This letter proposes a lightweight parallel recurrent–convolutional scheme to improve generalization capability and recognition accuracy while maintaining low computational complexity in resource-constrained underwater acoustic channels. In this scheme, the lightweight convolutional network is used to extract time–frequency features, and the lightweight recurrent network with gated recurrent units is used to capture long-term temporal phase correlations, thereby alleviating the Doppler-induced phase rotation and inter-symbol interference in time-varying multipath underwater acoustic channels. Sea-trial data are collected during shallow-water sea trials with strictly separated training and evaluation datasets. Experimental results on ten underwater acoustic modulation types show that the proposed scheme improves recognition accuracy by 6.2% and reduces computational cost by 22.4%, while exhibiting stronger generalization capability compared with benchmark schemes.

Existing adaptive modulation and coding schemes suffer from performance degradation under time-varying underwater acoustic channels and jamming attacks, resulting in increased video jitter and energy consumption, as well as reduced peak signal-to-noise ratio (PSNR). In this paper, we first propose a reinforcement learning (RL)-based underwater video transmission scheme, which jointly optimizes the channel coding methods, subcarrier modulation order, quantization parameter, and transmit power against jamming. The direct sequence spread spectrum mechanism is used to reduce the jamming power in each subcarrier frequency band, which ensures that the transmitter obtains a reliable feedback message. In addition, a multi-frame statistical scheme is designed to support transmission policy selection, which mitigates the impact of channel instability by averaging the observed transmission performance and channel gain. To further enhance communication performance and robustness under large state-action spaces and dynamic channel conditions, we propose a deep RL (DRL)-based anti-jamming video transmission scheme to compress the excessive state-action space, mitigate quantization errors, and improve transmission stability. Two target networks are employed in DRL to ensure learning stability by mitigating fluctuations in Q-value and R-value updates caused by the time-varying channel. In addition, the performance bounds of transmission delay and energy consumption related to modulation order and channel coding rate are derived. Simulation and experimental results demonstrate that our schemes improve the video transmission performance by reducing transmission delay, energy consumption, and video jitter while increasing PSNR and video quality compared with the benchmarks.

In the Internet of Underwater Things, which is entirely composed of autonomous underwater vehicles (AUVs) without fixed beacons, synchronized underwater acoustic (UWA) localization systems are employed. These systems estimate the relative locations of the AUVs, enabling the formation of an optimal network topology. The localization accuracy is affected by the AUV motion, the localization and communication signal (LCS) bandwidth and duration, and the power of LCSs. In this article, a reinforcement learning (RL)-based smart AUV localization scheme is proposed first. By optimizing the localization time windows and the signal weights, the RL-based localization scheme reduces the localization time delay and energy consumption while increasing the localization accuracy without fixed anchor nodes. Furthermore, a double deep Q-network (DDQN)-based hierarchical structure localization scheme is proposed to optimize the allocation of the substrategies for the follower AUVs, aiming to reduce the localization error and the energy consumption. Computational complexity and Cramér-Rao lower bounds (CRBs) are analyzed to evaluate the optimal localization performance. Simulation results show that the proposed schemes improve the localization accuracy, and reduce the time delay and the energy consumption compared with the benchmarks.

Underwater video transmission has to ensure quality-of-service (QoS) against jamming with severe multipath effect and narrow bandwidth limitation that degrade the communication performance under variable channel state. In this paper, we propose a reinforcement learning (RL)-based QoS-aware underwater video transmission scheme to optimize the video compression ratio, modulation format and transmit power based on the state consisting of the channel gain and previous transmission performance. This scheme evaluates the risk level that indicates the probability of failing the QoS and the long-term expected utility of each transmission policy under the current state to improve the anti-jamming communication performance. We derive the performance bound of the utility and analyze its relationship with transmission policy. Simulation results illustrate that our scheme improves the QoS by reducing the frame loss rate (FLR), transmission delay and increasing spatial-spectral entropy-based quality (SSEQ) compared with the benchmark.

In mobile edge computing (MEC) systems, multiple unmanned aerial vehicles (UAVs) can be utilized as aerial servers to provide computing, communication, and storage services for edge users, called UAV-assisted MEC, which has emerged as a promising technology to improve both the computing and communication performances. Unlike existing works without considering jamming attacks, we investigate a multi-UAV-assisted-MEC scenario under multiple malicious jammers and then propose a resource management approach with the objective of minimizing both the system energy consumption and latency. Due to the time-varying nature of communication environments, we design a multi-agent deep reinforcement learning (MADRL)-based resource management approach to dynamically adjust the CPU frequency, communication bandwidth, and channel access selection of UAVs to enhance the system performance against jamming attacks. On this basis, in order to enhance the algorithm learning efficiency, we propose a multi-agent twin-delayed deep deterministic policy algorithm in combination with the prioritized experience replay mechanism (PER-MATD3) to effectively search for the joint resource management strategy under high-dimensional state and action spaces, where the time-varying channel state information and imperfect attack behavior information are also effectively trained to improve the learning capacity and convergence speed. Simulation and experimental results verify that the proposed approach can significantly decrease the overall system latency (i.e., computing and communication latency) and energy consumption compared to other benchmark algorithms under different real-world settings.

Unmanned aerial vehicles (UAVs) have been increasingly employed as aerial servers in mobile edge computing (MEC) systems, providing essential computing, communication, and storage services for edge users. This UAV-assisted MEC paradigm shows great promise in enhancing both computing and communication performances. However, the presence of malicious jammers poses significant challenges to the system's reliability and efficiency. In this study, we explore the resource management problem in a multi-UAV-assisted MEC scenario under the influence of multiple malicious jammers. To mitigate the impact of jamming attacks, we propose a resource management approach with the primary objective of minimizing system energy consumption and latency while adhering to UAV energy constraints. Due to the dynamic and time-varying nature of the communication environment, we present a deep reinforcement learning (DRL)-based algorithm that dynamically adjusts the CPU frequency and communication bandwidth of the UAV to optimize the system performance even under jamming attacks. Through simulations, we demonstrate the effectiveness of the proposed algorithm in significantly reducing the overall system latency (both computational and communication latency) as well as minimizing energy consumption.

Advanced modulation and channel equalization techniques are essential for improving the performance of medium-range single-carrier underwater acoustic communications. In this paper, an enhanced detection scheme, hybrid time-frequency domain decision feedback equalizer (DFE) combined with complementary code keying (CCK) remodulator, is presented. CCK modulation technique provides strong tolerance to intersymbol interference caused by multipath propagation in underwater acoustic channels. The conventional hybrid DFE, using a frequency domain feedforward filter and a time domain feedback filter, provides good performance along with low computational complexity. The error propagation in the feedback filter, caused by feedbacking wrong decisions prior to CCK demodulation, may lead to great performance degradation. In our proposed scheme, with the help of CCK coding gain, more accurate remodulated CCK chips can be used as feedback. The proposed detection scheme is tested by the practical ocean experiments. The experimental results show that the proposed detection scheme ensures robust communications over 10-kilometre underwater acoustic channels with the data rate at 5 Kbits/s in 3 kHz of channel bandwidth.