Interference elimination method and system for sensor network with non-uniform structure

Abstract

The invention discloses an interference elimination method and system for a sensor network with a non-uniform structure. The method comprises the following steps: calculating an optimal clustering number in the sensor network with non-uniform distribution; calculating an average distance in each cluster, and optimizing selection of an initial clustering center according to the average distance to obtain an improved K-means clustering algorithm; analyzing various indexes selected by a cluster head according to an entropy weight method, and adding a signal strength factor to determine the cluster head; establishing a system model in which relay nodes simultaneously transmit and receive data; establishing a general model of a self-interference channel; listing a filter tap weight vector function; and iteratively estimating a self-interference channel factor, and eliminating the self-interference according to the self-interference channel factor. And the reliability of a node communication link and the utilization efficiency of spectrum resources are remarkably improved, so that the anti-interference performance and the communication efficiency of the whole network system are enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless sensors, and in particular relates to an interference elimination method and system for a sensor network with a non-uniform structure. Background Art

[0002] Wireless Sensor Networks (WSN) is a self-organizing network system composed of many micro sensor nodes densely and unevenly deployed in the monitoring area. The nodes are interconnected by wireless communication. Its core function is to collaboratively sense, collect and process target information within the network coverage, and then transmit the processing results to the observer. In complex scenarios where sensor nodes are densely and unevenly distributed, clustering strategies are usually adopted. Specifically, cluster head nodes are elected. These nodes are responsible for collecting data within the cluster and exchanging data with other cluster heads or aggregation nodes through multi-hop communication to achieve data integration and efficient transmission. This process not only gives full play to the advantages of wireless communication technology, but also demonstrates the high flexibility and excellent adaptability of wireless sensor networks in complex environments.

[0003] However, this communication mode also brings a challenge, namely energy consumption and interference issues.

[0004] The energy consumption problem refers to the fact that the power of sensor nodes cannot be repeatedly supplied. When the node power is exhausted, the node fails. When all nodes in the network run out of energy, the entire network fails. Therefore, how to reduce the energy consumption of sensor nodes and thus extend the use time of the network is one of the hot issues in current sensor network research.

[0005] There are two main types of interference problems, namely external interference and self-interference. External interference refers to the fact that during the operation of the wireless sensor network, it is also interfered by factors outside the network. This interference will cause the quality of the signal received by the sensor node to deteriorate, increase the bit error rate, and may even cause the interruption of the communication link. Self-interference refers to the fact that when the relay node receives and forwards data at the same time, strong self-interference may occur because the transmitted signal and the received signal are in the same frequency band and overlap in time. This self-interference may drown out the signals expected to be received from other nodes, affecting the communication quality, and even causing data loss or communication failure.

[0006] Therefore, when designing and implementing wireless sensor networks, while considering energy consumption balance, effective interference elimination technology must be adopted to ensure the communication quality between nodes and the stability of the network. Summary of the invention

[0007] The present invention provides an interference elimination method and system for a non-uniform structure sensor network, which is used to solve the technical problem that self-interference may drown out signals expected to be received from other nodes, affect communication quality, and even cause data loss or communication failure.

[0008] In a first aspect, the present invention provides an interference elimination method for a non-uniform structure sensor network, comprising:

[0009] Computing the optimal number of clusters in non-uniformly distributed sensor networks;

[0010] Calculate the average distance avgdist within each cluster, optimize the selection of the initial cluster center according to the average distance avgdist, and obtain an improved K-means clustering algorithm;

[0011] According to the entropy weight method, the various indicators of cluster head selection are analyzed, and the signal strength factor is added to determine the cluster head;

[0012] Establish a system model in which relay nodes send and receive data simultaneously;

[0013] Establish a general model of self-interfering channels;

[0014] List the filter tap weight vector function;

[0015] The self-interference channel factor is estimated iteratively, and the self-interference is eliminated according to the self-interference channel factor.

[0016] In a second aspect, the present invention provides an interference elimination system for a non-uniform structure sensor network, comprising:

[0017] A calculation module configured to calculate an optimal number of clusters in a non-uniformly distributed sensor network;

[0018] A selection module is configured to calculate an average distance avgdist within each cluster, and optimize the selection of initial cluster centers according to the average distance avgdist to obtain an improved K-means clustering algorithm;

[0019] A determination module is configured to analyze various indicators of cluster head selection according to the entropy weight method and add a signal strength factor to determine the cluster head;

[0020] A first building block is configured to establish a system model in which a relay node simultaneously sends and receives data;

[0021] a second building block configured to establish a general model of a self-interference channel;

[0022] an enumeration module configured to list filter tap weight vector functions;

[0023] The processing module is configured to iteratively estimate a self-interference channel factor and eliminate self-interference according to the self-interference channel factor.

[0024] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the interference elimination method for a non-uniform structure sensor network according to any embodiment of the present invention.

[0025] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of the interference elimination method for a non-uniform structure sensor network according to any embodiment of the present invention.

[0026] The interference elimination method and system of the non-uniform structure sensor network of the present application, in the sensor network with non-uniform deployment, adds the signal strength evaluation index to elect the cluster head, which can effectively reduce the external interference when the nodes in the cluster send data to the cluster head. At the same time, an adaptive self-interference elimination algorithm is introduced, which significantly improves the reliability of the node communication link and the utilization efficiency of the spectrum resources, thereby enhancing the anti-interference performance and communication efficiency of the entire network system. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 A flowchart of a method for eliminating interference in a sensor network with a non-uniform structure provided by an embodiment of the present invention;

[0029] Figure 2 A structural block diagram of an interference elimination system for a non-uniform structure sensor network provided by an embodiment of the present invention;

[0030] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] See also Figure 1 , which shows a flow chart of an interference elimination method for a non-uniform structure sensor network of the present application.

[0033] like Figure 1 As shown, the interference elimination method of the non-uniform structure sensor network specifically includes the following steps:

[0034] Step S101, calculating the optimal number of clusters in a non-uniformly distributed sensor network.

[0035] In this step, it is assumed that there are N sensor nodes in a rectangular area with a side length of M×M, and the nodes are divided into k clusters. The expression of the energy consumed by each cluster head and node in the cluster is:

[0036]

[0037] In the formula, E elec 、E da are the energy consumed for transmitting and fusing 1 bit of data, d toBs is the distance between the cluster head and the base station, d toCH is the distance between the node and the corresponding cluster head, ε fs , ε mp are the power amplifier parameters for the free space channel model and the multipath fading model respectively;

[0038] Assuming that the nodes in the network are evenly distributed and the distribution density is ρ(x, y), the distance between the node and the corresponding cluster head is expressed as:

[0039]

[0040] Calculate the energy consumption E of a single cluster in each round cluster , and calculate the total energy consumption E of all clusters in each round based on the energy consumption of a single cluster in each round total , the expression is:

[0041]

[0042] Take the derivative of k, let the first-order derivative of k equal to 0, and solve for the optimal number of clusters. The expression is:

[0043]

[0044] Where k is the optimal number of clusters for a non-uniform network.

[0045] Step S102, calculating the average distance avgdist within each cluster, optimizing the selection of the initial cluster center according to the average distance avgdist, and obtaining an improved K-means clustering algorithm.

[0046] In this step, the expression for calculating the average distance avgdist within each cluster is:

[0047]

[0048] Where d(i, j) is the distance between node i and node j.

[0049] The selection of the initial cluster center is optimized according to the average distance avgdist, and the improved K-means clustering algorithm includes:

[0050] Input data set D, optimal number of clusters k′ and maximum number of iterations;

[0051] Initialization, including:

[0052] Randomly select the first cluster center v1;

[0053] Select the point farthest from v1 from the dataset as v2;

[0054] Select the points farthest from the selected cluster center in turn until k cluster centers are selected;

[0055] For each node x_j: calculate the distance from x_j to all cluster centers;

[0056] Assign x_j to the cluster corresponding to the nearest cluster center;

[0057] Update each cluster center, including:

[0058] Calculate the mean of each node in the cluster and update the cluster center''

[0059] Cluster structure optimization, including:

[0060] For each node, calculate the node degree, residual energy and distance to the base station;

[0061] Redistribute nodes to optimize cluster structure;

[0062] Calculate the objective function J;

[0063] If the cluster center no longer changes or the maximum number of iterations is reached, the iteration is stopped;

[0064] Output the clustering results.

[0065] Step S103: Analyze various indices of cluster head selection according to the entropy weight method, and add the signal strength factor to determine the cluster head.

[0066] In this step, the evaluation index is determined, and the evaluation index includes: the key evaluation index for selecting the cluster head, including the remaining energy, node degree, node position, the total number of rounds of network operation, and the number of rounds that the current node has served;

[0067] Construct an indicator matrix, including:

[0068] Assuming there are n nodes, each node has m indicators, including RSS value, to construct the indicator matrix, specifically:

[0069]

[0070] In the formula, x nm is the mth index value of the nth node;

[0071] Standardization of indicators, including:

[0072] The indicators of different dimensions are standardized and the range standardization is adopted. The expression is:

[0073]

[0074] For negative indicators, the standardized formula is:

[0075]

[0076] The general form of the normalized matrix is:

[0077]

[0078] Calculate the entropy value of each indicator, including:

[0079] Calculate the ratio of each normalized value using the expression:

[0080]

[0081] Calculate the entropy weight of each indicator, and calculate the weight of each indicator through the entropy value. The expression is:

[0082]

[0083] Calculate the comprehensive score, the expression is:

[0084]

[0085] The node with the highest score will be selected as the cluster head.

[0086] Step S104: establishing a system model in which a relay node simultaneously sends and receives data.

[0087] Step S105: establishing a general model of the self-interference channel.

[0088] Step S106, listing the filter tap weight vector function.

[0089] Step S107, iteratively estimate the self-interference channel factor, and eliminate the self-interference according to the self-interference channel factor.

[0090] In a specific embodiment, a system model in which a relay node simultaneously sends and receives data is first established. The signal received by a node at a certain instant in time index l is given by the following formula:

[0091] y u [l] = h u [l]*s u [l]+h si [l]*S d [l]+n[l],

[0092] Among them, (*) is the convolution operator, s u [l] and s d [l] is the data sequence sent and received, n[l] is the white noise at the node, h u [l] and h si [l] are the channels for sending and receiving data, respectively, h si [l] is a self-interference channel.

[0093] Given the general model of the self-interference channel, the expression is:

[0094]

[0095] where κ is the coefficient. Note that h nlos [l] is a fixed value that depends on the geometry of the node receiving antenna, while h nlos [l] is non-line of sight (NLOS) wireless transmission, that is, the distance between the node receiving and transmitting antennas is completely blocked by obstacles, and the signal can only reach the receiving end through reflection, scattering and diffraction.

[0096] Training and estimating h si [l]:

[0097] In this phase, the relay nodes exchange training frames through the internal and external paths. In this phase, we apply the steepest descent to construct the coefficients of the Finite Impulse Response (FIR) filter taps, which is the estimate of the self-interference SI channel. The training frame contains an OFDM symbol (cyclic prefix and pilot) generated from the QPSK symbol. The output of the training phase is the self-interference SI channel estimate

[0098] Further, we consider a cost function I(w), which is a continuous differentiable function of some unknown weight vector w. This function maps the vector elements of w into real numbers. Our goal is to obtain the optimal solution w0 that satisfies the following conditions.

[0099] I(w0)≤I(w(l)for all w,

[0100] We start with an initial guess w0, and then we generate a sequence of weight vectors w(1), w(2) such that the cost function I(w) decreases in each adaptation cycle as follows:

[0101] I(w(l+1))≤I(w(l)),

[0102] Further:

[0103]

[0104] Further define time u(l) = [u(l), u(l-1), ..., u(l-L+1)] T , L is the number of filter taps and (·) T is the transposition operator. In the lth adaptation cycle, the estimated error is:

[0105] e(l)=d(l)-w H (l)u(l),

[0106] in,(·) H is the Hermitian operator, e(l) and d(l) are the estimated error and expected error respectively. If the training sequence u(l) and the expected sequence d(l) are jointly stationary, then the mean square error I(w(l)) at time index l is a quadratic function of the filter tap weight vector w.

[0107]

[0108] in, is the variance of the desired signal, p is the cross-correlation vector between the input signal u(l) and the desired signal d(l). R is the autocorrelation matrix of the input training signal u(l).

[0109] From this we can see that

[0110]

[0111] Therefore, the estimate of the output weight vector is expressed as

[0112] w(l+1)=w(l)+μ[p-Rw(l)]

[0113] It describes the formula for the steepest descent algorithm for Wiener filtering.

[0114] A necessary and sufficient condition for the steepest descent to converge or stabilize is that the step size μ must satisfy the double inequality:

[0115]

[0116] where is the maximum eigenvalue of the correlation matrix r. In the following, we denote the step size σmax as

[0117]

[0118] The output weight vector w is the self-interference channel h si .

[0119] Once the SI channel is estimated in the above stage, we subtract the estimated SI channel from the received signal. The received signal before ADC and after interference cancellation is expressed as:

[0120]

[0121] In summary, the method of the present application, for unevenly deployed sensor networks, adds a signal strength evaluation index to elect cluster heads, which can effectively reduce external interference when nodes in the cluster send data to the cluster head. At the same time, an adaptive self-interference elimination algorithm is introduced, which significantly improves the reliability of node communication links and the utilization efficiency of spectrum resources, thereby enhancing the anti-interference performance and communication efficiency of the entire network system.

[0122] See also Figure 2 , which shows a structural block diagram of an interference elimination system for a non-uniform structure sensor network of the present application.

[0123] like Figure 2 As shown, the interference cancellation system 200 includes a calculation module 210 , a selection module 220 , a determination module 230 , a first construction module 240 , a second construction module 250 , a listing module 260 and a processing module 270 .

[0124] Wherein, the calculation module 210 is configured to calculate the optimal number of clusters in a non-uniformly distributed sensor network;

[0125] A selection module 220 is configured to calculate an average distance avgdist within each cluster, and optimize the selection of initial cluster centers according to the average distance avgdist to obtain an improved K-means clustering algorithm;

[0126] The determination module 230 is configured to analyze various indicators selected by the cluster head according to the entropy weight method and add the signal strength factor to determine the cluster head;

[0127] A first building module 240 is configured to establish a system model in which a relay node simultaneously sends and receives data;

[0128] A second building module 250 is configured to build a general model of a self-interference channel;

[0129] An enumeration module 260 configured to list filter tap weight vector functions;

[0130] The processing module 270 is configured to iteratively estimate a self-interference channel factor and eliminate self-interference according to the self-interference channel factor.

[0131] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0132] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor is caused to execute the interference elimination method for a non-uniform structure sensor network in any of the above method embodiments;

[0133] As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows:

[0134] Computing the optimal number of clusters in non-uniformly distributed sensor networks;

[0135] Calculate the average distance avgdist within each cluster, optimize the selection of the initial cluster center according to the average distance avgdist, and obtain an improved K-means clustering algorithm;

[0136] According to the entropy weight method, the various indicators of cluster head selection are analyzed, and the signal strength factor is added to determine the cluster head;

[0137] Establish a system model in which relay nodes send and receive data simultaneously;

[0138] Establish a general model of self-interfering channels;

[0139] List the filter tap weight vector function;

[0140] The self-interference channel factor is estimated iteratively, and the self-interference is eliminated according to the self-interference channel factor.

[0141] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the interference elimination system of the non-uniform structure sensor network, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the interference elimination system of the non-uniform structure sensor network via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0142] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3 In the example, the bus connection is used. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the interference elimination method of the non-uniform structure sensor network of the above-mentioned method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the interference elimination system of the non-uniform structure sensor network. The output device 340 may include display devices such as display screens.

[0143] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0144] As an implementation mode, the electronic device is applied to an interference elimination system of a non-uniform structure sensor network, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can:

[0145] Computing the optimal number of clusters in non-uniformly distributed sensor networks;

[0146] Calculate the average distance avgdist within each cluster, optimize the selection of the initial cluster center according to the average distance avgdist, and obtain an improved K-means clustering algorithm;

[0147] According to the entropy weight method, the various indicators of cluster head selection are analyzed, and the signal strength factor is added to determine the cluster head;

[0148] Establish a system model in which relay nodes send and receive data simultaneously;

[0149] Establish a general model of self-interfering channels;

[0150] List the filter tap weight vector function;

[0151] The self-interference channel factor is estimated iteratively, and the self-interference is eliminated according to the self-interference channel factor.

[0152] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for eliminating interference in a non-uniform structure sensor network, characterized in that: include: Computing the optimal number of clusters in non-uniformly distributed sensor networks; Calculate the average distance avgdist within each cluster, optimize the selection of the initial cluster center according to the average distance avgdist, and obtain an improved K-means clustering algorithm; According to the entropy weight method, the various indicators of cluster head selection are analyzed, and the signal strength factor is added to determine the cluster head; Establish a system model in which relay nodes send and receive data simultaneously; Establish a general model of self-interfering channels; List the filter tap weight vector function; The self-interference channel factor is estimated iteratively, and the self-interference is eliminated according to the self-interference channel factor.

2. The interference elimination method for a non-uniform structure sensor network according to claim 1, characterized in that: The step of calculating the optimal number of clusters in a non-uniformly distributed sensor network includes: Assuming that there are N sensor nodes in a rectangular area with a side length of M×M, and the nodes are divided into k clusters, the expression for the energy consumed by each cluster head and node in the cluster is: In the formula, E elec 、E da are the energy consumed for transmitting and fusing 1 bit of data, d toBs is the distance between the cluster head and the base station, d toCH is the distance between the node and the corresponding cluster head, ε fs , ε mp are the power amplifier parameters for the free space channel model and the multipath fading model respectively; Assuming that the nodes in the network are evenly distributed and the distribution density is ρ(x, y), the distance between the node and the corresponding cluster head is expressed as: Calculate the energy consumption E of a single cluster in each round cluster , and calculate the total energy consumption E of all clusters in each round based on the energy consumption of a single cluster in each round total , the expression is: Take the derivative of k, let the first-order derivative of k equal to 0, and solve for the optimal number of clusters. The expression is: Where k is the optimal number of clusters for a non-uniform network.

3. The interference elimination method for a non-uniform structure sensor network according to claim 1, characterized in that: in, The expression for calculating the average distance avgdist within each cluster is: Where d(i, j) is the distance between node i and node j.

4. The interference elimination method for a non-uniform structure sensor network according to claim 1, characterized in that: The method of optimizing the selection of the initial cluster center according to the average distance avgdist to obtain the improved K-means clustering algorithm includes: Input data set D, optimal number of clusters k′ and maximum number of iterations; Initialization, including: Randomly select the first cluster center v1; Select the point farthest from v1 from the dataset as v2; Select the points farthest from the selected cluster center in turn until k cluster centers are selected; For each node x_j: calculate the distance from x_j to all cluster centers; Assign x_j to the cluster corresponding to the nearest cluster center; Update each cluster center, including: Calculate the mean of each node in the cluster and update the cluster center'' Cluster structure optimization, including: For each node, calculate the node degree, residual energy and distance to the base station; Redistribute nodes to optimize cluster structure; Calculate the objective function J; If the cluster center no longer changes or the maximum number of iterations is reached, the iteration is stopped; Output the clustering results.

5. The interference elimination method for a non-uniform structure sensor network according to claim 1, characterized in that: The method of analyzing various indicators of cluster head selection according to the entropy weight method and adding the signal strength factor to determine the cluster head includes: Determine the evaluation indicators, which include: the key evaluation indicators for selecting cluster heads, including residual energy, node degree, node location, the total number of rounds of network operation, and the number of rounds that the current node has served; Construct an indicator matrix, including: Assuming there are n nodes, each node has m indicators, including RSS value, to construct the indicator matrix, specifically: In the formula, x nm is the mth index value of the nth node; Standardization of indicators, including: The indicators of different dimensions are standardized and the range standardization is adopted. The expression is: For negative indicators, the standardized formula is: The general form of the normalized matrix is: Calculate the entropy value of each indicator, including: Calculate the ratio of each normalized value using the expression: Calculate the entropy weight of each indicator, and calculate the weight of each indicator through the entropy value. The expression is: Calculate the comprehensive score, the expression is: The node with the highest score will be selected as the cluster head.

6. An interference elimination system for a non-uniform structure sensor network, characterized in that: include: A calculation module configured to calculate an optimal number of clusters in a non-uniformly distributed sensor network; A selection module is configured to calculate an average distance avgdist within each cluster, and optimize the selection of initial cluster centers according to the average distance avgdist to obtain an improved K-means clustering algorithm; A determination module is configured to analyze various indicators of cluster head selection according to the entropy weight method and add a signal strength factor to determine the cluster head; A first building block is configured to establish a system model in which a relay node simultaneously sends and receives data; a second building block configured to establish a general model of a self-interference channel; an enumeration module configured to list filter tap weight vector functions; The processing module is configured to iteratively estimate a self-interference channel factor and eliminate self-interference according to the self-interference channel factor.

7. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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