le on each hop. Therefore, we can get the maximum rate on the second hop to be and, on the third hop, For every location (x,y), we can now determine the best rate out of {r1, V2, r3}, which was used to generate the result figures. One of the three rates is maximum, and the index i of the maximum ri determines the "best server" (i.e., it shows the station with which the UT at that location should be associated). The system capacity is determined by assuming an equal traffic load for each user terminal and a homogeneous user density over the area [17]. This means that a UT far outside that has a low PhyMode requires more share of the resources than a UT close to the BS. The following equation for the capacity C considers this: 8.5.1 Sequential Multihop Scenario For the first impression on relay performance, assume one BS1 at a center position, the next (interfering) BS2 at a distance of 9,000 m, and two relays, RN1 and RN2, positioned at 3,000 and 6,000 m, respectively. Let there be non-LOS (NLOS) propagation from the BS and RN to UTs and LOS between the BS and RN1. Figure 8.15 shows the SINR condition at any receiving UT on the line between BS1 and BS2. The BSs were placed such that the SINR is strong enough to carry at least the lowest PhyMode within the range of BS1, RN1, and RN2. The relays themselves receive enough SINR, SO let the first hop carry the highest PhyMode. Figure 8.16 shows the effective rate that can be achieved around the BS and around RN1 and RN2. According to the preceding formula, the maximum rate around RN1 and RN2 is, of course, reduced because of the resources used for the (first and second) hops. Nevertheless, there is a high rate around RN1 and RN2, even up to a distance of 7,500 m. Intuitively, this shows that the capacity (high rate) can be increased and the coverage is extended (7,500 instead of 2,000 m). The next sce- narios show this in a cellular context, which is the typical geometry used for radio network planning. A Manhattan scenario [19] is not studied here. C 2009 by Taylor & 