An unmanned aerial vehicle ad hoc network access method based on collective adjacent interference potential

Abstract

The application discloses a kind of unmanned aerial vehicle ad hoc network access methods based on collective adjacent interference potential, belong to wireless communication technical field.It includes using the power value of received data to carry out channel sensing, judge whether there is idle channel;According to the user density around unmanned aerial vehicle node, user channel interference situation calculates the collective adjacent potential of this unmanned aerial vehicle node, then according to the historical access situation of user channel, the collective adjacent interference potential of this unmanned aerial vehicle node in each idle channel is calculated;The transmission probability of unmanned aerial vehicle node is calculated, if transmission probability is greater than threshold value, then send, otherwise wait for next time slot to carry out channel sensing again.The present application can be in large-scale unmanned aerial vehicle ad hoc network scene, realize large-scale unmanned aerial vehicle ad hoc network access based on adjacent interference potential, satisfy future large-scale unmanned aerial vehicle centerless ad hoc network under the condition of low delay high efficiency access demand.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, in particular to a UAV ad hoc network access method based on collective adjacent interference potential. BACKGROUND

[0002] With the rapid development of UAV technology, communication and network technology, and the increasing demand for UAV applications around the world, UAV group networking technology has become a technology field that attracts much attention. Traditional UAV communication systems rely on a centralized networking architecture, which is controlled, coordinated and managed by ground base stations or air base stations. However, when the central node cannot work, the UAV group cannot communicate.

[0003] In a multi-UAV end-to-end service coordination ad hoc network, UAV nodes have the same function and equal status, and the problem of a single UAV will not affect the communication of the entire UAV group, which can well solve the problem of centralized control of the central network.

[0004] In a UAV ad hoc network, the access method combining carrier sense multiple access with collision avoidance (CSMA / CA) and time division multiple access (TDMA) has attracted widespread attention from domestic and foreign researchers and institutions. However, due to the scarcity of wireless channel resources and the change of service data volume, UAVs will cause collisions when accessing and will interfere with each other. Therefore, how to dynamically access and network UAVs to adapt to large-scale UAV ad hoc network scenarios and changes in services is the key to ensuring UAV group communication. SUMMARY

[0005] Therefore, the present application proposes a UAV ad hoc network access method based on collective adjacent interference potential. This method can realize fast and efficient access of UAVs in large-scale UAV scenarios, especially can realize UAV node density calculation and contention avoidance under UAV group interference, and meet the low-latency and high-efficiency access requirements of future large-scale UAV centerless ad hoc networks.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0007] A UAV ad hoc network access method based on collective adjacent interference potential, applied to a UAV node, comprising the following steps:

[0008] (1) When the UAV node has data to be sent, the power value of the received data is used for channel sensing to determine whether there is an idle channel;

[0009] (2) If there is no idle channel, wait for the next time slot to re-perform channel sensing, when there is an idle channel, calculate the collective neighborhood potential of the UAV node according to the user density around the UAV node and the user channel interference condition, and then calculate the collective neighborhood interference potential of the UAV node on each idle channel according to the user channel historical access condition;

[0010] (3) According to the collective neighborhood interference potential calculated in step (2), calculate the transmission probability of the UAV node, if the transmission probability is greater than the threshold, then transmit, otherwise wait for the next time slot to re-perform channel sensing.

[0011] Further, the collective neighborhood potential of the UAV node n is defined as:

[0012]

[0013] Wherein:

[0014]

[0015]

[0016] In the above formula, x(n t ) represents the position of the UAV node n at t, x(n' t ) represents the position of the UAV node n' at t, the UAV node n' is the virtual center of the UAV cluster, and the virtual centers of the same UAV cluster are the same, N is a set of all UAV nodes in the UAV cluster; a, b, c are three constants, a is the number of UAVs in communication within the communication range of the UAV node n, b is the number of UAVs within the communication range of the UAV node n, and satisfies 0 r σ and d σ are constants, r σ is the flight speed of the UAV node n' at t, d σ is the diameter of the communication area of the UAV node n' at t; s, z, k are used to represent the independent variables of the function.

[0017] Further, the user channel historical access condition is a channel access condition evaluation matrix in the past ΔT time, which is represented as:

[0018]

[0019] Wherein, t is the current time, J≤M, M is the total number of accessible channels, represents the occupation of channel j by the UAV node n at t-ΔT, if occupied, Otherwise

[0020] According to the collective proximity potential of the UAV node n and the historical access situation of the user channel, the collective proximity interference potential of the UAV node n on the channel j at the time t is calculated

[0021]

[0022] Wherein, Trace represents the trace of the calculation matrix;

[0023] Thus, the collective proximity interference potential state set of all idle channels of the UAV node n at the time t is obtained

[0024] Further, the specific manner of step 3 is:

[0025] The influence weight of the access situation in the past ΔT time on the access channel j is calculated:

[0026]

[0027] Wherein, the weight is the number of the UAV nodes accessing the channel j at the time t-ΔT, divided by the total number of the UAV nodes accessing the channel j in the ΔT time;

[0028] The access probability of the UAV node n on each channel at the time t is calculated:

[0029]

[0030] The access probability threshold is set as P0, and the UAV node n makes the access decision according to

[0031] If all elements in are less than P0, the UAV node n will give up the access initiation operation at the present time and postpone the access probability judgment to the next time;

[0032] If there is an element greater than P0 in, the access is initiated on the channel corresponding to the element;

[0033] If there are multiple elements greater than P0 in, the channel corresponding to the maximum element is selected to initiate the access;

[0034] If there are multiple elements greater than P0 in and there are multiple same maximum elements, the channel corresponding to the maximum element is randomly selected to initiate the access.

[0035] ​Compared with the prior art, the present application has the following beneficial effects:

[0036] 1、The present application can realize the rapid and efficient access networking of unmanned aerial vehicles in large-scale unmanned aerial vehicle scenes, and can realize unmanned aerial vehicle node density calculation and contention avoidance under unmanned aerial vehicle group interference, thereby meeting the low-latency and high-efficiency access requirements under the condition of future large-scale unmanned aerial vehicle centerless ad hoc networking.

[0037] 2、The present application considers the channel condition around the unmanned aerial vehicle node and the access condition in the previous period of time, adopts the adjacent interference potential to feed back the networking state of the unmanned aerial vehicle group in real time, solves the multiple retransmission problem caused by unmanned aerial vehicle interference, and can reduce the access number initiated by the unmanned aerial vehicle.

[0038] 3、The present application is based on dynamic time slot contention access, takes the maximum access success rate as the objective function, and can realize flexible and efficient access of unmanned aerial vehicle ad hoc networking. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The figure is a flowchart of a kind of unmanned aerial vehicle ad hoc networking access method based on collective adjacent interference potential in the embodiment of the present application.

[0040] Figure 2 The figure is a schematic diagram of the average access success rate of different simulation times.

[0041] Figure 3 The figure is a schematic diagram of the average access success rate of different simulation times.

[0042] Figure 4 The figure is a schematic diagram of the average access success rate of different simulation times.

[0043] Figure 5 The figure is a schematic diagram of the average access success rate of different simulation times.

[0044] Figure 6 The figure is a schematic diagram of the average access success rate of different simulation times.

[0045] Figure 7 The figure is a schematic diagram of the average access success rate of different simulation times. DETAILED DESCRIPTION

[0046] The present application will be described in further detail below in combination with the drawings and embodiments.

[0047] A kind of unmanned aerial vehicle ad hoc networking access method based on collective adjacent interference potential, applied to unmanned aerial vehicle access initiation end, as shown in Figure 1 The figure is a flowchart of a kind of unmanned aerial vehicle ad hoc networking access method based on collective adjacent interference potential in the embodiment of the present application.

[0048] (1) When the UAV node has data to be sent, the power value of receiving data is used for channel sensing to determine whether there is an idle channel.

[0049] (2) If there is no idle channel, wait for the next time slot to re-sense the channel, when there is an idle channel, calculate the collective proximity potential of the UAV node according to the user density around the UAV node and the user channel interference, and then calculate the collective proximity interference potential of the UAV node in each idle channel according to the user channel historical access situation.

[0050] The collective proximity potential of the UAV node n is defined as:

[0051]

[0052] Wherein:

[0053]

[0054] In the above formula, x(n t ) represents the position of the UAV node n at t, x(n' t ) represents the position of the UAV node n' at t, the UAV node n' is the virtual center of the UAV cluster, and the virtual center of the same UAV cluster is the same, N is the set of all UAV nodes in the UAV cluster; a, b, c are three constants, a is the number of UAVs in communication within the communication range of the UAV node n, b is the number of UAVs within the communication range of the UAV node n, and satisfies 0 r σ and d σ are constants, r σ is the flight speed of the UAV node n' at t, d σ is the diameter of the communication area of the UAV node n' at t; s, z, k are used to represent the independent variables of the function.

[0055] The user channel historical access situation is the channel access situation evaluation matrix in the past ΔT time, which is represented as:

[0056]

[0057] Wherein, t is the current time, J≤M, M is the total number of accessible channels, represents the occupation of channel j by the UAV node n at t-ΔT, if occupied, then Otherwise

[0058] According to the collective proximity potential of the UAV node n and the user channel history access situation, the collective proximity interference potential of the UAV node n in the channel j at the time t is calculated

[0059]

[0060] wherein, Trace represents the trace of the calculation matrix;

[0061] Thus, the collective proximity interference potential state set of all free channels of the UAV node n at the time t is obtained

[0062] (3) According to the collective proximity interference potential calculated in step (2), the transmission probability of the UAV node is calculated, if the transmission probability is greater than a threshold value, transmission is performed, otherwise, waiting for the next time slot to re-perform channel sensing; the specific way is:

[0063] The influence weight of the access situation in the past ΔT time on the access channel j decision is calculated:

[0064]

[0065] wherein, the weight is the number of the UAV nodes accessing the channel j at the time t-ΔT, divided by the total number of the UAV nodes accessing the channel j in the ΔT time;

[0066] The access probability of the UAV node n in each channel at the time t is calculated:

[0067]

[0068] The access probability threshold value is set as P0, and the UAV node n makes access decision according to

[0069] If all elements in are less than P0, the UAV node n will give up the access initiation operation at the present time, and postpone to re-perform the access probability judgment at the next time;

[0070] If there is an element greater than P0, the access is initiated in the channel corresponding to the element;

[0071] If there are multiple elements greater than P0, the channel corresponding to the largest element is selected to initiate access;

[0072] If there are multiple elements greater than P0 and there are multiple same maximum elements, the channel corresponding to the largest element is randomly selected to initiate access. ​

[0073] The following is a more specific example:

[0074] Consider a UAV network consisting of N UAVs as shown in Figure 2 The network is a centralized architecture. Each UAV node is equal in status and can communicate with each other. The set of UAVs is denoted as N = {1, 2, …, N}. There are M channels that can serve all the UAV nodes in the network, and N is much larger than M. When two or more UAV nodes in a certain interference range choose the same channel, interference occurs. We define the distance between UAV i and UAV j as d ij If the distance is less than the threshold interference distance d, the two UAVs are adjacent UAVs, and the transmission on the same channel interferes with each other.

[0075] When UAV node n has data to send, UAV node n listens to the status of channel m at time t. The status of channel m perceived by UAV node n at time t is denoted as where, denotes that channel m is an idle channel, denotes that channel m is a busy channel. In this way, UAV node n can obtain the set of all channel states at time t Then, according to the status of the channel, the channel is selected for access.

[0076] After selecting the channel, the UAV node will make an access decision according to an access probability to select immediate access or delayed access. denotes the access probability of UAV node n to channel m at time t.

[0077] At the same time, a variable is defined. When , it means that UAV node n chooses to access channel m at time t, and vice versa. In this way, the set of transmission conditions of UAV node n at time t can be obtained Because the UAV cannot choose two or more channels for access at the same time, the set can contain at most one 1 element.

[0078] The access success rate of UAV node n at time slot t can be represented as:

[0079]

[0080] where i in the above formula represents other UAVs in the same interference area.

[0081] The UAV node n initiates L times of access in time T. In order to adapt to the dynamic network change, the UAV node n adjusts the access in different time slots, and the average access success rate in time T is maximized as the target:

[0082]

[0083] The UAV density around the UAV node n can be described using the term of collective adjacent potential. The collective adjacent potential of the UAV is defined as V(n t )=∑ n，n′∈N ψ(||x(n t )-*x(n′ t )|| σ ).

[0084] Wherein, x(n t ) represents the position of the UAV node n, and x(n′ t ) represents the position of the UAV n′, which is the virtual center of the UAV cluster. The virtual center of the same UAV cluster is the same.

[0085] Wherein:

[0086]

[0087] In the above formula, N is a set of all UAV nodes in the UAV cluster; a, b, c are three constants, a is the number of UAVs in communication within the communication range of the UAV node n, b is the number of UAVs within the communication range of the UAV node n, and satisfies 0 r σ and d σ are constants, r σ is the flight speed of the UAV node n′ at t, d σ is the diameter of the communication area of the UAV node n′ at t; s, z, k are used to represent the independent variables of the function. The user channel history access condition is the channel access condition evaluation matrix in the past ΔT time, which is represented as:

[0088]

[0089] Wherein, t is the current time, J≤M, M is the total number of accessible channels, represents the occupation of channel j by the UAV node n at t-ΔT, if occupied Otherwise

[0090] The UAV swarm adjusts its position according to the surrounding environment during the execution of the task. The UAV generates a safe and efficient path while avoiding obstacles and collisions with other UAVs. The UAV determines the use of the channel according to the power intensity of each channel m. Users close to the UAV node n transmit on channel m, and the high signal energy generated causes them to be interfered by co-channel interference, affecting the channel selection of the UAV node n. Users far from the UAV node n transmit on channel m, and the low energy signal generated has a low impact on the channel selection of the UAV node n. Therefore, a collective proximity interference potential of channel m can be set for channel m to measure the impact of co-channel interference.

[0091] According to the collective proximity potential of the UAV node n and the user channel history access, the collective proximity interference potential of the UAV node n on channel j at time t is calculated as

[0092]

[0093] wherein Trace represents the trace of the calculation matrix;

[0094] Thus, the collective proximity interference potential state set of all free channels of the UAV node n at time t is obtained as

[0095] Meanwhile, the influence weight of the access situation in the past ΔT time on the access channel determination is and satisfies The weight is the number of UAV nodes accessing channel i at time t-ΔT, divided by the total number of UAV nodes accessing channel j in ΔT time. The access probability of the UAV node n on each channel at time slot t can be written as

[0096]

[0097] The access probability threshold is set as P0, and the UAV node n makes an access decision according to

[0098] If all elements in are less than P0, the UAV node n will give up the access initiation operation at this time and postpone the access probability judgment to the next time;

[0099] If there is an element greater than P0 in, the access is initiated on the channel corresponding to the element;

[0100] If there are multiple elements greater than P0 in, the channel corresponding to the largest element is selected to initiate access; ​

[0101] If If there are multiple elements greater than P0 and multiple same maximum elements in the set, randomly select the channel corresponding to the maximum element to initiate access.

[0102] Figure 3 The average access success rate of different simulation times obtained by the above embodiment method, wherein P-CSMA represents the probability CSMA access method, and CNIP-CSMA represents the unmanned aerial vehicle ad hoc network access method based on the collective adjacent interference potential proposed by the application.

[0103] From Figure 3 It can be seen from that, with the increase of simulation time, the average access success rate of users is increasing. Secondly, affected by the total number of users, with the increase of the total number of users, the competition among users increases, and the access success rate of users decreases. However, CNIP-CSMA can determine the probability of initiating access according to the access application of the surrounding user nodes, reduce the competition probability among users, and effectively improve the average access success rate of the unmanned aerial vehicle group.

[0104] Figure 4 and Figure 5 The number of initiating access of different user numbers and the average access success rate of different user numbers obtained by the above embodiment method are respectively.

[0105] From Figure 4 It can be seen from that, the number of initiating access of different user numbers and the average access success rate of different user numbers obtained by the above embodiment method are respectively. Figure 5 It can be seen from that, the average access success rate of CNIP-CSMA is obviously higher than that of P-CSMA, and in the case of the same number of initiating access, the effective initiation of CNIP-CSMA is higher. It can be seen that the CNIP-CSMA method can effectively increase the access efficiency of the whole unmanned aerial vehicle group and reduce the access delay.

[0106] Figure 6 and Figure 7 The number of initiating access of different user numbers and the average access success rate of different user numbers obtained by the above embodiment method are respectively.

[0107] From Figure 6 It can be seen that, under the condition of the same simulation time and user number, the number of initiating access increases with the increase of the number of access channels, and the number of initiating access of different access methods is basically the same. From Figure 7 It can be seen that, with the increase of the number of access channels, the average access success rate increases, and with the increase of the simulation time, the average access success rate also increases.

[0108] In summary, the number of users, simulation time and the number of channels will affect the average access success rate of the UAV group, and the CNIP-CSMA method can effectively improve the access efficiency and average access success rate of the UAV group compared with the ordinary P-CSMA method, and reduce the access delay.

[0109] The application can realize large-scale UAV ad hoc network access based on adjacent interference potential in a large-scale UAV ad hoc network scene, and meet the low-delay and high-efficiency access requirements under the condition of future large-scale UAV ad hoc network.

Claims

1. A method for accessing a drone ad hoc network based on collective neighboring interference potential, characterized in that: Applied to drone nodes, it includes the following steps: (1) When the UAV node has data to send, it uses the power value of the received data to sense the channel and determine whether there is an idle channel; (2) If there is no idle channel, wait for the next time slot to re-perform channel sensing. When there is an idle channel, calculate the collective neighbor potential of the drone node based on the user density and user channel interference around the drone node, and then calculate the collective neighbor interference potential of the drone node in each idle channel based on the user channel historical access situation; (3) Calculate the access probability of the UAV node based on the collective neighboring interference potential calculated in step (2). If the access probability is greater than the threshold, access is performed; otherwise, wait for the next time slot to re-perform channel sensing. The collective neighbor potential of drone node n is defined as: ； in: ； ； ； ； ； In the above formulas, represents the position of drone node n at time t, Represents the drone node at time t Location, drone node It is the virtual center of the drone cluster. The virtual center of the same drone cluster is the same. N is the set of all drone nodes in the drone cluster. ; ; a, b, c are three constants, a is the number of drones communicating within the communication range of drone node n, b is the number of drones within the communication range of drone node n, and they satisfy , ; and is a constant, is the drone node at time t The flight speed, is the drone node at time t The diameter of the communication area; s, z, k are used to represent the independent variables of the function; The user channel access history is in the past The channel access evaluation matrix within time is expressed as: ； Where t is the current time, , M is the total number of accessible channels, Indicates The occupation of channel j by drone node n at the moment, if occupied ,otherwise ; According to the collective neighbor potential of drone node n and the historical access of user channels, the collective neighbor interference potential of drone node n in channel j at time t is calculated. : ； Among them, Trace represents the trace of the calculation matrix; Thus, the collective neighboring interference potential state set of all idle channels of drone node n at time t is obtained ; The specific method of step 3 is: Calculate the past The influence weight of the access situation within time on the access channel j decision: ； Among them, the weight for, The number of UAV nodes accessing channel j at time ΔT divided by the total number of UAV nodes accessing channel j during ΔT time; Calculate the access probability of drone node n in each channel at time t: ； Set the access probability threshold to , UAV node n according to To make access decisions: if All elements are less than , then the drone node n will give up the access initiation operation at this moment and postpone the access probability judgment to the next moment; if There is an element greater than , then initiate access to the channel corresponding to the element; if There are multiple elements greater than , then select the channel corresponding to the largest element to initiate access; if There are multiple elements greater than If there are multiple identical maximum elements, the channel corresponding to the largest element is randomly selected to initiate access.

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