As the maritime industry continues to thrive and maritime services diversify, the demand for highly reliable maritime communication systems has become increasingly prominent. However, harsh marine conditions pose significant challenges to communication systems. In this work, we propose a Maritime AutoEncoder (MAE) communication system based on Attention Mechanisms (AMs) and DenseBlock (namely AM-Dense-MAE). AM-Dense-MAE utilizes DenseBlock and long short-term memory to extract deep features and capture spatio-temporal relationships, addressing the issue of “long-term dependency”. Furthermore, the decoder incorporates spatial attention modules and convolutional block attention module to enhance the preservation of crucial information and suppress irrelevant data. We employ the Rician fading channel model to simulate maritime communication channels. A substantial volume of data is utilized for model training and parameter optimization. Simulation results demonstrate that, in comparison to the benchmarks, the proposed AM-Dense-MAE exhibits better block error rate performance under various signal-to-noise ratio conditions and showcases generalization capabilities across diverse parameter settings.

With the exponential growth of maritime wireless devices and the rapid development of maritime applications, traditional maritime communication networks encounter communication and computation limitations in supporting computation-intensive and latency-critical tasks. Edge computing and Intelligent Reflecting Surface (IRS) have emerged as promising techniques to improve communication and computation services for maritime devices with limited computation capabilities and battery capacity. This paper studies an IRS Mounted on Unmanned Aerial Vehicle (UIRS) assisted maritime edge computing network, in which the UIRS is deployed to assist the transmission from Unmanned Surface Vehicles (USVs) to the edge server via Non-Orthogonal Multiple Access (NOMA) protocol. We propose a resource management and trajectory optimization scheme by jointly optimizing subslot duration, offloading ratios, transmit power, edge computation capability allocation, UIRS phase shifts and UIRS trajectory, aiming at minimizing the overall energy consumption. Since the non-convex nature of the optimization problem, we propose a two-layered method by decomposing the original problem into two subproblems. The top-layered subproblem is solved by the Semi-Definite Relaxation (SDR) method and the underlying-layered subproblem is solved by the Deep Deterministic Policy Gradient (DDPG) algorithm. Numerical results demonstrate that our proposed scheme can effectively and efficiently reduce overall energy consumption.