Low Complex Methods for Robust Channel Estimation in Doubly Dispersive Environments

Wireless communications play a significant role in facilitating several mobile applications like unmanned aerial vehicles, high-speed railway, and vehicular communications. Particularly, the concept of connected vehicles brings a new level of connectivity to vehicles. Along with novel on- board computing and sensing technologies, vehicular networks serve as a key enabler of intelligent transportation systems and smart cities. However, in such environments, the propagation medium between the network nodes is highly time-varying leading to considerable reliability challenges. Ensuring communication reliability by the means of accurate channel estimation in such environments is very important. Initially, vehicular communications standards apply the basic least square (LS) estimation that is not enough for the dynamic vehicular environment. Moreover, the frame structure has low pilot density, making channel tracking a difficult task to achieve, especially in high mobility scenarios. Conventional estimators either employ data subcarriers besides pilots in the estimation process, or the estimated channel and noise statistics. Therefore they suffer from significant performance degradation due to high error probability resulting from hard symbol demapping and the sensitivity against the change in the employed channel statistics. The motivation behind this paper is to overcome this challenge by proposing a low complex and robust channel estimation scheme based on truncated discrete Fourier transform (T-DFT) that updates the channel estimates using DFT interpolation without the need for data subcarriers decisions and the estimated channel statistics. Moreover, further performance improvement can be achieved by considering temporal averaging on top of T-DFT estimation. Analytical and simulation results carried out using different vehicular channel models reveal the performance superiority of the proposed schemes compared to conventional estimators while recording a significant decrease in computational complexity and execution time.