Deep Learning-Assisted Channel Estimation in Frequency Selective UWA Communication Systems

Envisioned future wireless gives rise to the possibility of exploiting underwater applications with high spectral efficiency and reliable communication. This paper presents a method to combine channel estimation in the time domain with neural networks and supervised learning to improve orthogonal frequency-division multiplexing (OFDM) systems in underwater communications. The pilot structure is designed in such a way that the propagation channels are tracked from symbol to symbol. Moreover, the low-density parity-check code (LDPC) channel coding method is applied to overcome the severe fading and attenuation effects in underwater acoustic environments. We consider this proposed system’s performance in a measurement-based channel model to evaluate it. In particular, the system’s performance is evaluated for different levels of channel mobility. Numerical results show that, with the assistance of deep learning, the channel estimation performance can be improved, depending on how fast the channel changes. Compared with state-of-the-art benchmarks, our proposal offers better system performance in terms of bit error ratio and normalized mean square error