On the Performance of Imperfect Channel Estimation for Vehicular Ad-Hoc Networks

Abstract

In this paper, we investigate the effect of imperfect channel estimation on the performance of inter-vehicular communication systems. In particular, we derive the bit error rate (BER) expression in the presence of channel estimation errors for a space-time block coded (STBC) inter-vehicular communication system based on cascaded Rayleigh fading. Our diversity analysis reveals that the underlying scheme is able to extract the full spatial diversity. Based on the derived BER expression, we formulate a power-allocation problem to optimally allocate power to both training and data transmission phases.

Vehicular Environment Identification Based on Channel State Information and Deep Learning

“…At the receiver side, a channel estimation task is mandatory; this aims to calculate the CSI, which is required in order to recover the transmitted data. Because channel estimation is quite important in V2X communications, a great deal of research work has been carried out in this field [24][25][26]. The channel estimation approaches in the literature are mainly based on observation and long training sequences (LTS).…”

Performance Improvements for Wireless Mobile Networks via Deep Reinforcement Learning

“…where the estimated CSI of the current time instant is simply taken as the predicted CSI for the next time instant. Considering the time-varying nature of wireless environments, this kind of memoryless channel assumption is not realistic [64], especially in vehicular environments due to high mobility [65]. In addition, it is difficult to obtain the perfect CSI due to channel estimation errors, communication latency and backhaul link constraints [66,67].…”