Modeling and simulation method, device and computer equipment of MIMO molecular communication system

Abstract

The present application relates to the technical field of communication simulation, in particular to a kind of MIMO molecular communication system modeling simulation method, device, system and storage medium, based on the diffusion-cylinder flow-diffusion environment induced diffusion-cylinder flow-diffusion environment induced diffusion-laminar coupling effect, the actual receiver mechanism that fits biological system is built, the binding process of a variety of information molecules to compete for limited receptor can be dynamically simulated, the number of receptor and various information molecules of the receiver is effectively quantified Binding quantity situation, so as to convert ideal concentration signal into receptor occupancy signal, the accurate, fast simulation of the communication performance of MIMO molecular communication system is realized, and the basis for the co-channel interference evaluation of MIMO molecular communication system is provided.

Description

Technical Field

[0001] This invention relates to the field of communication simulation technology, and in particular to a modeling and simulation method, apparatus, computer equipment, and storage medium for a MIMO molecular communication system. Background Technology

[0002] Molecular communication (MC) is a communication paradigm that uses biochemical molecules as information carriers to encode, transmit, receive, and decode information. In its overall architecture, molecular communication still follows the classic Shannon communication model, and its complete communication link also consists of five core components: source, transmitter, transmission channel, receiver, and sink.

[0003] However, in cylindrical flow-diffusion environments, existing MIMO molecular communication system models do not consider the diffusion-laminar coupling effect caused by the parabolic velocity distribution resulting from laminar flow. This leads to severe broadening and distortion of signal pulses during transmission along the flow direction, which in turn causes strong inter-symbol interference (ISI), making it difficult to accurately and quickly simulate the communication performance of MIMO molecular communication systems. Summary of the Invention

[0004] Based on this, the purpose of this invention is to provide a modeling and simulation method, device, computer equipment, and storage medium for MIMO molecular communication systems. Based on the diffusion-laminar coupling effect induced by a cylindrical flow-diffusion environment, it constructs a receiving mechanism that fits the actual biological system. It can dynamically simulate the binding process of various information molecules competing for a limited number of receptors, effectively quantifying the number of receptors and various information molecules binding to the receiver, thereby converting the ideal concentration signal into a receptor occupancy signal. This achieves accurate and rapid simulation of the communication performance of MIMO molecular communication systems and provides a foundation for the evaluation of co-channel interference in MIMO molecular communication systems.

[0005] In a first aspect, embodiments of this application provide a modeling and simulation method for a MIMO molecular communication system, the MIMO molecular communication system comprising a plurality of transmitters, a cylindrical pipe, and a plurality of receivers; each transmitter releases information molecules in the cylindrical pipe, the information molecules flowing through the fluid within the cylindrical pipe to each receiver, comprising the following steps:

[0006] Construct a state matrix for the information molecules released by each transmitter, wherein the state matrix is ​​used to represent the convection-diffusion coupling effect of the information molecules in the cylindrical pipe; Construct input vectors for the information molecules released by each transmitter and time vectors for a preset number of moments, wherein the input vectors represent the instantaneous concentration of the information molecules released by the transmitter; and the time vectors represent the intensity of the information molecules released by the transmitter at the current moment. State vector transformation is performed based on the state matrix, input vector, and time vector of the information molecules released by each transmitter at each time moment to obtain the state vector of the information molecules released by each transmitter at each time moment. The state vector is used to represent the concentration distribution of information molecules in the cylindrical pipe. An observation matrix is ​​constructed for each receiver, which is used to represent the sampling weight of the information molecule concentration at the location of the receiver; the information molecule concentration is calculated based on the state vector of the information molecules released by each transmitter at each time and the observation matrix of each receiver, so as to obtain the information molecule concentration of each type of information molecule at each time. Based on the concentration of various information molecules at each receiver at each time point, the binding and dissociation cycles of receptors and information molecules are simulated to obtain the number of receptors and various information molecules bound to each receiver at each time point, which serves as the modeling and simulation result of the MIMO molecular communication system.

[0007] Secondly, embodiments of this application provide a modeling and simulation apparatus for a MIMO molecular communication system. The MIMO molecular communication system includes several transmitters, a cylindrical pipe, and several receivers. Each transmitter releases information molecules within the cylindrical pipe, and these information molecules flow through fluid within the cylindrical pipe to each receiver, including: A state matrix construction module is used to construct the state matrix of the information molecules released by each transmitter, wherein the state matrix is ​​used to represent the convection-diffusion coupling effect of the information molecules in the cylindrical pipe; The information molecule vector construction module is used to construct the input vector of the information molecules released by each transmitter and the time vector of a preset number of time moments, wherein the input vector is used to represent the concentration distribution of the information molecules; and the time vector is used to represent the intensity of the information molecules released by the transmitter at the current moment. The state vector conversion module is used to perform state vector conversion based on the state matrix, input vector, and time vector of the information molecules released by each transmitter at each time, so as to obtain the state vector of the information molecules released by each transmitter at each time. The state vector is used to represent the concentration distribution of information molecules in the cylindrical pipe. The information molecule concentration calculation module is used to construct the observation matrix of each receiver, which represents the sampling weight of the information molecule concentration at the location of the receiver; the information molecule concentration is calculated based on the state vector of the information molecules released by each transmitter at each time and the observation matrix of each receiver to obtain the information molecule concentration of each type of information molecule at each time. The module for calculating the number of binding compounds is used to simulate the binding and dissociation cycles of receptors and information molecules based on the concentration of various information molecules at each receiver at each time point, and to obtain the number of binding compounds of receptors and various information molecules at each receiver at each time point, which serves as the modeling and simulation result of the MIMO molecular communication system.

[0008] Thirdly, embodiments of this application provide a computer device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, it implements the steps of the modeling and simulation method for the MIMO molecular communication system as described in the first aspect.

[0009] Fourthly, embodiments of this application provide a storage medium storing a computer program, which, when executed by a processor, implements the steps of the modeling and simulation method for the MIMO molecular communication system as described in the first aspect.

[0010] This application provides a modeling and simulation method, apparatus, computer equipment, and storage medium for a MIMO molecular communication system. Based on the diffusion-laminar coupling effect induced by a cylindrical flow-diffusion environment, it constructs a receiving mechanism that closely resembles the actual biological system. It can dynamically simulate the binding process of various information molecules competing for a limited number of receptors, effectively quantifying the number of receptors and various information molecules binding to the receiver. This converts the ideal concentration signal into a receptor occupancy signal, achieving accurate and rapid simulation of the communication performance of the MIMO molecular communication system and providing a foundation for the co-channel interference assessment of the MIMO molecular communication system.

[0011] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0012] Figure 1 An application environment for a modeling and simulation method of a MIMO molecular communication system provided in one embodiment of this application; Figure 2 A schematic flowchart illustrating a modeling and simulation method for a MIMO molecular communication system provided in one embodiment of this application; Figure 3A flowchart illustrating step S1 in a modeling and simulation method for a MIMO molecular communication system provided in one embodiment of this application; Figure 4 This is a flowchart illustrating step S11 of a modeling and simulation method for a MIMO molecular communication system provided in one embodiment of this application. Figure 5 A flowchart illustrating step S2 in a modeling and simulation method for a MIMO molecular communication system provided in one embodiment of this application; Figure 6 A flowchart illustrating step S3 in a modeling and simulation method for a MIMO molecular communication system provided in one embodiment of this application; Figure 7 A flowchart illustrating step S4 in a modeling and simulation method for a MIMO molecular communication system provided in one embodiment of this application; Figure 8 A flowchart illustrating step S5 of a modeling and simulation method for a MIMO molecular communication system provided in one embodiment of this application; Figure 9 A schematic diagram of the structure of a modeling and simulation device for a MIMO molecular communication system provided in one embodiment of this application; Figure 10 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. Detailed Implementation

[0013] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0014] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0015] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0016] Please see Figure 1 , Figure 1 The application environment for the modeling and simulation method of the MIMO molecular communication system provided in one embodiment of this application is as follows: the MIMO molecular communication system includes several transmitters 1, cylindrical pipes 2 and several receivers 3; each transmitter 1 releases information molecules 4 in the cylindrical pipes 2, and the information molecules 4 flow to each receiver 3 through the fluid in the cylindrical pipes 2.

[0017] Please see Figure 2 , Figure 2 The flowchart illustrates a modeling and simulation method for a MIMO molecular communication system according to an embodiment of this application. The method includes the following steps: S1: Construct the state matrix of the information molecules released by each transmitter.

[0018] The execution subject of the modeling and simulation method of the MIMO molecular communication system is the simulation device of the modeling and simulation method of the MIMO molecular communication system (hereinafter referred to as the simulation device). In an optional embodiment, the simulation device can be a computer device, a server, or a server cluster composed of multiple computer devices.

[0019] In this embodiment, the simulation device constructs a state matrix of information molecules released by each transmitter, wherein the state matrix is ​​used to represent the convection-diffusion coupling effect of information molecules in the cylindrical pipe.

[0020] Please see Figure 3 , Figure 3 The flowchart of step S1 in the modeling and simulation method of the MIMO molecular communication system provided in one embodiment of this application is shown below, including steps S11 to S13: S11: Construct a diagonal matrix of information molecules released by each transmitter.

[0021] In this embodiment, the simulation device constructs a diagonal matrix of information molecules released by each transmitter, wherein the diagonal matrix is ​​used to represent the diffusion process of information molecules.

[0022] Please see Figure 4 , Figure 4 The flowchart of step S11 in the modeling and simulation method of the MIMO molecular communication system provided in one embodiment of this application is shown below, including steps S111 to S113: S111: The first wave number of the cylindrical pipe is obtained by solving the problem based on the radius of the cylindrical pipe and the basis function of the cylindrical pipe in the radial direction.

[0023] In this embodiment, the simulation device solves the problem based on the radius of the cylindrical pipe and the basis functions of the cylindrical pipe in the radial direction to obtain the first wave number of the cylindrical pipe. The basis functions of the cylindrical pipe in the radial direction adopt a type of Bessel function. The first wave number is used to represent the spatial variation frequency of particle concentration on the cross-sections of the cylindrical pipe in the radial and angular directions.

[0024] S112: The second wave number of the cylindrical pipe is obtained by solving the problem based on the length of the cylindrical pipe and the basis function of the cylindrical pipe in the axial direction.

[0025] In this embodiment, the simulation device solves the problem based on the length of the cylindrical pipe and the basis function of the cylindrical pipe in the axial direction to obtain the second wave number of the cylindrical pipe. The basis function of the cylindrical pipe in the axial direction is a sinusoidal basis function, and the second wave number is used to represent the spatial variation frequency of particle concentration in the axial direction of the cylindrical pipe.

[0026] S113: Obtain the diffusion coefficient of the information molecules released by each transmitter; construct a diagonal matrix based on the diffusion coefficient of the information molecules released by each transmitter, the first wave number and the second wave number of the cylindrical pipe, and obtain the diagonal matrix of the information molecules released by each transmitter.

[0027] In this embodiment, the simulation equipment obtains the diffusion coefficient of the information molecules released by each transmitter; based on the diffusion coefficient of the information molecules released by each transmitter, the first wave number and the second wave number of the cylindrical pipe, a diagonal matrix is ​​constructed to obtain the diagonal matrix of the information molecules released by each transmitter, wherein the diagonal matrix includes several diagonal elements, and the diagonal elements are:

[0028] In the formula, For the first One diagonal element, Where is the diffusion coefficient. For the first wave number, Let be the order of the Bessel function. For use The number of order Bessel functions, This is the second wave number. The number of sine functions used to construct the basis functions of the cylindrical pipe in the axial direction.

[0029] S12: Obtain the radius, length, and flow velocity of the cylindrical pipe; construct a feedback matrix based on the radius and length of the cylindrical pipe to obtain the first feedback matrix and the second feedback matrix of the cylindrical pipe.

[0030] In this embodiment, the simulation device obtains the radius, length, and flow velocity of the cylindrical pipe; the simulation device constructs a feedback matrix based on the radius and length of the cylindrical pipe to obtain a first feedback matrix and a second feedback matrix of the cylindrical pipe. The first feedback matrix is ​​used to represent the influence of the uniform component of the flow velocity of the fluid in the cylindrical pipe on the propagation of information molecules; the second feedback matrix is ​​used to represent the influence of the parabolic component of the flow velocity of the fluid in the cylindrical pipe on the propagation of information molecules.

[0031] S13: Based on the diagonal matrix of the information molecules released by each transmitter, the flow velocity and length of the cylindrical pipe, the feedback matrix, and the preset state matrix construction algorithm, obtain the state matrix of the information molecules released by each transmitter.

[0032] In this embodiment, the simulation device obtains the state matrix of the information molecules released by each transmitter based on the diagonal matrix of the information molecules released by each transmitter, the flow velocity and length of the cylindrical pipe, the feedback matrix, and a preset state matrix construction algorithm. The state matrix construction algorithm is as follows:

[0033] In the formula, For the first i The state matrix of the information molecules released by the transmitter. For the first i A diagonal matrix of information molecules released by each transmitter. The flow velocity in the cylindrical pipe is [value]. Let be the radius of the cylindrical pipe. This is the first feedback matrix. This is the second feedback matrix.

[0034] S2: Construct the input vector of the information molecules released by each transmitter and the time vector of several preset moments.

[0035] In this embodiment, the simulation device constructs input vectors for the information molecules released by each transmitter and time vectors for several preset moments. The input vectors represent the instantaneous concentration of the information molecules released by the transmitter, and the time vectors represent the intensity of the information molecules released by the transmitter at the current moment.

[0036] Please see Figure 5 , Figure 5 The flowchart of step S2 in the modeling and simulation method of the MIMO molecular communication system provided in one embodiment of this application is as follows: S21: Obtain the position coordinate data of each transmitter and the effective release size data of each transmitter.

[0037] In this embodiment, the simulation device obtains the position coordinate data of each transmitter and the effective release size data of each transmitter. The position coordinate data includes position coordinates in several spatial dimensions, and the effective release size data includes effective release sizes in several spatial dimensions.

[0038] S22: Based on the position coordinate data of each transmitter, the effective release size data of each transmitter, and the preset information molecule concentration distribution calculation algorithm, obtain the information molecule concentration distribution data released by each transmitter.

[0039] In this embodiment, the simulation device obtains the information molecule concentration distribution data released by each transmitter based on the position coordinate data of each transmitter, the effective release size data of each transmitter, and a preset information molecule concentration distribution calculation algorithm. The information molecule concentration distribution calculation algorithm is as follows: ; For any spatial dimension Its one-dimensional function can be expressed as:

[0040] In the formula, This refers to the concentration distribution data of information molecules released by the transmitter. Representing spatial dimension Information molecule concentration distribution These are the position coordinates of the transmitter in the corresponding spatial dimension. , , , This represents the position coordinates in the corresponding spatial dimension. This refers to the effective release size of the transmitter in the corresponding spatial dimension. , , , This indicates the effective release size of the corresponding spatial dimension.

[0041] S23: Based on the position coordinate data of each transmitter, the information molecule concentration distribution data of each transmitter, and the preset input vector construction algorithm, obtain the input vector of each transmitter's information molecule.

[0042] In this embodiment, the simulation device obtains the input vector of the information molecules released by each transmitter based on the position coordinate data of each transmitter, the information molecule concentration distribution data of each transmitter, and a preset input vector construction algorithm. The input vector includes several elements, and the input vector construction algorithm is as follows:

[0043]

[0044] In the formula, For the first One element, The result of the operation on the data calculated using basis functions. These are the basis functions for a cylindrical pipe.

[0045] S3: Based on the state matrix, input vector, and time vector of the information molecules released by each transmitter, perform state vector transformation to obtain the state vector of the information molecules released by each transmitter at each time.

[0046] In this embodiment, the simulation device performs state vector transformation based on the state matrix, input vector, and time vector of the information molecules released by each transmitter at each time moment to obtain the state vector of the information molecules released by each transmitter at each time moment. The state vector is used to represent the concentration distribution of information molecules in the cylindrical pipe. Please see Figure 6 , Figure 6 The flowchart of step S3 in the modeling and simulation method of the MIMO molecular communication system provided in one embodiment of this application is shown below, including step S31: S31: Based on the state matrix, input vector, time vector at each moment, and preset state vector calculation algorithm of the information molecules released by each transmitter, obtain the state vector of each information molecule released by each transmitter at each moment.

[0047] In this embodiment, the simulation device obtains the state vector of each transmitter's released information molecules at each time step based on the state matrix, input vector, time vector at each moment, and a preset state vector calculation algorithm. The state vector calculation algorithm is as follows:

[0048] In the formula, For the first t The first moment i The state vector of the information molecules released by the transmitter. For the first i The input vector of the information molecules released by each transmitter. For the first t The first moment i The time vector of the information molecules released by the transmitter.

[0049] S4: Construct the observation matrix for each receiver, which represents the sampling weight of the information molecule concentration at the location of the receiver; calculate the information molecule concentration based on the state vector of the information molecules released by each transmitter at each time and the observation matrix of each receiver, and obtain the information molecule concentration of each type of information molecule at each time.

[0050] In this embodiment, the simulation device constructs an observation matrix for each receiver, wherein the observation matrix is ​​used to represent the sampling weight of the information molecule concentration at the location of the receiver.

[0051] The simulation equipment calculates the information molecule concentration based on the state vector of the information molecules released by each transmitter at each time and the observation matrix of each receiver, thereby obtaining the information molecule concentration of each receiver at each time.

[0052] Please see Figure 7 , Figure 7 The flowchart of step S4 in the modeling and simulation method of the MIMO molecular communication system provided in one embodiment of this application is shown below, including step S41: S41: Based on the state vectors of the information molecules released by each transmitter at each time, the observation matrices of each receiver, and the preset information molecule concentration calculation algorithm, obtain the information molecule concentrations of each receiver at each time.

[0053] In this embodiment, the simulation device obtains the information molecule concentrations of various information molecules at each receiver at each time step based on the state vectors of the information molecules released by each transmitter at each time step, the observation matrices of each receiver, and a preset information molecule concentration calculation algorithm. The information molecule concentration calculation algorithm for the observation matrix is ​​as follows:

[0054] In the formula, For the first t The first moment a The first receiver i The concentration of information molecules in a given type of information molecule. For the first a The observation matrix of each receiver.

[0055] S5: Based on the information molecule concentrations of various information molecules at each receiver at each time point, perform a cyclic simulation of receptor-information molecule binding and dissociation to obtain the number of receptor-information molecule combinations at each receiver at each time point, which serves as the modeling and simulation result of the MIMO molecular communication system.

[0056] In this embodiment, the simulation device performs a cyclic simulation of receptor-information molecule binding and dissociation based on the information molecule concentration of each receiver at each time point, and obtains the number of receptor-information molecule combinations at each receiver at each time point, which serves as the modeling and simulation result of the MIMO molecular communication system.

[0057] Please see Figure 8 , Figure 8 The flowchart of step S5 in the modeling and simulation method of the MIMO molecular communication system provided in one embodiment of this application is shown below, including steps S51 to S52: S51: Obtain the number of idle receptors in each receiver at the current time; based on the number of idle receptors in each receiver at the current time, the information molecule concentration of each information molecule in each receiver at the current time, and the preset binding rate calculation algorithm, obtain the binding rate of receptors and various information molecules in each receiver at the current time.

[0058] In this embodiment, the simulation device obtains the number of idle receptors for each receiver at the current moment. Based on the number of idle receptors for each receiver at the current moment, the information molecule concentrations of various information molecules for each receiver at the current moment, and a preset binding rate calculation algorithm, the simulation device obtains the binding rate between receptors and various information molecules for each receiver at the current moment. The binding rate calculation algorithm is as follows:

[0059]

[0060] In the formula, For the first t The first moment a The receiver of the first receiver and the first i The binding rate of information molecules, For the first a The receiver of the first receiver and the first i The binding rate constant of information molecules, For the first t The first moment a The number of idle receptors in each receiver. For the first aThe total number of receivers in each receiver. For the first t The first moment a The receiver of the first receiver and the first i The number of combinations of information molecules, at the first moment, ; S52: Obtain the dissociation rate of the receptors and various information molecules of each receiver at the current moment; based on the dissociation rate of the receptors and various information molecules of each receiver at the current moment, the binding rate of the receptors and various information molecules of each receiver at the current moment, the number of bindings of the receptors and various information molecules of each receiver at the current moment, and a preset binding quantity calculation algorithm, obtain the number of bindings of the receptors and various information molecules of each receiver at the next moment.

[0061] In this embodiment, the simulation device obtains the dissociation rate of the receptors and various information molecules of each receiver at the current moment; based on the dissociation rate of the receptors and various information molecules of each receiver at the current moment, the binding rate of the receptors and various information molecules of each receiver at the current moment, the number of binding compounds of the receptors and various information molecules of each receiver at the current moment, and a preset binding compound quantity calculation algorithm, the simulation device obtains the number of binding compounds of the receptors and various information molecules of each receiver at the next moment, wherein the binding compound quantity calculation algorithm is as follows:

[0062]

[0063] In the formula, For the first The first moment a The receiver of the first receiver and the first i The number of compounds that bind to information molecules. For time step, For the first t The first moment a The receiver of the first receiver and the first i The dissociation rate of information molecules. For the first a The receiver of the first receiver and the first i The dissociation rate constant of a type of information molecule.

[0064] The simulation equipment repeats the above steps until the number of receptors and various information molecules bound to each receiver at the last moment is obtained. This number of receptors and various information molecules bound to each receiver at each moment is used as the modeling and simulation result of the MIMO molecular communication system. Based on the diffusion-laminar coupling effect induced by a cylindrical flow-diffusion environment, a receiving mechanism closely resembling the actual biological system is constructed. This mechanism can dynamically simulate the process of various information molecules competing for binding to a limited number of receptors, effectively quantifying the number of receptors and various information molecules bound to each receiver. This converts the ideal concentration signal into a receptor occupancy signal, achieving accurate and rapid simulation of the communication performance of the MIMO molecular communication system and providing a foundation for evaluating co-channel interference in MIMO molecular communication systems.

[0065] Please refer to Figure 9 , Figure 9 This is a schematic diagram of a modeling and simulation device for a MIMO molecular communication system provided in one embodiment of this application. This device can be implemented in whole or in part through software, hardware, or a combination of both. The device 9 includes: The state matrix construction module 91 is used to construct the state matrix of the information molecules released by each transmitter, wherein the state matrix is ​​used to represent the convection-diffusion coupling effect of the information molecules in the cylindrical pipe; The information molecule vector construction module 92 is used to construct the input vector of the information molecules released by each transmitter and the time vector of a preset number of time moments, wherein the input vector is used to represent the concentration distribution of the information molecules; and the time vector is used to represent the intensity of the information molecules released by the transmitter at the current moment. The state vector conversion module 93 is used to perform state vector conversion based on the state matrix, input vector and time vector of the information molecules released by each transmitter, to obtain the state vector of the information molecules released by each transmitter at each time, wherein the state vector is used to represent the concentration distribution of the information molecules in the cylindrical pipe. The information molecule concentration calculation module 94 is used to construct the observation matrix of each receiver, which is used to represent the sampling weight of the information molecule concentration at the location of the receiver; the information molecule concentration is calculated based on the state vector of the information molecules released by each transmitter at each time and the observation matrix of each receiver to obtain the information molecule concentration of each type of information molecule at each time. The binding quantity calculation module 95 is used to perform a cyclic simulation of the binding and dissociation of receptors and information molecules based on the information molecule concentrations of various information molecules at each receiver at each time, and to obtain the binding quantity of receptors and various information molecules at each receiver at each time, which serves as the modeling and simulation result of the MIMO molecular communication system.

[0066] In this embodiment, a state matrix construction module is used to construct a state matrix for the information molecules released by each transmitter, wherein the state matrix represents the convection-diffusion coupling effect of the information molecules in the cylindrical pipe; an information molecule vector construction module is used to construct the input vector of the information molecules released by each transmitter and a preset number of time vectors, wherein the input vector represents the concentration distribution of the information molecules; the time vector represents the intensity of the information molecules released by the transmitter at the current time; a state vector conversion module is used to perform state vector conversion based on the state matrix, input vector, and time vectors of the information molecules released by each transmitter to obtain the state vector of the information molecules released by each transmitter at each time, wherein the state vector... The quantity is used to represent the concentration distribution of information molecules in the cylindrical pipe; through the information molecule concentration calculation module, the observation matrix of each receiver is constructed, and the observation matrix is ​​used to represent the sampling weight of the information molecule concentration at the location of the receiver; based on the state vector of the information molecules released by each transmitter at each time and the observation matrix of each receiver, the information molecule concentration of each type of information molecule at each time is calculated to obtain the information molecule concentration of each receiver at each time; through the binding quantity calculation module, based on the information molecule concentration of each type of information molecule at each time, the binding and dissociation cycle simulation of the receptor and information molecule is performed to obtain the binding quantity of the receptor and various information molecules at each receiver at each time, which is used as the modeling and simulation result of the MIMO molecular communication system. Based on the diffusion-laminar coupling effect induced by the cylindrical flow-diffusion environment, a receiving mechanism that fits the actual biological system is constructed. It can dynamically simulate the binding process of various information molecules competing for a limited number of receptors, effectively quantifying the number of receptors and various information molecules binding to the receiver. Thus, the ideal concentration signal is converted into a receptor occupancy signal, realizing accurate and rapid simulation of the communication performance of MIMO molecular communication systems, and providing a foundation for the co-channel interference assessment of MIMO molecular communication systems.

[0067] Please refer to Figure 10 , Figure 10This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. The computer device 10 includes: a processor 101, a memory 102, and a computer program 103 stored in the memory 102 and executable on the processor 101. The computer device can store multiple instructions, which are adapted to be loaded and executed by the processor 101. Figures 1 to 8 The method steps shown can be found in the following document for detailed execution process. Figures 1 to 8 The specific details shown will not be repeated here.

[0068] The processor 101 may include one or more processing cores. The processor 101 connects to various parts of the server using various interfaces and lines, and executes various functions and processes data of the MIMO molecular communication system modeling and simulation device 9 by running or executing instructions, programs, code sets, or instruction sets stored in memory 102, and by calling data stored in memory 102. Optionally, the processor 101 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 101 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required to be displayed on the touch screen; and the modem is used for wireless communication. It is understood that the modem may also not be integrated into the processor 101 and may be implemented as a separate chip.

[0069] The memory 102 may include random access memory (RAM) or read-only memory. Optionally, the memory 102 may include a non-transitory computer-readable storage medium. The memory 102 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 102 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 102 may also be at least one storage device located remotely from the aforementioned processor 101.

[0070] This application embodiment also provides a storage medium that can store multiple instructions, which are adapted to be loaded and executed by a processor as described above. Figures 1 to 8 The method steps shown can be found in the following document for detailed execution process. Figures 1 to 8 The specific details shown will not be repeated here.

[0071] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0072] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0073] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the algorithm. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0074] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0077] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms.

[0078] This invention is not limited to the above-described embodiments. If any modifications or variations to this invention do not depart from the spirit and scope of this invention, and if such modifications and variations fall within the scope of the claims and equivalent technologies of this invention, then this invention also intends to include such modifications and variations.

Claims

1. A modeling and simulation method for a MIMO molecular communication system, the MIMO molecular communication system comprising a plurality of transmitters, a cylindrical pipe, and a plurality of receivers; each transmitter releases information molecules in the cylindrical pipe, the information molecules flowing through fluid within the cylindrical pipe to each receiver, characterized in that, Includes the following steps: Construct a state matrix for the information molecules released by each transmitter, wherein the state matrix is ​​used to represent the convection-diffusion coupling effect of the information molecules in the cylindrical pipe; Construct input vectors for the information molecules released by each transmitter and time vectors for a preset number of moments, wherein the input vectors represent the instantaneous concentration of the information molecules released by the transmitter; and the time vectors represent the intensity of the information molecules released by the transmitter at the current moment. State vector transformation is performed based on the state matrix, input vector, and time vector of the information molecules released by each transmitter at each time moment to obtain the state vector of the information molecules released by each transmitter at each time moment. The state vector is used to represent the concentration distribution of information molecules in the cylindrical pipe. An observation matrix is ​​constructed for each receiver, which is used to represent the sampling weight of the information molecule concentration at the location of the receiver; the information molecule concentration is calculated based on the state vector of the information molecules released by each transmitter at each time and the observation matrix of each receiver, so as to obtain the information molecule concentration of each type of information molecule at each time. Based on the concentration of various information molecules at each receiver at each time point, the binding and dissociation cycles of receptors and information molecules are simulated to obtain the number of receptors and various information molecules bound to each receiver at each time point, which serves as the modeling and simulation result of the MIMO molecular communication system.

2. The modeling and simulation method for a MIMO molecular communication system according to claim 1, characterized in that, The construction of the state matrix of the information molecules released by each transmitter includes the following steps: Construct a diagonal matrix of information molecules released by each transmitter, wherein the diagonal matrix is ​​used to represent the diffusion process of information molecules; The radius, length, and flow velocity of the cylindrical pipe are obtained; a feedback matrix is ​​constructed based on the radius and length of the cylindrical pipe to obtain a first feedback matrix and a second feedback matrix of the cylindrical pipe. The first feedback matrix is ​​used to represent the influence of the uniform component of the flow velocity of the fluid in the cylindrical pipe on the propagation of information molecules; the second feedback matrix is ​​used to represent the influence of the parabolic component of the flow velocity of the fluid in the cylindrical pipe on the propagation of information molecules. Based on the diagonal matrix of the information molecules released by each transmitter, the flow velocity and length of the cylindrical pipe, the feedback matrix, and a preset state matrix construction algorithm, the state matrix of the information molecules released by each transmitter is obtained. The state matrix construction algorithm is as follows: In the formula, For the first i The state matrix of the information molecules released by the transmitter. For the first i A diagonal matrix of information molecules released by each transmitter. The flow velocity in the cylindrical pipe is [value]. Let be the radius of the cylindrical pipe. This is the first feedback matrix. This is the second feedback matrix.

3. The modeling and simulation method for a MIMO molecular communication system according to claim 2, characterized in that, The construction of the diagonal matrix of information molecules released by each transmitter includes the following steps: The first wave number of the cylindrical pipe is obtained by solving the problem based on the radius of the cylindrical pipe and the basis functions of the cylindrical pipe in the radial direction. The basis functions of the cylindrical pipe in the radial direction adopt a type of Bessel function. The first wave number is used to represent the spatial variation frequency of particle concentration in the radial and angular cross sections of the cylindrical pipe. The second wave number of the cylindrical pipe is obtained by solving the problem based on the length of the cylindrical pipe and the basis function of the cylindrical pipe in the axial direction. The basis function of the cylindrical pipe in the axial direction is a sine basis function, and the second wave number is used to represent the spatial variation frequency of particle concentration in the axial direction of the cylindrical pipe. Obtain the diffusion coefficient of the information molecules released by each transmitter; construct a diagonal matrix based on the diffusion coefficient of the information molecules released by each transmitter, the first wave number, and the second wave number of the cylindrical pipe, to obtain the diagonal matrix of the information molecules released by each transmitter, wherein the diagonal matrix includes several diagonal elements, and the diagonal elements are: In the formula, For the first One diagonal element, Where is the diffusion coefficient. For the first wave number, Let be the order of the Bessel function. For use The number of order Bessel functions, This is the second wave number. The number of sine functions used to construct the basis functions of the cylindrical pipe in the axial direction.

4. The modeling and simulation method for a MIMO molecular communication system according to claim 3, characterized in that, The process of constructing the input vector of the information molecules released by each transmitter and the time vectors of several preset time points includes the following steps: Obtain the position coordinate data of each transmitter and the effective release size data of each transmitter, wherein the position coordinate data includes position coordinates in several spatial dimensions and the effective release size data includes effective release sizes in several spatial dimensions; Based on the position coordinate data of each transmitter, the effective release size data of each transmitter, and a preset information molecule concentration distribution calculation algorithm, the information molecule concentration distribution data released by each transmitter is obtained. The information molecule concentration distribution calculation algorithm is as follows: ； For any spatial dimension Its one-dimensional function can be expressed as: In the formula, This refers to the concentration distribution data of information molecules released by the transmitter. Representing spatial dimension Information molecule concentration distribution These are the position coordinates of the transmitter in the corresponding spatial dimension. , , , This represents the position coordinates in the corresponding spatial dimension. This refers to the effective release size of the transmitter in the corresponding spatial dimension. , , , Indicates the effective release size of the corresponding spatial dimension; Based on the position coordinates of each transmitter, the concentration distribution data of the information molecules released by each transmitter, and a preset input vector construction algorithm, the input vector of the information molecules released by each transmitter is obtained. The input vector includes several elements, and the input vector construction algorithm is as follows: In the formula, For the first One element, The result of the operation on the data calculated using basis functions. These are the basis functions for a cylindrical pipe.

5. The modeling and simulation method for a MIMO molecular communication system according to claim 4, characterized in that, The step of performing state vector transformation based on the state matrix, input vector, and time vector of the information molecules released by each transmitter to obtain the state vector of each information molecule released by each transmitter at each time step includes the following steps: Based on the state matrix, input vector, time vector at each moment, and a preset state vector calculation algorithm of the information molecules released by each transmitter, the state vector of each information molecule released by each transmitter at each moment is obtained. The state vector calculation algorithm is as follows: In the formula, For the first t The first moment i The state vector of the information molecules released by the transmitter. For the first i The input vector of the information molecules released by each transmitter. For the first t The first moment i The time vector of the information molecules released by the transmitter.

6. The modeling and simulation method for a MIMO molecular communication system according to claim 5, characterized in that, The step of calculating the information molecule concentration based on the state vectors of the information molecules released by each transmitter at each time and the observation matrices of each receiver to obtain the information molecule concentrations of various information molecules at each receiver at each time includes the following steps: Based on the state vectors of the information molecules released by each transmitter at each time moment, the observation matrices of each receiver, and a preset information molecule concentration calculation algorithm, the information molecule concentrations of various information molecules at each receiver at each time moment are obtained. The algorithm for calculating the information molecule concentration using the observation matrix is ​​as follows: In the formula, For the first t The first moment a The first receiver i The concentration of information molecules in a given type of information molecule. For the first a The observation matrix of each receiver.

7. The modeling and simulation method for a MIMO molecular communication system according to claim 6, characterized in that, The method of simulating the binding and dissociation cycles of receptors and information molecules based on the concentration of various information molecules at each receiver at each time point to obtain the amount of receptors and various information molecules bound to each receiver at each time point includes the following steps: Obtain the number of idle receptors for each receiver at the current time; based on the number of idle receptors for each receiver at the current time, the information molecule concentrations of various information molecules for each receiver at the current time, and a preset binding rate calculation algorithm, obtain the binding rate of receptors to various information molecules for each receiver at the current time, wherein the binding rate calculation algorithm is as follows: In the formula, For the first t The first moment a The receiver of the first receiver and the first i The binding rate of information molecules, For the first a The receiver of the first receiver and the first i The binding rate constant of a type of information molecule, For the first t The first moment a The number of idle receptors in each receiver. For the first a The total number of receivers in each receiver. For the first t The first moment a The receiver of the first receiver and the first i The number of combinations of information molecules, at the first moment, ; Obtain the dissociation rate of the receptors and various information molecules at each receiver at the current moment; based on the dissociation rate of the receptors and various information molecules at the current moment, the binding rate of the receptors and various information molecules at the current moment, the number of binding compounds at the current moment, and a preset binding compound number calculation algorithm, obtain the number of binding compounds at each receiver at the next moment, wherein the binding compound number calculation algorithm is as follows: In the formula, For the first The first moment a The receiver of the first receiver and the first i The number of compounds that bind to information molecules. For time step, For the first t The first moment a The receiver of the first receiver and the first i The dissociation rate of information molecules. For the first a The receiver of the first receiver and the first i The dissociation rate constant of a type of information molecule.

8. A modeling and simulation device for a MIMO molecular communication system, the MIMO molecular communication system comprising a plurality of transmitters, a cylindrical pipe, and a plurality of receivers; each transmitter releases information molecules in the cylindrical pipe, the information molecules flowing through fluid within the cylindrical pipe to each receiver, characterized in that, include: A state matrix construction module is used to construct the state matrix of the information molecules released by each transmitter, wherein the state matrix is ​​used to represent the convection-diffusion coupling effect of the information molecules in the cylindrical pipe; The information molecule vector construction module is used to construct the input vector of the information molecules released by each transmitter and the time vector of a preset number of time moments, wherein the input vector is used to represent the concentration distribution of the information molecules; and the time vector is used to represent the intensity of the information molecules released by the transmitter at the current moment. The state vector conversion module is used to perform state vector conversion based on the state matrix, input vector, and time vector of the information molecules released by each transmitter at each time, so as to obtain the state vector of the information molecules released by each transmitter at each time. The state vector is used to represent the concentration distribution of information molecules in the cylindrical pipe. The information molecule concentration calculation module is used to construct the observation matrix of each receiver, which represents the sampling weight of the information molecule concentration at the location of the receiver; the information molecule concentration is calculated based on the state vector of the information molecules released by each transmitter at each time and the observation matrix of each receiver to obtain the information molecule concentration of each type of information molecule at each time. The module for calculating the number of binding compounds is used to simulate the binding and dissociation cycles of receptors and information molecules based on the concentration of various information molecules at each receiver at each time point, and to obtain the number of binding compounds of receptors and various information molecules at each receiver at each time point, which serves as the modeling and simulation result of the MIMO molecular communication system.

9. A computer device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor; the computer program, when executed by the processor, implements the steps of the modeling and simulation method for a MIMO molecular communication system as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the modeling and simulation method for the MIMO molecular communication system as described in any one of claims 1 to 7.

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