Optimization method for offshore distributed power distribution unit unmanned aerial vehicle cooperative secure communication network

Abstract

The application discloses an offshore distributed power distribution unit unmanned aerial vehicle cooperative security communication network optimization method, comprising the following steps: multi-communication link channel modeling; signal transmission strategy modeling; signal eavesdropping risk link modeling, the eavesdropping channel capacity of two scheduling links of a control center to a potential eavesdropper and unmanned aerial vehicle relay to the potential eavesdropper is evaluated; information security problem formulation; scheduling problem optimization scheme design; output optimized power distribution network scheduling parameters, including transmission power matrix, unmanned aerial vehicle trajectory and power distribution unit grouping parameters. The application designs a set of power optimization algorithm and trajectory optimization algorithm, which improves the communication rate, maximizes the security and reliability of the power distribution unit scheduling system, and meets the power distribution network operation management demand of offshore large-scale distributed energy safe access.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication networks of power systems, and particularly relates to an optimization method for a UAV cooperative secure communication network of offshore distributed power distribution units. BACKGROUND

[0002] With large-scale development and utilization of renewable energy, offshore distributed energy access has become an important direction of energy transformation. However, due to the particularity of the offshore environment and the distributed characteristics of the power distribution network, there are still many challenges in realizing the safe and efficient access of offshore distributed energy. Due to the complexity of the offshore environment, the construction cost of wired networks is high, the construction difficulty is great, and wired networks are easily damaged when encountering bad weather, resulting in insufficient communication reliability.

[0003] Although the traditional wireless communication scheme has flexibility, the existing communication mode based on fixed stations often cannot meet the dynamic scheduling demand when facing large-scale distributed power distribution units, the communication coverage range is limited, and the signal transmission stability is poor. In addition, in a multi-user distributed system, the existing communication protocol is low in efficiency in terms of spectrum resource allocation and interference management, and it is difficult to meet the real-time and reliability requirements of dynamic control of offshore large-scale power distribution networks. Especially in terms of security, since the offshore communication environment is exposed to an open space, it is easy to be eavesdropped and attacked, and the existing technology often sacrifices part of the communication rate when protecting the communication link, which obviously cannot meet the demand of efficient and secure communication for offshore distributed energy access.

[0004] At the same time, the traditional UAV relay communication mode does not take into account the communication environment of the offshore composite channel. The UAV and power supply equipment are affected by the sea waves, and need to be optimized after modeling the complex channel. Further, due to the uncertainty of the offshore environment, the specific position and channel state information of the eavesdropper cannot be accurately obtained, and the communication power optimization and UAV trajectory optimization need to be based on the fuzzy channel state information of the eavesdropper. SUMMARY

[0005] The purpose of the present application is to provide an optimization method for a UAV cooperative secure communication network of offshore distributed power distribution units. By introducing a cooperative architecture of a control center, a multi-antenna UAV relay and a single-antenna distributed power distribution unit, a power optimization algorithm and a trajectory optimization algorithm are designed, which can improve the communication rate while maximizing the security and reliability of the power distribution unit scheduling system, and meet the operation and management requirements of the power distribution network for offshore large-scale distributed energy access.

[0006] Technical Solution: The present invention provides a method for optimizing an offshore distributed power distribution unit (DPU)-UAV collaborative secure communication network. The communication network hardware includes an offshore distributed power distribution unit (DPU), a UAV relay, and a control center. The method includes the following steps:

[0007] Step 1: Model the multi-communication link channels from the control center to the distributed distribution units;

[0008] Step 2: Model the signal transmission strategy between the control center and the drone relay;

[0009] Step 3: Model the signal eavesdropping risk link and evaluate the eavesdropping channel capacity of the two scheduling links: the control center to the potential eavesdropper and the drone relay to the potential eavesdropper;

[0010] Step 4: Formulate information security issues;

[0011] Step 5: Design an optimization solution for the scheduling problem;

[0012] Step 6: Output the optimized distribution network dispatching parameters, including the transmission power matrix, UAV trajectory and distribution unit grouping parameters.

[0013] Furthermore, in step 1, the multi-communication link channel from the control center to the distributed power distribution unit is modeled; specifically, the steps include:

[0014] Step 1.1: Model the direct transmission link from the control center to the distributed distribution unit, using the Alpha-beta-gamma channel model (ABG) to approximate the path loss of actual communication.

[0015] Step 1.2: Model the relay transmission link from the control center to the relay UAV using a small-scale Rayleigh fading model. Considering the impact of sea waves on the UAV, the line-of-sight and non-line-of-sight components coexist in the model.

[0016] Step 1.3: The link from the relay UAV to the distributed distribution unit also uses the coexistence of line-of-sight and non-line-of-sight components, and the channel form is a dual form.

[0017] Furthermore, in step 2, the signal transmission strategy modeling for the control center and the drone relay includes the following steps:

[0018] Step 2.1: The control center directly schedules link modeling, including the transmit signal model, receive signal model, and channel capacity assessment.

[0019] Step 2.2, UAV relay scheduling link modeling, including the signal model sent from the control center to the UAV relay, the signal model sent from the UAV relay to the distribution unit, and the scheduling link channel capacity evaluation method finally accepted by the distribution unit.

[0020] Furthermore, in step 4, the information security problem is formulated, which specifically includes the following steps:

[0021] Step 4.1: Model the secure communication links from the control center to the distributed distribution unit and from the drone to the distributed distribution unit, and provide an expression for the average secure capacity of multiple time slots.

[0022] In step 4.2, the optimization problem is decomposed, and the problem of maximizing confidentiality capacity is decomposed into maximizing the confidentiality capacity of the direct scheduling link and maximizing the confidentiality capacity of the relay scheduling link.

[0023] Furthermore, in step 5, the scheduling problem optimization solution design specifically includes the following steps:

[0024] Step 5.1: Use K-means clustering algorithm to group distributed distribution units;

[0025] Step 5.2: Optimize the power of the direct scheduling link based on the zero-forcing method;

[0026] Step 5.3: Optimize the UAV relay scheduling link power;

[0027] Step 5.4: Use deep Q network to optimize the UAV flight trajectory.

[0028] Furthermore, step 5.1 specifically includes the following steps:

[0029] Step 5.1.1, initialize the cluster centers of the K-means clustering algorithm;

[0030] Step 5.1.2: Calculate the distance between each data point and the cluster center and assign the data point to the cluster center.

[0031] Step 5.1.3: Update the centroid of the K-means clustering algorithm and repeat the iteration.

[0032] Step 5.1.4: Use the distributed distribution unit clustering algorithm to group the distribution units.

[0033] Furthermore, step 5.3 specifically includes the following steps:

[0034] Step 5.3.1. Decompose the channel matrix based on generalized singular value decomposition and transform the original problem form;

[0035] Step 5.3.2: Based on the transformed power optimization problem, construct the Lagrangian function and KKT conditions and solve them;

[0036] Step 5.3.3: Based on the optimal solution of the Langladimir function, the sub-channel auxiliary parameters are determined to determine the optimal transmission power of the sub-channel.

[0037] Furthermore, step 5.4 specifically includes the following steps:

[0038] Step 5.4.1. Construct a reward function based on the UAV motion pattern and scheduling problem.

[0039] Step 5.4.2: Obtain supervised learning parameters of the deep Q network by calculating the Bellman equation;

[0040] Step 5.4.3. Design the loss function for deep Q-network training.

[0041] Step 5.4.4: Determine the loss gradient during deep Q-network training based on the loss function.

[0042] Step 5.4.5: Optimize the UAV trajectory using the UAV trajectory optimization algorithm of the distributed distribution unit scheduling system.

[0043] The present invention discloses a cooperative secure communication network for offshore distributed power distribution units and unmanned aerial vehicles (UAVs). The hardware body includes a control center with multiple antennas, a multi-antenna UAV dispatch relay, and multiple single-antenna offshore distributed power distribution units; the software body includes a dispatch and power optimization unit located in the control center, and a power optimization unit and trajectory optimization unit located in the UAVs.

[0044] Furthermore, the control center equipped with M antennas transmits control signals to the distributed power distribution units with K single antennas at the same frequency through direct communication or by using the drone equipped with N antennas. The signal transmission adopts space division multiple access and time division multiple access technology. The control center directly sends the control signals to the K b The power distribution terminal transmits the control command and relays it to K r The distribution terminals are controlled, and K = K b +K r .

[0045] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0046] (1) The present invention proposes a UAV cooperative communication network for offshore distributed distribution units. By introducing a collaborative architecture of a control center, a multi-antenna UAV relay, and a single-antenna distributed distribution unit, a set of power optimization algorithms and trajectory optimization algorithms are designed. While improving the communication rate, the security and reliability of the distribution unit scheduling system are maximized, meeting the distribution network operation and management needs for the safe access of large-scale distributed energy at sea.

[0047] (2) The present invention solves the problems of limited communication coverage, insufficient real-time dispatching capability, and weak communication security in large-scale distributed energy access at sea, improves the operating efficiency and security of the distribution network, and meets the needs for efficient communication and safe dispatching in new energy access scenarios.

[0048] (3) The present invention uses convex optimization combined with machine learning to optimize the secure communication network. While ensuring good optimization results, it does not occupy high computing power, effectively saving the system's computing resources and costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Schematic diagram of the coordinated secure communication network for offshore distributed power distribution units and drones;

[0050] Figure 2 Flowchart of the method for optimizing the collaborative secure communication network of UAVs in offshore distributed power distribution units;

[0051] Figure 3 This is a comparison chart of the maximum transmission rates in different scenarios;

[0052] Figure 4 The following is a comparison chart of the maximum confidential transmission rate in different scenarios. DETAILED DESCRIPTION

[0053] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0054] like Figure 1 The figure shows a schematic diagram of the coordinated safety communication network of the offshore distributed power distribution unit and the drone. The control center equipped with M antennas can communicate directly or use the drone equipped with N antennas to relay the control signals with K single-antenna distributed power distribution units at the same frequency. The signal transmission adopts space division multiple access and time division multiple access technology. The control center directly sends the K b The power distribution terminal transmits the control command and relays it to K r The distribution terminals are controlled, and K = K b +K r .

[0055] To fully convey the dispatch signal, the transmission rate between the control center and the drone relay must be maximized. At the same time, to ensure transmission security, which could potentially prevent eavesdroppers from eavesdropping on the dispatch signal and potentially create security risks for the communication system, this paper designs a power optimization algorithm and a trajectory optimization algorithm to maximize the security of the communication network while meeting the dispatch link communication rate requirements.

[0056] In order to maximize the secure transmission rate of the communication network, it is necessary to optimize the scheduling parameters, such as Figure 2 The flow chart of the method for optimizing the cooperative secure communication network of the offshore distributed power distribution unit UAV is shown. The technical solutions of the application will be described in detail below in combination with the drawings:

[0057] 1. Multi-communication link channel modeling

[0058] (1) Control center-distributed power distribution unit link

[0059] The Alpha-beta-gamma (ABG) channel model is used for the ground-to-ground communication channel to describe the communication channel between the control center and potential eavesdroppers and between the distributed power distribution unit and potential eavesdroppers. This model is the closest approximation model to the path loss of actual 5G ground communication measurement results and is adopted by standardization organizations such as ITU-R, 3GPP, mmMAGIC, and QuaDRiGa. It is defined as:

[0060]

[0061] where d gg represents the two-dimensional distance between the transmitting node and the receiving node, is the intercept, ρ G and γ G correspond to the exponents related to the distance and frequency, respectively. The shadow fading coefficient χ G2G,σ is modeled as a Gaussian random variable with a mean of zero and a standard deviation of σ sh .

[0062] (2) Control center-relay UAV link

[0063] UAV-power distribution unit communication link: When modeling the air-to-ground (A2G) communication channel between the UAV and the ground receiver, the present application adopts a small-scale Rayleigh fading model (Rician Fading), in which the line-of-sight (LoS) component coexists with the non-line-of-sight (NLoS) component. Both the ground control center and the UAV receiver (UAV relay) are equipped with uniform linear arrays (ULAs) consisting of M and N antennas, respectively. The channel model between the control center and the relay UAV is a composite channel with LoS (line-of-sight) and NLoS (non-line-of-sight) superimposed, and its path loss is expressed as follows:

[0064]

[0065] where λ0represents the path loss per 1 meter of distance; d TR is the three-dimensional distance between the control center and the relay UAV; α is the path loss exponent; and β is the Rayleigh fading factor.

[0066] Let the elements of the non-line-of-sight channel component be independent and identically distributed, following the distribution Line of sight channel component may be represented as an angle of arrival component G AoA and an angle of departure component G AoD , i.e.:

[0067]

[0068] where the angle of arrival component G AoA and the angle of departure component G AoD between the control center and the relay UAV can be represented as and respectively, where λ is the carrier wavelength, Y is the antenna spacing, Λ TR = cos Θ sin φ is the component of the angle of arrival, Θ is the azimuth angle, and φ is the elevation angle, is the component of the angle of departure, where is the elevation angle, and ψ is the azimuth angle. These parameters are used to describe the signal propagation characteristics between the control center and the relay UAV.

[0069] (3) Relay UAV - Distributed Power Unit Link

[0070] Considering a single antenna setup at each distributed power unit, the transmission path loss from the relay UAV to the distributed power unit is expressed as follows:

[0071]

[0072] where the non-line of sight channel component obeys a distribution The line of sight channel component is similar in form to equation (3).

[0073] 2. Signal transmission strategy modeling

[0074] The control center equipped with M antennas can communicate control signals with K single-antenna distributed power units either directly or through the relay of the UAV equipped with N antennas at the same frequency. The signal transmission adopts spatial division multiple access (SDMA) and time division multiple access (TDMA) technology. The control center directly communicates control instructions to K b power terminals, and controls K r power terminals through the relay of the UAV, i.e. K = K b + K r . The corresponding control signal transmission link model and transmission rate will be described in detail below.

[0075] 1) Control center direct scheduling link

[0076] For direct communication, the control center performs beamforming design to the distributed distribution terminals based on space division multiple access and time division multiple access modes. The transmit beam assigns a beam vector to each distribution unit. However, transmit power leakage may occur between different beams, resulting in interference between multiple distribution terminals. The present invention considers the downlink transmission scenario, in which the control center sends K b Distributed distribution units transmit K b data streams. Its transmission signal model is:

[0077]

[0078] in is the beamforming vector, s k is the information symbol transmitted for the kth distribution unit. The beamforming or precoding matrix of the control center contains K b beamforming vectors, in Then, by beamforming vector ||w b,k || 2 The square norm of is used to calculate the transmission power allocated to the kth distribution unit. b The received signal of each distribution unit is expressed as

[0079]

[0080] For the kth distribution unit, the first element on the right side of the above formula (6) is the channel coefficient h from the distribution unit to the control center 0,k , beamforming vector w b,k and information symbols k The rest of the signal is a useful control signal for the power distribution unit. k , are all interference. Including sending to the rest of K b -1 device’s signal, represented by the beamforming vector w b,i and information i Composition, and Gaussian noise in the environment Then, we get the signal-to-interference-and-noise ratio

[0081]

[0082] in, represents the MISO channel from the control center to the kth distribution unit. The channel capacity of the direct link is obtained by the following formula:

[0083] C b,k =log2(1+γ b,k ). (8)

[0084] (2) UAV relay scheduling link

[0085] Assume that the control center transmission and the UAV relay transmission are synchronized based on time slots. In odd time slots (phases), the control center sends K r Data streams are transmitted to the drone relay, each corresponding to a power distribution unit. The transmission between the control center and the drone relay can be modeled as a standard point-to-point MIMO channel. The signal received by the drone relay can be expressed as:

[0086]

[0087] in, is the signal vector received by the drone relay; It is the MIMO communication channel between the control center and the UAV relay; is the noise vector, which conforms to the complex Gaussian distribution, has a mean of zero, and the covariance matrix is ​​the identity matrix; is the signal sent by the control center, where w b,k is the beam vector of the kth distribution unit, s k is the data flow of the kth distribution unit.

[0088] In even time slots, the drone relays the transmission signal:

[0089] x r =W r y1, (10)

[0090] in, The transmission signal relayed by the drone, is the beamforming matrix. K r The signal received by each power distribution unit can be expressed as:

[0091]

[0092] in, is the signal vector received by the distribution unit, It is a drone relay and K r A2G communication channel between the power distribution units, is a noise vector that conforms to a complex Gaussian distribution with a mean of zero and a covariance matrix that is the identity matrix.

[0093] The signal received by the kth power distribution unit is:

[0094]

[0095] in, represents the MISO channel relayed from the UAV to the kth distribution unit, is additive noise.

[0096] The signal-to-noise ratio (SINR) of the communication link from the control center to the kth distribution unit through the drone relay can be calculated as:

[0097]

[0098] The channel capacity C of the indirect link r,k It can be obtained by formula (8), where γ r,k Instead of γ b,k It is important to note that H1 and H2 are directly affected by the motion of the UAV (e.g., changes in distance, altitude, and orientation relative to the ground receiver).

[0099] 3. Signal eavesdropping risk link modeling

[0100] (1) Direct scheduling link eavesdropping

[0101] The eavesdropper monitors the direct link between the control center and the relevant distribution units, and the received signal is:

[0102]

[0103] in, represents the communication channel between the control center and the eavesdropper, n e is the noise at the eavesdropper's end, satisfying Based on the beamforming of the distribution unit k, the SINR of the direct link between the control center and the eavesdropper can be expressed as

[0104]

[0105] Therefore, the direct link eavesdropping capacity from the control center to the kth distribution unit is

[0106]

[0107] (2) Relay scheduling link eavesdropping: The eavesdropper can eavesdrop on the A2G relay communication link between the drone relay and the power distribution unit. The received signal can be expressed as

[0108]

[0109] The capacity of the eavesdropper's associated relay link is expressed as

[0110]

[0111] Among them, γ r,e is SINR, represents the channel between the drone and the eavesdropper, n 2,e is the noise at the eavesdropper's end, satisfying

[0112] 4. Formulation of information security issues

[0113] Confidentiality capacity is a measure of the rate at which information can be transmitted securely without being eavesdropped. It is defined as the difference between the achievable data rate of the legitimate receiver and the achievable data rate of the eavesdropper, taking into account the channel conditions and the security measures adopted. Confidentiality capacity corresponds to the rate at which the eavesdropper cannot decode any data. For the system model in Section 1, K scheduled directly by the control center b The average total confidentiality capacity of a distributed distribution unit in T time slots is

[0114]

[0115] Similarly, K r The average total confidentiality capacity of a distributed distribution unit in T time slots can be expressed as

[0116]

[0117] Among them, the symbol [·] + The meaning of is to take non-zero value, that is The total confidentiality capacity is the sum of the capacity of the direct transmission link and the capacity of the UAV relay transmission link, which can be expressed as

[0118] C T =C sec,b +C sec,r (twenty one)

[0119] The above formulas (19)-(21) describe the quantitative information security indicators of the distributed distribution network dispatching network, with the aim of minimizing the channel capacity of the eavesdropper on the basis of maximizing the confidentiality capacity.

[0120] Considering the two scheduling modes of directly scheduling links or scheduling distribution units through drones, the present invention decomposes the problem into two optimization problems:

[0121] 1) Direct dispatch link: For the directly dispatched distribution unit, the optimization problem can be expressed as

[0122]

[0123] In the above formula, C sec,b is the confidentiality capacity defined by formula (19), P b,max is the maximum dispatch signal transmission power of the control center, P b is the actual dispatch signal transmission power of the dispatch center, which can be expressed as The superscript of is the conjugate transpose. The optimization goal of the above problem (22) requires understanding the situation of the eavesdropping channel, but this is an unrealistic assumption in the actual communication environment. Therefore, the present invention assumes that the location of the eavesdropper and its channel state information (CSI) are unknown, which is a common situation in practice. In this case, due to the passive nature of the eavesdropper, it is difficult to detect or estimate its existence, location or channel. Therefore, the present invention can only consider the capacity of the legitimate distribution unit and reformulate the optimization problem as follows:

[0124]

[0125] The eavesdropper's location and channel are only used to calculate the resulting confidentiality capacity for performance evaluation.

[0126] 2) UAV relay scheduling link: For distributed power distribution units scheduled via UAV relay, since the eavesdropper’s location and channel state information (CSI) are unknown, the optimization problem can be defined as

[0127]

[0128] Among them, the objective function is the confidentiality capacity, P r,max is the maximum transmit power of the drone, is the actual transmission power of the UAV, (x r ,y r ,z r ) is bound to the spatial domain (L x ,L y ,L z )’s three-dimensional coordinates of the drone.

[0129] 5. Design of optimization solutions for scheduling problems

[0130] To solve the above scheduling optimization problem, this paper proposes an optimization method that decomposes the original problem into a direct scheduling link optimization problem and a UAV scheduling link optimization problem. The direct scheduling link optimization problem is characterized by the beamforming problem of the scheduling signal, and the UAV scheduling link optimization problem is characterized by the beamforming problem and trajectory optimization problem of the UAV. The specific solution steps are as follows:

[0131] (a) Distribution unit grouping

[0132] The goal of distribution unit grouping is to divide K distribution units into two groups, one of which is directly dispatched by the control center and the other is dispatched by the drone relay.

[0133] In order to solve the problem of grouping distribution units, an exhaustive search method can be used, but this method has a high computational complexity and increases exponentially with the increase in the number of distribution units. Therefore, the present invention adopts the K-means clustering grouping method, which is an unsupervised machine learning algorithm used to group a group of objects so that the similarity of members within the group is maximized and the difference with other group members is maximized. K-means clustering is applicable to any single-dimensional or multi-dimensional data measurement and can be grouped according to the target number of clusters defined by the distribution unit. Compared with graph theory, fuzzy c-means clustering and hierarchical clustering, K-means clustering has higher computational efficiency.

[0134] The present invention combines the characteristics of wireless communication systems to determine the similarity of data points. Since the goal is to assign distribution units to different scheduling links, one is a direct scheduling link and the other is a relay scheduling link, the present invention uses the normalized channel coefficients between distributed distribution units and the control center as data points for the K-means clustering algorithm. This method captures the channel gain variations caused by RF propagation effects (such as small-scale fading and shadow fading). Therefore, it can be defined

[0135]

[0136] in, is the normalized channel gain, h b,k is the channel gain between the control center and the kth distribution unit, and |.|² is the L² vector norm. Using these channel gains as data points, the present invention applies a K-means clustering algorithm to determine cluster centers or centroids, and further identifies the distribution unit associated with each centroid. The goal is to leverage the similarity of the channels between the control center and the distributed distribution units to divide them into two groups: one group dispatched directly by the control center and the other dispatched via drone relays. The specific steps involved are as follows:

[0137] (a1) Initialize the centroid: Randomly select the initial centroid C = {c1, c2, ...c n}, as K available data points U={u1,u2,...,u k ...,u K In this paper, two clusters are considered: c1 corresponds to the direct scheduling link, c2 corresponds to the drone relay scheduling link, and among the K distributed distribution units, the data point of the kth distribution unit is

[0138] (a2) Data Point Assignment: Calculate the distance between each data point (e.g., channel state) and the cluster center, and assign the data point to the nearest cluster center. Distances between data points can be calculated using a variety of metrics, such as Euclidean distance and Manhattan distance. The present invention uses the L2 norm (i.e., Euclidean distance).

[0139] (a3) Update the centroid: Update the centroid to minimize the sum of the squared distances between the distribution unit and its centroid, Among them, d r,k =|u k -c r |2 2 is the center of mass and the Euclidean distance between R=2 (representing the number of clusters, in this invention, the clusters are two groups). For example, the distance between the normalized channel gain and the centroid is

[0140] Combining the above steps, a distributed distribution unit clustering algorithm is given. The specific process is as follows

[0141] Algorithm 1: Distributed Distribution Unit Clustering

[0142] Input: U and C

[0143] Output: K c and C,

[0144] 1. Initialize the cluster center set: And set c=1;

[0145] 2. While loop: When c ≤ C:

[0146] a. Randomly select a cluster center from U

[0147] b. Update

[0148] c. Set c = c + 1;

[0149] 3. End While

[0150] 4. Repeat the cycle:

[0151] a. For each distribution unit m∈U, calculate its The minimum distance;

[0152] b. Assign each distribution unit to the nearest cluster center;

[0153] c. Update cluster centers is the average value of all distribution units m;

[0154] 5. Until the cluster distribution unit no longer changes.

[0155] (b) Direct scheduling link power optimization problem

[0156] Problem (23) is a traditional beamforming power control problem involving a control center and a distribution unit, which has been widely studied. The beamforming vector of the control center can be obtained by applying the weighted minimum mean square error (WMMSE) algorithm, which is an iterative closed-form solution that optimizes the precoding vectors of the transmitter and receiver to maximize the total dispatch rate of all distribution units under the power constraint of the control center. The precoding solution for the direct communication link is

[0157]

[0158] In the above formula, Q=diag{q1,...,q k} is the receiving end precoding matrix, F=diag{f1,...,f k} is the weight matrix, I M is the covariance matrix.

[0159] (c) UAV relay scheduling link power optimization problem

[0160] Next, the present invention will solve the beamforming and power control problems in relay communication. Inspired by the ZeroForing criterion and the channel singular value decomposition (SVD) structure, the present invention first proposes a beamforming matrix structure (i.e., W r ) to eliminate interference between the distribution unit dispatching signals. This transforms the optimization problem (24) into a simplified convex optimization problem. The present invention then uses the Lagrangian function and the KKT condition to solve the modified optimization problem to obtain the optimal beamforming matrix of the UAV.

[0161] 1. Beamforming Matrix Design: Beamforming is performed at the dispatch center and the UAV relay. Each beamforming matrix provides a dispatch signal for a group of distribution units. The dispatch signal sent by the UAV relay to the $K$th distribution unit can be expressed as:

[0162] y2=H2W r H1W br s K +H2W r n1+n2 (27)

[0163] In the above formula, Represents K groups of signals sent to K distribution units. The zero-forcing criterion requires H2W r H1W bris a diagonal matrix of rank K, which means Rank(H1) ≥ K and Rank(H2) ≥ K. Based on the generalized singular value decomposition (GSVD) theory, H2 and H1 can be expressed as:

[0164]

[0165] Among them, U i and V i (for i=1,2) is the identity matrix, Is a diagonal matrix with positive diagonal elements. Knowing the channel coefficients of the control center and the UAV, in order to satisfy the ZF criterion, the beamforming matrix W of the scheduling link is directly br for Beamforming matrix W of the UAV relay scheduling link r for Among them, Λ b 、 and Λ r Both are K × K diagonal matrices. To simplify the problem, we can assume that the elements of these two diagonal matrices are non-negative, representing the beamforming power allocated by the control center and the UAV relay, respectively.

[0166] Based on the above decomposition method, the objective function defined in formula (24) can be written as

[0167]

[0168] Among them, λ r,k Yes r (k,k). Furthermore, the beamforming power constraint defined in equation (24) can be expressed as

[0169]

[0170] Among them, σ 2,n is Σ2(n,n). Combining equations (29) and (30), the beamforming power optimization problem of the UAV at any location can be written as:

[0171]

[0172] Among them, P r,max is the maximum available transmission power of the UAV, λ r,max is the maximum allocated power of each antenna. The Lagrangian function of the optimization problem can be expressed as:

[0173]

[0174] Among them, α1,α 2,l and α 3,lis the non-negative Lagrange multiplier corresponding to the first and second constraints. It is known that the UAV antenna array has a total of N antennas, and it is necessary to design a beamforming power matrix for each sub-antenna. Define an auxiliary parameter as

[0175]

[0176] Assume that the lth antenna meets the condition Then the power matrix of the lth antenna is 0. Assume that the lth antenna meets the condition Then the power matrix of the lth antenna is Assume that the lth antenna meets the condition Then the power matrix of the lth antenna is λ r,max Among them, the Lagrange multiplier It can be obtained by substituting (33) into the first constraint of (31) and making it hold. The optimal beamforming matrix and transmission power formula defined above will be used for UAV trajectory optimization.

[0177] (d) UAV relay flight trajectory optimization problem

[0178] The objective function (24) is the parameter x r ,y r ,z r ,P and the constraints are non-convex, and the problem is a non-deterministic polynomial. Therefore, the present invention proposes a machine learning solution to update the UAV trajectory based on the transition process of the current system state. Since the next system state is independent of the previous state and action, the process can be modeled as a Markov decision process. In order to avoid the computational difficulties brought about by the high-dimensional state-action space, the present invention proposes a deep Q-network. It should be noted that the deep Q-network proposed below is based on basic reinforcement learning algorithms such as Q-learning and deep reinforcement learning. The purpose is to replace traditional optimization tools with deep Q-networks to solve this non-deterministic polynomial optimization problem while reducing power consumption and computing resource consumption. Depending on the specific application, the framework can be expanded to more advanced learning models to adapt to specific usage scenarios.

[0179] The Markov decision process of the drone agent consists of the state space Action Space Reward Space and the transition probability space At time slot t, the agent observes the state and take actions according to its strategy According to the transition probability distribution, the agent will move to the new state s t+1Since the transition probability is specific to the operating environment, the present invention chooses the Q-learning method as a model-free algorithm to find the best strategy for each action in each state. This means that there is no need to know But states, actions, and rewards need to be carefully defined.

[0180] The state set of this network is defined as Where t is the time slot index. Each state s t The corresponding 3D coordinates of the drone and the distribution unit that the drone relay is responsible for dispatching. The drone status is based on the defined action set. transfer, where each action consists of three parts related to the movement of the drone, namely a t ={δ x ,δ y ,δ z}, where δ x , δ y and δ z Indicates taking action a in the x, y, z directions t After that, the drone relay will receive a reward R t (s t ,a t ). The drone will receive more rewards for actions that lead to higher safe scheduling link capacity. The reward function is defined as:

[0181]

[0182] The Deep Q Network, originally proposed by Google Deep Mind, combines reinforcement learning and deep learning methods. This technology uses nonlinear functions, specifically deep neural networks (DNNs), to approximate Q values ​​and handle high-dimensional state-action problems. The Deep Q Network uses two DNNs with the same structure: a training network and a target network. The training network outputs the Q value associated with the drone's action in each state, and the target network calculates the Bellman equation to obtain the target Q value, thereby supervising the training network. The Bellman equation is expressed as

[0183]

[0184] Where s and a represent the next state and action, respectively. The parameter γ∈(0,1) is a discount factor that indicates the importance of future rewards. The target value is compared with the output of the trained network to minimize the loss function:

[0185]

[0186] The Q value of the first term comes from the target network, and the Q value of the second term comes from the training network. and θ represent the weights of the target network and the training network, respectively. The coefficients of are updated every few time steps to ensure the stability of the target value, thus promoting stable learning.

[0187] When the drone flies, the system generates an experience record. At time step t, the experience contains the current state s t 、Action a t , reward r t and the next state s t+1 , forming a quadruple e t =(s t ,a t ,r t ,s t+1 ). Each experience is stored in a replay memory with a capacity of N, denoted as M = {e1,...,e t ,...,e N This memory is a queue-like buffer that stores the latest N experience vectors. A mini-batch of samples from the replay memory is used as input for training the network. The main reason for using mini-batches is to break the sequential dependencies of the environment states, thereby promoting generalization.

[0188] During the training of a neural network, the loss gradient is required, which is mathematically expressed as

[0189]

[0190] In order to update the weight parameters θ and The goal is to minimize the prediction error. Finally, the present invention adopts an ∈-greedy algorithm to select actions to balance the exploration and exploitation of the drone in the environment. In this algorithm, the drone explores the environment by selecting random actions with a probability of ∈. More specifically, the drone exploits the environment by selecting actions that maximize the Q-value function with a probability of 1-∈, i.e. Initially, ∈ is set to a high value so that the drone spends more time exploring. As the agent gradually acquires more information about the environment, the value of ∈ is gradually reduced to make full use of existing experience and select the best action instead of continuing to explore extensively.

[0191] Combining the above processes and formulas, a UAV trajectory optimization algorithm for distributed distribution unit scheduling system is proposed. The specific process is as follows:

[0192] Algorithm 2: UAV trajectory optimization algorithm for distributed distribution unit scheduling system

[0193] 1. Initialize the exploration rate ε start , ε end and decay rate decay

[0194] 2. Initialize T time steps, rounds

[0195] 3. Initialize playback memory M, capacity N

[0196] 4. Initialize θ, γ,α,B

[0197] Start loop Each round performs the following operations

[0198] Reset the environment

[0199] Start the loop t=1,2,...,T and perform the following operations

[0200] Get the initial observation state s t

[0201] If ∈>random(0,1)then

[0202] Randomly select action a from the action space A t

[0203] otherwise

[0204] Select action a t =argmax a∈A Q(s t ,a;θ)

[0205] End sub-judgment

[0206] The drone performs actions in the environment t ;

[0207] Observe the state transition s t+1 ,r t ;

[0208] The storage state is transferred to M: M←M∪{s t ,a t ,r t ,s t+1};

[0209] Randomly sample a mini-batch of samples from M: e i =(s i ,a i ,r i ,s i+1 ); train the deep neural network and calculate the estimated Q value;

[0210] Calculate the loss between the estimated Q value and the calculated Q value;

[0211] Derive the loss gradient and update the parameters θ in the training network;

[0212] Every B steps, copy θ to Update target network;

[0213] End sub-loop

[0214] Update ∈ by ∈-greedy algorithm

[0215] Store the rewards for each round

[0216] End loop output: optimized drone trajectory.

[0217] like Figure 3 The following chart compares the maximum transmission rates under different scenarios. The chart shows the maximum transmission rates for drone trajectory optimization combined with beamforming optimization under different numbers of distribution units. As can be seen, the maximum transmission rate increases with the number of distribution units. The proposed drone trajectory optimization and power optimization strategies can significantly improve the information transmission rate of the control center.

[0218] like Figure 4 The following chart compares the maximum secure transmission rates under different scenarios, showing the maximum secure transmission rates for different numbers of power distribution units when using drone trajectory optimization combined with beamforming optimization. As can be seen from the figure, the proposed drone trajectory optimization strategy and power optimization strategy significantly improve the secure transmission rate of the target control center, especially when comparing the scenario with trajectory optimization alone and the scenario without any optimization.

Claims

1. A method for optimizing a coordinated secure communication network of an offshore distributed power distribution unit and a drone, characterized in that: The communication network hardware includes an offshore distributed power distribution unit, a drone relay, and a control center. The method includes the following steps: Step 1: Model the multi-communication link channels from the control center to the distributed distribution units; In step 1, the modeling of multiple communication link channels from the control center to the distributed power distribution unit specifically includes the following steps: Step 1.1: Model the direct transmission link from the control center to the distributed distribution unit. Use the Alpha-beta-gamma channel model (ABG) to approximate the actual communication path loss. Step 1.2: Model the relay transmission link from the control center to the relay UAV using a small-scale Rayleigh fading model. Considering the impact of sea waves on the UAV, the line-of-sight and non-line-of-sight components coexist in the model. Step 1.3: The link from the relay drone to the distributed distribution unit also uses the coexistence of line-of-sight and non-line-of-sight components, and the channel form is a dual form. Step 2: Model the signal transmission strategy between the control center and the drone relay; In step 2, the signal transmission strategy modeling for the control center and the drone relay includes the following steps: Step 2.1: The control center directly schedules link modeling, including the transmit signal model, receive signal model, and channel capacity assessment. Step 2.2: Modeling the UAV relay scheduling link, including the signal model from the control center to the UAV relay, the signal model from the UAV relay to the power distribution unit, and the scheduling link channel capacity evaluation method ultimately accepted by the power distribution unit; Step 3: Model the signal eavesdropping risk link and evaluate the eavesdropping channel capacity of the two scheduling links: the control center to the potential eavesdropper and the drone relay to the potential eavesdropper; Step 4: Formulate information security issues; In step 4, the information security problem is formulated, which specifically includes the following steps: Step 4.1: Model the secure communication links from the control center to the distributed distribution unit and from the drone to the distributed distribution unit, and provide an expression for the average secure capacity of multiple time slots. Step 4.2: Decompose the optimization problem and split the problem of maximizing confidentiality capacity into maximizing the confidentiality capacity of the direct scheduling link and maximizing the confidentiality capacity of the relay scheduling link. Step 5: Design an optimization solution for the scheduling problem; In step 5, the optimization scheme for the scheduling problem is designed, which specifically includes the following steps: Step 5.1: Use K-means clustering algorithm to group distributed distribution units; Step 5.2: Optimize the power of the direct scheduling link based on the zero-forcing method; Step 5.3: Optimize the UAV relay scheduling link power; Step 5.4: Use the deep Q network to optimize the UAV flight trajectory; Step 6: Output the optimized distribution network dispatching parameters, including the transmission power matrix, UAV trajectory and distribution unit grouping parameters.

2. The optimization method of the offshore distributed power distribution unit UAV collaborative secure communication network according to claim 1, characterized in that: Step 5.1 specifically includes the following steps: Step 5.1.1, initialize the cluster centers of the K-means clustering algorithm; Step 5.1.2: Calculate the distance between each data point and the cluster center and assign the data point to the cluster center. Step 5.1.3: Update the centroid of the K-means clustering algorithm and repeat the iteration. Step 5.1.4: Use the distributed distribution unit clustering algorithm to group the distribution units.

3. The optimization method of the offshore distributed power distribution unit UAV collaborative secure communication network according to claim 1, characterized in that: Step 5.3 specifically includes the following steps: Step 5.3.

1. Decompose the channel matrix based on generalized singular value decomposition and transform the original problem form; Step 5.3.2: Based on the transformed power optimization problem, construct the Lagrangian function and KKT conditions and solve them; Step 5.3.3: Based on the optimal solution of the Langladimir function, the sub-channel auxiliary parameters are determined to determine the optimal transmission power of the sub-channel.

4. The method for optimizing an offshore distributed power distribution unit-UAV collaborative secure communication network according to claim 1, characterized in that: Step 5.4 specifically includes the following steps: Step 5.4.

1. Construct a reward function based on the UAV motion pattern and scheduling problem. Step 5.4.2: Obtain supervised learning parameters of the deep Q network by calculating the Bellman equation; Step 5.4.

3. Design the loss function for deep Q-network training. Step 5.4.4: Determine the loss gradient during deep Q-network training based on the loss function. Step 5.4.5: Optimize the UAV trajectory using the UAV trajectory optimization algorithm of the distributed distribution unit scheduling system.

5. A marine distributed power distribution unit-drone collaborative safety communication system, used to implement the method according to claim 1, characterized in that: The hardware body includes a control center with multiple antennas, a multi-antenna drone scheduling relay and multiple single-antenna offshore distributed power distribution units; the software body includes a scheduling and power optimization unit located in the control center, as well as a power optimization unit and trajectory optimization unit located in the drone.

6. The offshore distributed power distribution unit-UAV collaborative safety communication system according to claim 5, characterized in that: assembly The control center of each antenna communicates directly or uses the installed The drone relay with 1 antenna operates at the same frequency as The distributed power distribution unit with a single antenna transmits the control signal. The signal transmission adopts space division multiple access and time division multiple access technology. The control center directly sends The power distribution terminal transmits the control command and relays it to the The distribution terminals are controlled to obtain .

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