CN109067482A - Reconfigurable network channel simulation method and device towards car networking communication - Google Patents
Reconfigurable network channel simulation method and device towards car networking communication Download PDFInfo
- Publication number
- CN109067482A CN109067482A CN201810921350.XA CN201810921350A CN109067482A CN 109067482 A CN109067482 A CN 109067482A CN 201810921350 A CN201810921350 A CN 201810921350A CN 109067482 A CN109067482 A CN 109067482A
- Authority
- CN
- China
- Prior art keywords
- unit
- signal
- channel
- node
- pxi
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/391—Modelling the propagation channel
- H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a kind of reconfigurable network channel simulation method and devices towards car networking communication, the car networking channel simulation of extensive multinode is decomposed into several basic analogue units of restructural PXI (being not limited only to PXI equipment) by the present invention, each analogue unit supports two inputs, two output channel matrix decline to simulate and be superimposed, the simulator has general, flexible and restructural hardware configuration, the car networking channel simulation suitable for arbitrary number communication node.
Description
Technical field:
The present invention relates to a kind of reconfigurable network channel simulation method and devices towards car networking communication, belong to wireless communication
Transmission field is ceased, particular for the simulator and analogy method of wireless channel between multinode in car networking communication system.
Background technique:
World Health Organization's statistical data shows have every year close to 1,300,000 people and die of road traffic accident, injured by road traffic accident
Evil has become global high proportion safety problem.Car networking is intended to establish the network communicating system centered on vehicle,
It can be realized traffic intelligent management and Vehicular intelligent control, congestion in road can be effectively reduced and improve road safety.Car networking system
System mainly includes vehicle-mounted terminal equipment, cloud computing platform and Data Analysis Platform, and vehicle-mounted terminal equipment is as realization car networking
Key component, the reliability and stability of communication function be user and researcher concern emphasis.Currently, in order to assess
Communication efficiency of the vehicle-mounted terminal equipment in true environment, tester need to do a large amount of outfield actual measurement for varying environment,
It is not only time-consuming and laborious, but also find the problem and be also difficult to reappear same scene.In order to simulate true environment indoors to vehicle-mounted
The influence of terminal equipment in communication shortens the R&D cycle, reduces research and development cost, it is necessary to develop a kind of for car networking communication test
Channel simulation device.
Car networking communication mainly includes V2V (Vehicle-to-Vehicle, V2V), V2P (Vehicle-to-People)
With V2R (Vehicle-to-Road) scene.Wherein, V2V communication scenes are in random motion state due to receiving and dispatching both ends, lead
Cause radio signal propagation situation the most complicated, and V2P and V2R can regard its special case situation as.It is managed with being directed under mobile context
Go deep by modeling and measurement research, scholars have found that multipath fading, Doppler power spectra and delay profile of V2V channel etc. have
There are time-varying characteristics, thus belongs to non stationary channel.In addition, car networking is also the 5th Generation Mobile Communication System (the fifth of future
Generation, 5G) one of application scenarios, therefore combine multiple-input and multiple-output (multiple-input multiple-
Output, MIMO) the V2V Channel Modeling of technology is also one of the hot spot of current academia research.
On the other hand, car networking communication system have very strong sociability, any moment have a large amount of vehicle, pedestrian,
The communication nodes such as roadside equipment and base station collect, and entire communication link is by between complicated point-to-point communication or multiple spot
After the catenet communication system of the forms such as communication composition, traditional point-to-point channel simulator will be difficult to complete system testing to test
The work of card.Therefore, developing and combining the non-stationary scene lower network channel simulator of MIMO technology is to solve current car networking to lead to
Believe the important solutions of equipment performance test problem.
Summary of the invention:
The present invention is to provide a kind of weighing towards car networking communication to solve the above-mentioned problems of the prior art
Network forming network channel simulation method and device, the device can be under dynamic analog car networking communication scenes, Dynamic Scale different communication
Non stationary channel fading profiles between node.
The present invention adopts the following technical scheme: a kind of reconfigurable network channel simulation method towards car networking communication, packet
Include following steps:
The first step, user input communication scenes, communication node on master control server subsystem, through user interaction unit
The parameters such as number and vehicle driving trace；
Second step, GPU arithmetic element according to the parameter of input, carry out antenna performance modeling, space channel modeling, node it
Between interference signal modeling and interchannel noise modeling, and based on model generate emulation data, fixed point quantization after be stored in disk array
Unit；
Third step, by stellar chain MXI bus by the antenna performance matrix of disk array unit, spatial channel matrix, interference
Signal matrix and interchannel noise matrix are respectively transmitted in each channel matrix decomposition unit, while channel matrix decomposition unit pair
Each matrix data passes through PXI bus transfer to each signal processing unit after decomposing；
The analog signal of 4th step, each transmitting node input becomes number by the analog-to-digital conversion process of signal processing unit
Signal；
5th step, signal processing unit will input digital signal and channel data is overlapped, and obtains each subchannel output
Each digital component of signal；
Each digital component of 6th step, channel output signal obtains each communication by PXI FPGA signal pre-synthesis unit
Each component of node reception signal；
The correlated components of the same receiving node are connected to the data of FPGA resource pond signal synthesis subsystem by the 7th step
Synthesis unit is exported to each receiving node using D/A conversion unit after Data Synthesis.
Further, second step specifically generates that steps are as follows:
1) the practical communication scene that user inputs is abstracted into the scatterer of random distribution, acquisition only retains main scatterer
Simplification scene map；
2) the sending and receiving end antenna type and physical size inputted according to user obtains polarization factor matrix Q and antenna coupling
Coefficient matrix Cr、Ct；
3) according to the vehicle movement track of user's input and communication scenes type, the multipath number mean value of time varying channel is obtained
And random walk number N (t) is generated using Poisson process, the method is as follows:
Wherein, λGAnd λRIt goes out parameter for the relevant life of communication scenes；P(t；Δ t) indicates the life of each diameter in Δ t time interval
Go out probability；
4) according to vehicle and the geometry geographical relationship of speed of moving body and position is scattered, iterates to calculate vehicle and scatterer
Position vector is calculated the path delay of time τ of each diameter time-varying by position vectorn(t), path power P and is further obtainedn(t), method
It is as follows:
Wherein, rτAnd στRespectively time delay distribution and delay spread；YnFor Gaussian distributed random variable；
5) according to sending and receiving end position vector and dual-mode antenna location matrix, the Doppler phase shift of each scattering branch is obtainedWith mobile caused extra phase shiftAnd then obtain the s root transmitting antenna and u root of any two node
The fading factor of nth propagation path between receiving antenna is
Wherein, M is the scattering number of branches that the path includes；For random initial phase；
6) according to the network topology structure of sending and receiving end adjacent node, the equivalent model for obtaining receiving end interference signal is as follows,
Wherein, I is effective interfering nodes number；For independent identically distributed multiple Gauss stochastic variable；Indicate k-th of interference source signal and its at a distance from receiving end；
7) according to node device noise temperature, ambient noise and signal bandwidth, interchannel noise is modeled as additive Gaussian and is made an uproar
Sound
8) by step 1) -7) analogue simulation generate data fixed point quantization after be stored in disk array unit.
The present invention also adopts the following technical scheme that a kind of reconfigurable network channel simulation device towards car networking communication,
Subsystem is synthesized including master control server subsystem, PXI signal processing platform subsystem and FPGA resource pond signal；The master control
Server subsystem includes user interaction unit, GPU arithmetic element and disk array unit；PXI signal processing platform
System includes channel matrix decomposition unit, signal processing unit and PXI FPGA data pre-synthesis unit；The FPGA resource pond
It includes Data Synthesis unit and D/A conversion unit that signal, which synthesizes subsystem,；The output interface of the disk array unit with it is described
The input interface of channel matrix decomposition unit is connected with stellar chain MXI bus；The output interface of the channel matrix decomposition unit with
The input interface of the signal processing unit is connected with PXI bus；The output interface of the signal processing unit and the PXI
The input interface of FPGA signal pre-synthesis unit is connected with optical port；The output interface of the PXI FPGA signal pre-synthesis unit with
The input interface of the Data Synthesis unit is connected with optical port；The output interface of the Data Synthesis unit and the digital-to-analogue conversion
The input interface of unit is connected.
The invention has the following beneficial effects:
(1) the car networking channel simulation of extensive multinode is decomposed into several restructural PXI and simulated substantially by the present invention
Unit (is not limited only to PXI equipment), and each analogue unit supports two inputs, two output channel matrix decline to simulate and be superimposed, the mould
Quasi- device has general, flexible and restructural hardware configuration, the car networking channel simulation suitable for arbitrary number communication node.
(2) feature symmetrical for car networking network channel, the present invention use the framework of GPU concurrent operation, solve big
Scale multinode network Channel Modeling and the problem that parameter computational efficiency is low, real-time is poor.
(3) the characteristics of being directed to car networking communication scenes, the channel model that the present invention establishes have comprehensively considered antenna physical spy
Property, sending and receiving end random movement and a variety of factors for influencing signals and propagating of scattering scene dynamic change etc., while when the model supports
Become dynamic channel parameter, and can guarantee to export the continuity of channel fading power and phase.
Detailed description of the invention:
Fig. 1 is car networking multinode representative communication scene.
Fig. 2 is the implementation of inventive network channel simulation device.
Specific embodiment:
The present invention will be further described below with reference to the drawings.
The reconfigurable network channel simulation device that the present invention is communicated towards car networking, including master control server subsystem, PXI
Signal processing platform subsystem and FPGA resource pond signal synthesize subsystem；Master control server subsystem includes that user's interaction is single
Member, GPU arithmetic element and disk array unit；PXI signal processing platform subsystem includes channel matrix decomposition unit, at signal
Manage unit and PXI FPGA data pre-synthesis unit；It includes Data Synthesis unit and digital-to-analogue that FPGA resource pond signal, which synthesizes subsystem,
Converting unit；The input interface of the output interface of disk array unit and the channel matrix decomposition unit is with stellar chain MXI bus
It is connected；The output interface of channel matrix decomposition unit is connected with the input interface of the signal processing unit with PXI bus；Signal
The output interface of processing unit is connected with the input interface of the PXI FPGA signal pre-synthesis unit with optical port；PXI FPGA letter
The output interface of number pre-synthesis unit is connected with the input interface of the Data Synthesis unit with optical port；Data Synthesis unit it is defeated
Outgoing interface is connected with the input interface of the D/A conversion unit.
The reconfigurable network channel simulation method that the present invention is communicated towards car networking, includes the following steps:
The first step, user input communication scenes, communication node on master control server subsystem, through user interaction unit
The parameters such as number and vehicle driving trace；
Second step, GPU arithmetic element according to the parameter of input, carry out antenna performance modeling, space channel modeling, node it
Between interference signal modeling and interchannel noise modeling, and based on model generate emulation data, fixed point quantization after be stored in disk array
Unit；
Third step, by stellar chain MXI bus by the antenna performance matrix of disk array unit, spatial channel matrix, interference
Signal matrix and interchannel noise matrix are respectively transmitted in each channel matrix decomposition unit, while channel matrix decomposition unit pair
Each matrix data passes through PXI bus transfer to each signal processing unit after decomposing；
The analog signal of 4th step, each transmitting node input becomes number by the analog-to-digital conversion process of signal processing unit
Signal；
5th step, signal processing unit will input digital signal and channel data is overlapped, and obtains each subchannel output
Each digital component of signal；
Each digital component of 6th step, channel output signal obtains each communication by PXI FPGA signal pre-synthesis unit
Each component of node reception signal；
The correlated components of the same receiving node are connected to the data of FPGA resource pond signal synthesis subsystem by the 7th step
Synthesis unit is exported to each receiving node using D/A conversion unit after Data Synthesis.
Wherein, second step specifically generates that steps are as follows:
1) the practical communication scene that user inputs is abstracted into the scatterer of random distribution, acquisition only retains main scatterer
Simplification scene map；
2) the sending and receiving end antenna type and physical size inputted according to user obtains polarization factor matrix Q and antenna coupling
Coefficient matrix Cr、Ct；
3) according to the vehicle movement track of user's input and communication scenes type, the multipath number mean value of time varying channel is obtained
And random walk number N (t) is generated using Poisson process, the method is as follows:
Wherein, λGAnd λRIt goes out parameter for the relevant life of communication scenes；P(t；Δ t) indicates the life of each diameter in Δ t time interval
Go out probability；
4) according to vehicle and the geometry geographical relationship of speed of moving body and position is scattered, iterates to calculate vehicle and scatterer
Position vector is calculated the path delay of time τ of each diameter time-varying by position vectorn(t), path power P and is further obtainedn(t), method
It is as follows:
Wherein, rτAnd στRespectively time delay distribution and delay spread；YnFor Gaussian distributed random variable.
5) according to sending and receiving end position vector and dual-mode antenna location matrix, the Doppler phase shift of each scattering branch is obtainedWith mobile caused extra phase shiftAnd then obtain the s root transmitting antenna and u root of any two node
The fading factor of nth propagation path between receiving antenna is
Wherein, M is the scattering number of branches that the path includes；For random initial phase.
6) according to the network topology structure of sending and receiving end adjacent node, the equivalent model for obtaining receiving end interference signal is as follows,
Wherein, I is effective interfering nodes number；For independent identically distributed multiple Gauss stochastic variable；Indicate k-th of interference source signal and its at a distance from receiving end；
7) according to node device noise temperature, ambient noise and signal bandwidth, interchannel noise is modeled as additive Gaussian and is made an uproar
Sound
8) by step 1) -7) analogue simulation generate data fixed point quantization after be stored in disk array unit.
Consider the car networking communication system (as shown in Figure 1) that 4N two-way communication node is constituted, wherein X=[x1 x2 …
x4N]TFor the signal phasor of all transmitting nodes, Y=[y1 y2 … y4N]TFor the signal phasor of all receiving nodes, the present invention
Entire car networking network channel is modeled as
Wherein, L is the multipath number of clusters mesh of each subchannel；hij(τ, t) is j-th of hair after considering mutual coupling of antenna and polarity effect
Penetrate the channel fading matrix between node and i-th of receiving node；τlFor the path delay of time of each subchannel l diameter；J (t) and N
It (t) is respectively equivalent interference signal vector sum noise vector.On this basis, the present invention is further basic with 4 nodes
Hardware simulation unit carries out matrix decomposition, enables
X′j=[x4j-3 x4j-2 x4j-1 x4j], j=1,2 ..., N (14)
Therefore, 4 meshed network channels of each basic hardware analogue unit can be modeled as
Wherein, Y (t), J (t) and N (t) are the output, interference and noise matrix of basic hardware analogue unit, and three
Equal dimensionality reduction is 4N × 1.
To make the object, technical solutions and advantages of the present invention clearer, below to be built in W1NNER+ standard channel model
For the city microcellular communications scene of view, it is assumed that it is 2 × 2 mimo channel between 8 communication nodes and each node, in conjunction with
Attached drawing of the invention carries out clear, complete description to technical solution, and following case study on implementation is for explaining only the invention, but unlimited
Make application range of the invention.
The mimo channel for being 2 × 2 using the method for matrix decomposition of the present invention, between 8 communication nodes and each node, network
Channel model can be expressed as
It can be seen that needing 2 channel matrix decomposition lists between 8 nodes and each node for 2 × 2 mimo channel simulation
Member, 8 signal processing units, 2 PXI FPGA signal pre-synthesis units and 8 signal synthesis units and digital-to-analogue conversion list
Member, specific implementation step are as follows:
The first step, user input City scenarios, communication on master control server subsystem, through user interaction unit 1-1
The parameters such as interstitial content and vehicle driving trace；
Second step, GPU arithmetic element 1-2 carry out antenna performance modeling, space channel modeling, section according to the parameter of input
Interference signal modeling and interchannel noise modeling between point, and emulation data are generated based on model, disk is stored in after fixed point quantization
Array element 1-3, the specific steps are as follows:
1) the communication scene that user inputs is abstracted into the scatterer of random distribution, acquisition only retains main scatterer
Simplification City scenarios map；
2) the sending and receiving end antenna type and physical size inputted according to user obtains polarization factor matrix Q and antenna coupling
Coefficient matrix Cr、Ct；
3) according to the vehicle movement track of user's input and communication scene, the multipath number mean value of time varying channel is obtained
And random walk number N (t) is generated using Poisson process, the method is as follows:
Wherein, λGAnd λRIt goes out parameter for the relevant life of scene；P(t；Δ t) indicates that the life of each diameter in Δ t time interval is gone out generally
Rate.For city microcellular communications scene feature, present case takes λG=0.8, λR=0.04, time interval Δ t=100ms.
4) according to vehicle and the geometry geographical relationship of speed of moving body and position is scattered, iterates to calculate vehicle and scatterer
Position vector is calculated the path delay of time τ of each diameter time-varying by position vectorn(t), path power P and is further obtainedn(t), method
It is as follows:
Wherein, rτAnd στRespectively time delay distribution and delay spread；YnFor the Gaussian distributed random variable for meeting (0,3) N.
5) according to sending and receiving end position vector and dual-mode antenna location matrix, the Doppler phase shift of each scattering branch is obtainedWith mobile caused extra phase shiftAnd then obtain the s root transmitting antenna and u root of any two node
The fading factor of nth propagation path between receiving antenna is
Wherein, M is the scattering number of branches that the path includes；For random initial phase.Consider communication scene
Feature and hardware computational efficiency, present case take M=128,It is then obedience [0,2 π] equally distributed stochastic variable.
6) according to the network topology structure of sending and receiving end adjacent node, the equivalent model for obtaining receiving end interference signal is as follows,
Wherein, I is effective interfering nodes number, when interfering signal power is higher than maximum interference source power in present case
When 5%, it is believed that it is effectively；For independent identically distributed multiple Gauss stochastic variable CN (0,1)；Indicate k-th of interference source signal and its at a distance from receiving end；
7) according to node device noise temperature, ambient noise and signal bandwidth, interchannel noise is modeled as additive Gaussian and is made an uproar
Sound
8) by step 1) -7) analogue simulation generate data fixed point quantization after be stored in disk array unit 1-3.
Third step, by stellar chain MXI bus by the antenna performance matrix of disk array unit 1-3, spatial channel matrix, dry
It disturbs signal matrix and interchannel noise matrix is respectively transmitted in each channel matrix decomposition unit 1-4, while channel matrix decomposition
Unit 1-4 passes through PXI bus transfer to each signal processing unit 1-5 after decomposing to each matrix data；
The analog signal of 4th step, each transmitting node input is become by the analog-to-digital conversion process of signal processing unit 1-5
Digital signal；
5th step, signal processing unit 1-5 will input digital signal and channel data is overlapped, and it is defeated to obtain each subchannel
Each digital component of signal out；
Each digital component of 6th step, channel output signal obtains respectively by PXI FPGA signal pre-synthesis unit 1-6
Each component of communication node reception signal；
The correlated components of the same receiving node are connected to the data of FPGA resource pond signal synthesis subsystem by the 7th step
Synthesis unit 1-7 is exported to each receiving node using D/A conversion unit 1-8 after Data Synthesis.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
It for member, can also make several improvements without departing from the principle of the present invention, these improvement also should be regarded as of the invention
Protection scope.
Claims (3)
1. a kind of reconfigurable network channel simulation method towards car networking communication, characterized by the following steps:
The first step, user input communication scenes, communication node number on master control server subsystem, through user interaction unit
With the parameters such as vehicle driving trace；
Second step, GPU arithmetic element carry out antenna performance modeling, space channel models, does between node according to the parameter of input
Signal modeling and interchannel noise modeling are disturbed, and emulation data are generated based on model, is stored in disk array unit after fixed point quantization；
Third step, by stellar chain MXI bus by the antenna performance matrix of disk array unit, spatial channel matrix, interference signal
Matrix and interchannel noise matrix are respectively transmitted in each channel matrix decomposition unit, while channel matrix decomposition unit is to each square
Battle array data pass through PXI bus transfer to each signal processing unit after decomposing；
The analog signal of 4th step, each transmitting node input becomes number letter by the analog-to-digital conversion process of signal processing unit
Number；
5th step, signal processing unit will input digital signal and channel data is overlapped, and obtains each subchannel output signal
Each digital component；
Each digital component of 6th step, channel output signal obtains each communication node by PXI FPGA signal pre-synthesis unit
Receive each component of signal；
The correlated components of the same receiving node are connected to the Data Synthesis of FPGA resource pond signal synthesis subsystem by the 7th step
Unit is exported to each receiving node using D/A conversion unit after Data Synthesis.
2. the reconfigurable network channel simulation method as described in claim 1 towards car networking communication, it is characterised in that: second
Steps are as follows for the specific generation of step:
1) the practical communication scene that user inputs is abstracted into the scatterer of random distribution, obtains the letter for only retaining main scatterer
Change scene map；
2) the sending and receiving end antenna type and physical size inputted according to user obtains polarization factor matrix Q and the antenna coefficient of coup
Matrix Cr、Ct；
3) according to the vehicle movement track of user's input and communication scenes type, the multipath number mean value of time varying channel and benefit are obtained
Random walk number N (t) is generated with Poisson process, the method is as follows:
Wherein, λGAnd λRIt goes out parameter for the relevant life of communication scenes；P(t；Δ t) indicates that the life of each diameter in Δ t time interval is gone out generally
Rate；
4) according to the geometry geographical relationship of vehicle and scattering speed of moving body and position, the position of vehicle and scatterer is iterated to calculate
Vector is calculated the path delay of time τ of each diameter time-varying by position vectorn(t), path power P and is further obtainedn(t), the method is as follows:
Wherein, rτAnd στRespectively time delay distribution and delay spread；YnFor Gaussian distributed random variable；
5) according to sending and receiving end position vector and dual-mode antenna location matrix, the Doppler phase shift of each scattering branch is obtainedWith
Extra phase shift caused by mobileAnd then obtain the s root transmitting antenna and u root receiving antenna of any two node
Between the fading factor of nth propagation path be
Wherein, M is the scattering number of branches that the path includes；For random initial phase；
6) according to the network topology structure of sending and receiving end adjacent node, the equivalent model for obtaining receiving end interference signal is as follows,
Wherein, I is effective interfering nodes number；For independent identically distributed multiple Gauss stochastic variable；Indicate k-th of interference source signal and its at a distance from receiving end；
7) according to node device noise temperature, ambient noise and signal bandwidth, interchannel noise is modeled as additive Gaussian noise
8) by step 1) -7) analogue simulation generate data fixed point quantization after be stored in disk array unit.
3. a kind of reconfigurable network channel simulation device towards car networking communication, it is characterised in that: including master control server
System, PXI signal processing platform subsystem and FPGA resource pond signal synthesize subsystem；The master control server subsystem includes
User interaction unit (1-1), GPU arithmetic element (1-2) and disk array unit (1-3)；The PXI signal processing platform subsystem
System includes channel matrix decomposition unit (1-4), signal processing unit (1-5) and PXI FPGA data pre-synthesis unit (1-6)；Institute
Stating FPGA resource pond signal synthesis subsystem includes Data Synthesis unit (1-7) and D/A conversion unit (1-8)；The disk battle array
The output interface of column unit (1-3) is connected with the input interface of the channel matrix decomposition unit (1-4) with stellar chain MXI bus；
The output interface of the channel matrix decomposition unit (1-4) and the input interface of the signal processing unit (1-5) are with PXI bus
It is connected；The output interface of the signal processing unit (1-5) and the input of the PXI FPGA signal pre-synthesis unit (1-6) connect
Mouth is connected with optical port；The output interface of the PXI FPGA signal pre-synthesis unit (1-6) and the Data Synthesis unit (1-7)
Input interface with optical port be connected；The output interface of the Data Synthesis unit (1-7) and the D/A conversion unit (1-8)
Input interface is connected.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810921350.XA CN109067482B (en) | 2018-08-14 | 2018-08-14 | Reconfigurable network channel simulation method and device for Internet of vehicles communication |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810921350.XA CN109067482B (en) | 2018-08-14 | 2018-08-14 | Reconfigurable network channel simulation method and device for Internet of vehicles communication |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109067482A true CN109067482A (en) | 2018-12-21 |
| CN109067482B CN109067482B (en) | 2021-02-26 |
Family
ID=64678386
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810921350.XA Active CN109067482B (en) | 2018-08-14 | 2018-08-14 | Reconfigurable network channel simulation method and device for Internet of vehicles communication |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109067482B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109901559A (en) * | 2019-03-29 | 2019-06-18 | 北京经纬恒润科技有限公司 | A kind of T-BOX test macro and method |
| CN110855385A (en) * | 2019-11-27 | 2020-02-28 | 深圳格林帕科技有限公司 | Hard simulation equipment aiming at dynamic wireless environment of mobile equipment of Internet of things |
| CN111211820A (en) * | 2019-09-09 | 2020-05-29 | 南京航空航天大学 | Vehicle-mounted communication equipment testing device and method for Internet of vehicles |
| CN111327473A (en) * | 2020-02-26 | 2020-06-23 | 腾讯科技（深圳）有限公司 | Network regulation and control method, device, network regulation and control system and electronic equipment |
| CN111669726A (en) * | 2020-06-03 | 2020-09-15 | 上海无线通信研究中心 | Deterministic channel simulator and channel simulation method for Internet of vehicles |
| CN113612559A (en) * | 2021-09-07 | 2021-11-05 | 南京航空航天大学 | Reconfigurable channel fading simulation device and fading twinning method thereof |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105574239A (en) * | 2015-12-11 | 2016-05-11 | 北京交通大学 | Calculating method of radar cross section of combined type scatterer |
| CN106059685A (en) * | 2016-05-17 | 2016-10-26 | 南京航空航天大学 | Large scale MIMO channel simulation apparatus under time evolution and simulation method thereof |
| CN107579789A (en) * | 2017-07-21 | 2018-01-12 | 南京航空航天大学 | Extensive unmanned plane junction network channel simulation device and GPU real-time emulation methods |
-
2018
- 2018-08-14 CN CN201810921350.XA patent/CN109067482B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105574239A (en) * | 2015-12-11 | 2016-05-11 | 北京交通大学 | Calculating method of radar cross section of combined type scatterer |
| CN106059685A (en) * | 2016-05-17 | 2016-10-26 | 南京航空航天大学 | Large scale MIMO channel simulation apparatus under time evolution and simulation method thereof |
| CN107579789A (en) * | 2017-07-21 | 2018-01-12 | 南京航空航天大学 | Extensive unmanned plane junction network channel simulation device and GPU real-time emulation methods |
Non-Patent Citations (3)
| Title |
|---|
| QIUMING ZHU等: "A Novel 3D non-stationary vehicle-to-vehicle channel model and its spatial-temporal correlation properties", 《IEEE ACCESS》 * |
| QIUMING ZHU等: "A Novel 3D Non-Stationary Wireless MIMO Channel Simulator and Hardware Emulator", 《IEEE TRANSACTIONS ON COMMUNICATIONS》 * |
| 闭宇铭: "非平稳无线信道建模及其仿真技术研究", 《中国博士学位论文全文数据库信息科技辑》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109901559A (en) * | 2019-03-29 | 2019-06-18 | 北京经纬恒润科技有限公司 | A kind of T-BOX test macro and method |
| CN109901559B (en) * | 2019-03-29 | 2021-02-23 | 北京经纬恒润科技股份有限公司 | T-BOX test system and method |
| CN111211820A (en) * | 2019-09-09 | 2020-05-29 | 南京航空航天大学 | Vehicle-mounted communication equipment testing device and method for Internet of vehicles |
| CN111211820B (en) * | 2019-09-09 | 2021-10-08 | 南京航空航天大学 | Vehicle-mounted communication equipment testing device and method for Internet of vehicles |
| CN110855385A (en) * | 2019-11-27 | 2020-02-28 | 深圳格林帕科技有限公司 | Hard simulation equipment aiming at dynamic wireless environment of mobile equipment of Internet of things |
| CN111327473A (en) * | 2020-02-26 | 2020-06-23 | 腾讯科技（深圳）有限公司 | Network regulation and control method, device, network regulation and control system and electronic equipment |
| CN111327473B (en) * | 2020-02-26 | 2021-03-19 | 腾讯科技（深圳）有限公司 | Network regulation and control method, device, network regulation and control system and electronic equipment |
| CN111669726A (en) * | 2020-06-03 | 2020-09-15 | 上海无线通信研究中心 | Deterministic channel simulator and channel simulation method for Internet of vehicles |
| CN113612559A (en) * | 2021-09-07 | 2021-11-05 | 南京航空航天大学 | Reconfigurable channel fading simulation device and fading twinning method thereof |
| CN113612559B (en) * | 2021-09-07 | 2022-05-03 | 南京航空航天大学 | Reconfigurable channel fading simulation device and fading twinning method thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109067482B (en) | 2021-02-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109067482A (en) | Reconfigurable network channel simulation method and device towards car networking communication | |
| Yuan et al. | 3D wideband non-stationary geometry-based stochastic models for non-isotropic MIMO vehicle-to-vehicle channels | |
| Zhu et al. | A novel 3D non-stationary wireless MIMO channel simulator and hardware emulator | |
| CN106483512B (en) | A kind of general multichannel distributed object analogue echoes method and if system | |
| CN103532644B (en) | Multi-path shadow compound fading channel simulation device and work method thereof | |
| Zhu et al. | 3D non‐stationary geometry‐based multi‐input multi‐output channel model for UAV‐ground communication systems | |
| He et al. | A dynamic wideband directional channel model for vehicle-to-vehicle communications | |
| CN109412673B (en) | Real-time simulation method of geometric random channel model for unmanned aerial vehicle communication | |
| CN111211820B (en) | Vehicle-mounted communication equipment testing device and method for Internet of vehicles | |
| CN107579789B (en) | Large-scale unmanned aerial vehicle relay network channel simulation device and GPU real-time simulation method | |
| CN111245480B (en) | Double-cluster geometric channel modeling method based on three-dimensional space | |
| CN104865844B (en) | A kind of Information Network comprehensive assessment demenstration method based on distributed platform | |
| Zhou et al. | Channel modeling for future high-speed railway communication systems: A survey | |
| Xiong et al. | Novel multi-mobility V2X channel model in the presence of randomly moving clusters | |
| CN105978647B (en) | A kind of modeling of three-dimensional double-movement MIMO propagation channels and emulation mode | |
| Yang et al. | Machine-learning-based scenario identification using channel characteristics in intelligent vehicular communications | |
| Yan et al. | Channel characterization for vehicle-to-infrastructure communications in millimeter-wave band | |
| Zhou et al. | Graph‐based stochastic model for high‐speed railway cutting scenarios | |
| CN110417498A (en) | A kind of method for building up of the virtual broadband geometry channel for three-dimensional space | |
| Bian et al. | A 3-D non-stationary wideband MIMO channel model allowing for velocity variations of the mobile station | |
| CN106059685B (en) | The extensive mimo channel simulator of time evolution and its analogy method | |
| Sun et al. | Channel modeling by RBF neural networks for 5G mm-wave communication | |
| CN114499721A (en) | Multi-scattering cluster vehicle-to-vehicle channel modeling method under tunnel scene | |
| CN104581750B (en) | A kind of opportunistic network can the construction method of computation model | |
| Liu et al. | Analyzing V2I Channel and Spatial Consistency through Simulation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |