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CRA I: FUNCTIONS, COMPONENTS, AND DESIGN RULES
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The functions of AACR exceed those of SDR. Reformulating the SDR network node to the AACR <Self/> asserts a peer capable of creating networks and needing functions by which the <Self/> accurately perceives the local scene including <RF/> and the <User/> and autonomously learns to tailor QoI to the speci c <User/> in the current <RF/> <Scene/> and situation. 5.1.1 AACR Functional Component Architecture
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The SDR components appear with the related cognitive components in Figure 5-1. The cognition components describe the SDR in Radio XML so that the resulting <Self/> knows that it is a radio and that its goal is to achieve high QoI tailored to its own users. RXML asserts a priori radio background and user stereotypes as well as dynamic knowledge of <RF/> and space time <Scenes/> perceived and experienced. This knowledge enables both structured reasoning with iCR peers and CWNs and ad hoc reasoning with users while learning from experience. The detailed allocation of functions to components with interfaces among the components requires closer consideration of the SDR component as the foundation of CRA. SDRs include a hardware platform with RF access and computational resources, with more than one software-de ned personality. The SDR Forum has de ned its software communications architecture (SCA) [27] and the Object Management Group (OMG) has de ned its software radio architecture (SRA) [61], similar ne-grain architecture constructs for next-generation plug and play. These SDR architectures are de ned in Uni ed Modeling Language (UML) object models [138], CORBA Interface De ni-
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Radio Knowledge Radio XML Representation Language Radio Knowledge (RKRL) Antenna
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Structured and Ad-Hoc Reasoning. Learning from Experience Equalizer ...
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FIGURE 5-1
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The CRA augments SDR with computational intelligence.
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CRA I: FUNCTIONS, COMPONENTS, AND DESIGN RULES
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tion Language (IDL) [139], and XML descriptions of the UML models. The SCA emphasizes plug-and-play wireless personalities on computationally capable mobile nodes where network connectivity is often intermittent at best, while the SRA focuses on making the Web wireless. The commercial cell phone community [140], on the other hand, led by Ericsson and Nokia, envisions a much simpler architecture for mobile wireless devices, consisting of two APIs, one for the service provider and another for the network operator. They de ne a knowledge plane in the future intelligent wireless networks that is not dissimilar from a distributed CWN. Their Wireless World Research Forum (WWRF) [328] promotes the business model of the user service provider network operator large manufacturer device, where the user buys mobile devices consistent with services from a service provider, and the technical emphasis is on intelligence in the network. This strategy no doubt will yield computationally intelligent networks in the near- to mid-term. The CRA developed in this text, however, envisions the computational intelligence to create low cost ad hoc networks with the intelligence in the mobile device. This technical perspective enables the business model of user device heterogeneous networks, typical of the Internet model where the user buys a device (e.g., a wireless laptop) that can connect to the World Wide Web wirelessly via any available Internet service provider (ISP). The CRA builds on both the SCA/SRA and the commercial API model but integrates semantic web intelligence in Radio XML for mobile devices to enable more of an Internet business model. SDR, AACR, and iCR form a continuum facilitated by RXML. The AACR node CRA consists of the minimalist set of six functional components of Figure 5-2. A functional component is a black box to which functions have been allocated, but for which implementing components are not speci ed. Thus, while the Applications component is likely to be primarily software, the details of those software components are unspeci ed.
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User Sensory Perception User Environment Sensor Functions Environment Effector Functions Cognitive Radio
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System Applications Radio Networks Software Defined Radio Functions Cognition Functions Other Networks
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FIGURE 5-2