用神经网络提升光无线通信在多径环境下的信道估计精度与稳定性。
Robust Channel Estimation for Optical Wireless Communications Using Neural Network
- 基于自适应神经网络选择机制，无需先验信道信息即可估计复杂光学信道。
- 在动态环境中实现更低的归一化均方误差（NMSE）和误码率（BER）。
- 适合追求高可靠性的室内高速光无线通信系统部署。
光无线通信（OWC）因其高速数据传输和高吞吐量而受到广泛关注。传统上假设光无线信道为平坦信道，但本文针对高速率或高度色散环境下的频率选择性信道进行评估。为此，提出一种低复杂度的鲁棒信道估计框架，以缓解频率选择性影响，从而提升系统可靠性与性能。该框架采用神经网络，可在无环境先验信息条件下估计通用光无线信道；根据估计结果及对应时延扩展，激活多个离线训练的候选神经网络之一进行信道预测。仿真结果表明，相比传统方法，所提方法在保持计算效率的同时，显著改善了归一化均方误差（NMSE）和比特误码率（BER）性能。这些发现突显了神经网络在室内多径光信道下实现高数据速率、高可靠性通信的潜力。
原文摘要 · Abstract (English)
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.
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