Unmanned-aerial-vehicle on-demand spectrum transaction system and method based on two-level stackelberg game

Abstract

Disclosed in the present invention are an unmanned-aerial-vehicle on-demand spectrum transaction system and method based on a two-level Stackelberg game. The system comprises: a macro base station, which serves as an authority and is used for supervising a spectrum transaction; a micro base station, which is used for storing and managing general movement situations and resource states of unmanned aerial vehicles in a region, and uploading to the macro base station transaction records of spectrum resource buyers and sellers, wherein an unmanned aerial vehicle chooses to become a transaction subject on the basis of its own spectrum resource conditions and resource demands, a participant with an increased communication demand serves as a buyer of an unmanned-aerial-vehicle spectrum, and a participant having free spectrum resources becomes a provider of the unmanned-aerial-vehicle spectrum; and an unmanned-aerial-vehicle user that is used for providing a communication service for a mobile user to satisfy user demands having low latency requirements. In the present invention, an optimal pricing and transaction volume strategy in a two-level Stackelberg game is effectively calculated by using a gradient-based iterative search algorithm, and a higher total system utility is obtained and the requirements of users for a transmission rate and quality of service are satisfied, thereby improving the spectrum transaction volume and the spectrum utilization rate.

Description

A UAV on-demand spectrum trading system and method based on double-layer Stackelberg game Technical Field

[0001] The present invention relates to an on-demand spectrum trading system and method for unmanned aerial vehicles (UAVs) based on a double-layer Stackelberg game, and belongs to the technical field of wireless communications. Background Art

[0002] In recent years, wireless communication data volumes have experienced explosive growth. Related spectrum allocation and sharing technologies are constantly emerging. In the 5G era, increasing communication capacity depends on three fundamental dimensions: spectrum, spectrum efficiency, and spatial reuse. However, physical constraints, such as limited spectrum and infrastructure availability, are major factors hindering operators from expanding their services. Spectrum resources in lower frequency bands are particularly scarce, making efficient spectrum reuse particularly crucial.

[0003] In urban areas with dense traffic hotspots, where the coverage of traditional cellular base stations falls short of demand, drones, with their high flexibility, low cost, and easy deployment, can rapidly provide ubiquitous coverage and traffic offload services to ground base stations in these hotspots, thereby forming a low-altitude heterogeneous converged network with broad application scenarios. However, the total available bandwidth of cellular networks is limited, and sharing a portion of this total bandwidth with drones can compromise the capacity of cellular base stations. In reality, drones and ground base stations typically belong to multiple different operators, each selfishly seeking to maximize their own profits. Cellular network operators are reluctant to share their spectrum with drone networks, and users also lack the incentive to share. Against this backdrop, establishing effective spectrum resource sharing models and applying these models to drone networks to achieve distributed and reliable communications remains a challenge. Summary of the Invention

[0004] The purpose of the present invention is to address the defects and shortcomings of the above-mentioned existing technologies and provide a drone on-demand spectrum trading system and method based on a double-layer Stackelberg game. By optimizing the system rate in drone-assisted heterogeneous network communication scenarios, it can improve the spectrum utilization of base stations while maximizing the utility of all three parties.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a drone on-demand spectrum trading system based on a two-layer Stackelberg game, which includes a macro base station, a micro base station, a drone spectrum buyer, M drone spectrum providers, and N drone users. The macro base station serves as an authoritative agency to supervise spectrum trading.

[0006] In addition to providing spectrum resources for drones and mobile users, the micro base station is also considered the operator manager of the trading system, used to store and manage the movement profiles and resource status of drones in the area, and upload the transaction records of spectrum resource buyers and sellers to the macro base station;

[0007] Drones choose to become trading roles based on their own spectrum resource status and resource needs. Participants with increased communication needs can act as drone spectrum buyers, and participants with idle spectrum resources can become drone spectrum providers to balance local spectrum resource needs.

[0008] The drone user is used to provide communication services to mobile users to meet the needs of users with low latency requirements.

[0009] In the context of smart cities supporting 5G heterogeneous networks, each area has a macro base station and several micro base stations, which are responsible for orchestrating communication resources within the cell. To meet the needs of some users with low latency requirements, drones can be introduced to assist user communications.

[0010] Furthermore, the process of on-demand spectrum trading for drones includes: the spectrum resource trader (drone) first obtains a digital certificate and encryption key from the micro base station, obtains transaction authorization, and chooses to become a trading role based on its own spectrum resource status and resource requirements; the drone spectrum buyer sends a spectrum demand application to the micro base station to meet the communication needs of mobile users; the micro base station broadcasts the demand information to all drone spectrum providers, and any drone spectrum provider with idle spectrum can choose to respond and become a spectrum provider; the micro base station reclaims the spectrum resources and then trades with the drone spectrum buyer; after the transaction is completed, the micro base station packages the transaction record and uploads it to the macro base station;

[0011] Furthermore, the process of the two-layer Stackelberg game includes: the drone spectrum buyer is the first-level leader, the micro base station is the second-level leader, and the drone spectrum provider is the follower; in the first stage, the drone spectrum buyer sets the price {r b1 ,r b2 ,...,r bN}, used to purchase the unit spectrum quantity within a unit time, and then the micro base station adjusts the price according to the price strategy given by the leader {r p1 ,r p2 ,...,r pN}, which is used to recover the unit spectrum per unit time from the drone spectrum provider. The optimization problem at this stage can be expressed as:

[0012] Where 0<w i <w piIndicates that the spectrum transaction amount of the i-th user does not exceed the idle spectrum amount of its transaction object, r bi >r pi Indicates that the unit spectrum price of the transaction between the micro base station and the drone spectrum provider is greater than the unit price of the idle spectrum reclaimed by the micro base station from the drone spectrum provider;

[0013] In the second stage, the drone spectrum provider adjusts the spectrum resource transaction quantity {w1,w2,...,w N} and sent to the micro base station, which then passes it to the drone spectrum buyer. The optimization problem at this stage can be expressed as:

[0014] The present invention also provides a method for on-demand spectrum trading for drones based on a double-layer Stackelberg game. The method considers the influence of spectrum marginal effects. The more spectrum allocated, the higher the transmission rate, and the more benefits the user should obtain. The benefits obtained by the drone spectrum buyer using the purchased spectrum are expressed as:

[0015] Where i represents the i-th user among the drone spectrum buyers, ρ is the conversion coefficient between transmission rate and revenue, is the transmission rate after the user purchases the spectrum, R i The transmission rate achieved by the spectrum provided by drone spectrum purchasers to users.

[0016] Furthermore, the transmission rate R i Respectively expressed as: R i =(w bi -w i )log2(1+SINR bi )

[0017] where w bi is the spectrum amount when the user achieves the ideal transmission rate, w i is the amount of spectrum traded, It is expressed as the signal-to-interference-and-noise ratio between the drone spectrum buyer and user i, where G is the channel gain, d ij is the distance between the drone spectrum buyer and user i, α is the path loss coefficient, σ 2 is the noise power.

[0018] Furthermore, the total utility of the drone spectrum buyer is expressed as:

[0019] where r biis the unit spectrum price traded between the micro base station and the drone spectrum buyer, and E1 is the energy consumed by the drone spectrum buyer when sending messages to each user.

[0020] Furthermore, the method further includes expressing the utility of the micro base station as:

[0021] where r pi It represents the unit price of the micro base station to reclaim the idle spectrum of the drone spectrum provider, and E2 represents the energy consumed by the micro base station each time it performs a transaction.

[0022] Furthermore, the method also includes expressing the utility of the drone spectrum provider as: U pi =r pi w i +γε(w i )

[0023] Where ε(w i ) is the satisfaction function of the drone spectrum provider, γ is the satisfaction coefficient, and the higher γ is, the more the drone spectrum provider pays attention to the utilization of local spectrum resources.

[0024] Furthermore, the satisfaction function is expressed as: ε(w i )=ln(w pi -w i -λ+1)

[0025] where w pi It is represented by the original idle spectrum of the drone spectrum provider, λ is a parameter to measure whether the drone spectrum provider is satisfied. When the remaining spectrum is greater than λ, the satisfaction function ε(w i ) is greater than zero, which can be regarded as the UAV spectrum provider is relatively satisfied. When the remaining spectrum is less than λ, the satisfaction function ε(w i ) is less than zero, then the drone spectrum provider is not satisfied.

[0026] Furthermore, the method includes an iterative search algorithm based on gradient descent, which can obtain a unique Nash equilibrium in the double-layer Stackelberg game, thereby maximizing the utility of the three parties: the drone spectrum buyer, the micro base station, and the drone spectrum provider, where r bi Each update is expressed as: Beneficial effects:

[0027] 1. This paper proposes an on-demand spectrum trading model for drones based on a two-layer Stackelberg game. In the drone-assisted user communication scenario, a three-party spectrum trading model is constructed among drone spectrum buyers, providers, and micro base stations to meet users' transmission rate and service quality requirements and improve spectrum utilization.

[0028] 2. This invention comprehensively considers the impact of spectrum marginal effects, greatly increases user benefits, uses a gradient-based iterative search algorithm to prove the existence of Nash equilibrium, effectively solves the optimal pricing and transaction volume strategies in the game, and maximizes the utility of the three parties: drone spectrum buyers, micro base stations, and drone spectrum providers.

[0029] 3. This paper proposes a drone spectrum on-demand trading model based on a two-layer Stackelberg game. Base station operators will reclaim idle drone spectrum and provide it to drone users who make requests, meeting users' transmission rate and service quality requirements, promoting the adoption of spectrum sharing, and incentivizing mutual cooperation between operators to jointly optimize the utility of buyers and sellers. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] FIG1 is a model diagram of a drone on-demand spectrum trading system based on a double-layer Stackelberg game according to the present invention.

[0031] FIG2 is a diagram of the gradient-based iterative search process of the present invention.

[0032] FIG3 is a comparison diagram of the total system benefits of different models of the present invention.

[0033] FIG4 is a comparison chart of spectrum transaction volumes of different models of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be described in further detail below with reference to the accompanying drawings.

[0035] Figure 1 shows the model of the on-demand drone spectrum trading system of the present invention. It consists of a macro base station, a micro base station, a drone spectrum buyer, M drone spectrum providers, and N drone users. In the context of a smart city supporting 5G heterogeneous networks, each area has a macro base station and several micro base stations, which are responsible for orchestrating communication resources within the cell. To meet the needs of some users with low latency requirements, drones can be introduced to assist in user communications. Considering that in a smart city, a drone spectrum buyer may be surrounded by multiple drones (potential spectrum resource providers), a spectrum resource transaction involving one buyer, one operator, and multiple providers is more suitable for specific scenarios. The macro base station acts as the authority overseeing spectrum transactions. In addition to providing spectrum resources to drones and mobile users, the micro base station serves as the operator manager of the trading system, storing and managing drone mobility profiles and resource status within its area, and uploading transaction records between spectrum buyers and sellers. Spectrum resource traders first obtain digital certificates and encryption keys from the micro base station to obtain transaction authorization. They then choose a trading role based on their spectrum resource availability and requirements. Participants with idle spectrum resources become sellers in the market to balance local spectrum resource demand. Mobile users can send spectrum resource status to drones, which then aggregate spectrum demand requests and pass them to micro base stations. The micro base stations then broadcast the information of legitimate buyers to sellers, matching spectrum supply and demand.

[0036] The present invention is based on the above system, and the on-demand spectrum trading process of drones includes:

[0037] The spectrum resource trader (drone) first obtains a digital certificate and encryption key from the micro base station, obtains transaction authorization, and then chooses to become a trading role based on its own spectrum resource status and resource requirements;

[0038] To meet the communication needs of mobile users, drone spectrum buyers send spectrum demand applications to micro base stations;

[0039] The micro base station broadcasts demand information to all drone spectrum providers, and any drone spectrum provider with idle spectrum can choose to respond;

[0040] The spectrum resources are recovered by micro base stations and then traded with drone spectrum buyers;

[0041] After the transaction is completed, the micro base station packages the transaction record and uploads it to the macro base station;

[0042] The double-layer Stackelberg game process of the present invention includes:

[0043] The drone spectrum purchaser is the first-level leader, the micro base station is the second-level leader, and the drone spectrum provider is the follower;

[0044] In the first phase, drone spectrum buyers set pricing {r b1 ,r b2 ,...,r bN}, used to purchase the unit spectrum quantity within a unit time, and then the micro base station adjusts the price according to the price strategy given by the leader {r p1 ,r p2 ,...,r pN}, used to recover the unit spectrum per unit time from the drone spectrum provider. The optimization problem at this stage can be expressed as:

[0045] Where 0<w i <w pi Indicates that the spectrum transaction amount of the i-th user does not exceed the idle spectrum amount of its transaction object, r bi >r pi Indicates that the unit spectrum price of the transaction between the micro base station and the drone spectrum provider is greater than the unit price of the idle spectrum reclaimed by the micro base station from the drone spectrum provider;

[0046] In the second stage, the drone spectrum provider adjusts the spectrum resource transaction quantity {w1,w2,...,w N} and sent to the micro base station, which then passes it to the drone spectrum buyer. The optimization problem at this stage can be expressed as:

[0047] The pricing strategy of micro base stations and drone spectrum buyers can be adjusted to charge the same or different prices. Here, the scheme in which micro base stations and drone spectrum buyers charge different prices is called a non-uniform pricing scheme. Considering that the more spectrum is allocated, the higher the transmission rate, and the more benefits the user should obtain, the benefit of a single user should be an increasing function of the spectrum amount. In this invention, the e f(x) Indicates increasing benefits for users.

[0048] The present invention also provides a method for on-demand spectrum trading for drones based on a double-layer Stackelberg game. The method considers the influence of spectrum marginal effects. The more spectrum allocated, the higher the transmission rate, and the more benefits the user should obtain. The benefits obtained by the drone spectrum buyer using the purchased spectrum are expressed as:

[0049] Where i represents the i-th user among the drone spectrum buyers, ρ is the conversion coefficient between transmission rate and revenue, is the transmission rate after the user purchases the spectrum, R i The transmission rate achieved by the spectrum provided by the drone spectrum purchaser to the user. Transmission rate Ri Respectively expressed as: R i =(w bi -w i )log2(1+SINR bi )

[0050] where w bi is the spectrum amount when the user achieves the ideal transmission rate, w i is the amount of spectrum traded, It is expressed as the signal-to-interference-and-noise ratio between the drone spectrum buyer and user i, where G is the channel gain, d ij is the distance between the drone spectrum buyer and user i, α is the path loss coefficient, σ 2 is the noise power.

[0051] The total utility of the drone spectrum buyer in this invention is expressed as:

[0052] where r bi is the unit spectrum price traded between the micro base station and the drone spectrum buyer, and E1 is the energy consumed by the drone spectrum buyer when sending messages to each user.

[0053] The utility of the micro base station in the present invention is expressed as:

[0054] where r pi It represents the unit price of the micro base station to reclaim the idle spectrum of the drone spectrum provider, and E2 represents the energy consumed by the micro base station each time it performs a transaction.

[0055] The utility of the drone spectrum provider in the present invention is expressed as: U pi =r pi w i +γε(w i )

[0056] Where ε(w i ) is the satisfaction function of the drone spectrum provider, γ is the satisfaction coefficient, and the higher γ is, the more the drone spectrum provider pays attention to the utilization of local spectrum resources.

[0057] The above satisfaction function of the present invention is expressed as: ε(w i )=ln(w pi -w i -λ+1)

[0058] where w pi It is represented by the original idle spectrum of the drone spectrum provider, λ is a parameter to measure whether the drone spectrum provider is satisfied. When the remaining spectrum is greater than λ, the satisfaction function ε(w i) is greater than zero, which can be regarded as the UAV spectrum provider is relatively satisfied. When the remaining spectrum is less than λ, the satisfaction function ε(w i ) is less than zero, then the drone spectrum provider is not satisfied.

[0059] The iterative search algorithm based on gradient descent in the present invention can obtain a unique Nash equilibrium in the double-layer Stackelberg game, thereby maximizing the utility of the three parties: the drone spectrum buyer, the micro base station, and the drone spectrum provider. bi Each update is expressed as:

[0060] The simulation experiments and data of the present invention are as follows:

[0061] Assume that there are four drone spectrum buyers in the trading system and four drone spectrum providers responding to the trading requests. In the simulation, each user has different spectrum resource requirements, and the amount of available spectrum at each drone spectrum provider also varies.

[0062] In the simulation, the conversion coefficient of transmission rate and benefit is ρ 0.08, the channel gain G is -80dBm, the path loss coefficient α is 0.5, and the noise power σ 2 is -114dBm, transmission power P t The energy consumed by the micro base station each time it executes a transaction is E2, and the energy consumed by the drone spectrum purchaser when sending messages to each user is E1, which is 10.

[0063] Figure 2 illustrates the process of finding an equilibrium solution for a specific user in the drone spectrum purchaser using a gradient-based iterative search algorithm. The solid line represents the trend of the benefit as the corresponding parameter changes, and the plotted dots represent each gradient-based search solution. (a) shows the process of finding an equilibrium solution for the benefits of the mobile user served by the drone spectrum purchaser; (b) shows the process of finding an equilibrium solution for the benefits of the drone spectrum provider; and (c) shows the process of finding an equilibrium solution for the benefits of the micro base station. Figure 2 further demonstrates that the proposed Stackelberg game has a unique equilibrium solution and that the optimal pricing and trading strategy can be obtained using a gradient-based iterative search algorithm.

[0064] The UBP scheme, which unifies the spectrum purchase price for all drone spectrum purchasers, is called the UBP scheme. The USP scheme, which unifies the spectrum price charged by base station operators to all drone spectrum providers, is called the USP scheme. The TSG scheme, which charges different prices to base station operators and drone spectrum purchasers, is called the TSG scheme. The performance of the non-unified pricing scheme is compared with the unified pricing scheme and the unified transaction spectrum volume scheme (AS).

[0065] With other parameters remaining the same, Figure 3 compares the system benefits of the four schemes, and Figure 4 compares the spectrum trading volumes of the three schemes. Figure 3 shows the total system benefit of our proposed TSG scheme under different signal-to-interference-noise ratio (SIN) conditions, and compares it with the other three schemes. It can be seen that, at the same SIN, our scheme significantly outperforms the other three schemes. At the same time, the gap in system benefits increases as the SIN increases. This is because the TSG scheme achieves the optimal pricing strategy, maximizing the benefits of all three parties, while the other schemes only achieve suboptimal benefits. Figure 4 shows the spectrum trading volumes of the TSG scheme and the other two schemes under different SIN conditions. It can be seen that the TSG scheme significantly increases the amount of spectrum traded compared to the other two schemes, and that this increases with increasing SIN. This demonstrates that, under the same conditions, the TSG scheme significantly improves the spectrum utilization of the drone-assisted user communication system.

[0066] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A drone on-demand spectrum trading system based on a two-layer Stackelberg game, characterized in that, Including: Macro base station, micro base station, UAV spectrum buyer, M UAV spectrum providers and N UAV users; The macro base station, as an authoritative institution, is used to supervise spectrum trading; In addition to providing spectrum resources for UAVs and mobile users, the micro base station is also regarded as the operator manager of the trading system, used to store and manage the movement profiles and resource status of UAVs in the area, and upload the transaction records of both parties of spectrum resource buying and selling to the macro base station; UAVs choose to become trading roles according to their own spectrum resource conditions and resource requirements. Participants with increased communication requirements become UAV spectrum buyers, and participants with idle spectrum resources become UAV spectrum providers to balance local spectrum resource requirements; The UAV users are used to provide communication services for mobile users to meet the user requirements of low latency.

2. The drone on-demand spectrum trading system based on the double-layer Stackelberg game according to claim 1, wherein, The process of on-demand spectrum trading of UAVs includes: the spectrum resource trader (UAV) first obtains a digital certificate and an encryption key from the micro base station, obtains trading authorization, and chooses to become a trading role according to its own spectrum resource conditions and resource requirements; the UAV spectrum buyer sends a spectrum demand application to the micro base station to meet the communication requirements of mobile users; the micro base station broadcasts the demand information to all UAV spectrum providers, and any UAV with idle spectrum can choose to respond and become a spectrum provider; the micro base station recovers the spectrum resources and then conducts a transaction with the UAV spectrum buyer; after the transaction is completed, the micro base station packages the transaction records and uploads them to the macro base station.

3. The drone on-demand spectrum trading system based on a two-layer Stackelberg game according to claim 1, characterized in that, The process of the described two-layer Stackelberg game includes: the UAV spectrum buyer is the first-level leader, the small base station is the second-level leader, and the UAV spectrum provider is the follower; the UAV spectrum buyer sets the pricing {r b1 , r b2 ,..., r bN}, which is used to purchase the unit spectrum amount per unit time. Then, according to the price strategy given by the leader, the small base station adjusts the pricing {r p1 , r p2 ,..., r pN}, which is used to recover the unit spectrum amount per unit time from the UAV spectrum provider; the UAV spectrum provider adjusts the spectrum resource trading quantity {w1, w2,..., w N} according to the pricing strategy given by the base station operator, and sends it to the small base station, and then the small base station forwards it to the UAV spectrum buyer.

4. A method for on-demand spectrum trading of unmanned aerial vehicles based on a two-layer Stackelberg game, characterized in that, Including considering the influence of spectrum edge effect, the revenue obtained by the UAV spectrum purchaser using the purchased spectrum is expressed as: where \(i\) represents the \(i\)-th user among the UAV spectrum buyers, and \(\rho\) is the conversion coefficient between the transmission rate and the revenue. The transmission rate R after the user purchases the spectrum i The transmission rate achieved by the spectrum provided by the UAV spectrum purchaser for the user.

5. The method for on-demand spectrum trading of unmanned aerial vehicles based on a two-layer Stackelberg game according to claim 4, wherein The transmission rate R i are respectively expressed as: R i =(w bi -w i )log2(1 + SINR bi ) where w bi is the spectrum amount when the user reaches the ideal transmission rate, w i is the spectrum quantity of the transaction, denotes the signal-to-interference-plus-noise ratio between the UAV spectrum buyer and user \(i\), where \(G\) is the channel gain, \(d\) ij is the distance between the UAV spectrum buyer and user \(i\), \(\alpha\) is the path loss coefficient, \(\sigma\) 2 is the noise power.

6. A method for on-demand spectrum trading of unmanned aerial vehicles based on a two-layer Stackelberg game according to any one of claims 4-5, characterized in that, The total utility of the drone spectrum buyer is expressed as: where r bi is the unit spectrum price for the transaction between the micro base station and the UAV spectrum purchaser, and E1 is the energy consumed when the UAV spectrum purchaser sends messages to each user.

7. A method for on-demand spectrum trading of unmanned aerial vehicles based on a two-layer Stackelberg game according to claim 4, characterized in that The utility including the micro base station is expressed as: where r pi represents the unit price of the idle spectrum provided by the spectrum provider of the micro base station recycling drone, and E2 represents the energy consumed by the micro base station for each execution of the transaction.

8. A method for on-demand spectrum trading of unmanned aerial vehicles based on a two-layer Stackelberg game according to claim 4, characterized in that The utility of the drone spectrum provider is expressed as: U pi = r pi w i + γε(w i ) where ε(w i ) is the satisfaction function of the UAV spectrum provider, γ is the satisfaction coefficient, and the higher γ is, the more the UAV spectrum provider pays attention to the utilization of local spectrum resources.

9. The method for on-demand spectrum trading of unmanned aerial vehicles based on a two-layer Stackelberg game according to claim 8, wherein The satisfaction function is expressed as: ε(w i ) = ln(w pi - w i - λ + 1) where w pi represents the original idle spectrum amount provided by the UAV spectrum provider, λ is a parameter to measure whether the UAV spectrum provider is satisfied. When the remaining spectrum amount is greater than λ, the satisfaction function ε(w i ) is greater than zero, and it is regarded that the UAV spectrum provider is relatively satisfied. When the remaining spectrum amount is less than λ, the satisfaction function ε(w i ) is less than zero, and at this time the UAV spectrum provider is not satisfied.

10. A method for on-demand spectrum trading of unmanned aerial vehicles based on a two-layer Stackelberg game according to claim 6, characterized in that Including finding the unique Nash equilibrium in the two-layer Stackelberg game using an iterative search algorithm based on gradient descent, so that the three parties of the UAV spectrum buyer, the small cell base station, and the UAV spectrum provider can maximize their utilities, where r bi Each update is expressed as:

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