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<Place> <Thing> Metropolis <Place> <Intersection> <Thing> <Street> Street-1 </Street> </Thing> <Thing> <Street> Street-2 </Street> </Thing> </Intersection> </Place> </Thing> </Place> The essential elements of a corresponding XG-like radio spectrum management space are as follows: aacr spectrum adaptation perception action space Perception Space Condition primitives (16 ) Scan [t]; noise/modulation [j, t] Range/bearing to goal {Recipient, Legacy} Band, mode, bandwidth, and power occupancy Action Space Transmission primitives (3) Power (dBm); burst timing (P(t)) Beamforming (P( )) Band, mode, bandwidth, and data rate
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Mines correspond to legacy users such as regions where TV reception favors normal TV usage versus ad hoc networking. The Genie TV spectrumuse case might employ this strategy to express <RF/> constraints spatially. Each RF region is a <Thing/> that exists in space time, where space is de ned using streets as boundaries. The AACR itself is a <Thing/> that consists of many other <Things/> including <TV-WLAN/>, the physical extent of which may be de ned in terms of city blocks as a function of mean adjacent building height as well as the transmitted power and the threshold power above which the <TV-WLAN/> interferes with normal <TV/> reception. The condition primitives are those sensory parameters that the AACR can sense via its SDR subsystem. CRs and CWNs estimate range and bearing between the <Self/> and the competing user or receiver. The CR with a street map of Metropolis can <Sketch/> locations of competing <TV-WLAN/> reported by a host CWN on its own map, along with regions of good reception of broadcast TV. As with all RXML LCS primal sketches, the coordinates are 3D or 4D to include time, so reception above the 5th oor of high rise buildings is easy to establish. The action parameters for conventional radio networks are just power and bandwidth, but with AACR, there is a choice of band and mode to leverage XG policy for secondary users. With MIMO, particularly on vehicles and xed facilities, beamforming shapes the transmission in space time. Thus, the evolutionary spectrum navigation process consists of the following: 1. AACR placed at origin (e.g., place of work) with random user movement plan. 2. Representative placement of interference, competing nodes with expected variations.
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3. Noise (Gaussian; false positives and negatives) added to re ect error syndromes. 4. Populations reach goal (or not) in time t(i, x) representative of AACR travel time. 5. Download most effective control law to AACR from CWN. The control law could include reaction to mitigate interference and overcome link impairments, for example, using a neural network to control power and MIMO parameters. The symbolic level of control adjusts the choice of unoccupied TV channel to smoothly avoid interfering with known primary and secondary users while maintaining connectivity. Deliberate control at the symbolic level may occur in the <Plan/> phase where the transition from UHF 33 to UHF 44 occurs at the <Intersection/> of <Street> 4th </Street> and <Street> Main </Street>, an abstract yet precisely speci ed time and place for control via RXML LCS primal sketches. To introduce <User/>-domain challenges into this apparently nicely solved problem, one only need consider the unpredictability of users. Seems like Ed and Diane live in Metropolis, comes the remark from the chase vehicle. You know, we haven t seen them in years, the <Owner/> replies. They sent us such a nice card over the holidays, and we all know what is coming next. Genie, can you see if you can reach Ed and Diane so we can say hello on our way through Metropolis. If Ed and Diane were known to <Genie/> then all would be well. <Genie/> looks them up in the <Directory/> and sets up a connection between <Diane/> (now known to <Genie/> and thus a conceptual primitive) and <Spouse/> that leads to an impromptu visit, which changes the path through <Metropolis/>. If <Genie/> advises the CWN of the change of plan, then it will be able to advise of a more ef cient spectrum-use plan for staying connected by <TV-WLAN/> as the vehicles transit different neighborhoods on entirely different time lines to visit Ed and Diane, stay the night, and be on their way in the morning. However, the technology challenges of autonomously maintaining primal sketches becomes clearer if <Genie/> is new to the family and has never heard of Ed or Diane. Introducing new types of knowledge to AACR is much more dif cult than merely instantiating known types of knowledge into a primal sketch. The successful solution of this class of problem without the mediation of expensive knowledge engineers calls for the autonomous incremental knowledge acquisition for broad classes of task-related knowledge. 11.2 AUTONOMOUS EXTENSIBILITY
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Autonomous incremental knowledge acquisition is another technology that has existed since the 1980s but that for a variety of technical and economic reasons has yet to be fully deployed, at least not in the <User/> domain of AACR-class radio engineering. This section explores the foundations of
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