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followed by a more detailed portrayal of the Geometrically Based Single-Bounce Elliptical Model (GBSBEM) The metrics used for characterising the performance of mobile cellular networks were presented under both LOS and multipath propagation conditions, with and without adaptive antenna arrays The network capacity was found to increase when using adaptive antenna arrays, with further increases achieved due to the adoption of power control An adaptive modulation mode switching algorithm with combined power control was developed and network capacity investigations were conducted Employing adaptive modulation using adaptive antenna arrayswas found to increase the network s capacity significantly, whilst providing a superior call quality and a higher mean modem throughput Our investigations in 4 initially focused on the non-wraparound or desert island type networks, where the outer cells of the simulation area are subjected to lower levels of co-channel interference, a scenario that may be encountered in the suburbs of large conurbations.

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Simulations were carried out for the FCA algorithm, and the LOLIA using nearest base station constraints of 7 and 19, when exposed to LOS propagation conditions The FCA algorithm offered the lowest network capacity, but benefited the most from employing adaptive antenna arrays Specifically, the network capacity of FCA increased by 67%, when employing two element antenna arrays at thebase stations, and 144%, when using four element arrays TheLOLIA using a nearest base station constraint of 7 cells supported a higher number of users, but the adaptive antenna arrays not result in such dramatic improvements did in network capacity Explicitly, a 22% increase was observed for the two element case, and a 58% when using four elements However, the network capacity supported by the LOLIA in conjunction with n = 7 always exceeded that of the FCA algorithm.

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When using a 19 base station constraint, the LOLIA resulted in the highest network capacity without employing adaptive antenna arrays, although the large frequency reuse distance of this algorithm resulted in a modest increase of the network capacity We then conducted further simulations in Section 4622 using a more realistic 3-ray multipath propagation environment Again, the FCA algorithm supported the lowest number of users, and gained the most from invoking adaptive antenna arrays Using a four element array instead of a two element array led to a network capacity increase of 35%, and replacing the four element array with one employing eight elements resulted in a 24-34% increase in the number of users supported The LOLIA employing n = 7 supported the greatest number of users, but did not benefit from the same capacity increases as the FCA algorithm with the advent of adaptive antenna arrays.

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The number of users supported increased by 18% upon upgrading the system from two to four element adaptive antenna arrays, and by between 5% and 15% upon using eight element arrays in place of the four element arrays Using a frequency reuseconstraint of 19 in conjunction with the LOLIA resulted in a network whose capacity was restricted by the high new call blocking probability associated with its large frequency reuse distance This large frequency reuse distance led to low levels ofco-channel interference, which could not be nulled effectively by the adaptive antenna arrays, and hence the network capacity did notincrease by more than 5% upon doubling the number of antenna elements comprisingthe array Hence, our futurestudies only considered the FCA algorithm and the LOLIA in conjunction with n = 7.

The network capacity gains accruing from the implementation of power control over the same 3-ray multipath channel, as in the previous section, were then investigated for the FCA algorithm and the LOLIA using R = 7 Significant network capacity increases were observed for all of the scenarios considered Specifically, the network capacity without power.

The second implementation of Wold s decomposition emphasizes the idea of spectral modeling using sinusoids as proposed by MacAulay and Quatieri [1986]. In this model, both signal components are produced by the same sum of sinusoids. The noise-like regular component can well be approximated by such a sum (as suggested by spectral representation theory) if the relative phases of the sinusoids are randomized frequently, at least once for each frame. This scheme offers the advantage of retaining the original phase information in the singular component (if the available bit rate allows us to code it, of course) and it is more exible in combining regular and

Figure 19.14 Hierarchical baseline elaboration (left) and Nonhierarchical baseline elaboration (right).

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