CN107579789A - Extensive unmanned plane junction network channel simulation device and GPU real-time emulation methods - Google Patents

Extensive unmanned plane junction network channel simulation device and GPU real-time emulation methods Download PDF

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CN107579789A
CN107579789A CN201710600190.4A CN201710600190A CN107579789A CN 107579789 A CN107579789 A CN 107579789A CN 201710600190 A CN201710600190 A CN 201710600190A CN 107579789 A CN107579789 A CN 107579789A
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CN107579789B (en
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朱秋明
胡续俊
方竹
陈小敏
江凯丽
祝梦卿
杨婧文
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Nanjing University of Aeronautics and Astronautics
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Abstract

The present invention discloses a kind of extensive unmanned plane junction network channel simulation device and GPU real-time emulation methods, wherein extensive unmanned plane junction network channel simulation device includes network node dynamic topology parameter input unit, network channel parameter estimation unit, network channel modeling and generation unit, network channel combination superpositing unit, network node transmission signal input block and network node reception signal output unit.Analogue system of the present invention is agile and all-purpose, supports network size and topological structure dynamic to adjust；Signal, interference and the noise of each repeated link are modeled using equivalent method, enormously simplify the complexity of system emulation；For different communication scene, different nodes are supported using different channel models and real-time update, consider interfering between network node.

Description

Extensive unmanned plane junction network channel simulation device and GPU real-time emulation methods
Technical field：
The present invention relates to a kind of extensive unmanned plane junction network channel simulation device and GPU real-time emulation methods, belong to Wireless information transfer field, particular for following extensive unmanned plane relay network system, each communication node and via node it Between wireless channel simulating device, and using GPU platform real-time emulation method..
Background technology：
Wireless channel directly affects the transmission quality and performance of wireless communication system as electromagnetic transmission medium.Nothing The modeling and generation of line channel refer to establish a channel model being consistent with actual propagation environment, and pass through computer software Emulation or hardware simulation accurately and effectively reduce its characteristic of channel, and it is for optimization design, assessment and validation of wireless communication system It is most important, while can also effectively shorten the system research and development cycle.
Unmanned plane (Unmanned Aerial Vehicle, UAV) is born in nineteen twenties, by early stage science and technology Horizontal limitation, Development of UAV is relatively slow before nineteen seventies.Hereafter, with communication, microelectronics, new material And the fast development of the science and technology such as aero-engine, have become research heat of the various countries on military or even civilian now Point.Military Application scope is not limited solely to aerial reconnaissance and combat and the traditional field such as assesses, be also widely used for dog fight, The military and police tasks such as attack, interception and anti-smuggling, anti-terrorism；At civilian aspect, unmanned plane, which can perform, takes photo by plane, is aerial survey, distant The tasks such as sense, environmental protection, disaster alarm and assessment, in recent years, part express company are also carrying out unmanned plane express delivery experiment.In addition, Radio mobile self-organizing network (Wireless Mobile Ad-Hoc Networks, MANET) based on unmanned plane relaying can be with Independent of any static infrastructure, have the advantages that networking fast and flexible, broad covered area, reliability are high and survivability is strong, Fig. 1 gives the MANET typical case scenes based on unmanned plane relaying.In Afghan War and the war in Iraq, unmanned plane As aerial trunking traffic station, the effect of partial information network node is assume responsibility for, they not only can be the letter of enemy's ground target Breath sends the ground combat troop of one's own side and aerial opportunity of combat to, can also realize ground forces, sky by the airborne equipment of itself Being in communication with each other between middle opportunity of combat and general headquarters.
However, UAV Communication environment is complicated, wireless signal can be by the factor shadow such as landform, atural object and atmospheric precipitation Ring, converted plus itself flight attitude, cause the quick random fading of reception signal, so as to cause whole communication network transmission It can decline.In order to ensure that unmanned plane can between communicating pair, control centre or other aircraft in flight course Order or the data information transfer of high quality are carried out, the channel transfer characteristic of unmanned plane junction network must just be carried out deep Research and analysis.Meanwhile the actual measurement difficulty of unmanned plane junction network channel is big, and usually require a large amount of unmanned planes of batch production For disposing junction network, the cost for causing communication equipment to be tested is high, cycle length, and efficiency is low.Therefore, how in ground experiment room Unmanned plane junction network channel propagation characteristics are simulated under environment, and then complete the emulation testing of UAV Communication system just It is particularly important.
In moving communicating field, the channel simulator of existing commercialization external at present, such as the Propsim of Yi Lai bit companies C8, it can support the channel model that the 3GPP such as geometrical model (SCM), SCM extended models (SCME) recommend, and highest can be supported 12500km/h doppler spread, maximum delay expand to 6.4ms, can meet the mould of mobile telecommunication channel under most of scene Intend testing requirement；The broadband channel simulator CS8007 of AEROFLEX companies, can emulate Doppler frequency and Doppler upshift degree, Passband amplitude, phase distortion and various declines can be simulated, while accurate white Gauss noise and phase noise can also be superimposed；Think The SR5500 of Bo Lun companies can be directed to the advanced receivers with diversity Wave beam forming and MIMO, accurately the complicated width of emulation Band radio channel characteristic, its modularization framework can provide multiple combinations mode, can realize the mimo channel test of complexity.Business Can realize conventional channel fading model with the channel simulator of change, as Rayleigh fading, Lay this decline, Nakagami decline, it is right Number shadow fading and Suzuki fading models, however, such simulator is typically just for cellular mobile communication occasion, and often Only consider that single statistical model is realized, the requirement of large-scale network node communication channel simulation can not be met.
The real-time Simulation operand of large-scale network node communication channel is very big and requirement of real-time is high, if hardware is realized, Consumed hardware resource is very big, causes to be difficult to；Realize that arithmetic speed is slower, can not according to software of the tradition based on PC Meet requirement of real-time.In recent years, image processor (Graphic Processing Unit, GPU) is with more than Moore's Law Speed is developing, and CPU is compared considerably beyond CPU, bandwidth of memory ability due to its computing capability of more processing units be present Also there is obvious advantage.GPU early stages are mainly used in image rendering, with increasing substantially for computing capability, more and more It is applied in general-purpose computations, such as oil exploration, biomedicine, weather forecast, hydrodynamics, marine environment simulation, the earth The fields such as science, financial analysis, big data processing and artificial intelligence, are also used for the application study of the communication of algorithms in recent years.
The content of the invention：
The present invention is to provide a kind of extensive unmanned plane junction network to solve the above-mentioned problems of the prior art Channel simulation device and GPU real-time emulation methods, suitable for extensive unmanned plane relaying and the real-time testing of apparatus for network node With checking field.
The present invention also adopts the following technical scheme that：A kind of extensive unmanned plane junction network channel simulation device, including net Network node dynamic topology parameter input unit, network channel parameter estimation unit, network channel modeling and generation unit, network letter Road combination superpositing unit, network node transmission signal input block and network node reception signal output unit；
The network node dynamic topology parameter input unit is connected with network channel parameter estimation unit, defeated for user Enter each node communication scenes parameter of network；
The network channel parameter estimation unit is used for each node in network node dynamic topology parameter input unit Communication scenes parameter is converted into the model parameter of each node channel of unmanned plane junction network, then will calculate gained according to it is discrete when Between priority carry out framing, and network based on GPU parallel computations is transferred to according to network channel state renewal interval successively and believed Road models and generation unit；
The network channel modeling and generation unit include ground launch node signal model, unmanned plane relay forwarding node Receipt signal model, unmanned plane relay reception node receipt signal model, ground receiver node signal model, ground node interference Signal model, ground node noise model, each sub- letter of network for the every frame being calculated according to network channel parameter estimation unit Road model parameter is sequentially generated unmanned plane junction network channel by model above, and by output data by PCIE buses successively The network channel combination superpositing unit being transferred in FPGA；
The network channel combination superpositing unit models network channel and generation unit produces unmanned plane junction network letter The baseband signal that the network node transmission signal input block that road decline is added in FPGA inputs, and it is sent to the net in FPGA Network node reception signal output unit；
The network node transmission signal input block converts the medium-frequency IF of input or radio frequency rf signal by down coversion For complex baseband signal, and it is transferred to network channel combination superpositing unit；
The network node reception signal output unit is by network channel combination superpositing unit input by unmanned plane Complex baseband signal after network channel is converted into intermediate frequency by up-conversion or radiofrequency signal exports.
The present invention also adopts the following technical scheme that：A kind of GPU of extensive unmanned plane junction network channel simulation device is real When emulation mode, comprise the following steps：
The first step, user input communication scenes parameter, communication scenes by network node dynamic topology parameter input unit Parameter is sent to network channel parameter estimation unit；
Second step, network channel parameter estimation unit calculate each node of unmanned plane junction network according to user's input parameter and believed The model parameter in road, it then will calculate gained and carry out framing according to the priority of discrete time, and updated according to network channel state Interval is transferred to network channel modeling and generation unit successively；
Every frame network of 3rd step, network channel modeling and the input of generation unit network channel parameter estimation unit is each Subchannel model parameter, establish unmanned plane relay forwarding node receipt signal model, unmanned plane relay reception node reception signal Model, ground node receipt signal model, ground node interference signal model and ground node noise model；
4th step, using above-mentioned model, the data for producing each bar unmanned plane junction network propagation channel are simulated by GPU, And data are transferred to network channel in FPGA by PCIE buses successively and combine superpositing unit, at the same time, intermediate frequency or penetrate Frequency signal is input to the network node transmission signal input block in FPGA, is converted into baseband signal by down coversion, and transmit Superpositing unit is combined to network channel；
5th step, network channel combination superpositing unit simulation unmanned plane junction network channel additive process, by network channel Modeling and generation unit produce unmanned plane junction network channel be added to network node transmission signal input block input base band Signal, and the network node reception signal output unit being sent in FPGA, will be through by network node reception signal output unit The baseband signal crossed after channel is converted into intermediate frequency by up-conversion or radiofrequency signal exports.
Further, in step 3：Ground launch node N1, by way of N number of unmanned plane via node R1~RN, eventually arrive at Ground receiver node N2The reception signal of a trunking traffic link be modeled as following equivalent model
In formula,Represent unmanned plane via node RiRepeater gain； Air-ground, vacant lot and unmanned plane via node R are represented respectivelyi-1With RiBetween transmitting signal loss factor, be by its value
α=32.44+20lg (fMHz)+20lg(dkm) (13)
Wherein, fMHzRepresent communication frequency, unit MHz；dkmExpression communication distance, unit km,Represent it is air-ground and The link cascade decline of two sections of vacant lot, is modeled as a stochastic variable, and corresponding probability density distribution is
In formula, mi,msi, i=1,2 embody respectively it is air-ground with the multipath fading of vacant lot link and shadow fading severe journey Degree；Air-ground and vacant lot link channel fading mean power is represented respectively；
Respectively the equivalent model with noise will be disturbed to be designated asWherein noise modeling is additive Gaussian noise, Following equivalent model is modeled as,
In formula, M represents interference source number；dk, k=1 ..., M represents k-th of interference source and N2Distance；Pk, k= 1 ..., M represents kth road interfering signal power；Kth road interference channel and interference source are represented respectively Signal, obey independent identically distributed multiple Gauss distribution CN (0,1)；L (d) represents large scale decay function, is represented by
In formula, Bl~B (1, pl) represent to obey the stochastic variable of Bernoulli distributions, wherein, plRepresent interference source and N2 The probability of los path be present；Ll~log (0, σl) and Ln~log (0, σn) represent to obey the stochastic variable of logarithm normal distribution, Wherein, σl,σnLos path and shadow fading degree under obstructed path are represented respectively；αl,αnWith βl,βnSighting distance road is represented respectively Footpath and the path loss index and intercept under obstructed path.
Further, in step 4：
The specific production method that channel fading, interference and noise are produced using GPU is as follows：
1) first pass through following method and produce Gaussian random process
Wherein, N expressions can not resolved scatter number of branches；σ2Represent variance；ωi,n=2 π fi,dcosαi,n, wherein, fi,d =f0/ vc represents maximum doppler frequency, f0, v, c correspond to carrier frequency, sending and receiving end relative moving speed and the light velocity respectively；αi,n, φi,nRefer to the incidence angle and initial phase of each scattering branch road respectively, by incident angle αi,nBe arranged to [0,2 π) in value at equal intervals, Initial phase φi,nBe arranged to [0,2 π) in be uniformly distributed at random, the characteristics of using GPU parallel computations, produce gaussian random mistake Journey；
2) one group of Gaussian random process u caused by step 1) method is utilizedi,0(t)~N (0,1), carry out nonlinear transformation and obtain To the random process for representing shadow fading, i.e.,
In formula, σx,μxThe respectively standard deviation and regional average value of shadow fading；
3) multigroup Gaussian random process is produced using step 1) method, carries out nonlinear transformation, generation represents multipath fading Random process, i.e.,
Wherein, ui(t)~N (0, σ2) and σ2=Ω/2m, Ω=E [x2] represent multipath fading mean power；M represents decline The factor, cause the fading severity of signal for describing different scattering environments；
4) repeat step 2) and generation process 3), and the equivalent decline for obeying formula (3) distribution is obtained using following formula,
Wherein,WithAir-ground and shadow fading and multipath of vacant lot link are represented respectively The random process of decline；
5) scene inputted according to user, obtain interference source and produce probability λGWith mean numberProfit is counted with the following method The instantaneous number for obtaining the i-th moment interference source is calculated,
Wherein, Br~B (1, Pr) be one obey Bernoulli distribution stochastic variable, PrRepresent each interference source in Δ t The probability of interior survival,
Wherein, Represent ground receiver node N2Speed；PF,Institute is represented respectively There are the average probability and average speed that interference source moves；DcRepresent coherence distance；
6) each parameter is calculated respectively and l (d are calculated using formula (5)k), k=1 ..., M, then pass through formula (6) Produce 4M road Gaussian random process uk(t)~N (0,0.5), k=1,2 ..., 4M, and then obtain M interference source signal；
7) two-way Gaussian random process u is produced by formula (6)i(t)~N (0, σ2), i=1,2, wherein σ2=PN/ 2, PN Multiple noise power is represented, two-way corresponds to the real and imaginary parts of multiple noise respectively.
The present invention has the advantages that：
(1) analogue system is agile and all-purpose, supports network size and topological structure dynamic to adjust；
(2) signal of each repeated link, interference and noise are modeled using equivalent method, and the system of enormously simplify is imitated Genuine complexity；
(3) different communication scene is directed to, supports different nodes using different channel models and real-time update, to consider network Interfering between node.
Brief description of the drawings：
Fig. 1 is the MANET typical case scenes based on unmanned plane relaying.
Fig. 2 is unmanned plane junction network communication environments modeling scheme.
Fig. 3 is the Gaussian random process real-time Simulation based on GPU.
Fig. 4 is the channel fading real-time Simulation based on GPU.
Embodiment：
The present invention is further illustrated below in conjunction with the accompanying drawings.
The present invention discloses a kind of extensive unmanned plane junction network channel simulation device, including network node dynamic topology ginseng Number input block 1-1, network channel parameter estimation unit 1-2, network channel modeling and generation unit 1-3, network channel combination Superpositing unit 1-4, network node transmission signal input block 1-5 and network node reception signal output unit 1-6.
Wherein network node dynamic topology parameter input unit 1-1 is connected with network channel parameter estimation unit 1-2, is used for User inputs the scenario parameters of each node communication of network, main including initial position of the ground node with unmanned plane relaying and movement Speed, communication environments parameter, noise parameter etc..
Wherein network channel parameter estimation unit 1-2 is used in network node dynamic topology parameter input unit 1-1 Node communication scenes parameter is converted into the model parameter of each node channel of unmanned plane junction network, including time delay, path loss, the moon The parameters such as shadow decline, multipath fading, noise power, it then will calculate gained and carry out framing, and root according to the priority of discrete time It is transferred to network channel modeling and generation unit 1-3 based on GPU parallel computations successively according to network channel state renewal interval.
Wherein network channel modeling and generation unit 1-3 include ground launch node signal model, unmanned plane relay forwarding Node receipt signal model, unmanned plane relay reception node receipt signal model, ground receiver node signal model, ground node Interference signal model, ground node noise model, according to the network of the network channel parameter estimation unit 1-2 every frames being calculated Each subchannel model parameter is sequentially generated unmanned plane junction network channel by model above, and output data is total by PCIE Line is transferred to the network channel combination superpositing unit 1-4 in FPGA successively.
Wherein network channel combination superpositing unit 1-4 models network channel and generation unit 1-3 produces unmanned plane relaying The baseband signal that the network node transmission signal input block 1-5 that network channel decline is added in FPGA is inputted, and be sent to Network node reception signal output unit 1-6 in FPGA.
The intermediate frequency (IF) of input or radio frequency (RF) signal are passed through lower change by wherein network node transmission signal input block 1-5 Frequency is converted into complex baseband signal, and is transferred to network channel combination superpositing unit 1-4.
Wherein network node reception signal output unit 1-6 combines network channel the process nothing of superpositing unit 1-4 inputs Complex baseband signal after man-machine junction network channel is converted into intermediate frequency by up-conversion or radiofrequency signal exports.
The GPU real-time emulation methods of extensive unmanned plane junction network channel simulation device of the invention, comprise the following steps：
The first step, user input the parameters such as communication scenes by network node dynamic topology parameter input unit 1-1, mainly Initial position and translational speed, communication environments parameter, noise parameter including ground node and unmanned plane relaying etc., these parameters It is sent to network channel parameter estimation unit 1-2.
Second step, network channel parameter estimation unit 1-2 calculate unmanned plane junction network according to user's input parameter and respectively saved The model parameter of point channel, mainly including the channel parameters such as path loss, shadow fading, multipath fading, and time delay, noise work( The parameters such as rate, it then will calculate gained and carry out framing according to the priority of discrete time, and updated and be spaced according to network channel state Network channel modeling and generation unit 1-3 are transferred to successively.
Every frame that 3rd step, network channel modeling and generation unit 1-3 input according to network channel parameter estimation unit 1-2 Each subchannel model parameter of network, establish unmanned plane relay forwarding node receipt signal model, unmanned plane relay reception node connects Receive signal model, ground node receipt signal model, ground node interference signal model and ground node noise model.
Without loss of generality, with ground launch node N1, by way of N number of unmanned plane via node R1~RN, eventually arrive at ground and connect Receive node N2A trunking traffic link exemplified by, illustrate the modeling method of receiving end signal, interference and noise：
1) reception signal of trunking traffic link is modeled as following equivalent model by this patent,
In formula,Represent unmanned plane via node RiRepeater gain； Air-ground, vacant lot and unmanned plane via node R are represented respectivelyi-1With RiBetween transmitting signal loss factor, its value is by this case
α=32.44+20lg (fMHz)+20lg(dkm) (24)
Wherein, fMHzRepresent communication frequency, unit MHz；dkmRepresent communication distance, unit km.Represent it is air-ground and The link cascade decline of two sections of vacant lot, this case are modeled as a stochastic variable, and corresponding probability density distribution is
In formula, mi,msi, i=1,2 embody respectively it is air-ground with the multipath fading of vacant lot link and shadow fading severe journey Degree；Air-ground and vacant lot link channel fading mean power is represented respectively.
2) this patent will disturb the equivalent model with noise to be designated as respectivelyWherein noise modeling is additive Gaussian Noise,Following equivalent model is modeled as,
In formula, M represents interference source number；dk, k=1 ..., M represents k-th of interference source and N2Distance；Pk, k= 1 ..., M represents kth road interfering signal power；Kth road interference channel and interference source are represented respectively Signal, obey independent identically distributed multiple Gauss distribution CN (0,1)；L (d) represents large scale decay function, is represented by
In formula, Bl~B (1, pl) represent to obey the stochastic variable of Bernoulli distributions, wherein, plRepresent interference source and N2 The probability of los path be present；Ll~log (0, σl) and Ln~log (0, σn) represent to obey the stochastic variable of logarithm normal distribution, Wherein, σl,σnLos path and shadow fading degree under obstructed path are represented respectively；αl,αnWith βl,βnSighting distance road is represented respectively Footpath and the path loss index and intercept under obstructed path.
4th step, using above-mentioned equivalent model, pass through the number of each bar unmanned plane junction network propagation channel of GPU simulation generations According to, and the network channel that data are transferred in FPGA successively by PCIE buses combines superpositing unit 1-4.At the same time, in Frequency or radiofrequency signal are input to the network node transmission signal input block 1-5 in FPGA, and being converted into base band by down coversion believes Number, and it is transferred to network channel combination superpositing unit 1-4.Wherein, the specific of channel fading, interference and noise is produced using GPU Production method is as follows：
1) first pass through following method and produce Gaussian random process
Wherein, N expressions can not resolved scatter number of branches；σ2Represent variance；ωi,n=2 π fi,dcosαi,n, wherein, fi,d =f0V/c represents maximum doppler frequency, f0, v, c correspond to carrier frequency, sending and receiving end relative moving speed and the light velocity respectively；αi,n, φi,nRefer to the incidence angle and initial phase of each scattering branch road respectively, this patent is by incident angle αi,nBe arranged to [0,2 π) in wait between Every value, initial phase φi,nBe arranged to [0,2 π) in be uniformly distributed at random.It is the characteristics of using GPU parallel computations, specific to produce The scheme of Gaussian random process is as shown in Figure 3.It is 32 that circuitry number is scattered in this patent, and each thread block distributes 32x8 thread, Each thread beam carries out correlation computations to each scattering branch road of synchronization, and is finally carrying out stipulations summation, and then obtains high This random process.
2) one group of Gaussian random process u caused by step 1) method is utilizedi,0(t)~N (0,1), carry out nonlinear transformation and obtain To the random process for representing shadow fading, i.e.,
In formula, σx,μxThe respectively standard deviation and regional average value of shadow fading.
3) multigroup Gaussian random process is produced using step 1) method, carries out nonlinear transformation, generation represents multipath fading Random process, i.e.,
Wherein, ui(t)~N (0, σ2) and σ2=Ω/2m, Ω=E [x2] represent multipath fading mean power；M represents decline The factor, cause the fading severity of signal for describing different scattering environments.
4) repeat step 2) and generation process 3), and the equivalent decline for obeying formula (25) distribution is obtained using following formula,
Wherein,WithAir-ground and shadow fading and multipath of vacant lot link are represented respectively The random process of decline.This case utilizes the characteristics of GPU parallel computations, specifically produces process such as Fig. 4 institutes of the equivalent channel fading Show, 2m+1 roads Gaussian random process is first produced in figure, and the shade for obtaining representing earth-to-space link after nonlinear change respectively declines Fall the random process with multipath fading, then the channel fading random process of earth-to-space link obtained by the two multiplication, then according to Same method obtains the channel fading random process of vacant lot link, and is multiplied to obtain with the channel fading random process of earth-to-space link Cascade decline random process.
5) scene inputted according to user, obtain interference source and produce probability λGWith mean numberProfit is counted with the following method The instantaneous number for obtaining the i-th moment interference source is calculated,
Wherein, Br~B (1, Pr) be one obey Bernoulli distribution stochastic variable, PrRepresent each interference source in Δ t The probability of interior survival,
Wherein, Represent ground receiver node N2Speed；PF,Institute is represented respectively There are the average probability and average speed that interference source moves；DcRepresent coherence distance.
6) each parameter is calculated respectively and l (d are calculated using formula (27)k), k=1 ..., M, then pass through formula (28) and Fig. 3 methods produce 4M road Gaussian random process uk(t)~N (0,0.5), k=1,2 ..., 4M, and then obtain M and do Disturb source signal.
7) two-way Gaussian random process u is produced by formula (28) and Fig. 3 methodsi(t)~N (0, σ2), i=1,2, wherein σ2=PN/ 2, PNMultiple noise power is represented, two-way corresponds to the real and imaginary parts of multiple noise respectively.
5th step, network channel combination superpositing unit 1-4 simulation unmanned plane junction network channel additive processes, network is believed Road models and generation unit 1-3 generation unmanned plane junction network channels are added to, and network node transmission signal input block 1-5 is defeated The baseband signal entered, and the network node reception signal output unit 1-6 being sent in FPGA, it is defeated by network node reception signal Go out unit 1-6 and the baseband signal after channel is converted into intermediate frequency or radiofrequency signal output by up-conversion.
Described above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art For member, some improvement can also be made under the premise without departing from the principles of the invention, and these improvement also should be regarded as the present invention's Protection domain.

Claims (4)

  1. A kind of 1. extensive unmanned plane junction network channel simulation device, it is characterised in that：Join including network node dynamic topology Number input block (1-1), network channel parameter estimation unit (1-2), network channel modeling and generation unit (1-3), network letter Road combination superpositing unit (1-4), network node transmission signal input block (1-5) and network node reception signal output unit (1-6)；
    The network node dynamic topology parameter input unit (1-1) is connected with network channel parameter estimation unit (1-2), is used for User inputs each node communication scenes parameter of network；
    The network channel parameter estimation unit (1-2) is used in network node dynamic topology parameter input unit (1-1) Each node communication scenes parameter is converted into the model parameter of each node channel of unmanned plane junction network, then will calculate gained according to The priority of discrete time carries out framing, and is transferred to successively based on GPU parallel computations according to network channel state renewal interval Network channel models and generation unit (1-3)；
    The network channel modeling and generation unit (1-3) include ground launch node signal model, unmanned plane relay forwarding section Point receipt signal model, unmanned plane relay reception node receipt signal model, ground receiver node signal model, ground node are done Signal model, ground node noise model are disturbed, the network for the every frame being calculated according to network channel parameter estimation unit (1-2) Each subchannel model parameter is sequentially generated unmanned plane junction network channel by model above, and output data is total by PCIE Line is transferred to the network channel combination superpositing unit (1-4) in FPGA successively；
    The network channel combination superpositing unit (1-4) models network channel and generation unit (1-3) produces unmanned plane relaying The baseband signal that the network node transmission signal input block (1-5) that network channel decline is added in FPGA inputs, and send Network node reception signal output unit (1-6) into FPGA；
    The network node transmission signal input block (1-5) converts the medium-frequency IF of input or radio frequency rf signal by down coversion For complex baseband signal, and it is transferred to network channel combination superpositing unit (1-4)；
    The network node reception signal output unit (1-6) combines network channel the process nothing of superpositing unit (1-4) input Complex baseband signal after man-machine junction network channel is converted into intermediate frequency by up-conversion or radiofrequency signal exports.
  2. A kind of 2. GPU real-time emulation methods of extensive unmanned plane junction network channel simulation device, it is characterised in that：Including such as Lower step：
    The first step, user input communication scenes parameter, communication scenes by network node dynamic topology parameter input unit (1-1) Parameter is sent to network channel parameter estimation unit (1-2)；
    Second step, network channel parameter estimation unit (1-2) calculate each node of unmanned plane junction network according to user's input parameter The model parameter of channel, it then will calculate gained and carry out framing according to the priority of discrete time, and according to network channel state more New interval is transferred to network channel modeling and generation unit (1-3) successively；
    Every frame that 3rd step, network channel modeling and generation unit (1-3) input according to network channel parameter estimation unit (1-2) Each subchannel model parameter of network, establish unmanned plane relay forwarding node receipt signal model, unmanned plane relay reception node connects Receive signal model, ground node receipt signal model, ground node interference signal model and ground node noise model；
    4th step, using above-mentioned model, the data for producing each bar unmanned plane junction network propagation channel are simulated by GPU, and will Data are transferred to network channel in FPGA by PCIE buses and combine superpositing unit (1-4) successively, at the same time, intermediate frequency or penetrate Frequency signal is input to the network node transmission signal input block (1-5) in FPGA, and baseband signal is converted into by down coversion, and It is transferred to network channel combination superpositing unit (1-4)；
    5th step, network channel combination superpositing unit (1-4) simulation unmanned plane junction network channel additive process, by network channel Modeling and generation unit (1-3) produce unmanned plane junction network channel and are added to network node transmission signal input block (1-5) The baseband signal of input, and the network node reception signal output unit (1-6) being sent in FPGA, received and believed by network node The baseband signal after channel is converted into intermediate frequency by up-conversion for number output unit (1-6) or radiofrequency signal exports.
  3. 3. the GPU real-time emulation methods of extensive unmanned plane junction network channel simulation device as claimed in claim 2, it is special Sign is：In step 3：Ground launch node N1, by way of N number of unmanned plane via node R1~RN, eventually arrive at ground receiver section Point N2The reception signal of a trunking traffic link be modeled as following equivalent model
    <mrow> <mover> <mi>y</mi> <mo>~</mo> </mover> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msqrt> <mrow> <msubsup> <mi>&amp;alpha;</mi> <msub> <mi>N</mi> <mn>1</mn> </msub> <msub> <mi>R</mi> <mn>1</mn> </msub> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </msqrt> <msqrt> <mrow> <msubsup> <mi>&amp;alpha;</mi> <msub> <mi>R</mi> <mi>N</mi> </msub> <msub> <mi>N</mi> <mn>2</mn> </msub> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </msqrt> <munderover> <mo>&amp;Pi;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>2</mn> </mrow> <mi>N</mi> </munderover> <msqrt> <mrow> <msubsup> <mi>&amp;alpha;</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> <mi>i</mi> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </msqrt> <munderover> <mo>&amp;Pi;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msubsup> <mi>A</mi> <mi>F</mi> <msub> <mi>R</mi> <mi>i</mi> </msub> </msubsup> <mover> <mi>h</mi> <mo>~</mo> </mover> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mover> <mi>x</mi> <mo>~</mo> </mover> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
    In formula,Represent unmanned plane via node RiRepeater gain； Air-ground, vacant lot and unmanned plane via node R are represented respectivelyi-1With RiBetween transmitting signal loss factor, be by its value
    α=32.44+20lg (fMHz)+20lg(dkm) (2)
    Wherein, fMHzRepresent communication frequency, unit MHz；dkmExpression communication distance, unit km,Represent air-ground and vacant lot Two sections of link cascade declines, are modeled as a stochastic variable, and corresponding probability density distribution is
    <mrow> <msub> <mi>f</mi> <mi>&amp;alpha;</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>2</mn> <mrow> <mi>x</mi> <mi>&amp;Gamma;</mi> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mi>&amp;Gamma;</mi> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>s</mi> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mi>&amp;Gamma;</mi> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mi>&amp;Gamma;</mi> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>s</mi> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> </mrow> </mfrac> <msubsup> <mi>G</mi> <mrow> <mn>0</mn> <mo>,</mo> <mn>4</mn> </mrow> <mrow> <mn>4</mn> <mo>,</mo> <mn>0</mn> </mrow> </msubsup> <mrow> <mo>(</mo> <mrow> <mfrac> <mrow> <msub> <mi>m</mi> <mn>1</mn> </msub> <msub> <mi>m</mi> <mrow> <mi>s</mi> <mn>1</mn> </mrow> </msub> <msub> <mi>m</mi> <mn>2</mn> </msub> <msub> <mi>m</mi> <mrow> <mi>s</mi> <mn>2</mn> </mrow> </msub> </mrow> <mrow> <msub> <mi>&amp;Omega;</mi> <mrow> <mi>s</mi> <mn>1</mn> </mrow> </msub> <msub> <mi>&amp;Omega;</mi> <mrow> <mi>s</mi> <mn>2</mn> </mrow> </msub> </mrow> </mfrac> <msup> <mi>x</mi> <mn>2</mn> </msup> <msubsup> <mo>|</mo> <mrow> <msub> <mi>m</mi> <mrow> <mi>s</mi> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>m</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>m</mi> <mrow> <mi>s</mi> <mn>2</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>m</mi> <mn>2</mn> </msub> </mrow> <mo>-</mo> </msubsup> </mrow> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow>
    In formula, mi,msi, i=1,2 embody respectively it is air-ground with the multipath fading of vacant lot link and shadow fading severe degree；Air-ground and vacant lot link channel fading mean power is represented respectively；
    Respectively the equivalent model with noise will be disturbed to be designated asWherein noise modeling is additive Gaussian noise,Modeling For following equivalent model,
    <mrow> <mover> <mi>i</mi> <mo>~</mo> </mover> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <msqrt> <mrow> <msub> <mi>P</mi> <mi>k</mi> </msub> <mi>l</mi> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> </mrow> </msqrt> <msub> <mover> <mi>h</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <msub> <mover> <mi>u</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>
    In formula, M represents interference source number；dk, k=1 ..., M represents k-th of interference source and N2Distance；Pk, k=1 ..., M Represent kth road interfering signal power；Kth road interference channel and interference source signal, clothes are represented respectively From independent identically distributed multiple Gauss distribution CN (0,1)；L (d) represents large scale decay function, is represented by
    <mrow> <mi>l</mi> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>B</mi> <mi>l</mi> </msub> <msub> <mi>L</mi> <mi>l</mi> </msub> <msub> <mi>&amp;beta;</mi> <mi>l</mi> </msub> <msup> <mi>d</mi> <mrow> <mo>-</mo> <msub> <mi>&amp;alpha;</mi> <mi>l</mi> </msub> </mrow> </msup> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msub> <mi>B</mi> <mi>l</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>L</mi> <mi>n</mi> </msub> <msub> <mi>&amp;beta;</mi> <mi>n</mi> </msub> <msup> <mi>d</mi> <mrow> <mo>-</mo> <msub> <mi>&amp;alpha;</mi> <mi>n</mi> </msub> </mrow> </msup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow>
    In formula, Bl~B (1, pl) represent to obey the stochastic variable of Bernoulli distributions, wherein, plRepresent interference source and N2In the presence of regarding Probability away from path；Ll~log (0, σl) and Ln~log (0, σn) represent to obey the stochastic variable of logarithm normal distribution, wherein, σl,σnLos path and shadow fading degree under obstructed path are represented respectively；αl,αnWith βl,βnRespectively represent los path with Path loss index and intercept under obstructed path.
  4. 4. the GPU real-time emulation methods of extensive unmanned plane junction network channel simulation device as claimed in claim 3, it is special Sign is：In step 4：
    The specific production method that channel fading, interference and noise are produced using GPU is as follows：
    1) first pass through following method and produce Gaussian random process
    <mrow> <msub> <mi>u</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msqrt> <mfrac> <mrow> <mn>2</mn> <msup> <mi>&amp;sigma;</mi> <mn>2</mn> </msup> </mrow> <mi>N</mi> </mfrac> </msqrt> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mi>c</mi> <mi>o</mi> <mi>s</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;omega;</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>n</mi> </mrow> </msub> <mi>t</mi> <mo>+</mo> <msub> <mi>&amp;phi;</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow>
    Wherein, N expressions can not resolved scatter number of branches；σ2Represent variance；ωi,n=2 π fi,dcosαi,n, wherein, fi,d=f0v/ C represents maximum doppler frequency, f0, v, c correspond to carrier frequency, sending and receiving end relative moving speed and the light velocity respectively；αi,n,φi,n Refer to the incidence angle and initial phase of each scattering branch road respectively, by incident angle αi,nBe arranged to [0,2 π) in value at equal intervals, initially Phasei,nBe arranged to [0,2 π) in be uniformly distributed at random, the characteristics of using GPU parallel computations, produce Gaussian random process；
    2) one group of Gaussian random process u caused by step 1) method is utilizedi,0(t)~N (0,1), carry out nonlinear transformation and obtain generation The random process of table shadow fading, i.e.,
    <mrow> <mi>&amp;beta;</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mi>e</mi> <mrow> <msub> <mi>&amp;sigma;</mi> <mi>x</mi> </msub> <msub> <mi>u</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>&amp;mu;</mi> <mi>x</mi> </msub> </mrow> </msup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow>
    In formula, σx,μxThe respectively standard deviation and regional average value of shadow fading；
    3) produce multigroup Gaussian random process using step 1) method, carry out nonlinear transformation, produce represent multipath fading with Machine process, i.e.,
    <mrow> <mi>&amp;gamma;</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msqrt> <mrow> <msubsup> <mi>u</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>1</mn> </mrow> <mn>2</mn> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msubsup> <mi>u</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>2</mn> </mrow> <mn>2</mn> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mn>...</mn> <mo>+</mo> <msubsup> <mi>u</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>2</mn> <mi>m</mi> </mrow> <mn>2</mn> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </msqrt> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow>
    Wherein, ui(t)~N (0, σ2) and σ2=Ω/2m, Ω=E [x2] represent multipath fading mean power；M represents fading factor, Cause the fading severity of signal for describing different scattering environments；
    4) repeat step 2) and generation process 3), and the equivalent decline for obeying formula (3) distribution is obtained using following formula,
    <mrow> <mover> <mi>h</mi> <mo>~</mo> </mover> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msqrt> <mrow> <msubsup> <mi>&amp;beta;</mi> <msub> <mi>N</mi> <mn>1</mn> </msub> <msub> <mi>R</mi> <mn>1</mn> </msub> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </msqrt> <msubsup> <mi>&amp;gamma;</mi> <msub> <mi>N</mi> <mn>1</mn> </msub> <msub> <mi>R</mi> <mn>1</mn> </msub> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <msqrt> <mrow> <msubsup> <mi>&amp;beta;</mi> <msub> <mi>R</mi> <mi>N</mi> </msub> <msub> <mi>N</mi> <mn>2</mn> </msub> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </msqrt> <msubsup> <mi>&amp;gamma;</mi> <msub> <mi>R</mi> <mi>N</mi> </msub> <msub> <mi>N</mi> <mn>2</mn> </msub> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow>
    Wherein,WithAir-ground and shadow fading and multipath fading of vacant lot link are represented respectively Random process；
    5) scene inputted according to user, obtain interference source and produce probability λGWith mean numberProfit calculates obtain with the following method The instantaneous number of the i-th moment interference source is obtained,
    <mrow> <msub> <mi>M</mi> <mi>i</mi> </msub> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mover> <mi>M</mi> <mo>&amp;OverBar;</mo> </mover> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>i</mi> <mo>=</mo> <mn>0</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <munderover> <mi>&amp;Sigma;</mi> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> </munderover> <msub> <mi>B</mi> <mi>r</mi> </msub> <mo>+</mo> <munderover> <mi>&amp;Sigma;</mi> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mn>2</mn> <mover> <mi>M</mi> <mo>&amp;OverBar;</mo> </mover> <mo>-</mo> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> </mrow> </munderover> <mrow> <mo>(</mo> <mrow> <mn>1</mn> <mo>-</mo> <msub> <mi>B</mi> <mi>r</mi> </msub> </mrow> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>...</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> </mrow>
    Wherein, Br~B (1, Pr) be one obey Bernoulli distribution stochastic variable, PrRepresent each interference source in Δ t internal memories Probability living,
    <mrow> <msub> <mi>P</mi> <mi>r</mi> </msub> <mo>=</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <mfrac> <mrow> <msub> <mi>&amp;lambda;</mi> <mi>R</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>&amp;delta;</mi> <mi>R</mi> </msub> <mo>+</mo> <msub> <mi>&amp;delta;</mi> <mi>I</mi> </msub> <mo>)</mo> </mrow> </mrow> <msub> <mi>D</mi> <mi>c</mi> </msub> </mfrac> </mrow> </msup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow>
    Wherein, Represent ground receiver node N2Speed； Represent all dry respectively Disturb the average probability and average speed of source movement；DcRepresent coherence distance；
    6) each parameter is calculated respectively and l (d are calculated using formula (5)k), k=1 ..., M, then produced by formula (6) 4M roads Gaussian random process uk(t)~N (0,0.5), k=1,2 ..., 4M, and then obtain M interference source signal；
    7) two-way Gaussian random process u is produced by formula (6)i(t)~N (0, σ2), i=1,2, wherein σ2=PN/ 2, PNRepresent Multiple noise power, two-way correspond to the real and imaginary parts of multiple noise respectively.
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CN113517921A (en) * 2021-07-05 2021-10-19 河海大学 IRS (unmanned aerial vehicle) -assisted low-altitude passive air relay control method based on unmanned aerial vehicle
CN113655468A (en) * 2021-07-16 2021-11-16 广州大学 Unmanned aerial vehicle auxiliary positioning method and system, storage medium and terminal equipment
CN113655468B (en) * 2021-07-16 2023-05-23 广州大学 Unmanned aerial vehicle auxiliary positioning method, unmanned aerial vehicle auxiliary positioning system, storage medium and terminal equipment
CN113612560A (en) * 2021-09-16 2021-11-05 西安交通大学 Unmanned aerial vehicle channel simulation method, device and system for three-dimensional MIMO channel simulation
CN114268397A (en) * 2021-12-09 2022-04-01 重庆邮电大学 Mountain terrain-based unmanned aerial vehicle air-to-air channel modeling method
CN114268397B (en) * 2021-12-09 2023-06-20 重庆邮电大学 Unmanned aerial vehicle air-air channel modeling method based on mountain terrain

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