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Hierarchical routing [46] is an adaptive hierarchical routing protocol that conforms to cluster-based networks adopted on the network and MAC layers. The salient features of HR are that routing changes only require an update of a forwarder ID, instead of whole path information. Each cluster updates at different frequencies and only entries that have changed. An entry is updated based on comparisons of generation/registration time values stored in a routing table of a clusterhead or central controller (CC). In this way, the count-to-in nity problem is averted. In each cluster, a CC is selected and aware of its terminal node membership. Each CC maintains a local routing table composed of N elds (one for each member terminal) and a table update time (T-up), which provides a last-change-of-table time. Each eld includes a path length (PL) measured in links between the CC and a terminal, a maximum transmission rate (MTR), which is the MTR over the PL via the weakest link, a eld generation time (T-gen), which indicates the information life of this eld, a eld registration time (T-reg), which provides a last-change of eld time, and the ID of a forwarder to the next cluster. There is no synchronization among clusters to update routing tables. To perform an update, a CC sends an update-request (URQ) containing its T-up to its neighboring CCs. Neighboring CCs compare the T-up of the URQ to its T-reg for the entire routing table. Each CC then transmits an update response (URP) which includes all the elds where T-reg is greater than T-up. The source CC processes the URP and compares its elds to the new elds and updates those elds that meet the update criteria. Performance evaluations between HR and an on-demand routing algorithm (ODRA) were conducted that showed HR outperformed ODRA in terms of

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All comparison results presented in this section have been obtained for UMTS network with regular and uniform triangular grid and the same size of hexagonal cells Each Node B consists of three sectors The Node B s requirements for the backhaul network have been calculated only for voice 122 kbps transmissions with a cell loading of 50 % Additionally, it was assumed that the voice service s data rate comprises 70 % of the total bit rate The remaining 30 % of the total bit rate are used for packet transmissions or control data The UMTS cell size has been varied by an additional attenuation (building attenuation) in the link budget In all cases, only LMDS base stations working in the 26 GHz frequency band and ITU rain zone E with an availability of 9999 % have been applied.

A first analysis has been carried out for the UMTS network in non-dense urban areas, where the main problem is to have an insufficient LMDS range In all of these three cases, the Node B s range amounts to 2043 km The network configurations are presented in Figure 1739 and summarised in Table 1721 To increase the LMDS range, the narrowest channel bandwidth has been chosen (Figure 1739a); however, the LMDS base station capacity is poor Thus, only less than half of all Node Bs in the LMDS range are served These Node Bs, which are served by the LMDS base station, are drawn as hexagonals; otherwise, they are drawn as a dot The achieved LMDS sector capacity allows to serve only two Node Bs An increase of the LMDS capacity with additional high capacity sectors (16QAM) does not offer the required effect (case b ).

(3.9)

The LMDS solution is hence more expensive than PTP radio lines, because additional equipment for LMDS base stations is needed The limited range of the 16QAM sectors allows to serve only one or two Node Bs, and the LMDS base station capacity factor for 16QAM sectors does not exceed 30 % The cost of the LMDS solution is a little bit lower when two out of 7 MHz radio channels are assigned to a single QPSK sector In this case (Figure 1739c), more LMDS equipment increases the total purchase cost Assigning one of the 14 MHz channels to each LMDS sector, instead of 2 7 MHz, seems to be a much better solution (Figure 1740) It allows achieving the same LMDS base station capacity as in.

off. Contrary to proactive earliest due date with exact channel knowledge, realisticproactive earliest due date does not have exact knowledge through acquisition. Since the channel information is not completely accurate, realistic-proactive earliest due date needs to estimate or predict the next time instant at which channel conditions will turn from good to bad so that the packet deadlines can be adjusted accordingly. Realistic-proactive earliest due date suggests predicting the next transition time Ti for user i as

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