Regression-Based Uplink Interference Identification and SINR Prediction for 5G Ultra-Dense Network

Ultra-dense network (UDN) is recognized as a key technology for the fifth generation (5G) network to deliver high capacity. Due to cell densification, UDN suffers from heavy intercell interference (ICI). To mitigate ICI, an accurate interference modeling is essential. Compared to distributed mechanism, centralized interference management is more adept at addressing server ICI problem, especially in dense deployments. Hence, we adapt centralized radio access network (C-RAN) architecture to obtain global channel state data. In this paper, a novel regression-based uplink interference identification and signal-to-interference ratio (SINR) prediction algorithm is proposed for C-RAN based 5G UDN. By leveraging big data technology with in-depth analysis of the cause of ICI, this algorithm can precisely model the interference relationship and thus achieve very accurate SINR prediction. In addition, the proposed algorithm is highly efficient in computing, as demonstrated by complexity analysis.