CN115665860B - Unmanned aerial vehicle ad hoc network resource allocation method based on characteristics of waiting bird group - Google Patents

Unmanned aerial vehicle ad hoc network resource allocation method based on characteristics of waiting bird group Download PDF

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CN115665860B
CN115665860B CN202211334082.4A CN202211334082A CN115665860B CN 115665860 B CN115665860 B CN 115665860B CN 202211334082 A CN202211334082 A CN 202211334082A CN 115665860 B CN115665860 B CN 115665860B
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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 a waiting bird group characteristic, 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 separated sensing stage and data transmission stage; establishing a resource allocation mechanism according to the unmanned aerial vehicle self-organizing network model, and determining an optimal perception range; sensing the phase CSI, wherein all nodes exchange their Hello information 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 group behavior characteristics of the bird group, the unmanned aerial vehicle self-organizing network with separated sensing stage and data transmission stage is established, the control resource expense and the data transmission income caused by environment sensing are comprehensively considered, a low-complexity network resource allocation scheme and a low-delay routing algorithm are provided, the network resource allocation complexity is effectively reduced, and the data transmission delay of the self-organizing network is reduced.

Description

Unmanned aerial vehicle ad hoc network resource allocation method based on characteristics of waiting bird group
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 characteristics of a waiting bird group.
Background
Efficient and intelligent environmental awareness is critical to resource-limited unmanned aerial vehicle ad hoc networks because small-scale environmental awareness can lead to poor routing policies of the network, thereby causing large data transmission delays. Moreover, in physical reality, the perception of the communication environment and the performance of data transmission are closely coupled to each other. Thus, conventional network optimization methods (e.g., zone routing protocol) that analyze perception and transmission alone are not able to meet the low latency and energy efficient transmission requirements of future unmanned self-organizing networks.
The multi-hop data transmission of the traditional unmanned plane self-organizing network mainly depends on a routing algorithm, and typical routing algorithms comprise: active routing (e.g., DSDV routing and OLSR routing), passive routing (e.g., AODV routing), and hybrid routing (e.g., ZRP), etc. In the above routing method, awareness of the communication environment and corresponding resource overhead are major concerns. However, in existing work, the multi-hop transmission performance of data is not considered in combination with communication environment awareness. In fact, communication environment awareness and wireless communication are two coupling issues that cannot be considered separately. For example, more context-aware costs may extend the channel-aware range of the node and find better paths, but since the total network resources, such as time and frequency, are fixed, the remaining data transmission resources may decrease and the transmission performance may decrease. Therefore, it is important to design an efficient network architecture for unmanned aerial vehicle ad hoc networks to balance communication environment awareness and multi-hop data transmission overhead, and to improve transmission performance in terms of limited network resources.
Disclosure of Invention
In view of the above, the present invention aims to provide a resource allocation method for an unmanned aerial vehicle ad hoc network based on characteristics of a waiting bird group, which establishes an unmanned aerial vehicle ad hoc network with separated control plane and service plane according to group behavior characteristics of the waiting bird group when predators and obstacles are avoided, comprehensively considers control resource overhead and data transmission benefits caused by environmental awareness, proposes a low-complexity network resource allocation scheme and a low-delay routing algorithm, effectively reduces network resource allocation complexity, and reduces ad hoc network data transmission delay.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the invention provides a resource allocation method of an unmanned aerial vehicle ad hoc network based on a waiting bird group characteristic, which comprises the following steps:
s1: establishing an unmanned aerial vehicle ad hoc network model with separated sensing stage and data transmission stage, and establishing node association of the unmanned aerial vehicle ad hoc network by broadcasting and forwarding environment sensing information, wherein the node association is established based on the relationship between the association degree between individuals in the candidate bird group and the distance of the individual bird group;
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 their Hello information through broadcasting so as to perform channel estimation and acquire the CSI of the 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 as follows:
a1: in the first CSI sensing time slot, each node broadcasts a hello message containing identity information and a pilot sequence thereof, and the hello message can be used for estimating the CSI from the source node;
a2: each node can obtain the CSI of one-hop neighbors after receiving and decoding hello information;
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 expand the CSI sensing range.
Further, in step S4, the specific steps for implementing the data transmission stage are:
b1 determining the total transmission delay according to the following formula
Figure BDA0003914707300000021
B2: calculating the transmission delay of each hop
Figure BDA0003914707300000022
B3: calculating minimum time delay from source node to destination node
Figure BDA0003914707300000023
B4: determining an optimal path of data transmission, and transmitting source node information to a destination node according to the optimal path;
Figure BDA0003914707300000024
where ζ (γ) is the total transmission delay of path γ, e m For the mth hop channel of the path, τ (e m ) For the transmission delay of this channel, co is the processing delay of the relay node, m= |γ| is the number of hops of γ for one path from the source node x to the destination node y, W is the total transmission data amount, B is the channel bandwidth, SNR m Is the signal-to-noise ratio of the mth 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 slots, and n is the perceived range.
Further, in step B2, the transmission time delay is calculated, and the channel time delay outside the perception range is replaced by the average value of the distribution thereof, namely
Figure BDA0003914707300000031
Further, in step S2, the optimal sensing range determining method is as follows: mapping the original unmanned aerial vehicle ad hoc network into an undirected graph G p = (V, E) and the optimal perception range is determined by the following formula:
Figure BDA0003914707300000032
s.t.n∈{1，2，…，k-1}
k∈N + ，
k≥2
ò≥0
wherein V is a node set, E is an edge set,
Figure BDA0003914707300000033
in order to select the delay of the shortest delay path from the source node S to the destination node D in the sensing range n, K is the total time slot number, p is the probability of communication between two nodes, and co is the node processing delay.
Further, in step S1, when the unmanned aerial vehicle ad hoc network model is established, N unmanned aerial vehicle single antenna nodes are set up randomlyDistributed in a circular area with radius R and passing through formula
Figure BDA0003914707300000034
Determining the signal-to-noise ratio of links between nodes, wherein a hop of adjacent nodes is defined as a node having a higher signal-to-noise ratio to the source node than a threshold value beta, i.e. +.>
Figure BDA0003914707300000035
The two-hop adjacent nodes are positioned as adjacent nodes of the one-hop adjacent node and are 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 a 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 candidate bird group and the distance r thereof is determined by the following formula:
Figure BDA0003914707300000036
in the middle of
Figure BDA0003914707300000037
Is constant.
Further, in step S2, when the resource allocation mechanism is established, a complete time period T sf Is divided into K time slots, each time slot has a length T slot The first n time slots are allocated to the control plane for the CSI sensing stage; the remaining time slots are allocated to traffic for the data transmission phase.
The invention has the beneficial effects that: according to the invention, according to the group behavior characteristics of the bird group when predators and obstacles are avoided, an unmanned aerial vehicle self-organizing network with separated sensing stage and data transmission stage is established, the control resource expense and the data transmission income caused by environment sensing are comprehensively considered, a low-complexity network resource allocation scheme and a low-delay routing algorithm are provided, the network resource allocation complexity is effectively reduced, and the data transmission delay of the self-organizing network is reduced.
Additional advantages, objects, and features of the invention will be set forth in part 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 objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
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In order to make the objects, technical solutions and advantageous effects of the present invention more clear, the present invention provides the following drawings for description:
fig. 1 is a schematic diagram of an unmanned aerial vehicle ad hoc network model 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 invention provides a resource allocation method of an unmanned aerial vehicle ad hoc network based on characteristics of a waiting bird group, which comprises the following steps:
s1: establishing an unmanned aerial vehicle ad hoc network model with separated sensing stage and data transmission stage, and establishing node association of the unmanned aerial vehicle ad hoc network by broadcasting and forwarding environment sensing information, wherein the node association is established based on the relationship between the association degree between individuals in the candidate bird group and the distance of the individual bird group;
when the unmanned aerial vehicle self-organizing network model is established, N unmanned aerial vehicle single antenna nodes are arranged in a circular area with radius R randomly, and the formula is adopted
Figure BDA0003914707300000041
Determining the signal-to-noise ratio of links between nodes, wherein a hop of adjacent nodes is defined as a node having a higher signal-to-noise ratio to the source node than a threshold value beta, i.e. +.>
Figure BDA0003914707300000042
Two-hop adjacent node is positioned as an adjacent node of one-hop adjacent node and is not in one-hop range of source nodeAn inner part;
where P is the transmit power of the node, σ 2 For receiving noise power, α is a 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 their Hello information through broadcasting so as to perform channel estimation and acquire the CSI of the 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 candidate bird group, the most intelligent state should be a state of "cluster tension", which is just between "ordered" and "unordered", similar to the phase transition critical point, where a group can maintain its stability, and the relationship between the degree of association C (r) between individuals in the candidate bird group and the distance r is
Figure BDA0003914707300000051
In->
Figure BDA0003914707300000052
As a constant, the node in the unmanned aerial vehicle ad hoc network is a long-distance association exceeding the maximum transmission radius according to 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 can not be the physical distance any more, but the hop count between the nodes, because the hop count can bring better stability to the unmanned aerial vehicle ad hoc network no matter what the density of the network is; for nodes with small hop distances, the relevance between the nodes is considered to be high, and accurate CSI of each link is necessary to be exchanged for routing; in contrast, for nodes whose hop count distance is large, the correlation between them is considered to be low; then it is sufficient to obtain the basic connection state of the node only; thus, as shown in FIG. 1, a sense phase is establishedThe unmanned aerial vehicle ad hoc network model is separated from the data transmission stage, the CSI sensing stage is introduced for obtaining accurate CSI of the nodes in the high correlation range, and the data transmission stage is introduced for transmitting service data; both the CSI-aware phase and the data transmission phase are allocated dedicated resources to implement the sensing and communication procedures.
The beneficial effects of the technical scheme are that: the unmanned aerial vehicle ad hoc network model with separated sensing stage and data transmission stage is established to avoid interference and conflict of CSI sensing signals and data transmission signals, improve network efficiency, balance communication environment sensing and multi-hop data transmission cost and improve transmission performance in the aspect of limited network resources.
In one embodiment of the invention, the sensing phase comprises the following specific steps:
a1: in the first CSI sensing time slot, each node broadcasts a hello message containing identity information and a pilot sequence thereof, and the hello message can be used for estimating the CSI from the source node;
a2: each node can obtain the CSI of one-hop neighbors after receiving and decoding hello information;
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 expand the CSI sensing range.
The working principle of the technical scheme is as follows: each node will broadcast hello messages periodically in n allocated time slots, the structure of hello messages and the principle of CSI sensing results are shown in fig. 2, in the first CSI sensing time slot, each node broadcasts a hello message, including its identity information and pilot sequence, which can be used to estimate CSI from the source node, then each node can obtain CSI of its one-hop neighbor after receiving and decoding hello messages, as shown in (a) and (d) in fig. 2, and node 1 can obtain CSI after the first CSI sensing time slot: { h 12 ,h 13 ,h 14 -CSI estimation by pilot sequences in hello messages sent by nodes 2, 3 and 4); in the second sensing time slot, the CSI obtained by each node is added into hello information, and all nodes broadcast hello information again to enlarge the CSI sensing range; as shown in fig. 2 (b) and (e), each nodeAll of its 1-hop CSI can be obtained in the same manner as the first perceived slot. In addition, 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 time slot and added to hello information at the beginning of the second time slot. By analogy, node 1 may obtain CSI for nodes within its 3 hops after the third sensing time slot, as shown in fig. 2 (c) and (f).
The beneficial effects of the 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 distributed to the CSI sensing stage, the node can obtain the CSI of the node with a longer distance, more channel information references are provided for the data transmission stage, and the routing quality is effectively improved.
In one embodiment of the invention, during a data transmission phase, a source node selects a path according to the CSI obtained from the sensing phase and transmits information to a destination through one or more hops; setting a path from source x to destination y as γ, the base m= |γ| is the hop count of this path, and the total propagation delay of path γ is defined as the sum of the propagation and processing delays 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, and the co is the processing delay (including decoding and forwarding processes) of the relay node;
the transmission delay of each hop is calculated by the following steps:
Figure BDA0003914707300000062
where W is the total amount of transmitted data, B is the channel bandwidth, SNR m Is the signal to noise ratio of the mth hop channel.
Setting all paths from the source node x to the destination node y to be phi (x, y), the minimum delay from x to y is:
Figure BDA0003914707300000063
since the perceived range is limited (the perceived range is equal to the number n of the allocated perceived time slots), the channel delays outside the perceived range are difficult to determine, and therefore, in the calculation of the transmission delay, the channel delays outside the perceived range are replaced by the average value of the distribution thereof, namely
Figure BDA0003914707300000064
Thus, during data transmission, the source node will choose a path that it deems optimal (under a priori knowledge of its CSI awareness) to pass information on to the next hop, and this optimal path is determined in such a way that:
Figure BDA0003914707300000071
after the source node passes the information on to the next hop, the next hop node becomes the new source node and continues to pass the information on 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: the low-complexity network resource allocation scheme and the low-delay routing algorithm are provided, so that the complexity of network resource allocation is effectively reduced, and the data transmission delay of the ad hoc network is reduced.
In one embodiment of the present invention, in step S2, the optimal sensing range is determined in the following manner: mapping the original unmanned aerial vehicle ad hoc network into an undirected graph G p = (V, E) and the optimal perception range is determined by the following formula:
Figure BDA0003914707300000072
s.t.n∈{1，2，…，k-1}
k∈N + ，
k≥2
ò≥0
wherein V is a node set, E is an edge set,
Figure BDA0003914707300000073
in order to select the delay of the shortest delay path from the source node S to the destination node D in the sensing range n, K is the total time slot number, p is the probability of communication between two nodes, and co is the node processing delay.
The working principle and the beneficial effects of the technical scheme are as follows: the number N of nodes, the total time slot number K, the inter-node connection probability P and the node processing delay co are input into an unmanned aerial vehicle ad hoc network model, and according to the optimization problem P 1 And determining the optimal resource allocation strategy of environment sensing and data transmission, reducing the complexity of a network resource allocation scheme and a delay routing algorithm, effectively reducing the complexity of network resource allocation and reducing the data transmission delay of the ad hoc network.
In one embodiment of the present invention, in step S2, a complete time period T is set up when the resource allocation mechanism is established sf Is divided into K time slots, each time slot has a length T slot T, i.e sf ＝KT slot The first n time slots are allocated to the control plane for the CSI sensing stage; 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, T d ＝(K-n)T slot 。
The beneficial effects of the technical scheme are that: interference and collision of the CSI sensing signal and the data transmission signal are effectively avoided, and network efficiency is improved.
Finally, it is noted that the above-mentioned preferred embodiments are only intended to illustrate rather than limit the invention, and that, although the invention has been described in detail by means of the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention as defined by the appended claims.

Claims (7)

1. The resource allocation method of the unmanned aerial vehicle ad hoc network based on the characteristics of the waiting bird group is characterized by comprising the following steps:
s1: establishing an unmanned aerial vehicle ad hoc network model with separated sensing stage and data transmission stage, and establishing node association of the unmanned aerial vehicle ad hoc network by broadcasting and forwarding environment sensing information, wherein the node association is established based on the relationship between the association degree between individuals in the candidate bird group and the distance of the individual bird group;
s2: establishing a resource allocation mechanism according to the unmanned aerial vehicle self-organizing network model, and determining an optimal perception range;
wherein, when establishing the resource allocation mechanism, a complete time period T sf Is divided into K time slots, each time slot has a length T slot The first n time slots are allocated to the control plane for the CSI sensing stage; the rest time slots are allocated to the service for the data transmission stage;
s3: sensing the CSI, wherein all nodes exchange their Hello information through broadcasting so as to perform channel estimation and acquire the CSI of the 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 resource allocation method of unmanned aerial vehicle ad hoc network based on the characteristics of a waiting bird group according to claim 1, wherein in step S3, the specific steps for implementing the sensing stage are as follows:
a1: in the first CSI sensing time slot, each node broadcasts a hello message containing identity information and a pilot sequence thereof, and the hello message can be used for estimating the CSI from the source node;
a2: each node can obtain the CSI of one-hop neighbors after receiving and decoding hello information;
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 expand the CSI sensing range.
3. The resource allocation method of unmanned aerial vehicle ad hoc network based on the characteristics of a waiting bird group according to 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 FDA0004204859310000011
B2: calculating the transmission delay of each hop
Figure FDA0004204859310000012
B3: calculating minimum time delay from source node to destination node
Figure FDA0004204859310000013
B4: determining an optimal path of data transmission, and transmitting source node information to a destination node according to the optimal path;
Figure FDA0004204859310000021
where ζ (γ) is the total transmission delay of path γ, e m For the mth hop channel of the path, τ (e m ) For the transmission delay of this channel, co is the processing delay of the relay node, m= |γ| is the number of hops of γ for one path from the source node x to the destination node y, W is the total transmission data amount, B is the channel bandwidth, SNR m Is the signal-to-noise ratio of the mth channel, phi (x, y) is the slave sourceThe set of all paths from node x to destination node y, K is the total number of slots, and n is the perceived range.
4. The resource allocation method of unmanned aerial vehicle ad hoc network based on the characteristics of a waiting bird group according to claim 3, wherein in step B2, the transmission time delay is calculated, and the channel time delay outside the perception range is replaced by the average value of the distribution thereof, namely
Figure FDA0004204859310000022
5. The resource allocation method of unmanned aerial vehicle ad hoc network based on the characteristics of a waiting bird group according to claim 1, wherein in step S2, the optimal perception range determining manner is as follows: mapping the original unmanned aerial vehicle ad hoc network into an undirected graph G p = (V, E) and the optimal perception range is determined by the following formula:
P 1 ：
Figure FDA0004204859310000023
s.t.n∈{1,2,…,k-1}
k∈N + ,
k≥2
ò≥0
wherein V is a node set, E is an edge set,
Figure FDA0004204859310000024
in order to select the delay of the shortest delay path from the source node S to the destination node D in the sensing range n, K is the total time slot number, p is the probability of communication between two nodes, and co is the node processing delay.
6. The resource allocation method of unmanned aerial vehicle ad hoc network based on bird waiting group characteristics according to claim 1, wherein in step S1, when establishing an unmanned aerial vehicle ad hoc network model, N unmanned aerial vehicle single antenna nodes are set upRandomly distributed in a circular area with radius R and passing through the formula
Figure FDA0004204859310000025
Determining the signal-to-noise ratio of links between nodes, wherein a hop of adjacent nodes is defined as a node having a higher signal-to-noise ratio to the source node than a threshold value beta, i.e. +.>
Figure FDA0004204859310000026
The two-hop adjacent nodes are positioned as adjacent nodes of the one-hop adjacent node and are 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 a path fading factor, d i,j Is the distance between any node i and node j.
7. The resource allocation method of unmanned aerial vehicle ad hoc network based on the characteristics of a waiting bird group according to claim 1, wherein in step S1, the relationship between the degree of association C (r) between individuals in the waiting bird group and the distance r thereof is determined by the following formula:
Figure FDA0004204859310000031
in the middle of
Figure FDA0004204859310000032
Is constant.
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