21 Part II of the Technical Report PART II: INTRODUCTION Energy efficient wireless communications has been gaining c onsiderable attention these days mainly due to two major reasons i) dramatically varying global climate [23], and ii) slowly progressing battery technology [24]. In this context, power adaptation has been evolved as an efficient approach to reduce per capita power consumption, control inter-cell interference (ICI) and increase fairness among users in future generation wireless networks such as Orthogonal Frequency Division Multiple Access (OFDMA). In OFDMA networks, the system bandwidth is decomposed into orthogonal subcarriers. These subcarriers are adaptively allocated among users within a cell based on a predefined scheduling scheme and user transmit power levels. The allocated users on the same subcarrier in neighboring cells can cause significant uplink ICI depending on their transmit power level and channel conditions with respect to the base station (BS) of interest. Most of the recent literature considered the modeling of ICI in the downlink where the location of interferers is usually deterministic [2], [3]. However, compared to the downlink, the modeling of ICI in the uplink is more challenging due to the arbitrary locations of the interferers and the powers associated with them. Some interesting analytical models for uplink ICI have been presented in [ ? ], [6]; however, none of them considered the impact of channel based scheduling and power adaptation on the uplink ICI. Recently, in [25], we presented a semi-analytical framework to derive the distribution of uplink ICI on a given subcarrier assuming greedy scheduling without power adaptation. In this paper, we generalize the developed semi-analytical framework to incorporate the impact of power adaptation on the uplink ICI. This power adaptation promises considerable power savings while allowing high degree of fairness among users. Several power adaptation mechanisms are discussed in [26] such as fast and slow power control, open-loop and closed-loop power control etc. In this paper we focus on slow power control considering that each user is capable of adapting its transmit power autonomously either by measuring its location through a global positioning system (GPS) or estimating its distance based on the power measurement of pilot signals from the surrounding BSs [14]. PART II: SYSTEM MODEL We consider a given cell surrounded by L interfering cells. For analytical convenience, the cells are assumed to be circular with radius R . Each cell contains U uniformly distributed users where each user is