Achieving high frequency reuse in dense cellular networks: A matrix graph approach

Small cell size and dense cell placement have been considered in the Long Term Evolution standard. However, small cell may result in high interference between cells. Moreover, the random geographic topology of small cell networks make them hard to analyze. In this paper, a new approach called matrix graph is proposed which is robust to interference and random topology, and takes advantage of the small cell size. Based on theoretical properties of matrix graphs, an asymptotically optimal algorithm is obtained to address the frequency allocation problem with interference constraints, which is usually NP-hard. The computation complexity decreases with cell size and grows linearly with network size. Simulation results also support the theoretical performance gains. The proposed matrix graph approach is capable of designing and analyzing the next-generation dense cellular networks.