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The maximum exibility in the use of cross-band knowledge contemplates the ideal multiband multimode radio (iMBMMR) with bands from HF through EHF, including SATCOM. An iMBMMR may not be the size of a cell phone soon because of antenna constraints, but might be con gured into a military or commercial SUV for humanitarian relief. Such an iMBMMR may be made computationally intelligent via RXML and AML for an ideal cognitive MBMMR (iCM). Each iCM uses a computational model of the <Self/> to reason over its own capabilities and limitations, dynamically choosing radio bands and modes to enhance QoI. Using That Knowledge: The iCM can summarize the main features of each band and mode of Table 7-5. It knows its own ability for each of these modes. It relates the strengths, weaknesses, and content of these radio bands to QoI. Depending on the <Scene/>, one iCM might advise a remote <User/> that HF propagates readily over thousands of miles at low data rates. Another might con gure itself for LVHF propagation for rough terrain. An iCM in a city could con gure EHF pencil beams for low cost spatial sharing of the radio spectrum at gigabit per second data rates if it isn t raining. 7.10.2 Mode Selection Knowledge Chunk

[12], the nodes of the network know the underlying topology (eg, this could be a map of the network), while the beacons are called anchors and are linearly ordered B1 B2 Bk Given that each node in the network knows the underlying network topology, it is able to compute its hop distance to each one of the k anchors Bi The beacon vectors (called pseudo-coordinates) are known to all the nodes of the network Thus, the network can be thought of as being embedded in a pseudo-k-dimensional space while each node knows in addition to its own coordinate the coordinates of all its direct neighbors When receiving a message containing the (pseudo-) coordinate of the destination, a node forwards the message to its geometrically closest (in the pseudogeometric space) neighbor to the destination.

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Consistent mode selection behaviors may be preprogramed per Table 7-6. The evolution of SDR toward iCR includes such steps, but since an iCM can learn, its behavior may be enhanced by training it to weight QoI parameters according to the features of a <Scene/>. Using That Knowledge: The iCM applies the critical mode selection decision parameters to enhance QoI. With reinforcement learning, multiple training instances from many information access experiences are aggregated into decision parameters. Algorithmic methods for analyzing such noisy and uncertain training include Q-learning and neural networks. 7.10.3 Mode Transition Planning Seamlessness entails advanced planning of band/mode transitions as current modes deteriorate and as the scene changes. Information access gaps give the user a sense of seams in the network, while continuity of information access

I call it the pip auction game Even bank traders can fall into this Every trade you take is simply an opportunity to bid for pips! You make a bid ME: In the form of margin HARVEY: No, not really You think that s what you re risking, but it s not What are you really risking when you take a trade ME: I m risking a loss The loss of capital if the trade doesn t work out HARVEY: And what was your risk threshold today ME: I didn t have one HARVEY: So your risk was infinite ME: Yes HARVEY: And your profit potential What was your profit target on these trades ME: Well, for some of them it was determined, and for others I didn t have a profit target.

The pseudo-geometric routing problem can be solved if there is a pseudo-geometric routing algorithm that guarantees message delivery To guarantee delivery, any routing algorithm must rst resolve the naming problem in that all nodes must have unique identi ers, since the destination could not be uniquely identi able otherwise, in general Once this problem is solved, it is necessary.

n Let xij denote the ow at edge (i, j) for the nth connection. Let ui,j denote the maximum capacity of an edge (i, j). The feasibility constraints for the classical MCF formulation is given in Eq. (7.4) (7.6). Equation (7.4) describe the limiting bound of each ow to be the maximum rate of the connection. For a given connection, each edge can carry a maximum load corresponding to the rate of the given connection. The bundle constraint for the given graph is given by Eq. (7.5) which limits the total ow at an edge not to exceed its capacity. The ow constraint in Eq. (7.6) speci es the demand requirement to be met at each node as the difference between the out ow and in ow [Eq. (7.3)]. n 0 xij rn n C, (i, j) E

TABLE 7-6

(7.4)

Critical Mode Selection Decision Parameters Parameter Bit rate Constant (CBR) or variable (VBR) None, real-time or near-realtime Maximum burst size Tolerance of parameter mismatch Error rates Bit (BER), symbol (SER) errors Transfer delay, variance, jitter Packet or cell loss rate (CLR) Probability (link), grade of service Quantity of relevant valid information Wall clock time delay for content Authentication, privacy Peak, off-peak, service-related Remarks Quality and coder complexity Minimum, maximum, sustained Data transfers are not isochronous Large les create long bursts See service quality parameters Losses and delays Requires error control Excessive delays result in loss Over ows reduce rate Link and network provisioning QoI metric implies accuracy QoI metric re ects server in the network Signi cant for e-commerce Can be the critical factor

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