CN114650603A - Time slot resource dynamic allocation method for unmanned aerial vehicle cluster self-organizing communication network - Google Patents
Time slot resource dynamic allocation method for unmanned aerial vehicle cluster self-organizing communication network Download PDFInfo
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Abstract
The invention provides a dynamic allocation method for time slot resources of an unmanned aerial vehicle cluster self-organizing communication network, which comprises the following steps: calculating node degree centrality, betweenness centrality, residual capacity coefficient and signal-to-noise ratio estimation by the network member nodes, and counting node service flow; the central node calculates the weighted coefficient of the importance of the member nodes; the central node calculates the weighted importance of the member nodes and the time slot distribution parameters of the nodes, counts the time slot resources to be distributed in the whole network, and further calculates the time slot quantity to be distributed by each member node. According to the invention, according to the current network topology structure and the service data volume generated by the nodes, the constraint conditions such as the node signal-to-noise ratio, the node residual electricity quantity and the like are comprehensively considered, the dynamic allocation algorithm of the node time slot resources in the network is provided, the utilization efficiency of the time slot resources is improved, the throughput of the unmanned trunking communication network is increased, the network transmission delay is reduced, and the life cycle of each node in the network is prolonged.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle communication, and particularly relates to a dynamic time slot resource allocation method for an unmanned aerial vehicle cluster self-organizing communication network.
Background
A channel access method generally adopted by the unmanned aerial vehicle cluster networking communication is a Time Division Multiple Access (TDMA) protocol. The TDMA multiple access protocol is used as a basic protocol for network channel sharing and data transmission, time slot resource allocation is carried out according to an identification number (ID) of each node, each node can only send information in a specific time slot, and the rest time slots are in a receiving state. Therefore, the allocation method of the time slot resource determines the throughput capacity and the transmission delay of the whole network. Time slot resource allocation strategies are important factors affecting Time Division Multiple Access (TDMA) network throughput and average forwarding delay.
The common slot resource allocation strategy usually adopts static slot allocation or CSMA contention access mechanism. The static time slot allocation is to pre-allocate a fixed time slot to each node, and the node transmits in the allocated time slot. The CSMA competition access mechanism is to realize the access of nodes through random competition time slot resources under the condition that each node has no pre-allocated fixed time slot.
A typical working procedure of the CSMA access mechanism is as follows: assuming that the total number of nodes in the network is N and the total number of time slots is M, N is less than or equal to M. After the node is powered on, the node first monitors the channel for at least 2M σ, where σ is the length of the time slot. If no control packet is received from other nodes within the listening time of 2M sigma, the current node is the first started node. At this time, the node will occupy the first time slot after the end of the monitoring time period and start broadcasting its own control packet. If during the listening the node receives control packets from other nodes and finds that there is a slot resource allocation conflict, the node continues listening until the conflict is resolved. If the received control packet prompts that the time slot allocation information has no conflict, the time slot is correctly allocated, the node occupies the corresponding time slot from the idle time slot as required, and updates the local time slot resource allocation list and sends the control packet when the corresponding time slot arrives. If the time slot allocation lists of the other node control packets received by the current node do not conflict, the time slot allocation in the current round is successful. If the distribution list has conflict, the distribution is failed, the nodes need to randomly delay a period of time, and the time slot scheduling is restarted.
Dynamic time slot resource allocation refers to secondary adjustment of time slot resources for optimizing network performance on the basis of primary allocation of time slot resources. The most common method for dynamic time slot resource allocation is to allocate the corresponding number of time slots according to the node traffic, and increase the occupied bandwidth of the node by increasing the number of the node time slots with large traffic; the number of time slots of the node with small traffic is reduced, so that the occupied bandwidth of the node is reduced. The dynamic time slot resource allocation method can match the network transmission bandwidth with the node service transmission requirement and improve the network transmission performance.
The traditional method only considers the service transmission requirements of each node, but ignores the factors such as network topology structure, node position, signal interference in node communication, node residual electricity quantity and the like, and the traditional dynamic resource time slot allocation method has certain one-sidedness.
Disclosure of Invention
In order to solve the above problems, the present invention provides a dynamic allocation method for time slot resources of an unmanned aerial vehicle cluster ad hoc communication network, wherein a network is provided with N nodes, and a calculation method for the time slot allocation number of each node comprises the following steps:
step1, calculating node degree centrality, betweenness centrality, residual electric quantity coefficient and signal-to-noise ratio estimation by each member node in the network, and counting node service flow; the result is broadcasted in the allocated time slot, wherein the broadcast information also comprises the degree centrality, the betweenness centrality, the residual electric quantity coefficient and the signal-to-noise ratio estimation result of the neighbor nodes;
and step2, the network central node calculates the weighted importance of the member nodes according to the degree centrality, the betweenness centrality and the node service flow of each member node, and the value of the weight coefficient is determined by analyzing the statistical characteristic values of the degree centrality, the betweenness centrality and the node service flow of the node at regular time.
Step3, the network member node adopts a signal-to-noise ratio estimation algorithm to estimate the signal-to-noise ratio of the node, when the signal-to-noise ratio is larger than a node communication threshold value, the node is considered to be capable of normally communicating, otherwise, the communication is considered to be interrupted;
step4, defining the residual capacity coefficient of each member node as the ratio of the node residual capacity to the total capacity, wherein the total capacity is a constant:
step5, the network central node calculates time slot distribution parameters of each member node, when the signal-to-noise ratio estimation value of the node is greater than the communication threshold value, the time slot distribution parameters are the product of the importance parameters of the node and the electric quantity coefficient, otherwise, the time slot distribution parameters of the node are assigned to be 0;
step6, the network central node calculates the proportion occupied by the time slot distribution parameters of the time slot nodes to be distributed in the sum of the time slot distribution parameters of all the nodes to be distributed;
step7, the central node of the network calculates the number of time slot resources to be allocated to each node, and the number is the product of the calculation result and the number of time slots to be allocated to the system in step 6;
further, the node importance weight coefficient α1(i),α2(i),α3(i) The determination method comprises a coefficient of variation method:
firstly, measuring the data of the degree centrality, the betweenness centrality and the service flow importance of the nodes in a certain time. Then, calculating the mean value and the variance of the degree centrality, the betweenness centrality and the service flow importance of the node by using the following formulas; and then calculating the coefficient of variation using a known formula. The weighting factor of the node importance is the result of normalizing the variation factor.
Further, the following formula is adopted to calculate the centrality d (i) of each node degree in the network:
wherein: the number of nodes in the network is N, kiIs the degree of node i.
Further, the following formula is adopted to calculate the betweenness centrality c (i) of each node:
wherein: n isjkIs the number of shortest paths between nodes j and k, njk(i) The number of shortest paths between nodes j and k that pass through node i.
Further, the traffic importance r (i) of each node adopts the following formula:
wherein: b (i) traffic generated for the node itself,
traffic flow generated for the entire network node.
Further, the estimation of the signal-to-noise ratio of each node is calculated by adopting an estimation algorithm, including a second-fourth order moment estimation calculation method, a maximum likelihood estimation algorithm or a minimum mean square error estimation algorithm.
Furthermore, the central node adopts fixed time slots for communication, the number of the time slots is fixed, and secondary distribution is not carried out. In order to ensure the network connectivity, if the signal-to-noise ratio estimation result of the node is smaller than the communication threshold, at least 1 network time slot is allocated to the node.
Further, the method for calculating the number of time slots to be allocated by the system comprises the following steps:
analyzing the number of nodes with time slot distribution parameter of 0 in the network, and distributing at least one time slot to the nodes to ensure the network connectivity;
and subtracting the allocated time slots (including the time slots occupied by the central node) from the total time slot number of the network, wherein the result is the network time slot number to be allocated by the system.
Further, the method also comprises the following steps:
(1) in the initial network establishment stage, the nodes establish a network by adopting a CSMA access mode or a time slot allocation mode by a central node, and each node in the network knows neighbor nodes;
(2) each member node in the network calculates node degree centrality, betweenness centrality, service flow, residual capacity coefficient and signal-to-noise ratio estimation in a certain time period, and the result is broadcasted in the time slot allocated by each member node, wherein the broadcast information also comprises the information of the neighbor nodes;
(3) the network center node calculates and assigns the importance weighting coefficients of the member nodes according to the degree centrality, the betweenness centrality and the service flow of the member nodes, and then calculates the importance of the member nodes;
(4) the network central node calculates the time slot distribution parameters of the member nodes according to the importance of the member nodes, the residual electric quantity coefficient and the signal-to-noise ratio estimation result;
(5) the network central node counts the time slot resources to be distributed in the whole network, and calculates the number of the time slots to be distributed by the member nodes according to the time slot distribution parameters; in order to ensure network connectivity, at least 1 network time slot should be allocated to the member node if the signal-to-noise ratio estimation result of the member node is smaller than a normal communication threshold value;
(6) the network center node broadcasts the time slot distribution result, and each member node acquires the self-distributed time slot;
(7) adjusting and opening network parameters;
(8) judging whether the timing updating period of the network parameters is up, if so, returning to the step2, and recalculating the network time slot allocation; if the timing is not reached, continuing communication according to the current time slot allocation strategy;
(9) if the network continues to operate, the current time slot allocation strategy is kept to continue communication, otherwise, the algorithm is ended.
The invention provides a dynamic allocation method for time slot resources of an unmanned aerial vehicle cluster self-organizing communication network, which has the beneficial effects that:
according to the present network topology structure and the service data volume generated by the nodes, the invention also comprehensively considers the constraint conditions of the signal-to-noise ratio of the nodes, the residual electric quantity of the nodes and the like, and provides a dynamic allocation algorithm of the time slot resources of the nodes in the network; compared with a time slot resource dynamic allocation algorithm which only takes the node traffic as a main basis, the method improves the time slot utilization efficiency, increases the throughput of the unmanned cluster communication network, reduces the network transmission delay, prolongs the life cycle of each node in the network, and systematically optimizes and improves the performance of the unmanned cluster self-organizing communication network which adopts TDMA as a channel access protocol.
Drawings
Fig. 1 is a block diagram of an algorithm for dynamically allocating time slot resources in an unmanned aerial vehicle cluster ad hoc communication network according to the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples, which are provided for the purpose of illustrating the general inventive concept and are not intended to limit the scope of the invention.
The traditional method only considers the service transmission requirements of each node, neglects factors such as node topological structure and the like, and has certain one-sidedness in the dynamic resource time slot allocation method. Since the more important the node is in the position of connectivity in the network, the more transmission requirements of the node as a relay node are predicted, and more time slots should be allocated to reduce the network delay and improve the transmission efficiency. Meanwhile, the more the traffic transmission amount of the node itself is, the more time slot resources should be allocated. On the other hand, if the node has higher energy (e.g. power), it can support allocating more timeslot resources, and the node has lower energy and even runs out to become a "dead end", then the number of timeslot allocations should be reduced to reduce the transmission frequency and thus reduce the energy consumption. In addition, whether signal interference is occurring around the node or not needs to be considered, if the situation that the communication signal-to-noise ratio is reduced below a normal communication threshold is predicted by analyzing the signal-to-noise ratio received by the node, the time slot allocation of the node needs to be temporarily reduced, and the matched number of time slots are reallocated after the signal-to-noise ratio of the node is recovered to be normal.
In summary, the number of timeslot resources allocated to a node is analyzed and determined from four angles: node location importance, node traffic importance, node remaining capacity, and node signal-to-noise ratio.
(1) Node location importance
Node betweenness centrality and node betweenness centrality are indexes for measuring node importance in the network. The degree of a node refers to the number of edges in the network topology that are connected to the node. The centrality can be considered as the larger the number of neighbors of a node is, the larger the influence is.
The node betweenness centrality reflects the number of shortest paths among any 2 nodes of a connected traffic network passed by a certain node, and the more the shortest paths passing through the node, the higher the betweenness centrality. The higher the betweenness centrality is, the more the number of the shortest paths passed by the network nodes is, and the greater the action and influence on the whole network is. In the unmanned cluster communication network, distribution characteristics of degree and betweenness reflect the positions of different nodes in the network, which has important significance for designing a time slot resource dynamic allocation algorithm.
(2) Node traffic importance
In the present invention, the importance of node traffic refers to the importance of network traffic carried by a node in the total network traffic. The calculation method is as follows: for each node, adding all the traffic passing through the node in the network to obtain the sum of the traffic passing through the node, and adding the sum of the traffic passing through all the nodes in the network to obtain the total traffic of the nodes in the network.
Because the value of the power connection importance index depends on a network routing strategy and end-to-end traffic, the power connection importance index not only contains network topology information, but also contains network flow information, and the importance of data bearing of the node in the current operation network can be well reflected.
(3) Node residual capacity
In the unmanned aerial vehicle cluster ad hoc network, especially in the microminiature unmanned aerial vehicle networking application, the platform energy is very limited, and the node energy consumption factor needs to be considered. When each communication node in the network sends data according to the allocated service time slot, each node needs certain transmission energy consumption. When the number of the time slots allocated by the node is too large, the transmission time of the node in the time slot is increased, the energy consumption of the node is reduced too fast, and when the electric quantity of the node is used up, the node becomes a 'dead node' and cannot transmit any data information. Therefore, when the service time slot is allocated to each node in the cluster ad hoc network, the time slot number should be positively correlated with the residual electric quantity of the node, the nodes with more electric quantity can comprehensively consider to allocate more service time slots, and the nodes with less electric quantity can allocate relatively less service time slots.
(4) Node signal-to-noise ratio
The node signal-to-noise ratio reflects the condition that the node is interfered by the surrounding electromagnetic environment, and under the environment with serious electromagnetic environment interference, the node signal-to-noise ratio is reduced and even lower than the normal communication threshold of the node, at the moment, the node can not normally communicate, and the number of time slots allocated to the node is reduced, so that the time slot resources are favorably allocated to other nodes, and the resource utilization efficiency is improved. And after the signal-to-noise ratio of the nodes is improved and the communication is recovered to be normal, the nodes with the matched number are redistributed. Node signal-to-noise ratio predictions can be obtained by a signal-to-noise ratio estimation method. The more accurate snr estimation error is typically less than 0.5 dB.
The following describes the implementation method proposed by the present invention in further detail with reference to fig. 1, and the present invention proposes a dynamic allocation method for time slot resources of an unmanned aerial vehicle cluster ad hoc communication network, which specifically comprises the following steps:
(1) the node number in the network is set as N, and the member node i calculates the centrality according to the following formula:
wherein: n, k 1, 2iIs the degree of node i.
(2) The member node i calculates the betweenness centrality of each node in the network according to the following formula:
wherein: n1, 2，njkIs the number of shortest paths between nodes j and k, njk(i) The number of shortest paths between nodes j and k that pass through node i.
(3) And calculating the residual capacity coefficient of each node by the member node i:
wherein: i is 1, 2.. N, P (i) is the remaining power of the node i, and P is the total power of the node, which is a fixed value.
(4) The member node counts the traffic flow, and the result is b (i) ((i 1, 2.. N)).
(5) Calculating the residual capacity coefficient of each node:
wherein: n, P (i) is the remaining power of the node i, and P is the total power of the node, which is a fixed value.
(6) The member nodes adopt a signal-to-noise ratio estimation algorithm to carry out signal-to-noise ratio estimation, and the result is SNR (i) (1, 2.. N).
(7) The member nodes broadcast node degree centrality, number centrality, service flow, remaining capacity coefficient and signal-to-noise ratio estimation in respective time slots.
(8) The central node calculates the flow importance of each member node in the network:
wherein: n, b (i) is traffic generated by node i,
traffic flows generated for the entire network member node.
(9) The central node calculates the weighted importance I (i) of each node:
I(i)＝α1(i)·D(i)+α2(i)·C(i)+α3(i)·R(i)
wherein alpha is1(i),α2(i),α3(i) The weight coefficients of the degree center D (i), the number center C (i), and the flow rate importance R (i) of the node i, respectively, are1(i)+α2(i)+α3(i)＝1。α1(i),α2(i),α3(i) The value of (2) is determined by analyzing the node degree centrality, the betweenness centrality and the statistical characteristic value of the node service flow at regular time, and a typical method comprises a coefficient of variation method.
Firstly, measure the data of degree centrality D (i), medium centrality C (i) and traffic flow importance R (i) of node i in a certain time, and set as { d }1,d2...dn}，{c1,c2...cn}，{r1,r2...rnAre given as the mean values thereof respectively
The mean and variance are then calculated using the following formulas:
then, the coefficient of variation is calculated by using the following formula:
calculating a weight coefficient of the node importance:
(10) calculating time slot distribution parameters T (i) of each node according to the weighted importance of the nodes, the residual capacity coefficient and the signal-to-noise ratio:
(11) the time slots of the central node do not participate in the allocation. Allocating n (n is more than or equal to 1) time slots to the member nodes with the time slot allocation parameter of 0 in the network to ensure the network connectivity; and subtracting the allocated time slot (including the time slot occupied by the central node) from the total time slot number of the network, and taking the result F as the time slot number of the network to be allocated.
(12) Calculating the proportion occupied by the time slot distribution parameter of the node of the time slot to be distributed in the sum of the network time slot distribution parameters according to the following formula:
wherein: n, t (i) assigns parameters to the time slots of the member node i,
the sum of the time slot distribution parameters of all the time slot nodes to be distributed in the whole network is obtained;
(13) calculating the number of time slots to be allocated by the node, wherein if the number of the time slots to be allocated by the system is F, the number of the time slots to be allocated by the node i, N (i), is:
(14) the network center node broadcasts the calculation result, and each member node acquires the time slot allocated by the member node;
(15) adjusting and opening network parameters;
(16) judging whether the timing updating period of the network parameters is up, if so, returning to the step2, and recalculating the time slot allocation result; if the timing is not reached, continuing communication according to the current time slot allocation strategy;
(17) if the network continues to operate, the current time slot allocation strategy is kept to continue communication, otherwise, the algorithm is ended.
The purpose of this step is to set a time node, let the system execute the above-mentioned step once every a period of fixed time, let the network system can be regularly updated, make the time slot resource allocation of unmanned aerial vehicle cluster self-organizing communication system in an optimal state all the time.
After the weighted importance of each node is calculated, the signal-to-noise ratio of each node needs to be calculated, the signal-to-noise ratio needs to be larger than a node communication threshold value to normally communicate, otherwise, communication is considered to be interrupted, but in order to ensure network connectivity, at least 1 time slot of the node needs to be allocated. The method for calculating the signal-to-noise ratio estimation of each node comprises estimation algorithms such as second-fourth moment, maximum likelihood, minimum mean square error and the like, and takes the second-fourth moment signal-to-noise ratio estimation algorithm as an example:
the received signal output by the matched filter is set as:
in the above formula, akIs { -1, +1}, S is the power factor of the signal, nkIs white gaussian noise with a mean of 0 and a variance of 1, and N is the noise power factor.
Its second and fourth moments are respectively:
M2＝S+N，
M4＝haS2+4SN+hwN2，
combining the above two equations, an estimate of S, N is obtained:
haand hwAre the parameter coefficients of the signal power and the noise power, respectively.
In practical applications, the second and fourth moments are calculated from the time average of the received sequence and can be approximated by the following expression:
the method also comprises the following steps:
(1) in the initial network establishment stage, the nodes establish a network by adopting a CSMA access mode or a time slot allocation mode by a central node, each node in the network knows the neighbor nodes of the nodes, and if the neighbor nodes are known, the nodes form an edge in the network;
(2) calculating node degree centrality, betweenness centrality, service flow, residual capacity coefficient and signal-to-noise ratio estimation in a certain time by member nodes in the network, and broadcasting the result in each allocated time slot, wherein the broadcast information also comprises the information of neighbor nodes;
(3) the network central node calculates and assigns the member node importance weighting coefficient according to the degree centrality, the betweenness centrality and the service flow of the member node, and further calculates the member node importance;
(4) the network central node calculates the time slot distribution parameters of the member nodes according to the importance of the member nodes, the residual electric quantity coefficient and the signal-to-noise ratio estimation result;
(5) the network central node counts the time slot resources to be distributed in the whole network, and calculates the number of the time slots to be distributed by the member nodes according to the time slot distribution parameters; in order to ensure network connectivity, at least 1 network time slot should be allocated to the member node if the signal-to-noise ratio estimation result of the member node is smaller than a normal communication threshold value;
(6) the network center node broadcasts the time slot distribution result, and each member node acquires the self-distributed time slot;
(7) adjusting and opening network parameters;
(8) judging whether the timing updating period of the network parameters is up, if so, returning to the step2, and recalculating the network time slot allocation; if the timing is not reached, continuing communication according to the current time slot allocation strategy;
(9) if the network continues to operate, the current time slot allocation strategy is kept to continue communication, otherwise, the algorithm is ended.
Therefore, the invention is not limited to the specific embodiments and examples, but rather, all equivalent variations and modifications are within the scope of the invention as defined in the claims and the specification.
Claims (10)
1. A time slot resource dynamic allocation method for an unmanned aerial vehicle cluster self-organizing communication network is characterized by comprising the following steps:
step1, calculating node degree centrality D (i), medium centrality C (i), residual electric quantity coefficient p (i) and signal-to-noise ratio estimation SNR (i) by the network member node, and counting the node service flow; the result is broadcasted in the allocated time slot, wherein the broadcast information also comprises the degree centrality, the betweenness centrality, the residual electric quantity coefficient and the signal-to-noise ratio estimation result of the neighbor nodes;
step2, the central node of the network calculates the weighted importance I (i) of the member node i: :
I(i)＝α1(i)·D(i)+α2(i)·C(i)+α3(i)·R(i)；
wherein alpha is1(i),α2(i),α3(i) The weight coefficients of the degree center D (i), the number center C (i), and the flow rate importance R (i) of the node i, respectively, are1(i)+α2(i)+α3(i)＝1。α1(i),α2(i),α3(i) The value of (2) is determined by analyzing the node degree centrality, the betweenness centrality and the statistical characteristic value of the node service flow at regular time, and a typical method comprises a coefficient of variation method.
Step3. defining the SNR estimate for each node as SNR (i), when the SNR is greater than the node communication threshold ThIf the communication is normal, otherwise, the communication is interrupted;
and Step4, defining the residual capacity coefficient of each node as p (i):
wherein: and P (i) is the residual electric quantity of the node, P is the initial total electric quantity of the node, and P is a constant.
Step5, the central node of the network calculates the time slot distribution parameters of each node:
step6, the network central node calculates the proportion M (i) occupied by the time slot distribution parameters of the time slot nodes to be distributed in the sum of the time slot distribution parameters of all the nodes to be distributed:
wherein:
the sum of the time slot distribution parameters of all the time slot nodes to be distributed in the whole network is obtained;
step7, the central node of the network calculates the time slot resource N (i) to be allocated to each node:
N(i)＝M(i)·F，
wherein: f is the number of time slots to be allocated by the system.
2. The method of claim 1, wherein the unmanned aerial vehicle cluster ad hoc network comprises a central node and member nodes. The central node is responsible for time slot distribution, and the member nodes communicate according to the time slots distributed by the central node.
Member node weighted importance weight coefficient alpha1(i),α2(i),α3(i) The determination method comprises a coefficient of variation method:
the central node firstly measures the data of the degree centrality D (i), the medium centrality C (i) and the traffic flow importance R (i) of the node i in a certain time, and the data are set as { d }1,d2...dn}，{c1,c2...cn}，{r1,r2...rnAre given as the mean values thereof respectively
The mean and variance are then calculated using the following formulas:
then, the coefficient of variation is calculated by using the following formula:
calculating a weight coefficient of the node importance:
3. the method according to claim 2, wherein the following formula is used to calculate the centrality d (i) of each node in the network:
wherein: the number of nodes in the network is N, kiIs the degree of node i. The degree of a node refers to the number of edges associated with the node.
4. The method of claim 3, wherein the following formula is adopted to calculate the centrality of betweenness of nodes C (i):
wherein: n isjkIs the number of shortest paths between nodes j and k, njk(i) Is the shortest between nodes j and kThe number of shortest paths in the path through node i.
5. The method according to claim 4, wherein the traffic importance R (i) of each node is represented by the following formula:
wherein: b (i) traffic generated for the node itself,
traffic flow generated for the entire network node.
6. The method of claim 5, wherein the method comprises the following steps: and calculating the signal-to-noise ratio estimation of each node by adopting an estimation algorithm, wherein the estimation algorithm comprises a second-order moment estimation algorithm, a maximum likelihood estimation algorithm or a minimum mean square error estimation algorithm and the like.
7. The method of claim 6, wherein the calculating of the SNR estimate for each node is performed by a second-fourth order moment estimation method:
the received signal output by the matched filter is set as:
in the above formula, akIs { -1, +1}, S is the power factor of the signal, nkIs white gaussian noise with a mean of 0 and a variance of 1, and N is the noise power factor.
Its second and fourth moments are respectively:
M2＝S+N，
M4＝haS2+4SN+hwN2，
haand hwAre the parameter coefficients of the signal power and the noise power, respectively.
Combining the above two equations, an estimate of S, N is obtained:
in practice, the second and fourth moments are calculated from the time average of the received sequence and can be approximated by the following expression:
8. the method of claim 7, wherein the method comprises the steps of: the central node adopts fixed time slots for communication, the number of the time slots is fixed, and secondary distribution is not carried out. In order to ensure the network connectivity, if the signal-to-noise ratio estimation result of the member node is smaller than the communication threshold, at least 1 network time slot is allocated to the member node.
9. The method for dynamically allocating time slot resources in an ad hoc communication network of an unmanned aerial vehicle cluster according to claim 8, wherein the method for calculating the number F of time slots to be allocated by the system comprises:
analyzing the number of nodes with time slot distribution parameter of 0 in the network, and distributing at least one time slot to the nodes to ensure the network connectivity;
and subtracting the allocated time slots (including the time slots occupied by the central node) from the total time slot number of the network, wherein the result is the network time slot number to be allocated by the system.
10. The method for dynamically allocating timeslot resources in a drone cluster ad hoc communication network as claimed in any one of claims 1 to 9, further comprising the steps of:
(1) in the initial network establishment stage, the nodes establish a network by adopting a CSMA access mode or a time slot allocation mode by a central node, and each node in the network knows neighbor nodes;
(2) each member node in the network calculates node degree centrality, betweenness centrality, service flow, residual capacity coefficient and signal-to-noise ratio estimation in a certain time period, and the result is broadcasted in the time slot allocated by each member node, wherein the broadcast information also comprises the information of the neighbor nodes;
(3) the network central node calculates and assigns the member node importance weighting coefficient according to the degree centrality, the betweenness centrality and the service flow of the member node, and further calculates the member node importance;
(4) the network center node calculates a member node time slot distribution parameter according to the member node importance, the residual capacity coefficient and the signal-to-noise ratio estimation result;
(5) the network central node counts the time slot resources to be distributed in the whole network, and calculates the number of the time slots to be distributed by the member nodes according to the time slot distribution parameters; in order to ensure network connectivity, if the signal-to-noise ratio estimation result of the member node is smaller than a normal communication threshold value, at least 1 network time slot should be allocated to the member node;
(6) the network center node broadcasts the time slot distribution result, and each member node acquires the self-distributed time slot;
(7) adjusting and opening network parameters;
(8) judging whether the timing updating period of the network parameters is up, if so, returning to the step2, and recalculating the network time slot allocation; if the timing is not reached, continuing communication according to the current time slot allocation strategy;
(9) if the network continues to operate, the current time slot allocation strategy is kept to continue communication, otherwise, the algorithm is ended.
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