Wireless channel capacity optimization method for intelligent network connection meteorological monitoring vehicle

Abstract

The invention discloses a wireless channel capacity optimization method for an intelligent network connection meteorological monitoring vehicle, and the method comprises the steps: building a communication system model suitable for the intelligent network connection meteorological monitoring vehicle in consideration of the Doppler effect; an original MIMO channel is equivalent to a plurality of parallel SISO paths through SVD decomposition, and a channel capacity optimization problem is constructed. An original optimization problem is decoupled into two sequentially processed sub-problems: IRS reflection unit phase shift optimization and base station precoding design. According to the coupling characteristic of the two sub-problems, a closed-form solution of an optimal IRS reflection matrix is obtained by indirectly optimizing path gain, and then the optimal precoding of the base station is solved by adopting a water injection algorithm. The algorithm provided by the invention has higher convergence speed and lower calculation complexity, provides an efficient scheme for IRS phase shift optimization, and can effectively adapt to the time-varying characteristic of a meteorological monitoring vehicle channel.

Description

TECHNICAL FIELD

[0001] The application relates to a wireless channel capacity optimization method for an intelligent networked meteorological monitoring vehicle and belongs to the cross field of atmospheric monitoring and wireless communication technology. BACKGROUND

[0002] Traditional meteorological monitoring vehicles are limited by fixed power transmission mode and static networking scheme, and it is difficult to cope with the time-varying characteristics of wireless channels in dynamic environments such as urban building groups and complex terrains, resulting in frequent data transmission interruptions and signal quality fluctuations during mobile observation, which seriously restricts the transmission of massive real-time monitoring data for high-precision numerical weather prediction model construction and sudden meteorological disaster warning and other key services. With the deep integration of 5G / 6G communication technology and edge computing architecture, especially the breakthrough of intelligent reflecting surface (IRS) technology in the sixth generation mobile communication system, a new paradigm is provided to break through the shackles of existing technologies. IRS, with its digital electromagnetic control capability composed of large-scale programmable metasurface units, not only can intelligently reconstruct the optimal signal path in non-line-of-sight propagation scenarios, but also can accurately compensate for the Doppler shift effect in high-speed mobile scenarios. Based on the intelligent networked meteorological monitoring vehicle enabled by multiple IRSs, the channel capacity performance of its wireless communication system can be deeply optimized to significantly improve the data transmission reliability in high-speed mobile state, realize the dynamic optimal matching of meteorological sensing business requirements and communication resource allocation, and provide strong technical support for smart city precise meteorological services, major disaster emergency response and other application scenarios.

[0003] However, the current domestic and foreign academic research on IRS-aided V2I system capacity optimization in application scenarios still has significant deficiencies. Although existing optimization methods such as semi-definite relaxation (SDR) and alternating optimization (AO) have certain effects, they have two key limitations: first, they do not fully consider the substantial impact of Doppler effect on system performance in high-speed mobile scenarios; second, the algorithm has high computational complexity. This makes it difficult to meet the stringent requirements of real-time and reliability in meteorological monitoring networking scenarios. This disconnection between theory and application seriously restricts the engineering application process of IRS technology in intelligent meteorological monitoring systems. SUMMARY

[0004] Inventive purpose: The application provides a low-complexity channel capacity optimization method for intelligent networked meteorological monitoring vehicles in a multi-intelligent reflecting surface assisted MIMO wireless communication system, considers the influence of Doppler effect on channel modeling, and designs a more efficient algorithm to maximize the MIMO system capacity, so as to overcome the high complexity of the wireless communication system architecture for intelligent networked meteorological monitoring vehicles, and dynamically adapt to the state change of the channel. The designed algorithm can be applied to time-varying communication systems, and has important application value and application value for future analysis and improvement of the performance of the wireless communication system of intelligent networked meteorological monitoring vehicles.

[0005] Technical scheme: A wireless channel capacity optimization method for intelligent networked meteorological monitoring vehicles, comprising the following steps:

[0006] Step S1: Establishing a time-varying wireless communication system model for intelligent networked meteorological monitoring vehicles, and modeling the total receiving channel of the meteorological monitoring vehicle;

[0007] Step S2: Using SVD decomposition to equivalently model the total receiving channel of the meteorological monitoring vehicle as a plurality of parallel SISO spatial paths, and constructing a channel capacity optimization problem accordingly;

[0008] Step S3: Solving the optimization problem constructed in step S2 to obtain the closed-form solution of the optimal IRS reflection matrix and the optimal precoding at the base station end, and obtaining the optimization result of the wireless channel capacity.

[0009] As a further optimization scheme of the application, the time-varying wireless communication system model in step S1 includes a base station, a meteorological monitoring vehicle moving at a speed and a direction , and IRS; each IRS is equipped with a uniform planar array having reflective elements; the base station and the meteorological monitoring vehicle are both equipped with uniform linear arrays having and antennas.

[0010] As a further optimization scheme of the application, for the time-varying wireless communication system channel model, the channel of the transceiver end includes sub-channels from the base station to the meteorological monitoring vehicle, from the base station to the first IRS, and from the first IRS to the meteorological monitoring vehicle; wherein, .

[0011] As a further optimization scheme of the application, the total receiving channel of the meteorological monitoring vehicle in step S1 is modeled as:

[0012]

[0013] wherein, Indicates in The time slot is a sub-channel from the base station to the meteorological monitoring vehicle; , Indicates in Time slot from the Sub-channel from IRS to the weather monitoring vehicle ; , Indicates from the base station to the... Sub-channels of each IRS; N represents the total number of reflective elements in all IRSs; Indicates in Time slot number The reflection matrix of each IRS Indicates in Time slot number The phase of the nth reflecting unit of an IRS .

[0014] As a further optimization of the present invention, assuming that all channels follow the Ricean fading model, and considering the Doppler spread of the non-line-of-sight (NLOS) components and the Doppler frequency shift of the line-of-sight (LOS) components, the NLOS components of each sub-channel are analyzed. , , As time t changes, it is modeled as following a Rayleigh distribution with Jakes power spread spectrum; for the LOS component of the channel , , Introducing the Doppler effect, the model is as follows:

[0015]

[0016]

[0017]

[0018] in, and They represent in The NLOS and LOS components of the sub-channel from the base station to the meteorological monitoring vehicle in the time slot; and They represent in Time slot from the NLOS and LOS components of the sub-channel from the IRS to the weather monitoring vehicle; and These represent the distance from the base station to the... NLOS and LOS components of subchannels of each IRS. The array steering vector representing the direction of reception from the base station to the meteorological monitoring vehicle; Indicates the first The array steering vector from the IRS to the meteorological monitoring vehicle receiving direction; Representing the Array steering vectors for each IRS receiving direction; The array steering vector representing the transmission direction from the base station to the meteorological monitoring vehicle; Representing the Array steering vectors for each IRS emission direction; Indicates the base station to the Array steering vectors for each IRS emission direction; The carrier wavelength; The speed of the weather monitoring vehicle; The size of each time slot; and These are the azimuth and elevation angles of the direction of arrival from the base station to the meteorological monitoring vehicle; It is the azimuth angle of the departure direction from the base station to the meteorological monitoring vehicle; and They are the first The azimuth and elevation angles of the IRS to the weather monitoring vehicle's direction of arrival; It is the base station to the first The azimuth angle of the departure direction of each IRS; and They are the first The azimuth and elevation angles of the departure direction of each IRS; and They are the first The azimuth and elevation angles of the arrival direction of each IRS.

[0019] As a further optimization of the present invention: the channel capacity optimization problem in step S2 is constructed as follows:

[0020]

[0021] in, express Channel capacity of time slots Represents the total transmission power. express The number of parallel SISO spatial paths in a time slot. express Power allocation matrix of time slots, express Time slots are allocated to the parallel first Transmit power of each SISO space path, Indicates size is The 0 matrix, express Time slot The A singular value, The variance of the noisy complex signal is represented by... express diagonal elements, Represents the equivalent phase shift matrix The phase magnitude of the nth reflecting unit.

[0022] As a further optimization of the present invention: the solution to the optimization problem constructed in step S2 in step S3 is specifically as follows:

[0023] S3.1, for Solving this problem transforms the channel capacity optimization problem into an optimization problem P2, and obtaining the optimal solution. Closed-form solution:

[0024] P2:

[0025] in, This represents the equivalent value to be optimized. Represents the equivalent phase shift matrix The phase magnitude of the nth reflecting unit.

[0026] S3.2, the optimal solution from step S3.1 Substitute the closed-form solution into the channel capacity optimization problem, and use the water-filling algorithm to obtain the optimal power allocation matrix;

[0027] Step 3.3: Based on the optimal power allocation matrix in step 3.2, the optimal precoding matrix is ​​obtained, and then the optimization result of the wireless channel capacity is calculated.

[0028] As a further optimization of the present invention, the specific steps of step S3.1 are as follows:

[0029] Step S3.1.1: Expand optimization problem P2 into optimization problem P3:

[0030]

[0031] in express The element in the m-th row and n-th column, for The phase magnitude of the reflecting unit in the m-th row and n-th column;

[0032] Step S3.1.2, for about Perform the first derivative:

[0033]

[0034] in and These represent the real and imaginary parts of a complex number, respectively. and The dimensions representing the m-th and n-th elements being 1, and the rest being 0, are respectively. Standard basis vectors; This represents the element in the nth row and mth column of the matrix;

[0035] Step S3.1.3, Introduction , Indicates will The matrix obtained by setting the element in the m-th row and n-th column to 0. The first-order derivative result of step S3.1.2 can be further expressed as:

[0036]

[0037] in ;

[0038] S3.1.4, for about Perform second-order differentiation:

[0039] ;

[0040] S3.1.5, when fixed except At all angles outside, It is about The period is The sine function, then when The solution that yields the maximum value when the value is less than 0 is: , where k is any integer;

[0041] S3.1.6, based on S3.1.5, is obtained through iterative updates. The nth diagonal element, thus obtaining the optimal Closed-form solution:

[0042]

[0043] in, Represents the result obtained in l iterations The nth diagonal element, Represents the result obtained in l-1 iterations The nth diagonal element, , , , , where l represents the number of iterations.

[0044] As a further optimization of the present invention: in step S3.2, the optimal power allocation matrix is ​​obtained by using the water injection algorithm, wherein the optimal... , To ensure The constant.

[0045] As a further optimization of the present invention: the optimal precoding matrix in step S3.3 It is concluded that, among them It is by The unitary matrix obtained by SVD decomposition.

[0046] Compared with existing technologies, the wireless channel capacity optimization method for intelligent connected meteorological monitoring vehicles provided by this invention has the following advantages:

[0047] (1) Low complexity

[0048] Compared with existing algorithms, the channel capacity optimization method proposed in this invention takes into account the influence of the Doppler effect and has a faster convergence speed. It can effectively adapt to the time-varying nature of the channel and helps to achieve stable and high-quality communication in high-speed mobile scenarios of meteorological monitoring vehicles.

[0049] (2) High accuracy

[0050] Compared to existing algorithms, the channel capacity optimization method for wireless communication systems of intelligent connected meteorological monitoring vehicles proposed in this invention not only considers achieving near-maximum capacity but also optimizes system performance. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the intelligent connected meteorological monitoring vehicle proposed in this invention.

[0052] Figure 2 This is a convergence analysis of the algorithm of this invention in the initial time slot.

[0053] Figure 3 Different transmission powers Impact on the average channel capacity of V2I systems.

[0054] Figure 4 The number of different IRS reflective elements Impact on the average channel capacity of V2I systems. Detailed Implementation

[0055] The present invention will be further illustrated below with reference to specific example figures. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0056] This invention discloses a wireless channel capacity optimization method for intelligent connected meteorological monitoring vehicles. First, considering the impact of the Doppler effect on channel modeling, a wireless communication system model suitable for intelligent connected meteorological monitoring vehicles is established. Second, SVD decomposition is used to convert the original MIMO channel into multiple parallel SISO spatial paths, and a channel capacity optimization problem is constructed accordingly. The channel capacity is affected by the base station's transmit precoding. and IRS reflection matrix Due to their combined influence, to achieve efficient solutions, the original coupled problem is decoupled and decomposed into two sequentially processed subproblems: the IRS reflection unit phase shift optimization subproblem and the base station precoding design subproblem. Taking into account the special coupling nature of these two subproblems, an optimal solution is obtained by indirectly optimizing the path gain and presenting a low-complexity algorithm. The closed-form solution involves the following four steps:

[0057] Step 1, establish as Figure 1 The communication system model shown for intelligent connected weather monitoring vehicles includes a static base station and a speed-based communication system. and direction Mobile weather monitoring vehicle, and Each IRS is equipped with a uniform planar array, having There are [number] reflective elements, and the total number of reflective elements in all IRS is [number]. Both the base station and the weather monitoring vehicle are equipped with uniform linear arrays, each with a certain number of antennas. and Focusing on a typical transmission frame containing T time slots, denoted as... The duration of each time slot is And compared to the minimum coherence interval of all the channels involved, It is chosen to be small enough so that the channel characteristics remain constant within each time slot.

[0058] In the time slot The base station transmits data to the meteorological monitoring vehicle. A parallel data stream, represented as ,satisfy And through linear precoding Simultaneously, the signal is broadcast to all RIS and weather monitoring vehicles. Assume... , , These respectively represent the route from the base station to the meteorological monitoring vehicle, and the route from the base station to the... IRS ( ) and from the A channel from an IRS to a weather monitoring vehicle, Representative at Time slot number The reflection matrix of each IRS Indicates in Time slot number The phase of the nth reflecting unit of an IRS Assuming all channels follow the Ricean fading model, i.e.

[0059]

[0060] in, Reference distance Path loss at point m It is the distance between the transmitting and receiving ends. It is the path loss index of the communication link.

[0061] For the non-line-of-sight components of the channel ,because , Related to the meteorological monitoring vehicle, and varying over time, it is therefore modeled as following a Rayleigh distribution with a Jakes power spread spectrum. Since it does not change over time, the entries are generated randomly and follow an independent and identically distributed complex Gaussian distribution with zero mean and unit variance.

[0062] For the line-of-sight component of the channel Due to the mobility of the weather monitoring vehicle, the Doppler effect is introduced. Since the base station and weather monitoring vehicle are equipped with ULA, and the IRS is equipped with UPA, the following applies:

[0063]

[0064]

[0065]

[0066] in, The array steering vector representing the direction of reception from the base station to the meteorological monitoring vehicle; Indicates the first The array steering vector from the IRS to the meteorological monitoring vehicle receiving direction; The array steering vector representing the transmission direction from the base station to the meteorological monitoring vehicle; Indicates the base station to the Array steering vectors for each IRS emission direction; Representing the Array steering vectors for each IRS receiving direction; Representing the The array steering vector for each IRS emission direction. Furthermore, For carrier wavelength, and These are the azimuth and elevation angles of the direction of arrival from the base station to the meteorological monitoring vehicle; It is the azimuth angle of the departure direction from the base station to the meteorological monitoring vehicle; and They are the first The azimuth and elevation angles of the IRS to the weather monitoring vehicle's direction of arrival; It is the base station to the first The azimuth angle of the departure direction of each IRS; and They are the first The azimuth and elevation angles of the departure direction of each IRS; and They are the first The azimuth and elevation angles of the arrival direction of each IRS.

[0067] Therefore, the total receiving channel matrix at the meteorological monitoring vehicle can be written as:

[0068]

[0069] For ease of representation, the channel matrix is ​​equivalently represented in a compact form:

[0070]

[0071] in , , .

[0072] Therefore, the meteorological monitoring vehicle is in the time slot The received signal can be represented as:

[0073] in This represents the maximum transmit power of the transmitter in any time slot. It has a mean of 0 and a variance of The additive white Gaussian noise vector. To maximize the spatial diversity of MIMO during the movement of the weather monitoring vehicle, consider... Therefore, the channel capacity is expressed as:

[0074] .

[0075] The second step, in a MIMO system, is to optimize the effective channel... By performing SVD decomposition, the precoding matrix can be obtained. Let's assume a certain time slot... SVD is ,in and It is a unitary matrix. It is a diagonal matrix, and the singular values ​​on the diagonal are arranged in descending order, i.e. Based on this decomposition, the optimal precoding matrix for this time slot can be constructed as follows: The power allocation matrix satisfy Characterizes the power configuration of each independent data stream. and Using this method, the original MIMO channel is equivalent to There are 3 parallel SISO spatial paths, each with a channel gain of 1. Therefore, the first The capacity of a feature channel in a time slot It can be represented as Therefore, the optimization problem is transformed into:

[0076]

[0077] Third step: For the sake of brevity, the time slot index will be omitted in the following steps. As can be seen from the above formula, given any... , obtained through SVD decomposition , Then, the power allocation matrix and optimal precoding The solution was obtained through a water-filling algorithm. Therefore, the original optimization problem can be decoupled into two sequentially processed subproblems: the IRS reflection unit phase shift optimization subproblem and the base station precoding design subproblem.

[0078] Fourth step: As can be seen from the optimization problem, the channel capacity increases with... Monotonically increasing, which indicates that it depends on Channel quality. In order to maximize To improve channel quality, it is proposed to maximize the total gain of the spatial path between the base station and the weather monitoring vehicle. ,Right now Therefore, for Solving for the given information transforms into indirectly optimizing the path gain; therefore, the optimization problem becomes P2:

[0079]

[0080] in This is the nth element on the diagonal of the total reflection matrix of the IRS. To effectively solve this problem, we first extend it to a more comprehensive form, P3:

[0081]

[0082] in This is the nth element in the m-th row of the total reflection matrix of the IRS. for The phase magnitude of the reflecting element in the m-th row and n-th column. Here, It is no longer a diagonal matrix, but a full matrix. about Differentiate:

[0083]

[0084] in and These represent the real and imaginary parts of a complex number, respectively. and It is a dimension of The standard basis vectors, where the first... One element is 1, and the rest are 0. (Introduction) ,in Depend on The elements are composed of , except that the (m, n)th element is 0, and in addition... The first derivative can be further expressed as:

[0085]

[0086] in From the above formula, we can see that Depends on In addition to All angles. Furthermore, when fixed except... At all angles outside, It is about A sine function with a period of Based on the properties of the sine function, within each period, There exists a maximum point and a minimum point, and these extreme points are called extrema points. Setting the above equation to 0, we get the extreme points as:

[0087]

[0088] in It is an integer. To determine the maximum point, we... about Find the second derivative:

[0089]

[0090] Since the maximum point is located within the concave interval, that is... Must be smaller than Therefore, the solution that yields the maximum value is... Where k is any integer.

[0091] Since P3 is an extended version of P2, and the matrix in P2... Since it is a diagonal matrix, the nth diagonal element... The above formula can be used for iterative updates. Specifically, in each iteration, the other diagonal elements are fixed. And updated through closed-form analytical solutions Therefore, the optimal phase configuration for each IRS unit can be expressed as:

[0092]

[0093] in , , For any , l represents the number of iterations.

[0094] Because this algorithm utilizes the following characteristic: for a specific In fixed In the case of time, about Sine transformation. And its principle is similar to the well-known block coordinate descent method: fixing other diagonal elements. Through alternating optimization To achieve Maximize.

[0095] For the obtained phase shift matrix The optimal power allocation matrix can be obtained by solving P1 using the water injection algorithm. Specifically, for , optimal It can be determined by the following formula:

[0096]

[0097] in, It is a constant used to guarantee Its value can be calculated using numerical methods. Once obtained... Optimal precoding matrix It can be directly derived that, among which It is by The unitary matrix obtained from the SVD decomposition is used to calculate the capacity. .

[0098] Based on the above derivation,Figures 2-4 The simulation performance of the channel capacity optimization method for intelligent connected meteorological monitoring vehicles proposed in this invention is presented. Figure 2 The convergence of the algorithm in the initial time slot is described. To facilitate analysis of the DSM algorithm's performance, a comparison with the ADMM algorithm is made. Under various transmit power conditions, the DSM algorithm exhibits superior convergence characteristics compared to the ADMM algorithm. Specifically, as the number of iterations increases, the channel capacity of the DSM algorithm stabilizes more quickly, and the stable channel capacity value is also higher than that of ADMM. For example, at a transmit power of 20 dBm, the DSM algorithm quickly reaches and stabilizes at approximately 23 bps / Hz after fewer iterations, while the ADMM algorithm not only converges more slowly but also has a significantly lower stable channel capacity, approximately 20 bps / Hz. This demonstrates that the algorithm of this invention has low complexity and can achieve near-maximum capacity.

[0099] Figure 3 The relationship between average channel capacity and transmit power in a V2I system is described. To evaluate the performance of the DSM algorithm of this invention, it is compared with the following five schemes: 1) Phase of each reflecting element is adjusted by step size... Exhaustive search as an upper bound on performance; 2) 1 / 2-bit IRS; 3) Randomly generated phase shift scheme; 4) Optimal precoding design without IRS. From Figure 3 It can be seen that the proposed DSM algorithm performs comparably to exhaustive search, but with significantly lower computational complexity, demonstrating the effectiveness of the algorithm in this invention. Furthermore, the channel capacity of the wireless communication system for intelligent connected weather monitoring vehicles is significantly higher than that of a V2I system without an IRS. This is attributed to the IRS improving channel quality through beamforming gain and inherent aperture gain, achieving quadratic-order gain. Additionally, despite hardware limitations, the use of discrete phase-shifting RIS can still significantly improve wireless communication performance in practice.

[0100] Figure 4 Describes the number of IRS reflective elements Impact on the average channel capacity of V2I systems. As the number of reflecting elements increases, the channel capacity of wireless communication systems for intelligent connected weather monitoring vehicles also increases, while the channel capacity of systems without an IRS remains low and does not change with the number of elements. This is because more reflecting elements can reflect more signals, thus enhancing the passive beamforming effect. Furthermore, as the number of reflecting elements increases, the performance gap between continuous phase-shift schemes based on the DSM algorithm and 1-bit and 2-bit discrete phase-shift schemes gradually widens. This phenomenon is attributed to the fact that the increase in the number of reflecting elements leads to the accumulation of quantization errors in the discrete phase-shift scheme, thereby affecting its performance.

[0101] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

Claims

1. A method for optimizing wireless channel capacity for intelligent connected meteorological monitoring vehicles, characterized in that, Includes the following steps: Step S1: Establish a time-varying wireless communication system model for intelligent connected meteorological monitoring vehicles, and model the total receiving channel of the meteorological monitoring vehicle; Step S2: Use SVD decomposition to convert the total receiving channel of the meteorological monitoring vehicle into multiple parallel SISO spatial paths, and construct a channel capacity optimization problem accordingly. Step S3: Solve the optimization problem constructed in step S2 to obtain the closed-form solution of the optimal IRS reflection matrix and the optimal precoding at the base station, thereby obtaining the optimization result of the wireless channel capacity.

2. The wireless channel capacity optimization method for intelligent connected meteorological monitoring vehicles as described in claim 1, characterized in that, The time-varying wireless communication system model in step S1 includes a base station and a system that uses a speed... and direction Mobile weather monitoring vehicle, and Each IRS is equipped with a uniform planar array, having Each reflective element; both the base station and the weather monitoring vehicle are equipped with a uniform linear array, each having... and One antenna.

3. The wireless channel capacity optimization method for intelligent connected meteorological monitoring vehicles as described in claim 2, characterized in that, For the channel model of a time-varying wireless communication system, the channels at the transceiver end include the path from the base station to the meteorological monitoring vehicle, and the path from the base station to the... The first IRS, and from the first Sub-channels from IRS to the weather monitoring vehicle; among which... .

4. The wireless channel capacity optimization method for intelligent connected meteorological monitoring vehicles as described in claim 3, characterized in that, The total receiving channel model for the meteorological monitoring vehicle in step S1 is as follows: ， in, Indicates in The time slot is a sub-channel from the base station to the meteorological monitoring vehicle; , Indicates in Time slot from the Sub-channel from IRS to the weather monitoring vehicle ; , Indicates from the base station to the... Sub-channels of each IRS; N represents the total number of reflective elements in all IRSs; Indicates in Time slot number The reflection matrix of each IRS, Indicates in Time slot number The phase of the nth reflecting unit of an IRS .

5. A method for optimizing wireless channel capacity for intelligent connected meteorological monitoring vehicles as described in claim 3 or 4, characterized in that, Assuming all channels follow the Ricean fading model, and considering the Doppler spread of the non-line-of-sight (NLOS) components and the Doppler shift of the line-of-sight (LOS) components, the NLOS components of each sub-channel are analyzed. , , As time t changes, it is modeled as following a Rayleigh distribution with Jakes power spread spectrum; for the LOS component of the channel , , Introducing the Doppler effect, the model is as follows: ， ， ， in, and They represent in The NLOS and LOS components of the sub-channel from the base station to the meteorological monitoring vehicle in the time slot; and They represent in Time slot from the NLOS and LOS components of the sub-channel from the IRS to the weather monitoring vehicle; and These represent the distance from the base station to the... NLOS and LOS components of subchannels of each IRS. The array steering vector representing the direction of reception from the base station to the meteorological monitoring vehicle; Indicates the first The array steering vector from the IRS to the meteorological monitoring vehicle receiving direction; Representing the Array steering vectors for each IRS receiving direction; The array steering vector representing the transmission direction from the base station to the meteorological monitoring vehicle; Representing the Array steering vectors for each IRS emission direction; Indicates the base station to the Array steering vectors for each IRS emission direction; The carrier wavelength; The speed of the weather monitoring vehicle; The size of each time slot; and These are the azimuth and elevation angles of the direction of arrival from the base station to the meteorological monitoring vehicle; It is the azimuth angle of the departure direction from the base station to the meteorological monitoring vehicle; and They are the first The azimuth and elevation angles of the IRS to the weather monitoring vehicle's direction of arrival; It is the base station to the first The azimuth angle of the departure direction of each IRS; and They are the first The azimuth and elevation angles of the departure direction of each IRS; and They are the first The azimuth and elevation angles of the arrival direction of each IRS.

6. The wireless channel capacity optimization method for intelligent connected meteorological monitoring vehicles as described in claim 4, characterized in that: The channel capacity optimization problem in step S2 is constructed as follows: ， in, express Channel capacity of time slots Represents the total transmission power. express The number of parallel SISO spatial paths in a time slot. express Power allocation matrix of time slots, express Time slots are allocated to the parallel first Transmit power of each SISO space path, Indicates size is The 0 matrix, express Time slot The A singular value, The variance of the noisy complex signal is represented by... express diagonal elements, Represents the equivalent phase shift matrix The phase magnitude of the nth reflecting unit.

7. The wireless channel capacity optimization method for intelligent connected meteorological monitoring vehicles as described in claim 6, characterized in that: The specific steps for solving the optimization problem constructed in step S2 in step S3 are as follows: S3.1, for Solving this problem transforms the channel capacity optimization problem into an optimization problem P2, and obtaining the optimal solution. Closed-form solution: P2： ， ， in, This represents the equivalent value to be optimized. Represents the equivalent phase shift matrix The phase magnitude of the nth reflecting unit. S3.2, the optimal solution from step S3.1 Substitute the closed-form solution into the channel capacity optimization problem, and use the water-filling algorithm to obtain the optimal power allocation matrix; Step 3.3: Based on the optimal power allocation matrix in step 3.2, the optimal precoding matrix is ​​obtained, and then the optimization result of the wireless channel capacity is calculated.

8. The wireless channel capacity optimization method for intelligent connected meteorological monitoring vehicles as described in claim 7, characterized in that: The specific steps of step S3.1 are as follows: Step S3.1.1: Expand optimization problem P2 into optimization problem P3: ， ， in express The element in the m-th row and n-th column, for The phase magnitude of the reflecting unit in the m-th row and n-th column; Step S3.1.2, for about Perform the first derivative: ， in and These represent the real and imaginary parts of a complex number, respectively. and The dimensions representing the m-th and n-th elements being 1, and the rest being 0, are respectively. Standard basis vectors; This represents the element in the nth row and mth column of the matrix; Step S3.1.3, Introduction , Indicates will The matrix obtained by setting the element in the m-th row and n-th column to 0. The first-order derivative result of step S3.1.2 can be further expressed as: in ; S3.1.4, for about Perform second-order differentiation: ； S3.1.5, when fixed except At all angles outside, It is about The period is The sine function, then when The solution that yields the maximum value when the value is less than 0 is: , where k is any integer; S3.1.6, based on S3.1.5, is obtained through iterative updates. The nth diagonal element, thus obtaining the optimal Closed-form solution: in, Represents the result obtained in l iterations The nth diagonal element, Represents the result obtained in l-1 iterations The nth diagonal element, , , , , where l represents the number of iterations.

9. The wireless channel capacity optimization method for intelligent connected meteorological monitoring vehicles as described in claim 7, characterized in that: In step S3.2, the optimal power allocation matrix is ​​obtained using the water injection algorithm, where the optimal... , To ensure The constant.

10. The wireless channel capacity optimization method for intelligent connected meteorological monitoring vehicles as described in claim 7, characterized in that: The optimal precoding matrix in step S3.3 It is concluded that, among them It is by The unitary matrix obtained by SVD decomposition.

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