Reinforcement learning (RL)-based anti-jamming communication schemes select the transmit power and channel to send images or point clouds, but have low transmission reliability due to lack of maritime environmental features and the delayed feedback under harsh channel conditions caused by sea surface reflections and wave fluctuations. In this paper, we propose an RL-based anti-jamming maritime communication scheme for LLM inference that enables user equipment (UE) to optimize uplink power, channel and data compression ratio for transmitting multi-modal data based on data size of each modality, number of received packets, environmental features such as weather conditions and jamming features such as the received jamming power with quality-of-service guarantee. The historical anti-jamming experiences are used to recover the delayed feedback or loss and reduce communication outage under harsh maritime channel. The upper bound in terms of latency, UE energy consumption and inference accuracy is provided based on a maritime anti-jamming game to show the impact of the number of modalities, bandwidth and channel gains. The proposed scheme is implemented based on UEs equipped with Jetson Orin, camera and BME280 sensors for transmitting 200-KB temperature, humidity and pressure as well as images to the control center for LLaVA-based environmental feature extraction and marine object detection. Experimental results based on 4 UEs at Harbor show the performance gain with 40.5% less latency, 46.4% lower UE energy consumption and 10.9% higher inference accuracy against the smart jammer.

Reinforcement learning (RL) based flying ad-hoc network (FANET) routing enables unmanned aerial vehicles (UAVs) to choose the next-hop, but the quality of service (QoS) and energy efficiency have to be enhanced against jamming due to the inaccurate path quality estimation. In this paper, we propose an RL based anti-jamming FANET routing with QoS guarantee to optimize the transmit power and the originator message broadcast interval for the route discovery to estimate the path quality to select the next-hop from the routing table. Based on the path availability history, the channel conditions, the received jamming power and the transmission quality regarding the number of the received originator messages and the bit error rate during route discovery, the routing policy is selected to enhance the throughput and the energy efficiency. The backward estimation of the throughput in the utility evaluation addresses the delayed feedback from the destination. The performance bound is derived in terms of network topology and channel gain based on the Nash equilibrium of the cooperative game among the UAVs. Simulation results provide the performance gain of the throughput and energy consumption over the benchmarks.

Reinforcement learning (RL)-based communication scheme selects the transmit power and channel to improve communication reliability, but energy consumption and transmission delay degrades due to multi-modal data transmission such as images and point clouds for object detection under harsh maritime channel against jamming. In this paper, we propose a RL-based anti-jamming maritime communication scheme for multi-modal perception that optimizes the uplink transmit power, channel and compression ratios of each modality to improve perception accuracy and reduce communication energy consumption and transmission delay. The transmission policy of multi-modal data is determined by the probability distribution based on the data size and maritime environmental features such as rainfall and wind speed. The upper bound in terms of transmission delay and communication energy consumption is provided based on the Nash equilibrium of anti-jamming perception game to show the impact of the number of modalities and channel gain. Simulation results for ship detection based on the images and radar point clouds show the performance gain over benchmark against the smart jammer.

Reinforcement learning (RL) based flying ad-hoc network (FANET) routing enables unmanned aerial vehicles (UAVs) to choose the next-hop to forward the packets, but the quality of service (QoS) and energy efficiency have to be enhanced due to the inaccurate path quality estimation under jamming attacks. In this paper, we propose an RL based energy-efficient anti-jamming FANET routing scheme with QoS guarantee to optimize both the transmit power and the originator message broadcast interval for the route discovery to estimate the path quality to select the next-hop from the routing table. Based on the path availability history, the channel conditions and the received jamming power, as well as the transmission quality regarding the number of the received originator messages and the bit error rate during route discovery, the routing policy is selected to enhance the throughput and the energy efficiency under jamming attacks with changing power. The backward estimation of the throughput in the utility evaluation addresses the delayed feedback from the destination under large-scale networks. The deep neural networks are further designed to address the quantization error of the transmission quality and the channel gain for UAVs with high mobility to enhance the path exploration efficiency. In addition, the upper bound in terms of network topology and channel gain is derived based on the Nash equilibrium of the anti-jamming routing game. The proposed routing scheme is implemented to improve the image transmission quality against jamming in outdoor environments. Experimental results based on UAVs equipped with Raspberry Pi show the performance gain of the throughput and the energy consumption.

The rise of containerization has led to the development of container cloud technology, which offers container deployment and management services. However, scheduling a large number of containers efficiently remains a significant challenge for container cloud service platforms. Traditional load prediction methods and scheduling algorithms do not fully consider interdependencies between containers or fine‐grained resource scheduling, leading to poor resource utilization and scheduling efficiency. To address these challenges, this paper proposes a new load prediction model CNN‐BiGRU‐Attention and a container scheduling strategy based on load prediction. The prediction model CNN and BiGRU focus on the local features of load data and long sequence dependencies, respectively, as well as introduce the attention mechanism to make the model more easily capture the features of long distance dependencies in the sequence. A container scheduling strategy based on load prediction is also designed, which first uses the load prediction model to predict the load state and then generates a scheduling strategy based on the load prediction value to determine the change of the number of container replicas in a fine‐grained manner based on the load prediction value in the next time window, while the established domain‐based container selection method is employed to facilitate the coarse‐grained online migration of containers. Experiments conducted using public datasets and open‐source simulation platforms demonstrate that the proposed approach achieves a 37.4% improvement in container load prediction accuracy and a 21.7% improvement in container scheduling efficiency compared to traditional methods. These results highlight the effectiveness of the proposed approach in addressing the challenges faced by container cloud service platforms.