Learning-based Blind Detection of Interference Parameters for NAICS System

In this paper, a supervised-learning based blind detection of interference parameters for network-assisted interference cancellation and suppression (NAICS) system is proposed. In order to enable joint detection or interference cancellation at a user equipment in NAICS system, we consider detection of the interference parameters including traffic-to-pilot power ratio (TPR), rank indicator (RI), and precoding matrix indicator (PMI). We divide overall process for detection of these interference parameters into two steps, and propose supervised learning based neural network architecture for detection of corresponding parameters in each steps. Link-level simulation results are provided to validate detection performance of the proposed neural network architecture and performance of NAICS system which uses the proposed learning based detection method with respect to block error rate (BLER).