Coordinated Multi-Point Clustering Schemes: A Survey

Mobile data traffic grew by 74% in 2015 and it is expected to grow eight-fold by 2020. Future wireless networks will need to deploy massive number of small cells to cope with this increasing demand. Dense deployment of small cells will require advanced interference mitigation techniques to improve spectral efficiency and enhance much needed capacity. Coordinated multi-point (CoMP) is a key feature for mitigating inter-cell interference, improve throughput and cell edge performance. However, cooperation will need to be limited to few cells only due to additional overhead required by CoMP due to channel state information (CSI) exchange, scheduling complexity, and additional backhaul limitation. Hence, small CoMP clusters will need to be formed in the network. This paper surveys the state-of-the-art on one of the key challenges of CoMP implementation: CoMP clustering. As a starting point, we present the need for CoMP, the clustering challenge for 5G wireless networks and provide a brief essential background about CoMP and the enabling network architectures. We then provide the key framework for CoMP clustering and introduce self organization as an important concept for effective CoMP clustering to maximize CoMP gains. Next, we present two novel taxonomies on existing CoMP clustering solutions, based on self organization and aimed objective function. Strengths and weaknesses of the available clustering solutions in the literature are critically discussed. We then discuss future research areas and potential approaches for CoMP clustering. We present a future outlook on the utilization of big data in cellular context to support proactive CoMP clustering based on prediction modeling. Finally, we conclude this paper with a summary of lessons learned in this field. This paper aims to be a key guide for anyone who wants to research on CoMP clustering for future wireless networks.