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25% of the potential revenue is lost even with 3G technology (third case). All of this revenue growth is from wireless email, electronic maps, stock broker services on the move, and other services that appear attractive to consumers. 6.3.4 Cognitive Shaping of Demand
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The postulated AACRs learn accurate user models in communications contexts to autonomously shape demand. They balance value to the user (e.g., rapid delivery of email, rapid retrieval of items from the Web) against cost (e.g., offering the demand during off-peak periods). The third vignette in Table 6-3 exhibits a pattern of digital traf c where 68% of the afternoon demand is email or le transfers. If only 30% of this demand is delayed by from 1 to 4 hours, total traf c increases by about 5%. This is attractive to network operators but not necessarily to consumers. Instead of being merely shifted in time AACRs divert the digital traf c that is not time sensitive to the corporate RF LAN where airtime is (regarded as) free. If only 25% of subscribers nd the high-band RF LAN convenient, the cellular service providers lose 4.8% of their revenue-bearing traf c, not an insigni cant loss. These value systems can be expressed in RXML, learned by AACRs and used for speci c users for situation-dependent load shaping. Expression 6-8 Knowing that the User s Value System Is Situation Dependent <Abstractions> <Radios> <iCR> <Software> . . . <Learns> <User> <Value-system/> </User> </Learns> </Software> </iCR> </Radios> <User> <Value-system> <Cost/> <Delay/> <Speed/> </Value-system> </User> . . . </Abstractions> <Physical-universe> . . . <Instances> <User> Charlie <Value-system> <NOT> <CONTAINS> <Situation/> <Unusual/> </CONTAINS> </NOT> <Cost> <Lowest/> </Cost> </Value-system> </User> <User> Charlie <Value-system> <CONTAINS> <Situation> <Urgent/> </Situation> </CONTAINS> <Delay> <Lowest/> </Delay> </Value-system> </User> </Instances> </Physical-universe> As illustrated in Expression 6-8, speci c iCRs learn the speci c value systems of their users through a combination of a priori models and an ability
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to recognize and tailor user value systems to speci c features of a situation. Ontological primitives like <Urgent/> must be de ned in terms of observables in a scene. In addition, temporal reasoning must translate <Lowest/> cost or delay into the acts of buffering the traf c to the RF LAN. Introducing AACRs in large numbers diverts traf c to RF LANs, increasing total traf c by 8.9% with cognitive delay shaping of 25% resulting in a loss of 4.8% of the revenue-bearing cellular/3G traf c to WLANs. As more traf c is shaped to corporate RF LANs, more revenue is shifted from the service provider to the corporate clients in the form of savings of the costs of mobile traf c. The simple economic model does not include price breaks for corporate clients or the costs of acquiring and operating the corporate RF LANs, but such simpli cations may not be unrealistic if the RF LANs are installed and operated to reduce costs of wiring (and rewiring) of ce spaces or manufacturing oors; this represents a small but bene cial use of an existing corporate infrastructure. This use case does not re ect any particular business case and its simpli cations may render the results inapplicable in many cases. Still, millions of autonomous decisions can shift revenue measurably if autonomy moves millions of bits off the cellular networks and onto the corporate RF LAN, or the wireless Web.
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Globally, there have been many military radio programs that envision the elding of military SDRs. These include the U.S. Joint Tactical Radio System (JTRS) [79], the U.K. Bowman Program, and the European Future Multiband Multimode Modular Tactical Radio (FM3TR). Without necessarily addressing any one of these in detail, one may de ne a generic Coalition Tactical Radio System (CTRS). In this use case, CTRS would be the basis for a global open architecture standard for intelligent radios to facilitate the formation of military teams, for example, to assist in humanitarian relief operations like the tsunami disaster of 2004. CTRS addresses military radio modes between 2 MHz and 6 GHz, LF/HF through SHF. These include HF Morse, HF ALE, frequency hop (FH), direct sequence spread spectrum (DSSS), analog AM/ FM push to talk, GPSGlonass navigation, digital data links, FM FDM, and digital PCM. Suppose there are four antenna sets per military vehicle: (1) LF/HF dual whip antennas (2 30 MHz), (2) dual midband whip antennas (30 500 MHz), (3) dual high-band whip antennas (0.5 2 GHz), and (4) quadrant directional antennas (0.9 6 GHz). CTRS enables communications among coalition vehicular radios such as ships, aircraft, and land vehicles. The functions of CTRS include agile radio spectrum management, mode management (the association of modes to speci c vehicles), priority (by vehicle, by user, and by content), preemption of a lower priority by a higher priority, power management, communications security (COMSEC), transmission security
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