In this paper, we propose a novel generalized orthogonal chirp division multiplexing (GOCDM) communication system under doubly selective channels. The GOCDM waveform consists of modulated Zadoff-Chu sequences parameterized on the root index λ, which specifies the chirp rate. Thus, GOCDM subsumes OCDM as a special case (λ = 1) and inherits the full double-spreading feature. To deal with doubly selective channels, the optimal pilot chirp-assisted GOCDM is designed based on a basis expansion model. The optimal structure, placement and number of pilot symbols are proposed. Moreover, the optimal power allocation between pilot and data symbols is also derived. These optimal pilot parameters not only minimize the mean square error of the channel estimation, but also maximize a lower bound of the average channel capacity. Furthermore, it is possible to design the root index λ to achieve a GOCDM system with the optimal training to have higher bandwidth efficiency than the OCDM system. Simulation results corroborate the superior performance of the proposed design for GOCDM.

This article deals with a joint multiple resources (relay, channel, and power) allocation problem for underwater acoustic (UWA) cooperative communication in time-varying Internet of Underwater Things scenarios. The strong coupling of multiple resources and the unknown time-varying characteristic of UWA communication scenes make the joint optimization problem full of challenges. To address this issue, the adversarial multiarmed bandit online learning model without any prior channel information and statistic assumptions is employed. Furthermore, a double closed-loop learning structure with multiple intelligent experts assistance is proposed. Multiple experts embedded in inner loop can intelligently learn the derived inferential information to provide more efficient advice for the player in outer loop, thereby enriching learning information and enhancing learning ability. In addition, the expert diversity learning mechanism is proposed to fully reflect the characteristics of seeking advantages and avoiding disadvantages in the double closed-loop learning structure. As a result, the learning speed and performance of the proposed algorithms are significantly improved. The superiorities of the proposed algorithms are demonstrated through numerical results.

Acoustic ranging is required to obtain the location of underwater mobile vehicles in the Internet of Underwater Things (IoUT). As seawater is an inhomogeneous medium, the sound velocity in the ocean is not constant, thereby causing sound waves to deviate from a straight line of propagation and bend. Thus, the travel time of the sound wave from the transmitter to receiver cannot be directly converted to a range value using a linear relationship as is done in the case of wireless radio ranging in the air. Therefore, the concept of effective sound velocity was introduced to account for the differences between the sound velocities at a transmitter and receiver. This enables conversion of the travel time to slant distance via a linear relationship. However, the existing methodologies for computing effective sound velocities are computationally intensive, which hinders the use of acoustic ranging in small underwater mobile vehicles operating in the deep sea. This study aimed to resolve this problem by developing an effective, computationally less demanding algorithm that could improve the precision of acoustic ranging on such platforms. The proposed algorithm converts global integrals into local integrals that correspond to depth variation ranges, thereby reducing the amount of integral calculation. The performance of the proposed algorithm is evaluated using extensive simulations and real deep-sea experimental data sets obtained at a depth of 3000 m. The results verify the efficacy of the algorithm in realizing real-time underwater acoustic ranging in deep sea. The proposed algorithm can improve the accuracy of real-time effective sound velocity measurement by small underwater mobile vehicles, and subsequently realize low-complexity underwater acoustic ranging in the deep-sea IoUT networks.

In Internet of Underwater Things, data collection is assisted by autonomous underwater vehicle (AUV) to enhance the reliable transmission. AUV acts as a mobile collector and transmits the collected data to the station via relay nodes. However, the highly mobile nature of AUV needs an adaptive and efficient relay selection scheme for achieving good capacity performance. In this article, we propose a new contextual multiarmed bandit with evolving relay set (CMAB-ERS) learning framework, which successfully addresses crucial issues, including dynamic environment conditions and evolving relay set. To deal with the evolving relay set, CMAB-ERS incorporates collaborative effects into inference as well as learning processes, the new relays will acquire prior knowledge by having experienced nodes sharing observations, reducing the learning time significantly. To overcome the uncertainty of environmental information, we exploit the contextual environment factors to assist relay reward estimation and execute time-sensitive parameter update after every transmit–receive cycle, aiming for minimizing potential loss due to the time-varying channel. Correspondingly, the collaboration-aware online contextual bandit learning (COCBL) algorithm is designed that enables AUV to switch optimal relay adaptively and promises high-capacity transmission. Further, we rigorously prove the convergence of the COCBL algorithm by considering the evolving relay set and give its upper bound on the cumulative regret. Finally, extensive simulation results elucidate the effectiveness of the proposed COCBL.

Relay selection solutions for underwater acoustic cooperative networks suffer significant performance degradation as they fail to adapt to incomplete information, noisy interference and overwhelming dynamics. To address this challenge, a hierarchical adversarial multi-armed bandit learning framework by proposing an online reward estimation layer is designed to improve adaptive relay decision control. In online reward estimation layer, adaptive Kalman filter estimator is developed to properly handle noisy observation to support accurate reward. Meanwhile, an online predict mechanism is projected for all relays to enrich learning information. Furthermore, based on estimate error variance, an adaptive exploration structure is developed to accelerate the balance between exploration and exploitation. All gathered information are exploited to learn relay quality for the decision-making. Accordingly, we present a Hierarchical Adversarial Bandit Learning (HABL) algorithm to fully exploit the heuristic interaction between the hierarchical framework. HABL integrates reward estimation, information prediction, adaptive exploration and decision making carefully in a holistic algorithm to maximize the learning efficiency. Thereby, the HABL-based relay selection algorithm has higher system throughput and lower communication cost. Further, we rigorously analyze the convergence of HABL algorithm and give its upper bound on the cumulative regret. Finally, extensive simulations elucidate the effectiveness of the HABL.

Pairwise ranging between nodes plays a crucial role in Internet-of-Underwater-Things (IoUT) networks, and it typically impacts the overall performance of such networks. As the sound speed depends on several parameters, time-of-flight-based techniques cannot work well under varying sound speeds in actual underwater conditions. Pairwise ranging algorithms that consider stratification should be studied to improve ranging accuracy. However, there is a tradeoff between underwater acoustic ranging accuracy and number of calculations. This makes it challenging to implement underwater acoustic ranging algorithms in IoUT networks. In this article, we propose an underwater acoustic horizontal ranging algorithm that rapidly and accurately estimates the horizontal range from a sender to a receiver under an arbitrary sound-speed profile in the deep sea. Simulation results show that the proposed algorithm accurately calculates the horizontal range with a low computational complexity. We validate the performance of the proposed algorithm using the data collected during an ultrashort baseline precision test in the South China Sea.

Underwater acoustic communications (UACs) are demanded in a wide range of marine applications. However, one of the main challenges for UACs is unexpected interferences from nearby acoustic activities, especially dynamic and short-period interferences (called shot interference), which are hard to detect, estimate, and mitigate over time- and frequency-selective acoustic channels. In this article, we propose a novel filter that mitigates the interference effects for both single-carrier and multi-carrier systems without any prior knowledge of the interference. An Adaptive Sliding Window Interference Detection (ASWID) algorithm is developed to detect the location and the number of interfered symbols. Meanwhile, a least trimmed squares (LTS) equalizer is presented for the shot interference estimation and mitigation based on robust regression. The proposed algorithms are evaluated through numerical simulations and real channel measurements. Our results show that the proposed designs can detect and mitigate shot interferences effectively for single-carrier and multi-carrier UAC systems.

We study the problem of joint relay selection and power allocation in a underwater cooperative system with multiple users assisted by multiple relays. Due to the harsh underwater environments, the channel state information (CSI) at the transmitter is imperfect, which leads to the performance degrading in the underwater cooperative acoustic system. Therefore, we analyze the cooperative underwater acoustic channel with limited feedback to increase the sum-rate of the system. Meanwhile, different from other researches, we do not only focus on the single system scenario, but also consider the presence of nearby acoustic activities and the problem of joint relay selection and power allocation is solved in a cognitive acoustic (CA) scenario. Thus the codebook of interference CSI and the codebook of quantized relay selection and power allocation strategy are designed, respectively. Simulation results show that a few bits feedback can significantly improve the performance of the CA cooperative acoustic system.