Zhang Jinya, Jiang Min, Zhang Jingyi, Gu Mengchen, Cao Ziping
Engineering Training Center, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
College of Telecommunications & Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Sensors (Basel). 2024 Mar 27;24(7):2141. doi: 10.3390/s24072141.
Ultrasound is extremely efficient for wireless signal transmission through metal barriers due to no limit of the Faraday shielding effect. Echoing in the ultrasonic channel is one of the most challenging obstacles to performing high-quality communication, which is generally coped with by using a channel equalizer or pre-distorting filter. In this study, a deep learning algorithm called a dual-path recurrent neural network (DPRNN) was investigated for echo cancellation in an ultrasonic through-metal communication system. The actual system was constructed based on the combination of software and hardware, consisting of a pair of ultrasonic transducers, an FPGA module, some lab-made circuits, etc. The approach of DPRNN echo cancellation was applied to signals with a different signal-to-noise ratio (SNR) at a 2 Mbps transmission rate, achieving higher than 20 dB SNR improvement for all situations. Furthermore, this approach was successfully used for image transmission through a 50 mm thick aluminum plate, exhibiting a 24.8 dB peak-signal-to-noise ratio (PSNR) and a about 95% structural similarity index measure (SSIM). Additionally, compared with three other echo cancellation methods-LMS, RLS and PNLMS-DPRNN has demonstrated higher efficiency. All those results firmly validate that the DPRNN algorithm is a powerful tool to conduct echo cancellation and enhance the performance of ultrasonic through-metal transmission.
由于不受法拉第屏蔽效应的限制，超声波在通过金属屏障进行无线信号传输方面极为高效。超声信道中的回波是实现高质量通信最具挑战性的障碍之一，通常通过使用信道均衡器或预失真滤波器来应对。在本研究中，研究了一种名为双路径递归神经网络（DPRNN）的深度学习算法，用于超声穿金属通信系统中的回波消除。实际系统基于软件和硬件的组合构建，由一对超声换能器、一个FPGA模块、一些自制电路等组成。DPRNN回波消除方法应用于传输速率为2 Mbps、具有不同信噪比（SNR）的信号，在所有情况下均实现了高于20 dB的SNR改善。此外，该方法成功用于通过50 mm厚铝板的图像传输，呈现出24.8 dB的峰值信噪比（PSNR）和约95%的结构相似性指数测量值（SSIM）。此外，与其他三种回波消除方法——LMS、RLS和PNLMS相比，DPRNN已证明具有更高的效率。所有这些结果都有力地验证了DPRNN算法是进行回波消除和提高超声穿金属传输性能的强大工具。
