allocation result con- verges to the Nash Equilibrium, that is, when the BSs do not change their current allocation strategies. To illustrate the effectiveness of using a more intelligent form of slicing than simply assigning fixed amounts of bandwidth, the game theory model was implemented for three slices with static reservation in Yang et al. [32], and also for the full resource sharing approach. In both cases a seven-cell cluster with wrap-around was implemented for an OFDMA downlink using a suburban channel model. Three types of service (S) were included and following the approach of Jiang et al. [35] these were specified as requiring different bandwidths - here S1 = 1 Mbit/s, S2 = 2 Mbit/s, and S3 = 3 Mbit/s. Each service was allocated to a separate slice, the fixed slices being allocated 30 RBs each (90 RBs overall per cell). 8.4 Radio Resource Allocation that Considers Spectrum Sharing Number of users of each class Slice-1 Slice-2 Slice-3 Spectrum shared Figure 8.10 Comparison between fixed slices and overall sharing (even load) ©IEEE 2017. As a benchmark, the users are distributed uniformly and one third of the users are of each service class. 1 In Figure 8.10 [32] the usage of RBs is compared between the fixed slice model and the spectrum sharing approach as the offered load on the system increases. This is expressed as the ratio of used RBs to total RBs for that RAE - i.e. for the slice in the fixed allocation and for the cell in the fully dynamic case. It is clear from these results that, as the load on the system starts to become heavy, the fixed model does not use all available resources, especially in Slice 1 where the bandwidth demands are lower. As the load increases, the whole system becomes saturated and there are almost no free RBs. The same tests, but with different distributions of service class, are shown in Figure 8.11: in case (a) 55%, 30%, 15% for S1, S2, S3 respectively and for case (b) 15%, 30%, 55%. With fixed slices, Figure 8.11 shows that there are RBs unused even as the network sat