CN112822750A - Large-scale unmanned aerial vehicle network emotion driving distributed relay selection method - Google Patents
Large-scale unmanned aerial vehicle network emotion driving distributed relay selection method Download PDFInfo
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Abstract
The invention relates to a large-scale unmanned aerial vehicle network emotion driving distributed relay selection method, which comprises the steps that firstly, in a large-scale unmanned aerial vehicle network, a source unmanned aerial vehicle forwards collected information to a sink through a relay of an idle unmanned aerial vehicle; then, in a limited time, determining a candidate relay node set which maximizes the total rate of the network in each time slot; and (4) selecting the relay node by the information source node according to the satisfaction degree of the transmission rate by adopting an emotion driving mechanism. According to the emotion driving distributed relay selection method for the large-scale unmanned aerial vehicle network, the emotion driving mechanism is adopted for distributed relay selection, so that the total system rate of the dynamic unmanned aerial vehicle network is maximized under the condition that information exchange is not needed, the transmission complexity of the communication network can be effectively reduced, huge communication overhead caused by a large amount of information exchange among nodes is reduced, the convergence speed is accelerated, and the stability of the system is improved.
Description
Technical Field
The invention relates to a large-scale unmanned aerial vehicle network emotion driving distributed relay selection method, in particular to a distributed relay selection method which is used for selecting a relay unmanned aerial vehicle between an information source node and a target node of a large-scale unmanned aerial vehicle network, adopts an emotion driving mechanism, enables all the information source nodes to make decisions according to the current state of the information source nodes and updates the decisions instantly according to feedback data.
Background
Unmanned aerial vehicle is widely used with advantages such as its low cost, high mobility, like emergency communication, thing networking data acquisition and traffic monitoring. With the increasing complexity and diversity of tasks, a single unmanned aerial vehicle may not meet the requirements, and a large-scale unmanned aerial vehicle network capable of more accurately and effectively completing complex tasks is rapidly developing. Due to limited wireless transmission power or complex communication environment, a diameter propagation link may fail for two remotely located drone nodes. In order to improve the system performance of the wireless communication network, an unmanned aerial vehicle can be used as a relay node to assist the communication between the source node and the target node. In a large-scale drone network, communication between a source node and a target node requires a large number of drone relays. However, improper relay selection may negatively impact system performance. In the literature "HERA: the centralized Relay selection method proposed in "(Yang D, Fang X, Xue G, IEEE Journal on Selected Areas in Communications, vol.30, No.2, pp: 245-253, 2012) can achieve good system performance. However, the document "Self-Organizing Relay Selection in UAV Communication Networks: a Matching Game active, "(d.liu, y.xu, j.wang, et al, IEEE Wireless Communications, to ap, 2018.) shows that, since the central controller needs to collect a lot of information, the communication overhead is too large, and the centralized method is difficult to be applied to a large-scale unmanned aerial vehicle network. In the literature, "centralized Learning-Based Relay assessment for Cooperative Communications" (Chen Z, Lin T, Wu C, IEEE Transactions on Vehicular Technology, vol.65, No.2, pp: 813-826.2016.), a distributed Relay selection method Based on reinforcement Learning achieves better system performance. In the method, the source nodes do not need to exchange information or acquire channel state information, and a random learning automaton is used for learning the optimal relay selection. However, this method has a slow convergence rate and poor stability, and requires that the network be static. However, large scale drone networks have two main dynamic characteristics: first, the number of source nodes and relay nodes is variable due to the complexity and diversity of the tasks. Second, the movement of the drone causes time-varying channel conditions, and the communication link may be in deep fade. Therefore, in a large scale drone network, the above method is not suitable.
Aiming at huge communication overhead of centralized relay selection and dynamic characteristics of a large-scale unmanned aerial vehicle network, the invention provides a large-scale unmanned aerial vehicle network emotion driving distributed relay selection method in order to reduce system communication overhead and adapt to a dynamic network. In the method, an optimal relay selection criterion with the aim of maximizing the total network rate is established, and a mood drive mechanism is adopted to perform distributed relay selection according to the dynamic network characteristics. The emotion-driven distributed relay selection method for the large-scale unmanned aerial vehicle network is not limited by the dynamic characteristics of the unmanned aerial vehicle network, reduces huge communication overhead generated by massive information exchange among nodes, accelerates convergence speed, and improves system stability.
Disclosure of Invention
The invention provides a large-scale unmanned aerial vehicle network emotion driven distributed relay selection method, aiming at overcoming the defects of the centralized relay selection method and the distributed relay selection method based on reinforcement learning.
The invention discloses a large-scale unmanned aerial vehicle network emotion driving distributed relay selection method which is characterized by comprising the following steps of: step one, in a large-scale unmanned aerial vehicle network, a source unmanned aerial vehicle forwards collected information to a sink through a relay of an idle unmanned aerial vehicle; determining a candidate relay node set which maximizes the total rate of the network in each time slot; and step three, selecting the relay node by the information source node according to the satisfaction degree of the transmission rate by adopting an emotion driving mechanism.
The invention discloses a large-scale unmanned aerial vehicle network emotion driving distributed relay selection method, which is implemented by the following steps:
a) distributing orthogonal channels to each unmanned aerial vehicle node to avoid mutual interference, and performing ith information source node siWith the jth relay node rjPreprocessing the communication between the two devices; ith source node siWith the jth relay node rjThe distance between can be expressed as:
information source node siTo the relay node rjThe received signal-to-noise ratio of (c) can be expressed as:
in the formulae (1) and (2), siIs the ith source node, rjIs the j-th relay node and,
is the source node siIs the signal power at a reference distance of 1 meter, alpha is the path loss exponent, sigma2Is additive white gaussian noise at the receiver;
b) information source node siSelecting a relay node rj(ii) a In the transmission process, one relay node can serve a plurality of information source nodes, and a single information source node can only select one relay node in a single time slot; when a relay node is selected by a plurality of source nodes, it serves the source nodes in a cyclic manner; information source node siSelecting a relay node rjThe result of (a) can be represented by the following formula:
in the formula (3), si→rjRepresenting source nodes siSelects the relay node rj(ii) a Selects the relay node rjThe number of source nodes of (a) can be expressed as:
c) determining a total system rate of relay transmission; in time slot n, source node siVia the relay node rjThe transmission rate to the sink node D can be expressed as:
in equation (5), W is the bandwidth of each channel. At time slot n, the total rate of the drone network may be expressed as:
the invention discloses a large-scale unmanned aerial vehicle network emotion driving distributed relay selection method, which is implemented by the following steps:
d) determining a candidate relay node set to maximize the network rate; due to the dynamic characteristic of the unmanned aerial vehicle network, the total system rate is variable at different time slots; the best relay selection criteria to maximize the network rate in each slot is:
according to the criterion of formula (7), a set of candidate relay nodes in each time slot that maximizes the network rate is determined.
The invention discloses a large-scale unmanned aerial vehicle network emotion driving distributed relay selection method, which is realized by the following steps:
e) establishing an emotion driving mechanism; an emotion driving mechanism is established to reflect the satisfaction degree of the information source node on the transmission rate, and the emotion driving mechanism comprises four emotions: (ii) satisfies (c), is less than (d), is hoped for (h), and is vigilant (w); information source node siThe state of (1) is as follows:
in the formula (8), mi(n) is the source node si∈SnThe mood of (a) is,
is a reference action for relay selection,
is the reference utility of the transmission rate;
f) selecting a relay node; at the beginning of time slot n, source node siSelecting a relay node according to the current state of the relay node, and expressing the action as ai(n); at the end of time slot n, the transmission rate R is detectedi(n +1), and updating the state; information source node siThe required maximum transmission rate is recorded as
Information source node siThe minimum required transmission rate is recorded as
g) Updating the state; at the beginning of time slot n, source node siAnd (4) judging: if it is not
Whether z isi(n) what state is, ai(n)＝ai(n-1); if it is not
Whether z isi(n) what state, source node siOther relays will be explored randomly, i.e.:
ai(n)＝random{Rn\ai(n-1)} (9)
if m isi(n) ═ c, then
Probability of being1-epsilon, wherein epsilon (0,1) is the exploration probability; at the end of slot n, the state is updated as:
if m isi(n) is h, then
At the end of slot n, the state updates are:
if m isi(n) is w, then
At the end of time slot n, the state updates to:
if m isiIf (n) is d, then the source node siExploring other relays, i.e. ai(n)＝random{Rn\ai(n-1) }; at the end of slot n, the state is updated as:
in equations (9) and (13), the exploration speed of the source node is determined by the parameter ε ∈ (0,1), G (Δ R) is a decreasing linear function with respect to Δ R, F { R ∈ (0,1) }i(n +1) } is for RiA decreasing linear function of (n +1), wherein
The invention has the beneficial effects that: according to the emotion driving distributed relay selection method for the large-scale unmanned aerial vehicle network, the emotion driving mechanism is adopted for distributed relay selection, so that the total system rate of the dynamic unmanned aerial vehicle network is maximized under the condition that information exchange is not needed. By using the method for selecting the large-scale unmanned aerial vehicle network emotion-driven distributed relay, the information source node makes decision and judges according to the current state of the information source node and updates the state according to feedback. The method is not limited by the dynamic characteristics of the network, can effectively reduce the complexity of communication network transmission, reduces huge communication overhead caused by a large amount of information exchange among nodes, accelerates the convergence speed and improves the stability of the system.
Drawings
Fig. 1 is a schematic diagram of a large-scale unmanned aerial vehicle network distributed relay communication system;
FIG. 2 is a graph of the convergence performance simulation results of the total rate obtained by the method of the present invention;
FIG. 3 is a diagram of simulation results of the variation of the average total rate with the proportion of the number of nodes obtained by the method of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
Consider a large scale network of drones, including drones in operation and idle drones, as shown in fig. 1. The unmanned aerial vehicle in the working state needs to transmit the acquired information to the information sink. But due to the large communication distances, diameter transmission may not work. Therefore, idle drones are selected as relay forwarding information, these idle drones as relays are also referred to as relay nodes.
The set of cluster members is denoted as X, and the number is K. Unmanned aerial vehicles and idle unmanned aerial vehicles in working state are named as source nodes and relay nodes respectively. Each member may be a source node or a relay node for a time period, but may change roles for different time periods. With NnAnd Mn(Nn+MnK) represents the number of source nodes and relay nodes in the slot n. Their set is correspondingly defined as
And
in addition, the target node is denoted as D. Due to source node si∈SnDirect transmission to D is also possible, we extend
Wherein r is0Representing a direct transmission.
Assuming that the length of each slot is small enough, the position of each drone can be considered unchanged in one slot. For any unmanned plane u e Sn∪RnIts coordinate at time slot n is expressed as { x }u(n)，yu(n)，zu(n) }, proceeding with the ith source node siWith the jth relay node rjPre-processing the communication between them. Ith source node siWith the jth relay node rjThe distance between can be expressed as:
and an orthogonal channel is distributed to each unmanned aerial vehicle node, so that mutual interference is avoided. If the source node siIs constant in transmission power
Then from the source node siTo the relay node rjThe received signal-to-noise ratio of (c) can be expressed as:
in the formula (I), the compound is shown in the specification,
is the source node siIs a signal at a reference distance of 1 meterNumber power, α is the path loss exponent. Sigma2Is additive white gaussian noise at the receiver.
In the transmission process, one relay node can serve a plurality of information source nodes, and a single information source node can only select one relay node in a single time slot; when a relay node is selected by multiple source nodes, it serves the source nodes in a round-robin fashion, as shown in fig. 2.
Information source node siSelecting a relay node rjThe result of (a) can be represented by the following formula:
in the formula (3), si→rjRepresenting source nodes siSelects the relay node rj(ii) a Selects the relay node rjThe number of source nodes of (a) can be expressed as:
the network adopts an amplification forwarding mode and a decoding forwarding mode, and an information source node s is arranged at a time slot niVia the relay node rjThe transmission rate to the sink node D can be expressed as:
where W is the bandwidth of each channel. At time slot n, the total rate of the drone network may be expressed as:
due to the dynamic characteristic of the unmanned aerial vehicle network, the total system rate is variable at different time slots; thus, in each slot, the best relay selection criterion to maximize the network rate is:
and determining a candidate relay node set which maximizes the network rate in each time slot according to the above criteria.
As mentioned above, the role of the node may change at any time, for example, a relay node converts to a source node when receiving a task, and the source node converts to a relay node when its task is completed. In addition, nodes can move at high speed due to the task requirements, so that the channel state is time-varying. Obviously, the conventional convex optimization method is not suitable for solving the optimal relay selection criterion for maximizing the network rate due to the dynamic characteristic thereof. Although the optimal allocation scheme may be obtained when global information is known, collecting global information consumes a large amount of communication resources and incurs a significant cost. Therefore, in order to solve the optimal relay selection criterion for maximizing the network rate, the invention provides a large-scale unmanned aerial vehicle network emotion driving distributed relay selection method.
In the method for selecting the large-scale unmanned aerial vehicle network emotion driving distributed relay, an emotion driving mechanism is established to reflect the satisfaction degree of an information source node on the transmission rate, and the emotion driving mechanism comprises four emotions: (ii) satisfaction of (c), dissatisfaction of (d), hope (h), and vigilance (w). In any time slot n, the source node siThe state of (1) is as follows:
wherein m isi(n) is the source node si∈SnThe mood of (a) is,
is a reference action for relay selection,
is a basis for the transmission rateQuasi-utility. At the beginning of time slot n, source node siSelecting a relay node according to the current state of the relay node, and expressing the action as ai(n); at the end of time slot n, the transmission rate R is detectedi(n +1), and updates the state. The relay selection and status update rules are as follows:
h) at the beginning of time slot n, source node siAnd (4) judging: if it is not
Whether z isi(n) what state is, ai(n)＝ai(n-1); if it is not
Whether z isi(n) what state, source node siOther relays will be explored randomly, i.e.:
ai(n)＝random{Rn\ai(n-1)} (9)
if it is not
Steps i) -l) are performed.
i) If m isi(n) ═ c, then
The probability is 1-epsilon, wherein epsilon (0,1) is the exploration probability; at the end of slot n, the state is updated as:
j) if m isi(n) is h, then
At the end of slot n, the state updates are:
k) if m isi(n) is w, then
At the end of time slot n, the state updates to:
1) if m isiIf (n) is d, then the source node siExploring other relays, i.e. ai(n)＝random{Rn\ai(n-1) }; at the end of slot n, the state is updated as:
in the formulae (9) and (13),
is the source node siThe maximum transmission rate that is required is,
is the source node siThe minimum required transmission rate, the exploration speed of the source node, is determined by the parameter ε ∈ (0,1), G (Δ R) is a decreasing linear function with respect to Δ R, F { R ∈i(n +1) } is for RiA decreasing linear function of (n +1), wherein
The large-scale unmanned aerial vehicle network emotion driving distributed relay selection method is not easily influenced by network dynamic characteristics, and can obtain a higher total system rate. The total system rate obtained by the method provided by the invention is verified through simulation experiments. In the simulation, 20 source nodes and 30 relay nodes are set, and the maximum and minimum required target rates are set to be 2Mbps and 4Mbps respectively. In the iterative process, the roles of the source node and the relay node can be exchanged randomly. For comparison, the simulation experiment results are added with the comparison of the system total rate with the random relay selection method and the static network.
Figure 2 shows the results of the total rate of the system as a function of the number of iterations. From simulation experiment results, compared with a random relay selection method, the method provided by the invention obtains a significantly higher total system rate. Although the overall system rate achieved by the method is low compared to that of static networks, the method can work with dynamic networks. It should be noted that as the number of iterations increases, the total system rate obtained by the method of the present invention has a certain fluctuation, which is caused by the dynamic characteristics of the network, but the total system rate obtained is still much higher than that of the random relay selection method.
Fig. 3 shows the result of the variation of the average total rate in proportion to the number of nodes. For comparison, the average total rate achieved for diameter transmission is given in fig. 3. As can be seen from fig. 3, when the ratio of the number of source nodes to the number of relay nodes is small, the method of the present invention can achieve a larger average total rate than the diameter transmission. As the ratio of the number of source nodes to the number of relay nodes increases, the average total rate achieved by the method of the present invention gradually approaches the average total rate achieved by diameter propagation, since fewer and fewer relay nodes are available and diameter transmission becomes the primary mode of communication. Therefore, the method of the present invention is able to achieve a higher average total rate when there are enough relay nodes available in the network.
In summary, according to the emotion driving distributed relay selection method for the large-scale unmanned aerial vehicle network, an emotion driving mechanism is adopted to perform distributed relay selection, so that the total rate of the dynamic unmanned aerial vehicle network is maximized under the condition of avoiding information exchange. By using the method, the information source node carries out decision judgment according to the current state of the information source node and updates the state according to feedback, thereby determining the selected relay node. The method is not limited by the dynamic characteristics of the network, effectively reduces the complexity of network transmission, reduces huge communication overhead caused by a large amount of information exchange among nodes, accelerates the convergence speed and ensures the stable transmission of information.
The above-described embodiment is only one embodiment of the present invention, and it will be apparent to those skilled in the art that various modifications and variations can be easily made based on the application and principle of the present invention disclosed in the present application, and the present invention is not limited to the method described in the above-described embodiment of the present invention, so that the above-described embodiment is only preferred, and not restrictive.
Claims (4)
1. A large-scale unmanned aerial vehicle network emotion driving distributed relay selection method is characterized by comprising the following steps: step one, in a large-scale unmanned aerial vehicle network, a source unmanned aerial vehicle forwards collected information to a sink through a relay of an idle unmanned aerial vehicle; determining a candidate relay node set which maximizes the total rate of the network in each time slot; and step three, selecting the relay node by the information source node according to the satisfaction degree of the transmission rate by adopting an emotion driving mechanism.
2. The large-scale drone network emotion driven distributed relay selection method of claim 1, wherein step one is implemented by the sub-steps of:
a) distributing orthogonal channels to each unmanned aerial vehicle node to avoid mutual interference, and performing ith information source node siWith the jth relay node rjPreprocessing the communication between the two devices; ith source node siWith the jth relay node rjThe distance between can be expressed as:
information source node siTo the relay node rjThe received signal-to-noise ratio of (c) can be expressed as:
in the formulae (1) and (2), siIs the ith source node, rjIs the j-th relay node and,
is the source node siIs the signal power at a reference distance of 1 meter, alpha is the path loss exponent, sigma2Is additive white gaussian noise at the receiver;
b) information source node siSelecting a relay node rj(ii) a In the transmission process, one relay node can serve a plurality of information source nodes, and a single information source node can only select one relay node in a single time slot; when a relay node is selected by a plurality of source nodes, it serves the source nodes in a cyclic manner; information source node siSelecting a relay node rjThe result of (a) can be represented by the following formula:
in the formula (3), si→rjRepresenting source nodes siSelects the relay node rj(ii) a Selects the relay node rjThe number of source nodes of (a) can be expressed as:
c) determining a total system rate of relay transmission; in time slot n, source node siVia the relay node rjThe transmission rate to the sink node D can be expressed as:
in equation (5), W is the bandwidth of each channel. At time slot n, the total rate of the drone network may be expressed as:
3. the large-scale unmanned aerial vehicle network emotion driven distributed relay selection method of claim 1, wherein the second step is realized by the following substeps:
d) determining a candidate relay node set to maximize the network rate; due to the dynamic characteristic of the unmanned aerial vehicle network, the total system rate is variable at different time slots; the best relay selection criteria to maximize the network rate in each slot is:
according to the criterion of formula (7), a set of candidate relay nodes in each time slot that maximizes the network rate is determined.
4. The large-scale unmanned aerial vehicle network emotion driven distributed relay selection method of claim 1, wherein step three is implemented by the following sub-steps:
e) establishing an emotion driving mechanism; an emotion driving mechanism is established to reflect the satisfaction degree of the information source node on the transmission rate, and the emotion driving mechanism comprises four emotions: (ii) satisfies (c), is less than (d), is hoped for (h), and is vigilant (w); information source node siThe state of (1) is as follows:
in the formula (8), mi(n) is the source node si∈SnThe mood of (a) is,
is a reference action for relay selection,
is the reference utility of the transmission rate;
f) selecting a relay node; at the beginning of time slot n, source node siSelecting a relay node according to the current state of the relay node, and expressing the action as ai(n); at the end of time slot n, the transmission rate R is detectedi(n +1), and updating the state; information source node siThe required maximum transmission rate is recorded as
Information source node siThe minimum required transmission rate is recorded as
g) Updating the state; at the beginning of time slot n, source node siAnd (4) judging: if it is not
Whether z isi(n) what state is, ai(n)＝ai(n-1); if it is not
Whether z isi(n) what state, source node siOther relays will be explored randomly, i.e.:
ai(n)＝random{Rn\ai(n-1)} (9)
if m isi(n) ═ c, then
The probability is 1-epsilon, wherein epsilon (0,1) is the exploration probability; at the end of slot n, the state is updated as:
if m isi(n) is h, then
At the end of slot n, the state updates are:
if m isi(n) is w, then
At the end of time slot n, the state updates to:
if m isiIf (n) is d, then the source node siExploring other relays, i.e. ai(n)＝random{Rn\ai(n-1) }; at the end of slot n, the state is updated as:
in equations (9) and (13), the exploration speed of the source node is determined by the parameter ε ∈ (0,1), G (Δ R) is a decreasing linear function with respect to Δ R, F { R ∈ (0,1) }i(n +1) } is for RiA decreasing linear function of (n +1), wherein
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