CN112188588A - Offshore relay communication transmission efficiency optimization method based on unmanned aerial vehicle network - Google Patents
Offshore relay communication transmission efficiency optimization method based on unmanned aerial vehicle network Download PDFInfo
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Abstract
The invention relates to an offshore relay communication transmission efficiency optimization method based on an unmanned aerial vehicle network, which comprises the following steps: establishing an offshore relay system model based on an aerial base station, wherein the model comprises a shore-based base station, the aerial base station and N offshore users; respectively modeling communication channels between a shore-based base station and an air base station and between the air base station and an offshore user, and respectively obtaining the channel capacity of a downlink between the shore-based base station and the air base station, the channel capacity of an uplink, the channel capacity of the downlink between the air base station and the ith offshore user, the channel capacity of the uplink and the downlink capacity; the method comprises the steps of obtaining the minimum transmission capacity of offshore users and the actual deployable range of the aerial base station, obtaining the optimal position of the aerial base station by using channel capacity, uplink and downlink capacity, the minimum transmission capacity and the basic deploying range in the air as variables and utilizing particle swarm algorithm convergence iteration, and being capable of flexibly deploying the position of the offshore base station to maximize the offshore network efficiency.
Description
Technical Field
The invention relates to the technical field of communication, in particular to an offshore relay communication transmission efficiency optimization method based on an unmanned aerial vehicle network.
Background
In recent years, the development of the marine industry has been receiving increasing attention from countries around the world. In order to meet the major development requirements of various marine industries, a perfect marine information network must be built so as to realize seamless, efficient and reliable information coverage in a wide sea area. However, compared with the more developed land-based information network, the marine information network has great differences in electromagnetic environment, user distribution, node performance, etc., and communication and network resources such as energy, bandwidth, storage, computation, etc. are relatively in short supply, so that these factors severely limit the service efficiency and capability, i.e., network performance, of the marine information network.
Particularly in the offshore area, most ships statistically sail in the offshore range of about 50 kilometers from the coast, but the shore-based mobile communication base station is mainly aimed at land users and generally only covers the range of several kilometers, so that most offshore ship users cannot directly access the shore-based network. Therefore, the ships in the offshore sea area can not communicate, and information transmission and receiving are influenced.
Due to the complex offshore environment, the electromagnetic wave transmission is simultaneously interfered by factors such as onshore obstacles, sea surface reflection and the like, and in addition, the position of the offshore ship is constantly changed, so that the high efficiency and flexibility of the relay base station deployment method need to be realized; finally, the time-varying uplink and downlink services of the marine communication cannot reasonably utilize the channel, so that the offshore network efficiency is low.
Disclosure of Invention
Therefore, the invention provides an offshore relay communication transmission efficiency optimization method based on an unmanned aerial vehicle network, which can flexibly deploy the position of an offshore base station and maximize the efficiency of an offshore network.
In order to achieve the above object, the present invention provides an optimization method for offshore relay communication transmission efficiency based on an unmanned aerial vehicle network, comprising: establishing an offshore relay system model based on an aerial base station, wherein the model comprises a shore-based base station, the aerial base station and N offshore users; respectively modeling communication channels between a shore-based base station and an air base station and between the air base station and an offshore user, and respectively obtaining the channel capacity of a downlink between the shore-based base station and the air base station, the channel capacity of an uplink, the channel capacity of the downlink between the air base station and the ith offshore user, the channel capacity of the uplink and the downlink capacity; acquiring the minimum transmission capacity of the offshore user and the actual deployable range (xmin, xmax), (ymin, ymax), (hmin, hmax) of the aerial base station, wherein the minimum transmission capacity and the actual deployable range (xmin, xmax), (ymin, ymax), (hmin, hmax) respectively represent the plane deployment range and the highest and lowest flight heights of the aerial base station; and converging and iterating by using the channel capacity, the uplink and downlink capacity, the minimum transmission capacity and the basic deployment range in the air as variables through a particle swarm algorithm to obtain the optimal position of the air base station.
Further, the aerial base station carries a first transceiver and a second transceiver, the first transceiver is used for communicating with the shore-based base station, the second transceiver is used for communicating with the offshore user, and the first transceiver and the second transceiver both adopt directional antennas for transmitting and receiving.
Further, the establishing an offshore relay system model based on the aerial base station comprises: the plane coordinates of the shore-based base station are (0,0), the coordinates of the N offshore users are (xM1, yM1), … …, (xMN, yMN), the three-dimensional space coordinates of the aerial base station are (xU, yU, hU), the carrier frequencies and bandwidths adopted by the links between the shore-based base station and the aerial base station and the links between the aerial base station and the offshore users are f1, B1, f2 and B2, and the links between the aerial base station and all the N offshore users share the same channel; the link between the aerial base station and the offshore user, and thetai represents the antenna direction of the aerial base station and the included angle between the aerial base station and the ith offshore user, namely the deflection angle of the antenna, so thatThe intersection between the antenna and the sea level is denoted by (xA, yA). The antenna transmitting power of the shore-based base station, the air base station to offshore users and the N offshore users is pB, pU1, pU2, pM1, … … and pMN respectively, and Zeta i is epsilon to [0,1]Represents the proportion of the upstream flow of the i-th offshore user to the total flow, and has zeta ═ zeta 1, … …, zeta N for all users]. Similarly, λ ═ λ 1, … …, λ N is used]And γ ═ γ 1, … …, γ N]Indicating the time slot division, and lambdai indicates the proportion of the time slot number allocated to the ith maritime user to the total time slot, wherein
And using γ i ∈ [0,1 ]]Indicating the proportion of the uplink time slot allocated to the ith marine subscriber to the total time slots allocated to that subscriber.
Further, the modeling the communication channels between the shore-based base station and the aerial base station, and between the aerial base station and the offshore user respectively comprises: for a transmission link between the shore-based base station and the aerial base station, analyzing by using a channel model based on probability statistics, wherein the line-of-sight transmission of electric waves is positively correlated with the emission elevation angle of the base station, and the probability of line-of-sight transmission is higher if the included angle between the transmission link and the horizontal plane is larger; the path loss of line-of-sight transmission between the shore-based base station and the aerial base station may be expressed as:
PLLoS(d1,f1)＝FSPL(d1,f1)+ηLoS(dB)
instead, the path loss of the non-line-of-sight transmission can be expressed as:
PLNLoS(d1,f1)＝FSPL(d1,f1)+ηNLoS(dB)
where FSPL (d, f) (dB) represents the free space path loss at a frequency of f MHz at a distance of dkm, and η LoS and η NLoS represent the excess loss for line-of-sight and non-line-of-sight transmissions.
Further, the channel capacity of the downlink between the shore-based base station and the air base station can be expressed as:
the channel capacity of the uplink may be expressed as:
where σ 2 denotes a power density of white noise in the environment, g1 denotes a channel gain coefficient, and g1 denotes a channel gain coefficient of 10-PL1/10。
Further, the downlink and uplink channel capacities between the airborne base station and the ith offshore user may be expressed as:
and
Further, the offshore users have 5, the offshore user coordinate at the center is (l1,0), the offshore user coordinate at the periphery is (l1 ± l2/√ 2, ± l2/√ 2), the shore-based base station, the aerial base station and the offshore users, the transmission power is respectively pB 10W, pU1 pU2 pU 5W and pM1 … … pMN 2W, the gaussian white noise power density in the environment is σ 2-174 dB/Hz, the upstream traffic occupancy ratio of the offshore users is ζ [0.1,0.3,0.5,0.7,0.9], the deployment horizontal position range of the aerial base station is xmin 0km, xmin 30km, ymin-10 km and ymin 10km, the deployment range of the height is hmin 1km and hmin 10km, the deployment horizontal position range of the aerial base station is 10km, the antenna location range is pointing to the aerial link at the same base station, and the aerial link pointing to the aerial base station, the center frequency of communication is set to be f1 GHz, the bandwidth is B2 kHz, the extra loss η LoS is set to be 0.1dB, η NLoS is set to be 21dB, the line-of-sight communication probability model coefficient a is 4.88, B is 0.429 for sparse buildings, and the extra loss η LoS is set to be 1dB and η NLoS is set to be 20dB, the line-of-sight communication probability model coefficient a is 9.61, B is 0.158 for dense buildings. For an air-sea link between an air base station and a sea user, the center frequency of communication is set to be f 2-5.8 GHz, the bandwidth is set to be B2-250 kHz, the path loss coefficient n is set to be 1.6, and the extra loss eta M is set to be 109.8 dB.
Further, in the particle swarm optimization, the number of particles S is set to 100000, the number of iterations T is set to 100, the distance l1 between the shore-based base station and the offshore user is 20km, the distance l2 between the offshore users is 1km, the optimal deployment position of the aerial base station is about (11.0,0,4.9) km, and the optimal antenna pointing direction is about (19.9,0) km, which is close to the center of the offshore user.
Compared with the prior art, the method has the advantages that the position deployment and time slot allocation strategy joint optimization method of the aerial relay base station is provided, and the network transmission efficiency and the coverage range of the offshore network are effectively improved by reasonably deploying and configuring the unmanned aerial vehicle base station between the shore-based base station and the offshore user. Aiming at an offshore relay transmission system, the transmission characteristics of an air-offshore channel and an air-onshore channel are considered, and the time-varying characteristics of uplink and downlink services of a marine ship user are combined, so that the problem of air base station deployment is abstracted into an optimization problem based on network capacity. Aiming at the characteristics of problems, a combined particle swarm optimization algorithm of an air base station deployment position, antenna pointing and time slot allocation strategy is provided, and the balance of link capacity and uplink and downlink flows and the fairness of user services are realized. Finally, simulation tests are carried out on the deployment algorithm of the unmanned aerial vehicle relay base station facing various offshore relay scenes, simulation results show that the method has high efficiency and reliability, efficient and flexible configuration can be carried out according to actual communication environments and network services, network transmission efficiency is effectively improved, and related achievements can be used for deployment of the unmanned aerial vehicle relay base station in the offshore network and can be expanded to various scenes such as a remote buoy network and ship networking.
Drawings
Fig. 1 is a schematic structural diagram of an offshore relay communication transmission efficiency optimization method based on an unmanned aerial vehicle network according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an offshore relay communication network according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an example structure of an offshore relay communication network according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are only for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, a method for optimizing transmission efficiency of offshore relay communication based on an unmanned aerial vehicle network according to an embodiment of the present invention includes:
s100, establishing an offshore relay system model based on an aerial base station, wherein the model comprises a shore-based base station, the aerial base station and N offshore users;
s200, respectively modeling communication channels between a shore base station and an air base station and between the air base station and an offshore user, and respectively obtaining the channel capacity of a downlink between the shore base station and the air base station, the channel capacity of an uplink, the channel capacity of the downlink between the air base station and the ith offshore user, the channel capacity of the uplink and the downlink;
s300, acquiring the minimum transmission capacity of the offshore user and the actual deployable range (xmin, xmax), (ymin, ymax), (hmin, hmax) of the aerial base station, wherein the minimum transmission capacity and the actual deployable range (xmin, xmax), (ymin, ymax), (hmin, hmax) respectively represent the plane deployment range and the highest and lowest flight heights of the aerial base station;
and S400, converging and iterating by using the channel capacity, the uplink and downlink capacity, the minimum transmission capacity and the basic deployment range in the air as variables through a particle swarm algorithm to obtain the optimal position of the air base station.
Specifically, as shown in fig. 2, the relay system includes a shore-based base station, an unmanned aerial vehicle air relay base station, and N marine vessel users. The plane coordinates of the shore-based base station are (0,0), the coordinates of the N marine vessel users are (xM1, yM1), … …, (xMN, yMN), and the heights of the two are ignored in the embodiment of the invention. In addition, the three-dimensional space coordinate of the air relay base station is (xU, yU, hU). The relay system adopts a time division duplex mode for forwarding, so that only the uplink or downlink unidirectional transmission of a unique ship user and a shore-based base station is supported in the same time slot, and the uplink and downlink transmission of the same link section also shares the same channel. The carrier frequency and bandwidth adopted by the links between the shore-based base station and the aerial base station and the links between the aerial base station and the ship users are respectively f1, B1, f2 and B2, wherein the links between the aerial base station and all N offshore users share the same channel. In addition, the aerial base station carries two sets of independent transceivers for communication with the shore-based base station and the offshore users, and the two transceivers adopt directional antennas for transmitting and receiving. For the link between the aerial base station and the ship, thetai represents the pointing direction of the aerial base station antenna and the included angle between the aerial base station antenna and the ith offshore user, namely the deflection of the antennaThe angle, in the present embodiment, (xA, yA) is used to indicate the intersection point between the antenna and the sea level. The antenna transmission power of the shore-based base station, the air base station to the sea user and the N sea users are pB, pU1, pU2, pM1, … … and pMN respectively. Considering the time variation and imbalance of the uplink and downlink flow of the maritime communication service, zeta i belongs to [0,1 ]]Represents the proportion of the upstream flow of the i-th offshore user to the total flow, and has zeta ═ zeta 1, … …, zeta N for all users]. Similarly, λ ═ λ 1, … …, λ N is used]And γ ═ γ 1, … …, γ N]Indicating the slot division scheme of this time division duplex system. In particular, λ i represents the ratio of the number of time slots allocated to the ith marine user to the total time slots, where
And using γ i ∈ [0,1 ]]Indicating the proportion of the uplink time slot allocated to the ith marine subscriber to the total time slots allocated to that subscriber.
Given the complexity of the offshore communication environment, it is desirable to model the communication channels between shore-based base stations and aerial base stations, and between aerial base stations and offshore users, respectively. First, for a transmission link between a shore-based base station and an aerial base station, since obstacles and shelters generally exist around the shore-based base station, which may affect transmission quality, a channel model based on probability statistics is often used for analysis. Specifically, the radio wave transmission can be divided into two cases of line-of-sight transmission and non-line-of-sight transmission (NLOS), and the occurrence probability of the line-of-sight transmission is related to the angle between the transmission link and the horizontal plane, and can be approximately expressed as a Sigmoid function as follows:
PLoS(ψ)＝1/{1+a×exp[-b×(ψ-a)]} (2-1)
wherein, a and b are environmental parameters of the probability of line-of-sight communication, and ψ is the transmission elevation angle of the shore-based base station, namely:
ψ＝arcsin(hU/d1)(°) (2-2)
wherein,
the european distance between the shore-based base station and the air relay base station is represented, and the probability of occurrence of non-line-of-sight transmission is represented by PNLo S (ψ) ═ 1-PLo S (ψ), and it can be found that the larger the angle ψ between the transmission link and the horizontal plane is, the larger the probability of occurrence of line-of-sight transmission is, but shortening the transmission distance and increasing the deployment height to increase the angle ψ may cause an increase in the link path loss between the air relay base station and the offshore user, and therefore, it is necessary to determine the optimal deployment position by an optimization means.
In this model, the path loss of line-of-sight transmission between the shore-based base station and the aerial relay base station can be expressed as:
PLLoS(d1,f1)＝FSPL(d1,f1)+ηLoS(dB) (2-3)
instead, the path loss of the non-line-of-sight transmission can be expressed as:
PLNLoS(d1,f1)＝FSPL(d1,f1)+ηNLoS(dB) (2-4)
in the embodiment of the present invention, FSPL (d, f) (d B) is used to express the free space path loss under the condition of distance d km and frequency f MHz, that is:
FSPL(d,f)＝20log10d+20log10f+32.44(dB) (2-5)
of the two types of path loss, η Lo S and η NLo S are used to represent the extra loss of line-of-sight transmission and non-line-of-sight transmission, respectively, and the specific values are related to the onshore environment. Therefore, the average path loss between the shore-based base station and the air relay base station is:
PL1(d1,f1)＝FSPL(d1,f1)+PLoS(ψ)ηLoS+PNLoS(ψ)ηNLoS(dB) (2-6)
thus, the channel capacity of the downlink between the shore-based base station and the air base station can be expressed as:
the channel capacity of the uplink may be expressed as:
whereinThe channel gain coefficient g1 is 10-PL1/10Where σ 2 is used to represent the power density of white noise in the environment.
For a transmission link between an aerial base station and a marine user, since there are almost no large obstacles and shelters on the sea surface, the transmission link can be regarded as a sight transmission link approximately, an experience-based channel model is generally adopted, and the loss can be expressed as:
PL2i＝10n log10(d2i)+ηM(f2)+ηA(θi)(dB) (2-9)
specifically, the first two terms in (2-9) represent the path loss of the air-offshore transmission link, which is generally close to the path loss in free space, where n is the path loss coefficient and has a value range of [1,3], and η M is the extra loss of the offshore transmission, which is mainly determined by the carrier frequency. The last term in (2-9) represents the antenna directivity loss caused by the deviation of the antenna pointing direction from the actual transmission link direction, and is expressed as:
ηA(θi)＝min{12×(θi/15°)2,20dB} (2-10)
thus, the downlink and uplink channel capacities between the over-the-air relay base station and the ith offshore user can be expressed as:
and
wherein g2i ═ 10-PL2i/10Is the channel gain factor on that link. In addition, because the system adopts the time division duplex mode for forwarding, the actual transmission capacity is the product of the channel capacity and the proportion of the allocated time slots. For example, the actual capacity of the uplink transmission link from the ith maritime user to the air relay base station is λ i γ iCMUi。
From the above analysis, the constraints of this problem can be summarized as the following four aspects: 1 link capacity balance; 2, balancing the uplink and downlink flow; 3 user quality of service fairness and 4 drone deployment security.
In the base station deployment configuration problem, the main Optimization variables include a base station deployment position, an antenna direction and a time slot allocation proportion, and the three are continuous variables, so that the problem is efficiently solved by adopting a Particle Swarm Optimization (PSO) based heuristic algorithm. The basic concept of the particle swarm algorithm is derived from the simulation and abstraction of the prey of the bird swarm, an individual in the bird swarm is abstracted into a feasible solution in the problem feasible domain and is called as a particle in the particle swarm, and the optimal solution of the problem is found through information interaction and iterative search of the particle in the problem feasible domain.
In the simulation, it is assumed that there are 5 marine vessel users, the distribution of which is shown in fig. 3, where the marine user coordinates at the center are (l1,0) and the marine user coordinates at the periphery are (l1 ± l2/√ 2, ± l2/√ 2). In addition, according to different environments around the shore-based base station, two conditions of sparse obstacles and dense obstacles around the shore-based base station are respectively considered, and the related parameters refer to Suburban (suban) and Urban (Urban) scenes in the corresponding international telecommunication union recommendation.
For a shore-based base station, an aerial unmanned aerial vehicle relay base station and a marine user, it is assumed that transmission power is respectively pB 10W, pU1 pU2 pU 5W pM1 pM … … pM N2W, gaussian white noise power density in the environment is σ 2-174 d B/Hz, uplink traffic occupancy of the marine user is ζ 0.1,0.3,0.5,0.7,0.9, a deployment horizontal position range of the aerial relay base station is xmin 0km, xmax 30km, ymin 10km and ymin 10km, a deployment height range is hmin 1km and hmax 10km, and an antenna pointing range is the same as a horizontal deployment position. For an air-shore link between a shore-based base station and an air relay base station, the center frequency of communication is set to be f 1-2 GHz, the bandwidth is B2-250 k Hz, for the case of sparse buildings, the extra loss η LoS is set to be 0.1dB, η NLoS is set to be 21dB, the line-of-sight communication probability model coefficients a are 4.88 and B are 0.429, and for the case of dense buildings, the extra loss η Lo S is set to be 1dB and η NLo S is set to be 20dB, the line-of-sight communication probability model coefficients a are 9.61 and B are 0.158. For an air-sea link between an air relay base station and a sea user, the center frequency of communication is set to be f 2-5.8 GHz, the bandwidth is set to be B2-250 kHz, the path loss coefficient n is set to be 1.6, and the extra loss eta M is set to be 109.8d B. In the particle swarm optimization, the number of particles S is set to 100000, the number of iterations T is set to 100, the individual learning and global learning coefficients are set to S1 to S2 to 2, the inertia coefficients for the initial and end of the iteration are set to ω 1 to 0.7, and ω T to 0.4.
The embodiment of the invention provides a position deployment and time slot allocation strategy combined optimization method of an aerial relay base station, which aims at solving the problem of aerial base station deployment configuration of an offshore relay system. Aiming at an offshore relay transmission system, the transmission characteristics of an air-offshore channel and an air-onshore channel are considered, and the time-varying characteristics of uplink and downlink services of a marine ship user are combined, so that the problem of air base station deployment is abstracted into an optimization problem based on network capacity. Aiming at the characteristics of problems, a combined particle swarm optimization algorithm of an air base station deployment position, antenna pointing and time slot allocation strategy is provided, and the balance of link capacity and uplink and downlink flows and the fairness of user services are realized. Finally, simulation tests are carried out on the deployment algorithm of the unmanned aerial vehicle relay base station facing various offshore relay scenes, simulation results show that the method has high efficiency and reliability, efficient and flexible configuration can be carried out according to actual communication environments and network services, network transmission efficiency is effectively improved, and related achievements can be used for deployment of the unmanned aerial vehicle relay base station in the offshore network and can be expanded to various scenes such as a remote buoy network and ship networking.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An offshore relay communication transmission efficiency optimization method based on an unmanned aerial vehicle network is characterized by comprising the following steps:
establishing an offshore relay system model based on an aerial base station, wherein the model comprises a shore-based base station, the aerial base station and N offshore users;
respectively modeling communication channels between a shore-based base station and an air base station and between the air base station and an offshore user, and respectively obtaining the channel capacity of a downlink between the shore-based base station and the air base station, the channel capacity of an uplink, the channel capacity of the downlink between the air base station and the ith offshore user, the channel capacity of the uplink and the downlink capacity;
acquiring the minimum transmission capacity of the offshore user and the actual deployable range (xmin, xmax), (ymin, ymax), (hmin, hmax) of the aerial base station, wherein the minimum transmission capacity and the actual deployable range (xmin, xmax), (ymin, ymax), (hmin, hmax) respectively represent the plane deployment range and the highest and lowest flight heights of the aerial base station;
and converging and iterating by using the channel capacity, the uplink and downlink capacity, the minimum transmission capacity and the basic deployment range in the air as variables through a particle swarm algorithm to obtain the optimal position of the air base station.
2. The method of claim 1, wherein the aerial base station carries a first transceiver for communicating with the shore-based base station and a second transceiver for communicating with the offshore user, and wherein the first transceiver and the second transceiver both transmit and receive using directional antennas.
3. The method of claim 1, wherein the establishing an airborne base station-based offshore relay system model comprises:
the plane coordinates of the shore-based base station are (0,0), the coordinates of the N offshore users are (xM1, yM1), … …, (xMN, yMN), the three-dimensional space coordinates of the aerial base station are (xU, yU, hU), the carrier frequencies and bandwidths adopted by the links between the shore-based base station and the aerial base station and the links between the aerial base station and the offshore users are f1, B1, f2 and B2, and the links between the aerial base station and all the N offshore users share the same channel; the link between the aerial base station and the offshore user, thetai represents the antenna pointing direction of the aerial base station and the included angle between the aerial base station and the ith offshore user, namely the antenna deflection angle, the intersection point between the antenna and the sea level is represented by (xA, yA), the antenna transmitting powers of the shore base station, the aerial base station to the offshore user and the N offshore users are pB, pU1, pU2 and pM1, … …, pMN respectively, and Zeta i is epsilon to [0,1 ∈ [0,1 ]]Represents the proportion of the upstream flow of the i-th offshore user to the total flow, and has zeta ═ zeta 1, … …, zeta N for all users]Using λ ═ λ 1, … …, λ N]And γ ═ γ 1, … …, γ N]Indicating the time slot division, and lambdai indicates the proportion of the time slot number allocated to the ith maritime user to the total time slot, wherein
And using γ i ∈ [0,1 ]]Indicating the proportion of the uplink time slot allocated to the ith marine subscriber to the total time slots allocated to that subscriber.
4. The method of claim 3, wherein the separately modeling communication channels between the shore-based base station and the aerial base station, and between the aerial base station and the offshore user comprises: for a transmission link between the shore-based base station and the aerial base station, analyzing by using a channel model based on probability statistics, wherein the line-of-sight transmission of electric waves is positively correlated with the emission elevation angle of the base station, and the probability of line-of-sight transmission is higher if the included angle between the transmission link and the horizontal plane is larger;
the path loss of line-of-sight transmission between the shore-based base station and the aerial base station may be expressed as:
PLLoS(d1,f1)＝FSPL(d1,f1)+ηLoS(dB)
instead, the path loss of the non-line-of-sight transmission can be expressed as:
PLNLoS(d1,f1)＝FSPL(d1,f1)+ηNLoS(dB)
where FSPL (d, f) (dB) represents the free space path loss at a frequency of f MHz at a distance of dkm, and η LoS and η NLoS represent the excess loss for line-of-sight and non-line-of-sight transmissions.
5. The method of claim 3, wherein the channel capacity of the downlink between the shore-based base station and the aerial base station is expressed as:
the channel capacity of the uplink may be expressed as:
where σ 2 denotes a power density of white noise in the environment, g1 denotes a channel gain coefficient, and g1 denotes a channel gain coefficient of 10-PL1/10。
6. The method of claim 3, wherein downlink and uplink channel capacities between the airborne base station and the ith offshore user are expressed as:
and
7. The method for optimizing transmission efficiency of offshore trunking communication based on drone network according to any of claims 1-6, wherein there are 5 offshore users, the coordinates of the offshore users at the center are (l1,0), the coordinates of the offshore users at the periphery are (l1 ± l2/√ 2, ± l2/√ 2), the transmission power is pB 10 ± 10W, pU1 pU2 ═ 5W and pM1 ═ … … ═ pMN ═ 2W for the shore-based base station, the aerial base station and the offshore users, respectively, the gaussian white noise power density in the environment is σ 2-174 dB/Hz, the upstream traffic ratio of the offshore users is ζ ═ 0.1,0.3,0.5,0.7,0.9, the deployment horizontal position range of the aerial base station is set to xmin ═ 0km, xmin ═ 30km, hmin ═ 10km, and hmax, the deployment horizontal position range is set to 10km and the height is set to 10km range, the antenna directivity range is set to be the same as the horizontal deployment position, the center frequency of communication is set to be f 1-2 GHz and the bandwidth is B2-250 kHz for the air-shore link between the shore-based base station and the air base station, the extra loss η LoS is set to be 0.1dB and η NLoS is set to be 21dB for the case of sparse buildings, the line-of-sight communication probability model coefficients a are 4.88 and B are 0.429, the extra loss η LoS is set to be 1dB and η NLoS is set to be 20dB for the case of dense buildings, the line-of-sight communication probability model coefficients a are 9.61 and B are 0.158, the center frequency of communication is set to be f 2-5.8 GHz and the bandwidth is B2-250, the path loss coefficient n is 1.6 and the extra loss M is 109.8dB for the air-offshore link between the air base station and the offshore user.
8. The method as claimed in claim 7, wherein in the particle swarm optimization, the number of particles S is 100000, the number of iterations T is 100, the distance l1 between the shore-based base station and the offshore user is 20km, the distance l2 between the offshore users is 1km, the optimal deployment location of the airborne base station is about (11.0,0,4.9) km, and the optimal antenna pointing direction is about (19.9,0) km, which is close to the center of the offshore user.
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