19 we perform rounding in the analysis, i.e., we consider zero active users in the rings where u$_{k}$ ≤ 0 . 5 . In Monte-Carlo simulations, we consider the probability of allocating a user in these rings to be zero which can also be verified from Fig. 2. In Fig. 3, the PMF of the distance between the allocated user in interfering cell l and the BS of interest, i.e., P (˜ r$_{sel}$ = ˜ r$_{m}$ ) , is presented. Numerical results are found to be in close agreement with the Monte-Carlo simulation results. For the opportunistic scheduling schemes, it is likely that a user close to its serving BS can get a subcarrier, thus, the PMF of the distance of allocated interfering users is expected to have high density in the middle. However, the slight descend in the central region in Fig. 3 is due to ignoring users that lie within the rings where the average number of users is less than half. Moreover, we can observe that the round robin scheduler is highly vulnerable to interference compared to the other schemes as high interference is expected to come from the cell edge users in the interfering cells. On the other hand, the greedy scheduler is expected to have allocations near the cell center and, thus, leads to less interference from neighboring cells. The proportional fair scheme lies in between the two extremes. Fig. 4 illustrates the CDF of the ICI considering different number of interfering cells and path loss exponents β for the greedy scheduling scheme. With the increase in the number of interferers, the interference level increases. Moreover, as β increases, the signal degrades rapidly and thus interference level is reduced considerably. At this point, it is important to mention that in this paper we derive and utilize the MGF of the cumulative ICI rather than the CDF of the cumulative ICI in order to evaluate important network performance metrics. Therefore, the analytical part of the provided figure of the CDF of the cumulative ICI is plotted using a technique mentioned in [22] to convert MGF into CDF numerically. Fig. 5 investigates the effect of increasing the number of competing users on a given sub-carrier considering all scheduling schemes. It can be observed that the increase in the number of users enhances the performance of the opportunistic scheduling schemes due to additional multiuser diversity gains. The greedy scheme achieves the best performance whereas the round robin scheme achieves the worst performance. As expected, the proportional fair scheme lies in between the two extremes. The average capacity of location based round robin over W = K time slots has been shown to be better than the conventional round robin scheme.The average capacity results of the greedy round robin scheme is presented for W = 3 and W = 6 . Clearly, for W = 1 , the scheme is equivalent to the greedy scheme; however, with the increase of time slots, performance degradation takes place due to the reduction of multiuser diversity caused by ignoring the users from previously allocated rings.