无线通信系统的研究

蜂窝无线系统的研究文献翻译题 目 蜂窝无线通信系统的研究学生姓名 党勇 专业班级 通信工程12-01班 学 号 541207040104 院 （系） 计算机与通信工程学院 指导教师（职称） 黄立勋（讲师） 完成时间 2016年5月30日 12蜂窝无线通信系统的研究摘要 蜂窝通信系统允许大量移动用户无缝地、同时地利用有限的射频(radio frequency, RF)频谱与固定基站中的无线调制解调器通信。基站接收每一个移动台发送来的射频信号，并把他们转换到基带或者带宽微波链路，然后传送到移动交换中心(MSC)，再由移动交换中心连入公用交换电话网(PSTN)。同样的，通信信号也可以从PSTN传送到基站，再从这里发送个移动台。蜂窝系统可以采用频分多址(FDMA)、时分多址(TDMA)、码分多址(CDMA)或者空分多址(SDMA)中的任何一种技术。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。当今大多数无线通信系统都是基于蜂窝无线电概念之上的。蜂窝通信系统允许大量移动用户无缝地、同时地利用有限的射频（radio frequency，RF）频谱与固定基站中的无线调制解调器通信。基站接收每一个移动台发送来的射频信号，并把他们转换到基带或者带宽微波链路，然后传送到移动交换中心（MSC），再由移动交换中心连入公用交换电话网（PSTN）。同样的，通信信号也可以从PSTN传送到基站，再从这里发送个移动台。蜂窝系统可以采用频分多址（FDMA）、时分多址（TDMA）、码分多址（CDMA）或者空分多址（SDMA）中的任何一种技术。无线通信链路具有恶劣的物理信道特征，比如由于传播途径中有再大的障碍物，会产生时变多径和阴影。此外，无线蜂窝系统的性能还会受限于来自其他用户的干扰，因此，对干扰进行准确的建模就很重要。很难用简单的解析模型来描述复杂的信道条件，虽然有集中模型确实易于解析求解并与信道实测数据比较相符，不过，即使建立了完美的信道解析模型，再把差错控制编码、均衡器、分集及网络模型等因素都考虑再链路中之后，要得出链路性能的解析在绝大多数情况下任然是很困难的甚至是不可能的。因此，在分析蜂窝通信链路的性能时，常常需要进行仿真。跟无线链路一样，对蜂窝无线系统的性能分析使用仿真建模时很有效的，这是由于在时间和空间上对大量的随机事件进行建模非常困难。这些随机事件包括用户的位置、系统中同时通信的用户个数、传播条件、每个用户的干扰和功率级的设置（power level setting）、每个用户的话务量需求等，这些因素共同作用，对系统中的一个典型用户的总的性能产生影响。前面提到的变量仅仅是任一时刻决定系统中的某个用户瞬态性能的许多关键物理参数中的一小部分。蜂窝无线系统指的是，在地理上的服务区域内，移动用户和基站的全体，而不是将一个用户连接到一个基站的单个链路。为了设计特定大的系统级性能，比如某个用户在整个系统中得到满意服务的可能性，就得考虑在覆盖区域内同时使用系统的多个用户所带来的复杂性。因此，需要仿真来考虑多个用户对基站和移动台之间任何一条链路所产生的影响。链路性能是一个小尺度现象，它处理的是小的局部区域内或者短的时间间隔内信道的顺时变化，这种情况下可假设平均接收功率不变。在设计差错控制码、均衡器和其他用来消除信道所产生的瞬时影响的部件时，这种假设时合理的。但是，在大量用户分布在一个广阔的地理范围内时，为了确定整个系统的性能，有必要引入大尺度效应进行分析，比如在大的距离范围内考虑单个用户受到的干扰和信号电平的统计行为时，忽略瞬时信道特征。我们可以将链路级仿真看作通信系统性能的微调，而将系统级仿真看作时整体质量水平粗略但很重要的近似，任何用户在任何时候都可预计达到这个水平。通过让移动台在不同的服务区内共享或者复用通信信道，蜂窝系统能达到较高的容量（比如，为大量的用户服务）。信道复用会导致公用同一信道的用户之间产生同频干扰，这是影响蜂窝系统容量和性能的主要制约因素之一。因此，在设计一个蜂窝系统时，或者在分析和设计消除同频干扰负面影响的系统方法时，需要正确理解同屏干扰对容量和性能的影响。这些影响主要取决于通信系统的状况，如共享信道的用户数和他们的位置。其他与传播信道条件关系更密切的方面，如路径损耗、阴影衰落（或叫阴影）、天线辐射模式等对系统性能的影响也很重要，因为这些影响也岁特定用户的位置而改变。本章我们将讨论在同频干扰情况下，包括一个典型系统中的天线和传播的影响。尽管本章考虑的例子比较简单，但提出的分析方法可以容易地进行扩展，以包括蜂窝系统的其他特征。2 蜂窝无线系统系统级描述：如图2-1所示，通过把地理区域分成一个个称为小区的部分，蜂窝系统可以在这个区域内提供无线覆盖。把可用的频谱也分成很多信道，每个小区分配一组信道，每个小区中的基站都配备了可以同移动用户进行通信的无线调制解调器。从基站到移动台这个发送方向使用的射频信道称为前向信道，而从移动台到基站这个发送方向使用的信道称为反向信道。前向信道和反向信道共同构成了双工蜂窝信道。当使用频分双工（FDD, frequency division duplex）时，前向信道和反向信道使用不同的频率；当使用时分双工时（TDD, time division duplex）时，前向信道和反向信道占用相同的频率，但使用不同的时隙进行传送。图2-1 蜂窝通信系统的基本结构高容量的蜂窝系统在小区间进行频率复用，同频小区（共用相同频率的小区）之间要离开足够的距离以减轻同频干扰。如图2-2所示，N个小区构成一个簇（cluster，又叫“区群”），覆盖地理上的服务区，以实现信道复用，N是簇的大小。把服务区内可用的无线频谱都分配给每一个簇，使同一个簇内的小区不共用相同的信道。如果服务区内的可用频谱由M个信道构成，用户均匀分布在服务区内，则每个小区可以分得M/N个信道。因为簇在服务区内复制，复用信道将导致同频小区的层状结构（tier）。同频基站和移动台之间的射频能量传播，会引起同频干扰。例如，如果一个移动台同时接收来自本地小区基站的信号和邻近层的同频小区基站产生的信号，就会产生同频干扰。本例中，其中一个同频前向链路信号（基站到移动台的传输）是我们的有用信号，移动台接收到的其他同频信号就构成了对接机的同频干扰，同频干扰的功率级与同频小区之间的分隔距离密切相关。如果小区建模为如图2-2所示的六边形。两个同频小区中心之间的最小距离（叫做复用距离）等于 （2-1）式中R式小区的最大半径（这个六边形内接在半径为R的圆中）。因此，我们马上可以从图2-2看出，小簇（小复用距离）会引起同频小区间的大干扰。图2-2 小区簇：三小区复用模式的描述在一个指定小区中接收到的同频干扰的电平，还取决于任一时刻活跃的同频小区的数量。如前所述，在我们感兴趣的那个特定小区周围，同频小区组成一个个的层。在一个给定层中，同频小区的数量取决于层的阶次和用来表示小区的几何形状（如一个基站覆盖的面积）。对于典型的六边形，最近的同频小区在第一层，有六个同频小区，第二层有12个，第三层有18个，以此类推。因此，总的同频干扰时从所有层的全部同频小区发送出的同频干扰信号的总和。但是第一层的同频小区对总的干扰时从所有层的全部同频小区发送出的同频干扰信号的总和。但是第一层的同频小区对总的干扰有较强的影响，因为它们更靠近测量干扰的小区。人们认识到同频干扰时制约无线通信系统的容量和链路质量的主要因素之一。在系统容量（大尺度系统问题）和链路质量（小尺度系统问题）之间作折中时，它起到举足轻重的作用。例如，在不增加分配给系统的无线频谱带宽的前提下，得到高容量（大量的用户）的一种措施是，通过减小蜂窝系统簇的大小N，来缩短信道复用距离。然而，减少簇大小又增加了同频干扰，这会降低链路质量。蜂窝系统中的干扰电平在任何时候都是随机的，必须通过对蜂窝之间的射频传播环境和移动用户的位置进行建模才能仿真。另外，每个用户话务量的统计特性以及基站中信道分配方案的类型决定了瞬时干扰电平和系统的容量。同频干扰的影响可以用通信链路的信干比（SIR）来估计，这里信干比定义为有用信号的功率S与总干扰信号的功率I之比。由于无线传播影响，用户移动性以及话务量的变化，功率级S和I都是随机变量，SIR也是一个随机变量。因此，同频干扰对系统性能产生影响的严重程度，通常用系统的中断概率来进行分析。在这个特定场合下，中断概率定义为SIR低于给定阈值的概率，即 （2-2）其中是SIR的概率密度函数。要注意链路中断概率和系统中断概率之间的区别，前者是根据可接受的声音性能所需的特定误比特率（BER）或者Eb/N0阈值，确定是否为中断，而后者考虑的是一个典型用户可接受的移动性能所需的SIR阈值。 如前所述，用来估计蜂窝系统中断概率的解析方法，需要已知射频传播影响、用户移动性和话务量变化等随机量的易于处理的模型，以求得 的解析表达式。然而，由于这些影响和接受信号电平间的复杂关系，很难对这些影响采用解析模型。因此，主要靠仿真来估计蜂窝系统的中断概率，仿真还为分析提供了灵活性。本章我们给出了蜂窝通信系统的简单仿真示例，着重考虑通信系统的一些系统方面的问题，包括多用户性能、话务量工程和信道复用。为了进行系统级仿真，要考虑单个通信链路的许多方面，包括信道模型、天线辐射模式，以及Eb/N0（如SIR）和可接受性能之间的关系。RESEARCH OF CELLULAR WIRELESS COMMUNATION SYSTEMAbstract 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).1 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 todays 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 at any 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 2-1. 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 as forward 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.Figure 2-1 Basic architecture of a cellular communications system 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 2-2, where N is known as the cluster size.Figure 2-2 Cell clustering:Depiction of a three-cell reuse patternThe 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 2-2, the minimum distance between the center of two co-channel cells, called the reuse distance , is (2-1)where R is the maximum radius of the cell (the hexagon is inscribed within the radius). Therefore, we can immediately see from Figure 2-2 that a small cluster size (small reuse distance ), 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 total co-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 . This is (2-2)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 based 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 before, 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.8