Abstract
Long Term Evolution networks, which are cellular networks, are subject to many impairments due to the nature of the transmission channel used, i.e. the air. Intercell interference is the main impairment faced by Long Term Evolution networks as it uses frequency reuse one scheme, where the whole bandwidth is used in each cell. In this paper, we propose a full dynamic intercell interference coordination scheme with no bandwidth partitioning for downlink Long Term Evolution networks. We use a reinforcement learning approach. The proposed scheme is a joint resource allocation and power allocation scheme and its purpose is to minimize intercell interference in Long Term Evolution networks. Performances of proposed scheme shows quality of service improvement in terms of SINR, packet loss and delay compared to other algorithms.
Highlights
Long Term Evolution (LTE) is the solution proposed by the 3GPP consortium to provide high data rates to users in order to satisfy high bandwidth demand
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
We conducted simulations for an LTE network with 19 cells [16] with one enodeB located on each cell; a bandwidth of 5 MHz [16], which corresponds to 25 RBs to share among users; and a simulation time of 3000 time to interval (TTI)
Summary
Long Term Evolution (LTE) is the solution proposed by the 3GPP consortium to provide high data rates to users in order to satisfy high bandwidth demand. 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. 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. 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.
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