Channel Update Algorithm for VBLAST Architecture in Vehicular Ad-Hoc Networks

Abstract

Vehicular networks require accurate channel state information (CSI) to decode the received signal. Such knowledge is usually obtained via a training sequence. However in vehicular networks, the channel coherence time is very small due to the high speeds of the nodes, therefore the channel estimate from the training is likely to become inaccurate as the decoding proceeds. Using shorter packets can improve the performance at the cost of increased overhead. In this paper we introduce a novel channel tracking algorithm for VBLAST in vehicular networks with relatively little change in the overhead. The algorithm uses first order Kalman filters therefore it has less complexity than available tracking algorithms. The algorithm uses the detected symbols and received signal after the interference cancellation and detection processes of the VBLAST decoder to improve the channel estimation. Simulation results show considerable improvement in mean square error (MSE) and BER when using this algorithm compared to channel estimation by training only with small increase in hardware complexity.