Method for eliminating interference in ocean inductance and computer program product

Abstract

The invention discloses a method and a computer program product for eliminating interference in ocean communication. The method comprises the following steps: constructing a target function based on a maximum average realizable sum rate criterion; carrying out fractional programming simplification on the target function; processing the objective function which is simplified through fractional programming by utilizing an alternating optimization algorithm to obtain an objective function which is optimized alternately; using a continuous convex approximation algorithm to convert the alternately optimized objective function to obtain a converted objective function, and solving the converted objective function to obtain an optimal solution of the optimization parameter; and dynamically adjusting the system parameters of the communication and inductance integrated system by using the optimal solution of the optimized parameters so as to eliminate signal interference among users of the communication and inductance integrated system. According to the method and the device, the communication and sensing performance can be improved while the maximization of the user rate is ensured, and the interference of multiple users is reduced. In addition, the accuracy and the robustness of the optimal solution can be improved, so that the performance of the communication and inductance integrated system can be improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a method for eliminating interference in ocean synaesthesia and a computer program product. Background Art

[0002] With the continuous development of science and technology, wireless communication technology is also advancing rapidly. In the future Sixth-Generation (6G) wireless networks, sensing will occupy a pivotal position. With the application of large-scale Multiple-Input Multiple-Output (MIMO) technology and the shift of communication frequency bands to higher frequency bands, communication and sensing are extremely similar in terms of signal processing and system architecture, which has promoted researchers to study Integrated Sensing And Communication (ISAC). Non-Orthogonal Multiple Access (NOMA) is a traditional communication technology that shares spectrum resources by allocating power at the transmitting end, providing a way to alleviate the problem of tight spectrum resources. However, due to the non-orthogonality of NOMA system signals, interference management has become a new problem, and interference elimination has become increasingly important.

[0003] As the trend of globalization continues to develop, marine communications are of great significance. Compared with the rapidly developing land communications, marine communications are slightly behind. Currently, marine communications include underwater hydroacoustic communications, optical fiber and cable communications, and communications using satellites. Although satellite communications can achieve high-speed and reliable communications, its high launch and maintenance costs are still an issue that cannot be ignored. Considering the mutual communication, perception and positioning between ships sailing in the ocean, communication and perception integration has become a key technology. In a complex marine environment, the use of NOMA's ISAC system can solve the problem of tight frequency bands, and how to eliminate interference becomes a key issue. Summary of the invention

[0004] The purpose of this application is to provide a method for eliminating interference in ocean synaesthesia and a computer program product to solve the technical problems in the prior art that the communication and perception performance of the existing communication system is low and is easily subject to multi-user interference. The preferred technical solutions among the many technical solutions provided in this application can produce many technical effects as described below.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] The first aspect of the present application provides a method for eliminating interference in marine synaesthesia, comprising the following steps: S1, constructing an objective function based on the maximum average achievable and rate criteria, wherein the optimization parameters of the objective function include at least the weight coefficient of the communication signal and the weight coefficient of the dedicated perception signal; S2, performing fractional planning simplification on the objective function to obtain the objective function simplified by fractional planning; S3, processing the objective function simplified by fractional planning using an alternating optimization algorithm to obtain an alternately optimized objective function; S4, converting the alternately optimized objective function using a continuous convex approximation algorithm to obtain a converted objective function, solving the converted objective function to obtain an optimal solution for the optimization parameters; S5, dynamically adjusting the system parameters of the synaesthesia integration system using the optimal solution of the optimization parameters to eliminate signal interference between users of the synaesthesia integration system.

[0007] In some embodiments, the constraints of the objective function include at least a total power constraint of a transmitted signal, a minimum power constraint of a sensed signal, and a continuous interference cancellation constraint.

[0008] In some embodiments, the objective function is simplified by fractional planning to obtain the objective function simplified by fractional planning, including: introducing a first auxiliary variable, processing the objective function by Lagrange dual transformation, and obtaining the objective function after dual transformation; introducing a second auxiliary variable, processing the objective function after dual transformation by quadratic transformation, and obtaining the objective function simplified by fractional planning.

[0009] In some embodiments, the objective function simplified by fractional programming is processed using an alternating optimization algorithm to obtain an alternately optimized objective function, including: first freezing the optimization parameters, optimizing the first auxiliary variables and the second auxiliary variables; then freezing the first auxiliary variables and the second auxiliary variables, optimizing the optimization parameters to obtain the alternately optimized objective function.

[0010] In some embodiments, the optimizing the first auxiliary variable and the second auxiliary variable includes: performing a first-order partial derivative on the objective function simplified by fractional programming to obtain an optimal solution for the first auxiliary variable and an optimal solution for the second auxiliary variable.

[0011] In some embodiments, the optimizing the optimization parameters to obtain the alternately optimized objective function includes: simplifying the objective function simplified by fractional programming using continuous rank-one constraint relaxation to obtain the alternately optimized objective function.

[0012] In some embodiments, after constructing the objective function based on the maximum average achievable and rate criteria, the method for eliminating interference in ocean synaesthesia includes: expressing the objective function and the constraints of the objective function in the form of traces.

[0013] In some embodiments, the method for eliminating interference in ocean synaesthesia also includes: performing multiple iterative optimizations on the optimal solution of the first auxiliary variable, the optimal solution of the second auxiliary variable, and the optimal solution of the optimization parameter until a preset convergence threshold is met or a preset number of iterations is reached.

[0014] In some embodiments, the objective function is expressed by the following formula:

[0015]

[0016] Among them, h k represents the channel from the base station to the user, W i represents the weight coefficient of the beamformer used to transmit the communication signal to user i, represents the covariance matrix of the remaining part of the perceptual signal except the one embedded in the dedicated perceptual signal, W r，m A transmit beamformer representing the dedicated perceptual signal.

[0017] A second aspect of the present application provides a computer program product, which is stored on a data carrier and is designed to execute the method for interference elimination in marine synaesthesia as described above.

[0018] Implementing a technical solution in the above-mentioned technical solution of the present application has the following advantages or beneficial effects: in the present application, by constructing an objective function based on the maximum average achievable and rate criteria, and simplifying the objective function through a fractional programming algorithm and an alternating optimization algorithm, the optimal solution of the optimization parameters is finally obtained. In this case, compared with the prior art that only considers multi-user interference, or sacrifices the situation that the user can achieve the rate and the perception performance, the present application can improve the communication and perception performance while ensuring the user rate is maximized, while reducing the interference of multiple users. In addition, by processing the objective function using a fractional programming algorithm and an alternating optimization algorithm, the accuracy and robustness of the optimal solution can be improved, thereby further improving the performance of the synaesthesia integration system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. It is obvious that the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0020] Figure 1 is a flow chart of a method for eliminating interference in ocean synaesthesia according to an embodiment of the present application;

[0021] Figure 2 is an interactive schematic diagram of the synaesthesia integration system of an embodiment of the present application;

[0022] Figure 3 It is a curve diagram showing the variation of signal-to-noise interference ratio with distance in the simulation experiment;

[0023] Figure 4 It is a curve diagram showing the average achievable sum rate changing with the number of base station antennas in the simulation experiment;

[0024] Figure 5 It is a curve diagram showing the average achievable sum rate changing with the path attenuation coefficient in the simulation experiment;

[0025] Figure 6 It is a structural block diagram of the processing device of an embodiment of the present application.

[0026] In the figure: 1. processing device; 10. memory; 11. processor. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the present application clearer, the various exemplary embodiments to be described below will refer to the corresponding drawings, which constitute a part of the exemplary embodiments, wherein various exemplary embodiments that may be used to implement the present application are described. Unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation methods described in the following exemplary embodiments do not represent all implementation methods consistent with the present disclosure. It should be understood that they are only examples of processes, methods, and devices that are consistent with some aspects disclosed in the present application as detailed in the attached claims, and other embodiments may also be used, or the embodiments listed herein may be modified in structure and function without departing from the scope and essence of the present application.

[0028] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", etc. indicate the orientation or positional relationship based on the drawings, which is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the elements referred to must have a specific orientation, be constructed and operated in a specific orientation. The terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. The term "multiple" means two or more. The terms "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, an integral connection, a mechanical connection, an electrical connection, a communication connection, a direct connection, an indirect connection through an intermediate medium, and can be the internal connection of two elements or the interaction relationship between two elements. The term "and / or" includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0029] In order to illustrate the technical solution described in the present application, a specific embodiment is provided below, and only the parts related to the embodiment of the present application are shown.

[0030] like Figure 1 As shown, the present application provides a method for eliminating interference in ocean synaesthesia, comprising the following steps (step S1 to step S5):

[0031] S1. Construct an objective function based on the maximum average achievable and rate criteria, wherein the optimization parameters of the objective function may include at least a weight coefficient of the communication signal and a weight coefficient of the dedicated perception signal.

[0032] The Maximum Average Achievable Sum Rate Criterion is to maximize the average sum rate achievable by all users in the network.

[0033] The embodiments of the present application may be applied to an ISAC (Integrated Sensing And Communication) system, which may be referred to as a communication sensing system, a synaesthesia integration system or a synaesthesia system.

[0034] In some embodiments, Figure 2 As shown, the ISAC system may include a dual-function base station equipped with N antenna ULAs, K single-antenna users, and multiple sensing targets. The base station can provide services for K single-antenna users while receiving echo signals and tracking targets. The ISAC system may have a NOMA (Non-Orthogonal Multiple Access) mode.

[0035] In some embodiments, the transmission signal of the ISAC system in NOMA mode can be obtained by the following formula:

[0036]

[0037] in, Indicates the index value of the receiving user. Denotes a dedicated perception signal, Indicates the communication signal Transmitted to the beamformer of user i.

[0038] In some embodiments, the communication signal They can be independent, and the mean value of the communication signal and the unit power of the communication signal can both be 0.

[0039] In some embodiments, the dedicated sensing signal and the communication signal may be independent, and the covariance matrix of the dedicated sensing signal may be defined as:

[0040] In some embodiments, the dedicated sensing signal can be transmitted using multiple beams, that is, the covariance matrix R r Can have general rank.

[0041] In some embodiments, R r By performing eigenvalue decomposition, the dedicated sensing signal can be decomposed into multiple sensing beams, that is, it can be decomposed by the following formula:

[0042]

[0043] in, represents the eigenvalue, represents the feature vector, Represents the transmit beamformer for the dedicated sensing signal.

[0044] In some embodiments, in an ISAC system, NOMA technology may be used to embed information into a portion of a dedicated perception signal, and the perception signal with embedded information may be regarded as a virtual communication signal and placed on top of the real communication signal.

[0045] In some embodiments, the perception signal may be expressed by the following formula:

[0046]

[0047] In some embodiments, for the kth user, its received signal will be simultaneously interfered by multiple users, perceived signal interference and noise. Therefore, the received signal can be expressed as:

[0048]

[0049] Among them, h k represents the channel from the base station to the user, n k represents the noise signal, w represents the beamformer used to transmit the communication signal s to the user, r represents the dedicated sensing signal, Can represent interference between users, It can represent the perceived signal interference removed by SIC (Successive Interference Cancellation). It can represent the perceived signal interference that is not removed by SIC.

[0050] Generally speaking, interference from the virtual communication signal can be eliminated only when the power of the virtual communication signal is higher than the required communication signal. In some embodiments, the constraints of the objective function may include at least a total power constraint of the transmitted signal, a minimum power constraint of the sensed signal, and a continuous interference elimination constraint.

[0051] In some embodiments, the objective function can be expressed by the following formula:

[0052]

[0053] Among them, h k represents the channel from the base station to the user, W i represents the weight coefficient of the beamformer used to transmit the communication signal to user i, represents the covariance matrix of the remaining part of the perceptual signal except the one embedded in the dedicated perceptual signal, W r，m Represents the transmit beamformer for the dedicated sensing signal.

[0054] In some embodiments, the sensing signal minimum power constraint may be expressed by the following formula:

[0055] sP m ≥P th，m ,

[0056] Among them, P m represents the perceived power, P th,m Indicates the threshold of the perceived power, that is, to ensure that the perceived power is greater than the minimum threshold of the perceived power.

[0057] In some embodiments, the continuous interference cancellation constraint can be expressed by the following formula:

[0058]

[0059] In some embodiments, the total power constraint of the transmitted signal may be expressed by the following formula:

[0060] Tr(R)≤P max ,

[0061] Among them, P max represents the total transmit power. The total transmit signal power constraint can be expressed as the trace of the matrix R is less than P max .

[0062] In some embodiments, the constraint condition may further include a covariance matrix constraint, which may be expressed by the following formula:

[0063]

[0064] Among them, the covariance matrix constraint can indicate that the covariance matrix of the residual interference is semi-positive definite.

[0065] For the convenience of description, the objective function and the constraints of the objective function may be referred to as an optimization problem. That is, the optimization problem may include the objective function and the constraints. The processing of the optimization problem may be regarded as the processing of the objective function and / or the constraints. Therefore, the optimization problem may be expressed by the following formula:

[0066]

[0067] sP m ≥P th，m

[0068]

[0069] Tr(R)≤P max

[0070]

[0071] In some embodiments, after constructing the objective function based on the maximum average achievable sum rate criterion, the method for eliminating interference in ocean synaesthesia may include: expressing the objective function and the constraint conditions of the objective function in the form of a trace. Thus, the maximum user achievable sum rate desired can be converted into a concise mode. It can be expressed by the following formula:

[0072]

[0073] sttr(A(θ m )R)≥P th，m

[0074]

[0075] tr(R)≤P max

[0076]

[0077] In some embodiments, the perceived power may be expressed by the following formula:

[0078] P m =a H (θ m )Ra(θ m ),

[0079] Among them, P m represents the perceived power, a(θ m ) represents the steering vector of the transmitting antenna.

[0080] S2. Simplify the objective function by fractional programming to obtain the objective function simplified by fractional programming.

[0081] In some embodiments, performing fractional programming simplification on the objective function to obtain the objective function simplified by fractional programming may include: introducing a first auxiliary variable, processing the objective function using Lagrange dual transformation, and obtaining the objective function after dual transformation; introducing a second auxiliary variable, processing the objective function after dual transformation using quadratic transformation, and obtaining the objective function simplified by fractional programming.

[0082] In some embodiments, the Lagrangian dual transformation can simplify the weighted logarithmic summation problem by introducing auxiliary variables.

[0083] In some embodiments, the weighted logarithmic sum problem can be formulated as follows:

[0084]

[0085] In some embodiments, after introducing the auxiliary variable, the weighted logarithmic summation problem is transformed to obtain the following formula:

[0086]

[0087] Among them, γ = (γ1, γ2,..., γ M ), and γ m As each ratio term Auxiliary variables introduced.

[0088] In some embodiments, the objective function f r It can be expressed by the following formula:

[0089]

[0090] In some embodiments, the optimization problem corresponding to the dual transformed objective function can be expressed by the following formula:

[0091]

[0092] sttr(A(θ m )R)≥P th，m

[0093]

[0094] tr(R)≤P max

[0095]

[0096] The first auxiliary variable is s=(s1, s2, ..., s m ).

[0097] Assume a single-ratio FP problem, expressed by the following formula:

[0098]

[0099] in, represents a non-empty constraint set, represents a non-negative function, Represents a positive function.

[0100] Through the second transformation, the single-scale FP problem can be transformed into:

[0101]

[0102] Assuming a multi-scale FP problem, the quadratic transformation of the total scale can be expressed as:

[0103]

[0104] Through the quadratic transformation, the quadratic transformation of the total ratio can be converted to:

[0105]

[0106] In some embodiments, the optimization problem corresponding to the quadratically transformed objective function can be expressed by the following formula:

[0107]

[0108] sttr(A(θ m )R)≥P th，m

[0109]

[0110] tr(R)≤P max

[0111]

[0112] The second auxiliary variable is t=(t1, t2, ..., t K ).

[0113] In some embodiments, the objective function after quadratic transformation is the objective function simplified by fractional programming.

[0114] As described above, by referring to the first auxiliary variable and the second auxiliary variable, the problem of finding the weighted sum of logarithms can be converted into a problem of finding the sum of ratios, and then converted into a subtraction calculation through the quadratic transformation theorem.

[0115] S3. Using an alternating optimization algorithm to process the objective function simplified by fractional programming, to obtain an alternatingly optimized objective function.

[0116] In some embodiments, an alternating optimization algorithm is used to process the objective function simplified by fractional programming to obtain an alternatingly optimized objective function, which may include: first freezing the optimization parameters, optimizing the first auxiliary variable and the second auxiliary variable; then freezing the first auxiliary variable and the second auxiliary variable, and optimizing the optimization parameters to obtain an alternatingly optimized objective function.

[0117] In some embodiments, optimizing the first auxiliary variable and the second auxiliary variable may include: performing a first-order partial derivative on the objective function simplified by fractional programming to obtain an optimal solution for the first auxiliary variable and an optimal solution for the second auxiliary variable.

[0118] In some embodiments, the optimization parameters may also include the covariance matrix

[0119] In some embodiments, R can be used to represent the objective function simplified by fractional programming, that is:

[0120]

[0121] In some embodiments, the optimization parameter W may be k , W r,m as well as Keep it fixed, that is, freeze it. At this time, the objective function simplified by fractional programming can be a concave differential function of the first auxiliary variable and the second auxiliary variable. When , that is, the point where the first-order partial derivative of the objective function simplified by fractional programming with respect to the first auxiliary variable and the second auxiliary variable is 0 is calculated, the optimal solution of the first auxiliary variable and the optimal solution of the second auxiliary variable are obtained.

[0122] In some embodiments, the optimal solution of the first auxiliary vector can be expressed by the following formula:

[0123]

[0124] In some embodiments, the optimal solution of the second auxiliary vector can be expressed by the following formula:

[0125]

[0126] In some embodiments, optimizing the optimization parameters to obtain the alternately optimized objective function may include: using continuous rank-one constraint relaxation to simplify the objective function simplified by fractional programming to obtain the alternately optimized objective function.

[0127] In some embodiments, the continuous rank-one constraint may be expressed by the following formula:

[0128]

[0129] Among them, λ max (W k ) and λ max (W r,m ) are W k and W r,m The maximum eigenvalue of .

[0130] In some embodiments, if and only if tr(W k )-λ max (W k )=0 and tr(W r,m )-λ max (W r,m )=0, it can be considered that W k and W r,m There is only one eigenvalue, so the continuous rank-one constraint is satisfied.

[0131] In some embodiments, a penalty term may be obtained according to a continuous rank-one constraint. The objective function simplified by fractional programming may be relaxed and simplified based on the penalty term to obtain an objective function optimized by alternation. Specifically, a penalty coefficient may be introduced, and the penalty term may be brought into the objective function simplified by fractional programming, and terms unrelated to the optimization parameters may be removed.

[0132] In some embodiments, W k The penalty term can be expressed by the following formula:

[0133] tr(W k )-λ max (W k ).

[0134] In some embodiments, W r,m The penalty term can be expressed by the following formula:

[0135] tr(W r，m )-λ max (W r，m ).

[0136] In some embodiments, the optimization problem corresponding to the alternately optimized objective function can be expressed by the following formula:

[0137]

[0138] sttr(A(θ m )R)≥P th，m

[0139]

[0140] tr(R)≤P max

[0141]

[0142] Among them, ρ k and μ m represents the penalty coefficient for the continuous rank-one constraint.

[0143] In some embodiments, the goal of the optimization problem corresponding to the alternately optimized objective function may be to maximize the first term of the alternately optimized objective function and minimize the next two penalty terms.

[0144] S4. Use a continuous convex approximation algorithm to transform the alternately optimized objective function to obtain a transformed objective function, solve the transformed objective function, and obtain an optimal solution for the optimization parameters.

[0145] In some embodiments, the alternating optimized objective function is transformed using a continuous convex approximation algorithm, which can be expressed by the following formula:

[0146]

[0147] Among them, u(·) represents the unit eigenvector corresponding to the maximum eigenvalue, and the optimal feasible point of the first-order Taylor expansion is and

[0148] In some embodiments, the optimization problem corresponding to the converted objective function can be expressed by the following formula:

[0149]

[0150] sttr(A(θ m )R)≥P th,m

[0151]

[0152] tr(R)≤P max

[0153]

[0154] In some embodiments, the optimal solution of the first auxiliary variable and the optimal solution of the second auxiliary variable may be brought into the converted objective function and solved to obtain the optimal solution of the optimization parameter and the average achievable sum rate.

[0155] In some embodiments, the CVX toolbox may be used to solve the converted objective function to obtain the optimal solution for the optimization parameters.

[0156] In some embodiments, the method for eliminating interference in ocean synaesthesia may also include: performing multiple iterative optimizations on the optimal solution of the first auxiliary variable, the optimal solution of the second auxiliary variable, and the optimal solution of the optimization parameter until a preset convergence threshold is met or a preset number of iterations is reached.

[0157] In some embodiments, the preset convergence threshold may satisfy the following formula:

[0158]

[0159] Among them, g is the preset convergence threshold, and R is the average achievable sum rate.

[0160] S5. Dynamically adjust the system parameters of the synaesthesia integration system using the optimal solution of the optimization parameters to eliminate signal interference between users of the synaesthesia integration system.

[0161] As described above, the optimal solution using the optimization parameters can eliminate the perception interference between users, so as to eliminate signal interference and improve the communication performance of the synaesthesia integrated system without sacrificing the perception performance.

[0162] Simulation experiment:

[0163] This experiment designed a MU-MISO (multiple-input multiple-user single output) communication and perception integrated scenario, including a ULA dual-function base station with 4 antennas, 3 single-antenna users and 5 perception targets. The base station communicates and perceives the user targets, and compares the simulation effects of using the embodiments of the present application, not using the present application, and the ideal situation.

[0164] The simulation experiment parameters are set as shown in Table 1.

[0165]

[0166] Table 1

[0167] like Figure 3As shown, SINR (Signal-to-Interference plus Noise Ratio) decreases with the increase of distance. When the distance is fixed, the SINR of the embodiment of the present application is significantly better than "SDR, no SIC", and when using average power allocation, the embodiment of the present application still shows good performance. When the communication distance is relatively close, the path loss of the channel is relatively small. At this time, the embodiment of the present application has a relatively obvious performance advantage in eliminating the interference of the perceived signal. When the communication distance is relatively large, multi-user interference becomes the main interference factor. At this time, the use of the embodiment of the present application still greatly improves the performance. In the "average power allocation" mode, its performance is inferior to the case of total power constraint, but at this time, the embodiment of the present application still has obvious performance advantages compared to "SDR, no SIC".

[0168] Figure 4 is a curve showing the average achievable sum rate as a function of the number of base station antennas. The base station transmit antennas range from 4 to 7. "SIC-ISAC" indicates the waveform after using the embodiment of the present application, "SDR, no SIC" indicates the waveform without using FP-SCA interference elimination, and "average power allocation" indicates the waveform when equal power constraints are imposed on each transmit antenna.

[0169] like Figure 4 As shown, the average achievable sum rate increases with the increase in the number of base station antennas. When the number of base station antennas is fixed, the average achievable sum rate using the embodiment of the present application is significantly higher than "SDR, no SIC". In the "average power allocation" mode, the average achievable sum rate is less than the total power constraint. This is certain, because the average power constraint imposes more stringent constraints, it will cause certain losses to the performance of the communication, but because the average power allocation constraint has a larger application space in daily life, and the radar perception system often needs to execute equal power constraints on each antenna, this situation has a broader prospect of use. When the number of transmitting antennas increases, the system can send signals to specific users more accurately and directionally, so the interference will gradually decrease, and due to the marginal effect, its performance tends to be saturated.

[0170] Figure 5 Figure 2. Curves showing the average achievable sum rate as a function of the path attenuation factor, where the path attenuation factor ranges from 2.2 to 2.8.

[0171] like Figure 5As shown, the average achievable sum rate decreases with the increase of the path attenuation coefficient. This is because the larger the path attenuation coefficient, the greater the performance loss in the communication process, resulting in a smaller average achievable sum rate. When the path attenuation coefficient is fixed, the average achievable sum rate using the embodiment of the present application is still significantly higher than "SDR, no SIC". In the average power allocation mode, the average achievable sum rate is less than the implementation of the total power constraint. When the path attenuation coefficient is 2.6, the embodiment of the present application under the total power constraint has a performance improvement of 14.1% compared to the traditional SDR algorithm.

[0172] Those skilled in the art can understand that all or part of the features / steps of the above-mentioned method embodiments can be implemented by methods, data processing systems or computer programs, and these features can be implemented without hardware, entirely by software, or by a combination of hardware and software. The aforementioned computer program can be stored in one or more computer-readable storage media, and the storage medium stores the computer program. When the computer program is executed (such as by a processor), the steps of the above-mentioned method embodiment for eliminating interference in ocean synaesthesia are executed.

[0173] The aforementioned storage media that can store program codes include: static hard disks, solid-state hard disks, random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), optical storage devices, magnetic storage devices, flash memory, magnetic disks or optical disks and / or combinations of the above devices, that is, it can be implemented by any type of volatile or non-volatile storage device or a combination thereof.

[0174] like Figure 6 As shown, the present application also provides an embodiment of a processing device 1, comprising one or more processors 11 and a memory 10; wherein the memory 10 is used to store one or more computer programs, and the one or more processors 11 are used to execute one or more computer programs stored in the memory 10, so that the processor 11 executes the features / steps of the above-mentioned embodiment of the method for eliminating interference in ocean synesthesia.

[0175] The present application also relates to a computer program product, which can be stored on a data carrier and is designed to perform the method for eliminating interference in marine synaesthesia as described above. Therefore, the computer program product according to the present application produces the same advantages as those described in detail with reference to the device according to the present application. The computer program product can be executed as a computer-readable instruction code with each appropriate programming language such as JAVA, C++, etc. In addition, the computer program product can be provided on a network, such as the Internet, or a network, such as an Internet user, can download the computer program product from a network, such as the Internet when needed. The computer program product can be implemented by means of a computer program, i.e. software, or by means of one or more dedicated electronic circuits, i.e. hardware, or in any mixed form, i.e. by means of software components and hardware components, or in a mixed form of software, hardware, or software and hardware.

[0176] In the present application, by constructing the objective function based on the maximum average achievable and rate criterion, and simplifying the objective function by fractional programming algorithm and alternating optimization algorithm, finally the optimal solution of optimization parameters is obtained. In this case, compared to the prior art only considering multi-user interference, or sacrificing the situation of user achievable rate and perception performance, the present application can improve communication and perception performance while ensuring that the user rate is maximized, while reducing the interference of multiple users. In addition, by utilizing fractional programming algorithm and alternating optimization algorithm to process the objective function, the accuracy and robustness of the optimal solution can be improved, thereby the performance of the synaesthesia integration system can be further improved.

[0177] The above is only the preferred embodiment of the present application. Those skilled in the art will appreciate that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present application. In addition, under the guidance of the present application, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present application. Therefore, the present application is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of the present application belong to the protection scope of the present application.

Claims

1. A method for eliminating interference in ocean synaesthesia, characterized in that: The steps include: S1. Constructing an objective function based on the maximum average achievable and rate criteria, wherein the optimization parameters of the objective function include at least a weight coefficient of the communication signal and a weight coefficient of the dedicated perception signal; S2, performing fractional programming simplification on the objective function to obtain an objective function simplified by fractional programming; S3, using an alternating optimization algorithm to process the objective function simplified by fractional programming to obtain an alternating optimized objective function; S4, using a continuous convex approximation algorithm to transform the alternately optimized objective function to obtain a transformed objective function, and solving the transformed objective function to obtain an optimal solution for the optimization parameters; S5. Dynamically adjust the system parameters of the synaesthesia integration system using the optimal solution of the optimization parameter to eliminate signal interference between users of the synaesthesia integration system.

2. The method for eliminating interference in ocean synaesthesia according to claim 1, characterized in that: The constraint conditions of the objective function include at least a total power constraint of a transmitted signal, a minimum power constraint of a sensed signal, and a constraint on the establishment of continuous interference cancellation.

3. The method for eliminating interference in ocean synaesthesia according to claim 1, characterized in that: The objective function is simplified by fractional planning to obtain the objective function simplified by fractional planning, including: introducing a first auxiliary variable, processing the objective function by Lagrange dual transformation, and obtaining the objective function after dual transformation; introducing a second auxiliary variable, processing the objective function after dual transformation by quadratic transformation, and obtaining the objective function simplified by fractional planning.

4. The method for eliminating interference in ocean synaesthesia according to claim 3, characterized in that: The method of using an alternating optimization algorithm to process the objective function simplified by fractional programming to obtain an alternately optimized objective function includes: first freezing the optimization parameters and optimizing the first auxiliary variable and the second auxiliary variable; then freezing the first auxiliary variable and the second auxiliary variable, and optimizing the optimization parameters to obtain the alternately optimized objective function.

5. The method for eliminating interference in ocean synaesthesia according to claim 4, characterized in that: The optimizing the first auxiliary variable and the second auxiliary variable includes: performing a first-order partial derivative on the objective function simplified by fractional programming to obtain an optimal solution of the first auxiliary variable and an optimal solution of the second auxiliary variable.

6. The method for eliminating interference in ocean synaesthesia according to claim 4, characterized in that: The step of optimizing the optimization parameters to obtain the alternately optimized objective function includes: using continuous rank-one constraint relaxation to simplify the objective function simplified by fractional programming to obtain the alternately optimized objective function.

7. The method for eliminating interference in ocean synaesthesia according to claim 1, characterized in that: After constructing the objective function based on the maximum average achievable and rate criteria, the method for eliminating interference in ocean synaesthesia includes: expressing the objective function and the constraint conditions of the objective function in the form of traces.

8. The method for eliminating interference in ocean synaesthesia according to claim 4, characterized in that: The method for eliminating interference in ocean synaesthesia also includes: performing multiple iterative optimizations on the optimal solution of the first auxiliary variable, the optimal solution of the second auxiliary variable, and the optimal solution of the optimization parameter until a preset convergence threshold is met or a preset number of iterations is reached.

9. The method for eliminating interference in ocean synaesthesia according to claim 1, characterized in that: The objective function is expressed by the following formula: Among them, h k represents the channel from the base station to the user, W i represents the weight coefficient of the beamformer used to transmit the communication signal to user i, represents the covariance matrix of the remaining part of the perceptual signal except the one embedded in the dedicated perceptual signal, W r，m A transmit beamformer representing the dedicated perceptual signal.

10. A computer program product, characterized in that The computer program product is stored on a data carrier and is designed to execute the method for eliminating interference in marine synaesthesia according to any one of claims 1 to 9.

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