# A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks
## 1 Introduction  
Long Term Evolution (LTE) is the solution proposed by the 3 GPP consortium to provide high data rates to users in order to satisfy high bandwidth demand. LTE is a 4G technology and uses IP technology for data transport. It supports 100 Mbps data rates in the downlink and 50 Mbps data rates in the uplink [1]. LTE uses frequency reuse factor one that leads to intercell interference (ICI) [2]. LTE uses Orthogonal Frequency Division Multiplexing Access (OFDMA) in the downlink and Single Carrier Frequency Division Multiplexing Access (SC-FDMA) in the uplink to eliminate intra-cell interferences. The bandwidth is divided into orthogonal narrowband subcarriers of 180 kHz separated by guard band of 15 kHz. The main radio impairment faced by LTE is intercell interference as it uses frequency reuse one scheme where all frequencies are used by each cell. Cell edge users are likely to suffer interference causing by neighboring cells transmitting at the same frequencies while receiving weak signals from their serving cell due to the path loss that is higher with the distance to the serving enodeB [3]. Intercell interference mitigation techniques are used to address the ICI problem. There are three approaches to intercell mitigation defined by the 3 GPP consortium: intercell interference randomization, intercell interference cancellation and intercell-interference coordination/avoidance [4]. They can be combined in a system as their benefits are mutually exclusive [5]. The goal of intercell interference randomization is randomizing the interfering signals with cell-specific scrambling,cell-specific interleaving or frequency hopping. Intercell interference cancellation goal is to suppress interference at the user beyond what can be achieved by just exploiting the processing gain. This is done by using multiple antennas at the user or by detection and subtraction of the intercell interference. Intercell interference coordination/avoidance goal is to minimize intercell interference since it is impossible to prevent it as LTE uses reuse frequency 1 (RF1) scheme. This technique applies restrictions to the downlink resource management in a coordinated way between cells. Restrictions are made on transmit power and resource allocation both in time and frequency domain. The effect of these restrictions is the improvement of SINR, thus the data rate and coverage, especially for cell edges users. Basic intercell interference coordination techniques use static schemes [6; 7; 8] where available bandwidth is partitioned among users within each cell thanks to frequency reuse schemes. This bandwidth partitioning divide users in cell edge users and cell center users. However, some techniques combine dynamic schemes with static schemes [9; 10; 11; 12], dynamic schemes being used for cell edge users. Resources are likely to be waisted in some partitions if there are fewer users to use them, while users in other partitions could use them. In this paper, a full dynamic intercell interference coordination in a multi-cell environment scheme that combines resource allocation and dynamic power control in the downlink using reinforcement learning is proposed. The proposed scheme does not use bandwidth partitioning. Reinforcement learning is a computational intelligence approach which belongs to machine learning and is used in many areas. Among these area, we have robotics, games, control, and operations research [13; 14]. It has low computational requirements, medium memory requirements, high flexibility, and is optimal [15]. The proposed scheme has four steps: user selection, resource allocation, dynamic power control and packet to retransmit identification. We introduce a new way of handling packet retransmission, which, instead of waiting for a notification from the receiver, checks the channel conditions before sending a packet. When channel conditions are bad, the packet is immediately transferred in retransmission queue. The rest of the paper is organized as follows.