complex, and in the IoT, some terminals or sensors lack significant storage and computing power, making it difficult to configure and implement modern encryption algorithms. Given the low power consumption and computing power of IoT devices, which pose a threat to communication security, the goal is to utilize physical layer keys to generate encryption keys and ensure secure communications. Furthermore, the deployment of smart metasurfaces is becoming a hot topic in the future IoT, making the design of a smart metasurface-assisted physical layer key generation solution essential.

[0003] Patent No. ZL202110423339.2 discloses a wireless channel key generation method and device based on phase assistance of an intelligent reflective surface, and patent No. ZL202011435513.7 discloses a system and method for improving the physical layer key generation performance using an intelligent reflective array. When they use intelligent metasurfaces for physical layer key generation, the reflection coefficient of the intelligent metasurface is randomly selected, and the improvement on the key rate and key consistency is limited. Summary of the Invention

[0004] In response to the problems existing in the prior art, the present invention provides an intelligent metasurface-assisted key generation method and device, which improves the key rate and reduces the key inconsistency rate.

[0005] In order to solve the above technical problems, the present invention is implemented through the following technical solutions:

[0006] An intelligent metasurface-assisted key generation method, comprising:

[0007] The first device calculates the number N of units of the smart metasurface;

[0008] The first device and the second device perform N+1 mutual channel estimations, and obtain LM(N+1) estimated channel values ​​respectively, where L is the number of antennas of the first device and M is the number of antennas of the second device;

[0009] During the N+1 mutual channel estimation phase, when the first device and the second device perform each mutual channel estimation, the first device randomly selects the intelligent metasurface reflection coefficient, and obtains a total of N+1 sets of reflection coefficients;

[0010] The first device calculates LM(N+1) endogenous channel values ​​based on the LM(N+1) estimated channel values ​​and the N+1 groups of reflection coefficients;

[0011] The first device calculates, based on the LM(N+1) endogenous channel values, a smart metasurface reflection coefficient when the first device and the second device perform the N+2 mutual channel estimation, and configures the smart metasurface reflection coefficient during the N+2 mutual channel estimation to the smart metasurface;

[0012] After configuration, the second device performs an N+2th channel estimation to obtain an N+2th estimated channel value of the second device;

[0013] The first device calculates the N+2th estimated channel value of the first device based on the LM(N+1) endogenous channel values ​​and the intelligent metasurface reflection coefficient during the N+2th mutual channel estimation;

[0014] The first device and the second device quantize their respective LM(N+2) estimated channel values ​​to obtain a bit sequence;

[0015] The first device and the second device perform error correction negotiation on the bit sequence to obtain a completely consistent key.

[0016] Furthermore, the first device calculates the number N of units of the smart metasurface using the following formula:

[0017]

[0018] Where: T c is the coherence time; T p is the time required for the first device and the second device to perform a mutual channel estimation; floor(*) is a rounding-down function.

[0019] Furthermore, the first device calculates LM(N+1) endogenous channel values ​​based on LM(N+1) estimated channel values ​​and N+1 groups of reflection coefficients, and the calculation formula is:

[0020] h lm =(Φ) -1 a lm 1≤l≤L,1≤m≤M

[0021] H=[h 11 h 12 … h LM ] T

[0022] Where:

[0023] in, Indicates the estimated channel value from the lth antenna of the first device to the mth antenna of the second device obtained by the first device when the first device and the second device perform mutual channel estimation for the tth time;

[0024] Φ=[Φ1 Φ2 … Φ N+1 ] T represents N+1 groups of reflection coefficients, in, represents the coefficient of the nth smart metasurface unit in the tth group of reflection coefficients;

[0025] in, When the coefficient of the smart metasurface unit is 1, the intrinsic reflection channel value of the first device's lth antenna-nth smart metasurface unit-mth antenna of the second device, Indicates the intrinsic direct channel value from the lth antenna of the first device to the mth antenna of the second device;

[0026] H represents a matrix consisting of LM(N+1) endogenous channel values.

[0027] Furthermore, the first device calculates the intelligent metasurface reflection coefficient when the first device and the second device perform the N+2th mutual channel estimation based on the LM(N+1) endogenous channel values, and the calculation formula is as follows:

[0028]

[0029] In the formula, take the maximum amplitude of each column in the first N columns of the matrix H and record the row number where the maximum value is located, and get x n Indicates the row number corresponding to the maximum amplitude in the nth column, is the xth column in the matrix H n The number of rows corresponding to is the last column x in matrix H n The number corresponding to the row; exp(*) is the exponential function; angle(*) is the complex phase angle function.

[0030] Furthermore, the first device estimates the intelligent metasurface reflection coefficient Φ according to LM(N+1) endogenous channel values ​​and the N+2th mutual channel estimation. N+2 , calculate the N+2th estimated channel value of the first device, and the calculation formula is as follows:

[0031]

[0032] Where: Φ N+2 represents the intelligent metasurface reflection coefficient during the N+2th mutual estimation channel, represents the N+2th estimated channel value of the first device.

[0033] Furthermore, the first device and the second device quantize their respective LM(N+2) estimated channel values ​​to obtain a bit sequence, and the quantization method includes uniform quantization, double threshold quantization or CQ quantization.

[0034] Furthermore, the first device and the second device perform error correction negotiation on the bit sequence, and the negotiation method includes a Cascade error correction negotiation method or an LDPC error correction negotiation method.

[0035] An intelligent metasurface-assisted key generation device, comprising:

[0036] A first calculation module, configured in the first device, is used for the first device to calculate the number N of units of the smart metasurface;

[0037] a channel estimation module, configured in the first device and the second device, configured to perform N+1 mutual channel estimations between the first device and the second device, respectively obtaining LM(N+1) estimated channel values, where L is the number of antennas of the first device and M is the number of antennas of the second device; and configured to perform an N+2th channel estimation on the second device, obtaining an N+2th estimated channel value for the second device;

[0038] A random selection module is configured in the first device and is used to randomly select the intelligent metasurface reflection coefficient when the first device and the second device perform each mutual channel estimation in the N+1 mutual channel estimation stage, so as to obtain a total of N+1 sets of reflection coefficients;

[0039] A second calculation module is configured in the first device, and is used for the first device to calculate LM(N+1) endogenous channel values ​​based on the LM(N+1) estimated channel values ​​and the N+1 groups of reflection coefficients;

[0040] A third calculation module is configured in the first device, and is used for the first device to calculate the smart metasurface reflection coefficient when the first device and the second device perform the N+2th mutual channel estimation based on the LM(N+1) endogenous channel values, and configure the smart metasurface reflection coefficient during the N+2th mutual channel estimation to the smart metasurface;

[0041] a fourth calculation module, configured in the first device, for the first device to calculate the N+2th estimated channel value of the first device based on the LM(N+1) endogenous channel values ​​and the intelligent metasurface reflection coefficient during the N+2th mutual channel estimation;

[0042] A quantization module is configured in the first device and the second device, and is used for the first device and the second device to quantize their respective LM(N+2) estimated channel values ​​to obtain a bit sequence;

[0043] The error correction module is configured in the first device and the second device, and is used for the first device and the second device to perform error correction negotiation on the bit sequence to obtain a completely consistent key.

[0044] Compared with the prior art, the present invention has at least the following beneficial effects:

[0045] First, this invention divides the mutual estimation channel within a coherent time into two stages. In the first stage, the smart metasurface coefficients are randomly selected during each mutual estimation. In the second stage, the smart metasurface coefficients are calculated based on the current endogenous channel values. By combining these two stages, the key rate is further improved while the key inconsistency rate is reduced. Furthermore, utilizing the concept of "phase matching," a low-complexity method for calculating the smart metasurface reflection coefficient is proposed in the second stage.

[0046] Second, the present invention provides a rule for allocating time slots used by the mutual estimation channel within the coherence time. Within a coherence timeframe, different time slots are allocated to the first-stage mutual estimation channel and the second-stage mutual estimation channel. This allows the channel values ​​obtained in the first stage to both generate the key and determine the required smart metasurface reflection coefficient in the second stage. This allocation allows for better utilization of the smart metasurface and improves key performance.

[0047] Third, this invention provides a simple method for determining the number of reflective units on the intelligent metasurface. This method uses two parameters, coherence time and the time required for the first and second devices to estimate the channel, to determine the number of reflective units. This method does not require an unlimited number of reflective units. While improving key performance, it does so without excessively utilizing intelligent metasurface resources.

[0048] In summary, the intelligent metasurface-assisted key generation method provided by the present invention utilizes the controllable channel of the intelligent metasurface, has the characteristics of channel gain, improves the key rate and reduces the key inconsistency rate.

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the specific embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the specific embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 This is a simplified system diagram of the present invention;

[0052] Figure 2 It is a flow chart of an embodiment scheme;

[0053] Figure 3 It is a time slot diagram within the coherence time of the present invention;

[0054] Figure 4 This is a key rate comparison chart;

[0055] Figure 5 This is a comparison chart of key inconsistency rates. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0057] Smart metasurfaces have the ability to manipulate channels and increase signal strength, allowing them to change static or quasi-static environments that are unfavorable for physical layer key generation, while also increasing key capacity by improving the signal-to-noise ratio. During physical layer key extraction, since the endogenous channel remains essentially unchanged within a coherence time, from an information-theoretic security perspective, the key can only be extracted once within the coherence time. However, because smart metasurfaces can manipulate channels, the coherence time can be further divided to perform multiple key extractions. At the same time, the smart metasurface coefficients can also be matched to the current channel to increase the key rate. Of course, in situations where the direct channel between communication devices is blocked, the introduction of smart metasurfaces can bring gain to the reflected channel, improving initial key consistency.

[0058] As a specific embodiment of the present invention, a smart metasurface assisted key generation method specifically includes the following steps:

[0059] S1. The first device calculates the number N of units of the smart metasurface using the following formula:

[0060]

[0061] Where: T c is the coherence time; T p is the time required for the first device and the second device to perform a mutual channel estimation; floor(*) is a rounding-down function.

[0062] S2. The first device and the second device perform N+1 mutual channel estimations to obtain LM(N+1) estimated channel values, respectively, where L is the number of antennas of the first device and M is the number of antennas of the second device.

[0063] In the present invention, the first device and the second device may both be a base station and a terminal. When the first device is a base station, the second device is a terminal; when the first device is a terminal, the second device is a base station.

[0064] S3. In the N+1 mutual channel estimation stage, each time the first device and the second device perform mutual channel estimation, the first device randomly selects the intelligent metasurface reflection coefficient, and obtains a total of N+1 sets of reflection coefficients.

[0065] S4. The first device calculates LM(N+1) endogenous channel values ​​based on LM(N+1) estimated channel values ​​and N+1 groups of reflection coefficients. The calculation formula is:

[0066] h lm =(Φ) - 1a lm 1≤l≤L,1≤m≤M

[0067] H=[h 11 h 12 … h LM ] T

[0068] Where:

[0069] in, Indicates the estimated channel value from the lth antenna of the first device to the mth antenna of the second device obtained by the first device when the first device and the second device perform mutual channel estimation for the tth time;

[0070] Φ=[Φ1 Φ2 … Φ N+1 ] T represents N+1 groups of reflection coefficients, where in, represents the coefficient of the nth smart metasurface unit in the tth group of reflection coefficients;

[0071] in, is the intrinsic reflection channel value of the first device’s lth antenna – the nth smart metasurface unit – the second device’s mth antenna when the coefficient of the smart metasurface unit is 1 (i.e., the smart metasurface only plays a reflective role and does not change the signal value). Indicates the intrinsic direct channel value from the lth antenna of the first device to the mth antenna of the second device;

[0072] H represents a matrix consisting of LM(N+1) endogenous channel values.

[0073] S5. The first device calculates the intelligent metasurface reflection coefficient when the first device and the second device perform the N+2th mutual channel estimation based on the LM(N+1) endogenous channel values, and configures the intelligent metasurface reflection coefficient during the N+2th mutual channel estimation to the intelligent metasurface.

[0074] Specifically, the first device calculates the intelligent metasurface reflection coefficient when the first device and the second device perform the N+2th mutual channel estimation based on the LM(N+1) endogenous channel values. The calculation formula is as follows:

[0075]

[0076] In the formula, take the maximum amplitude of each column in the first N columns of the matrix H and record the row number where the maximum value is located, and get x n Indicates the row number corresponding to the maximum amplitude in the nth column, is the xth column in the matrix H n The number of rows corresponding to The xth column in the last column of matrix H n The number corresponding to the row; exp(*) is the exponential function; angle(*) is the complex phase angle function.

[0077] S6. After configuration, the second device performs the N+2th channel estimation to obtain the N+2th estimated channel value of the second device.

[0078] S7. The first device calculates the N+2th estimated channel value of the first device based on the LM(N+1) endogenous channel values ​​and the intelligent metasurface reflection coefficient during the N+2th mutual channel estimation. The calculation formula is as follows:

[0079]

[0080] Where: Φ N+2 represents the intelligent metasurface reflection coefficient during the N+2th mutual estimation channel, represents the N+2th estimated channel value of the first device.

[0081] S8. The first device and the second device quantize their respective LM(N+2) estimated channel values ​​to obtain a bit sequence.

[0082] Preferably, the quantization method includes but is not limited to uniform quantization, double threshold quantization or CQ quantization.

[0083] S9. The first device and the second device perform error correction negotiation on the bit sequence to obtain a completely consistent key.

[0084] Preferably, the negotiation method includes but is not limited to a Cascade error correction negotiation method or an LDPC error correction negotiation method.

[0085] The present invention combines two ideas: random selection of intelligent metasurface reflection coefficients and specific selection based on the current endogenous channel. At the same time, from the perspective of information theory, after the number of intelligent metasurface reflection units increases to a certain number, the key capacity increases slowly. Therefore, in practice, considering the overhead, it is necessary to determine the number of intelligent metasurface reflection units, rather than more is better. The present invention provides a simple method for determining the number of intelligent metasurface reflection units. Finally, based on the process of the overall solution, the allocation rules for the time slots used for the mutual estimation channel within the coherence time are also given. In short, compared with the existing methods, the present invention further improves the key rate and reduces the key inconsistency rate.

[0086] The present invention also provides an intelligent metasurface-assisted key generation device, comprising:

[0087] The first calculation module is configured in the first device and is used by the first device to calculate the number N of units of the smart metasurface.

[0088] The channel estimation module is configured in the first device and the second device, and is used for the first device and the second device to perform N+1 mutual channel estimations to obtain LM(N+1) estimated channel values ​​respectively, where L is the number of antennas of the first device and M is the number of antennas of the second device; it is also used for the second device to perform the N+2th channel estimation to obtain the N+2th estimated channel value of the second device.

[0089] The random selection module is configured in the first device and is used to randomly select the intelligent metasurface reflection coefficient when the first device and the second device perform each mutual channel estimation in the N+1 mutual channel estimation stage, and obtain a total of N+1 groups of reflection coefficients.

[0090] The second calculation module is configured in the first device, and is used by the first device to calculate LM(N+1) endogenous channel values ​​based on LM(N+1) estimated channel values ​​and N+1 groups of reflection coefficients.

[0091] The third calculation module is configured in the first device, and is used for the first device to calculate the intelligent metasurface reflection coefficient when the first device and the second device perform the N+2th mutual channel estimation based on LM(N+1) endogenous channel values, and configure the intelligent metasurface reflection coefficient during the N+2th mutual channel estimation to the intelligent metasurface.

[0092] The fourth calculation module is configured in the first device and is used for the first device to calculate the N+2th estimated channel value of the first device based on LM(N+1) endogenous channel values ​​and the intelligent metasurface reflection coefficient during the N+2th mutual channel estimation.

[0093] The quantization module is configured in the first device and the second device, and is used for the first device and the second device to quantize their respective LM(N+2) estimated channel values ​​to obtain a bit sequence.

[0094] The error correction module is configured in the first device and the second device, and is used for the first device and the second device to perform error correction negotiation on the bit sequence to obtain a completely consistent key.

[0095] An embodiment is provided below. In this embodiment, both the first device and the second device are single-antenna devices. The details are as follows:

[0096] Combine Figure 2 As shown, a smart metasurface assisted key generation method includes the following steps:

[0097] Step 1: Assume that the coherence time in the current channel environment is T c The time required for the first device and the second device to estimate the channel is T p The first device calculates the number of units of the smart metasurface Where floor(*) is the rounding down function.

[0098] Step 2: The first device and the second device perform N+1 mutual channel estimations.

[0099] At the beginning of each mutual estimation channel, the first device first randomly selects the intelligent metasurface reflection coefficient. For example, the intelligent metasurface reflection coefficient of the tth time is recorded as Among them, θ n ~U(0,2π),1≤n≤N, and then mutually estimate the channel. The first device and the second device store the channel values ​​estimated by each device. At the same time, the first device also stores the randomly selected smart metasurface reflection coefficient each time.

[0100] N+1 channel values ​​estimated by the first device: a=[a 1 a 2 … a N+1 ] T ;

[0101] N+1 channel values ​​estimated by the second device: b=[b 1 b 2 … b N+1 ] T ;

[0102] The first device knows N+1 sets of smart metasurface reflection coefficients: Φ=[Φ1 Φ2 … Φ N+1 ] T ;

[0103] It should be noted that φt The random selection method is not limited to the above method, and can also be selected in the DFT matrix.

[0104] Step 3: The first device calculates the endogenous channel value based on Φ and a stored in step 2:

[0105] h=Φ -1 a

[0106] Step 4: The first device calculates the intelligent metasurface reflection coefficient required for the N+2th mutual estimation channel based on h described in step 3. The calculation method is as follows:

[0107] Let h=[h 1 ,h 2 …h N+1 ] T , then the reflection coefficient of the smart metasurface during the N+2th mutual estimation channel is:

[0108]

[0109] Where exp(*) is the exponential function and angle(*) is the complex phase angle function.

[0110] Step 5: The first device sets the smart metasurface reflection coefficient value to φ calculated in step 4 N+2 , the first device sends a signal to the second device for the second device to perform channel estimation.

[0111] The second device estimates the channel value for the N+2th time as: b N+2 ;

[0112] The first device then uses h in the third step and φ in the fourth step N+2 The estimated channel value for the N+2th time is calculated:

[0113] Finally, all estimated channel values ​​obtained by the first device within the current coherence time are: All estimated channel values ​​obtained by the second device within the current coherence time are:

[0114] Step 6: To obtain a sufficiently long key, the first device and the second device can perform steps 1 and 5 multiple times at different coherence times. The estimated channel value obtained by the first device in the dth coherence time is: The estimated channel value obtained by the second device within the dth coherence time is: After D coherence times, all channel values ​​stored by the first device are: All channel values ​​stored in the second device are: The first device and the second device quantize all stored channel values ​​to obtain a bit sequence. In this embodiment, uniform quantization is adopted, and the channel amplitude quantization area is divided into 4, and the phase angle quantization area is divided into 4.

[0115] Step 7: The first device and the second device use the LDPC error correction negotiation algorithm to correct the bit sequence obtained in step 7 to obtain a completely consistent bit key sequence.

[0116] Simulations were performed for this embodiment to provide a comparative verification. This comparison scheme employs the key concepts of the wireless channel key generation method based on intelligent reflector phase assistance, as disclosed in patent number ZL202110423339.2. This comparison scheme uses the same quantization method as this method, and both consider the single-carrier scenario. The pre-negotiation key rate and key inconsistency rate of these two methods were compared.

[0117] In the simulation comparison, the parameters of this embodiment are selected as follows: the signal-to-noise ratio SNR is 5-15dB. Figure 1 As shown, the direct channel h between the first device and the second device AB Satisfying the distribution CN(0,P AB ), N is the number of smart metasurface units, which is 16. The reflection channel h from the first device to the smart metasurface unit to the second device AR *h RB Satisfying the distribution CN(0,P ARB ). The noise n at the first device end A and the noise n at the second device B Satisfy independent and identical distribution: CN(0,P N ). P AB =1,P ARB =0.1, T p =1s. Simulation results are shown in Figure 4 and Figure 5 ,from Figure 4 It can be seen from the above that this embodiment improves the key rate compared with the comparative solution. Figure 5 It can be seen from the figure that this embodiment further reduces the key inconsistency rate compared with the comparative solution.

[0118] The key generated by the present invention can be used for secure communication. The method first uses the characteristics of the intelligent metasurface to change the channel, so that the channel within the coherence time is randomized to achieve the effect of multiple key extractions, thereby improving the key rate. At the same time, within the same coherence time, the endogenous channel can be estimated using the results of the previous mutual estimation of the channel, and then the coefficient of the intelligent metasurface at the time of the last mutual estimation of the channel is determined using the idea of ​​"phase matching". Through this operation, the key rate is further improved, and at the same time, the key inconsistency rate is reduced. Based on the process of the overall solution, the present invention provides a simple method for determining the number of intelligent metasurface reflection units and the allocation rules for the time slots used for the mutual estimation channel within the coherence time, making the solution more practical. Through simulation comparison, the present invention is compared with other intelligent metasurface-assisted key generation methods, and the key rate is improved and the key inconsistency rate is reduced. It is a better intelligent metasurface-assisted key generation method.

[0119] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A smart metasurface assisted key generation method, characterized in that: include: The first device calculates the number N of units of the smart metasurface; The first device and the second device perform N+1 mutual channel estimations, and obtain LM(N+1) estimated channel values ​​respectively, where L is the number of antennas of the first device and M is the number of antennas of the second device; During the N+1 mutual channel estimation phase, when the first device and the second device perform each mutual channel estimation, the first device randomly selects the intelligent metasurface reflection coefficient, and obtains a total of N+1 sets of reflection coefficients; The first device calculates LM(N+1) endogenous channel values ​​based on the LM(N+1) estimated channel values ​​and the N+1 groups of reflection coefficients; The first device calculates, based on the LM(N+1) endogenous channel values, a smart metasurface reflection coefficient when the first device and the second device perform the N+2 mutual channel estimation, and configures the smart metasurface reflection coefficient during the N+2 mutual channel estimation to the smart metasurface; After configuration, the second device performs an N+2th channel estimation to obtain an N+2th estimated channel value of the second device; The first device calculates the N+2th estimated channel value of the first device based on the LM(N+1) endogenous channel values ​​and the intelligent metasurface reflection coefficient during the N+2th mutual channel estimation; The first device and the second device quantize their respective LM(N+2) estimated channel values ​​to obtain a bit sequence; The first device and the second device perform error correction negotiation on the bit sequence to obtain a completely consistent key.

2. The method for generating a key by using an intelligent metasurface according to claim 1, wherein: The first device calculates the number of units N of the smart metasurface using the following formula: Where: T c is the coherence time; T p is the time required for the first device and the second device to perform a mutual channel estimation; floor(*) is a rounding-down function.

3. The intelligent metasurface-assisted key generation method according to claim 1, characterized in that: The first device calculates LM(N+1) endogenous channel values ​​based on LM(N+1) estimated channel values ​​and N+1 groups of reflection coefficients, and the calculation formula is: H=[h 11 h 12 … h LM ] T Where: in, Indicates the estimated channel value from the lth antenna of the first device to the mth antenna of the second device obtained by the first device when the first device and the second device perform mutual channel estimation for the tth time; Φ=[Φ1 Φ2 … Φ N+1 ] T represents N+1 groups of reflection coefficients, in, represents the coefficient of the nth smart metasurface unit in the tth group of reflection coefficients; in, When the coefficient of the smart metasurface unit is 1, the intrinsic reflection channel value of the first device's lth antenna-nth smart metasurface unit-second device's mth antenna, Indicates the intrinsic direct channel value from the lth antenna of the first device to the mth antenna of the second device; H represents a matrix consisting of LM(N+1) endogenous channel values.

4. The method for generating a key by using an intelligent metasurface according to claim 3, wherein: The first device calculates the intelligent metasurface reflection coefficient when the first device and the second device perform the N+2th mutual channel estimation based on the LM(N+1) endogenous channel values. The calculation formula is as follows: In the formula, take the maximum amplitude of each column in the first N columns of the matrix H and record the row number where the maximum value is located, and get x n Indicates the row number corresponding to the maximum amplitude in the nth column, is the xth column in the matrix H n The number of rows corresponding to is the last column x in matrix H n The number corresponding to the row; exp(*) is the exponential function; angle(*) is the complex phase angle function.

5. The intelligent metasurface-assisted key generation method according to claim 4, characterized in that: The first device estimates the intelligent metasurface reflection coefficient Φ according to LM(N+1) endogenous channel values ​​and the N+2th mutual channel estimation. N+2 , calculate the N+2th estimated channel value of the first device, and the calculation formula is as follows: Where: Φ N+2 represents the intelligent metasurface reflection coefficient during the N+2th mutual estimation channel, represents the N+2th estimated channel value of the first device.

6. The method for generating a key by intelligent metasurface according to claim 1, wherein: The first device and the second device quantize their respective LM(N+2) estimated channel values ​​to obtain a bit sequence, and the quantization method includes uniform quantization, double threshold quantization or CQ quantization.

7. The intelligent metasurface-assisted key generation method according to claim 1, characterized in that: The first device and the second device perform error correction negotiation on the bit sequence, and the negotiation method includes a Cascade error correction negotiation method or an LDPC error correction negotiation method.

8. An intelligent metasurface-assisted key generation device, characterized in that: include: A first calculation module, configured in the first device, is used for the first device to calculate the number N of units of the smart metasurface; a channel estimation module, configured in the first device and the second device, configured to perform N+1 mutual channel estimations between the first device and the second device, respectively obtaining LM(N+1) estimated channel values, where L is the number of antennas of the first device and M is the number of antennas of the second device; and configured to perform an N+2th channel estimation on the second device, obtaining an N+2th estimated channel value for the second device; A random selection module is configured in the first device and is used to randomly select the intelligent metasurface reflection coefficient when the first device and the second device perform each mutual channel estimation in the N+1 mutual channel estimation stage, so as to obtain a total of N+1 sets of reflection coefficients; A second calculation module is configured in the first device, and is used for the first device to calculate LM(N+1) endogenous channel values ​​based on the LM(N+1) estimated channel values ​​and the N+1 groups of reflection coefficients; A third calculation module is configured in the first device, and is used for the first device to calculate the smart metasurface reflection coefficient when the first device and the second device perform the N+2th mutual channel estimation based on the LM(N+1) endogenous channel values, and configure the smart metasurface reflection coefficient during the N+2th mutual channel estimation to the smart metasurface; a fourth calculation module, configured in the first device, for the first device to calculate the N+2th estimated channel value of the first device based on the LM(N+1) endogenous channel values ​​and the intelligent metasurface reflection coefficient during the N+2th mutual channel estimation; A quantization module is configured in the first device and the second device, and is used for the first device and the second device to quantize their respective LM(N+2) estimated channel values ​​to obtain a bit sequence; The error correction module is configured in the first device and the second device, and is used for the first device and the second device to perform error correction negotiation on the bit sequence to obtain a completely consistent key.

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