3GPP LTE Downlink Channel Estimation in High-Mobility Environment Using Modified Extended Kalman Filter

Abstract

Estimation of time varying downlink channel for Long Term Evolution (LTE) systems in the high-mobility environment is a challenging task. In literature, the state-of-the-art techniques used for LTE channel estimation are extended Kalman filter (EKF), 2D interpolation using least square (2DILS), etc. Channel estimation has been performed by inserting pilot symbols in the frame. For fast time varying channels, Kalman filter based channel estimation is a fast and less complex technique compared to other conventional filters. The time correlation is modeled as a first order auto-regressive (AR) random process. Linearization of the state transition function is carried out using Taylor approximation. However, the higher bit error rate in fast fading channels discourages the use of aforementioned channel estimation techniques. This paper proposes a modified extended Kalman filter (MEKF) for joint estimation of the channel response and AR model coefficients. The bit error vs SNR performance of the proposed estimation technique has been demonstrated for different fast time varying LTE channels such as pedestrian user, vehicular user at different velocities such as 50 km/h and 70 km/h. The simulation results show that the proposed MEKF based approach leads to lower bit error rate than its two state-of-the-art counterparts based on EKF and 2DILS. © 2018 IEEE.