CN113848989A - Resource cooperative allocation method for unmanned aerial vehicle intensive formation communication network - Google Patents
Resource cooperative allocation method for unmanned aerial vehicle intensive formation communication network Download PDFInfo
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Abstract
The invention discloses a resource collaborative allocation method of an unmanned aerial vehicle intensive formation communication network, which belongs to the technical field of unmanned aerial vehicle communication, and comprises the steps of firstly, constructing an unmanned aerial vehicle formation communication scene containing cellular members and D2D members; selecting a leader member by calculating the communication power of each D2D member, selecting the leader member which meets the D2D communication distance and has the largest communication dependency degree for unique attachment by each residual member according to the Euclidean distance of the members to form attachment groups, matching each attachment group with the honeycomb members one by one, performing resource multiplexing on the whole, selecting the attachment groups with the transmission rate meeting the QoS on the premise of ensuring the fairness of formation information according to the change coefficient of the weight before and after multiplexing of each group, and completing the spectrum resource multiplexing of the honeycomb users; the invention can effectively improve the formation throughput while ensuring the fairness of formation information and improve the cooperative communication performance of the dense formation support network.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle communication, in particular to a resource cooperative allocation method for an unmanned aerial vehicle intensive formation communication network.
Background
The unmanned aerial vehicle formation support network is a self-organizing network which can enable formation members to recognize the position and the role of the members in the formation on the basis of ensuring the transmission and sharing of the information of the unmanned aerial vehicle intensive formation, can provide information for the cooperative action of the unmanned aerial vehicle intensive formation through a social dependence relationship network among the formation members, and can perform self-adjustment, self-adaptation, self-learning and self-innovation according to the change of the members in the formation and the social dependence relationship.
The end-to-end technology (D2D) can provide efficient network support for dense formation of unmanned aerial vehicles, can meet the requirements of the dense formation and has certain autonomy. Compared with other communication technologies which do not depend on infrastructure, the D2D communication technology is more versatile, and can perform communication not only through base station control but also without the infrastructure. Through D2D communication, the terminal equipment no longer need through the base station transfer, can directly transmit, and the link can produce the gain, and then can alleviate the load of base station, the gain that link and resource multiplexing produced, and the homoenergetic promotes frequency spectrum resource efficiency and throughput.
In the resource allocation research of D2D communication, the gains brought by communication link and resource multiplexing can improve the spectrum resource efficiency of the network, but at the same time, they also bring interference to the original cellular users, so resource allocation is the research focus in this field. Reference [1] proposes a heuristic network resource allocation method by analyzing the channel relationship between the network access of the D2D device and the analysis device, which can improve the network access rate and the network transmission rate. Document [2] introduces a grid-adaptive search strategy into cognitively-assisted D2D networks to solve the resource allocation problem. Document [3] allocates and controls resources and power by dividing the distant D2D users into a group, multiplexing the same system resources. Each D2D group multiplexes the cellular user's link closest to the base station to ensure the quality of service for each user in the overall communication link. Meanwhile, the D2D communication strategy based on power control is adopted, and the access volume of the D2D user is improved.
Although many research results are obtained in the present stage, and a certain advantage is presented for a typical system, due to practical factors such as the formation structure, the dynamic characteristics, and the member performance of the dense formation of the unmanned aerial vehicles, an applicable resource cooperative allocation method is also needed for the high-dynamic and large-scale dense formation of the unmanned aerial vehicles.
Reference documents:
[1]Wang X X,Lv S B,Wang X,et al.Greedy Heuristic Resource Allocation Algorithm for Device-to-device Aided Cellular Systems with System Level Simulations[J].KSII Transactions on Internet and Information Systems,2018,12(4):1415-1435.
[2]Ahmad M.Naeem M,Iqbal M,et al.Joint User Selection,Mode Assignment,and Power Allocation in Cognitive Radio-assisted D2D Networks[J].IET Communications,2018,12(10):1207-1214.
[3]Dinh-Van S,Shin Y,Shin O S.Resource Allocation and Power Control based on User Grouping for Underlay Device-to-device Communications in Cellular Networks[J],Transactions on Emerging Telecommunications Technologies,2017,28(1):1-12.
disclosure of Invention
Aiming at the existing requirements, the invention provides a resource cooperative allocation method of an unmanned aerial vehicle intensive formation communication network based on social communication dependence and information fairness on the premise of ensuring formation information fairness by measuring the communication power of formation members and the social communication dependence based on an end-to-end technology.
The method comprises the following specific steps:
step one, aiming at an unmanned aerial vehicle intensive formation communication network, constructing a formation D2D communication scene;
the scene comprises a formation main member m; c cell members { C1,c2,…,cCD2D members { D }1,d2,…,dD}; C. d is a positive integer.
The C cell members communicate with the main member m through independent channels which are mutually orthogonal, when the D2D members communicate, each D2D link multiplexes channel resources of different cell members for transmission, and the multiplexed channel resources are set as uplink channel resources.
Step two, respectively calculating the communication power of each D2D communication member in the unmanned aerial vehicle formation, and arranging in a descending order;
the method comprises the following specific steps:
step 201, aiming at the ith D2D communication member epsiloniCounting the member epsiloniWhen the communication quality satisfies the set threshold, the maximum number of members with which communication can be performed is recorded as the communication adjacency nTi；
Step 202, for Member εiAccording to the maximum transmission power pimaxA distance d from the abutmentimaxRatio of the calculated basic communication capability Cbi；
Wherein the adjacent distance dimax＝kimax·dis＞0，disIs a member epsiloniA safe distance of kimaxIs a member epsiloniThe adjacent coefficient of (2). The safe distance and the adjacent distance are set manually according to actual needs.
Step 203, calculating the formation member epsiloniTo member epsilonjSocial communication dependency of
And obtaining a social communication dependency matrix R through social dependencies among members, and obtaining the dense formation of the unmanned aerial vehicle and the member epsilon through analyzing rows and columns of the matrixjCommunication dependency between them;
wherein the content of the first and second substances,
social ability of a member;
an integrated communication capability for the member;
a basic communication capability is set for the member,
in the social communication dependency matrix R, the sum of each row
All represent the member epsilon corresponding to this rowiThe degree of social dependence on communication by other members in the formation, called Member epsiloniCommunication dependency on dense formation of unmanned aerial vehicles
Sum of each column
All represent the member epsilon corresponding to this columnjThe social influence degree on the communication of other members in the formation is called as the member epsilon of the unmanned aerial vehicle intensive formation pairjCommunication dependency of
Step 204, utilizing the member epsiloniThe communication adjacency, the basic communication capacity and the dependency of the unmanned aerial vehicle intensive formation on the members are calculated, and the member epsilon is calculatediCommunication power L ofi：
Step 205, similarly, calculating the communication power of each member, and sequencing the communication power from large to small;
step three, selecting a member epsilon with the maximum communication power each timeiTaking the leader member as a leader member, judging whether the relation between the remaining members and the leader member meets the condition, removing the remaining members which do not meet the condition, reordering, and repeatedly selecting the next leader member until all the leader members are completely selected;
the conditions are satisfied as follows:
step four, sequentially calculating member Euclidean distances between each member and each leader force member by each remaining member, and selecting the leader force members meeting the D2D communication distance to carry out candidate attachment;
step five, aiming at each residual member, calculating the communication dependency of the residual member on each leader member in each leader member to which the candidate member is attached; selecting the maximum communication dependency degree as the only leader member to attach until all the rest members are attached;
step six, forming an attaching group by each leader member and the formation members attached to the leader member, matching each attaching group with the honeycomb members one by one, performing resource multiplexing on the whole, and calculating the change coefficients of the weights before and after multiplexing of each group respectively;
for cell member ciWhen the cell member ciWhen only cellular network communication is carried out in the dense formation of unmanned aerial vehicles, the rate weight e of the unmanned aerial vehiclesiComprises the following steps:
wherein the content of the first and second substances,
periodically updating cell member c in time for a formationiThe average rate of (d);
is its overall average rate.
D2D member D when attached to group AjMultiplexing cellular Member ciWhen communicating with the spectrum resource of (b), the reuse of the cell members by all members of the attached group (a) is uniform, where djRefers to the entire attached group a; the rate weight becomes:
wherein the content of the first and second substances,
periodically updating D2D member D in time for a formationjWith cell member c being multiplexed by itiAverage rate of inter-communication;
is its overall average rate.
The attached group A multiplexing cell member ciThe front and rear weight variation coefficients are:
E＝[|ei-eij|]
wherein [. is a whole.
Step seven, arranging the weight variation coefficients of each group from small to large, selecting one by one in sequence, judging whether the transmission rate of each combination meets the QoS, and if so, determining the spectrum resource of the attached group multiplexing cellular user of the combination; otherwise, the combination is removed, the next combination is continuously selected for repeated judgment until all combinations are judged, and resource cooperative distribution of the unmanned aerial vehicle intensive formation communication network is completed.
When there are two or more groups at the same timeSelecting communication connection time IijThe smallest combination. If the attachment group or the candidate honeycomb member in the combination is already confirmed to have the multiplexing relationship, the combination is removed, and other combinations are judged again until all combinations are confirmed.
Compared with the prior art, the invention has the following positive effects:
(1) the invention discloses a resource cooperative allocation method for an unmanned aerial vehicle intensive formation communication network, which enables formation members to be aware of self capacity, determines the mutual relation through social communication dependency and other parameters, and reuses appropriate cellular member resources.
(2) A resource cooperative allocation method for an unmanned aerial vehicle intensive formation communication network can effectively improve formation throughput and improve cooperative communication performance of an intensive formation support network while guaranteeing formation information fairness.
Drawings
Fig. 1 is a flow chart of a resource cooperative allocation method of an unmanned aerial vehicle intensive formation communication network according to the present invention;
FIG. 2 is a diagram of an example communication model of formation D2D constructed by the present invention;
FIG. 3 is a schematic diagram of communication attachment groups constructed for formation members according to Euclidean distance according to the present invention;
FIG. 4 is a schematic diagram comparing the present invention with the maximum rate method in terms of network transmission rate;
FIG. 5 is a diagram comparing the information fairness of the present invention with the existing two methods;
Detailed Description
The present invention will be described in further detail and with reference to the accompanying drawings so that those skilled in the art can understand and practice the invention.
The invention provides a resource collaborative allocation method of an unmanned aerial vehicle intensive formation communication network, which comprises the steps of finding out a leader force member in a communication area of a D2D member in a formation by calculating the communication power of the D2D member in the formation according to the communication power and the member distance, judging whether the distance between the member and the leader force member meets the D2D communication requirement or not by each attachment member, and selecting an attachment object according to the social communication dependency degree in the leader force member meeting the condition to form a communication attachment group; repeating the above process until all the dependent members are selected; enumerating distribution combinations of all attached groups and candidate honeycomb members in the formation support network, calculating rate weights, screening from the combination with the minimum rate weight, and determining a multiplexing relation according to the transmission rate of the combination; until all combinations are confirmed; and on the premise of ensuring the fairness of formation information, the resource reuse is carried out on the cellular members meeting the QoS.
The invention can effectively improve the formation throughput while ensuring the fairness of formation information and improve the cooperative communication performance of the dense formation support network.
The unmanned aerial vehicle dense formation communication network comprises unmanned aerial vehicle members for cellular communication and unmanned aerial vehicle members for D2D communication, the resource collaborative allocation method of the unmanned aerial vehicle dense formation communication network of the invention prepares members for multiplexing cellular member resources in unmanned aerial vehicle formation, and as shown in figure 1, the specific steps are as follows:
step one, aiming at an unmanned aerial vehicle intensive formation communication network, constructing a formation D2D communication scene;
as shown in fig. 2, the scene includes a formation master member m; c cell members { C1,c2,…,cCD2D members { D }1,d2,…,dD}; C. d is a positive integer.
The C cell members communicate with the main member m through independent channels which are mutually orthogonal, and the D2D member sets the uplink channel resource to be multiplexed when the communication is carried out, considering that the uplink traffic in the cellular network is lower than the downlink traffic in general.
Step two, respectively calculating the communication power of each D2D communication member in the unmanned aerial vehicle formation, and arranging in a descending order;
the method comprises the following specific steps:
step 201, aiming at the ith D2D communication member epsiloniCounting the member epsiloniWhen the communication quality satisfies the set threshold, the maximum number of members with which communication can be performed is recorded as the communication adjacency nTi；
The specific steps that the communication quality meets the set threshold are as follows:
first, in a densely-queued communication network, the signal-to-interference-and-noise ratio γ of cellular linkscComprises the following steps:
the availability of the D2D link information is as follows:
wherein the content of the first and second substances,
a transmission power for a cell member;
transmission power of D2D member; gcmPath loss for a cell member to a primary member; gddPath loss between members of D2D; h iscmChannel gain for cellular members to primary members; h isddChannel gain among D2D members; i iscInterference experienced by the primary member; i isdInterference experienced by members of D2D; n is a radical of0Interfering noise for the interactive background environment.
The corresponding communication constraints are:
γc≥γc_th (3)
γd≥γd_th (4)
in the formula, gammac_thIs the lowest communication threshold of the cell members, gammad_thThe lowest communication threshold for member D2D. Equations (3) and (4) are used to ensure that the signal to interference plus noise ratio of the cell members and D2D members is not less than the minimum communication threshold.
Step 202, for Member εiAccording to the maximum transmission power pimaxA distance d from the abutmentimaxRatio of the calculated basic communication capability Cbi；
Member epsiloniBasic communication capability C ofbiIs a member epsiloniAbility to have without the aid of social dependencies, represented by ∈iMaximum transmission power pimaxA distance d from the abutmentimaxThe ratio of the two components is obtained:
wherein the adjacent distance dimax＝kimax·dis＞0，disIs a member epsiloniA safe distance of kimaxIs a member epsiloniThe adjacent coefficient of (2). For setting the safety distance and the abutment distance, see the literature: wu Sen Tang cooperative flight control system [ M ]]Beijing, science publishers, 2016.
Step 203, calculating the formation member epsiloniTo member epsilonjSocial communication dependency of
And obtaining a social communication dependency matrix R through social dependencies among members, and obtaining the dense formation of the unmanned aerial vehicle and the member epsilon through analyzing rows and columns of the matrixjCommunication dependency between them;
social communication dependency
Is used to characterize the social communication dependencies of the members. Social communication dependencies refer to the interdependencies of social communications formed between members in a formation and between members and formations. The larger the social communication dependency, the stronger the social communication dependency between the members, and the higher the communication quality.
Social communication dependency
The specific calculation formula is as follows:
wherein the content of the first and second substances,
social ability of a member;
an integrated communication capability for the member;
a basic communication capability is set for the member,
member epsiloniSocial ability of
Means unmanned aerial vehicle communication member epsiloniAnd member epsilonjThe relation between the signal-to-interference-and-noise ratio and the communication connection time is calculated by the following formula:
wherein, γijIs a member epsiloniAnd member epsilonjSignal to interference plus noise ratio of;
is a member epsiloniTransmission power of gijIs a member epsiloniTo member epsilonjPath loss of gcjIs cell member c to epsilonjPath loss of hijIs a member epsiloniTo member epsilonjChannel gain of hcjC as cell member to epsilonjThe channel gain of (a); i isijIs a member epsiloniAnd member epsilonjWhen connecting to a communication
A (c) is added; the social communication dependency matrix R is calculated by the following formula:
in the social communication dependency matrix R, the sum of each row
All represent the member epsilon corresponding to this rowiThe degree of social dependence on communication by other members in the formation, called Member epsiloniCommunication dependency on dense formation of unmanned aerial vehicles
Sum of each column
All represent the member epsilon corresponding to this columnjThe social influence degree on the communication of other members in the formation is called as the member epsilon of the unmanned aerial vehicle intensive formation pairjCommunication dependency of
Step 204, utilizing the member epsiloniThe communication adjacency, the basic communication capacity and the dependency of the unmanned aerial vehicle intensive formation on the members are calculated, and the member epsilon is calculatediCommunication power L ofi：
Step 205, similarly, calculating the communication power of each member, and sorting the communication power in a descending order from big to small;
step three, selecting a member epsilon with the maximum communication power each timeiTaking the leader member as a leader member, judging whether the relation between the remaining members and the leader member meets the condition, removing the remaining members which do not meet the condition, reordering, and repeatedly selecting the next leader member until all the leader members are completely selected;
the conditions are satisfied as follows:
lambda is a safety factor; see literature for settings: wu Sentang, cooperative flight control System [ M ]. Beijing, scientific Press, 2016.
at t0Time of day, formation member epsiloniAnd epsilonjAre respectively located at the coordinates
The velocity components in the x, y and z axes are
After the formation periodic update time delta t, the formation member epsiloniAnd epsilonjThe coordinates are as follows:
then the formation member epsilon at this timeiAnd epsilonjThe euclidean distance between can be expressed as:
step four, sequentially calculating member Euclidean distances between the remaining members and the leadership members
Selecting a leader member satisfying the distance allowed by the D2D communication as an attachment candidate member;
step five, aiming at each residual member, calculating the communication dependency of the residual member on each leader member in each leader member serving as an attachment candidate; selecting the maximum communication dependency degree as the only leader member to attach until all the rest members are attached;
after the leadership member is selected, firstly, each remaining member judges the member distance between the member and each leadership member according to the formula (10), and the leadership member which cannot meet the communication distance D2D is not attached;
in the leadership member satisfying the communication condition, the rest members epsilon are calculatediTo the leader force member epsilonjSocial dependency of
And member basic communication capability
And after the calculation is finished, selecting the leader member with the maximum social communication dependency degree as an attachment object. Thus, the leader member and the team members attached to it form C communication-attached groups.
Step six, forming an attaching group by each leader member and the formation members attached to the leader member, matching the honeycomb members forming a mapping relation with the attaching group, performing resource reuse on the whole, and respectively calculating the change coefficients of the weight values before and after the reuse of each group;
for cell member ciWhen the cell member ciWhen only cellular network communication is carried out in the dense formation of unmanned aerial vehicles, the rate weight e of the unmanned aerial vehiclesiComprises the following steps:
wherein the content of the first and second substances,
periodically updating cell member c in time for a formationiThe average rate of (d);
is its overall average rate.
D2D member D when attached to group AjMultiplexing cellular Member ciWhen communicating with the spectrum resource of (b), the reuse of the cell members by all members of the attached group (a) is uniform, where djRefers to the entire attached group a; the rate weight becomes:
wherein the content of the first and second substances,
periodically updating D2D member D in time for a formationjWith cell member c being multiplexed by itiAverage rate of inter-communication;
is its overall average rate.
The attached group A multiplexing cell member ciThe front and rear weight variation coefficients are:
E＝[|ei-eij|] (13)
wherein [. is a whole.
In the unmanned aerial vehicle network, the reuse of the spectrum resources of the cell members by the D2D members means that interference is generated on the cell members to be reused, and in order to reduce the difference between the cell members to be reused and other cell members in normal communication as much as possible, the damage to the fairness of the whole dense formation is reduced, so that a weight variation coefficient is set. The smaller the coefficient, the less impact on the multiplexed cell members.
Enumerating distribution combinations between all attached groups and candidate honeycomb members, arranging weight variation coefficients of all groups from small to large, screening the groups from the combination with the minimum rate weight in sequence, judging whether the transmission rate of each group meets the QoS (quality of service), and if so, determining the spectrum resources of the attached groups multiplexing honeycomb users of the group; otherwise, the combination is removed, other combinations in the mapping combination set are judged repeatedly until all combinations are judged, and resource cooperative allocation of the unmanned aerial vehicle intensive formation communication network is completed.
If the attachment group or the candidate honeycomb member in the combination is already confirmed to have the multiplexing relationship, the combination is removed, and other combinations are judged again according to the steps until all combinations are confirmed.
When two or more groups of combinations have the same variation coefficient at the same time, selecting the communication connection time IijThe smallest combination. If the attachment group or the candidate honeycomb member in the combination is already confirmed to have the multiplexing relationship, the combination is removed, and other combinations are judged again until all combinations are confirmed.
If there are 3 attached groups and 3 cellular member scenes, the sequence is: 1, 2 and 3; bee 1, bee 2 and bee 3; there were initially 9 combinations: the weight change coefficient (according to 1, bee 1) is 1, the weight change coefficient (according to 1, bee 2) is 2, the weight change coefficient (according to 1, bee 3) is 3, the weight change coefficient (according to 2, bee 1) is 4, the weight change coefficient (according to 2, bee 2) is 5, the weight change coefficient (according to 2, bee 3) is 5, but the connection time is short; the weight change coefficient (according to 3, bee 1) is 6, (according to 3, bee 2) is 7, and (according to 3, bee 3) is 8;
since the weight (in 1, bee 1) is the smallest, the multiplexing is true. Bee 1 is occupied by bee 1 alone, at this time (by 2, bee 1) and (by 3, bee 1) are eliminated, and the combination becomes: (yi 2, bee 2) (yi 2, bee 3) (yi 3, bee 2) (yi 3, bee 3);
since the weights (by 2, bee 2) and (by 2, bee 3) are the same, but the connection time (by 2, bee 3) is short, the multiplexing is established. Bee 3 is occupied by bee 2 alone, at this time, the bee 3 (in 3) is removed, and the residual combination becomes (in 3, bee 2) which is a unique combination and is multiplexed.
In the unmanned aerial vehicle dense formation communication network, the transmission power of the cellular member c and the D2D member D after path loss is as follows:
wherein the content of the first and second substances,
including the transmission power lost from the distance to the primary member for cell member c;
including for the source member the transmission power lost from the distance to the destination member; τ is the path loss constant; α is the path loss factor.
The interference experienced by the cell members and D2D members, including the loss of member range, is:
for cooperative allocation of unmanned aerial vehicle intensive formation communication network resources, when a cell member c in the unmanned aerial vehicle intensive formation communicates with an attached group multiplexing spectrum resources thereof, the transmission rate of the cell member c is as follows:
likewise, the transmission rate of D2D member D is:
accordingly, the sum of the transmission rates of the attached group when the spectrum resources of the cell members are multiplexed is:
example (b):
in the embodiment of the invention, the formation area is 500m, the noise power spectral density is-174 dBm/Hz and is 60m at most, the formation periodic updating time is 100ms, the path loss factor is 2, the maximum transmitting power is 24dBm, and the cell member gamma isc_thIs 3dB, D2D member gammad_thIs 3 dB.
The simulation result is shown in fig. 4, which compares the communication performance of the formation system based on the social communication dependency and information fairness allocation method and the formation network resource cooperative allocation method based on the maximum rate allocation method in the same formation scale. As can be seen from the cumulative distribution function of the formation transmission rate, when the intensive unmanned aerial vehicle formation adopts the resource allocation method provided by the invention, the intensive unmanned aerial vehicle formation generally has higher network transmission rate. Although the method based on the maximized rate has a high communication transmission rate of part of the formation members, the system resources with good quality are already allocated to the formation members with good channel quality firstly because of the aim of maximizing the formation throughput, and the fairness is lost for the rest formation members with relatively poor channel quality, so that the performance is generally poor.
Through the formation comprehensive resource collaborative allocation method based on social communication dependency and information fairness, compared with the relationship between the D2D member logarithm and the information fairness factor of the other two methods, as shown in FIG. 5, the formation information fairness of the three allocation methods is gradually reduced along with the increase of the D2D member logarithm. The more the number of pairs of D2D members in the formation is, the more the spectrum resources of the cellular members are multiplexed, the weight change coefficient before and after multiplexing is increased, and the overall fairness of the formation is gradually reduced, wherein the two methods considering the fairness can greatly improve the overall information fairness of the formation from the viewpoint of a method not considering the fairness. Although the formation information fairness of the three allocation methods has a similar descending trend under the condition that the number of pairs of the D2D members is increased, the allocation method provided by the invention can select a proper honeycomb member for multiplexing according to the weight change coefficient before and after the honeycomb members are multiplexed, so that the guide and regulation effects are played, the influence of the multiplexed honeycomb member before and after the multiplexing is reduced as much as possible, the overall fairness of the formation is improved, and the performance of the method is better than that of the other two methods.
Experiments prove that the fairness and the cooperative communication performance of the system are superior to those of the prior art by adopting the resource cooperative allocation method.
Claims (6)
1. A resource cooperative allocation method for an unmanned aerial vehicle intensive formation communication network is characterized in that the following steps are executed for unmanned aerial vehicle members in unmanned aerial vehicle formation which prepare multiplexing cellular member resources for communication:
firstly, constructing a unmanned aerial vehicle formation communication scene comprising C cellular members and D2D members; respectively calculating the communication adjacency degree, the basic communication capacity and the social communication dependency degree among the D2D communication members, further calculating the communication power of all the members in the D, and arranging the communication power in a descending order;
selecting a member with the largest communication power as a leader member each time, judging whether the relation between the rest members and the leader member meets the condition, removing the rest members which do not meet the condition, reordering, and repeatedly selecting the next leader member again until all the leader members are completely selected;
each remaining member sequentially calculates the Euclidean distance between the member and each leader force member, the leader force member which meets the D2D communication distance and has the largest communication dependency is selected for unique attachment, and each leader force member and the formation member attached to the leader force member form an attachment group;
each attached group is matched with the honeycomb members one by one, resource reuse is carried out on the whole, and the change coefficients of the weight values before and after the reuse of each group are respectively calculated; enumerating all the distribution combinations of the attached groups and the candidate honeycomb members, selecting the weight variation coefficients one by one after ascending order, judging whether the transmission rate of each combination meets the QoS, and if so, determining the spectrum resources of the attached groups multiplexing honeycomb users of the combination; otherwise, the combination is removed, the next combination is continuously selected for repeated judgment until all combinations are judged, and resource cooperative distribution of the unmanned aerial vehicle intensive formation communication network is completed.
2. The method according to claim 1, wherein the communication scenario for the formation of unmanned aerial vehicles includes a formation master member m; c cell members are { C1,c2,…,cCD2D members are D1,d2,…,dD}; C. d is a positive integer; the C cell members communicate with the main member m through independent channels orthogonal to each other, and the D2D member sets uplink channel resources to be multiplexed when performing communication.
3. The method for the cooperative resource allocation of the densely-organized communication network of unmanned aerial vehicles according to claim 1, wherein the communication power comprises the following specific steps:
step 201, aiming at the ith D2D communication member epsiloniCounting the member epsiloniWhen the communication quality satisfies the set threshold, the maximum number of members with which communication can be performed is recorded as the communication adjacency nTi；
Step 202, for Member εiAccording to the maximum transmission power pimaxA distance d from the abutmentimaxRatio of the calculated basic communication capability Cbi；
Wherein the adjacent distance dimax＝kimax·dis＞0，disIs a member epsiloniA safe distance of kimaxIs a member epsiloniThe adjacency coefficient of (a); setting the safe distance and the adjacent distance manually according to actual needs;
step 203, calculating the formation member epsiloniTo member epsilonjSocial communication dependency of
And obtaining a social communication dependency matrix R through social dependencies among members, and obtaining the dense formation of the unmanned aerial vehicle and the member epsilon through analyzing rows and columns of the matrixjCommunication dependency between them;
social communication dependency
The calculation formula of (2) is as follows:
wherein the content of the first and second substances,
social ability of a member;
an integrated communication capability for the member;
a basic communication capability is set for the member,
the calculation formula of the social communication dependency matrix is as follows:
in the social communication dependency matrix R, the sum of each row
All represent the member epsilon corresponding to this rowiThe degree of social dependence on communication by other members in the formation, called Member epsiloniCommunication dependency on dense formation of unmanned aerial vehicles
Sum of each column
All represent the member epsilon corresponding to this columnjThe social influence degree on the communication of other members in the formation is called as the member epsilon of the unmanned aerial vehicle intensive formation pairjCommunication dependency of
Step 204, utilizing the member epsiloniThe communication adjacency, the basic communication capacity and the dependency of the unmanned aerial vehicle intensive formation on the members are calculated, and the member epsilon is calculatediCommunication power L ofi：
Step 205, similarly, calculating the communication power of each member, and sorting the communication powers in the order from big to small.
4. Such as rightThe resource collaborative allocation method for the unmanned aerial vehicle dense formation communication network according to claim 1, wherein the member distance condition is as follows:
5. The method according to claim 1, wherein the coefficient of change of the before-after-multiplexing weight values is calculated by:
for cell member ciWhen the cell member ciWhen only cellular network communication is carried out in the dense formation of unmanned aerial vehicles, the rate weight e of the unmanned aerial vehiclesiComprises the following steps:
wherein the content of the first and second substances,
periodically updating cell member c in time for a formationiThe average rate of (d);
is its overall average rate;
D2D member D when attached to group AjMultiplexing cellular Member ciWhen the spectrum resource is communicated, the rate weight value is changed into:
wherein the content of the first and second substances,
periodically updating D2D member D in time for a formationjWith cell member c being multiplexed by itiAverage rate of inter-communication;
is its overall average rate;
the attached group A multiplexing cell member ciThe front and rear weight variation coefficients are:
E＝[|ei-eij|]
wherein [. is a whole.
6. The method of claim 1, wherein in the resource multiplexing process, if a multiplexing relationship has been confirmed for a certain combination of attached groups or candidate cell members, the combination is removed, and other combinations are determined again until all combinations are confirmed;
when two or more groups of combinations have the same weight change coefficient, selecting the combination with the minimum communication connection time, if the attached group or the candidate honeycomb member in the combination is confirmed to have the multiplexing relationship, rejecting the combination, and judging other combinations again until all the combinations are confirmed.
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