UAV-assisted high signal-to-noise ratio visible light communication method, system, device and medium

Abstract

The present invention relates to the field of communications technology and discloses a method, system, device, and medium for drone-assisted high-signal-to-noise ratio visible light communication. The method comprises the following steps: based on a multi-user, multi-input, multi-output system model, transmitting an optical signal containing a precoding matrix via an LED array; establishing a joint optimization problem with the goal of maximizing the system signal-to-noise ratio in the presence of channel-dependent noise; employing an alternating iterative optimization method to decompose the joint optimization problem into a transmission precoding subproblem and a dimming precoding subproblem, and solving the problem; and utilizing the dynamic deployment capabilities of drones to adjust the drone's position and attitude based on real-time channel state information, collaboratively optimizing the precoding strategy. This method effectively overcomes the signal transmission bottleneck in traditional static scenarios and can also improve the system's robustness and adaptability in complex environments.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method, system, device, and medium for drone-assisted high signal-to-noise ratio visible light communication. Background Art

[0002] Visible light communication (VLC) offers the advantages of high spectral efficiency, wide bandwidth resources, and freedom from radio spectrum constraints. Utilizing visible light bands for information transmission, VLC not only meets high data rate demands but also offers the potential for compatibility with existing lighting equipment, making it a key area of ​​future communication technology. Furthermore, the rapid development of drone technology, coupled with its flexible deployment capabilities and excellent maneuverability, offers a new dimension of support for future communication systems. Drones can serve as relay nodes or dynamic base stations in VLC systems, adjusting their positions to optimize channel quality and effectively address communication needs in complex environments. However, the mobility of drones and their limited battery life also present new challenges, necessitating the design of targeted algorithms and system solutions to fully realize their potential in VLC.

[0003] However, because visible light communication (VLC) channel characteristics are affected by multiple factors, including the light propagation environment and receiver noise characteristics, especially the presence of channel-dependent noise, optimizing the system's signal-to-noise ratio (SNR) has become a critical research topic. Existing research has proposed SNR optimization methods to maximize system capacity in the presence of channel-dependent noise, laying a theoretical foundation for improving VLC system performance. Existing research has also investigated the SNR performance of sine, square, and triangle waves in the context of VLC systems. The results show that square waves are less sensitive to noise. The system's SNR can be further improved through non-orthogonal multiple access (NOMA) precoding technology. By combining existing precoding techniques with convex optimization methods, it is possible to design a VLC system that maximizes SNR.

[0004] Several existing signal-to-noise ratio (SNR) optimization schemes for visible light communication systems aim to maximize system capacity in the presence of channel-dependent noise. These schemes primarily focus on optimizing signal scheduling and resource allocation within static devices or fixed scenarios. However, these schemes struggle to cope with rapidly changing channel conditions in dynamic environments and are unable to consistently provide optimal SNR improvements as channel characteristics fluctuate. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a UAV-assisted high signal-to-noise ratio visible light communication method, which can utilize visible light precoding technology to introduce UAVs into the field of visible light communication, taking into account channel-dependent noise while ensuring that the system can obtain a high signal-to-noise ratio.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a UAV-assisted high signal-to-noise ratio visible light communication method, comprising: based on a multi-user multi-input multi-output system model, sending an optical signal containing a precoding matrix through an LED array; establishing a joint optimization problem with the goal of maximizing the system signal-to-noise ratio under the background of channel-dependent noise; using an alternating iterative optimization method to decompose the joint optimization problem into a transmission precoding subproblem and a dimming precoding subproblem, and solving them; combining the dynamic deployment capability of the UAV, adjusting the position and attitude of the UAV according to real-time channel state information, and collaboratively optimizing the precoding strategy.

[0008] As a preferred solution of the UAV-assisted high signal-to-noise ratio visible light communication method of the present invention, wherein: the sending of the optical signal containing the precoding matrix includes, based on the MU-MIMO model, setting that there are N transmitting end LEDs and K receiving end users in the current system, adding a DC bias The LED sends a signal Expressed as:

[0009] ,

[0010] in, is the original digital signal to be sent to the kth user, is the precoding matrix to be optimized.

[0011] As a preferred solution of the UAV-assisted high signal-to-noise ratio visible light communication method of the present invention, wherein: the joint optimization problem includes optimizing the transmission precoding matrix and optimizing the dimming precoding matrix;

[0012] The optimization of the transmission precoding matrix includes fixing the dimming precoding matrix and solving the transmission precoding matrix using a fixed-point FP method based on quadratic transformation;

[0013] The optimization of the dimming precoding matrix includes fixing the transmission precoding matrix, introducing auxiliary variables to model the dimming precoding matrix, determining the feasible value range of the auxiliary variables by bisection, and solving the DC bias vector that meets the dimming control constraints.

[0014] As a preferred solution of the UAV-assisted high signal-to-noise ratio visible light communication method of the present invention, wherein: the alternating iterative optimization method includes initializing feasible values ​​of the transmission precoding matrix and the dimming precoding matrix;

[0015] The transmission precoding sub-problem and the dimming precoding sub-problem are optimized alternately, and the optimal solution is gradually approached by updating auxiliary variables and solving convex problems until the convergence threshold is met.

[0016] As a preferred solution of the UAV-assisted high signal-to-noise ratio visible light communication method of the present invention, the dynamic deployment capability of the UAV includes allocating non-orthogonal multiple access (NOMA) power to different users based on the channel gain vector sorting;

[0017] Optimize the spatial coverage of the optical signal by adjusting the drone's height, position, and the directivity of the LED array at the transmitter;

[0018] System constraints are established, including peak light intensity constraints of the LED array, dimming level constraints based on eye safety, total electrical power constraints, and item-by-item interference cancellation (SIC) power allocation constraints.

[0019] As a preferred solution of the UAV-assisted high signal-to-noise ratio visible light communication method of the present invention, the real-time channel state information includes: obtaining a channel vector based on channel estimation, and modeling the channel error as a Gaussian distribution variable;

[0020] The combined effects of noise correlated with the input signal and additive noise are introduced into the receiver signal expression.

[0021] As a preferred solution of the UAV-assisted high signal-to-noise ratio visible light communication method of the present invention, the collaborative optimization precoding strategy includes supporting adaptive adjustment of the number of dynamic users. When the number of users is less than the number of LEDs, sufficient resources are allocated in an underload mode. When the number of users is greater than or equal to the number of LEDs, power allocation is optimized using the NOMA principle to maintain system performance.

[0022] At the same time, by dynamically adjusting the optical signal coverage and precoding strategy through drones, real-time networking and data synchronization between mobile nodes can be achieved, supporting efficient information exchange between vehicles, roadside equipment and traffic control centers; utilizing the rapid deployment of drones, highly reliable visible light communication links can be established in complex obstruction environments, and data can be transmitted stably in disaster relief or temporary networks; by collaboratively optimizing the spatial directivity and power distribution of optical signals through multiple drones, the real-time data interaction needs of multiple users can be met, enabling low-latency communication between drone clusters, aerial base stations and ground terminals.

[0023] As a preferred solution of the UAV-assisted high signal-to-noise ratio visible light communication system of the present invention, it includes: a UAV communication module, a precoding optimization module, a channel state monitoring module and a dynamic deployment control module;

[0024] The drone communication module is used to carry the LED array, generate and send optical signals according to the precoding matrix, and receive feedback information from the receiving end user;

[0025] The precoding optimization module is used to perform joint optimization of transmission precoding and dimming precoding, solving the optimal precoding matrix through an alternating iterative method to maximize the system signal-to-noise ratio;

[0026] The channel state monitoring module is used to obtain channel state information (CSI) in real time, including channel estimation, error modeling, and noise analysis, and transmit the data to the precoding optimization module;

[0027] The dynamic deployment control module is used to adjust the position and height of the drone and the directionality of the LED array based on channel state information and precoding optimization results, thereby optimizing the spatial coverage and power distribution of the optical signal.

[0028] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, steps of a method for drone-assisted high signal-to-noise ratio visible light communication are implemented.

[0029] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for drone-assisted high signal-to-noise ratio visible light communication.

[0030] The present invention achieves the following beneficial effects: By optimizing transmission precoding and dimming precoding and utilizing convex optimization techniques to dynamically adjust the power distribution and directionality of optical signals, the present invention maximizes the system signal-to-noise ratio (SNR) in the presence of channel-dependent noise, significantly improving communication quality and stability. By using drones as auxiliary communication nodes, the system overcomes the spatial limitations of traditional fixed visible light communication equipment, significantly improving the system's coverage and service capabilities, and is suitable for large-scale, multi-user dynamic network environments.

[0031] Adaptive optimization based on channel state information (CSI) adjusts precoding parameters, effectively reducing the impact of channel-dependent noise on system performance and improving system robustness and reliability. This method is applicable to a variety of practical scenarios, including intelligent transportation, emergency communications, and aerial networks, and can provide efficient communication solutions under diverse conditions such as complex obstructions, user mobility, and environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 A schematic flow chart of a method for UAV-assisted high signal-to-noise ratio visible light communication according to one embodiment of the present invention.

[0034] Figure 2 A flowchart of solving the transmission precoding subproblem of a UAV-assisted high signal-to-noise ratio visible light communication method provided by one embodiment of the present invention.

[0035] Figure 3 This is a performance curve diagram of different methods of the drone-assisted high signal-to-noise ratio visible light communication method provided by one embodiment of the present invention, and changes with channel error when the number of PDs is 2.

[0036] Figure 4 This is a performance curve diagram of different methods of the drone-assisted high signal-to-noise ratio visible light communication method provided by one embodiment of the present invention, and changes with channel error when the number of PDs is 3.

[0037] Figure 5 This is a performance curve diagram of different methods of the drone-assisted high signal-to-noise ratio visible light communication method provided by one embodiment of the present invention, and changes with channel error when the number of PDs is 6.

[0038] Figure 6 The UAV-assisted high signal-to-noise ratio visible light communication method provided by one embodiment of the present invention is different Joint Optimization Resource Allocation Method Variation performance curve (K=4) diagram.

[0039] Figure 7 This is a graph of the iterative convergence performance of the joint optimization resource allocation method under different total powers for the UAV-assisted high signal-to-noise ratio visible light communication method provided by one embodiment of the present invention.

[0040] Figure 8 The UAV-assisted high signal-to-noise ratio visible light communication method provided by one embodiment of the present invention is different Joint optimization resource allocation method with dimming factor Performance curve diagram of the change.

[0041] Figure 9 The UAV-assisted high signal-to-noise ratio visible light communication method provided by one embodiment of the present invention is Performance curve of the joint optimization resource allocation method when =0.001.

[0042] Figure 10 The UAV-assisted high signal-to-noise ratio visible light communication method provided by one embodiment of the present invention is Performance curve of the joint optimization resource allocation method when =0.005.

[0043] Figure 11 The UAV-assisted high signal-to-noise ratio visible light communication method provided by one embodiment of the present invention is Performance curve of the joint optimization resource allocation method when =0.01.

[0044] Figure 12 The UAV-assisted high signal-to-noise ratio visible light communication method provided by one embodiment of the present invention is Performance curve of the joint optimization resource allocation method when =0.02. DETAILED DESCRIPTION

[0045] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0046] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a UAV-assisted high signal-to-noise ratio visible light communication method, comprising:

[0047] S1: Based on the multi-user multiple-input multiple-output system model, an optical signal containing a precoding matrix is ​​sent through the LED array;

[0048] S2: Under the background of channel-dependent noise, a joint optimization problem is established to maximize the system signal-to-noise ratio.

[0049] S3: Using an alternating iterative optimization method, the joint optimization problem is decomposed into a transmission precoding sub-problem and a dimming precoding sub-problem, and the results are solved.

[0050] S4: Combined with the dynamic deployment capability of UAVs, the position and attitude of UAVs are adjusted according to real-time channel status information, and the precoding strategy is collaboratively optimized.

[0051] Furthermore, this method jointly optimizes the transmission and dimming precoding matrices to address resource allocation in signal-dependent noise-free space optical communication (SDN-FSO) channels. To solve this joint optimization problem, an alternating optimization approach is employed. First, the dimming precoding matrix is ​​fixed, and a fixed-point (FP) method based on a quadratic transform is used to optimize the transmission precoding matrix. Next, using the optimized transmission precoding matrix, auxiliary variables are introduced to optimize the dimming precoding matrix, and the value range of the auxiliary variables is determined using a bisection method.

[0052] Since this method is based on the discussion of the Multi-User Multiple-Input Multiple-Output (MU-MIMO) SDN-FSO channel system model, and based on the MU-MIMO model, it is assumed that there are N transmitting LEDs and K receiving users in the system, the DC bias is added. The LED sends a signal It can be expressed as:

[0053] ,

[0054] represents the precoding matrix of the kth user, is the precoding matrix to be optimized, is the original digital signal to be sent to the kth user, represents the field of real numbers, and Respectively Row 1 Column and OK A matrix of columns.

[0055] To further illustrate the CSI error in the case of imperfect CSI, the actual channel vector of the kth user is It can be expressed as:

[0056] ,

[0057] Likewise, is the channel vector obtained by channel estimation, represents the channel error vector of the kth user, and the channel error is usually caused by noise, so can be modeled as a variable that follows a Gaussian distribution:

[0058] ,

[0059] In the above formula is the covariance matrix, which can be further expressed as . Indicates that the variable exhibits a Gaussian distribution; represents the variance of the channel estimation error of the kth user, express The identity matrix of order.

[0060] A similar expression for the received signal of the kth user can be written as:

[0061] ,

[0062] in, Indicates the The signal received by each user. represents the transpose of the k-th user channel gain vector, represents the product of the precoding matrix and the signal vector; Represents the product of the i-th LED signal and the corresponding precoding matrix. represents the noise associated with the input signal and It is additive noise that is unrelated to the input signal.

[0063] According to the above NOMA principle, in order to maintain generality, the estimated channel gain vectors of the users are arranged in ascending order. , where the user assigned higher power will first decode its own signal and then regard the signals of other users as interference. The SINR can be written as

[0064] ,

[0065] It should be noted that ,in Indicates the The transpose of the gain vector of the user estimated channel. and Respectively represent and The precoding matrix of the user is Indicates the first The square of the 2-norm of the precoding matrix of each user. In order to ensure that users with better channel gain can successfully decode the information of users with worse channel gain, the SINR needs to satisfy the following expression relationship:

[0066] ,

[0067] , Respectively represent and By observing the above inequality relationship, we can intuitively realize that the form of this inequality is relatively complex and difficult to handle.

[0068] Lemma 1: If the channel gain vector between users satisfies the following inequality relationship Then the above inequality relationship will also be satisfied.

[0069] Due to the non-negativity of visible light signal propagation, the following constraints will be imposed on the precoding matrix:

[0070] ,

[0071] In addition, since the LED array has the ability to illuminate, the transmitted light intensity of each LED array should be limited by a predefined peak light intensity A:

[0072] ,

[0073] On the other hand, in order to protect human eyesight, dimming control constraints are introduced:

[0074] ,

[0075] in, is the required dimming level. Represents the DC component of the signal, which actually averages the signal. Indicates the number of emitting LEDs, Indicates peak light intensity.

[0076] From the perspective of power energy, it is also necessary to consider that the average power of the signal will be limited by the total electrical power:

[0077] ,

[0078] In the above formula, Indicates the average power of the signal; Indicates averaging the items in brackets. is the total electrical power constraint.

[0079] In summary, the problem of jointly optimizing the dimming and transmission precoding matrix based on maximizing the average user SINR can be expressed as:

[0080] ,

[0081] in, It is the successive interference cancellation (SIC) power constraint for the NOMA system. This inequality constraint helps to increase the power allocated to weaker users. Without this constraint, most communication resources are likely to be allocated to users with better channel conditions.

[0082] The power allocation vector is optimized and converted into the optical precoding matrix, which is the DC bias. Optimization. and The relationship is highly coupled, and the objective function of this problem is a typical multi-FP addition form, that is, a non-convex and unsolvable complex problem. Therefore, the original joint optimization problem will be solved by alternating iterative optimization. First, the feasible transmission and dimming precoding matrices are initialized, the dimming precoding matrix is ​​fixed, and the transmission precoding matrix is ​​optimized and solved; then, the optimized transmission precoding matrix value is fixed, and the dimming precoding matrix optimization problem is solved; the above steps are repeated until the solution of the joint optimization problem reaches the convergence threshold. The threshold range is when the change between adjacent iterations is less than 1%.

[0083] According to the form of the original optimization problem, the original problem is decomposed into transmission precoding sub-problem and dimming precoding sub-problem to be solved separately. When fixed, only the transmission precoding matrix For optimization, the original optimization problem can be transformed into:

[0084] ,

[0085] Therefore, in order to transform the objective function formula, we first introduce the auxiliary variable ,make

[0086] ,

[0087] in, , Indicates the Auxiliary variables, as previously shown , so when optimizing the average SINR, only the minimum SINR when decoding user k information is considered, that is, The arithmetic mean of .

[0088] When the transmission precoding matrix When fixed, the left side of the above inequality can be replaced by Take the derivative and set it equal to zero. The derivation process can be expressed as:

[0089] ,

[0090] Therefore, the optimal value It can be expressed as:

[0091] ,

[0092] Get the best of this generation After the value is obtained, it is substituted into the changed optimization problem as a fixed value. At this time, the problem is about the precoding matrix It is a convex problem and can be solved directly using the CVX toolkit.

[0093] When the transmission precoding matrix When fixed, the dimming precoding vector is also the DC bias vector Optimize, then the original optimization problem can be transformed into:

[0094] ,

[0095] The dimming precoding subproblem can be equivalent to the following form:

[0096] ,

[0097] Introducing auxiliary variables ,

[0098] ,

[0099] The interval bounds can be defined as:

[0100] ,

[0101] ,

[0102] Where, Indicates the lower power limit.

[0103] Find by dichotomy The feasible solution is as follows:

[0104] 1) According to the formula defined by the interval bounds Solve the range of values ​​and get and The initial value of

[0105] 2) Set the midpoint value ,Will Substitute the value of into the problem to solve; if the problem with the introduction of auxiliary variables is solvable, then let Otherwise, ;

[0106] 3) Repeat step 2 until , and obtain the final optimal feasible solution . Get the optimal feasible solution Then, substitute it into the original problem of introducing auxiliary variables to obtain the optimal DC bias vector The value of .

[0107] In order to further explore the performance of the proposed method in the presence of SIC error, represents the percentage of SIC residual interference, so the SINR with imperfect SIC operation in the NOMA scenario can be expressed as:

[0108] ,

[0109] It should be noted that if Figure 2 As shown in Figure 1, after the function and constraints for maximizing SNR in the UAV communication channel are established, the problem is split into the transmission precoding subproblem and the light modulation subproblem. The solution to the transmission precoding subproblem includes the following steps:

[0110] S1: Set the initial number of iterations;

[0111] S2: Initialize the feasible precoding matrix value;

[0112] S3: Update the value of the introduced variable y;

[0113] S4: Fix the value of y and solve the convex problem about power;

[0114] S5: the number of iterations increases;

[0115] S6: Determine whether the problem converges.

[0116] Through the above steps, the transmission precoding sub-problem can be solved. As can be seen from the following example diagram, the method of the present invention requires fewer iterations and has lower computational complexity.

[0117] Example 2, referring to the system parameter settings in Table 1, is an embodiment of the present invention, providing a method for drone-assisted high signal-to-noise ratio visible light communication. This example illustrates the system parameter settings for simulations of this patent, using a room 6 meters long, 6 meters wide, and 3 meters high. At the drone's transmitting end, the number of LED arrays is set to 3, the half-power angle is 60°, the LED height is 3 meters, the peak light intensity is 5V, and the dimming level is set to 0.5. The modulation format of the transmitted signal is 2-PAM. At the receiving end, the number of photodetectors (PDs) is set to different values ​​for subsequent comparison: 2, 3, and 6. The PDs have an effective receiving area of ​​1 square centimeter, a field of view (FoV) of 60°, an optical filter gain of 1, and a concentrator refractive index of 1.5. The PD height is set to 0.7 meters.

[0118] Table 1 System parameter setting table

[0119] ,

[0120] Example 3, reference Figure 3-Figure 5 , the third embodiment of the present invention, shows the SNR performance of the proposed method under different channel errors. As can be seen from the figure, the proposed method has high signal-to-noise ratio characteristics. Compared with fixed transmission precoding, the SNR is improved by more than 10dB; compared with fixed dimming precoding, the SNR is improved by 2 to 5dB. Furthermore, when the number of PDs at the receiving end is 2 or 3, the system SNR is high; however, when the number of PDs at the receiving end is 6, the system SNR is low, because the number of PDs at the receiving end exceeds the system's carrying capacity.

[0121] from Figure 3-Figure 5 It can be seen from the observation that when the channel error increases, the average user SINR performance of all methods will increase. This is because the error causes the instability of the overall system performance. When the dimming precoding matrix is ​​fixed, no matter the number of users is 2, 3 or 6, the transmission precoding is The performance is better than The reason behind this is that the system performance is improved at the expense of some current energy. However, due to the nonlinear working range of LED, it does not mean that The larger the value of , the more linearly the system performance improves. Furthermore, it was found that the performance of all methods decreases as the number of users, K, increases. When the number of users, K, is 2, the system is underloaded because the number of users is smaller than the number of LEDs. This can be simply understood as a situation where there are more resources but fewer users using them. Therefore, the resources allocated to each user are relatively sufficient, resulting in a relatively high average user SINR. When the number of users, K, is 6, the number of users is twice the number of LEDs, indicating a resource shortage. It is necessary to allocate resources to each user according to the NOMA principle in the optimization algorithm to maintain overall system performance. Finally, it was found that performing only dimming precoding does not significantly improve system performance. This is because transmission precoding optimization can eliminate interference between users, which is a major factor affecting MU-MISO system performance. In this chapter, dimming precoding is only related to SDN terms and some optical energy constraints. Therefore, while it can help improve performance, its main focus is on the allocation and configuration of optical energy.

[0122] Example 4, reference Figure 6 , which is the fourth embodiment of the present invention, and is Joint optimization resource allocation method under constraints with SDN ratio Performance trend of the change, where the number and position of LEDs are the same as in the previous figure, the number of users K=4 and the position coordinates of these 4 users are (2,1,0.7), (2.5,3,0.7), (4,3,0.7) and (5,5,0.7), respectively. The channel estimation error is set to .observe Figure 4-8 It can be found that: 1) When the SDN ratio When the total power increases, The SINR performance of the joint optimization resource allocation method under the same conditions is reduced. This is because the increase of SDN will cause the system loss to increase. However, since the joint method takes the impact of SDN into account during optimization, the overall performance of this method does not decrease much; 2) With the total power With the increase of , the average user SINR performance of the system is improved. When the total power limit of the system is increased, it can offset some of the adverse effects of channel errors or SDN, thereby improving the overall performance of the system.

[0123] Example 5, reference Figure 7 , which is the fifth embodiment of the present invention, and is a graph showing the iterative convergence performance of the joint optimization resource allocation method under different total powers, wherein the number of users K=4, the user positions and the channel errors are set to the same Figure 4 Consistent, SDN ratio is When initializing the transmission precoding matrix When , it is usually set to a smaller value that meets all the constraints, for example, All elements in are 0.1. Figure 5 It can be found that after only no more than 4 iterations, the total power The joint optimization methods under constraints can achieve good convergence performance.

[0124] Example 6, reference Figure 8 , which is the sixth embodiment of the present invention, and is a joint resource allocation method with dimming factor Variation of performance curves, where channel noise error and SDN ratio , the number of users K=4 and the location settings are the same as above. If the dimming factor When the DC bias increases The sum of should also increase accordingly. When it increases, it will lead to an increase in the SDN item, which will eventually deteriorate the overall performance of the system. Figure 8 It was found that the system SINR performance did not change with the dimming factor. Therefore, this indirectly illustrates that the joint transmission precoding and dimming precoding optimization algorithm can be used to optimize the transmission precoding and dimming precoding in the presence of channel noise error according to the dimming factor. The system performance is adjusted adaptively according to the changes in , which reflects the robustness of the joint optimization algorithm.

[0125] Example 7, reference Figures 9-12 , which is the seventh embodiment of the present invention, and is a performance curve of the joint optimization resource allocation method under different SIC error ratios, wherein the SDN ratio , dimming level , the number of users K=4 and the location settings are the same as above. Figures 9-12 It can be found that when the SIC error exists and its proportion increases, the interference introduced by the first k−1 users appears, and at the same time, the interference term of each user increases, thereby reducing the overall performance of the system. However, since the adaptive joint optimization resource allocation method takes this term into consideration and performs the optimization together, even when When the SIC error increases from 0.001 to 0.02, which means a 20-fold increase, the average user SINR performance of the system only drops by 1 / 2; when the system simultaneously has SDN, imperfect CSI error and non-ideal SIC, the average user SINR performance still maintains relatively impressive data, indicating that the adaptive joint optimization resource allocation method has the ability to simultaneously resist the noise caused by the nature of the LED light source itself, the noise error caused by channel estimation and the SIC error in the NOMA technology. This method can adapt to relatively harsh communication environments and has a certain degree of robustness.

[0126] Example 8 is an embodiment of the present invention, which is different from the other embodiments in that:

[0127] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0128] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0129] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0130] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0131] Example 9, an embodiment of the present invention, provides a UAV-assisted high signal-to-noise ratio visible light communication system, including a UAV communication module, a precoding optimization module, a channel state monitoring module, and a dynamic deployment control module;

[0132] The drone communication module is used to carry the LED array, generate and send optical signals according to the precoding matrix, and receive feedback information from the receiving end user;

[0133] The precoding optimization module is used to perform joint optimization of transmission precoding and dimming precoding, solving the optimal precoding matrix through an alternating iterative method to maximize the system signal-to-noise ratio;

[0134] The channel state monitoring module is used to obtain channel state information (CSI) in real time, including channel estimation, error modeling, and noise analysis, and transmit the data to the precoding optimization module;

[0135] The dynamic deployment control module is used to adjust the position and height of the drone and the directionality of the LED array based on channel state information and precoding optimization results, thereby optimizing the spatial coverage and power distribution of the optical signal.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A UAV-assisted high signal-to-noise ratio visible light communication method, characterized by: include, Based on the multi-user multiple-input multiple-output system model, an optical signal containing a precoding matrix is ​​sent through a light-emitting diode (LED) array. The sending of the optical signal including the precoding matrix includes: setting, based on the multi-user-multiple input multiple output MU-MIMO model, that there are N transmitting end LEDs and K receiving end users in the current system, adding a DC bias I DC The LED sends a signal x expressed as: Among them, d k is the original digital signal to be sent to the kth user, p k represents the precoding matrix of the kth user, is the precoding matrix to be optimized, represents the field of real numbers, and Represent matrices with N rows and 1 column and N rows and K columns respectively; In the context of channel-dependent noise, a joint optimization problem is established to maximize the system signal-to-noise ratio; The joint optimization problem includes optimizing the transmission precoding matrix and optimizing the dimming precoding matrix; The optimization of the transmission precoding matrix includes fixing the dimming precoding matrix and solving the transmission precoding matrix using a fixed-point FP method based on quadratic transformation; The optimization of the dimming precoding matrix includes fixing the transmission precoding matrix, introducing auxiliary variables to model the dimming precoding matrix, determining the feasible value range of the auxiliary variables by bisection, and solving the DC bias vector that meets the dimming control constraints; Using an alternating iterative optimization method, the joint optimization problem is decomposed into a transmission precoding subproblem and a dimming precoding subproblem, and then solved; The alternating iterative optimization method includes initializing feasible values ​​of a transmission precoding matrix and a dimming precoding matrix; The transmission precoding sub-problem and the dimming precoding sub-problem are optimized alternately, and the optimal solution is gradually approached by updating auxiliary variables and solving convex problems until the convergence threshold is met. Combined with the UAV's dynamic deployment capability, the position and attitude of the UAV are adjusted according to real-time channel status information, and the precoding strategy is collaboratively optimized; The collaborative optimization precoding strategy includes supporting adaptive adjustment of the number of dynamic users. When the number of users is less than the number of LEDs, sufficient resources are allocated in an underload mode. When the number of users is greater than or equal to the number of LEDs, power allocation is optimized using the NOMA principle to maintain system performance. At the same time, by dynamically adjusting the optical signal coverage and precoding strategy through drones, real-time networking and data synchronization between mobile nodes can be achieved, supporting efficient information exchange between vehicles, roadside equipment and traffic control centers; utilizing the rapid deployment of drones, highly reliable visible light communication links can be established in complex obstruction environments, and data can be transmitted stably in disaster relief or temporary networks; by collaboratively optimizing the spatial directivity and power distribution of optical signals through multiple drones, the real-time data interaction needs of multiple users can be met, enabling low-latency communication between drone clusters, aerial base stations and ground terminals.

2. The UAV-assisted high signal-to-noise ratio visible light communication method according to claim 1, wherein: The dynamic deployment capability of the drone includes allocating non-orthogonal multiple access (NOMA) power to different users based on the channel gain vector sorting; Optimize the spatial coverage of the optical signal by adjusting the drone's height, position, and the directivity of the LED array at the transmitter; System constraints are established, including peak light intensity constraints of the LED array, dimming level constraints based on eye safety, total electrical power constraints, and item-by-item interference cancellation (SIC) power allocation constraints.

3. The UAV-assisted high signal-to-noise ratio visible light communication method according to claim 2, wherein: The real-time channel state information includes obtaining a channel vector based on channel estimation and modeling the channel error as a Gaussian distribution variable; The combined effects of noise correlated with the input signal and additive noise are introduced into the receiver signal expression.

4. The UAV-assisted high signal-to-noise ratio visible light communication method according to claim 3, wherein: The collaborative optimization precoding strategy includes supporting adaptive adjustment of the number of dynamic users and allocating sufficient resources in an underload mode when the number of users is less than the number of LEDs; When the number of users is greater than or equal to the number of LEDs, the power distribution is optimized through the NOMA principle to maintain system performance; At the same time, by dynamically adjusting the optical signal coverage and precoding strategy through drones, real-time networking and data synchronization between mobile nodes can be achieved, supporting efficient information exchange between vehicles, roadside equipment and traffic control centers; utilizing the rapid deployment of drones, highly reliable visible light communication links can be established in complex obstruction environments, and data can be transmitted stably in disaster relief or temporary networks; by collaboratively optimizing the spatial directivity and power distribution of optical signals through multiple drones, the real-time data interaction needs of multiple users can be met, enabling low-latency communication between drone clusters, aerial base stations and ground terminals.

5. A system for drone-assisted high signal-to-noise ratio visible light communication, applying the drone-assisted high signal-to-noise ratio visible light communication method according to any one of claims 1 to 4, characterized in that: It includes UAV communication module, precoding optimization module, channel status monitoring module and dynamic deployment control module; The UAV communication module is used to carry an LED array, generate and send optical signals according to the precoding matrix, and receive feedback information from the receiving end user; The precoding optimization module is used to perform joint optimization of transmission precoding and dimming precoding, and solve the optimal precoding matrix through an alternating iterative method to maximize the system signal-to-noise ratio; The channel state monitoring module is used to obtain channel state information (CSI) in real time, including channel estimation, error modeling and noise analysis, and transmit the data to the precoding optimization module; The dynamic deployment control module is used to adjust the position and height of the drone and the directionality of the LED array according to the channel state information and the precoding optimization results, thereby optimizing the spatial coverage and power distribution of the optical signal.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the drone-assisted high signal-to-noise ratio visible light communication method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the drone-assisted high signal-to-noise ratio visible light communication method according to any one of claims 1 to 4 are implemented.

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