论文标题
使用FSK调制和水下机器人深度学习解调的电通系统
An Electrocommunication System Using FSK Modulation and Deep Learning Based Demodulation for Underwater Robots
论文作者
论文摘要
对于通常具有严格的功率和尺寸约束的小型水下机器人，水下通信非常具有挑战性。在我们以前的工作中，我们开发了人工电通系统，该系统可能是小型水下机器人通信的替代方法。本文进一步提出了一种新的电通通信系统，该系统利用了二进制频移键合（2FSK）调制和对水下机器人的深度学习解调。我们首先得出了一个水下电通信模型，该模型涵盖了近场区域和近场区域以外的大型过渡区域。采用2FSK调制以提高电信号的抗干扰能力。深度学习算法用于通过接收器解码电信号。模拟和实验表明，在相同的测试条件下，新通信系统在通信距离和数据传输速率上都优于先前的系统。在具体而言，新开发的通信系统以5 kbps的数据传输速率在10 m的距离内实现稳定的通信，功率消耗少于0.1W。通信距离的实质性增加进一步提高了水下机器人中电沟通的可能性。
Underwater communication is extremely challenging for small underwater robots which typically have stringent power and size constraints. In our previous work, we developed an artificial electrocommunication system which could be an alternative for the communication of small underwater robots. This paper further presents a new electrocommunication system that utilizes Binary Frequency Shift Keying (2FSK) modulation and deep-learning-based demodulation for underwater robots. We first derive an underwater electrocommunication model that covers both the near-field area and a large transition area outside of the near-field area. 2FSK modulation is adopted to improve the anti-interference ability of the electric signal. A deep learning algorithm is used to demodulate the electric signal by the receiver. Simulations and experiments show that with the same testing condition, the new communication system outperforms the previous system in both the communication distance and the data transmitting rate. In specific, the newly developed communication system achieves stable communication within the distance of 10 m at a data transfer rate of 5 Kbps with a power consumption of less than 0.1 W. The substantial increase in communication distance further improves the possibility of electrocommunication in underwater robotics.
