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.

The Voxel-based point cloud neural networks (PNNs) have demonstrated exceptional accuracy across various 3D point cloud tasks. However, the practical deployment of Voxel-based PNNs on resource-constrained edge devices faces significant challenges, primarily stemming from two memory-access intensive operations: the map search operation and the sparse 3D convolution (Spconv3D) operation. Specifically, three key challenges remain to be addressed, including: (1) the extensive sequential coordinate comparisons and irregular off-chip memory access patterns generated by map search operation; (2) the considerable data movement requirements associated with data-intensive convolution operation; (3) significant workload imbalance resulting from point cloud sparsity and irregularity.In this paper, we propose Voxel-PIM, an efficient SRAM-based accelerator for Voxel-based PNNs that leverages in-memory search and compute techniques. The Voxel-PIM architecture primarily contains two Processing-in-Memory (PIM) cores: a Content-Addressable Memory (CAM)-based Search Core and a Compute-in-Memory (CIM)-based Computing Core. For the CAM core, we propose the KD-Tree-based Partitioning and Searching (KDPS) strategy, which activates only a fraction of the array and reduces search energy consumption during search operation. For the CIM array, we introduce a novel submatrices mapping method to flexibly support both Spconv3D and Conv2D operations. Furthermore, we propose a Weight Workload Balanced (W2B) method to mitigate computational imbalance. The hardware performance of Voxel-PIM is estimated for a 22nm technology. The simulation results demonstrate that Voxel-PIM achieves averagely 1.53~7.26× higher energy efficiency, 1.03~2.98× speed up in detection task, and 1.4~11.4× speed up in segmentation task compared to state-of-the-art (SOTA) point cloud accelerators and general-purpose processors.

Digital Compute-in-Memory (DCIM), which inserts logic circuits into SRAM arrays, presents a significant advancement in CIM architecture. DCIM has shown great potential in applications, and the diversity of applications requires rapid hardware iteration. However, the hardware design flow from user specifications to layout is extremely tedious and time-consuming for manual design. Commercial EDA tools are limited by restrictive licenses and the inability to specifically optimize the datapath, which calls for an open-source end-to-end hardware compiler for DCIM. This paper proposes$\mathbf{O}\mathbf{p}\mathbf{e}\mathbf{n}\mathbf{C}^{2}$, the first open-source end-to-end development framework for DCIM macro compilation.$\mathbf{O}\mathbf{p}\mathbf{e}\mathbf{n}\mathbf{C}^{2}$provides a template-based generation platform for DCIM macros across various technologies, sizes, and configurations. It can automatically generate a datapath-optimized, compact DCIM macro layout based on a hierarchical physical design methodology. Our experiment results show that$\mathbf{O}\mathbf{p}\mathbf{e}\mathbf{n}\mathbf{C}^{2}$'s compact design on FreePDK45 delivers over 30% area reduction and over 40% improvement in area efficiency compared to AutoDCIM on TSMC40.

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.