CN115665860A - Unmanned aerial vehicle ad hoc network resource allocation method based on migratory bird swarm characteristics - Google Patents

Unmanned aerial vehicle ad hoc network resource allocation method based on migratory bird swarm characteristics Download PDF

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CN115665860A
CN115665860A CN202211334082.4A CN202211334082A CN115665860A CN 115665860 A CN115665860 A CN 115665860A CN 202211334082 A CN202211334082 A CN 202211334082A CN 115665860 A CN115665860 A CN 115665860A
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白琳
王景璟
王佳星
苏阳
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Beihang University
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Abstract

The invention discloses a resource allocation method of an unmanned aerial vehicle ad hoc network based on the characteristics of a migratory bird group, which belongs to the technical field of ad hoc network communication and comprises the following steps: establishing an unmanned aerial vehicle ad hoc network model with a sensing stage and a data transmission stage separated from each other; establishing a resource allocation mechanism according to the unmanned aerial vehicle self-organizing network model, and determining an optimal perception range; sensing phase CSI, wherein all nodes exchange Hello information of the nodes through broadcasting; and in the data transmission stage, the data information is transmitted from the source node to the destination node by selecting an optimal multi-hop path according to the CSI sensing result. According to the method, the unmanned aerial vehicle self-organizing network with separated sensing stage and data transmission stage is established according to the group behavior characteristics of the bird waiting group, the control resource overhead and the data transmission benefit brought by environment sensing are comprehensively considered, a low-complexity network resource allocation scheme and a low-delay routing algorithm are provided, the network resource configuration complexity is effectively reduced, and the self-organizing network data transmission delay is reduced.

Description

Unmanned aerial vehicle ad hoc network resource allocation method based on migratory bird swarm characteristics
Technical Field
The invention belongs to the technical field of ad hoc network communication, and particularly relates to a resource allocation method of an unmanned aerial vehicle ad hoc network based on the characteristics of a migratory bird group.
Background
Efficient and intelligent environment sensing is of great importance to unmanned aerial vehicle ad hoc networks with limited resources, because small-range environment sensing can cause the routing strategy of the network to be poor, and therefore large data transmission delay is caused. Moreover, in physical reality, the perception of the communication environment and the performance of data transmission are tightly coupled to each other. Therefore, the traditional network optimization method (such as zone routing protocol) for separately analyzing sensing and transmission cannot meet the requirements of low delay and high energy efficiency transmission of the future unmanned aerial vehicle self-organizing network.
The multi-hop data transmission of the traditional unmanned aerial vehicle self-organizing network mainly depends on a routing algorithm, and a typical routing algorithm comprises the following steps: active routing (such as DSDV routing and OLSR routing), passive routing (such as AODV routing), and hybrid routing (such as ZRP). In the above routing method, awareness of the communication environment and the corresponding resource overhead are its main concerns. However, in the existing work, the multi-hop transmission performance of data is not considered integrally with the perception of the communication environment. In fact, communication context awareness and wireless communication are two coupled issues that cannot be considered separately. For example, more environment-aware costs may extend the channel-aware range of the node and find a better path, but since the total network resources, such as time and frequency, are fixed, the remaining data transmission resources may be reduced and the transmission performance may be degraded. Therefore, it is crucial to design an efficient network architecture for the ad hoc network of the drone to balance the communication environment sensing and the multi-hop data transmission overhead and improve the transmission performance in terms of limited network resources.
Disclosure of Invention
In view of the above, the present invention aims to provide a method for allocating resources of an unmanned aerial vehicle ad hoc network based on the characteristics of a waiting bird group, which establishes an unmanned aerial vehicle ad hoc network with a separated control plane and a service plane according to the group behavior characteristics of the waiting bird group when predators and obstacles are avoided, and provides a low-complexity network resource allocation scheme and a low-delay routing algorithm by comprehensively considering control resource overhead and data transmission benefits brought by environment sensing, thereby effectively reducing the complexity of network resource allocation and reducing ad hoc network data transmission delay.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention provides a resource allocation method of an unmanned aerial vehicle ad hoc network based on the characteristics of a migratory bird swarm, which comprises the following steps:
s1: establishing an unmanned aerial vehicle ad hoc network model with a sensing stage and a data transmission stage separated from each other, and establishing node association of the unmanned aerial vehicle ad hoc network through broadcasting and forwarding environment sensing information, wherein the node association is established based on the relationship between the association degree of individuals in a bird waiting group and the distance of the individuals;
s2: establishing a resource allocation mechanism according to the unmanned aerial vehicle self-organizing network model, and determining an optimal perception range;
s3: sensing the CSI, wherein all nodes exchange Hello information of the nodes through broadcasting so as to carry out channel estimation and acquire the CSI of a remote node;
s4: and in the data transmission stage, the data information calculates a routing table according to the CSI sensing result, and an optimal multi-hop path is selected according to the routing table to be transmitted from the source node to the destination node.
Further, in step S3, the specific steps for implementing the sensing stage are:
a1: in a first CSI sensing time slot, each node broadcasts a hello message which comprises identity information and a pilot frequency sequence and can be used for estimating CSI from a source node;
a2: each node can obtain CSI of one-hop neighbor after receiving and decoding the hello message;
a3: in the second sensing time slot, the CSI obtained by each node is added to the hello message, and all nodes broadcast the hello message again to enlarge the CSI sensing range.
Further, in step S4, the specific steps for implementing the data transmission stage are as follows:
b1, determining the total transmission delay according to the following formula
Figure BDA0003914707300000021
B2: calculating the transmission time delay of each hop
Figure BDA0003914707300000022
B3: calculating a minimum latency from a source node to a destination node
Figure BDA0003914707300000023
B4: determining an optimal path for data transmission, and transmitting source node information to a destination node according to the optimal path;
Figure BDA0003914707300000024
where ξ (γ) is the total propagation delay of path γ, e m For the mth hop channel of the path, τ (e) m ) For the transmission delay of this channel, oa is the processing delay of the relay node, M = | γ | is the number of hops of a path γ from the source node x to the destination node y, W is the total transmitted data volume, B is the channel bandwidth, SNR m Is the signal-to-noise ratio of the mth hop channel, Φ (x, y) is the set of all paths from the source node x to the destination node y, K is the total number of time slots, and n is the sensing range.
Further, in step B2, the transmission delay is calculated and the channel delays outside the sensing range are replaced by the average of their distribution, i.e. the transmission delay is calculated
Figure BDA0003914707300000031
Further, in step S2, the optimal sensing range determination method is as follows: no one will be presentMapping a machine ad hoc network into an undirected graph G p = V, E, and the optimal sensing range is determined by the following formula:
Figure BDA0003914707300000032
s.t.n∈{1，2，…，k-1}
k∈N + ，
k≥2
ò≥0
in the formula, V is a node set, E is an edge set,
Figure BDA0003914707300000033
for the time delay of the shortest delay path from the source node S to the destination node D that can be selected within the sensing range n, K is the total number of time slots, p is the probability of communication between two nodes, oa is the node processing delay.
Further, in step S1, when the ad hoc network model of the unmanned aerial vehicle is established, N single-antenna nodes of the unmanned aerial vehicle are randomly distributed in a circular area with a radius of R, and the circular area is determined according to a formula
Figure BDA0003914707300000034
Determining the signal-to-noise ratio of the link between nodes, wherein a one-hop neighbor node is defined as a node whose signal-to-noise ratio to the source node is above a threshold β, i.e. a node with a signal-to-noise ratio to the source node
Figure BDA0003914707300000035
The two-hop adjacent node is positioned as the adjacent node of the one-hop adjacent node and is not in the one-hop range of the source node;
where P is the transmit power of the node, σ 2 For receiving noise power, α is the path fading factor, d i，j Is the distance between any node i and node j.
Further, in step S1, the relationship between the degree of association C (r) between individuals in the cluster of birds and their distance r is determined by the following formula:
Figure BDA0003914707300000036
in the formula
Figure BDA0003914707300000037
Is a constant.
Further, in step S2, when the resource allocation mechanism is established, a complete time period T is set sf Is divided into K time slots, each time slot having a length of T slot The first n timeslots are allocated to the control plane for the CSI sensing phase; the remaining time slots are allocated to traffic for the data transmission phase.
The invention has the beneficial effects that: according to the method, the unmanned aerial vehicle self-organizing network with the separated sensing stage and data transmission stage is established according to the group behavior characteristics of bird waiting groups when predators and obstacles are avoided, the control resource overhead and data transmission benefits brought by environment sensing are comprehensively considered, a low-complexity network resource allocation scheme and a low-delay routing algorithm are provided, the network resource configuration complexity is effectively reduced, and the self-organizing network data transmission delay is reduced.
Additional advantages, objects, and features of the invention will be set forth in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof.
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In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
fig. 1 is a schematic diagram of an ad hoc network model of an unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the sensing principle of the embodiment of the present invention;
fig. 3 is a schematic diagram of resource allocation according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1 to 3, the present invention provides a method for allocating resources of an unmanned aerial vehicle ad hoc network based on the characteristics of a migratory bird group, comprising the following steps:
s1: establishing an unmanned aerial vehicle ad hoc network model with a sensing stage and a data transmission stage separated from each other, and establishing node association of the unmanned aerial vehicle ad hoc network through broadcasting and forwarding environment sensing information, wherein the node association is established based on the relationship between the association degree of individuals in a bird waiting group and the distance of the individuals;
when the unmanned aerial vehicle self-organizing network model is established, N unmanned aerial vehicle single-antenna nodes are randomly distributed in a circular area with the radius of R, and the N unmanned aerial vehicle single-antenna nodes are distributed in the circular area through a formula
Figure BDA0003914707300000041
Determining the signal-to-noise ratio of the link between nodes, wherein a one-hop neighbor node is defined as a node whose signal-to-noise ratio to the source node is above a threshold β, i.e. a node with a signal-to-noise ratio to the source node
Figure BDA0003914707300000042
The two-hop adjacent node is positioned as the adjacent node of the one-hop adjacent node and is not in the one-hop range of the source node;
where P is the transmit power of the node, σ 2 For receiving noise power, α is the path fading factor, d i,j Is the distance between any node i and node j.
S2: establishing a resource allocation mechanism according to the unmanned aerial vehicle self-organizing network model, and determining an optimal perception range;
s3: sensing the CSI, wherein all nodes exchange Hello information of the nodes through broadcasting so as to carry out channel estimation and acquire the CSI of a remote node;
s4: and in the data transmission stage, the data information calculates a routing table according to the CSI sensing result, and an optimal multi-hop path is selected according to the routing table to be transmitted from the source node to the destination node.
The working principle of the technical scheme is as follows: for a bird waiting group, the most intelligent state should be a certain 'conglomeration tension' state, which is just between 'order' and 'disorder', and is similar to the critical point of phase transition, at this time, a group can keep its stability, and between individuals in the bird waiting groupThe relationship between the degree of association C (r) and its distance r is
Figure BDA0003914707300000051
In the formula
Figure BDA0003914707300000052
The nodes in the unmanned aerial vehicle ad hoc network are also long-distance association exceeding the maximum transmission radius by adopting a constant and imitating the characteristic, and the association can be established by broadcasting and forwarding environment perception information; in addition, the index for measuring the distance between the nodes cannot be the physical distance but the hop count between the nodes, and the hop count can bring better stability to the unmanned aerial vehicle ad hoc network no matter how dense the network is; for nodes with small hop distance, the relevance between the nodes is considered to be high, and accurate CSI of each link needs to be exchanged for routing; conversely, for nodes with large hop count distances, the correlation between them is considered to be low; then it is sufficient to obtain the basic connection state of the node; therefore, as shown in fig. 1, an unmanned aerial vehicle ad hoc network model with a sensing phase and a data transmission phase separated is established, the CSI sensing phase is introduced for obtaining accurate CSI of nodes in a high correlation range, and the data transmission phase is introduced for transmitting service data; both the CSI sensing phase and the data transmission phase are allocated dedicated resources to implement the sensing and communication processes.
The beneficial effects of the above technical scheme are that: interference and conflict of CSI sensing signals and data transmission signals are avoided by establishing an unmanned aerial vehicle ad hoc network model with a sensing stage and a data transmission stage separated, network efficiency is improved, communication environment sensing and multi-hop data transmission overhead are balanced, and transmission performance is improved in the aspect of limited network resources.
In one embodiment of the present invention, the specific steps of the sensing stage are as follows:
a1: in a first CSI sensing time slot, each node broadcasts a hello message which comprises identity information and a pilot frequency sequence and can be used for estimating CSI from a source node;
a2: each node can obtain CSI of one-hop neighbor after receiving and decoding the hello message;
a3: in the second sensing time slot, the CSI obtained by each node is added to the hello message, and all nodes broadcast the hello message again to enlarge the CSI sensing range.
The working principle of the technical scheme is as follows: each node broadcasts a hello message periodically in n allocated time slots, the structure of the hello message and the principle of the CSI sensing result are shown in fig. 2, each node broadcasts a hello message in the first CSI sensing time slot, which contains identity information and a pilot sequence thereof, and can be used for estimating CSI from a source node, and then each node can obtain CSI of its hop neighbor after receiving and decoding the hello message, as shown in (a) and (d) in fig. 2, node 1 can obtain CSI after the first CSI sensing time slot: { h 12 ,h 13 ,h 14 } (by performing CSI estimation on pilot sequences in hello messages sent by nodes 2, 3 and 4); in a second sensing time slot, the CSI obtained by each node is added into the hello message, and all the nodes broadcast the hello message again to enlarge the CSI sensing range; as shown in fig. 2 (b) and (e), each node can obtain CSI of all its 1-hops in the same manner as the first sensing slot. It can also obtain its 2-hop neighbor CSI, e.g., h, by decoding messages from nodes 2, 3, and 4 23 、h 25 And h 47 Wherein the CSI is obtained at the end of the first slot and added to the hello information at the beginning of the second slot. By analogy, node 1 may obtain the CSI of nodes within its 3 hops after the third sensing slot, as shown in fig. 2 (c) and (f).
The beneficial effects of the above technical scheme are that: accurate CSI of different distances in a network is obtained, a better path is selected for data transmission, more time slots are allocated to a CSI sensing stage, the nodes can obtain the CSI of the nodes with longer distances, more channel information references are provided for the data transmission stage, and the routing quality is effectively improved.
In one embodiment of the invention, in a data transmission phase, a source node selects a path according to CSI obtained from a sensing phase and transmits information to a destination through one hop or multiple hops; setting a path from source x to destination y as γ, the radix M = | γ | of which is the number of hops of this path, and the total transmission delay of path γ is defined as the sum of the transmission and processing delay of each hop, i.e.
Figure BDA0003914707300000061
Wherein e m Is the mth hop channel of the path, τ (e) m ) Is the transmission delay of the mth hop channel, which is the processing delay of the relay node (including the decoding and forwarding processes);
the calculation mode of the transmission delay of each hop is as follows:
Figure BDA0003914707300000062
where W is the total amount of data transmitted, B is the channel bandwidth, SNR m Is the signal-to-noise ratio of the mth hop channel.
Setting the set of all paths from source node x to destination node y to Φ (x, y), the minimum delay from x to y is:
Figure BDA0003914707300000063
since the sensing range is limited (the sensing range is equal to the number n of allocated sensing time slots), the channel delay outside the sensing range is difficult to determine, so in the calculation of the transmission delay, the channel delay outside the sensing range is replaced by the average value of the distribution thereof, i.e. the channel delay outside the sensing range is calculated
Figure BDA0003914707300000064
Therefore, during data transmission, the source node selects a path that it considers optimal (under its CSI-aware a priori knowledge) to transmit information to the next hop, and the optimal path is determined by:
Figure BDA0003914707300000071
after the source node passes the information to the next hop, the next hop node becomes the new source node and continues to pass the information to the next hop in the same manner until the information reaches its destination node.
The working principle and the beneficial effects of the technical scheme are as follows: a low-complexity network resource allocation scheme and a low-delay routing algorithm are provided, so that the complexity of network resource allocation is effectively reduced, and the transmission delay of the ad hoc network data is reduced.
In an embodiment of the present invention, in step S2, the optimal sensing range determining manner is: mapping the original unmanned aerial vehicle ad hoc network into an undirected graph G p = V, E, and the optimal sensing range is determined by the following formula:
Figure BDA0003914707300000072
s.t.n∈{1，2，…，k-1}
k∈N + ，
k≥2
ò≥0
in the formula, V is a node set, E is an edge set,
Figure BDA0003914707300000073
for the time delay of the shortest delay path from the source node S to the destination node D that can be selected within the sensing range n, K is the total number of time slots, p is the probability of communication between two nodes, oa is the node processing delay.
The working principle and the beneficial effects of the technical scheme are as follows: by inputting the number N of nodes, the total number K of time slots, the connection probability P between the nodes, the node processing time delay oa according to the optimization problem P in the unmanned aerial vehicle ad hoc network model 1 The optimal resource allocation strategy of environment perception and data transmission is determined, the complexity of a network resource allocation scheme and a time delay routing algorithm is reduced, the complexity of network resource allocation is effectively reduced, and the self-networking data transmission is reducedAnd (4) time delay.
In an embodiment of the present invention, in step S2, when the resource allocation mechanism is established, a complete time period T is set sf Is divided into K time slots, each time slot having a length of T slot I.e. T sf ＝KT slot The first n timeslots are allocated to the control plane for the CSI sensing phase; the remaining time slots are allocated to traffic for the data transmission phase.
The working principle of the technical scheme is as follows: as shown in FIG. 3, the total number of time slots T sf ＝KT slot Wherein the first n are assigned to CSI sensing phases, i.e. T c ＝nT slot The remaining n + 1-K time slots are allocated to the data transmission phase, i.e. T d ＝(K-n)T slot 。
The beneficial effects of the above technical scheme are that: the interference and conflict of the CSI sensing signal and the data transmission signal are effectively avoided, and the network efficiency is improved.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.

Claims (8)

1. A resource allocation method of an unmanned aerial vehicle ad hoc network based on the characteristics of migratory bird groups is characterized by comprising the following steps:
s1: establishing an unmanned aerial vehicle ad hoc network model with a sensing stage and a data transmission stage separated from each other, and establishing node association of the unmanned aerial vehicle ad hoc network through broadcasting and forwarding environment sensing information, wherein the node association is established based on the relationship between the association degree of individuals in a bird waiting group and the distance of the individuals;
s2: establishing a resource allocation mechanism according to the unmanned aerial vehicle self-organizing network model, and determining an optimal perception range;
s3: sensing the CSI, wherein all nodes exchange Hello information of the nodes through broadcasting so as to carry out channel estimation and acquire the CSI of a remote node;
s4: and in the data transmission stage, the data information calculates a routing table according to the CSI sensing result, and an optimal multi-hop path is selected according to the routing table to be transmitted from the source node to the destination node.
2. The method for allocating the resources of the unmanned aerial vehicle ad hoc network based on the migratory bird swarm characteristic of claim 1, wherein in step S3, the specific steps for implementing the sensing stage are as follows:
a1: in a first CSI sensing time slot, each node broadcasts a hello message which comprises identity information and a pilot frequency sequence and can be used for estimating CSI from a source node;
a2: each node can obtain CSI of one-hop neighbor after receiving and decoding the hello message;
a3: in the second sensing time slot, the CSI obtained by each node is added to the hello message, and all nodes broadcast the hello message again to enlarge the CSI sensing range.
3. The method for allocating the resources of the ad hoc network of unmanned aerial vehicles based on the characteristics of the migratory bird population as claimed in claim 1, wherein in step S4, the specific steps for implementing the data transmission stage are as follows:
b1, determining the total transmission delay according to the following formula
Figure FDA0003914707290000011
B2: calculating the transmission time delay of each hop
Figure FDA0003914707290000012
B3: calculating a minimum latency from a source node to a destination node
Figure FDA0003914707290000013
B4: determining an optimal path for data transmission, and transmitting source node information to a destination node according to the optimal path;
Figure FDA0003914707290000014
where ξ (γ) is the total propagation delay of path γ, e m For the mth hop channel of the path, τ (e) m ) For the transmission delay of this channel, oa is the processing delay of the relay node, M = | γ | is the number of hops of a path γ from the source node x to the destination node y, W is the total transmitted data volume, B is the channel bandwidth, SNR m Is the signal-to-noise ratio of the mth hop channel, Φ (x, y) is the set of all paths from the source node x to the destination node y, K is the total number of time slots, and n is the sensing range.
4. The method according to claim 3, wherein in step B2, the transmission delay is calculated, and the channel delay outside the sensing range is replaced by the average value of the distribution of the channel delay, that is, the channel delay is calculated
Figure FDA0003914707290000021
5. The method for allocating the resources of the ad hoc network of unmanned aerial vehicles based on the migratory bird population characteristics as claimed in claim 1, wherein in step S2, the optimal sensing range determination method is as follows: mapping the original unmanned aerial vehicle ad hoc network into an undirected graph G p = V, E, and the optimal sensing range is determined by the following formula:
P 1 ：
Figure FDA0003914707290000022
s.t.n∈{1,2,…,k-1}
k∈N + ,
k≥2
ò≥0
in the formula, V is a node set, E is an edge set,
Figure FDA0003914707290000023
for the time delay of the shortest delay path from the source node S to the destination node D that can be selected within the sensing range n, K is the total number of time slots, p is the probability of communication between two nodes, oa is the node processing delay.
6. The method for allocating resources of an unmanned aerial vehicle ad hoc network based on the migratory bird swarm characteristics as claimed in claim 1, wherein in step S1, when the unmanned aerial vehicle ad hoc network model is established, N unmanned aerial vehicle single antenna nodes are set to be randomly distributed in a circular area with a radius of R, and the circular area is processed by a formula
Figure FDA0003914707290000024
Determining the signal-to-noise ratio of the link between nodes, wherein a one-hop neighbor node is defined as a node whose signal-to-noise ratio to the source node is above a threshold β, i.e. a node with a signal-to-noise ratio to the source node
Figure FDA0003914707290000025
The two-hop adjacent node is positioned as the adjacent node of the one-hop adjacent node and is not in the one-hop range of the source node;
where P is the transmit power of the node, σ 2 For receiving noise power, α is the path fading factor, d i,j Is the distance between any node i and node j.
7. The method for allocating resources of an ad hoc network of unmanned aerial vehicles based on the characteristics of the waiting bird population as claimed in claim 1, wherein in step S1, the relationship between the degree of association C (r) between individuals in the waiting bird population and the distance r thereof is determined by the following formula:
Figure FDA0003914707290000031
in the formula
Figure FDA0003914707290000032
Is a constant.
8. The method according to claim 1, wherein in step S2, when the resource allocation mechanism is established, a complete time period T is set up sf Is divided into K time slots, each time slot having a length of T slot The first n timeslots are allocated to the control plane for the CSI sensing phase; the remaining time slots are allocated to traffic for the data transmission phase.
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CN114449608A (en) * 2022-01-21 2022-05-06 重庆邮电大学 Unmanned aerial vehicle ad hoc network self-adaptive routing method based on Q-Learning

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CN116506877B (en) * 2023-06-26 2023-09-26 北京航空航天大学 Distributed collaborative computing method for mobile crowd sensing

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