# A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks
## 2 Related Work  
Intercell interference being the main problem in LTE networks, intensive research has been carried out to address it. Surveys have been carried out on intercell interference mitigation techniques in LTE downlink networks [16; 17], on intercell interference coordination techniques in OFDMA-based cellular networks [18] and on ICIC techniques in OFDMA based-cellular networks and LTE [19]. ICIC techniques can be classified in two groups [18]: static ICIC or frequency reuse based schemes and dynamic ICIC or cell coordination schemes. In static ICIC schemes, a common technique is Fractional Frequency Reuse (FFR). Users are divided into two groups: cell edge users and center cell users. The available resources are divided into two groups: one group for cell edge users and one group for center cell users. FFR is divided into three main classes [18]: Partial Frequency Reuse (PFR) schemes, Soft Frequency Reuse (SFR) schemes and intelligent reuse schemes. Performance of reuse-1, reuse-3, FFR, and SFR are investigated regarding network load and user distribution in [20]. Some hybrid schemes associate a static scheme to another techniques, for example in [21] where the authors proposed a hybrid mechanism using Fractional Frequency Reuse (FFR) and Almost Blank Sub-frame (ABS) schemes to mitigate inter-cell interference caused to Device-to-Device (DUE-Rxs) receivers by cellular user equipments (CUEs) in sensor networks underlying LTE-A. On the other side, coordination-based schemes can be categorized into four categories [18]: centralized, semi-distributed, coordinated-distributed, and autonomous-distributed. The level of coordination can be differentiated by four dimensions [18]: optimization objective, power control technique, channel allocation recommender and fairness to user. In [22], a joint resource block allocation and transmission power allocation in the uplink communication for multi-cell D2D enabled networks scheme is proposed. The proposed scheme uses bilateral symmetric interaction game for RB allocation problem and linear programming for the power allocation problem. In [23], the authors proposed a coordinated multi point interference mitigation scheme that uses destructive spatial phase coding as a pre-coding scheme to reduce the SNR. The scheme is proposed for heterogeneous OFDM networks. Intercell interference coordination is an optimization problem that can be better addressed using computational intelligence (CI). In this paper, we consider centralized cell coordination schemes. Obviously, the problem to solve in coordination schemes is resources allocation in multi-cells networks. Most studies addressing resource allocation found in the literature consider single cell networks. There are however some studies that consider multi cell networks. In [24], an ICIC technique based on user's ratio and frequency allocation is proposed for OFDM based networks. Cell-center users are allocated resources that are allocated to cell-edge users in neighboring cells. In [25], an ICIC scheme using downlink multi-cell chunk allocation with dynamic intercell coordination is proposed with two level-algorithm, at a base station and at a central controller. In [26], a heuristic intercell coordination scheme for the uplink of LTE networks is proposed. The proposed scheme is formulated as an integer programming problem. The basic idea of the scheme is to switch users on and off to maximize a certain utility function. Coordination is carried out between two cells at a time, and then iterative improvement is implemented for the whole coordination. Power control is not done in this scheme. The scheme just mitigates the intercell interference by resource allocation. The performance of the scheme has been evaluated in terms of uplink average throughput and delay via Monte Carlo simulations with a two-cell network case. The proposed scheme has been compared with no coordination schemes using proportional fairness (PF) and round robin (RR) schedulers.