A blockchain-based method for spectrum trading between drones and ground networks

Abstract

A blockchain-based method for implementing spectrum transactions between drones and ground networks belongs to the field of drone and air-ground communication technology. The method comprises the following steps: a drone applies to join the blockchain network and its identity is verified. The drone and the ground network perform spectrum pricing and interference coordination based on interference. The ground network verifies the transaction to determine whether the transaction is written to the next block. The present invention adopts an interference-based spectrum pricing model to reduce the weighted cumulative interference caused by drones to ground base stations in spectrum transactions. It fully utilizes the flexibility of drone base station deployment in spectrum transactions, implements a drone interference coordination mechanism based on a particle swarm algorithm, and further reduces the interference caused by drones to ground base stations. The method utilizes the joint deployment of multiple drones to further reduce the interference caused by drones to ground base stations compared to single drone deployment. By designing parameter interference weight factors, a higher quality of service is achieved.

Description

Technical Field

[0001] The present invention relates to a method for implementing spectrum transactions between unmanned aerial vehicles (UAVs) and ground networks based on blockchain, and belongs to the technical field of UAVs and air-ground communications. Background Art

[0002] With the rapid development of wireless communications and internet technologies, network architectures are no longer limited to terrestrial networks, but are gradually evolving towards heterogeneous networks that integrate air, space, land, and sea. In this future integrated network architecture, air-based networks, primarily drones, can be deployed on demand to flexibly provide services to terrestrial users, such as emergency rescue, data offloading, hotspot coverage, and military operations. Deploying drones as base stations in hotspots such as stadiums can effectively enhance user experience. However, drones currently primarily utilize the 2.4GHz and 5.8GHz public frequency bands. The limited bandwidth and broadcast characteristics of these bands make it difficult to meet the needs of massive device access and reliable communication for drone users.

[0003] Existing drone spectrum trading schemes mainly include the following types:

[0004] 1. Blockchain-based spectrum trading solution

[0005] Blockchain technology, due to its decentralized, tamper-proof, and highly secure nature, is considered a key technology for 6G spectrum management. For example, Qiu J et al., in a 2019 article published in the IEEE Internet of Things Journal, proposed a blockchain-based approach to secure spectrum transactions between drones and operators. They also designed an incentive mechanism for operators, maximizing both the benefits of operators and drones through both non-uniform and uniform pricing. In a 2022 article published in Computers & Electrical Engineering, Lijuan Liu et al. proposed a drone spectrum trading method based on a distributed blockchain consortium system, which outperformed other systems in terms of security and user privacy.

[0006] 2. Spectrum trading scheme based on economic model

[0007] Spectrum trading schemes based on economic models primarily focus on the various parties involved in the transaction, aiming to maximize their benefits. They typically use economic methods such as game theory and contract theory to model and analyze the transactions. For example, in a 2020 article published in the journal IEEE Network, Ansari et al. proposed a competitive open market model for spectrum trading to support spectrum trading between multiple drones and multiple operators. Under this model, spectrum prices ultimately reach equilibrium among drones, and the impact of drone willingness-to-pay factors on spectrum trading prices was analyzed. In a 2018 article published in the journal IEEE Transactions on Wireless Communications, Hu Z et al. established a spectrum trading model between operators and drones based on contract theory, derived an optimal pricing strategy for fixed bandwidth allocation, and proposed a dynamic programming algorithm to calculate the optimal bandwidth allocation in polynomial time.

[0008] 3. Spectrum trading scheme based on relay services

[0009] In a spectrum trading scheme based on relay services, drones take advantage of their own line-of-sight channels, borrowing terrestrial spectrum while providing relay services to terrestrial users in return. For example, in an article published in IEEE Wireless Communications Letters in 2022 by Wang D et al., drones used the spectrum of terrestrial users to simultaneously send their own uplink information and that of terrestrial users. Based on this, a game model was established, verifying that this method can effectively improve the overall transmission rate of the network. In an article published in IEEE Access in 2020, Alireza Shamsoshoara et al. studied the spectrum sharing model for drones in disaster emergency scenarios, dividing drones into relay drones and sensor drones that use the acquired spectrum to perform disaster relief missions. The performance and convergence of the proposed method were analyzed.

[0010] Currently, research on spectrum trading using drones as base stations is relatively limited. These studies primarily consider the economic benefits of each trading entity, such as designing incentive mechanisms for spectrum providers (e.g., operators) to maximize the benefits for both providers and drones. Other research establishes game models between spectrum providers and drones to determine the spectrum price at a Nash equilibrium. Because drone-to-ground communications can be approximated as line-of-sight channels, drone base stations could potentially cause significant interference to ground base stations. However, current drone spectrum trading schemes fail to account for variations in interference relationships, potentially causing significant disruption to ground networks. Summary of the Invention

[0011] In order to overcome the shortcomings of the existing technology, the present invention provides a blockchain-based method for implementing spectrum transactions between drones and ground networks.

[0012] A blockchain-based method for implementing spectrum transactions between drones and ground networks includes the following steps:

[0013] Step 1: The drone applies to join the blockchain network and its identity is verified.

[0014] Step 2: UAVs and ground networks conduct spectrum pricing and interference coordination based on interference.

[0015] Step 3: The ground network verifies the transaction to determine whether it is included in the next block.

[0016] The advantage of this invention is that it provides a spectrum trading solution for drone-ground networks based on blockchain, designs an efficient, energy-saving, flexible, low-interference and highly secure spectrum sharing method, and provides a basis for improving spectrum utilization in heterogeneous networks of air, land, and sea.

[0017] The present invention proposes a blockchain-based method for implementing secure spectrum trading between drones and ground networks, and proposes an interference-based spectrum pricing model. By using price factors, drones are encouraged to select frequency bands with lower interference to ground base stations in spectrum trading, thereby reducing the serious interference to ground base stations caused by drone spectrum trading. A blockchain-based interference coordination mechanism for spectrum trading between drones and ground networks is proposed. By using a particle swarm optimization algorithm, drones are flexibly deployed during spectrum trading, further reducing the weighted cumulative interference of drones to ground base stations. In the blockchain-based spectrum trading verification mechanism, payment verification and interference verification mechanisms of spectrum trading are taken into account. Compared with the case without interference pricing and interference coordination, the proposed spectrum trading implementation method significantly improves the probability of successful transactions under interference verification, defines interference weight factors of drones to ground base stations, takes into account the differences in interference caused by spectrum trading to different services, and has a certain protective effect on services with higher service quality requirements.

[0018] The beneficial effects achieved by the present invention are as follows:

[0019] (1) The UAV-ground network spectrum transaction based on blockchain technology ensures the openness, transparency, traceability and non-tamperability of the transaction, thus avoiding the occurrence of illegal transactions.

[0020] (2) Taking into account the serious interference problem that may arise in drone spectrum trading, an interference-based spectrum pricing model is adopted to reduce the weighted cumulative interference caused by drones to ground base stations in spectrum trading.

[0021] (3) By making full use of the flexibility of drone base station deployment in spectrum trading, a drone interference coordination mechanism is implemented based on the particle swarm algorithm, further reducing the interference caused by drones to ground base stations.

[0022] (4) The joint deployment of multiple drones further reduces the interference caused by drones to ground base stations compared to single drone deployment.

[0023] (5) Through the interference-based pricing model and blockchain spectrum trading interference coordination mechanism, the success probability of drone spectrum trading is effectively improved.

[0024] (6) By designing parameter interference weight factors, better protection of services with higher service quality requirements is achieved when UAVs trade spectrum with ground networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] When considered in conjunction with the accompanying drawings, the present invention can be more completely and better understood and its many attendant advantages can be easily known by referring to the following detailed description. However, the drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention, as shown in the figure:

[0026] Figure 1 Flowchart of the present invention.

[0027] Figure 2 This is the information interaction diagram of the present invention.

[0028] Figure 3 This is an interactive diagram for applying for a link to the drone of the present invention.

[0029] Figure 4 This is the particle swarm algorithm optimization flow chart of the UAV deployment position and power.

[0030] Figure 5 This is a schematic diagram of the interference verification area for drone spectrum trading in the present invention.

[0031] Figure 6 Schematic diagram of the drone-ground network spectrum transaction of the present invention.

[0032] Figure 7 Schematic diagram of the deployment of the UAV and ground network nodes of the present invention.

[0033] Figure 8 This is a comparison diagram of the cumulative interference of the drone base station spectrum trading solution of the present invention.

[0034] Figure 9 This is a comparison chart of the maximum interference of the drone base station spectrum trading solution of the present invention.

[0035] Figure 10 This is a comparison chart of the success probability of drone spectrum transactions in the present invention. DETAILED DESCRIPTION

[0036] The present invention will be further described below with reference to the accompanying drawings and examples.

[0037] Obviously, many modifications and variations made by those skilled in the art based on the purpose of the present invention fall within the protection scope of the present invention.

[0038] The terms "first" and "second" are used for descriptive purposes only and should not be construed to indicate or imply relative importance or implicitly specify the quantity of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. In the description, "plurality" means two or more, unless otherwise specifically defined.

[0039] Unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," and "fixed" should be interpreted broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediary; and internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in the present invention based on specific circumstances.

[0040] Those skilled in the art will understand that unless otherwise defined, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by those skilled in the art.

[0041] To facilitate understanding of the embodiments, further explanations will be given below, and each embodiment does not constitute a limitation of the embodiments.

[0042] Example 1: Figure 1 、 Figure 2 As shown in the figure, a blockchain-based method for spectrum trading between drones and ground networks mainly solves the following technical problems:

[0043] There is a spectrum shortage problem when drones are used as mobile base stations to cover hot spots.

[0044] Security issues that exist when drone base stations trade spectrum with ground networks.

[0045] Serious air-ground interference problems arise when drone base stations trade spectrum with ground networks.

[0046] When drone base stations conduct spectrum transactions with ground networks, the probability of successful transactions is low due to severe interference.

[0047] The problem of drone deployment location and power optimization when drone base stations conduct spectrum trading with ground networks.

[0048] In order to solve the above problems, realize safe spectrum trading between drones and ground networks, improve the spectrum efficiency of drones and suppress the interference of drones on ground networks, the present invention proposes a method for realizing spectrum trading between drones and ground networks based on blockchain technology and swarm intelligence algorithm.

[0049] The present invention discloses a blockchain-based method for implementing spectrum transactions between drones and ground networks, which can be used to enable drones deployed in hotspot areas to conduct spectrum transactions with ground networks, while suppressing the serious interference that spectrum transactions may cause to ground networks.

[0050] A blockchain-based method for implementing spectrum transactions between drones and ground networks includes the following steps:

[0051] Step 1: The drone applies to join the blockchain network and its identity is verified.

[0052] Step 2: UAVs and ground networks conduct spectrum pricing and interference coordination based on interference.

[0053] Step 3: The ground network verifies the transaction to determine whether it is included in the next block.

[0054] Step 1, "The drone applies to join the blockchain network and verifies its identity," includes the following steps:

[0055] As a decentralized distributed computing paradigm, blockchain can establish a peer-to-peer trust mechanism in a non-trust environment. Due to its distributed ledger structure, transactions are verified across all nodes in the network, making spectrum transactions open, transparent, traceable, and tamper-proof, ensuring the security of spectrum transactions. Before drones can conduct spectrum transactions with ground networks, they must first apply for and obtain a blockchain network access certificate. Figure 3 shown.

[0056] The process of joining the drone chain includes two steps: application and verification:

[0057] Application steps: First, the drone provides information such as device number and antenna configuration to apply for an access certificate from the certificate management agency. After the certificate management agency reviews and verifies the application, the drone will be issued a digital authentication certificate. The data structure of the digital authentication certificate is shown in formula (1):

[0058] C U ={Version,N S ,i U , K U , D U , VU , Issuer, Algorithm, Signature} (1)

[0059] Among them, Version represents the version number of the digital certificate.

[0060] N S Indicates the digital certificate serial number,

[0061] i U Indicates the identity id of the drone.

[0062] K U Indicates the public key and public key algorithm information disclosed by the drone.

[0063] D U Indicates the issuance date and validity period of the digital certification.

[0064] Issuer: The name of the device that issued the certificate.

[0065] Algorithm indicates the signature algorithm used by the certificate authority to issue certificates.

[0066] Signature represents the digital signature of the certificate management structure. The digital signature is the result of taking the hash value of the application information to obtain the summary A, and then encrypting it using the private key of the certificate authority.

[0067] Verification steps:

[0068] A drone applies to join the blockchain by carrying a valid digital authentication certificate. The blockchain network verifies the drone's digital authentication certificate using an asymmetric encryption algorithm. It first reads the relevant plaintext information in the certificate and uses the same hash algorithm to obtain digest B. The signature data is then decrypted using the public key of the certificate management structure to obtain digest A. By comparing digests A and B for consistency, the validity of the drone's digital authentication certificate is determined. If the certificate is valid, the drone is allowed to join the blockchain.

[0069] Step 2, "Interference-based spectrum pricing and coordination between drones and ground networks," involves the following steps: After joining the blockchain, the drone broadcasts its location, power, antenna configuration, and spectrum requirements, along with a spectrum trading request. Nodes in the ground network with a certain amount of remaining spectrum can trade spectrum with drones based on the blockchain to generate revenue.

[0070] First, taking the base station as an example, the ground network calculates the potential interference caused by drones to the ground in spectrum trading through information such as the drone's location, power, and antenna configuration, and then performs spectrum pricing based on the potential interference.

[0071] In addition to considering the center frequency, transaction bandwidth, spectrum usage duration, and transmission power of the spectrum demander of the traded spectrum, the interference-based spectrum pricing model also considers the weighted cumulative interference of unmanned vehicles on the terrestrial network. The greater the weighted cumulative interference, the higher the spectrum transaction price, and vice versa.

[0072] The interference-based spectrum pricing model is shown in the following formula (2):

[0073]

[0074] Among them, sp j is the spectrum price when the j-th drone conducts spectrum trading,

[0075] f c is the center frequency of the traded spectrum,

[0076] B is the transaction bandwidth,

[0077] T is the spectrum usage duration,

[0078] P j is the transmission power of the j-th UAV,

[0079] Indicates the cumulative interference caused by drones to ground networks.

[0080] represents the interference caused by the j-th UAV to the n-th ground network node,

[0081] w n It represents the interference weight factor of the nth ground network node. It is used to measure the interference tolerance of different ground services. Services with high service quality requirements have higher interference weight factors.

[0082] For example, as the main mode of vehicle-to-vehicle communication, roadside units have very high requirements for transmission delay and reliability, and the interference caused by them is more serious. Therefore, the interference weight factor is higher and the spectrum price will also be higher.

[0083] Secondly, after the ground base station obtains the spectrum price based on the interference pricing model, the drone selects a trading plan based on the price and performs interference coordination. The drone leverages its deployment flexibility to optimize its deployment location and transmit power while ensuring the signal-to-interference-to-noise ratio (SINR) for the users it serves, further reducing the weighted cumulative interference it causes on the ground network. The interference coordination problem can be transformed into the following optimization problem:

[0084]

[0085] stSINR min >SINR th

[0086] Among them, x and y represent the horizontal and vertical position coordinates of the drone respectively.

[0087] h represents the flight altitude of the drone,

[0088] P j represents the transmission power of the jth UAV,

[0089] represents the interference caused by the j-th UAV to the n-th ground network node,

[0090] w n represents the interference weight factor of the nth ground network node,

[0091] SINR min represents the minimum value of the signal-to-interference-and-noise ratio among drone service users,

[0092] SINR th Indicates the signal-to-interference-and-noise ratio threshold for drone service users.

[0093] For the optimization problem under this constraint, the particle swarm algorithm in the swarm intelligence algorithm can be used to solve it.

[0094] In this algorithm, the weighted cumulative interference of the drone on the ground can be set as the fitness function, the three-dimensional spatial coordinates and transmission power of the drone can be used as the parameters to be optimized, and the signal-to-interference-noise ratio is based on the idea of ​​a penalty function to assign a larger value to the fitness of particles that do not meet the constraints. The algorithm flow is as follows Figure 4 shown.

[0095] First, the algorithm parameters must be initialized, such as the initial population size, spatial dimension, maximum number of iterations, inertia weight, self-learning factor, and group learning factor. The population's position information must also be randomly initialized. Then, within the iterative loop, the particles continuously update their position vectors (drone position and power) and velocity vectors (direction for the next iteration), obtaining the individual optimal value pbest and the group optimal value gbest with each iteration.

[0096] Finally, when the number of iterations is reached or convergence is achieved, the group optimal value gbest is used as the optimal position and power value for the drone deployment. After optimizing its own deployment position and power, the drone interacts with the trading partner and updates the spectrum trading data.

[0097] Step 3, "The ground network verifies the transaction to determine whether it is included in the next block," includes the following steps:

[0098] After the drone reaches a transaction with the ground network node, other nodes on the blockchain network need to verify the transaction to prevent illegal transactions. Blockchain spectrum transaction verification includes payment verification and interference verification. Payment verification mainly verifies whether the drone account balance is sufficient, whether the seller node has the right to use the transaction frequency band, and whether there is a "double-spending attack" in the transaction. Interference verification is performed by the ground network node whose distance from the drone is less than a certain threshold. It is mainly used to prevent transactions that cause serious interference to the ground network from passing. The schematic diagram of interference verification is shown below. Figure 5 As shown in the figure, ground network nodes within the drone interference verification zone verify whether the interference caused by the drone spectrum transaction exceeds the interference threshold set by the node. If the interference caused by the drone to all nodes within the interference verification zone is below the threshold, the interference verification passes; otherwise, it fails. If the transaction passes, the miner will package the transaction and write it into the next block.

[0099] Example 2: Figure 1 、 Figure 2 As shown, the method for implementing spectrum trading between a UAV and a ground network based on blockchain as described in Example 1 further includes the following steps:

[0100] Step 1: Ground base station spectrum allocation and user location generation.

[0101] The drone spectrum trading solution proposed in this invention is mainly aimed at scenarios where drones are used as base stations to serve hotspots with high user density, such as Figure 6 As shown in Figure 1, the change in interference relations caused by spectrum trading will cause potential interference to the ground network. Before simulating spectrum trading, the spectrum of the ground network is first allocated using the greedy graph coloring algorithm. The number of nodes in the ground network is 150, the number of colors is 20, and the nodes with interference relations are colored differently, as shown in Figure 1. Figure 7 Second, user information needs to be generated in the scenario. In this paper, it is assumed that the number of drone service users (hotspot users) is 25,000, evenly distributed within a radius of 500m; the number of non-hotspot users is 100,000, randomly distributed within a range of 10km×10km. It is also assumed that each user is associated with the base station closest to it. The simulation scenario parameters are shown in Table 1.

[0102] Table 1 Simulation scenario parameters

[0103]

[0104] Step 2: UAVs and ground networks conduct spectrum pricing and interference coordination based on interference.

[0105] The pricing of spectrum resources is generally related to the central spectrum, bandwidth, spectrum usage duration, and transmission power of the spectrum acquirer, as shown in formula (4):

[0106] sp=f(f c , B, T, P j ) (4)

[0107] Where sp represents the spectrum price during spectrum trading

[0108] f c is the center frequency of the traded spectrum,

[0109] B is the transaction bandwidth,

[0110] T is the spectrum usage duration,

[0111] P j is the transmission power of the j-th UAV,

[0112] The interference-based spectrum pricing model not only considers the above factors, but also the weighted cumulative interference of drones on ground network nodes, as shown in formula (2). Spectrum pricing should be an increasing function of the weighted cumulative interference of drones on ground network nodes. This simulation verification uses formula (5) for pricing verification:

[0113]

[0114] In the formula, sp represents the spectrum price during spectrum trading, α represents the spectrum price per unit bandwidth, per unit time, and per unit power, B represents the transaction bandwidth, T represents the spectrum usage duration, and P represents the spectrum price per unit bandwidth, per unit time, and per unit power. j is the transmission power of the j-th UAV, is the interference price adjustment factor, where I th It represents the interference price threshold. Spectrum transactions above the interference price threshold will increase the price, and vice versa. is the weighted interference generated by the drone on the ground, represents the interference caused by the j-th UAV to the n-th ground network node, w n represents the interference weight factor of the nth ground network node.

[0115] In contrast to this scheme is the non-interference-based spectrum pricing method, as shown in formula (6):

[0116] sp=α·B·T·P j (6)

[0117] Where sp represents the spectrum price during spectrum trading, α represents the spectrum price per unit bandwidth, per unit time, and per unit power, B is the transaction bandwidth, T is the spectrum usage duration, and P j is the transmission power of the jth UAV.

[0118] The two pricing schemes compared in the simulation verification both ignored the impact of center frequency and supply and demand in transactions on spectrum prices.

[0119] from Figure 8 It can be seen that the interference-based spectrum pricing model reduces the cumulative interference to the ground by 1dB-11dB, significantly reducing the cumulative interference of drones to the ground during spectrum trading. Figure 9 It can be seen that the interference-based spectrum pricing model reduces the maximum interference to ground base stations by 1.5dB-15dB, significantly reducing the maximum interference of drones to the ground during spectrum trading. Figure 10 It can be seen that under the same interference verification threshold, the interference-based spectrum pricing model significantly improves the success probability of drone spectrum transactions.

[0120] The process of UAV interference coordination is regarded as solving the optimization problem of formula (3). The interference coordination simulation parameter table based on particle swarm optimization is shown in Table 2, where the fitness function of particle i is defined as formula (7):

[0121]

[0122] In the formula, is the weighted interference generated by the drone on the ground, represents the interference caused by the j-th UAV to the n-th ground network node, w n represents the interference weight factor of the nth ground network node. If the particle position does not meet the constraints of the optimization problem (3), then α i =1, otherwise, if the particle position satisfies the constraint condition, then α i =0.

[0123] Table 2 Interference coordination simulation parameters based on particle swarm optimization

[0124]

[0125] according to Figure 8 It can be seen that compared with the scheme without interference coordination mechanism, the UAV interference coordination mechanism can reduce the weighted interference of UAV to the ground by 3dB-4dB. Figure 9 It can be seen that the UAV interference coordination mechanism can reduce the maximum interference of UAVs to the ground by about 2dB-5dB, which significantly reduces the interference caused by UAVs to ground base stations.

[0126] At the same time, compared with a single UAV base station, multiple UAV base stations can be deployed more flexibly, thereby further reducing the interference of UAVs on the ground. Figure 8 and Figure 9It can be seen that multiple drone base stations can reduce the weighted interference of drones to the ground by 0-1dB, and the maximum interference of drones to the ground can be reduced by about 2.5dB. However, an increase in the number of drone base stations also means an increase in deployment costs for drone service providers.

[0127] Step 3: The ground network verifies the transaction to determine whether it is included in the next block

[0128] In the simulation verification, the interference verification of ground network nodes on transactions is mainly considered. Figure 10 As can be seen, as the interference threshold continues to increase, the probability of successful transactions for different schemes gradually increases. In addition, at the same interference threshold, the probability of successful transactions is better when the interference coordination mechanism based on the interference pricing model (multi-UAV) is better than the interference coordination mechanism based on the interference pricing model (single UAV), which is better than the interference-based pricing model and better than the non-interference-based pricing model. This proves that the interference-based pricing model and interference coordination mechanism can effectively reduce UAV-to-ground interference, thereby increasing the success probability of UAV spectrum transactions.

[0129] During the UAV interference coordination process, the algorithm for obtaining the optimal position and power can be replaced by other optimization algorithms such as genetic algorithms, reinforcement learning algorithms, convex optimization theory, etc.

[0130] Interference-based spectrum pricing models can also achieve similar effects by designing pricing models with the same meaning but different forms.

[0131] Miners are individuals or organizations that maintain blockchain networks in exchange for virtual currency rewards. Their primary job is to confirm transactions, package data, and compete for blockchain "bookkeeping rights" through a consensus algorithm. Miners who secure these rights then write the packaged transactions into the next block.

[0132] As described above, the embodiments of the present invention have been described in detail. However, it is obvious to those skilled in the art that many variations are possible without departing from the spirit and effects of the present invention. Therefore, all such variations are included within the scope of protection of the present invention.

Claims

1. A blockchain-based method for spectrum trading between drones and ground networks, characterized in that , contains the following steps: Step 1: The drone applies to join the blockchain network and verifies its identity. Step 2: Spectrum pricing and interference coordination between drones and ground networks based on interference. Step 3: The ground network verifies the transaction to determine whether it is written into the next block. Step 1, "Drone applies to join the blockchain network and verifies its identity," includes the following steps: Before drones can trade spectrum with ground networks, they must first apply for and obtain verification of access to the blockchain network. The process of drone access includes two steps: application and verification: Application steps: First, the drone provides the device number and antenna configuration information and applies for an access certificate from the certificate management agency. After the certificate management agency reviews and verifies the application, the drone is issued a digital authentication certificate. The data structure of the digital authentication certificate is shown in formula (1): C U ={Version,N S ,i U ,K U ,D U ,V U ,Issuer,Algorithm,Signature} (1) Among them, Version represents the version number of the digital certificate. N S Indicates the digital certificate serial number, i U Indicates the identity id of the drone. K U Indicates the public key and public key algorithm information disclosed by the drone. D U Indicates the issuance date and validity period of the digital certification. Issuer: The name of the device that issued the certificate. Algorithm indicates the signature algorithm used by the certificate authority to issue certificates. Signature represents the digital signature of the certificate management structure. The digital signature is the result of taking the hash value of the application information to obtain the summary A, and then encrypting it with the private key of the certificate authority. Verification steps: The drone applies to join the chain with a valid digital authentication certificate. The blockchain network verifies the drone's digital authentication certificate based on an asymmetric encryption algorithm. First, it reads the relevant plaintext information in the certificate and uses the same hash algorithm to obtain summary B. Then, it decrypts the signature data using the public key of the certificate management structure to obtain summary A. By comparing whether summary A and summary B are consistent, it determines whether the digital authentication certificate carried by the drone is legal. If the certificate is legal, the drone is allowed to join the chain.

2. The method for implementing spectrum trading between drones and ground networks based on blockchain according to claim 1 is characterized in that In step 2, "spectrum pricing and interference coordination between drones and ground networks based on interference" includes the following steps: After entering the chain, the drone broadcasts its own location, power, antenna configuration, and spectrum demand information, and sends a spectrum transaction request. Nodes with a certain amount of remaining spectrum in the ground network can conduct spectrum transactions with drones based on the blockchain to obtain certain benefits. First, the ground network takes the base station as an example, and calculates the potential interference caused by drones to the ground in spectrum trading through the drone's location, power and antenna configuration information, and then performs spectrum pricing based on the potential interference. The interference-based spectrum pricing model not only considers the center frequency, transaction bandwidth, spectrum usage duration, and transmission power of the spectrum demander, but also considers the weighted cumulative interference of unmanned vehicles on the terrestrial network. The greater the weighted cumulative interference, the higher the spectrum transaction price, and vice versa. The interference-based spectrum pricing model is shown in the following formula (2): Among them, sp j is the spectrum price when the j-th drone conducts spectrum trading, f c is the center frequency of the traded spectrum, B is the transaction bandwidth, T is the spectrum usage duration, P j is the transmission power of the jth UAV, It represents the cumulative interference caused by drones to ground networks. represents the interference caused by the j-th UAV to the n-th ground network node, w n It represents the interference weight factor of the nth ground network node, which is used to measure the tolerance of different ground services to interference. The interference weight factor of services with high service quality requirements is higher. After the ground base station obtains the spectrum price based on the interference pricing model, the drone selects a trading plan based on the price and performs interference coordination. The drone uses its own deployment flexibility to optimize its deployment location and transmit power while ensuring the signal-to-interference-to-noise ratio of the drone service users, further reducing the weighted cumulative interference of the drone on the ground network. The interference coordination problem is transformed into the following optimization problem: stSINR min >SINR th Among them, x and y represent the horizontal and vertical position coordinates of the drone respectively. h represents the flight altitude of the drone, P j represents the transmission power of the jth UAV, represents the interference caused by the j-th UAV to the n-th ground network node, w n represents the interference weight factor of the nth ground network node, SINR min represents the minimum value of the signal-to-interference-and-noise ratio among drone service users, SINR th represents the signal-to-interference-and-noise ratio threshold of the drone service user, For optimization problems, the particle swarm algorithm in the swarm intelligence algorithm can be used to solve them. In this algorithm, the weighted cumulative interference of the drone on the ground is set as the fitness function, the three-dimensional spatial coordinates and transmission power of the drone are used as parameters to be optimized, and the signal-to-interference-noise ratio is based on the idea of ​​a penalty function to assign a larger value to the fitness of particles that do not meet the constraints. First, it is necessary to initialize the algorithm parameters, including the initial population size, spatial dimension, maximum number of iterations, inertia weight, self-learning factor and group learning factor. At the same time, the position information of the population needs to be randomly initialized. Then, within the iterative loop, the particles continuously update their own position vectors of the drone's position and power and the velocity vector of the next iteration direction. Each iteration obtains the individual optimal value pbest and the group optimal value gbest. Finally, when the number of iterations is reached or convergence has been achieved, the group optimal value gbest is used as the optimal position and power value for drone deployment. After optimizing its own deployment position and power, the drone interacts with the trading object and updates the spectrum trading data.

3. The method for implementing spectrum trading between drones and ground networks based on blockchain according to claim 1 is characterized in that In step 3, "the ground network verifies the transaction to determine whether the transaction is written into the next block", it includes the following steps: After the drone reaches a transaction with the ground network node, other nodes on the blockchain network are required to verify the transaction to avoid illegal transactions. Blockchain spectrum transaction verification includes payment verification and interference verification. Payment verification mainly verifies whether the drone account balance is sufficient, whether the seller node has the right to use the transaction frequency band, and whether there is a "double-spending attack" in the transaction. Interference verification is performed by ground network nodes whose distance from the drone is less than a certain threshold. It is mainly used to prevent transactions that cause serious interference to the ground network from passing. Ground network nodes located in the drone interference verification area verify whether the interference to themselves after the drone spectrum transaction is greater than the interference threshold set by themselves. If the interference of the drone to all nodes in the interference verification area is lower than the threshold, the interference verification passes, otherwise it fails. If the transaction passes, the miner will package the transaction and write it into the next block.

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