1、 * STD.CEPT ERC REPORT b-ENGL 2000 = t32bqL4 00Lb725 1i52 ERC REPORT 68 European Radiocommunications Committee (ERC) within the European Conference of Postal and Telecommunications Administrations (CEPT) MONTE CARLO RADIO SIMULATION METHODOLOGY Naples, February 2000 STD.CEPT ERC REPORT 68-ENGL 2000
2、W 23264L4 00Lb726 O99 W * ERC REPORT 68 Copyright 2000 the European Ccnference of Postai and Telecommunications Administrations (CEPT) INTRODUCTION In this report background information on a Monte-Carlo Radio Simulation methodology is given. Apart from giving general information this text also const
3、itutes a specification for the first generation of SEAMCAT software which implements the Monte-Carlo methodology applied to radicommunication scenarios. 1 GENERAL REMARKS The problem of unwanted emissions, as a serious factor affecting the efficacy of radio spectrum use, is being treated in depth in
4、 various fora, internal and external to the CEPT. As the need to reassess the limits for unwanted emissions within RR-Appendix 8 is observed, it is widely recognised that a generic method is preferable for this purpose. One of numerous reasons why generic methods are favoured is their a priori poten
5、tial to treat new communication systems and technologies as they emerge. Other reason is that only generic method can aspire to become a basis for a widely recognised analysis tool. The Monte-Carlo Radio Simulation tool described in this Report was developed, based on above considerations, within th
6、e ERC process. 2 SEAMCAT SEAMCAT Radio Tool is the implementation of a Monte-Carlo Radio Simulation tool managed by the group of CEPT Administrations, ETSI members and international scientific bodies. SEAMCAT is public domain software distributed by the CEPT European Radiocommunications Office, Cope
7、nhagen. - STD-CEPT ERC REPORT bB-ENGL 2000 M 23ZbYLY 00lgb728 Yb1 ERC REPORT 68 Page 2 INDEX TABLE Introduction Background Monte-Carlo Simulation Technique: An Overview Architecture requirements List of parameters Event Generation Engine Propagation model Power control function Distribution definiti
8、ons Pseudo-random number generation dRSS calculation flow chart iRSS due to unwanted and blocking calculation Receiver Blocking iRSS due to intermodulation Intermodulation in the Receiver Influence of different bandwidths Distribution Evaluation Engine Chi-Squared Goodness-of-Fit Test Interference C
9、alculation Engine 1 3 3 6 11 17 29 35 36 37 38 39 40 42 43 45 59 52 53 . ERC REPORT 68 Page 3 Monte Carlo Radio Compatibility Tool 1. Background In order to reassess the limits for unwanted emissions within RR - Appendix 8, it is desirable to develop an analytical tool to enable us to evaluate the l
10、evel of interference which would be experienced by representative receivers. It has been agreed in the TG-1/3 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 arri
11、ve at this probability of interference, statistical modelling of interference scenarios will be required and this paper 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
12、 technique. The term “Monte-Carlo“ was adopted by von Neumann and Ulan during World War II, as a codename for the secret work on solving statistical problems related to atomic bomb design. Since that time, the Monte-Carlo method has been used for the simulation of random processes and is based upon
13、the principle 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. Monte Carlo approach is seen not to have an alternative in development of a m
14、ethodology for analysing unwanted emission interference. The approach is: 0 generic - A diversity of possible interference scenarios can be handled by single model. flexible - The approach is very flexible, and may be easily devised in a such way to handle the composite interference scenarios. 2. Mo
15、nte-Carlo Simulation Technique: An Overview The Monte Carlo method can address virtually ali radio-interference scenarios. This flexibility is achieved by the way the parameters of the system are defined. The input form of each variable parameter (antenna pattern, radiated power, propagation path,.
16、. .) is its statistical distribution function. It is therefore possible to model even very complex situations by relatively simple elementary functions. Number of diverse systems can be treated, such as 0 broadcasting (terrestrial and satellite) 0 mobile (terrestrial and satellite) point to point 0
17、point to multipoint etc. The principle is best explained on a following example, which considers only unwanted emissions as the interfering mechanism. In general the Monte Carlo method addresses also other effects present in the radio environment such as out of band emissions, receiver blocking and
18、intermodulation. STDDCEPT ERC REPORT 68ENGL 2000 2326434 0036730 SIT ERC REPORT 68 Page 4 2.1 Illustrative example (only un wanted emissions, most influent interferer). For interference to occur, it has been assumed that the minimum C/i is not satisfied at the receiver input. In order to calculate t
19、he C/I experienced by the receiver, it is necessary to establish 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 bandwidth have been considered to cont
20、ribute towards interference. For the mobile to fixed interference scenario, an example is shown in figure 2.1. Many potential mobile transmitters are illustrated. Only some of the transmitters are actively transmitting and still fewer emit unwanted energy in the victim receiver bandwidth. It is assu
21、med 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 example of Monte-Carlo simulation process as applied to calculati
22、ng probability of interference due to unwanted emission is given in figure 2.2. 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 unwanted level at the receiver input is derived from the receivers
23、 CA figure. For 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 determines the maximum unwanted level which may be radiated by any of the transmi
24、tters 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 values of the different input parameters to the model and given an appropriate d
25、ensity of interferers, it is possible to analyse a large spectra of interference scenarios. ERC REPORT 68 Page 5 Mobile radio. in a call a spurnS in vidim R bandmdth wim hesl coupling loss Mobile radio. in a call a spunouS in rx bandwih Mobile radio. in a call Mobile radio, receive-mly de FIGURE 2.1
26、 An example of interference scenario involving TV receiver and portable radios. Distribution of wanted Receiver Coverage Loss due to other mechanisms Loss distribution or unwanted signal QI c Median Propagation Loss for distribution MiSc. Maximum Losses Interference e.g. wd at Receiver Level tolleta
27、bli! Loss- Maximum tollerable interfew powerfmm most influent I Interferer interferer for triai FIGURE 2.2 An example formulation of the Monte-Carlo evaluation process. - STDmCEPT ERC REPORT hB-ENGL 2000 232641il 0016732 392 , ERC REPORT 68 Page 6 3. Architecture requirements One of the main require
28、ments is to select such an architectural structure for 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, spu
29、rious emission, intermodulation ) are involved and can be treated concurrently. Other requirements would be that the proposed architecture lend itself for modular development and is versatile enough to allow treatment of the composite interference scenarios. The proposed Monte Carlo architecture whi
30、ch meets these constraints is presented in Fig. 3.1. The proposed architecture is basically of a sequential type and consists of four processing engines: event generation engine dis tribu tion evaluation engine interference calculation engine limits evaluation engine The schematic view on the entire
31、 tool is on Figure 3.1. w V l! c; Y E-EVENT GENERATION -1 t D-DISTRIBUTION EVALUATION , I 4 C-INTEWERENCE CALCULATION J L-LIMITS EVALUATION STD.CEPT ERC REPORT 68-ENGL 2000 = 232h414 00Lb733 229 EKC REPORT 68 Page 7 The list of interference parameters and their relevance to one or more of the proces
32、sing engines is shown in Appendix A. 3.1 Event Generation Engine The event Eeneration engine (EGE) takes the relevant parameters from the submitted interference scenario and generates information on the received signal strength of the desired as well as on the strength for each of the interfering si
33、gnals included in the composite interference scenario. This process is 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 and all interfering signals are stored in separate data arrays of the len
34、gth N. The trials on parameters being common for desired and interfering radio paths are done concurrently in order to capture possible correlation between desired and interfering signals. Such an implementation will not cover only those seldom cases of interference in which one interference mechani
35、sm is excited by another interference (e.g. a strong transmitter mixes with spurious emission of the second transmitter and .produce an intermodulation type of interference). The flow chart description and detailed algorithm description for the EGE are presented in the ANNEX B. List of potential sou
36、rces of interference to be found in a radio habitat includes: Transmitter interference phenomena: e spurious emissions e wideband Noise e intermodulation b adjacent Channel e Co-channel Receiver interference phenomena: b spurious radiation B ackgr o u nd noise : e antenna noise e man-made noise Othe
37、r receiver interference susceptibility parameters: e blocking in termodulatio n reject ion b adjacent and Co-channel rejections b spurious response rejection b Ali of the above sources can be classified into three generic interference mechanism categories: undesired emission, intermodulation and rec
38、eiver susceptibility. Each of the above three categories requires a different model for physical processes being characteristic for that STD-CEPT ERC REPORT bB-ENGL 2000 232b4L4 0016734 Lb5 EHC REPORT 68 Page 8 interfering mechanism. The man-made noise and the antenna temperature noise can be consid
39、ered as an increase of the thermal noise level, decreasing thus the sensitivity of a receiver, and can be entered in the simulation when the criteria of interference is I/N or C/I+N. 3.2 Distribution Evaluation Engine The distribution evaluation engine DEE takes arrays of the data generated by the E
40、GE and processes the data with aims of: assessing whether or not the number of samples is sufficient to produce statistically stable results calculating correlation between the desired signal and interfering signal data and between different types of the interfering signal (e.g. blocking vs. Unwante
41、d emissions) calculating a known “continuous“ distribution function as the best fit to the RSS random variable. First and well known goodness-of-fit algorithms for general distributions such as the Kolmogorov-Smirnov test. Applicability of the fit to this specific task is to be further investigated
42、when the first generation of software is available. third of the above points can be achieved using If DEE detect unacceptable variation in discrete distribution parameters estimated in two successive estimations using N and N+AN sample size, the EGE is instructed to generate another AN of additiona
43、l samples. This test is repeated until a tolerable variation of the parameters is measured over the pre defined number of successive tests. Three different kind of outputs are possible fi-om the DEE engine: data arrays of the wanted and interfering signals. This is the output in the case that a high
44、 degree of correlation is detected between the wanted and any of the interfering signals. 0 discrete distributions of the wanted and interfering signals are passed in the case of a weak correlation between the signals or in the case that there was no correlation between the signals but no continuos
45、distribution approximation with satisfactory accuracy was possible. continuous distribution functions of the wanted and interfering signals are passed to ICE in the case that signals were decorelated and discrete distributions were successfully approximated with continuos distribution functions The
46、proposed flow chart and detailed algorithm specification is presented in ANNEX C. 3.3 Interference Calculation Engine The interference calculation engine ICE is the heart of the proposed architecture. Here, information gathered by the EGE and processed by DEE are used to calculate probability of ERC
47、 REPORT 68 Page 9 interference. Depending on which kind of information was passed from DEE to ICE three possible modes of calculating the probability of interference are identified, as shown in ANNEX D. Mode 1: data arrays for dRSS and inRSS passed by DEE to the ICE, and vector representing the comp
48、osite interfering signal I is calculated as a sum of the inRSS data vectors. Mode 2: distribution function for the composite interfering signal is calculated by taking random samples for inRSS distributions and linearly adding them up. Mode 3: The IRSS is calculated using numerical or analytical int
49、egration of the supplied distribution functions for each of the interference sources. Mode 4: All signals are assumed to be mutually independent and the overall probability for interference is identrfied as the probability to be disturbed by at least one kind of interference. Different criteria for calculation of interference probability can be accommodated within the processing engine. A cumulative probability functions can be calculated for C/N+I or N/N+I random variables. The flow of information together with associated processes is shown in form of a flo
