Channel Estimation Based on Extreme Learning Machine for High Speed Environments

Due to the complexity and extensive application of wireless systems, channel estimation has been a hot research issue, especially for high speed environments. High mobility challenges the speed of channel estimation and model optimization. Unlike conventional estimation implementations, this paper proposes a new channel estimation method based on extreme learning machine (ELM) algorithm. Simulation results of path loss estimation and channel type estimation show that the ability of ELM to provide extremely fast learning make it very suitable for estimating wireless channel for high speed environments. The results also show that channel estimation based on ELM can produce good generalization performance. Thus, ELM is an effective tool in channel estimation.