通信工程外文翻译---蜂窝无线通信系统的仿真

SIMULATION OF A CELLULAR RADIO SYSTEM———taken from《Prentice Hall - Principles Of Communication SystemsSimulation With Wireless Aplications》page672-6761 . IntroductionA wide variety of wireless communication systems have been developed to provide access to the communications infrastructure for mobile or fixed users in a myriad of operating environments. Most of today’s wireless systems are based on the cellular radio concept. Cellular communication systems allow a large number of mobile users to seamlessly and simultaneously communicate to wireless modems at fixed base stations using a limited amount of radio frequency (RF) spectrum. The RF transmissions received at the base stations from each mobile are translated to baseband, or to a wideband microwave link, and relayed to mobile switching centers (MSC), which connect the mobile transmissions with the Public Switched Telephone Network (PSTN). Similarly, communications from the PSTN are sent to the base station, where they are transmitted to the mobile. Cellular systems employ either frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), or spatial division multiple access (SDMA) .Wireless communication links experience hostile physical channel characteristics, such as time-varying multipath and shadowing due to large objects in the propagation path. In addition, the performance of wireless cellular systems tends to be limited by interference from other users, and for that reason, it is important to have accurate techniques for modeling interference. These complex channel conditions are difficult to describe with a simple analytical model, although several models do provide analytical tractability with reasonable agreement to measured channel data . However, even when the channel is modeled in an analytically elegant manner, in the vast majority of situations it is still difficult or impossible to construct analytical solutions for link performance when error control coding, equalization, diversity, and network models are factored into the link model. Simulation approaches, therefore, are usually required when analyzing the performance of cellular communication links.Like wireless links, the system performance of a cellular radio system is most effectively modeled using simulation, due to the difficulty in modeling a large number of random events over time and space. These random events, such as the location of users, the number of simultaneous users in the system, the propagation conditions, interference and power level settings of each user, and the traffic demands of each user,combine together to impact the overall performance seen by a typical user in the cellular system. The aforementioned variables are just a small sampling of the many key physical mechanisms that dictate the instantaneous performance of a particular user at any time within the system. The term cellular radio system,therefore, refers to the entire population of mobile users and base stations throughout the geographic service area, as opposed to a single link that connects a single mobile user to a single base station. To design for a particular system-level performance, such as the likelihood of a particular user having acceptable service throughout the system, it is necessary to consider the complexity of multiple users that are simultaneously using the system throughout the coverage area. Thus, simulation is needed to consider the multi-user effects upon any of the individual links between the mobile and the base station.The link performance is a small-scale phenomenon, which deals with the instantaneous changes in the channel over a small local area, or small time duration, over which the average received power is assumed constant . Such assumptions are sensible in the design of error control codes, equalizers, and other components that serve to mitigate the transient effects created by the channel. However, in order to determine the overall system performance of a large number of users spread over a wide geographic area, it is necessary to incorporate large-scale effects such as the statistical behavior of interference and signal levels experienced by individual users over large distances, while ignoring the transient channel characteristics. One may think of link-level simulation as being a vernier adjustment on the performance of a communication system, and the system-level simulation as being a coarse, yet important, approximation of the overall level of quality that any user could expect atany time.Cellular systems achieve high capacity (e.g., serve a large number of users) by allowing the mobile stations to share, or reuse a communication channel in different regions of the geographic service area. Channel reuse leads to co-channel interference among users sharing the same channel, which is recognized as one of the major limiting factors of performance and capacity of a cellular system. An appropriate understanding of the effects of co-channel interference on the capacity and performance is therefore required when deploying cellular systems, or when analyzing and designing system methodologies that mitigate the undesired effects of co-channel interference. These effects are strongly dependent on system aspects of the communication system, such as the number of users sharing the channel and their locations. Other aspects, more related to the propagation channel, such as path loss, shadow fading (or shadowing), and antenna radiation patterns are also important in the context of system performance, since these effects also vary with the locations of particular users. In this chapter, we will discuss the application of system-level simulation in the analysis of the performance of a cellular communication system under the effects of co-channel interference. We will analyze a simple multiple-user cellular system, including the antenna and propagation effects of a typical system. Despite the simplicity of the example system considered in this chapter, the analysis presented can easily be extended to include other features of a cellular system.2 Cellular Radio SystemSystem-Level Description：Cellular systems provide wireless coverage over a geographic service area by dividing the geographic area into segments called cells as shown in Figure 17.1. The available frequency spectrum is also divided into a number of channels with a group of channels assigned to each cell. Base stations located in each cell are equipped with wireless modems that can communicate with mobile users. Radio frequency channels used in the transmission direction from the base station to the mobile are referred to asforward channels, while channels used in the direction from the mobile to the base station are referred to as reverse channels. The forward and reverse channels together identify a duplex cellular channel. When frequency division duplex (FDD) is used, the forward and reverse channels are split in frequency. Alternatively, when time division duplex (TDD) is used, the forward and reverse channels are on the same frequency, but use different time slots for transmission.High-capacity cellular systems employ frequency reuse among cells. This requires that co-channel cells (cells sharing the same frequency) are sufficiently far apart from each other to mitigate co-channel interference. Channel reuse is implemented by covering the geographic service area with clusters of N cells, as shown in Figure 17.2, where N is known as the cluster size.The RF spectrum available for the geographic service area is assigned to each cluster, such that cells within a cluster do not share any channel . If M channels make up the entire spectrum available for the service area, and if the distribution of users is uniform over the service area, then each cell is assigned M/N channels. As the clusters are replicated over the service area, the reuse of channels leads to tiers of co-channel cells, and co-channel interference will result from the propagation of RF energy between co-channel base stations and mobile users. Co-channel interference in a cellular system occurs when, for example, a mobile simultaneously receives signals from the base station in its own cell, as well as from co-channel base stations in nearby cells from adjacent tiers. In this instance, one co-channel forward link (base station to mobile transmission) is the desired signal, and the other co-channel signals received by the mobile form the total co-channel interference at the receiver. The power level of the co-channel interference is closely related to the separation distances among co-channel cells. If we model the cells with a hexagonal shape, as in Figure 17.2, the minimum distance between the center of two co-channel cells, called the reuse distance ND, isR3（17.1）D N Nwhere R is the maximum radius of the cell (the hexagon is inscribed within the radius). Therefore, we can immediately see from Figure 17.2 that a small cluster size (small reuse distance ND), leads to high interference among co-channel cells.The level of co-channel interference received within a given cell is also dependent on the number of active co-channel cells at any instant of time. As mentioned before, co-channel cells are grouped into tiers with respect to a particular cell of interest. The number of co-channel cells in a given tier depends on the tier order and the geometry adopted to represent the shape of a cell (e.g., the coverage area of an individual base station). For the classic hexagonal shape, the closest co-channel cells are located in the first tier and there are six co-channel cells. The second tier consists of 12 co-channel cells, the third, 18, and so on. The totalco-channel interference is, therefore, the sum of the co-channel interference signals transmitted from all co-channel cells of all tiers. However, co-channel cells belonging to the first tier have a stronger influence on the total interference, since they are closer to the cell where the interference is measured.Co-channel interference is recognized as one of the major factors that limits the capacity and link quality of a wireless communications system and plays an important role in the tradeoff between system capacity (large-scale system issue) and link quality (small-scale issue). For example, one approach for achieving high capacity (large number of users), without increasing the bandwidth of the RF spectrum allocated to the system, is to reduce the channel reuse distance by reducing the cluster size N of a cellular system . However, reduction in the cluster sizeincreases co-channel interference, which degrades the link quality.The level of interference within a cellular system at any time is random and must be simulated by modeling both the RF propagation environment between cells and the position location of the mobile users. In addition, the traffic statistics of each user and the type of channel allocation scheme at the base stations determine the instantaneous interference level and the capacity of the system.The effects of co-channel interference can be estimated by the signal-tointerference ratio (SIR) of the communication link, defined as the ratio of the power of the desired signal S, to the power of the total interference signal, I. Since both power levels S and I are random variables due to RF propagation effects, user mobility and traffic variation, the SIR is also a random variable. Consequently, the severity of the effects of co-channel interference on system performance is frequently analyzed in terms of the system outage probability, defined in this particular case as the probability that SIR is below a given threshold 0SIR . This isdx p ]SIR Pr[SIR P )x 0SIR 0SIR 0outpage （⎰=<= （17.2）Where is the probability density function (pdf) of the SIR. Note the distinction between the definition of a link outage probability, that classifies an outage SIR(x)pbased on a particular bit error rate (BER) or Eb/N0 threshold for acceptable voice performance, and the system outage probability that considers a particular SIR threshold for acceptable mobile performance of a typical user.Analytical approaches for estimating the outage probability in a cellular system, as discussed in Chapter 11, require tractable models for the RF propagation effects, user mobility, and traffic variation, in order to obtain an expression for . Unfortunately, it is very difficult to use analytical models for these effects, due to their complex relationship to the received signal level. Therefore, the estimation of the outage probability in a cellular system usually relies on simulation, which offers flexibility in the analysis. In this chapter, we present a simple example of a simulation of a cellular communication system, with the emphasis on the system aspects of the communication system, including multi-user performance, traffic engineering, and channel reuse. In order to conduct a system-level simulation, a number of aspects of the individual communication links must be considered. These include the channel model, the antenna radiation pattern, and the relationship between Eb/N0 (e.g., the SIR) and the acceptable performance.SIR(x)p蜂窝无线通信系统的仿真——摘自《通信系统仿真原理与无线应用》第672页-676页1 、概述人们开发出了许多无线通信系统，为不同的运行环境中的固定用户或移动用户提供了接入到通信基础设施的手段。