Spectrum occupancy identification and ad hoc network optimization method and device

Abstract

The invention relates to a spectrum occupancy identification and ad hoc network optimization method and device, and belongs to the technical field of wireless networks, and the method comprises the steps: carrying out the grid management of a wireless network in which the spectrum occupancy is to be identified; judging the size of the interference range of the wireless network and a preset range; if the judgment result is that the interference range of the wireless network is smaller than the preset range, constructing a Markov prediction model to predict a spectrum occupation probability for each grid; if the judgment result is that the interference range of the wireless network is greater than or equal to the preset range, constructing a spectrum occupancy probability corresponding to each grid by an Ising prediction model; when the network environment is abnormal, performing self-organizing in a wireless network grid by adopting a random domain mode to obtain a to-be-optimized self-organized network; determining the average delay and network jitter times of the to-be-optimized ad hoc network; and screening the to-be-optimized ad hoc network according to the average delay and the network jitter times. According to the invention, the problems of low frequency spectrum occupation identification efficiency and poor ad hoc network performance are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless networks, and in particular relates to a method and device for spectrum occupancy identification and self-organizing network optimization. Background Art

[0002] Identifying unoccupied spectrum is very important for improving the efficiency and reliability of future wireless networks, especially for cognitive radio networks. A quantitative and accurate understanding of the current spectrum occupancy can help subsequent users make more effective dynamic spectrum access decisions. In addition, understanding the spectrum occupancy helps analyze the connectivity, capacity and other characteristics of cognitive radio networks. However, obtaining spectrum occupancy data requires a large amount of data measurement activities, which is inefficient. The random domain method is used in radio networks for regional self-organizing networks. Due to the average delay of the regional self-organizing network and the false alarm of arbitrary packet jitter, the performance of the self-organizing network is poor. Summary of the invention

[0003] In view of the above deficiencies in the prior art, the purpose of the invention is to provide a method and device for spectrum occupancy identification and ad hoc network optimization, which solves the problems of low efficiency in spectrum occupancy identification and poor performance of ad hoc networks.

[0004] The first aspect of the present invention provides a spectrum occupancy identification and ad hoc network optimization method, comprising:

[0005] Use grid management for wireless networks with identified spectrum occupancy;

[0006] Determine the size of the interference range of the wireless network and the preset range;

[0007] If the result of the judgment is that the interference range of the wireless network is less than the preset range, a Markov prediction model is constructed to predict the spectrum occupancy probability of each grid; if the result of the judgment is that the interference range of the wireless network is greater than or equal to the preset range, an Ising prediction model is constructed to predict the spectrum occupancy probability corresponding to each grid;

[0008] When the network environment is abnormal, a random domain method is used in the wireless network grid to self-organize and obtain a self-organizing network to be optimized;

[0009] Determine the average delay and network jitter times of the self-organizing network to be optimized;

[0010] The self-organizing networks to be optimized are screened according to the average delay of the self-organizing networks to be optimized and the number of network jitters to obtain the preferred self-organizing network.

[0011] Furthermore, in the above-mentioned spectrum occupancy identification and self-organizing network optimization method, the wireless network whose spectrum occupancy is to be identified is managed in a grid-based manner, including:

[0012] The topological relationship of the mesh topology structure is marked, wherein no cross-mesh structure topology = 0, cross-mesh structure topology and neighbor relationship = 1, and cross-mesh structure topology and group relationship = 2.

[0013] Furthermore, in the above-mentioned spectrum occupancy identification and self-organizing network optimization method, a Markov prediction model is constructed to predict the spectrum occupancy probability of each grid, including:

[0014] Obtaining the probability of historical spectrum occupancy information of each grid in the wireless network, and constructing a Markov prediction model through the probability of historical spectrum occupancy information of each grid in the wireless network;

[0015] Obtain the current spectrum occupancy information of each grid in the wireless network to increase the transfer probability and the current spectrum occupancy information to reduce the transfer probability;

[0016] The current spectrum occupancy information increase transition probability and the current spectrum occupancy information decrease transition probability of each grid are input into the Markov prediction model to predict the next spectrum occupancy information increase probability and the next spectrum occupancy information decrease probability.

[0017] Furthermore, in the above-mentioned spectrum occupancy identification and self-organizing network optimization method, an Ising prediction model is constructed to correspond a spectrum occupancy probability to each grid, including:

[0018] Obtain spectrum occupancy information of each grid in the wireless network;

[0019] An energy function corresponding to the spectrum occupancy information of each grid in the wireless network is defined according to the energy function corresponding to the spectrum occupancy information of each grid;

[0020] In the Ising prediction model, a spectrum occupancy probability is assigned to each grid according to the probability distribution of spectrum occupancy information, the spectrum occupancy information of each grid, and the energy function corresponding to the spectrum occupancy information of each grid.

[0021] Furthermore, in the above-mentioned spectrum occupancy identification and ad hoc network optimization method, determining the average delay in the ad hoc network includes:

[0022] Analyze the topological relationship of ad hoc networks;

[0023] Receive data from each ad hoc network through the log channel;

[0024] Extract all self-organizing network data in the self-organizing network topology relationship;

[0025] Query each ad hoc network delay data from the ad hoc network data;

[0026] The average delay of each ad hoc network is obtained by weighted averaging the delay data of each ad hoc network.

[0027] Furthermore, in the above-mentioned spectrum occupancy identification and self-organizing network optimization method, determining the number of network jitters includes:

[0028] Build a distributed training topology-aware system;

[0029] The distributed training topology awareness system traverses the network jitter log of the self-organizing network composed of all possible neighbor relationships or group relationships recorded by the topology relationship mark;

[0030] The number of network jitters is determined based on the traversal results.

[0031] Furthermore, in the above-mentioned spectrum occupancy identification and ad hoc network optimization method, the ad hoc network to be optimized is selected according to the average delay of the ad hoc network to be optimized and the number of network jitters to obtain the preferred ad hoc network, including:

[0032] The self-organizing network with the smallest average delay and the smallest network jitter number is selected as the preferred self-organizing network.

[0033] The second aspect of the present invention further proposes a spectrum occupancy identification and ad hoc network optimization method, comprising:

[0034] Grid management module: used to manage wireless networks with identified spectrum occupancy in a grid-based manner;

[0035] Judgment module: used to judge the size of the interference range of the wireless network and the preset range;

[0036] Construction module: used to construct a Markov prediction model to predict the spectrum occupancy probability of each grid if the judgment result is that the interference range of the wireless network is less than the preset range; if the judgment result is that the interference range of the wireless network is greater than or equal to the preset range, construct an Ising prediction model to predict the spectrum occupancy probability corresponding to each grid;

[0037] Self-organizing module: when the network environment is abnormal, it uses random domain method to self-organize in the wireless network grid to obtain the self-organizing network to be optimized;

[0038] Determination module: used to determine the average delay and network jitter times of the self-organizing network to be optimized;

[0039] Screening module: used to screen the self-organizing networks to be optimized according to the average delay of the self-organizing networks to be optimized and the number of network jitters to obtain the preferred self-organizing network.

[0040] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor and a memory;

[0041] The processor is used to execute any one of the above spectrum occupancy identification and self-organizing network optimization methods by calling the program or instruction stored in the memory.

[0042] In a fourth aspect of the present invention, a computer-readable storage medium is further proposed, wherein the computer-readable storage medium stores a program or instruction, and the program or instruction enables a computer to execute any one of the above spectrum occupancy identification and self-organizing network optimization methods.

[0043] The beneficial effects of the present invention are as follows: the present invention manages the wireless network whose spectrum occupancy is to be identified by gridding; determines the size of the interference range of the wireless network and the preset range; if the result of the judgment is that the interference range of the wireless network is less than the preset range, constructs a Markov prediction model to predict the spectrum occupancy probability for each grid; if the result of the judgment is that the interference range of the wireless network is greater than or equal to the preset range, constructs an Ising prediction model to predict the spectrum occupancy probability corresponding to each grid; when the network environment is abnormal, uses a random domain method to self-organize in the wireless network grid to obtain a self-organizing network to be selected; determines the average delay and network jitter number of the self-organizing network to be selected; and selects the self-organizing network to be selected according to the average delay and network jitter number of the self-organizing network to be selected to obtain the preferred self-organizing network. The present invention solves the problems of low efficiency in spectrum occupancy identification and poor performance of self-organizing networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are only used to illustrate specific embodiments and are not considered to limit the present invention. In the entire drawings, the same reference symbols represent the same components. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0045] Figure 1 A spectrum occupancy identification and ad hoc network optimization method diagram provided by an embodiment of the present invention;

[0046] Figure 2 A method diagram of constructing a Markov prediction model to predict the spectrum occupancy probability of each grid provided by an embodiment of the present invention;

[0047] Figure 3 A method diagram of constructing an Ising prediction model to correspond a spectrum occupancy probability to each grid provided by an embodiment of the present invention;

[0048] Figure 4 A method diagram for determining average delay in an ad hoc network provided by an embodiment of the present invention;

[0049] Figure 5 A method diagram for determining the number of network jitters provided by an embodiment of the present invention;

[0050] Figure 6 A spectrum occupancy identification and self-organizing network optimization device diagram provided by an embodiment of the present invention;

[0051] Figure 7 A schematic block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work should fall within the scope of protection of the present invention.

[0053] Furthermore, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts disclosed in the present invention.

[0054] In the description of the present invention, the terms "first", "second", and "third" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be internal communication between two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0055] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of methods and systems consistent with some aspects of the present invention as detailed in the appended claims.

[0056] The present invention proposes a spectrum occupancy identification and self-organizing network optimization method, device, electronic device and storage medium, which solve the problems of low spectrum occupancy identification efficiency and poor self-organizing network performance.

[0057] Before introducing the embodiments of the present invention, professional terms involved in the present invention are first introduced.

[0058] Ad hoc network: a decentralized wireless network without fixed infrastructure support. In an ad hoc network, each user terminal has both the functions of a router and a host and can move freely, resulting in the network topology constantly changing and highly dynamic.

[0059] Method Embodiment

[0060] Figure 1 A diagram of a spectrum occupancy identification and self-organizing network optimization method provided by an embodiment of the present invention.

[0061] In the first aspect of the present invention, a spectrum occupancy identification and self-organizing network optimization method is proposed, combining Figure 1 , including six steps from S1 to S6:

[0062] S1: Use grid management to manage wireless networks with identified spectrum occupancy.

[0063] Specifically, in the embodiment of the present invention, the wireless network whose spectrum occupancy is to be identified is managed in a grid-based manner, and the topological relationship of the grid topology is marked, which is divided into a non-cross-grid structure topology, a neighbor relationship topology, and a group relationship topology.

[0064] S2: Determine the size of the interference range of the wireless network and the preset range.

[0065] Specifically, in the embodiment of the present invention, the size of the preset range can be flexibly set according to actual conditions, and is used as a reference for determining the size of the interference range of the wireless network.

[0066] S3: If the judgment result is that the interference range of the wireless network is less than the preset range, a Markov prediction model is constructed to predict the spectrum occupancy probability for each grid; if the judgment result is that the interference range of the wireless network is greater than or equal to the preset range, an Ising prediction model is constructed to predict the spectrum occupancy probability corresponding to each grid.

[0067] Specifically, in the embodiment of the present invention, the Markov prediction model is used when the interference range of the wireless network is smaller than the preset range, that is, the interference range is small, and the Ising prediction model is used when the interference range of the wireless network is greater than or equal to the preset range, that is, the interference range is large.

[0068] S4: When the network environment is abnormal, a random domain method is used in the wireless network grid to self-organize to obtain a self-organizing network to be optimized.

[0069] Specifically, in an embodiment of the present invention, when the network environment is abnormal, such as the network environment is subjected to security attacks, intrusion attacks, node destruction and electromagnetic interference, in order to meet the wireless network scheduling requirements, upper-layer application requirements, business service quality requirements, etc., the wireless network grid is reorganized using a random domain method to obtain a self-organizing network to be optimized.

[0070] S5: Determine the average delay and the number of network jitters of the self-organizing network to be optimized.

[0071] Specifically, in the embodiment of the present invention, the method for determining the average delay and the number of network jitters of the self-organizing network to be optimized is described in detail below.

[0072] S6: Filter the self-organizing networks to be optimized according to the average delay and the number of network jitters of the self-organizing networks to be optimized to obtain the preferred self-organizing network.

[0073] Specifically, in the embodiment of the present invention, the self-organizing network to be selected as the preferred self-organizing network has the smallest average delay and the smallest number of network jitters.

[0074] Furthermore, in the above-mentioned spectrum occupancy identification and self-organizing network optimization method, the wireless network whose spectrum occupancy is to be identified is managed in a grid-based manner, including:

[0075] The topological relationship of the mesh topology structure is marked, wherein no cross-mesh structure topology = 0, cross-mesh structure topology and neighbor relationship = 1, and cross-mesh structure topology and group relationship = 2.

[0076] Specifically, in the embodiment of the present invention, the neighbor relationship is that for any grid that is not at the edge of the network, the four adjacent grids are considered to be its neighbors; the group relationship is a set of grids that are neighbors of each other, and the topology without cross-grid structure is an independent grid topology structure.

[0077] Figure 2 A method diagram for constructing a Markov prediction model to predict the spectrum occupancy probability of each grid is provided in an embodiment of the present invention.

[0078] Furthermore, in the above-mentioned spectrum occupancy identification and self-organizing network optimization method, a Markov prediction model is constructed to predict the spectrum occupancy probability of each grid, combined with Figure 2 , including three steps from S21 to S23:

[0079] S21: Obtain the probability of historical spectrum occupancy information of each grid in the wireless network, and construct a Markov prediction model based on the probability of historical spectrum occupancy information of each grid in the wireless network;

[0080] S22: obtaining the current spectrum occupancy information of each grid in the wireless network to increase the transfer probability and the current spectrum occupancy information to decrease the transfer probability;

[0081] S23: Inputting the current spectrum occupancy information increase transition probability and the current spectrum occupancy information decrease transition probability of each grid into the Markov prediction model to predict the next spectrum occupancy information increase probability and the next spectrum occupancy information decrease probability.

[0082] Specifically, in an embodiment of the present invention, a Markov prediction model is constructed through the probability of historical spectrum occupancy information of each grid in the wireless network, and the transition probability of an increase in the current spectrum occupancy information and the transition probability of a decrease in the current spectrum occupancy information of each grid in the wireless network are input into the Markov prediction model to predict the probability of the next spectrum occupancy information increase and the probability of the next spectrum occupancy information decrease, thereby predicting the spectrum occupancy probability for each grid.

[0083] Figure 3 A method diagram of constructing an Ising prediction model to correspond a spectrum occupancy probability to each grid is provided in an embodiment of the present invention.

[0084] Furthermore, in the above-mentioned spectrum occupancy identification and self-organizing network optimization method, an Ising prediction model is constructed to correspond a spectrum occupancy probability to each grid, and combined with Figure 3 , including three steps from S31 to S33:

[0085] S31: Obtain spectrum occupancy information of each grid of the wireless network;

[0086] S32: defining a corresponding energy function according to the spectrum occupancy information of each grid in the wireless network to obtain an energy function corresponding to the spectrum occupancy information of each grid;

[0087] S33: In the Ising prediction model, a spectrum occupancy probability is assigned to each grid according to the probability distribution of the spectrum occupancy information, the spectrum occupancy information of each grid, and the energy function corresponding to the spectrum occupancy information of each grid.

[0088] Specifically, in the embodiment of the present invention, the spectrum occupancy information of each grid of the wireless network is expressed as:

[0089] X={x0,…,x i , …, x n}∈Ω

[0090] The energy function corresponding to the spectrum occupancy information of each grid is expressed as:

[0091]

[0092] The probability distribution of spectrum occupancy information is expressed as:

[0093]

[0094]

[0095] Among them, each random variable X represents a grid in the network in the topological structure, Ω represents the sample space, α and β represent two parameters of the Ising model, namely the external influence parameter and the internal influence parameter, T represents the temperature, K represents the universal constant, Z represents the partition function, i represents the subscript of the grid, and ij represents the subscript of all adjacent grids.

[0096] Figure 4 A diagram of a method for determining average delay in an ad hoc network provided by an embodiment of the present invention.

[0097] Furthermore, in the above-mentioned spectrum occupancy identification and self-organizing network optimization method, the average delay in the self-organizing network is determined, combined with Figure 4 , including five steps from S41 to S45:

[0098] S41: Analyze the topology of the ad hoc network;

[0099] S42: receiving data of each ad hoc network through a log channel;

[0100] S43: extracting all ad hoc network data in the ad hoc network topology relationship;

[0101] S44: querying each ad hoc network delay data from the ad hoc network data;

[0102] S45: Obtain an average delay of each ad hoc network by weighted averaging the delay data of each ad hoc network.

[0103] Specifically, in an embodiment of the present invention, the wireless network grid topology relationship is parsed, the data of each ad hoc network is received through a log channel, all the ad hoc network data in the ad hoc network topology relationship are extracted, each ad hoc network delay data is queried from the ad hoc network data, and the delay is weighted and averaged to obtain the average delay of each ad hoc network.

[0104] Figure 5 A diagram of a method for determining the number of network jitters provided by an embodiment of the present invention.

[0105] Furthermore, in the above-mentioned spectrum occupancy identification and self-organizing network optimization method, the number of network jitters is determined, combined with Figure 5 , including three steps from S51 to S53:

[0106] S51: Building a distributed training topology-aware system;

[0107] S52: The distributed training topology awareness system traverses the network jitter log of the self-organizing network composed of all possible neighbor relationships or group relationships recorded by the topology relationship mark;

[0108] S53: Determine the number of network jitters according to the traversal result.

[0109] Specifically, in an embodiment of the present invention, a distributed training topology awareness system is used to traverse the network jitter logs of all possible ad hoc networks composed of neighbor relationships or group relationships to determine the number of network jitters.

[0110] Furthermore, in the above-mentioned spectrum occupancy identification and ad hoc network optimization method, the ad hoc network to be optimized is selected according to the average delay of the ad hoc network to be optimized and the number of network jitters to obtain the preferred ad hoc network, including:

[0111] The self-organizing network with the smallest average delay and the smallest network jitter number is selected as the preferred self-organizing network.

[0112] Specifically, in the embodiment of the present invention, the self-organizing network with the smallest average delay and the smallest number of network jitters is selected as the self-organizing network with the best performance.

[0113] Device Embodiment

[0114] Figure 6 A diagram of a spectrum occupancy identification and self-organizing network optimization device provided in an embodiment of the present invention.

[0115] In a second aspect of the present invention, a spectrum occupancy identification and self-organizing network optimization device is also proposed, combining Figure 6 ,include:

[0116] The grid management module 61 is used to manage the wireless network whose spectrum occupancy is to be identified by adopting a grid approach.

[0117] Specifically, in the embodiment of the present invention, the grid management module 61 manages the wireless network whose spectrum occupancy is to be identified by grid management, and marks the topological relationship of the grid topology structure, which is divided into non-cross-grid structure topology, neighbor relationship topology and group relationship topology.

[0118] The determination module 62 is used to determine the size of the interference range of the wireless network and the preset range.

[0119] Specifically, in the embodiment of the present invention, the size of the preset range can be flexibly set according to actual conditions, and is used as a reference for determining the size of the interference range of the wireless network.

[0120] Construction module 63: used to construct a Markov prediction model to predict the spectrum occupancy probability for each grid if the judgment result is that the interference range of the wireless network is less than the preset range; if the judgment result is that the interference range of the wireless network is greater than or equal to the preset range, construct an Ising prediction model to predict the spectrum occupancy probability corresponding to each grid.

[0121] Specifically, in an embodiment of the present invention, when the interference range of the wireless network is less than the preset range, that is, the interference range is small, the construction module 63 constructs a Markov prediction model; when the interference range of the wireless network is greater than or equal to the preset range, that is, the interference range is large, the construction module 63 constructs an Ising prediction model.

[0122] Self-organizing module 64: used for self-organizing in a random domain manner in a wireless network grid to obtain a self-organizing network to be optimized when the network environment is abnormal.

[0123] Specifically, in the embodiment of the present invention, when the network environment is abnormal, such as the network environment is subjected to security attacks, intrusion attacks, node destruction and electromagnetic interference, in order to meet the wireless network scheduling requirements, upper-layer application requirements, business service quality and other requirements, the self-organizing module 64 uses a random domain method to reorganize the wireless network grid to obtain the self-organizing network to be optimized.

[0124] Determination module 65: used to determine the average delay and network jitter times of the ad hoc network to be optimized.

[0125] Specifically, in the embodiment of the present invention, the determination module 65 determines the average delay and the number of network jitters of the self-organizing network to be optimized.

[0126] Screening module 66: used for screening the ad hoc networks to be optimized according to the average delay and the number of network jitters of the ad hoc networks to be optimized to obtain the preferred ad hoc network.

[0127] Specifically, in the embodiment of the present invention, the screening module 66 screens the self-organizing network with the smallest average delay and the smallest number of network jitters as the preferred self-organizing network.

[0128] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor and a memory;

[0129] The processor is used to execute any one of the above spectrum occupancy identification and self-organizing network optimization methods by calling the program or instruction stored in the memory.

[0130] In a fourth aspect of the present invention, a computer-readable storage medium is further proposed, wherein the computer-readable storage medium stores a program or instruction, and the program or instruction enables a computer to execute any one of the above spectrum occupancy identification and self-organizing network optimization methods.

[0131] Figure 7 It is a schematic block diagram of an electronic device provided by an embodiment of the present invention.

[0132] like Figure 7As shown, the electronic device includes: at least one processor 701, at least one memory 702 and at least one communication interface 703. The various components in the electronic device are coupled together through a bus system 704. The communication interface 703 is used to transmit information between external devices. It can be understood that the bus system 704 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 704 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 7 Various buses are labeled as bus system 704 .

[0133] It can be understood that the memory 702 in this embodiment can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0134] In some embodiments, the memory 702 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system and application programs.

[0135] The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. A program that implements any of the spectrum occupancy identification and self-organizing network optimization methods provided in an embodiment of the present invention can be included in the application.

[0136] In an embodiment of the present invention, the processor 701 calls a program or instruction stored in the memory 702, specifically, a program or instruction stored in an application, and the processor 701 is used to execute the steps of each embodiment of a spectrum occupancy identification and self-organizing network optimization method provided in an embodiment of the present invention.

[0137] Use grid management for wireless networks with identified spectrum occupancy;

[0138] Determine the size of the interference range of the wireless network and the preset range;

[0139] If the result of the judgment is that the interference range of the wireless network is less than the preset range, a Markov prediction model is constructed to predict the spectrum occupancy probability of each grid; if the result of the judgment is that the interference range of the wireless network is greater than or equal to the preset range, an Ising prediction model is constructed to predict the spectrum occupancy probability corresponding to each grid;

[0140] When the network environment is abnormal, a random domain method is used in the wireless network grid to self-organize and obtain a self-organizing network to be optimized;

[0141] Determine the average delay and network jitter times of the self-organizing network to be optimized;

[0142] The self-organizing networks to be optimized are screened according to the average delay of the self-organizing networks to be optimized and the number of network jitters to obtain the preferred self-organizing network.

[0143] Any of the spectrum occupancy identification and self-organizing network optimization methods provided in the embodiments of the present invention can be applied to the processor 701, or implemented by the processor 701. The processor 701 can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 701 or instructions in the form of software. The above processor 701 can be a general-purpose processor, a digital signal processor (DigitalSignal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0144] The steps of any method in the spectrum occupancy identification and self-organizing network optimization method provided in the embodiment of the present invention can be directly embodied as being executed by a hardware decoding processor, or being executed by a combination of hardware and software units in the decoding processor. The software unit can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 702, and the processor 701 reads the information in the memory 702 and completes the steps of the method in combination with its hardware.

[0145] Those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present invention and to form different embodiments.

[0146] Those skilled in the art will appreciate that the description of each embodiment has its own emphasis, and for parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0147] Although the embodiments of the present invention are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims. The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed by the present invention, and these modifications or substitutions should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

[0148] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A spectrum occupancy identification and ad hoc network optimization method, characterized in that: include: Use grid management for wireless networks with identified spectrum occupancy; Determine the size of the interference range of the wireless network and the preset range; If the result of the judgment is that the interference range of the wireless network is smaller than the preset range, a Markov prediction model is constructed to predict the spectrum occupancy probability for each grid; If the judgment result is that the interference range of the wireless network is greater than or equal to the preset range, constructing an Ising prediction model for the spectrum occupancy probability corresponding to each grid; When the network environment is abnormal, a random domain method is used in the wireless network grid to self-organize and obtain a self-organizing network to be optimized; Determine the average delay and network jitter times of the self-organizing network to be optimized; The self-organizing networks to be optimized are screened according to the average delay of the self-organizing networks to be optimized and the number of network jitters to obtain the preferred self-organizing network.

2. A spectrum occupancy identification and ad hoc network optimization method according to claim 1, characterized in that: Wireless networks with identified spectrum occupancy are managed in a grid-based manner, including: The topological relationship of the mesh topology structure is marked, wherein no cross-mesh structure topology = 0, cross-mesh structure topology and neighbor relationship = 1, and cross-mesh structure topology and group relationship = 2.

3. A spectrum occupancy identification and ad hoc network optimization method according to claim 1, characterized in that: Construct a Markov prediction model to predict the spectrum occupancy probability for each grid, including: Obtaining the probability of historical spectrum occupancy information of each grid in the wireless network, and constructing a Markov prediction model through the probability of historical spectrum occupancy information of each grid in the wireless network; Obtain the current spectrum occupancy information of each grid in the wireless network to increase the transfer probability and the current spectrum occupancy information to reduce the transfer probability; The current spectrum occupancy information increase transition probability and the current spectrum occupancy information decrease transition probability of each grid are input into the Markov prediction model to predict the next spectrum occupancy information increase probability and the next spectrum occupancy information decrease probability.

4. A spectrum occupancy identification and ad hoc network optimization method according to claim 1, characterized in that: The Ising prediction model is constructed to correspond a spectrum occupancy probability to each grid, including: Obtain spectrum occupancy information of each grid in the wireless network; An energy function corresponding to the spectrum occupancy information of each grid in the wireless network is defined according to the energy function corresponding to the spectrum occupancy information of each grid; In the Ising prediction model, a spectrum occupancy probability is assigned to each grid according to the probability distribution of spectrum occupancy information, the spectrum occupancy information of each grid, and the energy function corresponding to the spectrum occupancy information of each grid.

5. The spectrum occupancy identification and ad hoc network optimization method according to claim 1, characterized in that: Determine the average latency in an ad hoc network, including: Analyze the topology of ad hoc networks; Receive data from each ad hoc network through the log channel; Extract all self-organizing network data in the self-organizing network topology relationship; Query each ad hoc network delay data from the ad hoc network data; The average delay of each ad hoc network is obtained by weighted averaging the delay data of each ad hoc network.

6. A spectrum occupancy identification and ad hoc network optimization method according to claim 1, characterized in that: Determine the amount of network jitter, including: Building a distributed training topology-aware system The distributed training topology awareness system traverses the network jitter log of the self-organizing network composed of all possible neighbor relationships or group relationships recorded by the topology relationship mark; The number of network jitters is determined based on the traversal results.

7. A spectrum occupancy identification and ad hoc network optimization method according to claim 1, characterized in that: The method of selecting the optimal self-organizing network according to the average delay of the optimal self-organizing network and the number of network jitters to obtain the optimal self-organizing network includes: The self-organizing network with the smallest average delay and the smallest network jitter number is selected as the preferred self-organizing network.

8. A spectrum occupancy identification and ad hoc network optimization method, characterized in that: include: Grid management module: used to manage wireless networks with identified spectrum occupancy in a grid-based manner; Judgment module: used to judge the size of the interference range of the wireless network and the preset range; A construction module: used for constructing a Markov prediction model to predict the spectrum occupancy probability of each grid if the judgment result is that the interference range of the wireless network is smaller than the preset range; If the judgment result is that the interference range of the wireless network is greater than or equal to the preset range, constructing an Ising prediction model for the spectrum occupancy probability corresponding to each grid; Self-organizing module: when the network environment is abnormal, it uses random domain method to self-organize in the wireless network grid to obtain the self-organizing network to be optimized; Determination module: used to determine the average delay and network jitter times of the self-organizing network to be optimized; Screening module: used to screen the self-organizing networks to be optimized according to the average delay of the self-organizing networks to be optimized and the number of network jitters to obtain the preferred self-organizing network.

9. An electronic device, characterized in that: include: Processor and memory; The processor is used to execute the spectrum occupancy identification and self-organizing network optimization method as described in any one of claims 1 to 7 by calling the program or instruction stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or instruction, and the program or instruction enables a computer to execute a spectrum occupancy identification and self-organizing network optimization method as described in any one of claims 1 to 7.

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