A Pilot-based Hybrid and Reduced Complexity Channel Estimation Method for Downlink NB-IoT Systems

Efficient and low complexity channel estimation has become the prime concern in Narrowband Internet of Things (NB-IoT) receiver performance. The maximum likelihood estimator (MLE) is simple in complexity but subjected to low estimation precision, whereas well-known 2D Wiener filtering offers efficient performance to estimate the channel but entails severe complexity. Application of two 1D Wiener filter (for frequency and time domain) can reduce its complexity but degrades the performance. In this work, we proposed an efficient hybrid channel estimation method for the downlink NB-IoT systems by combining the time domain Wiener filter technique and computationally simple frequency domain MLE. The proposed technique achieves a good balance regarding both system efficiency and computational complexity. Computer simulations prove the significant improvement of the mean square error (MSE) and the block error rate (BLER) in comparison to the existing two 1D Wiener filtering and MLE technique. The reduced system complexity is shown by the comparison of the total number of complex multiplication in the aforementioned methods.