Autoregressive and Neural Network Model Based Predictions for Downlink Beamforming

Abstract

Abstract. In Time-Division-Duplex (TDD) wireless communications, downlink beamforming performance of a smart antenna system can be degraded due to variation of spatial signature vectors in vehicular scenarios. To mitigate this, downlink beams must be adjusted according to changing propagation dynamics. This can be achieved by modeling spatial signature vectors in the uplink period and then predicting them for new mobile position in the downlink period. This paper examines time delay feedforward neural network (TDFN), adaptive linear neuron (ADALINE) network and autoregressive (AR) filter to predict spatial signature vectors. We show that predictions of spatial signatures using these models provide certain level of performance improvement compared to conventional beamforming method under varying mobile speed and filter (delay) order conditions. We observe that TDFN outperforms ADALINE and AR modeling for downlink SNR improvement and relative error improvement with high mobile speed and higher filter order/delay conditions in fixed Doppler case in multipaths.