Research Information System
IET Communications, vol.20, no.1, 2026 (SCI-Expanded, Scopus)
Channel state information (CSI) is pivotal for assuring high performances of wireless communication systems. In particular, multiple-input multiple-output transmission is only beneficial when CSI is known. A large number of subcarriers are desired in Orthogonal Frequency Division Multiplex (OFDM) systems to boost overall throughput, which makes channel estimation a more challenging task, especially to extract channel features in a more dynamic environment without causing a significant overhead transmission. Conventional least squares-based methods are affected by the noise and interference that inherently exist in the acquired data for processing. We proposed the deep neural network (DNN)-based method to estimate CSI, and one distinguishing characteristic is to adopt a Discrete Fourier Transform (DFT) operation-based method to mitigate the impact of noise before carrying out the DNN procedure; hence, the accuracy of the learning outcome significantly improved. The effectiveness of the proposed scheme is verified with simulations under a variety of propagation scenarios. The proposed method has demonstrated a high performance for channel estimation. It has shown a particular advantage in more dynamic and noisy environments for wireless communications.