ITU-R REPORT SM 2028-1-2002 Monte Carlo simulation methodology for the use in sharing and compatibility studies between different radio services or systems《用蒙特卡洛模拟方法研究不同的无线业务或系统间的共.pdf

《ITU-R REPORT SM 2028-1-2002 Monte Carlo simulation methodology for the use in sharing and compatibility studies between different radio services or systems《用蒙特卡洛模拟方法研究不同的无线业务或系统间的共.pdf》由会员分享，可在线阅读，更多相关《ITU-R REPORT SM 2028-1-2002 Monte Carlo simulation methodology for the use in sharing and compatibility studies between different radio services or systems《用蒙特卡洛模拟方法研究不同的无线业务或系统间的共.pdf（67页珍藏版）》请在麦多课文档分享上搜索。

1、 Rep. ITU-R SM.2028-1 1 REPORT ITU-R SM.2028-1 Monte Carlo simulation methodology for the use in sharing and compatibility studies between different radio services or systems (Question ITU-R 211/1) (2001-2002) CONTENTS Page Summary 2 1 Background. 2 2 Monte Carlo simulation methodology: An overview

2、3 3 Architecture requirements 6 Annex 1 List of input parameters 10 Annex 2 Event generation engine . 13 Appendix 1 to Annex 2: Propagation model . 24 Appendix 2 to Annex 2: Power control function. 42 Appendix 3 to Annex 2: Distribution definitions 43 Appendix 4 to Annex 2: Pseudo-random number gene

3、ration . 44 Appendix 5 to Annex 2: dRSS calculation flow chart . 46 Appendix 6 to Annex 2: iRSS due to unwanted and blocking calculation 47 Appendix 7 to Annex 2: Receiver blocking 48 Appendix 8 to Annex 2: iRSS due to intermodulation 50 Appendix 9 to Annex 2: Intermodulation in the receiver 51 Appe

4、ndix 10 to Annex 2: Influence of different bandwidths 53 Appendix 11 to Annex 2: Radio cell size in a noise limited network . 57 Appendix 12 to Annex 2: Symmetric antenna pattern 58 Annex 3 Distribution evaluation engine . 59 Appendix 1 to Annex 3: Chi-squared goodness-of-fit test 61 Appendix 2 to A

5、nnex 3: Kolmogorov-Smirnov test of stability . 63 Annex 4 Interference calculation engine 63 2 Rep. ITU-R SM.2028-1 Summary In this Report background information on a Monte Carlo radio simulation methodology is given. Apart from giving general information this text also constitutes a specification f

6、or the first generation of spectrum engineering advanced Monte Carlo analysis tool (SEAMCAT) software which implements the Monte Carlo methodology applied to radiocommunication scenarios. General The problem of unwanted emissions, as a serious factor affecting the efficiency of radio spectrum use, i

7、s being treated in depth in various fora, internal and external to the European Conference of Postal and Telecommunications Administrations (CEPT). As the need to reassess the limits for unwanted emissions within Appendix 3 of the Radio Regulations (RR) is observed, it is widely recognized that a ge

8、neric method is preferable for this purpose. One of numerous reasons why generic methods are favoured is their a priori potential to treat new communication systems and technologies as they emerge. Another reason is that only a generic method can aspire to become a basis for a widely recognized anal

9、ysis tool. The Monte Carlo radio simulation tool described in this Report was developed, based on the above considerations, within the European Radiocommunication Committee (ERC) process. SEAMCAT SEAMCAT is the implementation of a Monte Carlo radio simulation model developed by the group of CEPT adm

10、inistrations, European Telecommunications Standards Institute (ETSI) members and international scientific bodies. SEAMCAT is a public object code software distributed by the CEPT European Radiocommunications Office (ERO), Copenhagen. The Web address is as follows: http./www.ero.dk The software is al

11、so available in the ITU-R software library. Further details can be provided by ERO, e-mail: eroero.dk. 1 Background In order to reassess the limits for unwanted emissions within RR Appendix 3, it is desirable to develop an analytical tool to enable us to evaluate the level of interference which woul

12、d be experienced by representative receivers. It has been agreed in the ITU-R that level of interference should be expressed in terms of the probability that reception capability of the receiver under consideration is impaired by the presence of an interferer. To arrive at this probability of interf

13、erence, statistical modelling of interference scenarios will be required and this Report describes the methodology and offers a proposal for the tool architecture. The statistical methodology described here and used for the tool development is best known as Monte Carlo technique. The term “Monte Car

14、lo” was adopted by von Neumann and Ulan during World War II, as a code-name for the secret work on solving statistical problems related to atomic Rep. ITU-R SM.2028-1 3 bomb design. Since that time, the Monte Carlo method has been used for the simulation of random processes and is based upon the pri

15、nciple of taking samples of random variables from their defined probability density functions. The method may be described as the most powerful and commonly used technique for analysing complex statistical problems. The Monte Carlo approach does not have an alternative in the development of a method

16、ology for analysing unwanted emission interference. The approach is: generic: a diversity of possible interference scenarios can be handled by a single model. flexible: the approach is very flexible, and may be easily devised in a such way as to handle the composite interference scenarios. 2 Monte C

17、arlo simulation methodology: An overview This methodology is appropriate for addressing the following items in spectrum engineering: sharing and compatibility studies between different radio systems operating in the same or adjacent frequency bands, respectively; evaluation of transmitter and receiv

18、er masks; evaluation of limits for parameters such as unwanted (spurious and out-of-band) blocking or intermodulation levels. The Monte Carlo method can address virtually all radio-interference scenarios. This flexibility is achieved by the way the parameters of the system are defined. The input for

19、m of each variable parameter (antenna pattern, radiated power, propagation path,) is its statistical distribution function. It is therefore possible to model even very complex situations by relatively simple elementary functions. A number of diverse systems can be treated, such as: broadcasting (ter

20、restrial and satellite); mobile (terrestrial and satellite); point-to-point; point-to-multipoint, etc. The principle is best explained with the following example, which considers only unwanted emissions as the interfering mechanism. In general the Monte Carlo method addresses also other effects pres

21、ent in the radio environment such as out-of-band emissions, receiver blocking and intermodulation. Some examples of applications of this methodology are: compatibility study between digital personal mobile radio (PMR) (TETRA) and GSM at 915 MHz; sharing study between FS and FSS; sharing study betwee

22、n short range devices (Bluetooth) and radio local area networks (RLANs) in the industrial, scientific and medical (ISM) band at 2.4 GHz; 4 Rep. ITU-R SM.2028-1 compatibility study for International Mobile Telecommunications-2000 (IMT-2000) and PCS1900 around 1.9 GHz; compatibility study for ultra wi

23、deband systems and other radio systems operating in these frequency bands. 2.1 Illustrative example (only unwanted emissions, most influential interferer) For interference to occur, it has been assumed that the minimum carrier-to-interference ratio, C/I, is not satisfied at the receiver input. In or

24、der to calculate the C/I experienced by the receiver, it is necessary to establish statistics of both the wanted signal and unwanted signal levels. Unwanted emissions considered in this simulation are assumed to result from active transmitters. Moreover, only spurii falling into the receiving bandwi

25、dth have been considered to contribute towards interference. For the mobile to fixed interference scenario, an example is shown in Fig. 1. Rap 2028-01Mobile radioreceive-onlymodeMobile radio,in a callMobile radio,in a call andspurious inreceiver bandwidthMobile radio, in a call andspurious in victim

26、 receiverbandwidth with lowestcoupling lossVictimreceiverWantedsignalFIGURE 1An example of interference scenario involving TV receiver and portable radiosRep. ITU-R SM.2028-1 5 Many potential mobile transmitters are illustrated. Only some of the transmitters are actively transmitting and still fewer

27、 emit unwanted energy in the victim receiver bandwidth. It is assumed that interference occurs as a result of unwanted emissions from the most influent transmitter with the lowest path loss (median propagation loss + additional attenuation variation + variation in transmit power) to the receiver. An

28、 example of Monte Carlo simulation process as applied to calculating the probability of interference due to unwanted emission is given in Fig. 2. For each trial, a random draw of the wanted signal level is made from an appropriate distribution. For a given wanted signal level, the maximum tolerable

29、unwanted level at the receiver input is derived from the receivers C/I figure. Rap 2028-02Histogram oftolerable interfererlevels Acceptable interferenceprobability for service Spurious emission levelrequired Monte Carlotrial valueSensitivitylevel Median propagation loss formost influent interferer r

30、angein given environment Maximum tolerable interfererpower from most influentinterferer for trial Distribution of wantedsignal Miscellaneouslossese.g. Wall lossesLoss distribution orunwanted signaldistributionCoverage lossdue to othermechanismsC/I Maximuminterferencelevel tolerableat receiverAntenna

31、lossesFIGURE 2An example formulation of the Monte Carlo evaluation processReceiver InterfererFor the many interferers surrounding the victim, the isolation due to position, propagation loss (including any variations and additional losses) and antenna discrimination is computed. The lowest isolation

32、determines the maximum unwanted level which may be radiated by any of the transmitters during this trial. From many trials, it is then possible to derive a histogram of the unwanted levels and for a given probability of interference, then to determine the corresponding unwanted level. By varying the

33、 values of the different input parameters to the model and given an appropriate density of interferers, it is possible to analyse a large spectra of interference scenarios. 6 Rep. ITU-R SM.2028-1 3 Architecture requirements One of the main requirements is to select such an architectural structure fo

34、r the simulation tool which would be flexible enough to accommodate analysis of composite interference scenarios in which a mixture of radio equipment sharing the same habitat and/or multiple sources of interference (e.g. out-of-band emission, spurious emission, intermodulation, .) are involved and

35、can be treated concurrently. Other requirements would be that the proposed architecture consists of modular elements and is versatile enough to allow treatment of the composite interference scenarios. The proposed Monte Carlo architecture which meets these constraints is presented in Fig. 3. The pro

36、posed architecture is basically of a sequential type and consists of four processing engines: event generation engine; distribution evaluation engine; interference calculation engine; limits evaluation engine. The schematic view of the entire tool is in Fig. 3. Rap 2028-03Limits evaluationInterferen

37、ce calculationDistribution evaluationEvent generationInterfaceSystemmanagerFIGURE 3Architecture of the simulation toolThe list of interference parameters and their relevance to one or more of the processing engines is shown in Annex 1. 3.1 Event generation engine The event generation engine (EGE) ta

38、kes the relevant parameters from the submitted interference scenario and generates information on the received signal strength (RSS) of the desired, as well as on the strength for each of the interfering, signals included in the composite interference scenario. Rep. ITU-R SM.2028-1 7 This process is

39、 repeated N times, where N is a number of trials which should be large enough to produce statistically significant results. Generated samples of the desired, as well as all interfering, signals are stored in separate data arrays of the length N. The trials on parameters being common for desired and

40、interfering radio paths are done concurrently in order to capture possible correlation between desired and interfering signals. Such an implementation will not cover those seldom cases of interference in which one interference mechanism is excited by another interference (e.g. a strong emission of t

41、he first transmitter mixes with a spurious emission of the second transmitter and produces an intermodulation type of interference). The flow chart description and detailed algorithm description for the EGE are presented in Annex 2. List of potential sources of interference to be found in a radio en

42、vironment includes: Transmitter interference phenomena: unwanted (spurious and out-of-band) emissions; wideband noise; intermodulation; adjacent channel; co-channel. Receiver interference phenomena: spurious emission. Background noise: antenna noise; man-made noise. Other receiver interference susce

43、ptibility parameters: blocking; intermodulation rejection; adjacent and co-channel rejections; spurious response rejection. All of the above sources can be classified into three generic interference mechanism categories: undesired emission, intermodulation and receiver susceptibility. Each of the ab

44、ove three categories requires a different model for physical processes being characteristic for that interfering mechanism. The man-made noise and the antenna temperature noise can be considered as an increase of the thermal noise level, decreasing thus the sensitivity of a receiver, and can be ente

45、red in the simulation when the criteria of interference is I/N (interference-to-noise ratio) or C/(I + N) (wanted signal-to-interference + noise). 8 Rep. ITU-R SM.2028-1 3.2 Distribution evaluation engine The distribution evaluation engine (DEE) takes arrays of the data generated by the EGE and proc

46、esses the data with the aim of: a) assessing whether or not the number of samples is sufficient to produce statistically stable results; b) calculating correlation between the desired signal and interfering signal data and between different types of the interfering signals (e.g. blocking vs. unwante

47、d emissions); c) calculating a known continuous distribution function, e.g. Gaussian, as the best fit to the generated distributions of the desired and interfering signal data. Items a) and c) can be achieved using well known goodness-of-fit algorithms for general distributions such as the Kolmogoro

48、v-Smirnov test. Applicability of the fit to this specific task is to be further investigated in the planned phase 2 of the development of the methodology. If DEE detects unacceptable variation in discrete distribution parameters estimated in two successive estimations using N and N + N sample sizes,

49、 the EGE is instructed to generate another N of additional samples. This test is repeated until a tolerable variation of the parameters is measured over the pre-defined number of successive tests. Three different kinds of outputs are possible from the DEE engine: data arrays of the wanted and interfering signals. This is the output in the case that a high degree of correlation is detected between the wanted and any of the interfering signals; discrete distributions of the wanted and inter