Unmanned aerial vehicle group spectrum access method and system oriented to data transmission stability

Abstract

The application discloses a kind of unmanned aerial vehicle group spectrum access method and system for data transmission stability, it is related to unmanned aerial vehicle communication technical field, the method comprises: constructing distributed unmanned aerial vehicle group communication network;If the channel use state of current time slot unmanned aerial vehicle is 0, then according to the channel access control parameter and next time slot channel access control parameter of the current time slot channel access control parameter corresponding to the unmanned aerial vehicle, determine the channel access probability of the unmanned aerial vehicle in next time slot;If the channel use state of current time slot unmanned aerial vehicle is 1, then according to the service data packet of backlog in the current time slot data transmission queue corresponding to the unmanned aerial vehicle, determine the channel access probability of the unmanned aerial vehicle in next time slot;If the channel use state of current time slot unmanned aerial vehicle is 2, then according to the current interference state information corresponding to the unmanned aerial vehicle, determine the channel access probability of the unmanned aerial vehicle in next time slot;The application can effectively alleviate the frequency use conflict of unmanned aerial vehicle group on each channel, improve channel effective utilization and system throughput.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle communication, in particular to a method and system for spectrum access of unmanned aerial vehicle group for data transmission stability. BACKGROUND

[0002] In recent years, unmanned aerial vehicles have attracted much attention in the industry due to their high mobility and low cost, and have been widely used in military and civilian fields such as reconnaissance and surveillance, auxiliary communication and information collection. With the rapid development of wireless communication technology, people's demand for information transmission rate is increasing, and the available frequency spectrum resources are becoming more and more scarce and dynamic, so how to effectively alleviate the spectrum conflict and realize efficient frequency use of unmanned aerial vehicle group is a key problem.

[0003] Due to the dynamic changes of the spatial position and network topology structure of the unmanned aerial vehicle group, the information transmission channel characteristics of the unmanned aerial vehicle group also present dynamic time-varying characteristics, while the traditional method usually considers quasi-static interference channels. In order to quickly and efficiently realize the frequency use coordination of the unmanned aerial vehicle group, most researches adopt centralized network architecture or establish public control channels for information exchange, which can reduce the frequency use conflict of the unmanned aerial vehicle group to a certain extent. For example, one document studies the transmission queue stability problem under the conditions of data burst and user mobility by using a centralized controller to uniformly control the user spectrum access and switching, and proposes a strategy of joint channel allocation and power control, which makes the system have good robustness. However, this strategy does not consider the highly dynamic wireless environment, nor does it consider the signaling overhead brought by centralized control. In order to overcome the shortcomings of centralized control and the spectrum overhead brought by control channel, other documents study the distributed channel selection problem for interference suppression in time-varying spectrum environment, and propose a completely distributed non-coupled stochastic learning algorithm. Although this method can effectively reduce the mutual interference between users in time-varying wireless environment, it does not consider the influence of user frequency demand change and network topology time variation, and the convergence speed of this algorithm is also slow. The existing technology also studies the use of deep reinforcement learning method to solve the mutual interference problem between users in the case of slow change of wireless environment. In the case of small number of users and available channels, the proposed algorithm can effectively suppress the frequency use conflict between users, but in the case of large number of users and channels, the effect of frequency use conflict alleviation between users is not good. In addition, the existing technology gives a solution based on multi-armed bandit for channel allocation problem, but it is not suitable for the fast-changing scenarios such as bursty traffic data, rapid movement of unmanned aerial vehicles and wireless communication.

[0004] In real applications, the unmanned aerial vehicle group mainly faces the following problems:

[0005] (1) The spatial location and network topology of the drone swarm change dynamically, and the wireless communication environment in which the drones are located is time-varying; (2) The spectrum resources of the drone swarm are limited and are more sensitive to communication overhead. It is very difficult to establish a public control channel in an agile spectrum environment; (3) Due to the volatility of the drone data acquisition rate, the frequency requirements of different users in the cluster change in real time. Summary of the Invention

[0006] The purpose of the present invention is to provide a spectrum access method and system for drone swarms aimed at data transmission stability, which can effectively alleviate the frequency conflicts of drone swarms on various channels and improve the effective utilization rate of channels and system throughput.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] A spectrum access method for a swarm of unmanned aerial vehicles (UAVs) for data transmission stability, comprising:

[0009] Construct a distributed UAV swarm communication network; the distributed UAV swarm communication network is used to provide relay services for ground user equipment or collect ground target information, and transmit arriving service data packets to the base station in real time; the distributed UAV swarm communication network includes N UAVs, a base station and M available channels; the bandwidth of each channel is B, the flight altitude of each UAV is h, and the transmission power of the i-th UAV in the current time slot n is p i (n);

[0010] When the next time slot is not the last time slot, determining a channel usage status of each drone in the distributed drone swarm communication network in the current time slot;

[0011] If the channel usage status of the drone in the current time slot is 0, the channel access probability of the drone in the next time slot is determined according to the current time slot channel access control parameter and the next time slot channel access control parameter corresponding to the drone; the channel usage status of the drone in the current time slot is 0, which means that there is no channel to transmit the backlog of service data packets in the data transmission queue of the drone in the current time slot n;

[0012] If the channel usage status of the drone in the current time slot is 1, the channel access probability of the drone in the next time slot is determined based on the backlog of business data packets in the data transmission queue of the current time slot corresponding to the drone; the channel usage status of the drone in the current time slot is 1, which means that there is a channel in the current time slot n to transmit the backlog of business data packets in the data transmission queue of the drone and there is no interference from other drones;

[0013] If the channel usage status of the drone in the current time slot is 2, the channel access probability of the drone in the next time slot is determined based on the current interference status information corresponding to the drone; the channel usage status of the drone in the current time slot is 2, which means that there is a channel in the current time slot n to transmit the backlog of business data packets in the drone's data transmission queue and it is interfered with by other drones.

[0014] A spectrum access system for drone swarms aimed at data transmission stability, comprising:

[0015] A distributed UAV swarm communication network construction module is used to construct a distributed UAV swarm communication network; the distributed UAV swarm communication network is used to provide relay services for ground user equipment or collect ground target information, and transmit arriving service data packets to the base station in real time; the distributed UAV swarm communication network includes N UAVs, a base station and M available channels; the bandwidth of each channel is B, the flight altitude of each UAV is h, and the transmission power of the i-th UAV in the current time slot n is p i (n);

[0016] A current time slot drone channel usage status determination module, configured to determine the channel usage status of each drone in the distributed drone swarm communication network in the current time slot when the next time slot is not the last time slot;

[0017] The module for calculating the probability of drone channel access in the next time slot is used to:

[0018] If the channel usage status of the drone in the current time slot is 0, the channel access probability of the drone in the next time slot is determined according to the current time slot channel access control parameter and the next time slot channel access control parameter corresponding to the drone; the channel usage status of the drone in the current time slot is 0, which means that there is no channel to transmit the backlog of service data packets in the data transmission queue of the drone in the current time slot n;

[0019] If the channel usage status of the drone in the current time slot is 1, the channel access probability of the drone in the next time slot is determined based on the backlog of business data packets in the data transmission queue of the current time slot corresponding to the drone; the channel usage status of the drone in the current time slot is 1, which means that there is a channel in the current time slot n to transmit the backlog of business data packets in the data transmission queue of the drone and there is no interference from other drones;

[0020] If the channel usage status of the drone in the current time slot is 2, the channel access probability of the drone in the next time slot is determined based on the current interference status information corresponding to the drone; the channel usage status of the drone in the current time slot is 2, which means that there is a channel in the current time slot n to transmit the backlog of business data packets in the drone's data transmission queue and it is interfered with by other drones.

[0021] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0022] This paper addresses the need for stable data transmission in the presence of bursty drone traffic in a time-varying spectrum environment. It establishes a model for generating drone traffic data packets and transmitting them in real time to a base station. Aiming to mitigate frequency conflicts between drones and increase system throughput, this paper proposes a spectrum access method based on multi-user uncoupled queuing. This method utilizes its own sensing capabilities to detect idle channels. Distributed drones do not require any information exchange. Based on their transmission requirements, the interference power they encounter, and historical channel usage, drones construct learning benefit functions to generate dynamic frequency usage decisions. Simulation experiments demonstrate that, while ensuring stable traffic data queues and fair frequency usage for all drones in the network, the proposed method effectively mitigates frequency conflicts on various channels within a drone swarm, improving channel utilization and system throughput. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 This is a flow chart of a spectrum access method for a drone swarm aimed at data transmission stability according to the present invention;

[0025] Figure 2 This is a model structure diagram of the distributed UAV swarm communication network of the present invention;

[0026] Figure 3 A graph showing the change in frequency conflict rate over time when the data transmission of the drone swarm service is in a critical stable state according to the present invention;

[0027] Figure 4 A graph showing the change in effective channel utilization over time when the data transmission of the drone swarm service is in a critical stable state;

[0028] Figure 5 The following is a graph showing the change of the average data backlog of the drone group over time under different average data arrival rates of the present invention;

[0029] Figure 6 is the maximum average stable arrival rate ρ of the drone swarm of the present invention max A graph showing the number of available channels M as a function of the available channels;

[0030] Figure 7This is a diagram showing a change process of the maximum data backlog difference of the UAV when the number of available channels M is 15 and the UAV is in a critical stable state;

[0031] Figure 8 This is a structural schematic diagram of a spectrum access system for drone swarms aimed at data transmission stability according to the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0034] Example 1

[0035] like Figure 1 As shown, this embodiment provides a spectrum access method for a drone swarm aimed at data transmission stability, including:

[0036] Step 100: Construct a distributed UAV swarm communication network; the distributed UAV swarm communication network is used to provide relay services for ground user equipment or collect ground target information, and transmit arriving service data packets to the base station in real time; the distributed UAV swarm communication network includes N UAVs, a base station and M available channels; the bandwidth of each channel is B, the flight altitude of each UAV is h, and the transmission power of the i-th UAV in the current time slot n is p i (n).

[0037] This embodiment considers a distributed UAV swarm communication network composed of multiple UAVs to provide relay services for ground user equipment (or collect ground target related information) and transmit the arriving data to the base station in real time, such as Figure 2The network contains 1 base station and N unmanned aerial vehicles, the set of unmanned aerial vehicle groups is represented as N = {1,..., i,... N}, the base station is represented by the symbol o, the unmanned aerial vehicle network contains M available channels, the set of which is represented as M = {1,..., m,... M}. h is the flight height of the unmanned aerial vehicle group, which is a fixed value, and N > M is set. This embodiment only considers the link of the unmanned aerial vehicle transmitting service data packets to the base station. It is assumed that the moving area of the unmanned aerial vehicle is a two-dimensional finite planar area at the same height, which is divided into multiple square grids of equal area; the base station is located on the ground with a height of 0, and its position is fixed. In order to simulate the random movement process of the unmanned aerial vehicle, it is assumed that all the unmanned aerial vehicles move independently and can only be at a certain grid point in each time slot, and the unmanned aerial vehicle will choose to continue to stay at the original grid point or move to a certain adjacent grid point in the next time slot; it is assumed that the unmanned aerial vehicle independently performs the task, and the amount of service data generated or arrived (hereinafter referred to as “arrived”) by the unmanned aerial vehicle and transmitted to the base station is measured by unit data packets.

[0038] When the unmanned aerial vehicle transmits data to the base station, it is assumed that all channels experience block fading, that is, the channel gain is the same in the same time slot and randomly changes between different time slots, so the instantaneous gain of the data transmission link between the unmanned aerial vehicle i and the base station o in the channel m in the time slot n is

[0039]

[0040] Wherein, m represents the channel number selected by the unmanned aerial vehicle i, d i,o (n) is the distance between the unmanned aerial vehicle i and the base station o in the time slot n, and a is the path loss index, is the instantaneous fading coefficient between the unmanned aerial vehicle i and the base station o in the time slot n, channel m, and the signal power p i,m (n) received by the base station o from the unmanned aerial vehicle i in the time slot n, channel m is represented as:

[0041]

[0042] If two unmanned aerial vehicles select the same channel in the same time slot, frequency use conflict will occur between them, and the interference signal strength I i (n) and the transmission rate R i (n) received by the base station o from the unmanned aerial vehicle i in the time slot n, channel m is represented as:

[0043]

[0044]

[0045] In the formula, S i (a i (n)) = {j e N \ i: a​j (n) = a i (n) denotes the set of all unmanned aerial vehicles using channel a i (n) in time slot n except unmanned aerial vehicle i; N\i denotes all unmanned aerial vehicles in set N except unmanned aerial vehicle i; a i (n) denotes the channel selected by unmanned aerial vehicle i in time slot n; a i (n) e M; a j (n) denotes the channel selected by unmanned aerial vehicle j in time slot n; p j (n) denotes the transmission power of unmanned aerial vehicle j in time slot n; is the instantaneous gain of the data transmission link between unmanned aerial vehicle j and base station o in channel m in time slot n. σ 2 is the noise power. It is assumed that each unmanned aerial vehicle has an unbounded queue for storing the service data arriving at the unmanned aerial vehicle. For any time slot n ≥ 0, the queue backlog of unmanned aerial vehicle i in the next time slot O i (n+1) can be expressed as:

[0046]

[0047] where O i (n) denotes the queue backlog of unmanned aerial vehicle i in time slot n; b i (n) = 1 indicates that unmanned aerial vehicle i occupies channel a to transmit data to the base station in time slot n; b i (n) = 0 indicates that unmanned aerial vehicle i does not occupy channel a to transmit data to the base station in time slot n; v i (n) is the number of service data packets transmitted by unmanned aerial vehicle i to the base station in time slot n; A i (n) denotes the number of service data packets arriving at unmanned aerial vehicle i in time slot n, and A i (n) ≤ A max . It is assumed that the number of service data packets A i (n) arriving at unmanned aerial vehicle i in time slot n follows a Poisson distribution with parameter ρ, i.e., when the unmanned aerial vehicle experiences enough time slots, ρ is the average number of service data packets arriving at unmanned aerial vehicle i in a single time slot, and the present embodiment refers to ρ as the average arrival rate of the unmanned aerial vehicle.

[0048] The primary problem to be solved in unmanned aerial vehicle spectrum allocation and access is to reduce or eliminate the frequency usage conflict among unmanned aerial vehicles and improve the effective utilization rate of channels. The present embodiment defines the frequency usage conflict rate (referred simply to as “frequency usage conflict rate”) K(n) among unmanned aerial vehicles in the first n time slots as:

[0049]

[0050] where c1(x) is the total number of UAVs that have a conflict with other UAVs at time slot x, and N is the total number of UAVs. The effective utilization of the channel L(n) in the first n time slots is defined as:

[0051]

[0052] where c2(x) is the number of channels that are effectively utilized at time slot x, and M is the total number of available channels; for A channel m is said to be effectively utilized if and only if there is one UAV using the channel m.

[0053] In addition, the embodiment aims to transmit more service data packets in a unit of time, and therefore, the maximum stable average data arrival rate p of the system should be maximized under the condition of ensuring the stable transmission of service data packets of all UAVs. max , which can be expressed as

[0054] Based on the above analysis, the objective of the embodiment is to find a balanced strategy s that can minimize the frequency conflict rate between UAVs, maximize the effective utilization of the channel, and maximize the maximum average stable arrival rate (system throughput) p max , that is,

[0055]

[0056]

[0057]

[0058] where C|D represents C that satisfies condition D. K(n)|s represents the frequency conflict rate K(n) that satisfies the balanced strategy s; L(n)|s represents the effective utilization L(n) that satisfies the balanced strategy s; and p max |s represents the maximum average stable arrival rate p that satisfies the balanced strategy s. max .

[0059] As can be seen from equations (8)-(10), not only the frequency conflict and the effective utilization of the channel of the system UAV group are considered, but also the change of the data backlog of the UAVs.

[0060] To ensure the stability of the data backlog in the service data packet transmission queue of the UAV, the system channel allocation strategy needs to be updated in real time according to the data backlog. To control the channel access of the UAV, z i (n) is introduced to represent the control parameter of the channel access of the UAV i at time slot n, z i (n)≥0, and z is used to represent the channel access control parameter vector of the UAV group.

[0061] The channel usage state of UAV i in time slot n is defined as STA i (n), where: STA i (n) = 0 means that there is no channel transmission of data in the data backlog of UAV i in time slot n; STA i (n) = 1 means that there is channel transmission of data in the data backlog of UAV i in time slot n and no interference from other UAVs; STA i (n) = 2 means that there is channel transmission of data in the data backlog of UAV i in time slot n and interference from other UAVs.

[0062] When the UAV uses the channel to transmit data in its data backlog, it can be determined whether it is interfered by other UAVs through the energy detection method.

[0063] Under the premise of ensuring that the data backlog in the service data packet transmission queue of all UAVs remains stable, for UAV i ∈ N, its spectrum access strategy is different when it is in different states, which is divided into the following three cases:

[0064] (1) When STA i (n) = 0, UAV i needs to find an empty channel to access in order to keep the data backlog of the UAV within a certain range, which meets the requirement of ρ max in formula (10), and to avoid frequency use conflicts between UAVs without information interaction, UAV i remains in state 0 for 1 time slot, and the update of its channel access control parameter is z i (n+1) = z i (n) + 1. UAV i remains in state 0 for at least N-M time slots, that is, when z i (n) ≥ N-M, UAV i can access the available empty channel, and the probability of accessing all available empty channels is the same. At this time, the probability of UAV i accessing channel r (r ∈ M) in the next time slot is:

[0065] When z i (n) ≥ N-M,

[0066]

[0067] When z i (n) ≤ N-M,

[0068]

[0069] where Y(n) = {1,... y,... Y(n)} is the set of idle channels that can be accessed in time slot n, and Y(n) is the number of idle channels in time slot n. q i,r(n+1) represents the probability that the i-th UAV accesses channel r in the next time slot n+1, q i,0 (n+1) represents the probability that the i-th drone does not access any channel in the next time slot n+1. C\D represents all elements that belong to C and not to D, that is, r∈M\Y(n) means that channel r belongs to the channel set M and does not belong to the idle channel set Y(n).

[0070] (2) When STA i When (n) = 1, if UAV i uses channel m, in order to avoid it occupying channel resources for a long time and causing other UAVs to be unable to use the channel effectively, when its data backlog is less than a certain value Δ, at the beginning of the next time slot, UAV i will release the occupied channel to allow STA j (n) = 0 and parameter z j (n)≥NM UAVs use, j∈N, and the channel usage status of UAV i in the next time slot becomes 0; when its data backlog is greater than Δ, UAV i continues to maintain its original status. Regardless of the channel usage status of UAV i in the previous time slot, UAV i has achieved the goal of occupying the channel alone, then its channel access parameter z in the next time slot i (n+1)=0. At this time, the probability that drone i accesses channel r in the next time slot is:

[0071] When O i (n)>Δ,

[0072]

[0073] When O i (n)<Δ,

[0074]

[0075] Among them, r∈M\m indicates that channel r belongs to channel set M but does not belong to channel m, Δ is the threshold for drone i to release the occupied channel, which is used to control drones to release occupied channels to ensure the interests of all drones; i (n) is the backlog of business data packets in the data transmission queue of the i-th UAV in the current time slot n; m represents the channel occupied by the i-th UAV in the current time slot n.

[0076] (3) When STA i When (n) = 2, if a drone accesses channel m, in order to avoid mutual interference between drones in channel m in the next time slot and to avoid frequency conflicts with other drones on other channels, the drone will exit or stay in the original channel according to probability. In the current time slot, drone i cannot effectively use the channel for data transmission due to interference from other drones and may exit the original channel in the next time slot. In order to reduce its impact, the channel access control parameter, i.e., z, is not changed.i (n+1)=z i (n), thus avoiding requeuing and effectively preventing a large increase in its data backlog. The probability q of drone i accessing channel r in the next time slot n+1 is i,r (n+1) is:

[0077]

[0078] where p i,r (n) and I i (n) are the signal strength of UAV i received by base station o in channel r in time slot n and the mutual interference signal strength of the signal received by UAV i, respectively. 2 is the noise power.

[0079] In time slot n, when two or more drones use channel m at the same time, mutual interference will occur. Satisfy condition a i To avoid the continued mutual interference between drones i and channel m, the probability of drone i choosing to stay in the original channel m at the beginning of the next time slot is as shown in the formula:

[0080] In summary, when a drone accesses an idle channel in state 0, it must queue up to access, thereby effectively avoiding conflicts caused by multiple drones randomly accessing the same channel at the same time when changing from state 2 to state 1 and from state 0 to state 1. In addition, when multiple drones access channel m at the same time, the conflicting drones will choose to exit or stay in channel m according to a certain probability, which effectively avoids the original channel from continuing to conflict to a certain extent. When drones frequently alternate using channels as needed based on the stability of business data transmission, they effectively avoid mutual interference with other drones. In time slot n, this can effectively reduce c1(n) and effectively improve c2(n), thereby effectively reducing K(n) and improving L(n). The improvement of L(n) makes the ρ of the drone group max The value is improved accordingly; in addition, when the drone channel usage state is 1 and its data backlog is less than the threshold Δ, the drone will release the channel it occupies in the next time slot, thereby avoiding long-term occupation of limited spectrum resources and improving ρ max value.

[0081] According to the above analysis, the channel access control parameter z without UAV i can be obtained i The corresponding relationship between (n) and the historical channel usage status is shown in Table 1.

[0082] Table 1 Channel access control parameters z of UAV i i (n) Correspondence table with historical channel usage status

[0083]

[0084] From Table 1, the unmanned aerial vehicle i can obtain the channel access control parameter z i (n) of the next time slot according to the channel access control parameter z i (n-1) of the current time slot and the channel access control parameter z i (n+1) of the previous time slot.

[0085] Wherein, STA i (n) = 0 indicates that the channel usage state of the i unmanned aerial vehicle in the current time slot n is 0, STA i (n-1) = 0 indicates that the channel usage state of the i unmanned aerial vehicle in the previous time slot n-1 is 0, STA i (n) = 1 indicates that the channel usage state of the i unmanned aerial vehicle in the current time slot n is 1, STA i (n-1) = 1 indicates that the channel usage state of the i unmanned aerial vehicle in the previous time slot n-1 is 1, STA i (n) = 2 indicates that the channel usage state of the i unmanned aerial vehicle in the current time slot n is 2, STA i (n-1) = 2 indicates that the channel usage state of the i unmanned aerial vehicle in the previous time slot n-1 is 2.

[0086] z i (n) indicates the channel access control parameter of the i unmanned aerial vehicle in the current time slot n, z i (n+1) indicates the channel access control parameter of the i unmanned aerial vehicle in the next time slot n.

[0087] Therefore, step 200 of the method of the embodiment is:

[0088] When the next time slot is not the last time slot, the channel usage state of each unmanned aerial vehicle in the distributed unmanned aerial vehicle group communication network in the current time slot is determined, specifically including: when the next time slot is not the last time slot, the channel usage state of each unmanned aerial vehicle in the distributed unmanned aerial vehicle group communication network in the current time slot is determined according to the channel access probability of each unmanned aerial vehicle in the distributed unmanned aerial vehicle group communication network in the current time slot.

[0089] Step 300 of the method of the embodiment is:

[0090] If the channel usage state of the unmanned aerial vehicle in the current time slot is 0, the channel access probability of the unmanned aerial vehicle in the next time slot is determined according to the channel access control parameter of the unmanned aerial vehicle in the current time slot and the channel access control parameter of the unmanned aerial vehicle in the next time slot; the channel usage state of the unmanned aerial vehicle in the current time slot is 0, which means that there is no channel transmission for the unmanned aerial vehicle to transmit the accumulated service data packets in the data transmission queue in the current time slot n;

[0091] If the channel usage status of the drone in the current time slot is 1, the channel access probability of the drone in the next time slot is determined based on the backlog of business data packets in the data transmission queue of the current time slot corresponding to the drone; the channel usage status of the drone in the current time slot is 1, which means that there is a channel in the current time slot n to transmit the backlog of business data packets in the data transmission queue of the drone and there is no interference from other drones;

[0092] If the channel usage status of the drone in the current time slot is 2, the channel access probability of the drone in the next time slot is determined based on the current interference status information corresponding to the drone; the channel usage status of the drone in the current time slot is 2, which means that there is a channel in the current time slot n to transmit the backlog of business data packets in the drone's data transmission queue and it is interfered with by other drones.

[0093] After executing step 200, the drone swarm spectrum access method further includes:

[0094] Step A: Calculate the channel access control parameters of each drone in the distributed drone swarm communication network in the next time slot.

[0095] Step B: Calculate the backlog of service data packets in the data transmission queue of each drone in the distributed drone swarm communication network in the next time slot.

[0096] Among them, step A: calculate the channel access control parameters of each drone in the distributed drone swarm communication network in the next time slot based on the channel usage status of each drone in the distributed drone swarm communication network in the current time slot and the channel usage status of each drone in the distributed drone swarm communication network in the previous time slot.

[0097] Furthermore, the channel access control parameters of each drone in the distributed drone swarm communication network in the next time slot are calculated according to the above Table 1.

[0098] Step B: Calculate the backlog of service data packets in the data transmission queue of each drone in the distributed drone swarm communication network in the next time slot according to the following formula; the following formula is:

[0099]

[0100] Among them, O i (n+1) represents the backlog of service data packets in the data transmission queue of the i-th UAV in the next time slot n+1, O i (n) represents the backlog of service data packets in the data transmission queue of the i-th UAV in the current time slot n, b i (n) = 1 means that the i-th UAV occupies the channel to transmit data to the base station in the current time slot n, b i(n) = 0 means that the i-th UAV does not occupy the channel to transmit data to the base station in the current time slot n; v i (n) is the number of service data packets transmitted by the i-th UAV to the base station in the current time slot n; A i (n) represents the number of service data packets arriving at the i-th UAV in the current time slot n.

[0101] If the channel usage state of the current time slot UAV is 0, the channel access probability of the next time slot of the UAV is determined according to the current time slot channel access control parameter and the next time slot channel access control parameter corresponding to the UAV, and specifically includes:

[0102] When STA i (n) = 0 and z i (n+1) > 5, all idle channels are detected by energy detection method; wherein, STA i (n) = 0 means that the channel usage state of the i-th UAV in the current time slot n is 0, z i (n+1) means the channel access control parameter of the i-th UAV in the next time slot n;

[0103] When z i (n) ≥ N-M,

[0104]

[0105] When z i (n) ≤ N-M,

[0106]

[0107] Wherein, z i (n) represents the channel access control parameter of the i-th UAV in the current time slot n, q i,r (n+1) represents the probability of the i-th UAV accessing the channel r in the next time slot n+1, Y(n) = {1,...y,...Y(n)} is the idle channel set accessible to the i-th UAV in the current time slot n, Y(n) is the number of idle channels corresponding to the i-th UAV in the current time slot n; r ∈ M\Y(n) means that the channel r belongs to the channel set M and does not belong to the idle channel set Y(n); r ∈ M means that the channel r belongs to the channel set M; r ∈ Y(n) means that the channel r belongs to the idle channel set Y(n); r = 0 means that the channel r does not exist.

[0108] If the channel usage state of the current time slot UAV is 1, the channel access probability of the next time slot of the UAV is determined according to the backlog of service data packets in the current time slot data transmission queue corresponding to the UAV, and specifically includes:

[0109] When O i (n) > Δ,

[0110]

[0111] When O i (n)<Δ,

[0112]

[0113] Among them, Δ is the threshold for the i-th UAV to release the occupied channel; O i (n) represents the backlog of service data packets in the data transmission queue of the i-th UAV in the current time slot n; m represents the channel occupied by the i-th UAV in the current time slot n; q i,r (n+1) represents the probability that the i-th UAV accesses channel r in the next time slot n+1; r∈M\m represents that channel r belongs to channel set M but not channel m; r∈M represents that channel r belongs to channel set M; r=m represents that channel r is channel m; r=0 represents that channel r does not exist.

[0114] If the channel usage status of the drone in the current time slot is 2, the channel access probability of the drone in the next time slot is determined according to the current interference status information corresponding to the drone, specifically including:

[0115]

[0116] Among them, q i,r (n+1) represents the probability that the i-th UAV accesses channel r in the next time slot n+1; p i,r (n) and I i (n) are the signal strength of the i-th UAV received by base station o in channel r in the current time slot n and the mutual interference signal strength of the i-th UAV signal received by base station o, respectively. 2 is the noise power; r∈M\m means that channel r belongs to channel set M and is not channel m; r=m means that channel r is channel m; r=0 means that channel r does not exist.

[0117] To solve the above UAV spectrum access problem, this embodiment proposes a spectrum access algorithm based on multi-user non-coupling queuing, as shown in Table 2. In time slot n, UAV i generates q i (n) Select channel access, (q i (n) is the probability set of UAV i accessing all channels in M ​​in time slot n, q i (n) = {q i,r (n)} r∈M ). After the UAV selects the channel access, the channel usage status of UAV i in time slots n and n-1 is given according to Section 2.1, as well as the channel access control parameter z in Table 1 i (n+1), when STA i(n) = 0 and z i (n+1) > 5, the energy detection method is used to obtain all the idle channel set Y(n); the UAV obtains its data backlog O i (n+1) and the channel usage state STA i (n), the idle channel set Y(n) and the channel access control parameter z i (n), the channel access probability vector q i (n+1).

[0118] Table 2 is a table of the multi-user non-coupled queuing spectrum access algorithm

[0119]

[0120]

[0121]

[0122] According to the algorithm and the complexity calculation method given in Table 2, the number of calculations of the algorithm is in linear relationship with 10M, i.e. O(10M). The random spectrum access algorithm (RSAA) used for comparative analysis in the embodiment of the present application is a method for multiple mobiles to randomly access a channel, and the complexity thereof is O(2M); the utility-based distributed subchannel allocation algorithm (UDSA)

[14] is an algorithm for controlling channel access by introducing an additional state variable "mood" for each local user, and the complexity thereof is O(2M). The complexity of the algorithm proposed in the present application is slightly higher than that of the existing algorithm, but it is relatively acceptable.

[0123] In order to verify the performance of the proposed method, the random spectrum access algorithm (RSAA) and the existing utility-based distributed subchannel allocation algorithm (UDSA) are taken as comparative objects. The number of UAVs is set to N = 20, the noise power is set to σ 2=-100dBm, channel bandwidth B = 3MHz, Δ = 2ρ, propagation path fading coefficient α = 2. The flight altitude of the drones is h = 50m, the range of motion is 2000m × 2000m, the grid area is 2m × 2m, the base station is located directly below the center of the plane and at an altitude of 0; the transmission power of the drones is p i =24.77dBm; the data packet size is 3×10 4 bits.

[0124] Figure 3 A time-varying curve of the frequency conflict rate for a swarm of drones (UAVs) is presented, with M = 15, when the data transmission is in a critically stable state. The figure shows that when the UAV system is in its respective critically stable state under different algorithms, the proposed algorithm significantly reduces the frequency conflict rate compared to the other two algorithms. This frequency conflict rate gradually decreases over time, ultimately stabilizing at around 3%. This is because in the proposed algorithm, UAVs in state 0 must queue to access idle channels, effectively avoiding conflicts caused by multiple UAVs randomly accessing the same channel simultaneously when transitioning from state 2 to state 1 and from state 0 to state 1. Furthermore, when multiple UAVs simultaneously access channel m, the conflicting UAVs will choose to exit or remain in channel m with a certain probability, effectively preventing further conflicts on the original channel. UAVs frequently alternate channels as needed based on the stability of data transmission, effectively avoiding interference with other UAVs.

[0125] Figure 4 The curve of the effective channel utilization rate of the drone swarm system when the data transmission of the drone swarm business is in a critical stable state is given, where M = 15. As can be seen from the figure, the effective channel utilization rate of the drone swarm system in the proposed method is significantly higher than that of the UDSA method and the RSAA method. As time goes by, the effective channel utilization rate gradually increases and finally stabilizes at around 89%. This trend is consistent with the Figure 3 same.

[0126] Figure 5 The curve of the average data backlog of the drone group changing with time under different ρ values ​​is given. Figure 5 (a) to Figure 5 In (d), the average data arrival rate ρ in the network is 9 packets / time slot, 10 packets / time slot, 11 packets / time slot and 33 packets / time slot respectively. From the simulation results, we can see that Figure 5 (a) Figure 5 (b) Figure 5 In (c), the data backlog of the UAV increases nonlinearly when the UDSA method and the RSAA method are used. Figure 5In (d), this change is linear. This is because, when the average data arrival rate is low, the UDSA and RSAA methods can still complete the transmission of the backlog data to a certain extent, so their average data backlogs show a certain degree of volatility. When the average arrival rate ρ reaches 33 packets / time slot, the amount of data transmitted per unit time by the UDSA and RSAA methods is almost negligible compared to the amount of data arriving per unit time and the data backlog. Therefore, when using these two methods, the average data backlog of the drone increases almost linearly. In sharp contrast, under different data arrival rates, the average data backlog of the drone in the proposed algorithm remains basically stable, thus verifying that under high traffic load, the proposed algorithm can rationally utilize spectrum resources to effectively complete data transmission.

[0127] Figure 6 The maximum stable average arrival rate ρ of the drone swarm under different available channel numbers M is given max As can be seen from the figure, the ρ of the drone swarm in the three methods max The values ​​of both increase with the number of channels, but the ρ of the drone swarm in the proposed algorithm max The value (or maximum transmission rate) is increased by 1.38 to 2.3 times compared with the UDSA algorithm and by 1.7 to 2.7 times compared with the USAA algorithm. This is because the effective channel utilization rate of the proposed method is significantly higher than that of the other two methods. In addition, when the channel usage status of the drone is 1 and its data backlog is less than the threshold Δ, the drone will release the occupied channel in the next time slot, thereby avoiding long-term occupation of limited spectrum resources and improving system throughput.

[0128] Since the drone data backlog is related to the average data arrival rate and transmission rate, that is, when the data backlog is constant, the drone transmission rate is directly affected by the average data arrival rate. The drone transmission rate increases with the increase of the average data arrival rate, thereby improving the throughput performance of the drone swarm.

[0129] Since the average arrival rate of drone service packets is different when the drone swarm is in a critical stable state under different algorithms, in order to compare the fairness of drone frequency utilization of the three algorithms, the ρ of the proposed algorithm is used. max The maximum data backlog difference of the UAV is normalized by the standard (i.e., the maximum UAV data backlog in the UAV group minus the minimum UAV data backlog). For example, the system ρ of the proposed algorithm and the UDSA algorithm is max The values ​​are 33 packets / time slot and 10 packets / time slot respectively, and the maximum data backlog difference of the drone in time slot n is O 1 (n) and O 2 (n), then for O 1 (n) and O2 (n) Normalization processing is performed:

[0130] Figure 7 The maximum data backlog difference change process of the UAV when the UAV is in a critical stable state is given when the number of available channels M = 15. From Figure 7 It can be seen that, compared with the other two algorithms, the maximum data backlog difference of the UAV in the proposed algorithm is smaller when the UAV is in a critical stable state, and its change is more stable, which effectively ensures the fairness of the frequency used between UAVs. This is because the proposed algorithm can make the UAV access the channel on demand, and when the UAV data backlog is lower than the threshold Δ, it will automatically release the occupied channel in the next time slot for other UAVs to use, effectively avoiding a single UAV from being unable to effectively use the channel for a long time (the channel use state is not 1), thereby effectively controlling the maximum UAV data backlog difference. In addition, when the UAV changes from channel use state 0 to channel use state 2, its channel access control parameter does not change in the next time slot, avoiding the re-queuing process, thereby to some extent avoiding the increase of its data backlog amount.

[0131] Embodiment two

[0132] In order to perform the method corresponding to the above-mentioned embodiment one to realize the corresponding functions and technical effects, the following provides a UAV group spectrum access system for data transmission stability.

[0133] As Figure 8 shown, the UAV group spectrum access system for data transmission stability provided by the embodiment comprises:

[0134] The distributed UAV group communication network construction module 1 is configured to construct a distributed UAV group communication network; the distributed UAV group communication network is configured to provide relay services for ground user equipment or collect ground target information, and transmit the arrived service data packets to a base station in real time; the distributed UAV group communication network comprises N UAVs, one base station and M available channels; the bandwidth of each channel is B, the flight height of each UAV is h, and the transmission power of the i-th UAV in the current time slot n is p i (n).

[0135] The current time slot UAV channel use state determination module 2 is configured to determine the channel use state of each UAV in the distributed UAV group communication network in the current time slot when the next time slot is not the last time slot.

[0136] The next time slot UAV channel access probability calculation module 3 is configured to:

[0137] If the channel usage status of the drone in the current time slot is 0, the channel access probability of the drone in the next time slot is determined according to the current time slot channel access control parameter and the next time slot channel access control parameter corresponding to the drone; the channel usage status of the drone in the current time slot is 0, which means that there is no channel to transmit the backlog of service data packets in the data transmission queue of the drone in the current time slot n;

[0138] If the channel usage status of the drone in the current time slot is 1, the channel access probability of the drone in the next time slot is determined based on the backlog of business data packets in the data transmission queue of the current time slot corresponding to the drone; the channel usage status of the drone in the current time slot is 1, which means that there is a channel in the current time slot n to transmit the backlog of business data packets in the data transmission queue of the drone and there is no interference from other drones;

[0139] If the channel usage status of the drone in the current time slot is 2, the channel access probability of the drone in the next time slot is determined based on the current interference status information corresponding to the drone; the channel usage status of the drone in the current time slot is 2, which means that there is a channel in the current time slot n to transmit the backlog of business data packets in the drone's data transmission queue and it is interfered with by other drones.

[0140] This paper investigates the spectrum access problem for drone swarms under the premise of stable service data transmission queues. Aiming to mitigate inter-UAV frequency conflicts and improve channel utilization and system throughput, a spectrum access method based on multi-user uncoupled queuing is proposed. Simulation results demonstrate that the proposed algorithm effectively reduces inter-UAV frequency conflicts, improves channel utilization and system throughput, and is suitable for scenarios with agile internal and external environments, such as those with bursty service data and agile wireless communication environments. However, this paper assumes that the average number of arriving service packets per drone is known. If the value of ρ cannot be determined in advance or the number of arriving service packets per drone time slot fluctuates erratically, the drones will be unable to adjust their power consumption in a timely manner, resulting in unnecessary energy consumption. Therefore, future research will explore dynamic spectrum access methods for drones in these practical scenarios.

[0141] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0142] The principles and implementation manners of the present application are described by using specific examples in the present application, and the above examples are only used to help understand the method of the present application and its core idea; meanwhile, for the general technical personnel in the art, the specific implementation manners and application ranges will be changed according to the idea of the present application. In conclusion, the content of the present specification should not be understood as the limitation of the present application.

Claims

1. A spectrum access method for drone swarms aimed at data transmission stability, characterized in that: include: Construct a distributed UAV swarm communication network; the distributed UAV swarm communication network is used to provide relay services for ground user equipment or collect ground target information, and transmit arriving service data packets to the base station in real time; the distributed UAV swarm communication network includes N UAVs, a base station and M available channels; the bandwidth of each channel is B, the flight altitude of each UAV is h, and the transmission power of the i-th UAV in the current time slot n is p i (n); When the next time slot is not the last time slot, determining the channel usage status of each drone in the distributed drone swarm communication network in the current time slot; If the channel usage status of the drone in the current time slot is 0, the channel access probability of the drone in the next time slot is determined according to the current time slot channel access control parameter and the next time slot channel access control parameter corresponding to the drone; the channel usage status of the drone in the current time slot is 0, which means that there is no channel to transmit the backlog of service data packets in the data transmission queue of the drone in the current time slot n; If the channel usage status of the drone in the current time slot is 1, the channel access probability of the drone in the next time slot is determined based on the backlog of business data packets in the data transmission queue of the current time slot corresponding to the drone; the channel usage status of the drone in the current time slot is 1, which means that there is a channel in the current time slot n to transmit the backlog of business data packets in the data transmission queue of the drone and there is no interference from other drones; If the channel usage status of the drone in the current time slot is 2, the channel access probability of the drone in the next time slot is determined based on the current interference status information corresponding to the drone; the channel usage status of the drone in the current time slot is 2, which means that there is a channel in the current time slot n to transmit the backlog of business data packets in the drone's data transmission queue and it is interfered with by other drones.

2. The spectrum access method for drone swarms aimed at data transmission stability according to claim 1 is characterized in that: When the next time slot is not the last time slot, determining the channel usage status of each drone in the distributed drone swarm communication network in the current time slot specifically includes: When the next time slot is not the last time slot, the channel usage status of each drone in the distributed drone swarm communication network in the current time slot is determined according to the channel access probability of each drone in the distributed drone swarm communication network in the current time slot.

3. The spectrum access method for drone swarms aimed at data transmission stability according to claim 1 is characterized in that: After determining the channel usage status of each drone in the distributed drone swarm communication network in the current time slot when the next time slot is not the last time slot, the drone swarm spectrum access method further includes: Calculating a channel access control parameter for each of the UAVs in the distributed UAV swarm communication network in the next time slot; Calculate the backlog of business data packets in the data transmission queue of each drone in the distributed drone swarm communication network in the next time slot.

4. The spectrum access method for drone swarms aimed at data transmission stability according to claim 3 is characterized in that: The calculating of the channel access control parameters of each drone in the distributed drone swarm communication network in the next time slot specifically includes: According to the channel usage status of each drone in the distributed drone swarm communication network in the current time slot and the channel usage status of each drone in the distributed drone swarm communication network in the previous time slot, the channel access control parameters of each drone in the distributed drone swarm communication network in the next time slot are calculated.

5. The spectrum access method for drone swarms aimed at data transmission stability according to claim 3 is characterized in that: The calculating of the channel access control parameters of each drone in the distributed drone swarm communication network in the next time slot specifically includes: Calculate the channel access control parameters of each drone in the distributed drone swarm communication network in the next time slot according to Table 1; Table 1 is: Among them, STA i (n) = 0 means that the channel usage status of the i-th UAV in the current time slot n is 0, STA i (n-1)=0 means that the channel usage status of the i-th UAV in the previous time slot n-1 is 0, STA i (n) = 1 means that the channel usage status of the i-th UAV in the current time slot n is 1, STA i (n-1)=1 means that the channel usage status of the i-th UAV in the previous time slot n-1 is 1, STA i (n) = 2 means that the channel usage status of the i-th UAV in the current time slot n is 2, STA i (n-1)=2 means that the channel usage status of the i-th drone in the previous time slot n-1 is 2; z i (n) represents the channel access control parameter of the i-th UAV in the current time slot n, z i (n+1) represents the channel access control parameters of the i-th UAV in the next time slot n.

6. The spectrum access method for drone swarms aimed at data transmission stability according to claim 3 is characterized in that: The calculating of the backlog of service data packets in the data transmission queue of each drone in the distributed drone swarm communication network in the next time slot specifically includes: Among them, O i (n+1) represents the backlog of service data packets in the data transmission queue of the i-th UAV in the next time slot n+1, O i (n) represents the backlog of service data packets in the data transmission queue of the i-th UAV in the current time slot n, b i (n) = 1 means that the i-th UAV occupies the channel to transmit data to the base station in the current time slot n, b i (n) = 0 means that the i-th UAV does not occupy the channel to transmit data to the base station in the current time slot n; ν i (n) is the number of service data packets transmitted by the i-th UAV to the base station in the current time slot n; A i (n) represents the number of service data packets arriving at the i-th UAV in the current time slot n.

7. The spectrum access method for drone swarms aimed at data transmission stability according to claim 3, 4 or 5, characterized in that: If the channel usage status of the drone in the current time slot is 0, the channel access probability of the drone in the next time slot is determined according to the current time slot channel access control parameter and the next time slot channel access control parameter corresponding to the drone, specifically including: When STA i (n) = 0 and z i When (n+1)>5, the energy detection method is used to detect all idle channels; among them, STA i (n) = 0 means that the channel usage status of the i-th UAV in the current time slot n is 0, z i (n+1) represents the channel access control parameter of the i-th UAV in the next time slot n; When z i (n)≥NM, When z i (n)≤NM, Among them, z i (n) represents the channel access control parameter of the i-th UAV in the current time slot n, q i,r (n+1) represents the probability that the i-th UAV accesses channel r in the next time slot n+1, Y(n)={1,...y,...Y(n)} is the set of idle channels that the i-th UAV can access in the current time slot n, and Y(n) is the number of idle channels corresponding to the i-th UAV in the current time slot n; r∈M\Y(n) means that channel r belongs to channel set M but not to idle channel set Y(n); r∈M means that channel r belongs to channel set M; r∈Y(n) means that channel r belongs to idle channel set Y(n); r=0 means that channel r does not exist.

8. The spectrum access method for drone swarms aimed at data transmission stability according to claim 3 or 6, characterized in that: If the channel usage status of the drone in the current time slot is 1, the channel access probability of the drone in the next time slot is determined based on the backlog of service data packets in the data transmission queue of the current time slot corresponding to the drone, specifically including: This O i (n)>Δ, This O i (n)<Δ, Among them, Δ is the threshold for the i-th UAV to release the occupied channel; O i (n) represents the backlog of service data packets in the data transmission queue of the i-th UAV in the current time slot n; m represents the channel occupied by the i-th UAV in the current time slot n; q i,r (n+1) represents the probability that the i-th UAV accesses channel r in the next time slot n+1; r∈M\m represents that channel r belongs to channel set M but not channel m; r∈M represents that channel r belongs to channel set M; r=m represents that channel r is channel m; r=0 represents that channel r does not exist.

9. The spectrum access method for drone swarms oriented to data transmission stability according to claim 1 is characterized in that: If the channel usage status of the drone in the current time slot is 2, the channel access probability of the drone in the next time slot is determined according to the current interference status information corresponding to the drone, specifically including: Among them, q i,r (n+1) represents the probability that the i-th UAV accesses channel r in the next time slot n+1; p i,r (n) and I i (n) are the signal strength of the i-th UAV received by base station o in channel r in the current time slot n and the mutual interference signal strength of the i-th UAV signal received by base station o, respectively. 2 is the noise power; r∈M\m means that channel r belongs to channel set M and is not channel m; r=m means that channel r is channel m; r=0 means that channel r does not exist.

10. A spectrum access system for drone swarms aimed at data transmission stability, characterized in that: include: A distributed UAV swarm communication network construction module is used to construct a distributed UAV swarm communication network; the distributed UAV swarm communication network is used to provide relay services for ground user equipment or collect ground target information, and transmit arriving service data packets to the base station in real time; the distributed UAV swarm communication network includes N UAVs, a base station and M available channels; the bandwidth of each channel is B, the flight altitude of each UAV is h, and the transmission power of the i-th UAV in the current time slot n is p i (n); A current time slot drone channel usage status determination module is used to determine the channel usage status of each drone in the distributed drone swarm communication network in the current time slot when the next time slot is not the last time slot; The module for calculating the probability of drone channel access in the next time slot is used to: If the channel usage status of the drone in the current time slot is 0, the channel access probability of the drone in the next time slot is determined according to the current time slot channel access control parameter and the next time slot channel access control parameter corresponding to the drone; the channel usage status of the drone in the current time slot is 0, which means that there is no channel to transmit the backlog of service data packets in the data transmission queue of the drone in the current time slot n; If the channel usage status of the drone in the current time slot is 1, the channel access probability of the drone in the next time slot is determined based on the backlog of business data packets in the data transmission queue of the current time slot corresponding to the drone; the channel usage status of the drone in the current time slot is 1, which means that there is a channel in the current time slot n to transmit the backlog of business data packets in the data transmission queue of the drone and there is no interference from other drones; If the channel usage status of the drone in the current time slot is 2, the channel access probability of the drone in the next time slot is determined based on the current interference status information corresponding to the drone; the channel usage status of the drone in the current time slot is 2, which means that there is a channel in the current time slot n to transmit the backlog of business data packets in the drone's data transmission queue and it is interfered with by other drones.

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