Channel Prediction for Coordinated Multipoint Transmission

Abstract: One of the currently explored strategies for interference avoidance and improving Signal to Noise Ratio (SNR) for mobile communication systems is Coordinated MultiPoint (CoMP) transmission. The general idea of the strategy is to let two or more base stations serve the same user. Due to delay factors, the channels from each serving base station needs to be predicted to obtain an adaptive CoMP system. In this thesis, a user interface is created to act as an experimental platform for a set of measured downlink channel data. The user interface supports editing of the channel data, model estimation, Kalman filtering and prediction and evaluation of the channel statistics. The user interface and the measured channel downlink data is then used to examine how well we can predict the weakest channel in a CoMP setup with three base stations. The predictions are carried out using an m-step Kalman predictor which uses an AR4 model, estimated from previous channel data. For the investigation, the user moves at pedestrian speed and the signals from the three different base stations use orthogonal Common Reference Signals (CRS). A comparison of different CRS patterns is also included in the investigation. It is concluded that 5 ms predictions of the weakest channel achieves a normalized mean squared error (NMSE) of -8 dB or lower provided that the weakest signal has an SNR of at least 5 dB and is no more than 15 dB lower than the combined received signal. Additionally, it is found that predictions are more accurate for CRS patterns spread over time than over subcarriers.