In high-mobility scenarios, wireless communications undergo time and frequency doubly selective fading, making channel estimation essential for accurately obtaining channel state information (CSI), which in turn enhances the perfor-mance of communication systems. The Time-Frequency Doubly Selective Channel is a channel model that characterizes signal fading with selective properties in both time and frequency dimensions. To address the challenges of channel estimation in such environments, deep learning methods have been widely adopted in recent years. Networks that originally excelled in computer vision and natural language processing, such as Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), have been applied to channel estimation techniques. However, due to significant differences in data characteristics and task objectives between channel estimation and image processing, these approaches still face numerous challenges. This study introduced a novel channel estimation deep learning algorithm based on a Channel Enhanced Deep Horblock Network (CEHNet). The proposed algorithm treats the time-frequency grid of the doubly selective channel as a two-dimensional image and employs a Super-Resolution (SR) network to reconstruct the CSI. Additionally, a preprocessing method that increases amplitude features is utilized to expand the dataset, and Lasso regression is incorporated as a constraint to accelerate the network convergence speed. Experimental results demonstrate that, across various channel models, the proposed CEHNet algorithm outperforms traditional channel estimation methods such as Super-Resolution Convolutional Neural Networks (SRCNN) when the number of pilots is limited. Furthermore, CEHNet exhibits significantly faster convergence rates, achieving a fourfold performance improvement over SRCNN at a signal-to-noise ratio (SNR) of 22 dB.

The water content of human soft tissues is as high as 65%, so ultrasound has advantages in low attenuation, low risk of disease, and limited interference range in human soft tissues, making it advantageous for communication in the body. However, the ultrasonic human channel has the characteristics of dense multipath, so the signals that reach the receiver through multiple paths of reflection and refraction overlap to generate multipath interference, which affects communication reliability. The existing research avoids multipath overlap and its interference by emitting extremely short pulses of ultrasound, but it is difficult to achieve ultra wideband ultrasound probes with large directional angles. The actual pulse width generated by the probe is not small enough, so multipath overlap will occur at the receiver and the interference cannot be ignored in signal judgment. In order to study the distribution characteristics of multipath interference in the body and its impact on wideband ultrasound human body communication, this paper used the k-Wave simulation toolkit to model the ultrasound human body channel in 3D. Then the channel impulse response was obtained through simulation experiments, and the statistical characteristics of multipath delay distribution were analyzed and curve fitting was performed. Based on the decision mechanism of the receiver, the multipath interference of the human channel was estimated, and the lower bound of the bit error rate (BER) for direct sequence spread spectrum ultrasonic broadband (DS-UsWB) was derived. The effectiveness of this lower bound of BER was verified through Monte Carlo experiments. Experimental results indicate that multipath interference cannot be ignored when the signal-to-noise ratio is low, and the communication performance can be improved by adjusting spread spectrum code length.

The traditional dual-mode ultrasound technology has greater potential for wearable implementation compared to the other emerging technologies. During pulse Doppler blood flow velocity estimation, dual-mode ultrasound needs to emit B-mode pulses simultaneously for imaging localization, which requires that B-mode pulses and Doppler pulses share sampling time. This issue can be addressed by using a sparse interval emission method based on single-frequency Doppler pulses. However, common sparse emission arrangements (such as nested emission, coprime emission, etc.) have the disadvantage of a long sampling time window. Especially, there is the problem of insufficient temporal resolution with significant changes in blood flow velocity. In addition, a longer time window contains more blood flow velocity components, which is prone to artifacts caused by sparse sampling, thereby affecting the accuracy of blood flow velocity estimation. Therefore, this paper proposed a novel blood flow velocity estimation method based on multi-frequency pulse sampling. Firstly, a multi-frequency pulse sampling echo model was constructed and derived. It is proved that under the assumption of stationary frequency band attenuation, this mathematical model is equivalent to the spare interval emission method of single-frequency Doppler pulses. That is, the multifrequency pulse sampling can achieve performance similar to that of the long time window of the single-frequency pulse sampling mode through a shorter time window, thereby improving the temporal resolution and estimation accuracy of the blood flow velocity spectrum. Subsequently, this paper proposed two methods for constructing the blood flow velocity spectrum for multi-frequency pulse sampling mode, namely the BMUSIC algorithm with lower complexity and the VMUSIC algorithm with better artifact suppression effect. The experimental results based on Field Ⅱ simulation data and in vivo data show that, compared with the sparse emission method of single-frequency Doppler pulses, the proposed method in this paper can not only use a shorter time window to improve the temporal resolution of the blood flow velocity spectrum, but also obtain continuous, clear, high-precision, and better artifact suppression effect of blood flow velocity estimation results. In the experiments with in vivo data, due to the limitation of experimental constraints, the proposed method cannot achieve non-integer multiples of frequency, and the performance advantage of the VMUSIC algorithm is not fully demonstrated.