Markov Cluster Algorithm Based Intra and Inter Cell Interference Management in 5g Wireless Mesh Networks

Abstract Due to the decreasing distance between cells, inter and intra cell interference management become more challenging in ultra-highly dense small cell cellular networks. The main objective of our research is to manage the inter-cell and intra-cell interferences in a cellular network. Initially, the inter-cell interference is eliminated by creating an interference-weighted graph and cell clustering of cellular networks. The user clustering process is fundamental for the cell clustering process. The markov clustering algorithm (MCA) is proposed to achieve cell clustering in the cellular network. The intra-cell interference is eliminated by allocating a channel to the user based on the markov model. The channel allocation is done using the markov prediction model, which effectively predicts the best optimal channel for each user in the cells. This prediction process dramatically reduces the channel selection timing during the clustering-based interference management process. The simulation is done using the MatLab platform. The performance parameters used by our proposed method are throughput, SINR (Signal-to-Interferenceplus-Noise-Ratio), bit error rate (BER), sum rate, total energy consumption, and cumulative distribution function (CDF). The robustness and proficiency of our system are represented by comparing various existing methods like resource availability maximization function (RAmax), almost blank subframe (ABS), and channel aggregation (CA) techniques.