外文翻译-作用于信号处理和通信系统上的仿真和实现工具.doc

Simulation and Implementation Tools for Signal Processing and Communication SystemsK. Sam ShanmuganIt is now possible to move easily from a top-level simulation model of a system toward its implementation. While this path is seamless and nearly complete for the digital implementation, gaps still exist for the implementation of RF and optical portions of communication systems.Simulation plays an important role in the design, analysis, and implementation of communication and signal processing systems. In the past, simulation has been used primarily to verify the design. The early applications of simulations were focused on high risk and high-cost systems such as geosynchronous communication satellites. Simulation now plays a much broader and more central role in the design and implementation of commercial products ranging from communication satellites to terminals for personal communication systems (PCS). The first generation of simulation tools addressed the “system” level design of communication systems and emphasized behavioral models that are far removed from implementation details. Circuit and gate level simulation tools on the other hand focused on implementation details, leaving a large gap between the two levels. This gap is narrowing considerably with the increasing emphasis on digital implementation. In the next generation of Electronic System Design Automation (ESDA) tools, simulation will be an integral part of the design process during all phases and the distinction between simulation models and prototype implementation will be minimal. Indeed, stand alone simulation tools without links to implementation will play only a minor role in ESDA. Simulation technology has matured considerably in the past 10 years, and simulation has been integrated into design automation (implementation)tools. Instead of the earlier approach of generating top level specifications using simulations and passing them on to a variety of implementation tools ,it is now possible to move down easily from a top level simulation model of a system progressively toward its implementation. While this path is seamless and nearly complete for the digital (hardware and software) implementation, gaps still exist for the implementation of RF and optical portions of communication systems. The primary focus of this article is to describe the state of the art in waveform (or functional) level simulation tools for communication and signal processing systems with links to digital implementation.BackgroundThe first generation of software packages for simulation of communication system was developed during the 60s as part of NASA programs on communication satellites and deep space exploration. These packages were text-based and were designed to run on mainframe computers in batch mode. One of the most comprehensive packages of this vintage, still in use after several upgrades, is SYSTID l, 21 which was developed by Hughes Aircraft for NASA. The European Space Agency sponsored the development of a similar package , TOPSIM 3,which also has gone through several upgrades and is still in use. With the development and widespread use of minicomputers and graphics terminals in the O OS, the second generation of simulation software were menu-driven and interactive, at least in parts. The development of the second generation of simulation software such as ICs and ICSSM 4,5 was supported by the U.S. Department of Defense. Computer hardware and software technologies have undergone significant changes in the past 10 years. With the availability of powerful workstations (and PCs) in networked environments, simulation software packages have moved towards interactive, hierarchical and graphical frameworks. The Block Oriented Systems Simulator (BOSS) l was the first among many workstation based software packages that were developed during the late 80s. Unlike the previous generations of simulation software packages which were developed primarily at Universities under government sponsorships, the current generation of software packages are developed and marketed by commercial vendors. SPW, COSSAP, and DSP Station6 -81a re the three leading commercial software packages for the simulation and implementation of communication systems. Digital signal processing algorithms play an important role in both the simulation and implementation of significant portions of communication systems. Algorithms used in receivers for equalization, synchronization, filtering, etc. can be implemented in hardware using application-specific integrated circuits (ASICs) or they can be implemented in software on a programmable DSP processor. It is indeed possible to include hardware implementation issues such as limited precision, clocking, resource sharing, and other details in the simulation framework and move from simulation to implementation using hardware description languages such as VHDL to provide the interface between the simulation framework and hardware design and implementation tools. If the implementation is in the form of software running on floating point DSP processors, then the distinction between the code that simulates the algorithms used in a receiver and the code that actually implements the receiver functions becomes very blurred. Indeed, the simulation itself can be carried out on one or more DSP processors attached to a workstation. In either case (hardware or software implementation), the simulation framework can and should be tightly integrated with implementation tools, a trend that is common in the current generation of simulation tools for communication and signal processing systems. Mathematical analysis also play an important role, in addition to simulations, during the early stages of the design of a communication system. Software packages like MatLab 9 and Mathematic alo are examples of analysis packages that can be used alone, or with simulations. However, these packages do not provide any links to implementation tools and hence are not covered in this paper.Model BuildingThe current generation of simulation tools use a hierarchical block diagram approach for graphically creating a simulation model of the communication system. Simulation models of end-to-end communication systems are built using building blocks from model libraries that come with the simulation software. Icons representing functional blocks such as information sources, encoders, modulators, multiplexers, channels, noise and interference sources, filters, demodulators, decoders and demultiplexers are selected from various model libraries, placed on the screen and “wired” together graphically to create the simulation model in the form a signal flow block diagram. Models can be built either from top-down or bottom-up, with the top-down view being the preferred choice of systems engineers. The bottom-up approach is used more often by hardware designers. The term “yoyo design” is used to refer to switching back and forth between bottom-up and top-down methodology during the design process. At the “leaf” level (i.e., the lowest level in the hierarchy), simulation models can have a number of representations depending on the context in which they will be used. For “system” level simulations for performance evaluation, the leaf level models (and the higher level models derived from them) are usually expressed as subroutines or procedures in a programming language such as C and the simulations are carried out in floating point arithmetic with high precision. Finite precision versions of the models as well as VHDL versions will be used when one moves from system level performance evaluation to implementation.Simulation TechniquesA variety of simulation techniques are used in the current generation of simulation software. These techniques can be classified into four categories:1) time-driven (single- and multi-rate), 2) event-driven,3) data- or stream-driven, and 4) mixed mode. These methods differ in how the blocks in the simulation model are invoked and they offer varying degrees of flexibility and improvement in the simulation speed.Time-Driven Simulation In the simplest case, each block in the simulation model is invoked once every “tick” of the simulation clock. The global time is updated by a constant increment, and all the blocks in the model are activated so they can update their states to correspond to the new global time.This method is computationally very inefficient since it uses a uniform sampling rate through out the system and there is considerable overhead in invoking each block in the model with one input sample from each input signal. Some improvement in simulation speed can be obtained by vector processing where in each block is invoked once every n samples with vectors (or blocks) of n samples of each input signal and producing n samples of each output signal. Vectorized processing will require buffering and it can be used as long as there are no feedback loops.In many systems such as CDMA, the bandwidth of signals and subsystems can vary over a wide range and a uniform sampling rate based on the signal with the largest bandwidth will result in vast over sampling of lower bandwidth signals. Multi rate simulation techniques use different sampling rates for different parts of the system, sampling each signal at a rate commensurate with its bandwidth. Decimation and interpolation operations will be used to provide appropriate sampling rate conversions at points where signals with different sampling rates merge. Some form of scheduling, static (determined prior to simulations) or dynamic will be required to sequence the invocation of blocks.Event-Driven Simulation In event-driven simulators, the global time is advanced to the time of the next event in the event queue. Each block needs to update its internal state to be consistent with the new global time. Typically, only a few blocks need to be activated to update their internal state. No processingtakes place during the inter-arrival time between the events.Event-driven simulations are computationally more efficient than time-driven simulations for network and logic systems because of their asynchronous nature. However, event-driven simulation will be slower for waveform level simulation of (synchronous) signal processing operations in a receiver. In some systems, it might be necessary to use a combination of simulation modes. The Ptolemy software environment and some commercial software packages support heterogeneous simulation modes.Data- or Stream-driven Simulation In data-driven simulators, the availability of all necessary data items at the inputs of a block triggers the activation of the block. If the availability of data items can be “pre-computed” from the fixed input/output data rates of the blocks, then the sequence in which the blocks will be activated can be determined prior to the start of the simulations. This is called static scheduling.If the blocks do not have a constant input/output rate, in fact this can vary from activation to activation, then static scheduling is not possible. The availability of data items in this case has to be checked at run time, during the simulation, and scheduling of activation has to be done dynamically.A fundamental difference between time-driven and data-driven simulators is that data-driven simulators typically do not have any notion of global time built into the simulation kernel. Instead, each block can have a notion of its own local time. The time semantics is introduced by the user of the simulator.COSSAP uses a dynamically scheduled data-driven approach for simulation. However, a static scheduling is used to generate code for real-time applications.Interactive SimulationIrrespective of the simulation methodology used, many of the state of the art simulation software packages also offer the capability for “interactive” simulation whereby the user can execute the simulation “one step” at a time and watch the evolution of the simulated waveforms and the results. The users can also stop the simulations when certain conditions are met (check pointing), change parameter or data values and resume executions. Interactive simulation is very useful for “debugging” a simulation model, and it also provides better insight into the dynamic behavior of the systemDistributed SimulationsSome simulation packages also offer the capability for partitioning a large simulation model for execution on multiple processors. While the partitioning is done manually, execution of the simulation on multiple processors and the inter-processor communications are handled automatically. Distributed simulations is a current area of research that is receiving much attention now. An area that is closely related to distributed simulations is the rapid prototyping, and in some cases, implementation of portions of communication systems on programmable DSP chips. Many simulation tools now offer the capability to partition a communication system design for implementation on multiple DSP chips. While the partitioning is done manually, generating the code for each partition and the code for controlling the communication (signal flow) between the partitions running on different processors is done automatically. Here the difference between accelerated simulations on multiple DSP chips, and prototype implementation of the system becomes very small.Links to ImplementationThe implementation of communication systems (transmitters and receivers) can be divided in two parts: the predominantly analog “RF/IF” frontend, which involves an RF or optical carrier component, modulators, mixers, power amplifiers, etc.) and the “base band portion,” which consists of signal processing operations such as encoders, decoders, multiplexers,demultiplexers, filters, etc. The availability of high-speed, low-cost digital hardware has made it possible to implement the “baseband” portions of communication systems using ASICs, FPGAs, or programmable DSP chips. We will now briefly discuss the links to implementation that have to be provided by the communication systems simulation framework, with a focus on the base band portion only.Low power ASIC technology is the preferred choice for power-limited situations such as battery operated handsets for PCS. For many otherapplications such as voice-band modems, software implementation of receiver algorithms on one or more programmable DSP chips is the current trend. This is also the method commonly used for rapid prototyping of communication systems since it offers more flexibility.In many complex systems a combination of ASICs and programmable DSP chips might be used for implementation.A key issue that has to be addressed here is the hardware/software partitioning, i.e., deciding what portion of the algorithms will be implemented in software and what portion will be implemented in ASICs. This partitioning is usually handled by “system” level design tools capable of handling resource allocation issues. Tools in this category include SES/Workbench 1 21 and BONeS Designer1 31.For the following discussion we will assume that the partitioning has already been done.Concluding RemarksThe “software” and “silicon” content of communication systems has increased significantly in recent years and this trend will continue in the future with further advances in sub-micron and low power integrated circuit technologies. With the exception of the front ends(RF/Optical portions),much of the remainder of the transmitters and receivers will consist of highly integrated, and programmable implementation of signal processing operations. Tools such as the ones described in this paper will play an increasingly important role in the design and implementationof the next generation of communication systems.AcknowledgmentsThe author would like to thank Paul Conflitti and Joachim Kunkel for their comments and inputs.作用于信号处理和通信系统上的仿真和实现工具 K. Sam Shanmugan如今，我们可以轻松地构建一个高水准的仿真模型来实现一个系统的运行。虽然这个方式是无接口的，几乎完全用于数字实现，但是无线电频率的实现和通信系统的光学部分还存在着隔阂。仿真在设计，分析，通信的实现和信号处理上有很大的作用。在过去，仿真主要用于确认设计的正确性。早先的仿真主要的应用是高风险高损耗系统，比如地球同步通信卫星。现在，在设计和经济性的产品从通信卫星到个人通信系统终端，仿真在他们的实现上有了更广泛更重要的作用。第一代仿真工具作用于通信系统的系统”级别的设计，而且强调大大离开了实现细节的行为模式。另一方面，电路和路径级别的仿真工具注重实现的细节，在两个级别之间留下了一个巨大的隔阂。这个隔阂正在通过不断增加强调实现的细节有了相当程度的缩小。在下一代电子系统设计自动化的工具，仿真在整个阶段将成为一个设计过程的完整部分，而且仿真模式和标准化的实现上的差别将会变的很小。离开了连接实现的单独标准的仿真确实将在电子系统设计自动化中起到很小的作用。在过去十年中，仿真技术有了相当的成熟，而且仿真已经整合进了设计自动化工具中。它替代了更早的运用仿真产生高级别的具体化方式，还给很多实现工具传递了它们，对于仿真方式的实现，仿真可以从一个高级别的系统仿真模式向下降低。这个路径是没有接口的，几乎完全作用于数字实现，但是隔阂仍然在无线电频率的实现和通信系统的光学部分上存在。这篇文章的首要目的是描述连接数字实现的通信和信号处理系统上的波形级的仿真的学术状态。背景在60世纪，第一代通信系统仿真软件程序包被开发出来，作为NASA在通信卫星和深度空间探索项目的一个部分。这些程序包是基于文本的，被设计出来在大型中央处理机上用批处理方式运行。其中一个最广泛的精品程序包systid在及格更新之后依旧在使用，它是Hughes Aircaraft开发的，提供给NASA。欧洲航天局投资了一个相似的程序包TOPSIM的开发。它也经过了几次更新还在一直被使用。随着迷你计算机和在操作系统上制图终端的开发和广泛使用，第二代的仿真软件是菜单驱动和交互式的，至少部分是这样的。第二代仿真软件比如ICs和ICSSM是美国国防部支持下的。在过去十年，计算机硬件和软件技术进行着重要的改变。随着在网络环境下功能强大的工作站的使用，仿真软件包转向相互的，等级制度的和图像的架构。BOSS是很多基于软件包的工作站中的第一个，它被开发于80实际。与先前的被开发于大学在政府的资金下的仿真软件包不同，现金的软件包是商业售主开发的.SPW,COSSAP, and DSP Station是三个主要的商业软件包，用于仿真和实现通信系统。数字信号处理算法在仿真和实现通信系统的重要部分有很大的作用。在均衡接收机，同步接收机和滤波器使用的算法可以在硬件上通过使用具体程序集成电路实现。或者他们可以在软件上通过数字信号处理机实现。这确实包括硬件实现问题比如受限精度，时钟，资源共享和别的在仿真架构下的细节，和从仿真到实现所使用的硬件描述语言比如VHDL，提供了仿真架构和硬件设计和实现工具之间的界面。如果实现是在软件运行浮动点数字信号处理机的格式下，那么模拟接收机算法的编码和实际上实现接收机功能的编码之间的差别变得非常模糊不清。确实，仿真本身可以实现一个或更多粘附于工作站的数字信号处理机。在两者之间的情况，仿真架构可以也应该紧密地与实现工作整合，在现今的通信和信号处理系统中是一个普遍的趋势。通信系统的设计早期的阶段，除了仿真，数学分析也有很大的作用。程序包像MATLAB和MATHEMATIC就是分析包的例子，他们可以单独使用，或者和仿真结合。然而这些软件包不提供任何与实现工具的链接，因此不会在这个论文中被论诉。模型建立现代的仿真技术使用分层的区块图表方式来建立一个图表式的通信系统仿真模型。终端对终端的通信系统仿真模型是建立在使用模型数据库的区块上的，这些区块来自仿真模型。图表代表功能区块比如信息资源，编码器，调制器，多路复用器，频道，噪音和界面资源，滤波器，解调器，解码器和反多路复用器，这些都可以从很多模型库里选择，然后放置在屏幕上用连线图表式地连接起来，去生成在信号流图式的仿真模型。模型可以建立从上向下或从下到上的形式。从顶到底的视图是系统工程师最好的选择。从底到顶方式更常用用于硬件工程师。而yoyo设计是用在从底到顶和从顶到底方法之间向后向前的选择。在最底层时，仿真模型有很多基于将要用到内容的表示。对于系统级别的性能评估，叶层模式在编程软件中（比如c语言和仿真语言）常常被用来表述成子程序或进程，他们用以表