4 function (MGF) of the ICI considering a single interfering cell. Next, we derive the MGF expression for the cumulative ICI experienced from all interfering cells over generalized fading channels, and present explicit expressions for three practical fading models. Finally, we demonstrate the importance of the derived expressions by utilizing them to evaluate important network performance metrics. Notation : Exp( λ ) represents an exponential distribution with parameter λ , Gamma( m$_{s}$, m$_{c}$ ) represents a Gamma distribution with shape parameter m$_{s}$ and scale parameter m$_{c}$ . K$_{G}$ ( m$_{c}$, m$_{s}$, Ω) represents the Generalized- K distribution with fading parameter m$_{c}$ , shadowing parameter m$_{s}$ and average power Ω . Γ( . ) represents the Gamma function. p ( A ) denotes the probability of event A. f ( . ) and F ( . ) denotes the probability distribution function (PDF) and cumulative distribution function (CDF), respectively. [ a, b ] denotes a discrete set of elements which ranges from a to b . Finally, E [ . ] denotes the expectation operator. II. PART I: SYSTEM MODEL AND PROPOSED FRAMEWORK We consider a given cell surrounded by L interfering neighboring cells. For analytical convenience, the cells are assumed to be circular with radius R . Each cell l is assumed to have U uniformly distributed users. The frequency reuse factor is assumed to be unity with each subcarrier reused in all cells. The bandwidth of a subcarrier is assumed to be less than the channel coherence bandwidth, thus, each subcarrier experiences flat fading. Time is divided into time slots of le ngth smaller than the channel coherence time and, thus, the channel variation within a given time slot is negligible. Generally, the scheduling strategies can be broadly categorized into two classes; (i) rate maximization (i.e., rate adaptation) while transmitting with constant/maximum power; (ii) power minimization (i.e., power adaptation) while achieving a fixed data rate. In this w ork, we focus on rate adaptive schemes where users transmit with their maximum power in order to maximize their rate depending on the existing channel and interference conditions. Therefore, for the scope of this paper, we assume that all users transmit with their maximum power P$_{max}$ on a given subcarrier with rate adaptation depending on their channel qualities. At this point, it is also important to emphasize that this is not a limitation and the approach can be extended for various uplink power control mechanisms. The instantaneous signal to noise ratio (SNR) γ of each user can then be written as follows: γ = P$_{max}$C r − $^{β}$ψη σ 2 = ¯ Kr − $^{β}$ζ (1) A. Description of the System Model