Perception enhanced communication interference integrated network node deployment and resource optimization method

Abstract

The invention provides a perception-enhanced communication interference integrated network node deployment and resource optimization method. The method comprises the following steps: step 1, establishing a communication interference integrated network model; step 2, establishing a communication interference integrated network node deployment and resource optimization problem; step 3, establishing a space loss field; 4, acquiring a channel gain sample, and estimating any link channel gain in a given region by using a Kriging interpolation method; and step 5, performing joint optimization on the working state of the communication interference integrated network node. According to the invention, the environment perception capability is improved, the estimation error of the link channel gain is reduced, the network integration index is improved, and the algorithm convergence speed is accelerated.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a perception-enhanced communication interference integrated network node deployment and resource optimization method. Background Art

[0002] Communication and jamming are two key elements in the field of electromagnetic countermeasures. Thanks to the technological breakthroughs in software-defined radio and signal processing, the integrated communication and jamming network (ICAJN) has developed rapidly. The network consists of multiple integrated communication and jamming nodes. By utilizing shared hardware platforms, databases and information processing units, communication and jamming functions are mutually reinforced in one system. This integration not only reduces hardware costs, but also reduces the need for information interaction between different systems. Although ICAJN has the above advantages, it faces at least two challenges: on the one hand, the distributed structure increases the network coverage and improves the uneven distribution of signal strength, but it brings the problem of network coordination difficulties and poses challenges to network connectivity maintenance. On the other hand, the communication between multiple nodes within ICAJN is affected by the propagation environment such as terrain and buildings, and environmental perception is required to achieve effective node deployment and resource optimization. Existing research on ICAJN node location deployment and resource allocation is mostly based on the circular domain coverage model caused by large-scale fading, which cannot simulate the actual signal coverage. This affects the effectiveness of network node location deployment and resource allocation and reduces network performance. Therefore, enhancing the network's environmental perception capabilities and improving the performance of ICAJN in complex geographical environments is a promising but unexplored direction.

[0003] In the field of electromagnetic environment perception, there are mainly two methods: deterministic channel state calculation and channel state estimation. Ray tracing is a typical deterministic channel state calculation method, which obtains the channel gain of the link by calculating the amplitude, phase, delay and polarization of each ray between the connection endpoints based on terrain data. This method is complex and requires the construction of a physical model in advance. It is suitable for scenarios with high requirements for estimation accuracy and insensitive to real-time requirements. Channel state estimation methods mainly use spatial interpolation techniques, such as inverse distance weighting (IDW) and Kriging, to estimate the channel state in a specific area by using sampled points obtained by distributed nodes. A key limitation of channel state estimation methods is that they can only predict the channel state between a specified point (usually the location of the transmitter) and other points in the area, but cannot predict links whose endpoints are not at the specified point.

[0004] Coverage and connectivity are the core indicators of ICAJN performance. From the perspective of network node deployment and resource allocation, coverage and connectivity show contradictory characteristics. The coverage indicator requires nodes to be far enough from each other to reduce overlapping coverage areas, while connectivity requires nodes to be close enough to ensure that the link between them is reachable. Some studies have been proposed to explore the design that satisfies both coverage and connectivity. The strip deployment mode has been shown to be close to optimal under equal transmission power and free space path loss models; the optimal deployment scheme when the coverage distance and communication distance are not equal has also been proposed. These works provide guidance for node deployment, but most of them only consider large-scale fading models, assuming that the signal attenuation in all directions is uniform. In fact, factors such as uneven terrain and buildings not only cause large-scale fading, but also shadow fading (mesoscale fading), small-scale fading and other effects on signal transmission. Considering only the circular domain coverage model caused by large-scale fading makes it challenging to simulate real signal coverage conditions. Summary of the invention

[0005] The present application provides a perception-enhanced communication interference integrated network node deployment and resource optimization method, which can be used to solve the technical problem of low accuracy in simulating real signal coverage.

[0006] The present application provides a perception-enhanced communication interference integrated network node deployment and resource optimization method, the method comprising:

[0007] Step 1: Establish a communication interference integrated network model.

[0008] The communication interference integrated network model includes K numbered Peer-to-peer integrated nodes; the network needs to perform dual tasks in adversarial scenarios: ensure communication coverage, the probability that there are authorized users in the target area; and in the suspicious area To achieve interference coverage, there is a probability of illegal users appearing in the target area;

[0009] In order to ensure the connectivity of the network and make the coverage more refined, the communication and interference integrated network workflow includes the environment perception stage and the execution stage. In the environment perception stage, all integrated nodes operate in the perception mode, sample the channel gain in pairs, and further infer the node coverage boundary through spatial interpolation. In the execution stage, the integrated nodes switch to the communication or interference mode according to the perception and calculation results of the environment perception stage to perform the communication and interference coverage tasks.

[0010] Consider starting from position x k to x l The wireless link, where x k ,x ldenote the positions of the kth and lth nodes respectively; the channel gain of the link is expressed as It is decomposed into three parts: path loss, shadow fading and small-scale fading. By eliminating the influence of small-scale fading, the channel gain The statistical model of (in decibels) is expressed as:

[0011]

[0012] d0 is the reference distance. Near the reference distance, the path loss is characterized by free space fading. ||·|| represents the 2-norm. γ is the path loss exponent, which is determined by the signal propagation environment. is the shadow fading part, which reflects the impact of obstacles on signal propagation and follows a Gaussian distribution;

[0013] For network connectivity, use To represent the adjacency matrix of the network, where the element c k,l Indicates whether there is a reachable link between the kth and lth nodes; when the received signal power exceeds the threshold p under the condition of maximum transmission power c,th When , there is a reachable communication link; based on this, c k,l It is expressed as:

[0014]

[0015] in is an indicator function whose value is 1 when the parameter satisfies the constraint and 0 otherwise. max is the maximum transmission power of the node; Substituting equation (1) into equation (2), the constraint condition is expressed in terms of link shadow fading, that is,

[0016] For communication coverage, consider that nodes work at different frequencies without interfering with each other; then, select the node with the highest signal strength in the communication group to provide service for location x; based on this, the effective communication coverage area is expressed as Written as:

[0017]

[0018] in is the target area, is the set of nodes in communication mode, is the node index of the service location x;

[0019] For interference coverage, the effective interference coverage area, It is expressed as:

[0020]

[0021] in Represents a collection of integrated nodes used for interference.

[0022] Step 2: Establish the communication interference integrated network node deployment and resource optimization problem;

[0023] After defining the coverage area as a measure of communication and interference performance, an integrated indicator is designed to comprehensively consider communication, interference performance and network energy consumption to achieve energy-saving communication and interference coverage. The optimization variables of the problem include the deployment location of the node, the transmission power and the grouping strategy, and the optimization quantity is integrated into the working state set To this end, the optimization problem is stated as follows:

[0024]

[0025] in represents the communication coverage rate, represents the interference coverage, and It is p k The resource consumption caused by this is: C1 represents the signal transmission power constraint of all nodes; C2 is the location constraint, which stipulates the location of the node in the target area. The distribution in , C3 represents the network connectivity constraint;

[0026] Problem (5), i.e., equation (5), is a mixed-integer nonlinear programming problem (MINLP), which is very difficult to solve. On the one hand, in the objective function, and The determination of and the solution of the Area(·) function are all about optimizing the variables The non-convex function of is a two-layer non-convex function, and the two layers of non-convex functions are nested, which increases the difficulty of solving. Among the constraints, the power calculation of the matrix in the network connectivity constraint is also a non-convex process. On the other hand, it can be seen from formulas (3) and (4) that when the node position is to be optimized, solving and The actual channel quality g of any link in a given area is required x,y , using deterministic channel state calculation and channel state estimation methods cannot meet the above requirements. Therefore, problem (5) cannot be solved directly.

[0027] Step 3: Establishment of spatial loss field;

[0028] Calculate the channel quality g of any link in a given area x,yIt is a necessary condition for solving problem (5). However, the current pilot channel estimation method can only solve the channel gain of a certain link, and the channel state estimation method can only solve the link channel gain from the transmitting node to any position, and cannot estimate the link channel gain where both end points are not at the transmitter position. Therefore, the present invention uses the perception capability of the integrated node in ICAJN to estimate the channel gain in the target area and guide the solution of problem (5);

[0029] According to formula (1), the channel gain includes path loss and shadow fading, where the path loss is obtained from the link distance; therefore, the challenge of estimating the channel gain lies in the shadow fading s k,l To solve this problem, this patent proposes the concept of spatial loss field, which considers that the shadow loss experienced on the link is the result of the potential spatial loss field p(x);

[0030] In a non-specific scenario, that is, the distribution of obstacles causing fading is in Poisson spatial randomness, the spatial loss field p(x) is an isotropic wide-sense stationary Gaussian random field with zero mean and exponential fading spatial correlation; that is, The covariance of the spatial loss value of any two points x and y in the target area is written as:

[0031]

[0032] where ||xy|| represents the Euclidean distance between points x and y, σ s is the standard deviation of shadow fading, and δ is a spatial constant;

[0033] As mentioned earlier, the shadow fading experienced on a link is the result of an underlying spatial loss field; specifically, the shadow fading loss s between any two points is x,y is the weighted line integral of the spatial loss field between two points:

[0034]

[0035] in As a weight coefficient, it reflects that the shadow fading effect of the same obstacle on the short path is greater than that on the long path. This is because there are more scatterers and greater diffraction possibilities on the long path, while the opposite is true for the short path.

[0036] Since all link shadow fading is a function of the spatial loss field, the correlation of link shadow fading is derived from the correlation of the spatial loss field; the shadow fading s on the link x→y and the link u→v x,y ,s u,v The cross-correlation function is written as:

[0037]

[0038] After obtaining the correlation of link shadow fading, the distributed characteristics of ICAJN nodes are used to sample the link shadow fading values ​​to obtain diverse samples, and the shadow fading values ​​of any link are estimated by interpolation and the irregular coverage boundary is inferred.

[0039] Step 4: Obtain channel gain samples and use Kriging interpolation to estimate the channel gain of any link in a given area;

[0040] The channel gain sample acquisition process is as follows:

[0041] In the sensing phase, nodes transmit pilot signals in sequence in a time-division manner, dividing a sensing cycle into K time slots. In time slot k, the kth node transmits a signal with a specific power, and other nodes receive the signal. The channel gain between the two nodes is obtained based on the power difference between the received signal and the transmitted signal. When the relative position of the receiver to the transmitter is known, the measured value of the shadow fading between the two points is obtained. The relationship between the measured and actual values ​​of shadow fading is modeled as where ε k,l Contains measurement errors and uncertainties caused by small-scale channel fading, and obeys a zero-mean Gaussian distribution. From the perspective of the kth node, it receives training signals from other K-1 nodes in one sensing cycle, and represents these K-1 nodes as a set definition is the signal received by the kth node in a sensing cycle A collection of; definition is the sampling data set of all sampling points; Also expressed as:

[0042]

[0043] where s and ε are respectively represented by {s k,l} and {ε k,l}, Reconstruction obtained;

[0044] Using the spatial correlation of link shadow fading s, the shadow fading s of any link x→y is estimated by the Kriging interpolation method. x,y Values:

[0045]

[0046] in Represents the covariance vector between the sampling point and the prediction point, and defines C s =cov{s},C ε = cov{ε}, Q = C s +C ε; p(x) is a random field with zero mean. According to the relationship in formula (7), s x,y is also a variable with zero mean, so In the shadow fading parameters When and δ are determined, and C s Through formula (8), we get:

[0047] The link channel gain is estimated using the Kriging interpolation method, and the process is as follows;

[0048] The input is the link endpoint value to be estimated x = (x1, x2), y = (y1, y2) and the estimated sampling error covariance C ε ;

[0049] The output quantity is the link channel gain estimate

[0050] Step 4-1: A sensing period T has K time slots, and K users work in time division multiple access mode; the transmission power of the kth user in the kth time slot is p k The pilot signal of

[0051] Step 4-2: The lth user in time slot The signal from the kth user is received, and the received power is expressed as Further calculation

[0052] Step 4-3: Calculate according to formula (1)

[0053] Step 4-4: Summarize the sampled signals of the lth user in a period And the sampling data of all sampling points

[0054] Step 4-5: Based on the sample data Get the current environment for An estimated value of

[0055] Step 4-6: Use formula (10) to calculate s x,y Perform interpolation prediction to obtain an estimated value

[0056] Step 4-7: Calculation

[0057] The whole process of link channel gain estimation in the target area is summarized in the above algorithm. Steps 4-1 to 4-4 complete the acquisition of link shadow fading samples within one sensing cycle; step 4-5 is the parameter estimation stage, and steps 4-6 to 4-7 give the estimation of link shadow fading partial and overall gain.

[0058] Complexity Analysis: The complexity of the algorithm is mainly caused by executing steps 4-6. In the worst case, the complexity of each link channel gain estimation can be estimated as Where p represents the number of partitions when calculating the link shadow fading covariance using the function integral2 in MATLAB according to equation (8), and the second term is the complexity of matrix inversion.

[0059] Step 5: Joint optimization of the working status of nodes in the communication interference integrated network;

[0060] According to the estimated information of link channel gain in step 4, the working state of the integrated communication and interference network is jointly optimized to optimize the working state including node deployment position, power allocation and grouping strategy, so as to achieve communication and interference coverage under the constraint of network connectivity. In order to cope with the challenges brought by the non-convexity of the objective function and constraints to the problem solving, the grey wolf optimization (GWO) algorithm is used to give the approximate optimal solution to the problem.

[0061] As an emerging swarm intelligence (SI) algorithm, the Gray Wolf Optimization Algorithm imitates the hunting process of wolves in nature, models the hunting techniques and social hierarchy of wolves, regards the optimal solution to the problem as prey, and approaches the prey (obtains the optimal solution) in a gradual iterative process. Compared with other swarm intelligence algorithms, the Gray Wolf Optimization Algorithm has the advantages of not needing to calculate the objective function gradient, having few defined parameters, being easy to adjust, and having clear physical meanings. It has been proven to be a convenient and reliable algorithm in many fields.

[0062] The standard gray wolf optimization algorithm operation process starts with initializing the feasible solution of the agent. The number of agents, also known as the gray wolf population, is defined as N. The position vector of each agent is the feasible solution of problem (5), y n It is expressed as:

[0063]

[0064] in, The value of the zth dimension of the position vector of the nth agent, with a total dimension of Z, which includes the positions of all integrated nodes, power allocation, and communication interference node grouping, defining the set of dimensions

[0065] After initialization, the gray wolf optimization algorithm enters the exploration phase; in this phase, the top three agents with the best fitness need to be found as αalpha wolf, βbeta wolf and δdelta wolf, and the others are defined as ωomega wolf. α, β and δ wolves are considered to be the wolves that can get closest to the prey in the current situation.

[0066] Enter the parameter update phase, that is, attack the prey; at this time, except for α, β and δ wolves, the agents are positioned as ω wolves, and iterative optimization is performed based on the positions of the first three wolves, and then the population with better fitness is selected to update the node positions of α, β and δ wolves;

[0067] In order to better model the exploration phase and parameter update phase, the iteration number is added to formula (11), and the position vectors of the top three agents in the lth iteration are defined as:

[0068]

[0069] Among them, the numbers of α, β, and δ meet the following conditions:

[0070]

[0071] Wherein, f(·) represents the calculation function of the objective function in formula (5);

[0072] After determining the positions of α, β, and δ wolves, the position of ω wolf is further updated. First, two state update vectors are defined:

[0073]

[0074] in, a z (l) is a parameter that decreases linearly with the number of iterations, ranging from 2 to 0, and is modeled as:

[0075]

[0076] On this basis, the relative distances between each ω wolf and α, β and δ wolves are expressed as and The specific calculation method is as follows:

[0077]

[0078] The position update formula of ω wolf is expressed as:

[0079]

[0080] When defining the lth iteration, the alpha, beta and delta solutions of the nth ω wolf are:

[0081]

[0082] The updated solution for the nth ω wolf:

[0083]

[0084] After completing the optimization iterations of all agents, we get

[0085] The working state joint optimization process is as follows:

[0086] The input is the initial positions of N agents defined by constraints 1 and 2 of problem (5). Maximum number of iterations l max ;

[0087] The output is the optimal working state y of the network node obtained by the algorithm α (l max );

[0088] Step 5-1: Input the positions of N agents into step 4, calculate the link channel gain, and further obtain their fitness function

[0089] Step 5-2: Verify whether the problem solution corresponding to the agent satisfies the connectivity constraint. If not, impose a penalty by multiplying the fitness function by the discount factor λ;

[0090] Step 5-3: Use equation (13) to calculate the indices of the top three best solutions so far, namely α(l), β(l) and δ(l);

[0091] Step 5-4: Calculate the search coefficient vector using equations (14) and (15);

[0092] Step 5-5: Update the new position y of the agent according to equations (16)-(19) n (l+1);

[0093] Step 5-6: Adjust y n The value in (l+1) exceeds the search space so that it corresponds to the space boundary;

[0094] Step 5-7: Update l=l+1 until l>l max When , it ends; otherwise, return to step 5-1.

[0095] Algorithm convergence and complexity analysis: Due to the working state set By using the GWO algorithm to optimize in an iterative manner, the objective function f(y n (0))≤f(y n (1))≤…≤f(y n (l max )). In addition, the work status set Subject to the constraints of C1 and C2, f(y n (l max)) will converge to a maximum limit point. The main idea of ​​the algorithm is to calculate the fitness function of the agent in each iteration. The complexity in the worst case can be estimated as Where p represents the number of partitions when calculating the link shadow fading covariance using the function integral2 in MATLAB according to formula (10). The complexity of calculating the fitness function of each agent is:

[0096] The perception-enhanced communication interference integrated network node deployment and resource optimization method proposed in the present invention has the following technical effects:

[0097] Improve environmental perception: Through the realization of spatial loss field, the system can more accurately quantify link shadow fading. By combining path loss and shadow fading, it can generate more realistic irregular coverage boundaries, thereby improving the accuracy of signal coverage. This can better adapt to the actual signal transmission environment and improve communication and interference performance.

[0098] Reducing the estimation error of link channel gain: The proposed Kriging estimation algorithm outperforms the traditional IDW estimation and path loss model estimation algorithms in terms of perceived performance, especially when the number of antennas is increased, the performance gain can reach about 4dB.

[0099] Improve network integration indicators: Under different prior information, the system can achieve higher integration indicators by adjusting the transmission power and optimizing the algorithm, that is, energy-saving communication and interference coverage while maintaining network connectivity.

[0100] Accelerate algorithm convergence speed: The GWO algorithm shows faster convergence speed and better performance than the PSO and ABC algorithms, which indicates that in practical applications, the system can find the optimal solution more quickly.

[0101] Dynamic adjustment capability of node deployment: ICAJN can dynamically adjust node positions according to the distribution of the spatial loss field to avoid links passing through high-loss areas, thereby achieving better communication and interference coverage effects under the constraints of network connectivity.

[0102] Improvement of connectivity rate: By optimizing algorithms and improving working protocols, the system has increased the proportion of trials that meet connectivity constraints in multiple experiments, that is, improved the connectivity rate of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Figure 1 A system model diagram and a protocol diagram of the communication interference integrated network provided by the present invention;

[0104] Figure 2 The spatial loss field realization and corresponding channel gain contour map provided by the present invention;

[0105] Figure 3 The integrated index and node quantity relationship diagram corresponding to the five algorithms provided by the present invention;

[0106] Figure 4 Two state-of-the-art node deployment schemes that consider both coverage and connectivity are provided for the present invention;

[0107] Figure 5 A graph showing the relationship between the estimation error and the number of nodes under different estimation algorithms provided by the present invention;

[0108] Figure 6 A graph showing the relationship between the objective function and the maximum transmit power under different prior information provided by the present invention;

[0109] Figure 7 This is a graph showing the relationship between the objective function and the number of iterations under different swarm intelligence algorithms provided by the present invention;

[0110] Figure 8 The spatial loss field and the corresponding position optimization results and signal strength distribution diagram provided by the present invention. DETAILED DESCRIPTION

[0111] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0112] The following first introduces the embodiments of the present application in conjunction with the accompanying drawings.

[0113] To evaluate the performance of the proposed method, an ICAJN with K = 6 nodes in a 200m × 200m area is analyzed, representing an urban microcell scenario, where each multifunctional node acts as a mobile base station to jointly provide communication and interference coverage. max =40dBm, network operating frequency f = 2570 ~ 2620MHz, path loss index γ = 3.2, communication receiving power threshold p c,th =-60dBm and effective interference power threshold p j,th =-60dBm. Shadow fading parameters δ = 50. The discount factor in the improved GWO algorithm is set to λ = 0.5. The suspicious area is defined as a square with a coordinate range of [0m:50m; 0m:50m].

[0114] Figure 2 (a) shows a specific implementation of the spatial loss field in a 200m×200m area, which is obtained using the spatial correlation relationship in equation (8). In order to prove that the spatial loss field is effective in quantifying link shadow fading, Figure 2(b) and 2(c) show the channel gain contour plots transmitted from the point [70.5m, 12.5m]. Figure 2 (b) shows the circular contour plot considering only path loss, while Figure 2 (c) shows the irregular contour map obtained by combining path loss and shadow fading. Figure 2 (b) Compared with Figure 2 (c) shows that when the link passes through the region of positive p(x), such as [15m:35m; 0m:25m], [10m:25m; 95m:110m], the spacing between two adjacent contour lines becomes larger, indicating that the signal attenuation becomes slower. On the contrary, when the link passes through the region of negative p(x), such as [120m:160m; 115m:145m], the spacing between two adjacent contour lines becomes smaller, indicating that the signal attenuation becomes faster and shadow fading has a reverse effect on signal propagation.

[0115] Figure 3 The relationship between the integration index and the number of nodes in five different cases is shown. The red curve represents the upper limit, which is the theoretical unreachable value obtained by the proposed algorithm using the exact coverage boundary. The blue and yellow curves are obtained using the estimated coverage boundary and the circular coverage boundary, respectively. As a benchmark solution, two node deployment schemes based on the circular domain coverage model that meet the network coverage and connectivity requirements are provided. These schemes are represented by black and green lines, corresponding to the strip and grid node deployment schemes, respectively, and their schematic diagrams are shown in Figure 4 . It is clear that the proposed method significantly outperforms the baseline method, and the improvement is mainly attributed to two aspects: perception gain and execution gain. The execution gain is represented by the yellow shading and is reflected in the difference between the proposed algorithm using a circular coverage boundary and the baseline scheme, both of which are based on a circular coverage model and have no environmental awareness. The perception gain comes from the reduction in link channel gain and coverage boundary estimation error, which is represented by the blue shading. In addition, as the number of nodes increases, the gap between the blue curve and the red curve decreases. This shows that increasing the number of sensing nodes can narrow the gap between the estimated coverage boundary and the exact coverage boundary.

[0116] Figure 5The perceived performance of the proposed Kriging estimation algorithm is compared with two other estimation algorithms: inverse distance weighted (IDW) estimation and path loss model estimation algorithm. The IDW estimation algorithm uses the link distance as the weight of the weighted average, and the link distance is expressed as the sum of the distances of the two endpoints. The path loss model estimation only considers the path loss part and gives the link channel gain according to the link length. This scheme is given as a benchmark. The normalized root mean square error (NRMSE) is used as the performance indicator.

[0117] Where Q is the total number of experiments, K val is the number of links in an experiment, is the estimated value of the verification link in the qth experiment, g val (q) are their true values. The position of the verification node is Figure 2 Indicated by the black square in (c), the parameter is set to K val =36 and Q = 100. As K increases, the NRMSE of the link channel gain of the two spatial interpolation estimation algorithms decreases, significantly reducing the NRMSE compared to the path loss model estimation. The proposed Kriging estimation algorithm outperforms the IDW estimation algorithm, and this gain becomes larger as the number of antennas increases, reaching a performance gain of about 4 dB when K = 8.

[0118] Figure 6 The influence of transmission power on comprehensive indicators under different prior information is shown. The proposed scheme refers to ICAJN working according to the proposed protocol. The upper bound and lower bound 1 are both obtained using step 6, the difference is that the upper bound is calculated using the exact coverage boundary, and the lower bound 1 is calculated using the circular coverage boundary. Based on lower bound 1, lower bound 2 improves the communication threshold p in the optimization process. c,th =-55dBm, so that it is easier to meet the network connectivity constraints, and its comprehensive index is better than the lower bound of 1. Figure 6 It is further shown that the sensing function in ICAJN can better adapt to the actual signal transmission environment by estimating the link shadow fading, thus enhancing the communication and interference capabilities. In addition, in all four schemes, the comprehensive index increases with the increase of the maximum transmission power.

[0119] In order to verify the effectiveness of the GWO algorithm used in this patent, Figure 7The relationship between the comprehensive index and the number of iterations of three swarm intelligence algorithms: GWO algorithm, particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm is shown. It can be seen that the GWO algorithm has a faster convergence speed and better algorithm performance than the PSO and ABC algorithms. When K=6, it converges after 100 iterations.

[0120] Figure 8 Two implementations of the spatial loss field are shown, along with the node position optimization results and signal strength distribution under the corresponding spatial loss fields. Figure 8 (a) The simulation shows a scenario where the distribution of attenuation obstacles follows a Poisson spatial random process. Figure 8 (b) Figure 8 Based on (a), the spatial loss field in the area [60m:100m; 100m:140m] is set to p(x) = -5 to simulate the impact of large obstacles on signal propagation in the precise area. Figure 8 (c) and Figure 8 (d) respectively give the Figure 8 (a) and Figure 8 (b) shows the node position optimization results and signal strength distribution under the spatial loss field, where the blue cross indicates the node deployment position in the communication mode, the red cross indicates the node deployment position in the interference mode, and the black straight line indicates the reachable link between two nodes. It can be found that in anisotropic signal propagation environments, ICAJN can dynamically adjust node positions to avoid links passing through locations with high spatial loss field values, thereby achieving better communication and interference coverage effects under network connectivity constraints.

[0121] In order to solve the problem of high computational complexity and inability to predict the channel gain of any link in a given area in the field of electromagnetic environment perception, the present invention introduces the concept of spatial loss field and derives the relationship between link channel gain and spatial loss field. On this basis, the channel gain of any link is inferred through sampling and spatial interpolation, and the coverage area of ​​the node is further inferred, thereby enhancing the environmental perception capability.

[0122] In order to solve the problem of difficult optimization of network coverage and connectivity in a specific propagation environment, the present invention formulates the problem of simultaneously satisfying coverage and connectivity. A comprehensive index is developed to optimize the deployment location, power allocation and grouping strategy of nodes. The problem is iteratively solved using the gray wolf optimization algorithm to determine the best working state in the current environment.

[0123] An extended simulation is provided in the urban microcell scenario. The simulation results show that the coverage boundary estimation and working state joint optimization algorithm have advantages in single domain capabilities compared with the baseline algorithm, and the designed protocol can coordinate multiple functions and show adaptability to dynamic electromagnetic environments.

[0124] The above-described embodiments of the present application do not constitute a limitation on the protection scope of the present application.

Claims

1. A perception-enhanced communication interference integrated network node deployment and resource optimization method, characterized in that: The method comprises: Step 1: Establish a communication interference integrated network model; Step 2: Establish the communication interference integrated network node deployment and resource optimization problem; Step 3: Establishment of spatial loss field; Step 4: Obtain channel gain samples and use Kriging interpolation to estimate the channel gain of any link in a given area; Step 5: Joint optimization of the working status of nodes in the communication and interference integrated network.

2. The method according to claim 1, characterized in that Step 1: Establish a communication interference integrated network model, including: The communication interference integrated network model includes K numbered Peer-to-peer integrated nodes; the network needs to perform dual tasks in adversarial scenarios: ensure communication coverage, the probability that there are authorized users in the target area; and in the suspicious area To achieve interference coverage, there is a probability of illegal users appearing in the target area; The communication and interference integrated network workflow includes the environment perception stage and the execution stage. In the environment perception stage, all integrated nodes operate in the perception mode, sample channel gains in pairs, and further infer the node coverage boundary through spatial interpolation. In the execution stage, the integrated nodes switch to the communication or interference mode according to the perception and calculation results of the environment perception stage to perform the communication and interference coverage tasks. Consider starting from position x k to x l The wireless link, where x k ,x l denote the positions of the kth and lth nodes respectively; the channel gain of the link is expressed as It is decomposed into three parts: path loss, shadow fading and small-scale fading. By eliminating the influence of small-scale fading, the channel gain The statistical model is expressed as: d0 is the reference distance. Near the reference distance, the path loss is characterized by free space fading. ||·|| represents the 2-norm. γ is the path loss exponent, which is determined by the signal propagation environment. is the shadow fading part, which reflects the impact of obstacles on signal propagation and follows a Gaussian distribution; For network connectivity, use To represent the adjacency matrix of the network, where the element c k,l Indicates whether there is a reachable link between the kth and lth nodes; when the received signal power exceeds the threshold p under the condition of maximum transmission power c,th When , there is a reachable communication link; based on this, c k,l It is expressed as: in is an indicator function whose value is 1 when the parameter satisfies the constraint and 0 otherwise. max is the maximum transmission power of the node; Substituting equation (1) into equation (2), the constraint condition is expressed in terms of link shadow fading, that is, For communication coverage, consider that nodes work at different frequencies without interfering with each other; then, select the node with the highest signal strength in the communication group to provide service for location x; based on this, the effective communication coverage area is expressed as Written as: in is the target area, is the set of nodes in communication mode, is the node index of the service location x; For interference coverage, the effective interference coverage area, It is expressed as: in Represents a collection of integrated nodes used for interference.

3. The method according to claim 2, characterized in that Step 2: Establish the communication interference integrated network node deployment and resource optimization problem; including: The optimization variables of the problem include the deployment location of the nodes, the transmission power and the grouping strategy, and the optimization quantity is integrated into the working state set Therefore, the optimization problem is expressed as follows: in represents the communication coverage rate, represents the interference coverage, and It is p k The resource consumption caused by this is: C1 represents the signal transmission power constraint of all nodes; C2 is the location constraint, which stipulates the location of the node in the target area. The distribution in, C3 represents the network connectivity constraint; Problem (5), i.e., equation (5), is a mixed integer nonlinear programming problem.

4. The method according to claim 3, characterized in that Step 3: Establishment of spatial loss field; include: By using the sensing capability of the integrated nodes in ICAJN, the channel gain in the target area is estimated to guide the solution of problem (5); According to formula (1), the channel gain includes path loss and shadow fading, where the path loss is obtained from the link distance. The concept of spatial loss field is proposed, and it is considered that the shadow loss experienced on the link is the result of the potential spatial loss field p(x). In a non-specific scenario, that is, the distribution of obstacles causing fading is in Poisson spatial randomness, the spatial loss field p(x) is an isotropic wide-sense stationary Gaussian random field with zero mean and exponential fading spatial correlation; that is, The covariance of the spatial loss value of any two points x and y in the target area is written as: where ||xy|| represents the Euclidean distance between points x and y, σ s is the standard deviation of shadow fading, and δ is a spatial constant; The shadow fading experienced on a link is the result of an underlying spatial loss field; specifically, the shadow fading loss s between any two points is x,y is the weighted line integral of the spatial loss field between two points: in As a weight coefficient, it reflects that the shadow fading effect of the same obstacle on the short path is greater than its shadow fading effect on the long path; Since all link shadow fading is a function of the spatial loss field, the correlation of link shadow fading is derived from the correlation of the spatial loss field; the shadow fading s on the link x→y and the link u→v x,y ,s u,v The cross-correlation function is written as: After obtaining the correlation of link shadow fading, the distributed characteristics of ICAJN nodes are used to sample the link shadow fading values ​​to obtain diverse samples, and the shadow fading value of any link is estimated by interpolation and the irregular coverage boundary is inferred.

5. The method according to claim 4, characterized in that Step 4: Obtain channel gain samples and use Kriging interpolation to estimate the channel gain of any link in a given area; including: The channel gain sample acquisition process is as follows: In the sensing phase, nodes transmit pilot signals in sequence in a time-division manner, dividing a sensing cycle into K time slots. In time slot k, the kth node transmits a signal with a specific power, and other nodes receive the signal. The channel gain between the two nodes is obtained based on the power difference between the received signal and the transmitted signal. When the relative position of the receiver to the transmitter is known, the measured value of the shadow fading between the two points is obtained. The relationship between the measured and actual values ​​of shadow fading is modeled as where ε k,l Contains measurement errors and uncertainties caused by small-scale channel fading, and obeys a zero-mean Gaussian distribution. From the perspective of the kth node, it receives training signals from other K-1 nodes in one sensing cycle, and represents these K-1 nodes as a set definition is the signal received by the kth node in a sensing cycle A collection of; definition is the sampling data set of all sampling points; Also expressed as: where s and ε are respectively represented by {s k,l } and {ε k,l }, Reconstruction obtained; Using the spatial correlation of link shadow fading s, the shadow fading s of any link x→y is estimated by the Kriging interpolation method. x,y Values: in Represents the covariance vector between the sampling point and the prediction point, and defines C s =cov{s},C ε = cov{ε}, Q = C s +C ε ; p(x) is a random field with zero mean. According to the relationship in formula (7), s x,y is also a variable with zero mean, so In the shadow fading parameters When and δ are determined, and C s Through formula (8), we get: The link channel gain is estimated using the Kriging interpolation method, and the process is as follows; The input is the link endpoint value to be estimated x = (x1, x2), y = (y1, y2) and the estimated sampling error covariance C ε ; The output quantity is the link channel gain estimate Step 4-1: A sensing period T has K time slots, and K users work in time division multiple access mode; the transmission power of the kth user in the kth time slot is p k The pilot signal of Step 4-2: The lth user in time slot k, The signal from the kth user is received within k≠l, and the received power is expressed as Further calculation Step 4-3: Calculate according to formula (1) Step 4-4: Summarize the sampled signals of the lth user in a period And the sampling data of all sampling points Step 4-5: Based on the sample data Get the current environment for An estimated value of Step 4-6: Use formula (10) to calculate s x,y Perform interpolation prediction to obtain an estimated value Step 4-7: Calculation Steps 4-1 to 4-4 complete the acquisition of link shadow fading samples within one sensing cycle; step 4-5 is the parameter estimation stage, and steps 4-6 to 4-7 provide estimates of the link shadow fading partial and overall gain.

6. The method according to claim 5, characterized in that Step 5: Joint optimization of the working state of the communication and interference integrated network nodes; Based on the estimated information of the link channel gain in step 4, the communication and interference integrated network working state is jointly optimized to optimize the working state including the node deployment position, power allocation and grouping strategy, and realize communication and interference coverage under the constraint of network connectivity, including: The grey wolf optimization (GWO) algorithm is used to give an approximate optimal solution to the problem. The number of agents, also known as the grey wolf population, is defined as N. The position vector of each agent is the feasible solution to problem (5), y n It is expressed as: in, The value of the zth dimension of the position vector of the nth agent, with a total dimension of Z, which includes the positions of all integrated nodes, power allocation, and communication interference node grouping, defining the set of dimensions After initialization, the gray wolf optimization algorithm enters the exploration phase; in this phase, the top three agents with the best fitness need to be found as αalpha wolf, βbeta wolf and δdelta wolf, and the others are defined as ωomega wolf. α, β and δ wolves are considered to be the wolves that can get closest to the prey in the current situation. Enter the parameter update phase, that is, attack the prey; at this time, except for α, β and δ wolves, the agents are positioned as ω wolves, and iterative optimization is performed based on the positions of the first three wolves, and then the population with better fitness is selected to update the node positions of α, β and δ wolves; Based on formula (11), the number of iterations is increased, and the position vectors of the top three agents in the lth iteration are defined as: Among them, the numbers of α, β, and δ meet the following conditions: Wherein, f(·) represents the calculation function of the objective function in formula (5); After determining the positions of α, β, and δ wolves, the position of ω wolf is further updated. First, two state update vectors are defined: in, a z (l) is a parameter that decreases linearly with the number of iterations, ranging from 2 to 0, and is modeled as: On this basis, the relative distances between each ω wolf and α, β and δ wolves are expressed as and The specific calculation method is as follows: The position update formula of ω wolf is expressed as: When defining the lth iteration, the alpha, beta and delta solutions of the nth ω wolf are: The updated solution for the nth ω wolf: After completing the optimization iterations of all agents, we get The working state joint optimization process is as follows: The input is the initial positions of N agents defined by constraints 1 and 2 of problem (5). Maximum number of iterations l max ; The output is the optimal working state y of the network node obtained by the algorithm α (l max ); Step 5-1: Input the positions of N agents into step 4, calculate the link channel gain, and further obtain their fitness function f(y n (l)), Step 5-2: Verify whether the problem solution corresponding to the agent satisfies the connectivity constraint. If not, impose a penalty by multiplying the fitness function by the discount factor λ; Step 5-3: Use equation (13) to calculate the indices of the top three best solutions so far, namely α(l), β(l) and δ(l); Step 5-4: Calculate the search coefficient vector using equations (14) and (15); Step 5-5: Update the new position y of the agent according to equations (16)-(19) n (l+1); Step 5-6: Adjust y n The value in (l+1) exceeds the search space so that it corresponds to the space boundary; Step 5-7: Update l=l+1 until l>l max When , it ends; otherwise, return to step 5-1.

Images