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It is well known that the underwater acoustic channel (UAC) has the physical characteristic of sparse structure due to the significant multipath effect. To improve the performance of UAC estimation, with the physical knowledge on channel sparsity in mind we propose a novel method called Deep Learning based UAC Estimation (DL-UACE) in this paper. The DL-UACE method combines the conventional iterative sparse recovery algorithm of approximate message passing (AMP) with deep neural network (DNN) to construct a sparsity-aware DNN for the deep learning of the inherent sparse structure of the UAC. Furthermore, the denoising convolutional neural network (DnCNN) is integrated into the sparsity-aware DNN as a denoiser to mitigate the impact of ubiquitous ambient noise that obeys Gaussian distribution on UAC estimation. Simulation results show that the proposed DL-UACE method is superior to the stateof-the-art methods in terms of estimation accuracy and spectrum efficiency, especially in severe conditions of low signal-to-noise ratio (SNR) or insufficient pilots.

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It has always been difficult to achieve accurate information of the channel for underwater acoustic communications because of the severe underwater propagation conditions, including frequency-selective property, high relative mobility, long propagation latency, and intensive ambient noise, etc. To this end, a deep unfolding neural network based approach is proposed, in which multiple layers of the network mimic the iterations of the classical iterative sparse approximation algorithm to extract the inherent sparse features of the channel by exploiting deep learning, and a scheme based on the Sparsity-Aware DNN (SA-DNN) for UAC estimation is proposed to improve the estimation accuracy. Moreover, we propose a Denoising Sparsity-Aware DNN (DeSA-DNN) based enhanced method that integrates a denoising CNN module in the sparsity-aware deep network, so that the degradation brought by intensive ambient noise could be eliminated and the estimation accuracy can be further improved. Simulation results demonstrate that the performance of the proposed schemes is superior to the state-of-the-art compressed sensing based and iterative sparse recovery schems in the aspects of channel recovery precision, pilot overhead, and robustness, particularly under unideal circumstances of intensive ambient noise or inadequate measurement pilots.

Due to the limitation of energy supply and the requirements of high reliability in the mission-critical Internet of Medical Things (IoMT), the efficient and reliable transmission of the sensing siganl of implantable medical devices (IMDs) is still a challenge. In order to improve the spectrum efficiency and transmission reliability, in this paper, a Generative Adversarial Network-enabled Sparse Compression and Recovery (GAN-SCR) scheme is proposed by exploiting the physical knowledge of sparsity, which compressively measures the sparse IMD sensing signal in the transmitter, and recovers the sensing signal in the receiver. In the stage of sparse measurement in the proposed GAN-SCR scheme, a pre-trained measurement discriminative network (MDN) is used to conduct signal compression at the transmitter, which enhances the restricted isometry property via learning. In the stage of sparse recovery, exploiting the temporal correlation and inherent sparsity of physiological signals, a pretrained representation generative network (RGN) is used to map the sensing signal to a low-dimensional latent vector for sparse representation learning. Subsequently, the projection from the latent vector onto the measurement vector is structured by jointly training an RGN and an MDN, by which accurate signal recovery can be implemented via online optimization. Simulation results verify that the proposed GAN-SCR scheme outperforms other state-of-art sparse reconstruction algorithms in the accuracy of sensing signal recovery. CCS CONCEPTS• Human-centered computing → Empirical studies in ubiquitous and mobile computing; Ubiquitous and mobile devices; • Networks → Mobile ad hoc networks.