## 2 Related Works  
Interference from the neighboring cell significantly affects the cell-edge user as well as the system performance. Therefore, inter-cell interference (ICI) has been studied previously to overcome the problems of poor QoS and low throughput of the cell-edge users [10; 11]. The authors consider the soft frequency reuse (SFR) technique, which is a form of inter-cell interference coordination (ICIC) for LTE systems in [12; 13; 14]. A decentralized SFR scheduling algorithm with low complexity is proposed in [12], which incorporates user classification and balance fairness and throughput among all users. Two types of frequency bands are used in a cell, namely, cell-center band (CCB) and cell-edge band (CEB). Moreover, users are also separated into schedules in CCB groups and in CEB groups. The users with higher interference from the adjacent cells are defined as the cell-edge users (CEUs) and the rest of the users are the cell-center users (CCUs). In [13], the authors proposed a softer frequency reuse (SerFR) method, which is the modified version of SFR. In this method, the frequency reuse factor (FRF) of cell edge and cell center is one. High power frequency bands are allocated to the cell edge users and low power bands are allocated to the cell center users. In [14], an adaptive SFR is considered by altering the transmit power and the number of major subcarriers for individual cells based on the traffic load. This method decentralizes the resource allocation decision by sharing the subcarrier, transmit power and rate information with the neighboring cells. The interference can vary due to other reasons as well as the geographical area, weather conditions, and user location. However, the authors did not consider any of these parameters to avoid the interference. Fractional frequency reuse (FFR) is used in [15] where the frequency band is divided into several sub-bands to avoid the interference. The authors consider the FRF one for cell center users and three for cell edge users. This method allocates different sub-bands for different cell edge areas. In [16], a method using frequency reuse and pilot sensing is proposed to mitigate the cross-tier co-channel interference. After deploying the FRF of three to the macrocell, the femtocell senses the pilot signals from the adjacent cell. The scheduler then allocates the remaining frequency to the femtocell, which has not been allocated to another cell. In [17], authors consider reducing the interference as a Gaussian random variate. They proposed three RB allocation techniques, namely, pure random allocation, compact random allocation and allocation with memory. The authors in [18] present an interference avoidance method to prevent strong inter-cell interference for the cell-edge user. Their proposed method consists of a two-tier algorithm--the first one at the eNB level and the second one at a central controller. They consider this controller as a group of neighboring eNBs that are connected to each other. According to their model, users send channel state information (CSI) with the interference of two sectors, whereas sectors send restriction request to the central controller. The controller then resolves the conflicts of the request and sends modified restriction lists to all involved eNBs. A downlink optimal power allocation scheme is used in [19] to maximize the spectral efficiency of the cell-edge user. The MLWDF scheduling algorithm has been studied in [20; 21; 22] to analyze the performance and maximize the QoS of cell user. A delay priority based algorithm is used in [20] where the user gets higher priority if the packet delay is close to the threshold for real-time data. However, the authors do not consider the non-real-time data packets. The authors in [21] show that MLWDF provides better QoS and Quality of Experience (QoE) than other scheduling algorithms in terms of packet loss ratio and delay.