# Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation
## 1 Introduction  
In many sensing and control scenarios, signal transmission through metal barriers is necessary without physically penetrating them as much as possible. This includes monitoring the internal running status of a nuclear reactor [1, 2], acquiring information from sensors deployed in high-pressure pipes [3, 4], and passing data from one side of a watertight bulkhead to the other [5]. Traditional wireless communication based on electromagnetic waves is ineffective due to the Faraday shielding effect. Ultrasound propagates readily in metal; therefore, it has been suggested to replace electromagnetic waves for through-metal communication [6, 7, 8, 9]. However, the acoustic impedance mismatch between the ultrasonic transducer and the metal structure leads to numerous acoustic reflections at interfaces. Consequently, the communication channel presents severe echoing, which further causes intersymbol interference (ISI) at a high data rate and limits the communication rate. The echoing in the ultrasonic channel is considerably similar to the multipath effect and frequency-selective fading phenomenon observed in radio frequency (RF) communications, which is typically addressed by an equalizer performed at the output of the channel. Some filtering algorithms including least mean square (LMS), normalize least mean square (NLMS) recursive least square(RLS) [10, 11, 12], subband adaptive filter (SAF), and sparse filter (SF) [13, 14, 15] have been applied for equalization in the acoustic channel. It was found that frequently-used LMS and NLMS could not satisfyingly remove the echoing impact exceptits simplicity [10; 11]. RLS, SAF and SF exhibited better performance, but their operation complexity partially prevented the actual utilization [13; 14; 15; 16]. The echo cancellation was also investigated by using an experimental-based or model-driven filter at the input of the channel to pre-distort signals, which might simplify the receiver's duties [17; 18; 19]. [PERSON] et al. introduced a canceling pulse at the transmitter of their communication system and destructively combined it with the echoes to suppress its amplitude [17]. Their report demonstrated that the data rate was significantly enhanced from 50 kbps to 1 Mbps. Following that, they further improved the echo cancelation process by building a more accurate model and using that model to derive a better pre-distorting filter [18]. Actually, the pre-distorting filter was constructed based on certain channel conditions, for instance, the metal type and wall thickness, and hence it easily lost robustness due to the diversity and variability of practical applications. On the other hand, orthogonal frequency division multiplexing (OFDM) is considered a powerful solution to the multi-path problem. The OFDM technique divides the frequency-selective channel into orthogonal flat fading bands, allowing for a marked increase in data rate without using highly complex equalizers to correct signal fading. Previous literature indicated that OFDM-based communications could accomplish high-data-rate transmission through metal barriers [20; 21; 22]; however, one shortcoming of OFDM is that it will result in a high peak-to-average power ratio (PAPR). High PAPR means that the system should consume high power and employ a power amplifier with a broad linear range for the transmitter. Over the past decade, deep learning (DL) has been widely used in engineering fields [23; 24], for example, natural language processing. Some DL algorithms have been proven to be greatly efficient for treating unintelligible subjects in the physical layer of wireless communication where the channel conditions are unknown or too complicated for analyzing [25; 26; 27]. Recent reports indicated that DL-based methods were substantially powerful for echo cancellation at audio frequency, implying its potential application in ultrasonic communication [28; 29].