CN112040449A - Relay optimization selection method for unmanned aerial vehicle intensive formation communication network - Google Patents
Relay optimization selection method for unmanned aerial vehicle intensive formation communication network Download PDFInfo
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Abstract
The invention discloses a relay optimization selection method for an unmanned aerial vehicle intensive formation communication network, and belongs to the technical field of unmanned aerial vehicle communication. The method selects a communication transmission mode according to the link quality, defines a relay selection area by analyzing the overlapping relation of adjacent domains of members of unmanned aerial vehicles at two ends of a formation relay, selects the most suitable relay member from candidate members according to the social dependence relation of a source member and the relay member, and considers the social dependence relation between the adjacent domains of the formation members and the formation members. The invention ensures the basic communication capacity of the dense formation of the unmanned aerial vehicles, and simultaneously optimizes and improves the throughput of the formation, thereby further improving the overall 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 relay optimization selection method for an unmanned aerial vehicle intensive formation communication network, which is a method for selecting an optimal relay of an unmanned aerial vehicle formation network according to social dependence relationship between adjacent domains of formation members and the formation members considered under the intensive formation of unmanned aerial vehicles.
Background
The unmanned aerial vehicle formation support network is a communication network capable of supporting transmission and sharing of information in formation, and formation members can further know the position of the unmanned aerial vehicle formation support network in formation and social dependence relationship with other formation members through the unmanned aerial vehicle formation support network. The Device-to-Device (D2D) technology can provide efficient network support and supplement for unmanned aerial vehicle formation, meets the requirement of cooperative formation and has certain autonomy. Compared with other communication technologies which do not depend on network infrastructure, the D2D technology is more versatile and can communicate not only under the control of a base station but also without the infrastructure. Through D2D communication, the terminals do not need to be directly transmitted through transfer, and the link can generate gain, so that the load of the base station can be reduced, and the spectrum resource efficiency and the throughput can be improved by the gain generated by the link and resource multiplexing.
Since relay users are distributed around the D2D source members, the problem of how to select excellent and reliable relay members when relaying cooperative communication becomes a hot spot for studying by domestic and foreign scholars. Reference [1] first constructs a model combining social factors and physical factors, and proposes a method for selecting an optimal relay based on prior probability, thereby improving throughput and reducing interruption probability of the system. Reference [2] proposes a relay selection method based on social perception capable of enhancing multi-hop collaboration efficiency by studying social and interactive contributions, and by means of the method, the power expenditure of the system can be further optimized. Reference [3] proposes a staged relay selection method through social similarity awareness, and improves system resource efficiency on the premise of ensuring system throughput and fairness. Although these studies present certain advantages in terms of improving throughput and optimizing systems of a typical system, when the existing method is applied to the dense formation of unmanned aerial vehicles, due to the characteristics of high dynamics and large scale of the unmanned aerial vehicle cluster in the dense formation communication network, the problems of poor communication capability of selected relay members, low overall throughput of the system and the like exist, and therefore a relay node optimization selection technology suitable for the dense formation communication network of unmanned aerial vehicles is needed.
Reference documents:
[1] xu Shao and Ying, Zhang Peng. D2D Relay selection and Power distribution based on social information in a cooperative communication network [ J ]. electronic and information bulletin, 2019, 39:1-8.
[2]Bidi Y,Amiya N.A Power-Efficient and Social-Aware Relay Selection Method for Multi-Hop D2D Communications[J].IEEE COMMUNICATIONS LETTERS,2018,22(7):1450-1453.
[3] Yuanrun, Min Jian Yong, Min Weijuan-based proportional fair selection of relay communication strategy [ J ] modern navigation, 2012, 134-.
Disclosure of Invention
Aiming at the existing requirements, the invention provides a relay optimization selection method of an unmanned aerial vehicle intensive formation communication network by considering the adjacent domains and the overlapping relationship of the formation members and the social dependence relationship among the formation members based on an end-to-end technology.
The unmanned aerial vehicle dense formation communication network comprises unmanned aerial vehicle members for cellular communication and unmanned aerial vehicle members for D2D communication, and the relay optimization selection method of the unmanned aerial vehicle dense formation communication network carries out the following steps for the members in unmanned aerial vehicle formation ready for D2D communication:
step one, setting members needing D2D communicationiAs a source member, the current members in the formation are combined with the source memberiNearest and owniMembers of a required resourcejIs a target member; memberiMultiplexing the uplink channel resources of the cellular member c for transmission;
step two, calculatingiSum of signal to interference and noise ratio of cellular link of multiplexing ciAndjjudging whether the signal-to-interference-and-noise ratio of the D2D link meets the corresponding lowest communication threshold, if so, judging that the member meets the lowest communication thresholdiWith the target memberjCarrying out direct communication to finish the method; otherwise, the relay communication is needed, and the step three is continuously executed;
step three, selecting members according to the overlapping relation of adjacent domainsiThe source member broadcasts a relay request to members of the unmanned aerial vehicle in the selected area;
and step four, selecting the optimal relay member from the relay selection area, receiving the relay request of the source member by the optimal relay member, establishing a link with the source member and the target member and carrying out relay cooperative communication.
In the third step, the method for selecting the relay selection area includes: by member ofiIs a sphere center,iThe adjacent distance of (a) is a spherical area of radiusiA neighboring domain of; by member ofjIs a sphere center,jThe adjacent distance of (a) is a spherical area of radiusjA neighboring domain of; by source memberiAnd target memberjThe connecting line of (1) is two hemisphere adjacent domains of the central line, the overlapped part of the two hemisphere adjacent domains is set as an I area, the rest parts of the two hemisphere adjacent domains are set as a II area, and the I area and the II area are selected relay selection areas.
In the fourth step, firstly, selecting an optimal relay member from the area I, if no idle unmanned aerial vehicle exists in the area I, selecting the optimal relay member from the area II, if no idle unmanned aerial vehicle exists in the area II, no unmanned aerial vehicle capable of serving as a relay currently exists, no suitable relay is found at this time, and the method is executed again after waiting for the next formation periodic update time; when only one idle unmanned aerial vehicle exists in the area I, the unmanned aerial vehicle is taken as an optimal relay member; when a plurality of idle unmanned aerial vehicles exist in the I area, selecting a source memberiThe idle unmanned aerial vehicle with the largest social dependence degree is used as an optimal relay member; when no idle unmanned aerial vehicle exists in the area I and a plurality of idle unmanned aerial vehicles exist in the area II, selecting a source member from the area IIiThe idle unmanned aerial vehicle with the largest social dependence degree is used as the optimal relay member.
In the fourth step, the source member is determinediSelecting the optimal relay member according to the social dependence, and setting the idle unmanned aerial vehicle asr，rTo pairiIs expressed as ρriThe calculation is as follows:
wherein, CriIs a member ofrOf (2) aAbility, by memberrObtaining the signal-to-interference-and-noise ratio; cbrIs a member ofrBy members ofrIs obtained as a ratio of the maximum transmission power of C to its adjacent distancebrIs more than or equal to 0. Will idle unmanned aerial vehicle to source memberiThe social dependency degrees are ranked from large to small, and the idle unmanned aerial vehicle with the largest social dependency degree is selected as the optimal relay member.
Compared with the prior art, the invention has the following positive effects:
(1) in consideration of the structural characteristics of unmanned aerial vehicle formation, the relay optimization selection method of the unmanned aerial vehicle dense formation communication network takes into account the overlapping relation of adjacent domains of unmanned aerial vehicle members in the formation and the social dependency relationship among the formation members, and is more suitable for high-dynamic and large-scale unmanned aerial vehicle dense formation compared with the prior art.
(2) The relay optimization selection method of the unmanned aerial vehicle intensive formation communication network can ensure the basic communication capacity of the unmanned aerial vehicle intensive formation and optimize and improve the throughput of the formation, thereby further improving the overall performance of the intensive formation support network.
Drawings
Fig. 1 is a diagram of an example of a dense formation communication network architecture for unmanned aerial vehicles to which the method of the present invention is applied;
FIG. 2 is a schematic diagram of the communication flow of formation D2D according to the present invention;
FIG. 3 is a schematic diagram of the overlapping distribution relationship in the neighborhood of the members in the formation according to the present invention;
fig. 4 is a schematic diagram comparing the system throughput of the present invention with two existing relay selection methods;
fig. 5 is a schematic diagram comparing system throughput of the present invention with selecting relays only with the social dependencies of the present invention.
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 relay optimization selection method of the unmanned aerial vehicle intensive formation communication network selects a communication transmission mode according to link quality, defines a relay selection area by analyzing the overlapping relation of adjacent domains of members of unmanned aerial vehicles at two ends of a formation relay, and selects the most suitable relay member from candidate members according to the social dependence relation of a source member and the relay member. The invention can ensure the basic communication capacity of the dense formation of the unmanned aerial vehicles, and simultaneously optimize and improve the throughput of the formation, thereby further improving the overall performance of the dense formation support network.
Fig. 1 shows a dense formation communication network for unmanned aerial vehicles to which the method of the present invention is applied. Wherein, contain in the unmanned aerial vehicle formation: a main member m; i free members I1,i2,…,iI}; c cell members { C1,c2,…,cC}; D2D members { D1,d2,…,dDAnd (4) including the source member and the target member. Wherein I, C, D are all positive integers. In the formation, the cell members communicate with the primary member m through independent channels that are orthogonal to each other and thus do not interfere with each other. I free members present in the formation for use by D2D members as relay members, I>C. In the formation D2D communication, each D2D link respectively multiplexes channel resources of different cell members for transmission. Considering that the uplink traffic in the cellular network is lower than the downlink traffic in general, the uplink channel resources are set to be multiplexed, and no surplus spectrum resources are separately allocated to D2D communication.
Signal to interference plus noise ratio y of cellular links of source member t and multiplexed cellular member c in the networkcComprises the following steps:
signal to interference plus noise ratio gamma of the D2D link of source member t and target member rdComprises the following steps:
wherein the content of the first and second substances,
is the transmission power of cell member c,
transmission power, g, for D2D member tcmPath loss, g, for cell member c to primary member mtmIs the path loss, h, from the source member t to the master member mcmChannel gain, h, for cell member c to primary member mtmChannel gain, g, for source member t to primary member mtrIs the path loss between the source member t to the target member r, gcrIs the path loss, h, of cell member c to target member rtrIs the channel gain, h, between the source member t and the target member rcrChannel gain for cell member c to target member, N0Interfering noise for the interactive background environment. The source member and the target member are D2D members.
In the D2D communication mode, data between two end users are directly transmitted without being transferred through the mobile communication network, and the communication link further generates gain; the efficiency of wireless spectrum resources can be greatly improved by gains generated by communication links and resource multiplexing, the network throughput is remarkably improved, and the application scene of the wireless spectrum resources is further expanded.
Since the D2D users reuse the cellular user channel resources and are affected by the neighboring structure and interference in the formation, the cellular link and the D2D link must satisfy their own minimum communication requirements at the same time to perform the direct communication conditionally, and the specific conditions are as follows:
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. When the link quality degrades, the transmission conditions of the receiving members may be satisfied by first increasing their transmission power. The direct communication is used when equations (3) and (4) are satisfied, and relay is not required.
When the link condition is poor and the transmission power capable of meeting the requirement is larger than the transmission power threshold, the direct communication used between the D2D member pairs cannot meet the quality of service (quality of service), and the members suitable for serving as the relay are selected from the idle members to perform relay cooperative communication. That is, as long as equation (5) or equation (6) is satisfied at one time, relay cooperative communication is used:
in the formula (I), the compound is shown in the specification,
is the maximum transmission power of cell member c,
the maximum transmission power of D2D member t.
Based on the communication mode of the D2D member, the method of the invention defines the following physical meanings:
in the arrangement of the unmanned aerial vehicle formation,i、jrepresenting any two unmanned aerial vehicle members in the formation, wherein i and j are unmanned aerial vehicle numbers; then:
(1) social dependencies refer to social interdependencies formed between members in a formation and between members and formations. The larger the social dependency, the stronger the social dependency between the members, and the higher the communication quality.
Team memberiTo members ofjThe degree of social dependence is rhoijThen, then
Wherein, CiRefer to a memberiComprehensive capacity of CijIs a member ofiSocial ability of CbiIs a member ofiBasic communication capability of Cbi≥0。
(2) Social competence of the Member, MemberiSocial ability of CijMeans unmanned plane memberiDependent on memberjThe ability possessed by the formed social dependency relationship. In the formation support network, the signal interference noise ratio of the member is defined as the social ability of the member, and then the memberiSocial ability of CijAs calculated by equation (2).
Wherein, member ofiMultiplexing the uplink channel resources of cell member c for transmission,
is a member ofiTransmission power of gijIs a member ofiTo membersjPath loss of gcjIs a cellular member c to memberjPath loss of hijIs a member ofiTo membersjChannel gain of hcjIs a cellular member c to memberjChannel gain of, N0Interfering noise for the interactive background environment.
(3) Basic communication capability of a Member, MemberiBasic communication capability C ofbiRefer to a memberiThe ability to have without the aid of social dependencies, isiMaximum transmission power pimaxA distance d from the abutmentimaxThe ratio of the two components is obtained.
Wherein, member ofiIs a distance d of abutmentimax＝kimax·dis＞0，disIs a member ofiA safe distance of kimaxIs a member ofiThe adjacent coefficient of (2). For the definition of the adjacency distance and the adjacency coefficient, see document [4 ]]: wu Sen Tang cooperative flight control system [ M ]]Beijing, science publishers, 2016.
When C is presentbiWhen 0, it means a memberiThe basic communication ability of (a) is completely annihilated, i.e., the member is in a state of being non-viable even by an external force. Thus, a normal memberiBasic communication capability C ofbi＞0。
(4) Safe distance of formation member, memberiIs denoted by dis. At time t, when the formation memberiWith other members in the formationjOr spacing d between barrier threatsij(t) is less than disWhen is dij(t)＜disThen members of formationiCorresponding collision prevention measures must be taken; when d isij(t)＝disMembers of formationiIt is in a ready state for taking corresponding collision avoidance measures.
Based on the above description, as shown in fig. 2, the relay optimization selection method for the dense formation communication network of unmanned aerial vehicles according to the present invention performs the following steps one to seven for members in the formation of unmanned aerial vehicles, who are ready to perform D2D communication.
Step one, unmanned aerial vehicle members with D2D communication requirementsiSending communication request information to the main member, and forming the current formation with the memberiNearest and owniMembers of a required resourcejAs a supply member.
Members with D2D communication requirementsiAs a source member, a feed memberjIs a target member.
At t0Time of day, formation memberiAndjare respectively located at the coordinates
The velocity components in the x, y and z axes are
After the formation periodically updates the time delta t, the formation membersiAndjthe coordinates are as follows:
then the formation member at this timeiAndjthe euclidean distance between can be expressed as:
wherein the content of the first and second substances,
representing formation membersiAndjat t0The distance after time at is updated.
Step two, calculating the membersiThe signal to interference and noise ratio of the cellular link of (a) and the signal to interference and noise ratio of the D2D link of (b), whether equation (3) or (4) above is satisfied, and if so,iwith the target memberjCarrying out direct communication and ending the process; otherwise, the relay communication is needed, and the step three is continuously executed.
Calculating membership according to equation (1)iSignal to interference and noise ratio of cellular link
Wherein, gimIs a member ofiPath loss to main member m, himIs a source memberiChannel gain to the primary member m.
Calculating membership according to equation (2)iAndjsignal to interference and noise ratio of the D2D link
Gamma to be calculatedc、γijRespectively with the lowest communication threshold value gammac_th、γd_thThe comparison is carried out in such a way that,and if the minimum communication requirements are met, the source member and the target member carry out direct communication, otherwise, relay communication is searched.
For simplicity, the following powers are all powers including path loss, i.e.:
in the formula, piRepresenting source membersiTransmission power, p, including path losscThe transmission power indicating cell member c contains the path loss, a denotes the path loss constant, and α denotes the path loss factor.
Representing formation membersiAnd cell member c is at t0The distance between times Δ t is updated. Since the multiplexed links are different cell member links, only the interference from the multiplexed cell member c is considered here.
Then when the D2D useriAndjthe signal to interference and noise ratio of the D2D link when using pass-through communication is denoted as γijThe following were used:
obtained according to the Shannon formulaiAndjthroughput R of link in straight-through communicationijComprises the following steps:
where B is the channel bandwidth.
When the communication quality of the through link is not ideal, a relay selection range is defined according to the overlapping relation of adjacent domains, and the optimal member is selected as the relay member according to the social dependency of the member to perform relay cooperative communication so as to improve the link transmission speed. In the selection of relay forwarding cooperation, the received information resource is detected by the decoding forwarding cooperation relay, so that the influence of the noise of relay members is reduced, and the method has the characteristic of high flexibility and is suitable for a formation network. Therefore, the decoding forwarding is selected as the cooperative relaying scheme.
Step three, in the source memberiThe adjacent area of the relay unmanned aerial vehicle defines a selection area of relay unmanned aerial vehicle members, and the source membersiA relay request is broadcast to the selected in-region members.
Arranging members of unmanned aerial vehicle formation to be distributed in a certain ellipsoidal areaiIn the belonging formation, there are idle members, cell members and D2D members, an example of which is shown in fig. 1.
Team memberiPosition in the inertial frame is li(t), the formation form composed of all the members of the unmanned aerial vehicle at time t is represented by l (t) ═ col { l { (t) }1(t),l2(t),...,ln(t), col represents a column vector, each position is a column vector of three-dimensional coordinates, and n represents the total number of unmanned aerial vehicles in the formation of the unmanned aerial vehicles.
In three-dimensional Euclidean space, members will form a teamiBeing the center of sphere, membersiIs a distance d of abutmentimaxDefined as members of a spherical region of radiusiOf the adjacent domain. According to the formation motion characteristics of the unmanned aerial vehicles, membersiForm members of the neighborhood ofiIs (a) a neighbor group v (l):
v(l)＝{(i,q)∈×:μiq＜dimax,i≠q} (14)
wherein, the set space formed by idle unmanned aerial vehicle members is represented; mu.siqIndicating membership of an unmanned aerial vehicleiWith idle unmanned aerial vehicle memberqEquation (14) indicates that the free drones located in the neighborhood constitute a neighborhood group.
Target memberjThen there is the same neighborhood that is the target memberjIs the center of the sphere, and is adjacent to the distance djmaxIs a radius.
As shown in FIG. 3, the source memberiAnd target memberjThe adjacent domain of the sub-area is divided into two sub-areas; by source memberiAnd target memberjThe overlapping portion of the two hemisphere-adjacent domains whose line of (a) is the center line is defined as a region I, and the remaining portion of the two hemisphere-adjacent domains is defined as a region II.
To obtain a better D2D transmission rate, the candidate relay should be at the sending memberiIn the I-zone of the adjacent domain, thus ensuring the member to sendiWhile also being compatible with the receiving memberjI.e. r ∈ I.
Compared with the prior art that the relay nodes are selected on the basis of two-dimensional planes, most of the relay nodes are selected under the static condition, the method provided by the invention considers the three-dimensional space where the unmanned aerial vehicle formation is located and the member distribution around the formation members, and considers the condition of high-speed motion of the unmanned aerial vehicle, so that the candidate relay unmanned aerial vehicle is selected according to the adjacent domain, and the method is more suitable for high-dynamic large-scale dense formation of the unmanned aerial vehicle.
And step four, putting the idle members in the area I into a relay candidate member set A, if the A is an empty set, executing the step five, if only one member in the A exists, taking the member as an optimal relay member, and executing the step seven, otherwise, executing the step six.
And step five, putting the idle members in the area II into a relay candidate member set B, if only one element exists in the member B, taking the member as an optimal relay member, and executing the step seven, otherwise, executing the step six.
Step six, calculating each member and the source member of the idle unmanned aerial vehicles in the relay candidate member set A or BiAnd sorting according to the sequence from big to small, selecting the members ranked at the top as the optimal relay members, and then executing the step seven.
For each candidate relay member in candidate set A or BrCalculatingrTo pairiSocial dependency of (1) < rho >riAnd (4) calculating according to the formulas (7) to (9).
Wherein, CriIs a member ofrAccording to the social ability of the memberrAndithe signal-to-interference-and-noise ratio of the D2D link is obtained, i.e.
Wherein the content of the first and second substances,
is a member ofrTransmission power of griIs composed ofrToiPath loss of gciIs a cell member c toiPath loss of hriIs a member ofrTo membersiChannel gain of hciIs a cellular member c to memberiThe channel gain of (a); cbrIs a member ofrBy members ofrMaximum transmission power prmaxA distance d from the abutmentrmaxThe ratio of (A) to (B) is obtained and is expressed as
Wherein p isrmaxIs composed ofrMaximum transmission power of drmaxIs composed ofrAdjacent distance of drmax＝krmax·drs，drsIs a member ofrA safe distance of krmaxIs a member ofiThe adjacent coefficient of (2).
The social dependency relationship among the formation members can be effectively obtained by measuring and calculating the social dependency degree among the formation members, and a basis is provided for the selection of the relay members. The invention takes the magnitude of social dependency among members as a judgment basis and selects the best relay member from relay candidate members.
Step seven: and the optimal relay member receives the relay request of the source member, establishes a link and performs relay cooperative communication.
For D2D relay cooperative communication, the communication process of the link comprises two time slots T due to the joining of the relay member1And T2Corresponding to D2D source member to relay member and relay member to target member, respectively. Mark optimal relay members asrAccording to the decodingIn the cooperative relay protocol, the relay member selects the throughput R of the complete decoding, the first hop and the second hopir、RrjRespectively as follows:
in the formula (I), the compound is shown in the specification,
representing source usersiWith relay membersrIn time slot T1The signal-to-interference-and-noise ratio in communication,
indicating relay membershiprWith the destination userjIn time slot T2Signal to interference plus noise ratio in communications.
D2D Source MemberiTo relay memberrSignal to interference and noise ratio gamma ofirThe calculation is as follows:
relay memberrTo D2D target MemberjSignal to interference and noise ratio gamma ofrjComprises the following steps:
transmission rate R corresponding to link in cooperative communication by using relayrComprises the following steps:
therefore, in the decode-and-forward cooperative relay method, the throughput R of the relay system is considered after the social dependency between the members in the formation is considereddComprises the following steps:
where ρ isijIs a member ofiTo members ofjSocial dependency of (2).
In the relay cooperative communication, the transmission speed and the system performance of the whole link are directly influenced as a result of the selection of the relay members so as to form the throughput R of the relay systemdAnd the transmission mode and the relay members are optimally selected as targets and measurement standards. The relay selection method in the prior art does not consider the connection between terminals and the conditions of the relay selection method, but considers the self capacity of the formation members and the social dependence relationship between the formation members based on the social dependence relationship, and is more suitable for high-dynamic large-scale unmanned aerial vehicle intensive formation.
Example (b): in the embodiment of the invention, the formation radius is 500m, the minimum distance between D2D members is 25m and 60m, the periodic update time of the formation is 100ms, the path loss factor is 2, the maximum transmitting power is 23dBm, the interference noise of the interactive background environment is-120 dBm/hz, and the channel bandwidth is 180 khz. The simulation results are shown in fig. 4 and 5.
As shown in fig. 4, the performance of the relay selection method provided by the present invention and the performance of the two existing relay selection methods on system Throughput (Throughput) are compared. Two existing relay selection methods are a relay selection method based on distance and a relay selection method based on signal-to-noise ratio (SNR). Through a Cumulative Distribution Function (CDF) of the formation throughput, it can be seen that when the relay selection method based on the invention is adopted for formation, the overall throughput of the formation can be improved compared with the other two methods, most of formation members have higher throughput, and the formation members with low throughput are obviously less than the other two selection methods. The selection method based on the distance is poor in performance of the three relay selection methods, and the condition of simply taking the distance between the formation members as the relay selection is not very suitable for high-dynamic large-scale unmanned aerial vehicle intensive formation. In the embodiment of the invention, because a small number of the members in the formation are few in the selectable relay members in the adjacent domain, the selection of the relay members is mainly determined according to the social dependency degree among the members, and the improvement amplitude is smaller compared with the selection method based on the SNR. And the other members have more relay members selected in the neighborhood, so that the number of the selectable members meeting the selection method conditions is increased directly, and the improvement is more obvious.
Fig. 5 compares the performance of the relay selection method provided by the present invention with the performance of the relay selection method based on the social dependency provided by the present invention in the formation throughput. Since only the social dependency is used as the selection factor, although the idle member with the highest social dependency is selected as the optimal relay member for communication, the uncertainty of communication is increased because the member with the high social dependency is likely to be far away from the connection line between the source member and the target member, even completely opposite to the target member. The relay selection method provided by the invention considers the adjacent domains of the members, the selected optimal relay is positioned in the adjacent domains of two hemispheres taking the connecting line of the source member and the target member as the central line, and the distance between the two hemispheres is considered, so that the relay selection method provided by the invention is generally superior to the relay selection method taking social dependence as a selection basis.
Experiments prove that the throughput of the system of the relay node selected by the method is superior to that of the prior art.
Claims (3)
1. A relay optimization selection method for an unmanned aerial vehicle dense formation communication network is characterized in that the following steps are executed for unmanned aerial vehicle members in unmanned aerial vehicle formation, which are prepared for D2D communication:
step one, setting members needing D2D communicationiAs a source member, the current members in the formation are combined with the source memberiNearest and owniMembers of a required resourcejIs a target member; let memberiMultiplexing the uplink channel resources of the cellular member c for transmission;
step two, calculatingiSum of signal to interference and noise ratio of cellular link of multiplexing ciAndjjudging whether the corresponding lowest communication threshold is met or not according to the signal-to-interference-and-noise ratio of the D2D link, and if so, determining that the member is in the lowest communication thresholdiWith the target memberjThe direct communication is carried out and the communication is carried out,finishing the method; otherwise, the relay communication is needed, and the step three is continuously executed;
step three, selecting members according to the overlapping relation of adjacent domainsiRelay selection area, memberiBroadcasting a relay request to members of the unmanned aerial vehicle in the relay selection area;
the method for selecting the relay selection area comprises the following steps: by member ofiIs a sphere center,iThe adjacent distance of (a) is a spherical area of radiusiA neighboring domain of; by member ofjIs a sphere center,jThe adjacent distance of (a) is a spherical area of radiusjA neighboring domain of; by source memberiAnd target memberjThe connecting line of the relay selection area is two hemisphere adjacent areas of the central line, the overlapped part of the two hemisphere adjacent areas is set as an I area, the rest parts of the two hemisphere adjacent areas are set as an II area, and the I area and the II area are selected relay selection areas;
step four, counting the number of idle unmanned aerial vehicle members in the area I, if the number of idle unmanned aerial vehicle members is 0, executing step five, if the number of idle unmanned aerial vehicle members is 1, taking only one idle unmanned aerial vehicle in the area I as an optimal relay member, executing step seven, and otherwise, entering step six to execute;
step five, counting the number of idle unmanned aerial vehicle members in the area II, if the number of idle unmanned aerial vehicle members is 0, finishing the method, if the number of idle unmanned aerial vehicle members is 1, taking only one idle unmanned aerial vehicle in the area II as an optimal relay member, executing the step seven, and otherwise, entering the step six to execute;
step six, selecting a source member from idle unmanned aerial vehicles in the area I or the area IIiThe idle unmanned aerial vehicle with the largest social dependence degree is used as an optimal relay member, and then the seventh step is executed;
let the candidate relay member ber，rTo pairiIs expressed as ρriThe calculation is as follows:
wherein, CriIs a member ofrSocial ability of the membersrAndid of (A)Obtaining the signal-to-interference-and-noise ratio of the 2D link; cbrIs a member ofrBy members ofrIs obtained as a ratio of the maximum transmission power of C to its adjacent distancebr≥0；
And step seven, the optimal relay member receives the relay request of the source member, establishes a link and performs relay cooperative communication.
2. The method of claim 1, wherein in step two, membersiThe signal-to-interference-and-noise ratio of the cellular link multiplexing cellular member c is expressed as
Wherein the content of the first and second substances,
is the transmission power of cell member c,
is a member ofiTransmission power of gcmPath loss, g, for cell member c to primary member m in the networkimIs a member ofiPath loss to main member m, hcmChannel gain, h, for cell member c to primary member mimIs a member ofiChannel gain to primary member m, N0Interfering noise for the interactive background environment;
memberiWith the target memberjThe signal-to-interference-and-noise ratio of the D2D link is expressed as
Wherein, gijIs composed ofiTojPath loss of gcjIs a cell member c tojPath loss of hijIs composed ofiTojChannel gain of hcjIs a cell member c tojThe channel gain of (a);
setting a minimum communication threshold for cell membersValue of gammac_thThe lowest communication threshold of the D2D member is gammad_th(ii) a When γ is satisfiedc≥γc_thAnd gammaij≥γd_thWhen the temperature of the water is higher than the set temperature,iandjdirect D2D communication is conducted.
3. The method of claim 1, wherein in step six, membersrSocial ability of CriIs calculated as follows:
wherein the content of the first and second substances,
is the transmission power of cell member c,
is a member ofrTransmission power of griIs a member ofrTo membersiPath loss of gciIs a cellular member c to memberiPath loss of hriIs a member ofrTo membersiChannel gain of hciIs a cellular member c to memberiChannel gain of, N0Interfering noise for the interactive background environment;
memberrBasic communication capability C ofbrThe calculation is as follows:
wherein p isrmaxIs composed ofrMaximum transmission power of drmaxIs composed ofrAdjacent distance of drmax＝krmax·drs，drsIs a member ofrA safe distance of krmaxIs a member ofiThe adjacent coefficient of (2).
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112804670A (en) * | 2020-12-30 | 2021-05-14 | 深圳市微网力合信息技术有限公司 | Communication method and communication equipment based on 5G and Wi-Fi6 |
| CN113848989A (en) * | 2021-08-09 | 2021-12-28 | 吴钟博 | Resource cooperative allocation method for unmanned aerial vehicle intensive formation communication network |
| CN114051224A (en) * | 2021-08-09 | 2022-02-15 | 吴钟博 | Cooperative relay selection method for unmanned aerial vehicle intensive formation support network |
| CN116709255A (en) * | 2023-08-04 | 2023-09-05 | 中国人民解放军军事科学院系统工程研究院 | Distributed selection method for relay unmanned aerial vehicle under incomplete information condition |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120250545A1 (en) * | 2009-07-17 | 2012-10-04 | France Telecom | Selection of dynamic relays for cooperative communications in a mobile network |
| CN107205256A (en) * | 2017-04-17 | 2017-09-26 | 西安交通大学 | Relay selection method based on Social Characteristics in a kind of D2D junction networks |
| US20170347285A1 (en) * | 2016-05-27 | 2017-11-30 | Hefei University Of Technology | Method and system for reoptimizing unmanned aerial vehicle formation communication topology based on minimum cost arborescence |
-
2020
- 2020-07-21 CN CN202010706781.1A patent/CN112040449A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120250545A1 (en) * | 2009-07-17 | 2012-10-04 | France Telecom | Selection of dynamic relays for cooperative communications in a mobile network |
| US20170347285A1 (en) * | 2016-05-27 | 2017-11-30 | Hefei University Of Technology | Method and system for reoptimizing unmanned aerial vehicle formation communication topology based on minimum cost arborescence |
| CN107205256A (en) * | 2017-04-17 | 2017-09-26 | 西安交通大学 | Relay selection method based on Social Characteristics in a kind of D2D junction networks |
Non-Patent Citations (2)
| Title |
|---|
| OLUWATOSIN AHMED AMODU; MOHAMED OTHMAN; NOR KAMARIAH NOORDIN; IDAWATY AHMAD: ""Relay-Assisted D2D Underlay Cellular Network Analysis Using Stochastic Geometry: Overview and Future Directions"", 《IEEE ACCESS ( VOLUME: 7)》 * |
| 吴钟博，易建强: ""无人机编队支撑网络的协同通信中继策略"", 《航空学报》, vol. 41, no. 2, pages 1 - 6 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112804670A (en) * | 2020-12-30 | 2021-05-14 | 深圳市微网力合信息技术有限公司 | Communication method and communication equipment based on 5G and Wi-Fi6 |
| CN113848989A (en) * | 2021-08-09 | 2021-12-28 | 吴钟博 | Resource cooperative allocation method for unmanned aerial vehicle intensive formation communication network |
| CN114051224A (en) * | 2021-08-09 | 2022-02-15 | 吴钟博 | Cooperative relay selection method for unmanned aerial vehicle intensive formation support network |
| CN114051224B (en) * | 2021-08-09 | 2023-12-05 | 吴钟博 | Cooperative relay selection method for unmanned aerial vehicle secret formation supporting network |
| CN116709255A (en) * | 2023-08-04 | 2023-09-05 | 中国人民解放军军事科学院系统工程研究院 | Distributed selection method for relay unmanned aerial vehicle under incomplete information condition |
| CN116709255B (en) * | 2023-08-04 | 2023-10-31 | 中国人民解放军军事科学院系统工程研究院 | Distributed selection method for relay unmanned aerial vehicle under incomplete information condition |
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