Tunnel satellite signal enhancement method and system based on reconfigurable intelligent surface

Abstract

The invention provides a tunnel satellite signal enhancement method and system based on a reconfigurable intelligent surface. The method comprises the steps that a reconfigurable intelligent surface RIS array is arranged at a tunnel portal and / or the inner wall of a tunnel; establishing a channel model of a tunnel wireless communication network, wherein the tunnel wireless communication network at least comprises a satellite, a target vehicle in a tunnel and an RIS array; the satellite transmits a signal to a target vehicle and the RIS array, and the RIS array reflects the signal to the target vehicle for signal enhancement; on the basis of the channel model, an optimization problem is constructed with the purpose of maximizing the minimum transmission rate of the signals, the optimization problem is solved through a preset iterative optimization algorithm, and a solving result is obtained; parameters of the channel model are optimized according to the solving result, and signal transmission in the tunnel is carried out according to the optimized channel model; by deploying the RIS array, intelligent reconstruction and beam forming of GNSS signals are realized, and the problem of signal shielding in a tunnel environment is effectively solved.

Description

Technical Field

[0001] This invention relates to the field of GNSS positioning technology, and more specifically, to a method and system for enhancing satellite signals through tunnels based on a reconfigurable intelligent surface (RIS). Background Technology

[0002] With the widespread application of Global Navigation Satellite System (GNSS) technology, in-vehicle navigation systems have become an important component of intelligent transportation and autonomous driving. GNSS technology provides real-time positioning services through satellite signals, laying a solid foundation for applications such as vehicle navigation, route planning, and autonomous driving. However, traditional GNSS technology faces severe challenges in enclosed environments such as tunnels, mainly manifested as complete signal blockage or severe attenuation, leading to positioning failure or a sharp decline in accuracy. This problem seriously restricts the reliability and applicability of in-vehicle navigation systems in complex urban environments.

[0003] In tunnels and other obstructed environments, GNSS signals cannot directly penetrate reinforced concrete structures, preventing vehicle-mounted receivers from acquiring effective satellite signals. Specifically, when a vehicle enters a tunnel, the satellite signal strength decreases exponentially, interrupting positioning data updates. This signal obstruction problem worsens with increasing tunnel length and depth, potentially causing positioning drift in short tunnels and complete loss of lock-on in long tunnels, posing a significant safety hazard to autonomous driving systems and emergency rescue applications. Summary of the Invention

[0004] To overcome the problem of weak satellite signals in tunnels and other shielded environments, this invention provides a method and system for enhancing satellite signals in tunnels based on reconfigurable smart surfaces. By deploying a RIS array, intelligent reconstruction and beamforming of GNSS signals are achieved, effectively solving the signal shielding problem in tunnel environments.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: A method for enhancing satellite signals through tunnels based on reconfigurable smart surfaces includes the following steps: Deploy a reconfigurable smart surface RIS array at the tunnel entrance and / or on the tunnel wall; A channel model for a tunnel wireless communication network is established, wherein the tunnel wireless communication network includes at least a satellite, a target vehicle inside the tunnel, and the RIS array; the satellite transmits signals to the target vehicle and the RIS array, and the RIS array reflects the signals back to the target vehicle for signal enhancement; Based on the channel model, an optimization problem is constructed with the goal of maximizing the minimum transmission rate of the signal. The optimization problem is then solved using a preset iterative optimization algorithm to obtain the solution result. The parameters of the channel model are optimized based on the solution results, and the signal transmission in the tunnel is carried out based on the optimized channel model.

[0006] Preferably, the RIS array includes several independently adjustable phase-controlled reflective units.

[0007] Preferably, the channel model is as follows: In each signal transmission time slot, the satellite transmits signals. It consists of K symbols, represented as:

[0008] in, The beamforming matrix for the transmitted signal satisfies: ; Indicates the index value of the target vehicle; Indicates the total number of target vehicles; The dimension is beamforming vector, This refers to the number of antennas on the satellite transmitter. The satellite's transmitted signal matrix satisfies: , Indicates matrix transpose; This represents the signal vector transmitted by the satellite to the k-th target vehicle; Assumption The total transmission power of the satellite is expressed as:

[0009] in, Expressing expectations; Represents the conjugate transpose of a matrix; Represents the identity matrix; Represents the trace of a matrix; This indicates the preset maximum total transmit power; In each time slot, the received signal of the k-th target vehicle Represented as:

[0010] in, This represents the direct channel vector between the satellite and the k-th target vehicle; This represents the channel vector between the RIS array and the k-th target vehicle; This represents the channel matrix from the satellite to the RIS array; Indicating RIS array A phase adjustment matrix of dimension, satisfying , Indicates the first RIS array Phase shift of each reflecting unit This represents the nth reflection unit in the RIS array. This represents the total number of reflective units; This represents the noise of the k-th target vehicle, specifically with a mean of 0 and a variance of... Additive white Gaussian noise.

[0011] Preferably, the transmission rate of the signal is expressed as:

[0012] in, This represents the signal transmission rate corresponding to the kth target vehicle; The optimization problem is specifically:

[0013] Where st represents the constraint condition.

[0014] Preferably, solving the optimization problem using a preset iterative optimization algorithm includes the following steps: Given phase adjustment matrix The optimization problem is transformed into an optimization problem. Optimize beamforming matrix The optimization problem Represented as:

[0015] Iteratively solve the optimization problem The optimal beamforming matrix is ​​obtained. ; Based on the optimal beamforming matrix The optimization problem is transformed into an optimization problem. Optimize the phase adjustment matrix The optimization problem Represented as:

[0016] in, Let be the i-th optimal beamforming vector; For dimension The optimization variables satisfy , For the nth optimized vector, ; Iteratively solve the optimization problem The optimal phase adjustment matrix is ​​obtained. ; The optimal beamforming matrix and the optimal phase adjustment matrix The results are saved together as the solution.

[0017] Preferably, the optimization problem is solved iteratively. The steps include: Define auxiliary variables and The optimization problem Rewrite as an optimization problem , represented as:

[0018] in, as auxiliary variables The corresponding vector satisfies ; definition constrain the conditions Represented as , equivalent to:

[0019]

[0020]

[0021] Based on the continuous convex approximation algorithm, an approximate solution is obtained using a first-order Taylor expansion:

[0022]

[0023]

[0024] in, and ; For the introduced auxiliary variables, as auxiliary variables The corresponding vector satisfies ; The optimization problem Transform into an optimization problem , represented as:

[0025] The optimization problem is solved iteratively using the CVX solver. The process continues until the first convergence condition is met, at which point the optimal beamforming matrix is ​​obtained. .

[0026] Preferably, the first convergence condition is:

[0027] in, For the j-th iteration auxiliary variable The value; This is the preset convergence accuracy.

[0028] Preferably, the optimization problem is solved iteratively. The steps include: Define auxiliary variables and The optimization problem Rewrite as an optimization problem , represented as:

[0029] because Define intermediate variables , constrain conditions Represented as: , equivalent to:

[0030] Based on the continuous convex approximation algorithm, an approximate solution is obtained using a first-order Taylor expansion:

[0031] in, and These are the two auxiliary variables introduced; and ; Define penalty function ,in, , As a preset value, the optimization problem is... Transform into an optimization problem , represented as:

[0032] in, and Auxiliary variables and The corresponding vector satisfies , ; The optimization problem is solved iteratively using the CVX solver. This continues until the second convergence condition is met, at which point the optimal optimization variable is obtained. ,according to Further obtain the optimal phase adjustment matrix. .

[0033] Preferably, the second convergence condition is:

[0034]

[0035] in, For the j-th iteration variable The value; This is the preset convergence accuracy.

[0036] The present invention also provides a tunnel satellite signal enhancement system based on reconfigurable smart surfaces, comprising: a satellite, a target vehicle inside the tunnel, a processor, and a reconfigurable smart surface (RIS) array deployed at the tunnel entrance and / or the tunnel wall. The satellite, the target vehicle inside the tunnel, and the RIS array constitute a tunnel wireless communication network; the satellite transmits signals to the target vehicle and the RIS array, and the RIS array reflects the signals back to the target vehicle to enhance the signal. The processor is used to establish a channel model for the tunnel wireless communication network and execute the above-described method to optimize the parameters of the channel model, and then transmit signals within the tunnel according to the optimized channel model.

[0037] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: This invention provides a method and system for enhancing satellite signals in tunnels based on reconfigurable smart surfaces. First, a reconfigurable smart surface (RIS) array is deployed at the tunnel entrance and / or on the tunnel wall. Next, a channel model of the tunnel wireless communication network is established, which includes at least a satellite, a target vehicle inside the tunnel, and the RIS array. The satellite transmits signals to the target vehicle and the RIS array, and the RIS array reflects the signals back to the target vehicle for signal enhancement. Then, based on the channel model, an optimization problem is constructed with the goal of maximizing the minimum signal transmission rate. This optimization problem is solved using a pre-defined iterative optimization algorithm, and the solution is obtained. Finally, the parameters of the channel model are optimized based on the solution results, and signal transmission within the tunnel is performed according to the optimized channel model. This invention optimizes beamforming at the signal transmitter and performs phase modulation on the RIS array, effectively reconstructing satellite signals obscured by tunnel structures based on traditional inertial navigation compensation techniques. This overcomes the positioning interruption problem caused by the complete absence of GNSS signals in enclosed environments, achieving continuous and accurate positioning within tunnels. This invention significantly improves the performance of satellite positioning systems in complex and obscured environments such as tunnels, providing reliable technical support for applications requiring high-precision positioning across all scenarios, such as intelligent transportation and tunnel engineering monitoring, and expanding the application scenarios and coverage of GNSS high-precision positioning technology. Attached Figure Description

[0038] Figure 1 This is a flowchart of a tunnel satellite signal enhancement method based on a reconfigurable smart surface, as provided in Example 1.

[0039] Figure 2 This is a diagram of the tunnel wireless communication network architecture provided in Example 2.

[0040] Figure 3 This is a structural diagram of a tunnel satellite signal enhancement system based on a reconfigurable smart surface, as provided in Example 3. Detailed Implementation

[0041] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this application. To better illustrate this embodiment, some parts in the accompanying drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions; It will be understood by those skilled in the art that certain well-known structures and their descriptions may be omitted in the accompanying drawings.

[0042] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0043] Example 1 like Figure 1 As shown, this embodiment provides a method for enhancing satellite signals through tunneling based on reconfigurable smart surfaces, including the following steps: S1: Deploy a reconfigurable smart surface RIS array at the tunnel entrance and / or on the tunnel inner wall; S2: Establish a channel model for a tunnel wireless communication network, which includes at least a satellite, a target vehicle inside the tunnel, and the RIS array; the satellite transmits signals to the target vehicle and the RIS array, and the RIS array reflects the signals back to the target vehicle for signal enhancement; S3: Based on the channel model, with the goal of maximizing the minimum transmission rate of the signal, an optimization problem is constructed, and the optimization problem is solved by a preset iterative optimization algorithm to obtain the solution result; S4: Optimize the parameters of the channel model based on the solution results, and perform signal transmission in the tunnel based on the optimized channel model.

[0044] In the specific implementation process, in order to improve the positioning failure and sharp decline in accuracy of existing GNSS vehicle navigation systems (navigation systems using single-point positioning) in enclosed environments such as tunnels due to complete obstruction or severe attenuation of satellite signals, this embodiment proposes a tunnel satellite signal enhancement method based on a RIS array. The core idea of ​​this method is to achieve intelligent relaying and beamforming of GNSS signals by deploying a dynamically configurable RIS array in the tunnel while maintaining system reliability, effectively solving problems such as signal blockage, so as to achieve continuous high-precision positioning in tunnel environments. Specifically, a reconfigurable smart surface RIS array is first deployed at the tunnel entrance and / or the inner wall of the tunnel. Next, a channel model for the tunnel wireless communication network is established. The tunnel wireless communication network includes at least a satellite, a target vehicle inside the tunnel, and a RIS array. The satellite transmits signals to the target vehicle and the RIS array, and the RIS array reflects the signals back to the target vehicle to enhance the signal. Then, based on the channel model, an optimization problem is constructed with the goal of maximizing the minimum transmission rate of the signal. The optimization problem is then solved using a pre-set iterative optimization algorithm to obtain the solution results. Finally, the parameters of the channel model are optimized based on the solution results, and the signal transmission in the tunnel is carried out according to the optimized channel model, ultimately achieving signal enhancement in the tunnel. This method achieves intelligent reconstruction and beamforming of GNSS signals by deploying RIS arrays at the tunnel entrance and / or on the tunnel wall, effectively solving the signal obstruction problem in tunnel environments. This method can significantly improve the positioning performance of vehicle navigation systems in tunnels, providing a more reliable technical solution for applications such as autonomous driving and intelligent traffic management.

[0045] Example 2 This embodiment provides a method for enhancing satellite signals through tunnels based on reconfigurable smart surfaces, including the following steps: S1: Deploy a reconfigurable smart surface RIS array at the tunnel entrance and / or on the tunnel inner wall; S2: Establish a channel model for a tunnel wireless communication network, which includes at least a satellite, a target vehicle inside the tunnel, and the RIS array; the satellite transmits signals to the target vehicle and the RIS array, and the RIS array reflects the signals back to the target vehicle for signal enhancement; S3: Based on the channel model, with the goal of maximizing the minimum transmission rate of the signal, an optimization problem is constructed, and the optimization problem is solved by a preset iterative optimization algorithm to obtain the solution result; S4: Optimize the parameters of the channel model based on the solution results, and perform signal transmission in the tunnel based on the optimized channel model.

[0046] In the specific implementation process, a reconfigurable smart surface (RIS) array is first deployed at the tunnel entrance and / or the inner wall of the tunnel; in this embodiment, the RIS array contains N independently adjustable phase reflection units; Next, a channel model for the tunnel wireless communication network is established, such as... Figure 2 As shown, the tunnel wireless communication network includes at least a satellite, a target vehicle inside the tunnel, and a RIS array; the satellite transmits signals to the target vehicle and the RIS array, and the RIS array reflects the signals back to the target vehicle to enhance the signal; In this embodiment, the constructed channel model is specifically as follows: In each signal transmission time slot, the satellite transmits signals. It consists of K symbols, represented as:

[0047] in, The beamforming matrix for the transmitted signal satisfies: ; Indicates the index value of the target vehicle; Indicates the total number of target vehicles; The dimension is beamforming vector, This refers to the number of antennas on the satellite transmitter. The satellite's transmitted signal matrix satisfies: , Indicates matrix transpose; This represents the signal vector transmitted by the satellite to the k-th target vehicle; Assumption The total transmission power of the satellite is expressed as:

[0048] in, Expressing expectations; Represents the conjugate transpose of a matrix; Represents the identity matrix; Represents the trace of a matrix; This indicates the preset maximum total transmit power; In each time slot, the received signal of the k-th target vehicle Represented as:

[0049] in, This represents the direct channel vector between the satellite and the k-th target vehicle; This represents the channel vector between the RIS array and the k-th target vehicle; This represents the channel matrix from the satellite to the RIS array; Indicating RIS array A phase adjustment matrix of dimension, satisfying , Indicates the first RIS array Phase shift of each reflecting unit This represents the nth reflection unit in the RIS array. This represents the total number of reflective units; This represents the noise of the k-th target vehicle, specifically with a mean of 0 and a variance of... Additive White Gaussian Noise (AWGN); At the receiving end (target vehicle), the transmission rate of the received signal can be expressed as:

[0050] Without using RIS technology, the transmission rate of the received signal can be expressed as:

[0051] Because satellite signals are almost completely blocked inside the tunnel, The value of approaches 0, therefore at this time It also approaches 0; that is, in the solution without RIS technology, the vehicle navigation system receives almost no satellite signals inside the tunnel; while in the solution using RIS technology (i.e., this method), Approaching 0, leading to Approximately equal to:

[0052] in, This represents the signal transmission rate corresponding to the kth target vehicle; Considering the overall performance of users, this method is based on the channel model described above, with the goal of maximizing the minimum transmission rate of the signal. An optimization problem is constructed, and the optimization problem is solved by a preset iterative optimization algorithm to obtain the solution result. Specifically, in this embodiment, the optimization problem is as follows:

[0053] Where st represents the constraint condition; Since this problem is not convex and cannot be solved directly, this embodiment proposes an algorithm based on Alternating Optimization (AO) to find the optimal beamforming matrix in order to find the optimal solution. and phase adjustment matrix To achieve a higher transmission rate; the solution steps are as follows: Step 1: Given the phase adjustment matrix Transform the optimization problem into an optimization problem Optimize beamforming matrix Optimization problem Represented as:

[0054] Iterative solution of optimization problems The optimal beamforming matrix is ​​obtained. ; In this embodiment, the optimization problem is solved iteratively. The steps include: Define auxiliary variables and The optimization problem Rewrite as an optimization problem , represented as:

[0055] in, as auxiliary variables The corresponding vector satisfies ; definition constrain the conditions Represented as , equivalent to:

[0056]

[0057]

[0058] Based on the continuous convex approximation algorithm, an approximate solution is obtained using a first-order Taylor expansion:

[0059]

[0060]

[0061] in, and ; For the introduced auxiliary variables, as auxiliary variables The corresponding vector satisfies ; The optimization problem Transform into an optimization problem , represented as:

[0062] question This is already a convex optimization problem, which can be solved iteratively using existing solvers (such as CVX) until the first convergence condition is met, thus obtaining the optimal beamforming matrix. In this embodiment, the first convergence condition is:

[0063] in, For the j-th iteration auxiliary variable The value; This is the preset convergence accuracy; Step one above is used for precoding at the system transmitter. The following pseudocode demonstrates the optimization process:

[0064] Step 2: Based on the optimal beamforming matrix Transform the optimization problem into an optimization problem Optimize the phase adjustment matrix Optimization problem Represented as:

[0065] in, Let be the i-th optimal beamforming vector; For dimension The optimization variables satisfy , For the nth optimized vector, since If it is always true, then ;therefore, It also holds true; Iterative solution of optimization problems The optimal phase adjustment matrix is ​​obtained. ; In this embodiment, the optimization problem is solved iteratively. The steps include: Define auxiliary variables and The optimization problem Rewrite as an optimization problem , represented as:

[0066] because Define intermediate variables , constrain conditions Represented as: , equivalent to:

[0067] Based on the continuous convex approximation algorithm, an approximate solution is obtained using a first-order Taylor expansion:

[0068] in, and These are the two auxiliary variables introduced; and ; also, It is also a non-convex constraint, which this method converts into And in order to To approximate 1, this method employs the penalty function approach, defining the penalty function as follows: ,in, It is a positive number used to enforce penalties; Since this optimization problem is a maximization problem, and the penalty function term is non-convex, a first-order Taylor expansion is used for approximation. To balance the trade-off between computational efficiency and computational accuracy, this method defines... , As a preset value, in this embodiment, ; Ultimately, the optimization problem will be addressed. Transform into an optimization problem , represented as:

[0069] in, and Auxiliary variables and The corresponding vector satisfies , ; question This is a convex optimization problem, which can be solved iteratively using existing solvers (such as CVX) until the second convergence condition is met, thus obtaining the optimal optimization variables. ,according to Further obtain the optimal phase adjustment matrix In this embodiment, the second convergence condition is:

[0070]

[0071] in, For the j-th iteration variable The value; This is the preset convergence accuracy; Step two above is used for the phase modulation matrix of the RIS array. The following pseudocode demonstrates the optimization process:

[0072] In summary, this embodiment uses algorithms 1 and 2 to jointly optimize and solve the constructed optimization problem. The overall algorithm pseudocode is as follows:

[0073] Ultimately, the optimal beamforming matrix will be determined. and the optimal phase adjustment matrix The results are saved together as the solution. The parameters of the channel model are optimized based on the solution. The signal transmission in the tunnel is carried out based on the optimized channel model, and finally the signal enhancement in the tunnel is achieved. This method utilizes a RIS array to sense the channel status in real time and dynamically adjust the electromagnetic characteristics to accurately guide satellite signals outside the tunnel to the target vehicle inside the tunnel, while suppressing multipath interference, thus significantly improving signal coverage and positioning accuracy. This method enhances satellite signals within tunnels using a RIS array, achieving a transmission rate far exceeding that of solutions without RIS technology. It innovatively solves the positioning challenges of in-vehicle navigation systems in obstructed environments such as tunnels. Furthermore, this method employs optimized algorithms for transmitter beamforming and RIS phase modulation, helping to find the optimal parameters for system performance. This significantly improves the reliability of in-vehicle navigation systems in complex environments, particularly for intelligent connected vehicles and autonomous driving systems. It not only overcomes the inherent limitations of traditional technologies but also provides crucial technical support for the comprehensive construction of intelligent transportation infrastructure and continuous high-precision positioning.

[0074] Example 3 like Figure 3 As shown, this embodiment provides a tunnel satellite signal enhancement system based on reconfigurable smart surfaces, including: a satellite 301, a target vehicle 302 in the tunnel, a reconfigurable smart surface RIS array 303 deployed at the tunnel entrance and / or the tunnel wall, and a processor 304. The satellite 301, the target vehicle 302 in the tunnel, and the RIS array 303 constitute a tunnel wireless communication network; the satellite 301 transmits signals to the target vehicle 302 and the RIS array 303, and the RIS array 303 reflects the signals back to the target vehicle 302 for signal enhancement; The processor 304 is used to establish a channel model for the tunnel wireless communication network and execute the method in Embodiment 1 or 2 to optimize the parameters of the channel model and transmit signals in the tunnel according to the optimized channel model.

[0075] In the specific implementation process, satellite 301 transmits signals to target vehicle 302 and RIS array 303, and RIS array 303 reflects the signals back to target vehicle 302 to enhance the signals; The processor 304 establishes a channel model of the tunnel wireless communication network and constructs an optimization problem with the goal of maximizing the minimum transmission rate of the signal. It solves the optimization problem through a preset iterative optimization algorithm, obtains the solution results, and finally optimizes the parameters of the channel model based on the solution results. The signal transmission in the tunnel is carried out based on the optimized channel model to achieve signal enhancement in the tunnel. This system achieves intelligent reconstruction and beamforming of satellite signals by deploying RIS arrays at tunnel entrances and / or on tunnel walls, effectively solving the signal obstruction problem in tunnel environments. This system can significantly improve the positioning performance of vehicle navigation systems in tunnels, providing a more reliable technical solution for applications such as autonomous driving and intelligent traffic management.

[0076] The same or similar labels correspond to the same or similar parts; The terms used to describe positional relationships in the accompanying drawings are for illustrative purposes only and should not be construed as limiting this application. Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for enhancing satellite signals through tunnels based on reconfigurable smart surfaces, characterized in that, Includes the following steps: Deploy a reconfigurable smart surface RIS array at the tunnel entrance and / or on the tunnel wall; A channel model for a tunnel wireless communication network is established, wherein the tunnel wireless communication network includes at least a satellite, a target vehicle inside the tunnel, and the RIS array; The satellite transmits a signal to the target vehicle and the RIS array, and the RIS array reflects the signal back to the target vehicle to enhance the signal. Based on the channel model, an optimization problem is constructed with the goal of maximizing the minimum transmission rate of the signal. The optimization problem is then solved using a preset iterative optimization algorithm to obtain the solution result. The parameters of the channel model are optimized based on the solution results, and the signal transmission in the tunnel is carried out based on the optimized channel model.

2. The method for enhancing tunneled satellite signals based on reconfigurable smart surfaces according to claim 1, characterized in that, The RIS array contains several reflective units whose phases can be independently adjusted.

3. The method for enhancing tunneled satellite signals based on reconfigurable smart surfaces according to claim 2, characterized in that, The channel model is specifically as follows: In each signal transmission time slot, the satellite transmits signals. It consists of K symbols, represented as: in, The beamforming matrix for the transmitted signal satisfies: ; Indicates the index value of the target vehicle; Indicates the total number of target vehicles; The dimension is beamforming vector, This refers to the number of antennas on the satellite transmitter. The satellite's transmitted signal matrix satisfies: , Indicates matrix transpose; This represents the signal vector transmitted by the satellite to the k-th target vehicle; Assumption The total transmission power of the satellite is expressed as: in, Expressing expectations; Represents the conjugate transpose of a matrix; Represents the identity matrix; Represents the trace of a matrix; This indicates the preset maximum total transmit power; In each time slot, the received signal of the k-th target vehicle Represented as: in, This represents the direct channel vector between the satellite and the k-th target vehicle; This represents the channel vector between the RIS array and the k-th target vehicle; This represents the channel matrix from the satellite to the RIS array; Indicating RIS array A phase adjustment matrix of dimension, satisfying , Indicates the first RIS array Phase shift of each reflecting unit This represents the nth reflection unit in the RIS array. This represents the total number of reflective units; This represents the noise of the k-th target vehicle, specifically with a mean of 0 and a variance of... Additive white Gaussian noise.

4. The tunneling satellite signal enhancement method based on a reconfigurable smart surface according to claim 3, characterized in that, The transmission rate of the signal is expressed as: in, This represents the signal transmission rate corresponding to the kth target vehicle; The optimization problem is specifically: Where st represents the constraint condition.

5. The tunneling satellite signal enhancement method based on a reconfigurable smart surface according to claim 4, characterized in that, The optimization problem is solved using a pre-defined iterative optimization algorithm, including the following steps: Given phase adjustment matrix The optimization problem is transformed into an optimization problem. Optimize beamforming matrix The optimization problem Represented as: Iteratively solve the optimization problem The optimal beamforming matrix is ​​obtained. ; Based on the optimal beamforming matrix The optimization problem is transformed into an optimization problem. Optimize the phase adjustment matrix The optimization problem Represented as: in, Let be the i-th optimal beamforming vector; For dimension The optimization variables satisfy , For the nth optimized vector, ; Iteratively solve the optimization problem The optimal phase adjustment matrix is ​​obtained. ; The optimal beamforming matrix and the optimal phase adjustment matrix The results are saved together as the solution.

6. The method for enhancing tunneled satellite signals based on reconfigurable smart surfaces according to claim 5, characterized in that, Iteratively solve the optimization problem The steps include: Define auxiliary variables and The optimization problem Rewrite as an optimization problem , is represented as: in, as auxiliary variables The corresponding vector satisfies ; definition constrain the conditions Represented as , equivalent to: Based on the continuous convex approximation algorithm, an approximate solution is obtained using a first-order Taylor expansion: in, and ; For the introduced auxiliary variables, as auxiliary variables The corresponding vector satisfies ; The optimization problem Transform into an optimization problem , is represented as: The optimization problem is solved iteratively using the CVX solver. The process continues until the first convergence condition is met, at which point the optimal beamforming matrix is ​​obtained. .

7. A method for enhancing tunneled satellite signals based on reconfigurable smart surfaces according to claim 6, characterized in that, The first convergence condition is: in, For the j-th iteration auxiliary variable The value; This is the preset convergence accuracy.

8. A method for enhancing tunneled satellite signals based on reconfigurable smart surfaces according to claim 5, characterized in that, Iteratively solve the optimization problem The steps include: Define auxiliary variables and The optimization problem Rewrite as an optimization problem , is represented as: because Define intermediate variables , constrain conditions Represented as: , equivalent to: Based on the continuous convex approximation algorithm, an approximate solution is obtained using a first-order Taylor expansion: in, and These are the two auxiliary variables introduced; and ; Define penalty function ,in, , The optimization problem is set to a preset value. Transform into an optimization problem , is represented as: in, and Auxiliary variables and The corresponding vector satisfies , ; The optimization problem is solved iteratively using the CVX solver. This continues until the second convergence condition is met, at which point the optimal optimization variable is obtained. ,according to Further obtain the optimal phase adjustment matrix. .

9. A method for enhancing tunneled satellite signals based on reconfigurable smart surfaces according to claim 8, characterized in that, The second convergence condition is: in, For the j-th iteration variable The value; This is the preset convergence accuracy.

10. A tunneling satellite signal enhancement system based on a reconfigurable smart surface, characterized in that, include: Satellites, target vehicles inside tunnels, processors, and reconfigurable smart surface RIS arrays deployed at tunnel entrances and / or tunnel walls; The satellite, the target vehicle inside the tunnel, and the RIS array constitute a tunnel wireless communication network; the satellite transmits signals to the target vehicle and the RIS array, and the RIS array reflects the signals back to the target vehicle to enhance the signal. The processor is used to establish a channel model for the tunnel wireless communication network and execute the method as described in any one of claims 1 to 9 to optimize the parameters of the channel model and perform signal transmission in the tunnel according to the optimized channel model.

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