Gray-model based SINR estimation for enhanced intercell interference coordination

The configuration of the almost blank subframe (ABS) is a critical technique for enhanced intercell interference coordination (eICIC) in heterogeneous network (HetNet). Due to great channel changes during the ABS and the normal subframes, it is challenging to estimate the signal to interference plus noise ratio (SINR) exactly. In this paper, a novel method based on gray model is proposed to estimate SINR. Gray model can use a small amount of ABS information as original series and get accumulative generation series to predict the current SINR of ABS. Performance evaluation shows that the proposed method can get more accurate SINR estimation and improve user average throughput when channel correlation is strong or the ratios of ABS are high. The proposed SINR estimation method is more appropriate for macro-pico network with strong correlated channels.