Shore-to-ship shortwave secure transmission device and method based on massive MIMO

Abstract

A massive MIMO-based shore-to-ship shortwave secure transmission device and method includes a description module, an establishment module, a precoding module, a fixed station selection module, and a transmit power optimization module. The description module describes the roles and relationships of the various elements in the massive MIMO-based shore-to-ship secure transmission method. The establishment module establishes a mathematical model for user location-based precoding schemes, fixed station selection, and achievable rate distribution. The precoding module solves the precoding problem for each fixed station. The fixed station selection module selects an appropriate fixed station based on the user's location. This invention effectively improves the low user rate and wide coverage-related security issues inherent in traditional shortwave sea wave communications.

Description

Technical Field

[0001] The present invention relates to the field of anti-eavesdropping communication technology, and in particular to a shore-to-ship shortwave secure transmission device and method based on massive MIMO. Background Art

[0002] The open nature of wireless channels makes communications potentially eavesdroppers, potentially posing a risk to communication security. With the rapid development of wireless communications, users have placed higher demands on the security of communication services. Consequently, physical layer security technologies, aimed at ensuring secure information transmission, have been extensively researched.

[0003] Most existing work is based on passive and active eavesdropping schemes. Countermeasures against passive eavesdropping typically utilize artificial noise, which allows legitimate users to identify and filter out the noise while preventing the eavesdropper from detecting it, significantly reducing the eavesdropper's signal-to-noise ratio (SINR). In active eavesdropping scenarios, the eavesdropper sends the same pilot signal as the legitimate user to a fixed station, causing the station to direct its beam toward the eavesdropper instead of the legitimate user, thereby ensuring better signal reception quality. Furthermore, technologies such as Coordinated Multipoint (CoMP) and Multiple-Input Multiple-Output (MIMO) can provide secure transmission for communication systems. Cooperative Multipoint (CoMP) is being applied in areas such as secure heterogeneous network coverage, secure drone communications, and multi-beam satellite communications.

[0004] The above work does not take into account the further combination of the cooperative characteristics of multi-point coordinated communication and the sparse characteristics of the channel in the angular domain in MIMO technology. They are often carried out separately, which shows that there are other aspects to improve the security of the communication system. Summary of the Invention

[0005] To address the above issues, the present invention proposes a shore-to-ship secure transmission method based on massive MIMO. This method effectively leverages the advantages of both MIMO and CoMP to address anti-eavesdropping issues. It also proposes a coordinated beamforming method within the angular domain and further leverages the spatial distribution of capacity in MIMO systems to enhance security. The details are as follows:

[0006] A shore-to-ship shortwave secure transmission device based on massive MIMO, comprising:

[0007] A description module is used to describe the role of each element in the shore-to-ship secure transmission method based on massive MIMO and the relationship between them;

[0008] Establishing a module for establishing a mathematical model for precoding schemes, fixed station selection, and achievable rate distribution based on user location;

[0009] Precoding module, used to solve the precoding problem of each fixed station;

[0010] A fixed station selection module is used to select a suitable fixed station according to the user's location;

[0011] The power optimization module is used to optimize the transmission power of the fixed station.

[0012] A shore-to-ship shortwave secure transmission method based on massive MIMO includes the following steps:

[0013] Step 1: Describe the roles of various elements in the shore-to-ship secure transmission method based on massive MIMO and the relationships between them;

[0014] Step 2: Build a mathematical model for the distribution of precoding schemes, fixed station selection, and achievable rates based on user location.

[0015] Step 3: Solve the precoding problem for each fixed station;

[0016] Step 4: Select a suitable fixed station based on the user's location;

[0017] Step 5: Perform power optimization of the selected fixed stations.

[0018] The present invention adopts the above technical solution and has the following advantages compared with the prior art:

[0019] 1. The precoding design of the present invention can maximize the traversal rate of the sea wave massive MIMO system;

[0020] 2. The fixed station selection design of the present invention can, on the one hand, select the appropriate fixed station for the user, thereby maximizing the user's traversal reachable rate. On the other hand, the combined scenario of multiple fixed stations can further increase the sparseness of the spatial distribution of the traversal reachable rate, further improving security performance.

[0021] 3. The fixed station transmission power design of the present invention can reduce the fixed station transmission power while ensuring the user's traversal reachable rate, thereby reducing the eavesdropper's traversal reachable rate and improving the traversal reachable security rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a structural diagram of the shortwave communication system based on shore-to-ship communication.

[0023] Figure 2 This is a curve showing how the achievable rate changes with frequency under different fixed station antenna numbers.

[0024] Figure 3 This is a curve showing how the achievable rate changes with frequency under different fixed station antenna numbers.

[0025] Figure 4 This is a curve showing how the achievable safe rate changes with the antenna array element spacing under different fixed station antenna numbers.

[0026] Figure 5 This is a curve showing how the achievable safe rate changes with the fixed station transmit power under different fixed station antenna numbers.

[0027] Figure 6 This is a schematic diagram of the regional distribution of achievable rates under different numbers of fixed station antennas. DETAILED DESCRIPTION

[0028] For a given user location, the core network calculates the main beam azimuth of the corresponding fixed station and instructs the fixed station to intersect its beam within a small area encompassing the user. Only terminals within this area can simultaneously receive multiple channels of information and combine them to recover the original information. Users outside this area lack at least one channel of information and cannot complete the combination, thus losing access to the original information.

[0029] A shore-to-ship shortwave secure transmission method based on massive MIMO includes the following steps:

[0030] Step 1: Describe the roles of various elements in the shore-to-ship secure transmission method based on massive MIMO and the relationships between them;

[0031] Step 2: Build a mathematical model for the distribution of precoding schemes, fixed station selection, and achievable rates based on user location.

[0032] Step 3: Solve the precoding problem for each fixed station;

[0033] Step 4: Select a suitable fixed station based on the user's location;

[0034] Step 5: Perform power optimization of the selected fixed stations.

[0035] The present invention describes the functions of various elements in a shore-to-ship shortwave secure transmission method based on massive MIMO and the relationships between them. The description includes:

[0036] like Figure 1 As shown, consider a shortwave communication system consisting of M fixed stations, one user, and one eavesdropper. At the transmitter, the mth fixed station is equipped with N m A uniform linear array of antennas. At the receiving end, the user and eavesdropper are equipped with a single antenna. In massive MIMO systems, the angular channel of the antenna array is sparse. As the number of antennas approaches infinity, the channels of different receivers become asymptotically orthogonal. Therefore, a fixed station equipped with an antenna array can form a directional beam, enabling users to achieve relatively high data rates while simultaneously reducing the data rates of eavesdroppers, thus achieving secure information transmission.

[0037] The present invention establishes a mathematical model for establishing a user location-based precoding scheme, fixed station selection, and achievable rate distribution, including the following:

[0038] Fixed station i equipped with N i The antenna array of fixed station i is a uniform linear array with half-wavelength spacing. Each antenna array element uniformly covers the arrival angle interval [0,π). The transmitted signal of the antenna array of fixed station i can be expressed as

[0039]

[0040] Among them, P i represents the transmit power, f i is the precoding vector and ||f i ||=1,s i represents the transmission symbol and |s| = 1. According to the mathematical model of uniform linear array, the array response vector of fixed station i is

[0041]

[0042] where j is the imaginary unit, θ i is the arrival angle between fixed station i and user, represents the arrival angle θ i The phase difference caused by the time delay between adjacent antenna elements under certain conditions.

[0043] Consider the far field situation, that is, the distance between the user and the fixed station is much greater than the distance between adjacent antenna units. In this case, it can be assumed that the direction from each antenna unit to the user is the same. Let x i and y i are the horizontal and vertical coordinates of fixed station i, is the user's position, the arrival angle can be expressed as

[0044]

[0045] where x(l), y(l), γ i (l) represents the horizontal coordinate, vertical coordinate and angle between the vector l and the X axis respectively;

[0046] Assume d i is the distance from fixed station i to the user, β i is the large-scale fading; the large-scale fading of the ground wave scene is

[0047]

[0048] Where λ represents the carrier wavelength, ε and σ are the dielectric constant and conductivity of the propagation medium, respectively. T and G R are transmitter gain and receiver gain respectively, set GT =G R =1.

[0049] The signal received by fixed station i from user is

[0050]

[0051] where v T (θ i ) is the transpose of the fixed station array side response vector, z is zero mean, and the variance is σ 2 Complex Gaussian noise, P i is the transmission power of fixed station i; the signal-to-noise ratio of the signal received by the fixed station is

[0052]

[0053] In order to maximize the direction The receiver's γ i , using conjugate beamforming, the precoding vector is set to

[0054]

[0055] Substituting (7) into (6), we can get When beamforming, at the arrival angle θ i The signal-to-noise ratio of the user can be expressed as

[0056]

[0057] When the number of antennas approaches infinity, the array response vectors for different angles are asymptotically orthogonal, that is,

[0058]

[0059] The original signal is divided into M sub-signals, which are transmitted through M channels respectively. i is the bandwidth of the signal sent by fixed station i, then the rate from fixed station i to the user can be expressed as

[0060]

[0061] Define vector w∈{0,1} M×1 Indicates which fixed stations are selected. Specifically, the i-th element of s is 1, which means the i-th fixed station is selected, and 0 means it is not selected. The achievable rate is defined as the maximum rate at which information can be transmitted without error in the system, which can be expressed as

[0062]

[0063] Assume that the positions of the user and the eavesdropper are u and e respectively, then the security rate of the system is expressed as

[0064] R sec =[R(u)-R(e)] + , (12)

[0065] Where R(u) and R(e) represent the traversal rates of the user and the eavesdropper, respectively;

[0066] The problem considered is to maximize the user rate by optimizing the precoding vector, transmit power, and fixed station selection scheme under the constraints of the minimum number of fixed station selections, the maximum transmit power of a single fixed station, and the two-norm of the precoding vector. The optimization problem is expressed as:

[0067]

[0068] Constraint C1 means that at least k fixed stations must be in working state, and constraint C2 means that the transmission power of each fixed station should be less than the maximum value P max Constraint C3 indicates that the precoding scheme of a single fixed station antenna array must satisfy the power constraint. Obviously, P1 belongs to the hybrid shaping optimization problem and is non-convex, so it cannot be solved directly.

[0069] Formula (11) describes the relationship between the achievable rate and the user's location. Formula (9) describes the sparseness of the angle domain. The achievable rates at different locations also exhibit similar characteristics, especially when a fixed station is equipped with a large number of antennas. The effective reception area is defined as the area covered by the main beams of all selected fixed stations. As the number of antennas approaches infinity, the effective reception area converges to a single point. Therefore, anti-eavesdropping communication can be achieved by intersecting the fixed station's beams at the user's location.

[0070] The present invention solves the precoding problem of each fixed station, specifically including:

[0071] Formula (11) shows that to maximize the achievable rate, we can maximize the rate from each fixed station to the user. Based on the monotonically increasing relationship between rate and signal-to-noise ratio and Formula (8), the precoding vector is set according to Formula (7). The specific process is summarized in Algorithm 1.

[0072]

[0073] The present invention selects a suitable fixed station according to the user's location, specifically including:

[0074] Based on the precoding design, the user's achievable rate can be maximized by fixed station selection. Define R ξ (s) is the achievable rate of the receiver ξ under the fixed station selection scheme s obtained by Algorithm 1. P1 can be written as

[0075]

[0076] If you use exhaustive search, there are a total of For M fixed station scenarios, when k is relatively small, the complexity of exhaustive search is O(2 M ).

[0077] Equation (11) shows that the achievable rate depends on the slowest sub-message. Therefore, we propose a more efficient algorithm to reduce the complexity of the problem. Specifically, we first calculate the signal-to-noise ratios (SNRs) from all fixed stations to the user according to Equation (8), sort them, and select the top k fixed stations. This process is summarized in Algorithm 3.

[0078]

[0079]

[0080] The power optimization of the selected fixed station of the present invention specifically includes:

[0081] A heuristic method for setting the transmission power is to set the transmission power of each fixed station to the maximum, so as to maximize the signal-to-noise ratio of each fixed station to the user. However, according to formula (11), the achievable rate is determined by the slowest one among all sub-messages. Therefore, we set the transmission power of the fixed station with the smallest link gain to the user to the maximum value, and at the same time, the other fixed stations reduce their own transmission power until the rate of all sub-messages is kept the same. This process is described as problem P3

[0082]

[0083] Among them, P m The transmission power of each fixed station, P max is the maximum transmission power of the fixed station, R m is the rate of sub-information m, R n is the rate of sub-information n, w is the fixed station selection scheme, [w] n is the nth element of w;

[0084] According to the monotonically increasing relationship between rate and signal-to-noise ratio and C2 in (15), we can get

[0085]

[0086] Then the transmission power of each fixed station is expressed as

[0087]

[0088] The specific process is summarized in Algorithm 2.

[0089]

[0090] A skywave massive MIMO secure communication system based on multi-station collaboration, comprising:

[0091] A description module is used to describe the role of each element in the shore-to-ship secure transmission method based on massive MIMO and the relationship between them;

[0092] Establishing a module for establishing a mathematical model for precoding schemes, fixed station selection, and achievable rate distribution based on user location;

[0093] Precoding module, used to solve the precoding problem of each fixed station;

[0094] A fixed station selection module is used to select a suitable fixed station according to the user's location;

[0095] The power optimization module is used to optimize the transmission power of the fixed station.

[0096] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0097] A specific embodiment of the present invention is as follows. The system simulation adopts Python language. The following embodiment examines the effectiveness of the shore-to-ship secure transmission method based on massive MIMO designed by the present invention.

[0098] In this section, the effectiveness of the proposed algorithm is demonstrated by presenting simulation results. First, the user rate of the proposed algorithm is compared with that of the exhaustive search method. Then, the regional distribution characteristics of the receiver rate are analyzed and the reception area of the proposed method is compared with that of a single fixed station omnidirectional antenna coverage scenario. Finally, the traversal safety rate performance of the proposed method is analyzed. Table 1 shows the parameter settings. 100 user locations are randomly generated within the simulation region {(x,y)|50≤x≤600,-400≤y≤400}. The azimuth angle can be calculated using the geometric relationship between the fixed station and the user positions.

[0099]

[0100]

[0101] Figure 2 and Figure 3 The achievable rate varies with frequency and transmit power for different numbers of fixed station antennas. As can be seen, the achievable rate increases with increasing the number of antennas and transmit power. This is because adding more antennas to a fixed station improves angular gain, and using higher transmit power enhances the signal-to-noise ratio at the receiver. Furthermore, the performance of the proposed method approaches that of an exhaustive search, demonstrating the effectiveness of the proposed low-complexity fixed station selection algorithm.

[0102] Figure 4The figure depicts how the achievable safe rate varies with antenna element spacing for different fixed station antenna numbers. It can be seen that when the antenna element spacing is less than half a wavelength (9.375 meters), the achievable safe rate gradually increases as the antenna element spacing increases. However, when the antenna element spacing is greater than half a wavelength, the achievable safe rate stabilizes. This is because a smaller antenna element spacing creates a wider main lobe, which increases the reception range.

[0103] Figure 5 The achievable secure rate varies with the fixed station's transmit power under different fixed station antenna numbers. It can be seen that the achievable secure rate gradually increases with increasing fixed station transmit power. For scenarios with the same number of antennas, the proposed method outperforms the heuristic method. This is because the proposed method can reduce transmit power while maintaining user rates, potentially reducing the eavesdropper's traversal secure rate and thus increasing the achievable secure rate.

[0104] Figure 6 The regional distribution of achievable rates for different fixed station antenna numbers is depicted. (a) corresponds to an 8-antenna scenario; (b) to a 32-antenna scenario; and (c) to a 128-antenna scenario. As the number of antennas increases, the receivable area shrinks, while the achievable rate at the user's location increases. This demonstrates that, while using MIMO technology in shortwave ground wave communication systems can improve user rates and energy efficiency, employing a collaborative model with multiple fixed stations can further reduce the effective reception area and enhance security.

[0105] The present invention has been described above in an illustrative manner using embodiments. Those skilled in the art should understand that the present disclosure is not limited to the embodiments described above, and that various changes, modifications, and substitutions may be made without departing from the scope of the present invention.

Claims

1. A shore-to-ship shortwave secure transmission method based on massive MIMO, characterized in that: The steps include: Step 1: Describe the roles of various elements in the massive MIMO-based shore-to-ship secure transmission method and their relationships. This description includes: A shortwave communication system consists of M fixed stations, one user, and one eavesdropper. At the transmitting end, the mth fixed station is equipped with N m A uniform linear array of antennas is constructed. At the receiving end, both the user and the eavesdropper are equipped with a single antenna. In a massive MIMO system, the angular domain channel of the antenna array is sparse. As the number of antennas approaches infinity, the channels of different receivers become asymptotically orthogonal. Fixed stations equipped with antenna arrays form directional beams, allowing users to receive relatively high data rates while eavesdroppers receive relatively low data rates, thus achieving secure information transmission. Step 2: Build a mathematical model for the user location-based precoding scheme, fixed station selection, and achievable rate distribution. This includes the following: Fixed station i equipped with N i The antenna array is a uniform linear array with half-wavelength spacing of 10 antennas. Each antenna array element uniformly covers the arrival angle interval [0,π). The transmission signal of the antenna array of fixed station i is expressed as Among them, P i represents the transmit power, f i is the precoding vector and ||f i ||=1,s i represents the transmission symbol and |s|=1; according to the mathematical model of uniform linear array, the array response vector of fixed station i is where j is the imaginary unit, θ i is the arrival angle between fixed station i and user, represents the arrival angle θ i The phase difference caused by the time delay between adjacent antenna elements under the conditions; Consider the far-field situation, that is, the distance between the user and the fixed station is much greater than the distance between adjacent antenna units. In this case, it is assumed that the direction from each antenna unit to the user is the same; let x i and y i are the horizontal and vertical coordinates of fixed station i, is the user's location, the arrival angle is expressed as where x(l), y(l), γ i (l) represents the horizontal coordinate, vertical coordinate and angle between the vector l and the X axis respectively; Assume d i is the distance from fixed station i to the user, β i is the large-scale fading; the large-scale fading of the ground wave scene is Where λ represents the carrier wavelength, ε and σ are the dielectric constant and conductivity of the propagation medium, respectively. T and G R are transmitter gain and receiver gain respectively, set G T =G R =1; The signal received by fixed station i from user is where v T (θ i ) is the transpose of the fixed station array side response vector, z is zero mean, and the variance is σ 2 Complex Gaussian noise, P i is the transmission power of fixed station i; the signal-to-noise ratio of the signal received by the fixed station is In order to maximize the direction The receiver's γ i , using conjugate beamforming, the precoding vector is set to Substituting formula (7) into formula (6), we can get When beamforming, at the arrival angle θ i The signal-to-noise ratio of the user is expressed as When the number of antennas approaches infinity, the array response vectors for different angles are asymptotically orthogonal, that is, The original signal is divided into M sub-signals, which are propagated through M channels respectively; let B i is the bandwidth of the signal sent by fixed station i, then the rate from fixed station i to the user is expressed as Define vector w∈{0,1} M×1 Indicates which fixed stations are selected; specifically, the i-th element of w is 1, indicating that the i-th fixed station is selected, and 0, indicating that it is not selected; the achievable rate is defined as the maximum rate at which information can be transmitted without error in the system, expressed as Formula (11) describes the relationship between the achievable rate and the user location, and formula (9) describes the sparsity of the angle domain; Assume that the positions of the user and the eavesdropper are u and e respectively, then the security rate of the system is expressed as R sec =[R(u)-R(e)] + , (12) Where R(u) and R(e) represent the traversal rates of the user and the eavesdropper, respectively; The problem considered is to maximize the user rate by optimizing the precoding vector, transmit power, and fixed station selection scheme under the constraints of the minimum number of fixed station selections, the maximum transmit power of a single fixed station, and the two-norm of the precoding vector. The optimization problem is expressed as: Constraint C1 means that at least k fixed stations must be in working state, and constraint C2 means that the transmission power of each fixed station is less than or equal to the maximum value P max ,Constraint C3 indicates that the precoding scheme of a single fixed station antenna array must meet the power constraint; Step 3: Solve the precoding problem for each fixed station; Step 4: Select a suitable fixed station based on the user's location; specifically: Based on the precoding design, the user's achievable rate is maximized through fixed station selection, and the definition is the achievable rate of the receiver ξ under the fixed station selection scheme s; P1 is written as If you use exhaustive search, there are a total of In the case of satisfying the constraints, for M fixed station scenarios, when k is relatively small, the complexity of exhaustive search is O(2 M ), calculate the signal-to-noise ratios of all fixed stations to users according to formula (8), and select the top k fixed stations after sorting; Step 5: Optimize the power of the selected fixed stations; specifically include: The transmission power of the fixed station with the smallest link gain to the user is set to the maximum value. At the same time, other fixed stations reduce their own transmission power until the rate of all sub-information is kept the same. This process is described as problem P3. P3:max P m s.t. C1:P m <P max (15) C2:R m =min{R n ∣[in] n =1} Among them, P m The transmission power of each fixed station, P max is the maximum transmission power of the fixed station, R m is the rate of sub-information m, R n is the rate of sub-message n, w is the fixed station selection scheme, [w] n is the nth element of w; According to the monotonically increasing relationship between rate and signal-to-noise ratio and C2 in (15), we can get Then the transmission power of each fixed station is expressed as 2. A shore-to-ship shortwave secure transmission device based on massive MIMO, used to implement the shore-to-ship shortwave secure transmission method based on massive MIMO as claimed in claim 1, characterized in that include: A description module is used to describe the role of each element in the shore-to-ship secure transmission method based on massive MIMO and the relationship between them; Establishing a module for establishing a mathematical model for precoding schemes, fixed station selection, and achievable rate distribution based on user location; Precoding module, used to solve the precoding problem of each fixed station; A fixed station selection module is used to select a suitable fixed station according to the user's location; The power optimization module is used to optimize the transmission power of the fixed station.

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