Spectrum sensing data fusion method, apparatus, device and medium

Abstract

The application discloses a spectrum sensing data fusion method, device, equipment and medium. The method is applied to a cognitive radio network, and comprises the following steps: acquiring sensing data of communication states of a primary user of a plurality of sensing users by a network fusion center; determining updated spectrum sensing data according to a consistency fusion rate and a sensing state in a first time slot based on a plurality of time slots divided by a sliding window, wherein the sensing state is whether the sensing user performs collaborative sensing with adjacent sensing users; determining an updated reputation value according to an initial reputation value and the sensing state; determining a malicious user by removing the sensing user with the updated reputation value smaller than the initial reputation value; and in the case that the updated spectrum sensing data of the plurality of sensing users does not satisfy a consistency condition, returning to the step of determining the updated spectrum sensing data in the next time slot until the consistency condition is satisfied, and obtaining a fusion result. According to the embodiment of the application, the quality of the fused data can be improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of cognitive radio, and particularly relates to a spectrum sensing data fusion method and device, equipment and a medium. BACKGROUND

[0002] In a complex electromagnetic environment, wireless communication is susceptible to interference from complex geographical conditions and electromagnetic radiation, and faces a variety of frequency using devices and their difficult-to-coordinate frequency using behaviors. Therefore, the information sensed by multiple nodes needs to be effectively integrated.

[0003] However, for traditional data fusion methods, if a malicious user's fake data attack is encountered, the authenticity of the data cannot be distinguished, resulting in incomplete or incorrect final fusion data, which affects the quality of the fusion data. SUMMARY

[0004] The embodiments of the present application provide a spectrum sensing data fusion method, device, equipment and medium to at least solve the problem of fusion data quality affected by malicious user's fake data attack in related technologies.

[0005] In a first aspect, the embodiments of the present application provide a spectrum sensing data fusion method applied to a fusion center of a cognitive radio network, the cognitive radio network including a primary user and a plurality of sensing users;

[0006] The method comprises:

[0007] Obtaining spectrum sensing data of the plurality of sensing users, the spectrum sensing data indicating a communication state of the primary user;

[0008] For each sensing user, in a first time slot of a plurality of time slots obtained by pre-dividing based on a sliding window, determining updated spectrum sensing data according to a pre-set consistency fusion rate and a sensing state, the sensing state being whether the sensing user cooperatively senses with a sensing user adjacent to itself;

[0009] Determining an updated reputation value of the sensing user according to a pre-set initial reputation value and the sensing state;

[0010] Determining the sensing user with the updated reputation value less than the initial reputation value as a malicious user, and removing the malicious user from the plurality of sensing users;

[0011] In a case where the updated spectrum sensing data of the plurality of sensing users does not satisfy the consistency condition, returning the step of determining the updated spectrum sensing data according to the preset consistency fusion rate and the sensing state in a next time slot of the first time slot until the updated spectrum sensing data of the plurality of sensing users satisfies the consistency condition, to obtain a spectrum sensing data fusion result.

[0012] In a second aspect, the embodiments of the present application provide a spectrum sensing data fusion device, applied to a fusion center of a cognitive radio network, the cognitive radio network comprising a primary user and a plurality of sensing users.

[0013] The device comprises:

[0014] An acquisition module, configured to acquire spectrum sensing data of the plurality of sensing users, the spectrum sensing data indicating a communication state of the primary user;

[0015] A first determination module, configured to, for each sensing user, determine updated spectrum sensing data according to a preset consistency fusion rate and a sensing state in a first time slot of a plurality of time slots pre-divided based on a sliding window, the sensing state being whether the sensing user performs collaborative sensing with a sensing user adjacent to the sensing user.

[0016] A second determination module, configured to determine an updated reputation value of the sensing user according to a preset initial reputation value and the sensing state.

[0017] A removal module, configured to determine a malicious user from the plurality of sensing users by determining that a sensing user has the updated reputation value less than the initial reputation value.

[0018] A fusion module, configured to, in a case where the updated spectrum sensing data of the plurality of sensing users does not satisfy the consistency condition, return the step of determining the updated spectrum sensing data according to the preset consistency fusion rate and the sensing state in a next time slot of the first time slot until the updated spectrum sensing data of the plurality of sensing users satisfies the consistency condition, to obtain a spectrum sensing data fusion result.

[0019] In a third aspect, the embodiments of the present application provide an electronic device, comprising a processor and a memory storing computer program instructions; the processor implements the steps of the spectrum sensing data fusion method in any one of the embodiments of the first aspect when executing the computer program instructions.

[0020] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium, and the computer readable storage medium stores computer program instructions. When the computer program instructions are executed by a processor, the steps of the spectrum sensing data fusion method in any one of the embodiments of the first aspect are implemented.

[0021] The spectrum sensing data fusion method, the apparatus, the device and the medium provided by the embodiments of the present application can obtain sensing data of a communication state of a primary user by a network fusion center in a cognitive radio network, distinguish malicious users in multiple sensing users by using a reputation mechanism to remove abnormal data, and obtain a data fusion result of multi-user cooperative spectrum sensing by using a weighted consistency data fusion method, so as to improve the quality of the fused data, and thus more comprehensively and accurately represent the state of the sensing object (i.e., the primary user). In addition, the sensing processes of all sensing users are processed in segments based on a sliding window in the processing of the multiple sensing users, so that the data fusion efficiency is improved. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly introduced. For those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0023] Figure 1 is a flowchart of a spectrum sensing data fusion method provided by the embodiments of the present application;

[0024] Figure 2 is an architecture diagram of a cognitive radio network provided by the embodiments of the present application;

[0025] Figure 3 is a structural diagram of a spectrum sensing data fusion apparatus provided by the embodiments of the present application;

[0026] Figure 4 is a structural diagram of an electronic device provided by the embodiments of the present application.

[0027] REFERENCE SIGNS

[0028] The spectrum sensing data fusion apparatus 300, the acquisition module 301, the first determination module 302, the second determination module 303, the removal module 304, the fusion module 305,

[0029] The electronic device 400, the processor 401, the memory 402, the communication interface 403, and the bus 410. DETAILED DESCRIPTION

[0030] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0031] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0032] In complex electromagnetic environments, wireless communication is susceptible to interference from complex geographical conditions and electromagnetic radiation. It also faces the challenge of diverse frequency-using devices and their difficult-to-coordinate frequency-using behaviors. Single-node sensing technologies have limitations; they can only perceive their own state and surrounding environment, unable to perceive the state and environment of other nodes. This leads to a lack of global information in cognitive radio systems, thus affecting the system's decision-making and control effectiveness. Therefore, multi-point cooperative sensing technology has been proposed. Multi-point cooperative sensing refers to the process where, under given communication conditions, multiple intelligent agents with limited sensing capabilities, based on a specific group cooperation strategy, exchange sensing information with each other.

[0033] In order to have a comprehensive understanding of the environment, it is necessary to effectively integrate the information perceived by multiple nodes. Among them, the information perceived by each node is regarded as heterogeneous data, which has different data structures, fingerprint characteristics, etc. The fusion of heterogeneous data can effectively utilize and process these heterogeneous data. At the same time, due to its ability to effectively reduce the data transmission volume of the wireless network, the energy consumption of the entire network is greatly reduced, effectively prolonging the life cycle of the entire network. Big data technology provides an efficient tool for penetrating the surface and reaching the essence. The massive amount of data rich in information provides a bottom foundation for revealing emerging themes. However, big data often has different sources, different media, and even different structures. The maximum expansion of the data category helps to improve the analysis capability, but the inherent conflict of data structure also brings challenges to analysis modeling. Data fusion can effectively utilize and process these heterogeneous data.

[0034] In order to adapt to the needs of various data fusion, various data fusion models are proposed. Roughly, it can be divided into two categories: one is a functional fusion model, which is mainly constructed by the order of the function realized by the data fusion in each node; the other is a data fusion model, which is mainly constructed by the extraction of data in data fusion.

[0035] During the processing of data fusion, with the elimination of redundant information, the data transmission volume in the network will inevitably be reduced. However, due to the complex and changeable wireless network communication environment, the perception data sometimes will appear loss, abnormality, data delay, and data quality will be inevitably affected. Therefore, the fusion technology related to the fusion quality also emerges as the times require.

[0036] Data quality refers to the correctness and integrity of the perception data received by the sink node. Under the condition of meeting the minimum limit of delay and energy, the maximum improvement of the fusion data quality is the core of this kind of fusion algorithm. However, for the traditional data fusion method, if it encounters a malicious user's fake data attack, due to the inability to distinguish the authenticity of the data, the final fusion data will be incomplete or incorrect, affecting the quality of the fusion data.

[0037] In order to solve the problems of related technologies, the embodiment of the present application provides a spectrum sensing data fusion method, device, equipment and medium.

[0038] The spectrum sensing data fusion method provided by the embodiment of the present application will be described in detail in combination with the specific embodiments and their application scenarios.

[0039] Figure 1 The flowchart of the spectrum sensing data fusion method 100 of the embodiment of the present application is shown.

[0040] As Figure 1As shown, the spectrum sensing data fusion method 100 can specifically include the following steps:

[0041] S101, acquiring spectrum sensing data of a plurality of sensing users, the spectrum sensing data indicating a communication state of a primary user;

[0042] S102, for each of the sensing users, based on a plurality of time slots pre-divided by a sliding window, in a first time slot of the plurality of time slots, determining updated spectrum sensing data according to a pre-set consistency fusion rate and a sensing state, the sensing state being whether the sensing user performs collaborative sensing with a sensing user adjacent to the sensing user;

[0043] S103, determining an updated reputation value of the sensing user according to a pre-set initial reputation value and the sensing state;

[0044] S104, determining a malicious user from the plurality of sensing users, if the updated reputation value of the sensing user is less than the initial reputation value;

[0045] S105, in a case where the updated spectrum sensing data of the plurality of sensing users does not satisfy a consistency condition, returning to the step of determining the updated spectrum sensing data according to the pre-set consistency fusion rate and the sensing state in a next time slot of the first time slot, until the updated spectrum sensing data of the plurality of sensing users satisfies the consistency condition, to obtain a spectrum sensing data fusion result.

[0046] It should be noted that the embodiments of the present application are applied to a fusion center of a cognitive radio network. Referring to Figure 2 , it is a schematic diagram of an architecture of a cognitive radio network. As shown in the figure, Figure 2 The cognitive radio network includes a primary user (main user) and a plurality of sensing users. Among them, the plurality of sensing users include a small number of malicious users and a large number of honest users.

[0047] Therefore, in the cognitive radio network, the network fusion center acquires sensing data of a plurality of sensing users on the communication state of a primary user respectively, uses a reputation mechanism to distinguish malicious users in the plurality of sensing users to remove abnormal data, and uses a weighted consistency data fusion method to obtain a data fusion result of multi-user collaborative spectrum sensing, which can improve the quality of the fusion data, so as to more comprehensively and accurately represent the state of the sensing object (i.e. the primary user); and in the processing of the plurality of sensing users, the sensing processes of all the sensing users are processed in segments based on a sliding window, which can improve the data fusion efficiency.

[0048] The specific implementation of each of the above steps will be described below.

[0049] In some embodiments, in S101, the spectrum sensing data may include the energy vector of the signal of the primary user obtained by spectrum sensing of the primary user, which can characterize the communication state of the primary user, so as to judge the licensed spectrum.

[0050] It should be noted that since there is one primary user and C sensing users in the cognitive radio network, and among the sensing users, there are M malicious users and H honest users (M < C), therefore, for the spectrum sensing data uploaded by multiple sensing users received by the fusion center, as Figure 2 shown, the honest users send the actual data energy sensed to the fusion center, while the malicious users make corresponding decisions according to their own purposes and forge the energy vector to send to the fusion center. Since the fusion center does not know any information related to the malicious users, such as their number and identity, it can only observe the energy vector sent by the users at the corresponding time points. Therefore, it is necessary to respond to such attacks to avoid being greatly affected by the subsequent data fusion results.

[0051] For the fusion center, the signal received from the sensing users can be formulated as a binary hypothesis testing problem, that is, the following formula (1):

[0052]

[0053] where, x i (t) represents the signal received by the fusion center; n i (t) represents Gaussian white noise with a mean of 0 and a variance of ; h i (t) represents the channel gain at time t; s i (t) represents the signal sent by the sensing users; H0 represents that the primary user is in an idle state; H1 represents that the primary user is occupying the licensed spectrum, that is, in an active state.

[0054] Furthermore, define the sensing sampling interval as τ and the sampling rate as f s , then the sampling point k = τf s , then when the state of the primary user is different, the energy vector observed at each sensing user can be expressed as the following formula (2):

[0055]

[0056] Then, define the set of the energy vectors transmitted by all sensing users received by the fusion center

[0057] It should be understood that when the malicious users send forged data, the actual energy mean e of the energy vector set meansCompared with the ideal energy mean e ideal An offset will be generated. Wherein, the ideal energy mean e ideal And the actual energy mean e means Can be expressed as formula (3) and (4) respectively:

[0058]

[0059] In addition, in some embodiments, for the spectrum sensing data of a plurality of sensing users, a spectrum sensing feature vector is obtained by using a mean shift clustering algorithm. Specifically, the minimum value of a class function is calculated to obtain an initial center point, the class function being constructed according to the spectrum sensing data of the plurality of sensing users; steps A to C are iteratively executed until the error between the center point updated in the current round and the center point updated in the last round is less than a preset threshold, to obtain the spectrum sensing feature vector.

[0060] Step A: according to the spectrum sensing data of the plurality of sensing users and the initial center point, a neighborhood corresponding to the initial center point and a bandwidth is determined, the bandwidth being determined according to the spectrum sensing data of the plurality of sensing users and the initial center point; Step B: according to the number of spectrum sensing data in the neighborhood, the spectrum sensing data and the initial center point, a mean shift vector is determined; Step C: according to the initial center point and the mean shift vector, an updated center point is determined.

[0061] In specific implementation, the class function can be defined as formula (5) as follows:

[0062]

[0063] In this way, by minimizing the class function, a robust initial center point e medoids Can be obtained, which can be expressed as formula (6)

[0064]

[0065] In specific implementation, the neighborhood can be determined according to formula (7) and (8) as follows:

[0066]

[0067] Wherein, S h (e i ) represents the neighborhood; e meanshift represents the initial center point; r represents the bandwidth; e i represents the spectrum sensing data of the i-th sensing user; and C represents the number of sensing users.

[0068] That is, the initial center point is taken as the initial energy vector e meanshift = e medoidsand define a neighborhood S meanshift with e h as the center and r as the bandwidth i ).

[0069] Further, the mean shift vector u can be derived, which is expressed as follows in equation (9):

[0070]

[0071] where B represents the number of spectrum sensing data in the neighborhood (e.g. the number of energy vectors in the neighborhood). Then, the initial center point can be updated, i.e.

[0072] e meanshift = e meanshift + u (10)

[0073] It can be understood that the energy center point is iteratively updated until a pre-set convergence condition is reached, i.e. the energy center points obtained by two consecutive iterations are controlled within a certain error range, that is, the error between the center point updated in the current round and the center point updated in the last round is less than a pre-set threshold. Wherein, the pre-set threshold can be set according to the specific experimental environment, application scenario and demand, but it must be small enough to ensure stability.

[0074] It should be noted that the energy center point is used as a feature vector for spectrum sensing, and the subsequent consistency requirement is that the error between the energy vector and the energy center point is less than the defined bandwidth r. In this way, based on the spectrum sensing feature vector, the error between the actual mean and the ideal mean is avoided, and the robustness to fake data attacks is achieved.

[0075] Further, in some embodiments, in S102, based on the pre-set consistency fusion rate and the sensing state, the updated spectrum sensing data can be determined according to the following equations (11), (12) and (13):

[0076]

[0077] where λ i represents the consistency factor of the i-th sensing user; V hi represents the set of honest neighboring sensing users of the i-th sensing user; δ represents the consistency fusion rate; represents the sensing state of the m-th sensing user; β0represents the initial reputation factor.

[0078] In some embodiments, in S103, based on the pre-set initial reputation value and the sensing state, the updated reputation value of the sensing user can be determined according to the following equation (14):

[0079]

[0080] where P init represents the initial reputation value; N i,j,1 (t) represents the set of neighboring sensing users selected by the i-th sensing user for collaborative sensing at time t, and N i,j,0 (t) represents the set of non-selected sensing users.

[0081] Then, the sliding window-based reputation mechanism divides the window into K time slots, and in each time slot, the state of the sensing user can be shown in the following formula (15). Thus, the sensing state is whether the sensing user collaborates with the neighboring sensing users or not.

[0082]

[0083] It can be seen that the more the sensing user collaborates, the higher the updated reputation value is, which indicates that the sensing result of the sensing user is more reliable, and thus the sensing user will not be wrongly decided as a malicious user. When a malicious user sends a fake energy vector to the fusion center, the reputation value of the malicious user will change after several iterations, i.e., the updated reputation value is less than the initial reputation value. Thus, in S104, the fusion center no longer trusts the data sent by the malicious user, so as to achieve the effect of distinguishing the malicious user. Then, all the sensing users except the malicious user can update their own reputation values after each iteration, and each iteration update is related to the neighboring sensing users.

[0084] As an optional embodiment, in S105, the consistency condition is that the error between the updated spectrum sensing data of the plurality of sensing users and the spectrum sensing feature vector is less than a bandwidth r, and the spectrum sensing feature vector is determined by using a mean shift clustering algorithm on the spectrum sensing data of the plurality of sensing users.

[0085] In some optional embodiments, the spectrum sensing data fusion result can be determined according to the following formula (16):

[0086]

[0087] where e i (k') represents the updated spectrum sensing data of the i-th sensing user at the k'-th iteration; V hi represents the set of neighboring sensing users of the i-th sensing user.

[0088] In this way, the spectrum sensing data (i.e., the transmission energy value) can reach the consistency requirement within a limited number of iterations, that is, by using the weighted consensus data fusion model based on the historical reputation factor, the cooperative cognitive network after iteration reaches consensus.

[0089] It is to be understood that the foregoing description is directed to embodiments of the application. Various embodiments can be devised without departing from the scope of the application. Some of the embodiments of the present application are described in the following clauses. In some cases, the acts or steps in a claim may be performed in an order different from the order described above and / or below and still achieve the desired result. Also, the processes depicted in the accompanying figures do not necessarily require the particular order shown or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0090] Based on the same technical concept, the present application also provides a spectrum sensing data fusion device 300 corresponding to any of the above-mentioned embodiment methods. Moreover, the spectrum sensing data fusion device 300 is applied to a fusion center of a cognitive radio network, which includes a primary user and a plurality of sensing users.

[0091] As shown in Figure 3 the spectrum sensing data fusion device 300 can include:

[0092] an acquisition module 301, configured to acquire spectrum sensing data of the plurality of sensing users, the spectrum sensing data indicating a communication state of the primary user;

[0093] a first determination module 302, configured to, for each of the sensing users, in a first time slot of a plurality of time slots obtained by pre-dividing based on a sliding window, determine updated spectrum sensing data according to a pre-configured consistency fusion rate and a sensing state, the sensing state being whether the sensing user cooperates with a sensing user adjacent to the sensing user in cooperative sensing;

[0094] a second determination module 303, configured to determine an updated reputation value of the sensing user according to a pre-configured initial reputation value and the sensing state;

[0095] a removal module 304, configured to determine a malicious user from the plurality of sensing users according to the updated reputation value being less than the initial reputation value, and remove the malicious user from the plurality of sensing users;

[0096] a fusion module 305, configured to, in a case where the updated spectrum sensing data of the plurality of sensing users does not satisfy a consistency condition, return to the step of determining the updated spectrum sensing data according to the pre-configured consistency fusion rate and the sensing state in a next time slot of the first time slot, until the updated spectrum sensing data of the plurality of sensing users satisfies the consistency condition, to obtain a spectrum sensing data fusion result.

[0097] In some embodiments, the consistency condition is that an error between the updated spectrum sensing data of the plurality of sensing users and a spectrum sensing feature vector determined from the spectrum sensing data of the plurality of sensing users using a mean shift clustering algorithm is less than a bandwidth.

[0098] In some embodiments, the spectrum sensing data fusion device 300 further comprises a third determining module (not shown in the figure) configured to calculate an initial center point from a minimum value of a class function constructed from the spectrum sensing data of the plurality of sensing users; and iteratively perform the following steps A to C until an error between a center point updated in a current round and a center point updated in a previous round is less than a preset threshold: Figure 3

[0099] Step A: determining a neighborhood corresponding to the initial center point and a bandwidth from the spectrum sensing data of the plurality of sensing users and the initial center point, wherein the bandwidth is determined from the spectrum sensing data of the plurality of sensing users and the initial center point; Step B: determining a mean shift vector from a number of spectrum sensing data in the neighborhood, the spectrum sensing data and the initial center point; and Step C: determining an updated center point from the initial center point and the mean shift vector.

[0100] In some embodiments, the third determining module is specifically configured to determine the neighborhood according to the following formula:

[0101] S h (e i )={y|(y-e meanshift )(y-e meanshift ) T <r};

[0102]

[0103] wherein S h (e i ) represents the neighborhood; e meanshift represents the initial center point; r represents the bandwidth; e i represents the spectrum sensing data of the i-th sensing user; and C represents the number of sensing users.

[0104] In some embodiments, the first determining module 302 is specifically configured to determine the updated spectrum sensing data according to the following formula:

[0105]

[0106] wherein λ i represents a consistency factor of the i-th sensing user; and V hi ​represents a set of neighboring cognitive users of the i th cognitive user; δ represents a consistency fusion rate; represents a cognitive state of the m th cognitive user; β 0 represents an initial reputation factor.

[0107] In some embodiments, the second determining module 303 is specifically configured to determine the updated reputation value of the cognitive user according to the following formula:

[0108]

[0109] wherein P init represents an initial reputation value; N i,j,1 (t) represents a set of neighboring cognitive users of the i th cognitive user at t time for cooperative sensing.

[0110] In some embodiments, the fusion module 305 is specifically configured to determine the fusion result of the spectrum sensing data according to the following formula:

[0111]

[0112] wherein e i (k') represents the updated spectrum sensing data of the i th cognitive user at the k' th iteration; V hi represents a set of neighboring cognitive users of the i th cognitive user.

[0113] It should be noted that, for the convenience of description, the above apparatus is described in various modules according to functions. Of course, in the implementation of the present application, the functions of the modules can be implemented in the same or multiple software and / or hardware.

[0114] The apparatus of the above embodiments is used to implement the corresponding spectrum sensing data fusion method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be described here.

[0115] Based on the same technical concept, corresponding to the method of any of the above embodiments, the present application further provides an electronic device.

[0116] Figure 4 A more specific hardware structure of an electronic device is shown.

[0117] The electronic device 400 can include a processor 401 and a memory 402 storing computer program instructions.

[0118] Specifically, the processor 401 can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0119] The memory 402 can include mass storage for data or instructions. As an example and not by way of limitation, the memory 402 can include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disc (e.g., a compact disc (CD) or a digital versatile disc (DVD)), a solid-state drive (SSD), a USB drive, or a combination of two or more of these. Where appropriate, the memory 402 can include removable or non-removable (or fixed) media, where appropriate. The memory 402 can be internal or external to the integrated gateway disaster recovery device. In particular embodiments, the memory 402 is non-volatile, solid-state memory.

[0120] In particular embodiments, the memory can include read-only memory (ROM), random access memory (RAM), magnetic disk storage mediums, optical storage mediums, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software that, when executed (by one or more processors), is operable to

[0121] The processor 401 implements the spectrum sensing data fusion method of any of the above embodiments by reading and executing computer program instructions stored in the memory 402.

[0122] In some examples, the electronic device 400 can also include a communication interface 403 and a bus 410. As shown, the processor 401, the memory 402, and the communication interface 403 are connected by the bus 410 and complete communication among each other. Figure 4

[0123] The communication interface 403 is mainly used to realize the communication between the modules, devices, units and / or equipment in the embodiments of the present application.

[0124] ​Bus 410 includes a hardware, software, or both that couples components of the online data traffic metering device to each other. As an example but not a limitation, bus 410 can include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand (IB) interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or another suitable bus or a combination of two or more of these. Where suitable, bus 410 can include one or more buses. Although particular buses have been described and illustrated, the present application contemplates any suitable bus or interconnect.

[0125] By way of example, electronic device 400 can be a mobile phone, a tablet computer, a notebook computer, a handheld computer, an on-board electronic device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc.

[0126] Based on the same technical concept, the present application also provides a non-transitory computer-readable storage medium corresponding to any of the above-mentioned embodiment methods. The computer-readable storage medium stores computer program instructions; the computer program instructions are executed by a processor to implement any of the above-mentioned spectrum sensing data fusion methods. Examples of the computer-readable storage medium include non-transitory computer-readable storage media, such as a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, etc.

[0127] Based on the same technical concept, the present application also provides a computer program product including computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processor to perform the spectrum sensing data fusion method. Corresponding to the execution subject of each step in each embodiment of the spectrum sensing data fusion method, the processor performing the corresponding step can belong to the corresponding execution subject.

[0128] It is to be understood that the application is not limited to particular configurations and processes described herein and shown in the drawings. The detailed description is not to be taken in a limiting sense, and the scope of the present application is defined by the appended claims. In the above embodiments, several specific steps are described and illustrated in order to provide a thorough understanding of the present application. However, the process of the present application can be carried out in some orders of the steps, under some conditions, and using some alternatives, all without departing from the spirit and scope of the application.

[0129] The functions of the elements of described above and illustrated in the figures can be implemented as hardware, software, firmware or any combination thereof. When implemented in hardware, for example, a circuit such as an application specific integrated circuit (ASIC) can be used. When implemented in software, the elements of the application are the program or code segments to perform the necessary tasks. The program or code segments can be stored in a machine readable medium, or transmitted by a carrier wave as data signals and can be downloaded into the machine readable medium. The machine readable medium can include any medium that can store or transfer information. Examples of the machine readable medium include an electronic circuit, a semiconductor memory device, a ROM, a flash memory, an erasable ROM (EROM), a floppy diskette, a CD-ROM, an optical disk, a hard disk, a fiber optic medium, a radio frequency (RF) link, etc. The code segments can be downloaded via the Internet, an intranet, etc.

[0130] It is also to be understood that the example embodiments described herein are based on a series of steps or apparatuses to describe some methods or systems. However, the present application is not limited to the order of the steps described above, that is, the steps can be performed in the order mentioned in the embodiments, or in an order different from the embodiments, or several steps can be performed simultaneously.

[0131] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. Alternatively, computer program implemented steps can be implemented by one or more of the components of the system described above. The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. Alternatively, computer program implemented steps can be implemented by one or more of the components of the system described above.

[0132] The above merely describes a specific implementation of the present application. Those skilled in the art can clearly understand the specific working processes of the system, modules and units described above for the convenience and brevity of description, and the corresponding processes in the foregoing method embodiments can be referred to, which will not be described herein again. It should be understood that the protection scope of the present application is not limited in this way, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed in the present application, and these modifications or replacements should be covered in the protection scope of the present application.

Claims

1. A method of spectrum sensing data fusion, the method comprising: A fusion center applied to a cognitive radio network, the cognitive radio network comprising a primary user and a plurality of sensing users; The method comprises: obtaining spectrum sensing data of the plurality of sensing users, the spectrum sensing data indicating a communication state of the primary user; for each of the sensing users, based on a plurality of time slots obtained by pre-dividing a sliding window, in a first time slot of the plurality of time slots, determining updated spectrum sensing data according to a pre-configured consistency fusion rate and a sensing state, the sensing state being whether the sensing user cooperates with a sensing user adjacent to the sensing user in cooperative sensing; determining an updated reputation value of the sensing user according to a pre-configured initial reputation value and the sensing state; determining a malicious user whose updated reputation value is less than the initial reputation value from the plurality of sensing users, and removing the malicious user from the plurality of sensing users; in a case where the updated spectrum sensing data of the plurality of sensing users does not satisfy a consistency condition, returning to the step of determining the updated spectrum sensing data according to the pre-configured consistency fusion rate and the sensing state in a next time slot of the first time slot until the updated spectrum sensing data of the plurality of sensing users satisfies the consistency condition, to obtain a spectrum sensing data fusion result.

2. The method of claim 1, wherein the consistency condition is that an error between the updated spectrum sensing data of the plurality of sensing users and a spectrum sensing feature vector is less than a bandwidth, the spectrum sensing feature vector being determined by a mean shift clustering algorithm on the spectrum sensing data of the plurality of sensing users.

3. The method of claim 1, wherein, in a case where the updated spectrum sensing data of the plurality of sensing users does not satisfy a consistency condition, returning to the step of determining the updated spectrum sensing data according to the pre-configured consistency fusion rate and the sensing state in a next time slot of the first time slot until the updated spectrum sensing data of the plurality of sensing users satisfies the consistency condition, to obtain a spectrum sensing data fusion result, the method further comprises: calculating a minimum value of a class function to obtain an initial center point, the class function being constructed according to the spectrum sensing data of the plurality of sensing users; iteratively performing the following steps A to C until an error between a current round of updated center point and a last round of updated center point is less than a pre-configured threshold value: Step A: determining a neighborhood corresponding to the initial center point and a bandwidth according to the spectrum sensing data of the plurality of sensing users and the initial center point, the bandwidth being determined according to the spectrum sensing data of the plurality of sensing users and the initial center point; Step B: determining a mean shift vector according to a number of spectrum sensing data in the neighborhood, the spectrum sensing data and the initial center point; Step C: determining an updated center point according to the initial center point and the mean shift vector.

4. The method of claim 1, wherein, the determining of the updated spectrum sensing data according to the pre-configured consistency fusion rate and the sensing state comprises: determining the updated spectrum sensing data according to the following formula: where λ i represents the consistency factor of the ith perceived user; V hi represents the set of neighboring perceived users of the ith perceived user; δ represents the consistency fusion rate; represents the perceived state of the mth perceived user; and β0represents the initial reputation factor.

5. The method of claim 1, wherein, the determining of the updated reputation value of the sensing user according to the pre-configured initial reputation value and the sensing state comprises: The updated reputation value of the sensing user is determined according to the following formula: where P init represents the initial reputation value; N i,j,1 (t) represents the set of neighboring sensing users that the ithsensing user collaboratively senses at time t.

6. The method of claim 1, wherein, If the updated spectrum sensing data of the multiple sensing users does not satisfy the consistency condition, the step of determining the updated spectrum sensing data according to the preset consistency fusion rate and the sensing state is returned in the next time slot of the first time slot until the updated spectrum sensing data of the multiple sensing users satisfies the consistency condition, and a fusion result of the spectrum sensing data is obtained, including: The fusion result of the spectrum sensing data is determined according to the following formula: where e i (k') represents the updated spectrum sensing data of the i-th sensing user at the k'-th iteration; V hi represents the set of neighboring sensing users of the i-th sensing user.

7. The method of claim 3, wherein, The neighborhood corresponding to the initial center point and the bandwidth is determined according to the spectrum sensing data of the multiple sensing users and the initial center point, including: The neighborhood is determined according to the following formula: S h (e i )={y(y-e meanshift )(y-e meanshift ) T <r}; where S h (e i ) denotes the neighborhood; e meanshift denotes the initial center point; r denotes the bandwidth; e i denotes the spectrum sensing data of the i-th sensing user; and C denotes the number of sensing users.

8. A spectrum sensing data fusion apparatus, characterized by A fusion center applied to a cognitive radio network, the cognitive radio network including a primary user and multiple sensing users; The device includes: An acquisition module is configured to acquire spectrum sensing data of the multiple sensing users, the spectrum sensing data indicating a communication state of the primary user; A first determination module is configured to, for each sensing user, determine updated spectrum sensing data according to a preset consistency fusion rate and a sensing state in a first time slot of multiple time slots pre-divided based on a sliding window, the sensing state being whether the sensing user cooperatively senses with a sensing user adjacent to the sensing user; A second determination module is configured to determine an updated reputation value of the sensing user according to a preset initial reputation value and the sensing state; A removal module is configured to determine a malicious user as a sensing user whose updated reputation value is less than the initial reputation value, and remove the malicious user from the multiple sensing users; A fusion module is configured to, if the updated spectrum sensing data of the multiple sensing users does not satisfy a consistency condition, return the step of determining the updated spectrum sensing data according to the preset consistency fusion rate and the sensing state in a next time slot of the first time slot until the updated spectrum sensing data of the multiple sensing users satisfies the consistency condition, and obtain a fusion result of the spectrum sensing data.

9. An electronic device, comprising: The device includes a processor and a memory storing computer program instructions; and the processor invokes the computer program instructions to implement the spectrum sensing data fusion method in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer program instructions, and the computer program instructions are invoked by the processor to implement the spectrum sensing data fusion method in any one of claims 1-7.

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