12 ˜ r$_{sel}$ is the distance of the allocated user in the interfering cell l from the BS of interest, r$_{sel}$ is the distance of the allocated user from its serving BS, i.e., (BS l ), θ ∈ [0 , 2 π ] and D = 2 R since we consider universal frequency reuse with one tier of interfering cells. The approach can be extended to any number of tiers in a straightforward manner. In order to determine the PMF of ˜ r$_{sel}$ where ˜ r$_{sel}$ ∈ [ D − R, D + R ] , first of all we compute ˜ r$_{i,k}$ for given θ$_{i}$ and r$_{k}$ using (65) as follows: where, ˜ r$_{i,k}$ denotes the interfering distance from a specified polar coor dinate ( r$_{k}$, θ$_{i}$ ) in the interfering cell to the BS of interest located at a distance D (see Fig. 1). In addition, it is worth to mention that ˜ r$_{i,k}$ are the points at which P (˜ r$_{sel}$ = ˜ r$_{i,k}$ ) can be defined using (63) as follows: The two dimensional data set of ˜ r$_{sel}$ , at which P (˜ r$_{sel}$ = ˜ r$_{i,k}$ ) is defined, can then be grouped into M segments of any arbitrary width Δ . This can be done by dividing the distance between D − R and D + R into M equal segments of width Δ and mapping ˜ r$_{i,k}$ accordingly. Clearly, by adding all the probabilities for which ˜ r$_{sel}$ lies in the m th segment we get the probability of ˜ r$_{sel}$ = ˜ r$_{m}$ : where ˜ r$_{m}$ denotes any discrete value that the RV ˜ r$_{sel}$ can take. Recall X = ¯ K ˜ r − $^{β}$χ , therefore the PDF of X conditioned on ˜ r$_{sel}$ can be determined by RV transformation as follows: Averaging over the PMF of ˜ r$_{sel}$ , the distribution of the ICI, f$_{X}$ ( x ) , from any cell l can be given as: It is important to emphasize that the derivation of the distribution of ICI is based on the scheduling decisions of interfering cells at a given time slot. Therefore, the parameter ˜ r$_{m}$ of the ICI distribution varies from one time slot to the other for the location based round robin and greedy round robin schemes. ˜ r 2 i,k = r 2 k + D 2 − 2 r$_{k}$D cos θ$_{i}$ ∀ r$_{k}$, ∀ θ$_{i}$ (26) P (˜ r$_{sel}$ = ˜ r$_{i,k}$ ) = P ( r$_{sel}$ = r$_{k}$ ) I (27) P (˜ r$_{sel}$ = ˜ r$_{m}$ ) = ∑ ˜ r$_{i,k}$ ∈ [˜ r$_{m}$ − Δ 2 , ˜ r$_{m}$ + Δ 2 ] P (˜ r$_{sel}$ = ˜ r$_{i,k}$ ) (28) f$_{X}$$_{|}$$_{˜}$ r$_{sel}$ = f$_{χ}$ ( x ˜ r β sel ¯ K − $^{1}$) ¯ K ˜ r − β sel (29) f$_{X}$ ( x ) = ˜ r$_{M}$ ∑ ˜ r$_{m}$ =˜ r 1 f$_{χ}$ ( x ˜ r β m ¯ K − $^{1}$) ¯ K ˜ r − β m P (˜ r$_{sel}$ = ˜ r$_{m}$ ) (30)