Marine Internet of Things construction method based on unmanned aerial vehicle base station

Abstract

The invention discloses an ocean Internet of Things construction method based on an unmanned aerial vehicle base station, and belongs to the technical field of wireless communication networks. The ocean Internet of Things at least comprises one low earth orbit satellite, N unmanned aerial vehicle base stations and J ocean Internet of Things devices, and the method comprises the steps that the J ocean Internet of Things devices are grouped through a load grouping model, so that the nth unmanned aerial vehicle station provides relays for the M ocean Internet of Things devices. The present invention ensures balanced distribution of user loads between unmanned aerial vehicle base stations.

Description

Technical Field

[0001] This invention relates to a method for constructing a marine Internet of Things (IoT) based on a drone base station, belonging to the field of wireless communication network technology. Background Technology

[0002] The Marine Internet of Things (MIOT) relies heavily on robust communication systems to operate effectively. However, the vast ocean domain, inadequate infrastructure, dynamic environmental conditions, and limited terrestrial network coverage pose significant obstacles to connectivity for small-scale island developing countries, presenting key accessibility challenges. These challenges underscore the urgent need for innovative network architectures particularly suited to marine conditions. Recently, there has been increasing interest in space-based communication solutions, particularly using low Earth orbit (LEO) satellites to support connectivity in marine environments. LEO satellites have recently gained considerable attention due to their reduced development costs and the ability of extended LEO satellite networks to substantially minimize transmission latency while mitigating the limitations associated with terrestrial networks in marine environments. However, limitations in the transmission power and processing capabilities of MIOT devices, along with the Earth's curvature creating dead zones within satellite coverage areas and weak line-of-sight communication between MIOT devices and LEO satellites, result in reduced low-elevation coverage within LEO satellite networks, posing significant challenges to large-scale data transmission from MIOT devices. Summary of the Invention

[0003] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a method for constructing a marine Internet of Things (IoT) based on UAV base stations. This method utilizes UAV base stations as relays to ensure seamless communication between marine IoT devices and low Earth orbit satellites, while also ensuring a balanced distribution of marine IoT devices among UAVs, thereby improving communication efficiency.

[0004] To achieve the aforementioned objective, this invention provides a method for constructing a marine Internet of Things (IoT) based on unmanned aerial vehicle (UAV) base stations. The marine IoT includes at least one low-Earth orbit satellite, N UAV base stations, and J marine IoT devices. The method includes:

[0005] The J sea network devices are grouped using a load grouping model, which is as follows: , In the formula, The j-th marine network device belongs to the component published by the n-th Gaussian and belongs to the n-th unmanned aerial vehicle base station. The probability of coverage area, where U is the set of drones: ; It is the mixing coefficient; and It is the nth Gaussian distribution Two parameters; For a Gaussian distribution set, .

[0006] Compared with existing technologies, the marine Internet of Things (IoT) construction method based on UAV base stations provided by this invention utilizes UAV base stations as relays to ensure seamless communication between marine IoT devices and low Earth orbit satellites. By allocating J marine IoT devices to each of N UAV base stations through a load grouping model, it can ensure a balanced distribution of marine IoT devices among UAVs and improve communication efficiency. Attached Figure Description

[0007] Figure 1 This is a flowchart of the marine Internet of Things (IoT) construction method based on UAV base stations provided by the present invention.

[0008] Figure 2 This is a block diagram of the control system of the nth UAV base station provided by the present invention. Detailed Implementation

[0009] It should be noted that, below, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. The advantages and features of the present invention, as well as the methods for achieving these advantages and features, will become clear from the accompanying drawings and the detailed embodiments described below.

[0010] However, the present invention is not limited to the embodiments disclosed below, and can be implemented in many different forms. This embodiment is only used to make the disclosure of the present invention more complete and to fully inform those skilled in the art of the present invention of the scope of the invention. The present invention is defined only by the scope of the claims.

[0011] While terms such as "first," "second," etc., are used to describe various elements, components, and / or parts, these elements, components, and / or parts are not limited by these terms. These terms are used only to distinguish one element, component, or part from other elements, components, or parts. Therefore, it is apparent that, within the technical spirit of this disclosure, the first element, first component, or first part mentioned below may also be a second element, second component, or second part, and the terminology used in this specification is for describing embodiments only and is not intended to limit this disclosure.

[0012] In this specification, unless otherwise specified in the text, the singular includes the plural. The use of "comprising" and / or "consisting of" in this specification does not exclude the presence or addition of one or more other structural elements, steps, actions, and / or components mentioned.

[0013] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Furthermore, terms defined in commonly used dictionaries shall not be interpreted ideally or excessively unless explicitly and specifically defined.

[0014] In this invention, the marine Internet of Things (IoT) comprises a low Earth orbit (LEO) satellite, N unmanned aerial vehicle (UAV) base stations, and J marine IoT devices. The LEO satellite operates at a constant speed and fixed altitude within a predefined orbital plane. Simultaneously, the N UAV base stations are deployed as airborne base stations to facilitate communication within the marine environment. For simplicity, this invention assumes that the LEO satellite remains within its designated coverage area during the observation period without switching over, thereby ensuring continuous service within the marine IoT. The marine IoT includes a set of UAV base stations. A collection of marine IoT devices distributed across ocean areas, communicating via low Earth orbit satellites and drone base stations. This invention defines the communication process over discrete time slots t and t={1,2,…,T}, with the system dynamically operating to optimize network performance at each moment. In this invention, the total duration is represented by T, during which the marine IoT consistently manages multiple communication and resource allocation tasks in each time slot within the originally allocated time frame T. Without loss of generality, this invention incorporates unmanned aerial vehicle (UAV) base stations. Position in 3D space is defined as: , In the formula, Indicates drone base station Latitude and longitude coordinates, This indicates its flight altitude. Similarly, marine IoT devices... The position is represented as: , in, Corresponding to the latitude and longitude of the marine IoT devices, altitude is assumed to be zero because these devices are located at sea level. To ensure uninterrupted service and optimal connectivity, the drone base station dynamically adjusts its location in each time slot based on the real-time location of the marine IoT devices. This adaptive repositioning allows the drone base station to maintain a stable and high-quality communication link. Notably, the drone base station maintains a link with a terrestrial base station (TBS) or a low-Earth orbit satellite, depending on its location relative to the TBS coverage area. When the drone base station is operating within the TBS coverage area, it connects via a high-speed, low-latency link. However, once the drone base station moves beyond the coastal area and leaves the TBS range, it seamlessly switches to a satellite link by connecting to a low-Earth orbit satellite, thus ensuring continuous connectivity in remote or maritime areas.

[0015] For downlink communication, this invention employs a marine network of low-Earth orbit satellites and UAV base stations integrating MIOTD devices. The low-Earth orbit satellites utilize Interleaved Frequency Division Multiple Access (OFDMA) and Non-Orthogonal Multiple Access (NOMA) communication methods. OFDMA divides the available bandwidth into multiple orthogonal subcarriers, thereby allowing flexible and adaptive subchannel allocation based on real-time service requirements. Each subchannel is allocated equal bandwidth. Where B represents the total system bandwidth and W represents the total number of sub-channels. This partitioning ensures efficient spectrum utilization and reduces interference. Meanwhile, NOMA enhances spectral efficiency by enabling multiple MIOTDs to utilize the same frequency band via power domain multiplexing. In this setup, different MIOTDs receive different power levels based on their respective channel conditions.

[0016] This invention defines connectivity decision as the nth UAV base station The function of the location relative to the coverage area of ​​TBS and low Earth orbit satellites. The coverage radius and the location of TBS are expressed as follows: and .

[0017] At time t, the nth drone base station The distance between and TBS is expressed as: , Connectivity is represented as: , ,but, ,otherwise .

[0018] Meanwhile, at time t, the nth drone base station and the jth sea network device The connection status of the link between them is represented as The j-th marine internet device is within the coverage area of ​​the n-th drone base station. ,otherwise ,Right now hour ,otherwise, , In the formula, For the nth UAV base station The coverage angle.

[0019] Figure 1 This is a flowchart of the marine Internet of Things (IoT) construction method based on UAV base stations provided by the present invention. Figure 1 As shown, the marine Internet of Things (IoT) construction method based on UAV base stations provided by this invention includes: S1: Group the J sea network devices using a load grouping model, where the load grouping model is as follows: , In the formula, The j-th marine network device belongs to the component published by the n-th Gaussian and belongs to the n-th unmanned aerial vehicle base station. The probability of coverage area, where U is the set of drones: ; It is the mixing coefficient; and It is the nth Gaussian distribution Two parameters; For a Gaussian distribution set, ; S2: Configured to allocate channels to N drone base stations via the channel allocation module; S3: Calculate the total performance index using the total performance index calculator; S4: The nth drone base station It updates its control parameter vector based on the overall performance index, provides signal relay to its M maritime joint defense devices according to the assigned channel, and also reports its parameters to low Earth orbit satellites, n=1,2,…,N.

[0020] In this invention, the overall performance index is represented by a set. , Indicates total throughput; Indicates overall communication fairness; This indicates the total communication coverage.

[0021] In this invention, the nth UAV base station throughput for: , In the formula, To be assigned to the nth drone base station The mth marine network device of the relay The bandwidth of the connection; , , From low Earth orbit satellites to drone base stations The transmission power; This refers to the altitude of the low Earth orbit satellite. For the nth UAV base station The altitude at which it is located; Noise power; The average channel gain is at a reference distance of 1m; , , For the nth UAV base station The power; For drone base stations Location, For the nth UAV base station The m-th marine network device of the relay The location.

[0022] In this invention, the total throughput of the low-Earth orbit satellites is: .

[0023] In this invention, the nth UAV base station Fairness of communication for: , In the formula, M is the nth drone base station Number of IoT devices served; For the nth UAV base station and the mth sea network device At time t, connectivity is 1 if connected, otherwise 0. , For coefficients; To be assigned to the nth drone base station The mth marine network device of the relay The bandwidth of the connection.

[0024] In this invention, the overall communication fairness of low-Earth orbit satellites is: .

[0025] In this invention, the nth UAV base station Communication coverage for: , In the formula, For the nth UAV base station and the mth sea network device At time t, connectivity is 1 for connectivity and 0 otherwise.

[0026] In this invention, the total communication coverage of low-Earth orbit satellites is: .

[0027] In this invention, the optimal locations of N unmanned aerial vehicle (UAV) stations are obtained according to the following formula; ,, In the formula, .

[0028] In this invention, the bandwidth allocated to N unmanned aerial vehicle (UAV) stations is obtained according to the following formula; ,, In the formula, .

[0029] In this invention, the nth UAV base station is obtained according to the following formula. Control functions: , It is the nth drone base station The parameter vector of the control function; It is the nth drone base station The state vector, the state vector Including power, location And attitude. Update the nth UAV base station using gradient raising method based on objective function. parameter vector No prior settings are required.

[0030] Figure 2 This is a block diagram of the control system for the nth UAV base station provided by the present invention. Figure 2 As shown, the control system of the nth UAV base station includes a first autonomous learning module, a position controller, and a first error calculator. The first error calculator calculates the error based on the data from the nth UAV base station. The measured location and The first autonomous learning module generates a sequence error signal and then generates a first state at time t based on the sequence error signal. The first self-learning module is based on the first state. Unmanned aerial vehicle (UAV) base stations weight and coverage angle Control strategy for generating position controller , This is the control parameter vector of the position controller at time t.

[0031] In this invention, the position controller outputs the angular velocity of the motor driving the UAV base station at time t. ; nth UAV base station The position at time t is L. n (t) and optimal position The error is Then we have: , In the formula, ; Written in matrix form: , In the formula, , .

[0032] In this invention, for a multi-rotor drone, the position controller outputs the angular velocities of the multiple motors that drive the drone.

[0033] Although the present invention is described with a control quantity of 3, it can also be K-1, where K is greater than or equal to 2.

[0034] In this invention, the position controller control parameters are affected not only by the state but also by the unmanned aerial vehicle (UAV) station. Coverage angle Impact and weight The impact.

[0035] In this invention, the neural network of the first autonomous learning module includes a first input layer, a first hidden layer, and a first output layer. The first input layer includes K+1 neurons, which respectively input the first state. K-1 elements, drone station Weight and coverage angle The first hidden layer comprises K+1 neurons; the first output layer comprises K neurons, and the first to K-1 neurons of the first output layer output K-1 control values ​​for the position controller. , In the formula, , and These are the center and width of the q-th Gaussian function in the neural network of the first autonomous learning module; These are the weights between the q-th neuron in the hidden layer and the k-th neuron in the output layer of the neural network for the autonomous learning module at time t; q=1,…,K+1; k=1,…,K-1. Represents the L2 norm, Indicates splicing; It is the nth drone base station The weight.

[0036] In the first embodiment, the first autonomous learning module, according to... Generate the nth UAV base station The first state-value function at time t The output of the Kth neuron in the output layer of the neural network of the first autonomous learning module is: , In the formula, It represents the weights between the q-th neuron in the hidden layer of the neural network of the first autonomous learning module and the K-th neuron in the output layer.

[0037] In the first embodiment, the first autonomous learning module further constructs a first objective function according to the following formula:

[0038] In the formula, , In the formula, ; The first state value function at time t; The first state value function at time t-1; , These are weighting coefficients used to adjust the dimensions of each value.

[0039] The first self-learning module updates the parameter vector according to the following formula. Parameters in: , , , , In the formula, , , , The learning coefficient; Let k be the weights between the q-th neuron in the hidden layer and the n-th neuron in the output layer of the neural network of the autonomous learning module at time t+1, where k=1,…,K-1; The weights between the q-th neuron in the hidden layer and the K-th neuron in the output layer of the neural network in the autonomous learning module at time t; These are the centers of the Gaussian function before the q-th update and at time t+1, respectively. Let be the bandwidth of the q-th Gaussian function at time t+1.

[0040] In the first embodiment, the first autonomous learning module also determines the first optimal function. Is it the smallest? If not, , , , Repeat the above steps. If yes, output... , , , As the optimal parameter for calculation and make Each of these parameters is assigned to one of the K-1 control parameters of the position controller.

[0041] The output of the speed controller at time t is It is the AC voltage signal used to drive the motor of the UAV base station; driving the nth UAV base station The measured angular velocity of the running motor at time t and the position controller output drives the nth UAV base station angular velocity of the running motor The bit error rate is Then we have: , In the formula, In the formula, The rotation angle of the motor driving the UAV station at time t.

[0042] Written in matrix form: , In the formula, , .

[0043] Although the attitude control quantity of this invention is described using 3 as an example, it can also be S-1, where S is greater than or equal to 2.

[0044] In this invention, for a multi-rotor drone, the speed controller controls the output AC voltage values ​​of the multiple motors that drive the drone.

[0045] The block diagram of the control system for the nth UAV base station provided by this invention further includes a second error calculator, a speed controller, and a second autonomous learning module. The second error calculator calculates the measured angular velocity of the nth UAV base station and the ideal angular velocity output by the speed controller. Generate the second error signal of the sequence The second self-learning module is based on The nth drone station Communication coverage angle Unmanned aerial vehicle (UAV) stations weight Control strategy for generating speed controller , This is the control parameter vector for the speed controller.

[0046] The neural network of the second autonomous learning module includes a second input layer, a second hidden layer, and a second output layer. The second input layer includes S+1 neurons, which are respectively input to the second state. The S-1 elements in the unmanned aerial vehicle station Communication coverage angle and weight The second hidden layer consists of S+1 neurons, and the second output layer consists of S neurons. The first to the (S-1)th neurons of the second output layer respectively input S-1 control variables to the speed controller. , In the formula, , and These are the center and width of the p-th Gaussian function in the neural network of the second autonomous learning module; These are the weights between the q-th neuron in the hidden layer and the n-th neuron in the output layer of the neural network in the second autonomous learning module at time t; p=1,…,S+1; s=1,…,S-1. This represents the L2 norm.

[0047] In the first embodiment, the second autonomous learning module, according to... Function for generating the second state value of the nth UAV base station The outputs of the S-th neuron in the second output layer of the neural network of the second autonomous learning module are as follows: , In the formula, It represents the weights between the p-th neuron in the hidden layer and the S-th neuron in the output layer of the neural network of the second autonomous learning module.

[0048] In the first embodiment, the first autonomous learning module further constructs a second objective function according to the following formula:

[0049] In the formula, , In the formula, ; For the nth unmanned aerial vehicle station The second state value function at time t-1; , These are weighting coefficients used to adjust the dimensions of each value; and For the nth unmanned aerial vehicle station The set attitude angle and the actual attitude angle at time t.

[0050] The second self-learning module updates the parameter vector according to the following formula. Parameters in: , , , , In the formula, , , , The learning coefficient; Let s be the weights between the p-th neuron in the hidden layer and the k-th neuron in the output layer of the neural network of the second autonomous learning module at time t+1, where s=1,…,S-1; The weights between the p-th neuron in the hidden layer of the neural network of the second autonomous learning module and the K-th neuron in the second output layer at time t; These are the centers of the p-th Gaussian function before the update and at time t+1, respectively. Let be the bandwidth of the p-th Gaussian function at time t+1.

[0051] In the first embodiment, the second autonomous learning module further determines the second objective function. Is it the smallest? If not, , , , Repeat the above steps. If yes, output... , , , As the optimal parameter for calculation and make Assign values ​​to the S-1 control parameters of the attitude controller respectively.

[0052] In this invention, , .

[0053] The present invention, through the above technical solution, eliminates the need to pre-set the control parameters of the unmanned aerial vehicle (UAV) station, and can actively learn.

[0054] The preferred embodiments of the present invention disclosed herein are merely for the purpose of illustrating the present invention. The preferred embodiments do not describe all the details exhaustively, nor do they limit the invention to specific implementation methods. Obviously, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for constructing a marine Internet of Things (IoT) based on unmanned aerial vehicle (UAV) base stations, wherein the marine IoT comprises at least one low Earth orbit satellite, N UAV base stations, and J marine IoT devices, characterized in that, The method includes: The J sea network devices are grouped using a load grouping model, which is as follows: ， In the formula, The j-th marine network device belongs to the component published by the n-th Gaussian and belongs to the n-th unmanned aerial vehicle base station. The probability of coverage area, where U is the set of drones: ; It is the mixing coefficient; and It is the nth Gaussian distribution Two parameters; For a Gaussian distribution set, .

2. The method for constructing a marine Internet of Things based on a drone base station according to claim 1, characterized in that, Also includes: Channels are allocated to N drone base stations using the channel allocation module; Calculate the overall performance index using the overall performance index calculator; The nth UAV base station updates its control parameter vector according to the overall performance index, and provides signal relay to M maritime joint defense devices within its coverage area according to the channel assigned to it. It also reports its own parameters to low Earth orbit satellites, n=1,2,…,N.

3. The method for constructing a marine Internet of Things based on a UAV base station according to claim 2, characterized in that, The overall performance index is represented by a set. , Indicates total throughput; Indicates overall communication fairness; This indicates the total communication coverage.

4. The method for constructing a marine Internet of Things based on a UAV base station according to claim 3, characterized in that, The nth drone base station throughput for: ， In the formula, To be assigned to the nth drone base station The mth marine network device of the relay The bandwidth of the connection; , , From low Earth orbit satellites to drone base stations The transmission power; This refers to the altitude of the low Earth orbit satellite. For the nth UAV base station The altitude at which it is located; Noise power; The average channel gain is at a reference distance of 1m; , , For the nth UAV base station The power; For drone base stations Location, For the nth UAV base station The m-th marine network device of the relay The location.

5. The method for constructing a marine Internet of Things based on a UAV base station according to claim 4, characterized in that, The nth drone base station Fairness of communication for: ， In the formula, M is the nth drone base station Number of IoT devices served; For the nth UAV base station and the mth sea network device At time t, connectivity is 1 if connected, otherwise 0. , For coefficients; To be assigned to the nth drone base station The mth marine network device of the relay The bandwidth of the connection.

6. The method for constructing a marine Internet of Things based on a UAV base station according to claim 5, characterized in that, The nth drone base station Communication coverage for: , In the formula, For the nth UAV base station and the mth sea network device At time t, connectivity is 1 for connectivity and 0 otherwise.

7. The method for constructing a marine Internet of Things based on a drone base station according to claim 6, characterized in that... , Obtain N drone stations according to the following formula. The best location; ； In the formula, ...

8. The method for constructing a marine Internet of Things based on a drone base station according to claim 7, characterized in that... , The bandwidth allocated to N unmanned aerial vehicle (UAV) stations is obtained using the following formula; ； In the formula, .

9. The method for constructing a marine Internet of Things based on a drone base station according to claim 8, characterized in that, The nth UAV base station is obtained according to the following formula. Control functions: , It is the nth drone base station The parameter vector of the control function; It is the nth drone base station The state vector, the state vector Including location and posture .

10. The method for constructing a marine Internet of Things based on a UAV base station according to claim 9, characterized in that, The nth UAV base station is updated using gradient descent based on the objective function. parameter vector .

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