With the massive development of underwater small robotic vehicles and matching acoustic modems, applications for the Internet of Underwater Things (IoUT) are emerging. IoUT involves communication between the nonsynchronized network nodes organized in a mesh. A limiting factor of such communication is the so-called near-far effect, where transmissions from a node (near) close to a common receiver block the transmissions of a farther node (far). Due to the high-power attenuation in the underwater acoustic channel, near-far effect is common in underwater acoustic communication networks, and the phenomena occurs even for a distance ratio of 80% between the near and far nodes to the receiver, and the large number of nodes in the IoUT compounds the effect of this phenomena. While the current approaches only consider the jamming effect to the far signal, in this article, we consider canceling the interference from both sources by estimating and equalizing the channels on parallel, thereby significantly improving the decoding of both signals. As a result, IoUT performance improves. To limit mutual interference, we propose an automatic switching mechanism that controls the cancelation operation both in channel estimation and channel equalization. The simulation results show that our approach obtains significant improvement in communication from both near and far nodes. Results from a designated sea trial demonstrate that when both nodes are affected by their mutual transmissions, our proposed method improves the output signal-to-noise ratio (SNR) significantly.

Channel estimation plays a crucial role in Internet of Underwater Things networks. Traditional channel estimation algorithms have limited performance improvement under the block-sparsity underwater acoustic channel, and the distributed compressed sensing (DCS) method has not been researched under block-sparsity recovery. In this article, we propose DCS with block sparsity for underwater acoustic channel estimation under short observation length and low SNR scenarios. Specifically, the underwater acoustic channels are partitioned some sub-blocks that have the identical size, and the simultaneous block orthogonal matching pursuit algorithm (SBOMP) is proposed to enhance the channel estimates that have common delays. However, the number of nonzero taps located in a partitioned sub-block does not equal to the size of partitioned sub-blocks, and the channel between adjacent two data blocks may have slightly difference, when the SBOMP algorithm is applied, there would be much estimated noise. In order to address this problem, in this article, we also propose dynamic block sparsity-based SBOMP which is referred to as DSBOMP. The proposed DSBOMP consists of the SBOMP algorithm, first-order derivative of the residual method, and parallel comparison strategy. The first-order derivative is used to remove some negligible taps and dynamically measure the number of significant taps; the parallel comparison strategy is used to check whether the current taps are available. Both simulation and sea trial data indicate that, our proposed SBOMP and DSBOMP algorithms have better channel estimation performance than tradition algorithms under short observation length and low SNR. Moreover, our proposed DSBOMP achieves the best communication performance.

With the increasing demand for marine sensing information, more and more research has been investigated on underwater wireless sensor networks (UWSNs). Underwater acoustic communication (UWAC) MODEMs which are used to exchange marine sensing data have attracted wide attention, because they are the most important part of UWSNs. Orthogonal signal-division multiplexing (OSDM) is a compromise between single-carrier communication and multiple-carrier communication. The OSDM communication system is less sensitive to multipath and Doppler shift, and has a lower peak-to-average power ratio. The OSDM UWAC recently has been investigated in theoretical analysis, but as far as we know, there is extremely limited research on real-time OSDM UWAC MODEM design. Aiming at addressing this gap, in this paper, a real-time OSDM MODEM is researched and implemented for UWAC, including hardware and software. Specifically, the pre-amplifier, the bandpass filter, the power amplifier, and the signal processor are introduced. Moreover, detailed procedures for modulation and demodulation are also presented. In addition, to accommodate the different underwater acoustic channels and to get the balance between bit error rate and data rate, the parameters such as the number of zero vectors and pilot vectors are investigated. Our OSDM MODEM achieves error-free with the data rate of 4.35 kbps by using the minimum-mean-square-error equalizer and polar decoding under slow time-varying channels, and the communication range reaches larger than 5 km.

It has been recognized that the performance of the traditional direction of arrival (DOA) estimation algorithms is highly dependent on array geometry parameters, especially the array size and element number. Unfortunately, significant performance degradation may be caused under unideal array geometry such as small size array onboard small underwater vehicle. Among all the possible directions, the presence of underwater target at a certain direction constitutes a type of spatial sparsity. Thus, spatial sparse recovery provides a potential way to address this challenging problem. As each DOA will correspond to a different transmission response pattern with respect to each array element, thus constitutes a direction-dependent transmission response (DDTR) that can be explored. However, random contribution of underwater acoustic (UWA) channel contained in DDTR makes it extremely difficult to directly construct dictionary matrix with DDTR. In this paper, a novel DDTR based small size array DOA estimation method is proposed. First, DDTRs are decomposed into time delay (TD) term and random channel response (RCR) term to decouple the adverse impact of UWA channel. Second, TD is conveniently constructed as dictionary matrix in terms of geometry relationship. Then, by accumulating the constructed DOA at frequency bins selected with energy or correlation coefficient, a selective frequency accumulation compressed sensing reconstruction (SFA-CS) algorithm is designed to mitigate the negative effect of UWA channel. Compared with the conventional DOA estimation algorithms, simulation results demonstrate that the proposed method can achieve small array DOA estimation with a lower root mean squared error (RMSE) and higher success rate (SR).

Due to its high spectral efficiency and robustness in multipath channels, orthogonal frequency division multiplexing (OFDM) has been considered one of the most promising coherent underwater acoustic (UWA) communication technologies. Channel estimation (CE) plays an important role in OFDM receivers for mitigating the negative impact of UWA channels, unfortunately, the performance of the conventional CE algorithms suffers from significant degradation under time-varying UWA channels due to the unavoidable mismatch between the CE and OFDM demodulation. As channel estimators are generally designed to optimize the signal-level cost functions. However, the results of which are used for bit-level OFDM demodulation. In this article, a minimum bit-error-ratio (BER) sparsity exploitation (MBSE) CE algorithm is proposed under the least-squares framework to address this mismatch. By tuning the sparsity exploitation parameters, i.e., threshold and diffuseness, in a novel manner, the proposed algorithm is designed to adapt to the time-varying UWA channels to improve OFDM demodulation. Specifically, while the sparsity exploitation threshold is obtained via minimum BER searching to determine the dominant multipath arrivals, the structural sparsity parameter, defined as multipath diffuseness, is symbol-wise updated to track the channel variations between the adjacent symbols. Numerical simulation and sea trial experiments verify the performance enhancement of the proposed algorithm in terms of output signal-to-noise ratio, CE mean-square error, and BER under artificial and physical time-varying UWA channels, respectively.

Due to the simultaneous existence of severe difficulties caused by multi-path, Doppler, and environmental noise caused by underwater acoustic channels, designing a stable and reliable underwater acoustic communication system (UWACS) is a challenging task. In addition to channel estimation and equalization, the technique of channel codes, which is capable of approaching Shannon’s capacity at a low encoding and decoding complexity, offers an efficient and reliable solution to improve the performance of the UWACS and thus draws extensive attention from the research community. By seeking a balance between communication efficiency and channel tolerance via multiple coding rate tuning, it has been recognized that the rate-compatible codes can achieve smooth rate-adaptive transmission under highly diverse underwater acoustic channel states. While there has been a substantial number of investigations on rate-compatible channel codes for the UWACS, in this paper, the research of rate-compatible channel codes has been summarized, reviewed, and compared, mainly focusing on the low-density parity-check (LDPC) and polar codes, which render flexibility in terms of code length and rate. Furthermore, typical research was evaluated and compared from the practical viewpoint of performance under real noise in a shallow water environment. Finally, problems encountered by the LDPC and polar codes for the UWACS were highlighted, and several future research issues were discussed.