In underwater Internet of Things (IoT) applications, efficient and reliable transmission is demanding. Recently, the nonorthogonal multicarrier (MC) modulation is utilized as a promising spectrum-efficient solution in the underwater acoustic (UWA) channel, where generally, the constellation mapping (CM) methods (such as 8-PSK and 16-QAM) are utilized to further increase the transmission rate. In this article, we focus on the transmission of sonar images among IoT nodes and a constellation diagram learning-based adaptive sparse nonorthogonal wavelet division multiplexing (CDLA-SNOWDM) system is proposed to combat complex channel states between IoT nodes. We found that different CM methods contribute to unique time-frequency characteristics in constellation diagrams. We first utilize the time-frequency characteristics in designing the nonorthogonal subcarriers. The complex-valued CM signals are regarded as a new type of image. We introduce dictionary learning (DL) to nonorthogonal subcarrier designing, with the idea that designing CM signal adaptive nonorthogonal subcarriers can be regarded as designing an adaptive subcarrier dictionary. The CDLA-SNOWDM modulation is performed as a projection of the CM image to the subcarriers with an adaptive dictionary. The learned adaptive subcarriers can represent constellation diagrams more efficiently and improve transmission reliability. Both simulations and experiments show that the proposed scheme improved the reliability in transmitting sonar images over other orthogonal and nonorthogonal modulation schemes in UWA scenarios.

This article considers underwater acoustic passive localization of a noncooperative broadband source in the presence of multipath propagation. A differential transmission-loss model-based passive localization method is proposed, in which 3-D localization can be implemented with only two hydrophones. Instead of spatial diversity, frequency diversity is exploited to achieve passive localization with a limited number of hydrophones and address the challenge of multipath propagation. Specifically, the received signals of both hydrophones are decomposed in the frequency domain, respectively, at first. Then, a set of differential transmission-loss model-based equations between the two hydrophones are established on multiple frequencies. Based on these equations, the passive localization is modeled as a multivariate optimization problem. Meanwhile, the nonline of sight (NLOS)-related parameters are also involved in the optimization problem. Therefore, accurate localization and NLOS mitigation can be accomplished simultaneously by solving the optimization problem. To simplify the multivariate optimization problem for a reliable solution, a cepstrum-autocorrelation-based multipath estimation algorithm is proposed, by which all the NLOS paths can be represented by an equivalent NLOS path. Consequently, the simplified optimization problem concerns only the desired coordinate of the source and a single NLOS-related parameter. Finally, a differential evolution algorithm is employed to solve the simplified optimization problem. Both simulation and lake trial results corroborate the effectiveness and the robustness of the proposed method.

Effective network planning improves performance in the radio-frequency identification (RFID) system. This article proposes an optimal deployment of phased array reader antennas for RFID network planning (RNP). For practical considerations, the RNP problem is analyzed and formulated based on a multipath propagation model, where each reader is equipped with a phased array antenna. The gains and radiation directions of the antennas are adjusted by voltage states instead of gestures, reducing the cumbersome antennas redeployment. An indicator called amplitude fluctuation under narrowband (AFN) is proposed to reflect the disturbance of frequency selective fading caused by the multipath effect. To effectively address the RNP problem, an improved chicken swarm optimization algorithm with two targeted strategies is developed. Simulation and experiment comparisons with the existing algorithms demonstrate the superiority of the proposed approach.

Received signal strength (RSS)-based ranging is a promising distance estimation approach in underwater acoustic sensor networks (UASNs). However, the multipath-rich underwater environment complicates acoustic propagations and derails the RSS-based ranging. To address the challenges, this article provides a novel ranging method, called channel state information (CSI)-based ranging for UASNs (CRUN). Instead of RSS, the measured CSI is modeled as a set of power-loss-based equations. Then, the ranging process under multipath scenarios is transformed as a multivariate optimization problem which involves parameters of all propagation paths. This optimization problem aims to simultaneously realize distance estimation and multipath mitigation. Noticing the large number of variables makes the solution numerically unstable, a threshold-window-based algorithm is proposed to simplify the multivariate optimization problem. In specific, the proposed algorithm extracts relative amplitude attenuations and relative time delays between the line-of-sight (LOS) path and each of the non-line-of-sight paths from CSI. The extracted parameters, being as equality constraints, simplify the multivariate optimization problem to a univariate optimization problem only concerning the desired LOS distance. Then, the simplified problem can be efficiently solved by the gradient descent algorithm. Statistical-channel-model-based simulations and lake experiments demonstrate that CRUN significantly improves the ranging accuracy and robustness compared with RSS-based approaches.

Due to the characteristics of the underwater acoustic (UWA) channel, the process at the receiver is complicated to match the channel. To simplify receiver design and match UWA channel better, this study proposes a deep neural network‐based orthogonal frequency division multiplexing receiver for UWA communication. Different from existing receivers needing a neural network and several other processing parts, the proposed receiver only uses a single neural network to implement the whole signal processing. Moreover, it is a general receiver which is suitable for other modulation schemes. Simulation results show that the proposed receiver offers better bit error rate performance over traditional ones.