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Appendix A: HTML 4 Data Types . . . . . . . . . . . . . . . . . . . . . . . 579 Appendix B: HTML 4 Elements Reference . . . . . . . . . . . . . . . . . 583 Appendix C: HTML 4 Attributes Reference . . . . . . . . . . . . . . . . . 653 Appendix D: HTML 4 Character Entities Reference . . . . . . . . . . . . 665 Appendix E: Cascading Style Sheets Reference . . . . . . . . . . . . . . 669 Appendix F: Language Codes Reference . . . . . . . . . . . . . . . . . . 713 Appendix G: Hex Notations Reference . . . . . . . . . . . . . . . . . . . 719 Appendix H: About the CD-ROM . . . . . . . . . . . . . . . . . . . . . . . 723 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 725 End-User License . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 744 CD-Rom Installation Instructions . . . . . . . . . . . . . . . . . . . . . . . 750

The difference between these two sets of waveforms is the variance of the jitter Ti . We would like to determine if jitter variance has an impact on the performance of the reciever. Note that for simulation purposes we assume that the limit waveforms are generated by N 4000 nodes, since these waveforms are indeed close to the limit waveform under the simulation parameters. In Figure 2.9 we also show the waveforms a0 (t) and a1 (t), which are approximations of s0 (t) and s1 (t), respectively, generated by much smaller networks. We will analyze the performance of the optimal detector when the signals generated by the network are not si (t), but instead are ai (t), for i 0, 1. We rst follow standard communication theory to design the optimal detector for two known signals s0 (t) and s1 (t) corrupted by additive white Gaussian noise. A detailed reference to this approach can be found in ref. [40]. We nd that the optimal detector is illustrated in Figure 2.10, where that orthonormal basis functions are f0(t) and f1 t , and s0 and s1 are the signal space representations for s0(t) and s1(t), respectively. The probability of bit error can be calculated as 0s 1 D D2 A Pe Q p Q@ 2N0 2 N0 =2 where D k 0 s1 k and Q is the standard Q-function. Recall that this is assuming s s(t) [ {s0 (t), s1 (t)}, so the actual limit waveform is sent and that each waveform is

In fact, it has been noted that even using a simple one step prediction algorithm (predict slot i + 1 is good if slot i is observed to be good, and bad otherwise) results in an acceptable first cut solution to this problem [11] A wireless fair scheduler needs precise state information to provide tight fairness guarantees to flows If the scheduler has perfect state information, it can try to swap slots between flows and avoid capacity loss However, if all flows perceive channel errors or the scheduler has imperfect channel state, then capacity loss is unavoidable In this sense, wireless fair queueing algorithms do not make any assumptions about the exact error model, though they assume an upper bound on the number of errors during any time window of size Ti, ie.