Robust Channel Estimation for Optical Wireless Communications Using Neural Network
This work addresses channel estimation challenges for optical wireless communication systems, particularly in indoor environments, but it is incremental as it applies neural networks to a known bottleneck in a specific domain.
The paper tackles the problem of frequency-selective channel effects in optical wireless communications by proposing a neural network-based framework for robust channel estimation, resulting in improved normalized mean square error and bit error rate performance compared to conventional methods while maintaining computational efficiency.
Optical Wireless Communication (OWC) has gained significant attention due to its high-speed data transmission and throughput. Optical wireless channels are often assumed to be flat, but we evaluate frequency selective channels to consider high data rate optical wireless or very dispersive environments. To address this for optical scenarios, this paper presents a robust channel estimation framework with low-complexity to mitigate frequency-selective effects, then to improve system reliability and performance. This channel estimation framework contains a neural network that can estimate general optical wireless channels without prior channel information about the environment. Based on this estimate and the corresponding delay spread, one of several candidate offline-trained neural networks will be activated to predict this channel. Simulation results demonstrate that the proposed method has improved and robust normalized mean square error (NMSE) and bit error rate (BER) performance compared to conventional estimation methods while maintaining computational efficiency. These findings highlight the potential of neural network solutions in enhancing the performance of OWC systems under indoor channel conditions.