Reinforcement learning (RL) has been used for uncrewed aerial vehicle (UAV)-based jamming detection by selecting hypothesis-test thresholds on received signal strength (RSS) to safeguard large language model (LLM) inference over wireless links. However, existing methods struggle against smart jammers with dynamic jamming probability. In this paper, we propose a UAV-based jamming detection scheme for LLM-enabled wireless networks to detect smart jamming. The proposed scheme augments traditional wireless features with an LLM-inferred communication environment indicator derived from multimodal sensing (e.g., images/videos from mobile devices), improving accuracy beyond RSS alone. Our approach jointly optimizes the test threshold and the UAV’s sniffer channel assignment, even with a limited number of antennas. We achieve this via a RL policy that leverages UAV imagery, e.g., suspicious radio devices and measuring relative distances, to accelerate detection speed. We implement the proposed scheme on a UAV equipped with a universal software radio peripheral to alert on jamming attacks in a wireless network integrated with a 7-billion-parameter vision-language multimodal LLM. Experiments with a UAV at 3 m detecting a smart jammer during image–text LLM inference across three mobile devices and an edge server demonstrate improvements in both detection accuracy and speed.

Network interface card (NIC) enabled active drone detection that analyzes the WiFi channel state information (CSI) variations due to drone vibrations and propeller rotations, which depends on frequent channel estimation to obtain sufficient CSI samples to distinguish drone motion-related information such as Doppler effects. However, the detection is inaccurate against interference due to the degraded channel estimation performance, such as pilot symbol error, and the excessive CSI sampling rate resulting in high energy consumption. In this paper, we propose a reinforcement learning assisted WiFi CSI based energy-efficient drone detection against interference, which optimizes the signal sampling across NICs regarding the transmit channel and the CSI sampling rate as well as the detection threshold. Based on the received signal strength indicator and the sampled CSI data in terms of amplitude and phase, the detection policy is chosen to enhance the utility as a weighted sum of the drone detection accuracy and energy consumption. The filtering mechanism quantifies policy similarity through the Kullback-Leibler divergences of both sampled data distributions and detection performance metrics to eliminate redundant policies, thereby improving policy exploration efficiency. Experiment results based on two NICs controlled by a Raspberry Pi and a laptop to detect a DJI Mavic 3 in the outdoor environment against interference show the performance gain over the benchmark.

Reinforcement learning (RL) based routing enables unmanned aerial vehicles (UAVs) to choose the next hop to transmit the packets, which has performance degradation against jamming and gray-hole attacks that disrupt communication and drop packets selectively, resulting in a substantial reduction in packet delivery ratio. In this paper, we propose an RL based jamming-resilient routing against gray-hole attacks in maritime UAV networks, which optimizes both the next hops to build multiple available routing paths with enhanced failure resilience and the transmit power to improve the communication reliability. Besides the channel gain and the hop count, based on the location of neighboring UAVs, the measured wind speed and jamming power, the service type and the moving speed, the multi-path routing is selected to enhance the packet delivery ratio with reduced latency and routing energy consumption following the quality of service constraints in the routing policy distribution. The UAV trust level is also exploited in the routing policy distribution formulation to enable rapid route repair against gray-hole attacks. The feedback recovery based on routing experience replay applies the future performance feedback from the destination to reconstruct the experience pool under fast changing maritime radio channel. Simulation results in offshore scenarios show the performance gain over the benchmarks.

Reinforcement learning based jamming detection that chooses the test threshold to evaluate the received signal strength indicator (RSSI) and the packet loss rate is inaccurate for unmanned aerial vehicles (UAVs) lacking target indication against smart jammers. In this paper, we propose a UAV swarm-enabled multimodal jamming detection scheme to optimize the test thresholds based on vision information such as object classes and relative distances, along with the RSSI, the channel gain and the communication performance, including packet delivery ratio, bit error rate and packet delivery delay. A machine learning classifier is used to assess the joint variation among RSSI, channel gain and communication performance, with the output outlier score compared with the test threshold to detect the jammer. The detection results shared from neighboring UAVs are exploited in the update of policy distribution to refine jamming signal resolution and thus reduce the miss detection rate. The utility bound is derived based on the Nash equilibrium of the jamming detection game between the UAV swarm and the jammer. Experimental results based on 5 UAVs to detect a smart jammer show that our proposed scheme enhances the detection accuracy compared with the benchmarks.

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 worm detection chooses the test threshold to evaluate the network traffic features such as the spectral flatness measure (SFM), but the detection of the evasive worm that modifies the scan rate and the worm propagation speed to manipulate the network traffic features is inaccurate due to the estimation and quantization error in the test threshold. In this paper, we propose an RL based accurate worm detection for smart grids that enables the control center to optimize the test threshold based on the number of meters, the infection time series and the number of connections to new destination IP addresses, besides the traffic log size received from each data concentrator and the number of the previously infected meters. A constraint on the maximum missed detection rate required by the smart grids is exploited in the detection policy distribution to support the reliable data transmission. A deep RL version addresses the quantization error in terms of the test threshold in the SFM evaluation and the infection time series in the state formulation, and compress the state space of the traffic log size received from a large number of data concentrators. Based on a worm detection game, the performance bound is provided under the specified detection window size and the propagation speed of evasive worm. Simulation results for 3600 meters show that the performance gain of the detection accuracy and latency against evasive worm over the benchmarks.

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.

Reinforcement learning (RL)-based collaborative perception in vehicular networks chooses the sub-frame of radio channel resources for connected autonomous vehicles (CAVs) to exchange sensing data to enhance the perception performance, but leads to inaccurate detection in the light detection and ranging (LiDAR)-based object detection due to the asynchronous scan period of the LiDAR point clouds. This paper proposes a RL-based collaborative perception scheme to choose the transmit power and sub-frame to share the feature maps extracted from point clouds. Based on the estimated packet timestamp, the network topology and the channel gains among CAVs, this scheme enhances the perception accuracy and latency against path-loss and interference. The collaborative risk in the policy distribution is formulated as a weighted sum of the perception latency and packet loss rate to avoid the time asynchronization and information loss of the feature map exchange. The performance bound of the perception accuracy and latency is provided based on a Nash equilibrium of the cooperative game among CAVs. Simulation results based on five CAVs show the performance gain of the perception accuracy and latency over the benchmarks.

Narrowband Internet of Things (NB-IoTs) with limited power supply and long range requirements can apply reinforcement learning (RL) based anti-jamming techniques to choose the transmit power and channel to enhance the communication reliability, but have low energy efficiency for the energy-constrained user equipment (UE). In this paper, we propose a RL-based energy-efficient anti-jamming NB-IoT communication scheme to choose the transmission policy including the UE transmit channel and power as well as whether to send them to UE over the downlink channel. Based on the received power on each channel, UE battery level and message size, the transmission policy distribution is formulated according to the risk value that indicates communication reliability exceeding the narrowband bit error rate (BER) requirement and the long-term utility as the weighted sum of UE energy consumption, BER level and frequency hopping overhead for higher energy efficiency and reliability. The similarity of the state-action pairs is evaluated using the risk value to update the long-term risk levels of feasible policies via the knowledge reuse technique for faster learning. The computational complexity and the upper bounds of our proposed scheme in terms of BER and energy consumption are provided. Simulation results show the performance gain over the benchmark against jamming.

Edge-assisted inference that enables each unmanned aerial vehicle (UAV) to offload marine tasks for maritime applications such as target tracking and data collection, but the inference speed and energy efficiency are affected by sea surface movement and wave occlusions under broad area maritime environment with instability channel. In this paper, we propose an RL-based edge-assisted inference scheme for maritime UAV networks to optimize the deep neural networks partition point, the transmit power and the collaborative edge server to enhance the utility as the weighted sum of the data-related energy consumption and the inference latency. Based on the average sea wave height, the channel gain, the number of marine tasks and the battery level, the self-correcting mechanism makes a trade-off between the overestimation and the underestimation in collaborative inference policy without additional computational cost. The bounds of the data-related energy consumption and the inference latency are derived under the specific data rate and inference computation amounts. Simulation results show the effectiveness of the proposed edge-assisted inference scheme.

Reinforcement learning (RL) based flying ad-hoc network (FANET) routing enables unmanned aerial vehicles (UAVs) to choose the next-hop to increase the packet delivery ratio, but the routing latency and energy consumption have to be further reduced over inaccurate feedback for large-scale networks. In this paper, we propose an RL based energy-efficient fast routing for each UAV to choose the forwarding decision and the power. Based on the state consisting of the battery level, channel conditions and forwarding decisions of the one-hop neighbors, the routing policy is chosen to enhance the utility as the weighted sum of the delivery success indicator, the latency and the energy consumption. The number of the latency violations and the learning parameters shared among the one-hop neighbors are exploited in the update of the routing policy distribution following the latency constraint with the reduced energy consumption. The deep neural networks address the state quantization error of the latency and the channel gain for UAVs with high mobility under large-scale networks. The performance bound regarding the end-to-end latency and the energy consumption is derived in terms of network topology and channel gain based on the packet forwarding game. The performance gain over the benchmark is provided via both simulation and experimental results.

Reinforcement learning (RL) enables flying ad-hoc networks (FANETs) to choose the next hop unmanned aerial vehicles (UAV s) with shorter routing path, but may raise the retransmission rate and fails to guarantee the quality of service (QoS) under the high mobility and fast fading channels. In this paper, we propose an RL based routing scheme that optimizes both the routing and the power allocation to protect the latency QoS and save routing energy consumption of the FANET. Based on the routing history, the channel conditions, the battery level and the shared knowledge from the neighbors, this scheme formulates the routing policy distribution with safe exploration to select the stable path and thus reduce the retransmission rate. Specifically, the risk value with respect to end-to-end latency constraint is designed to evaluate the routing policy and reduce the exploration probability of the high-latency routing. Based on the distributed value function approach, the learning parameter such as the state value functions shared among neighbors is exploited to accelerate the routing process and enhance the routing stability under the dynamic network topology. Simulation results verify the routing performance gain of our proposed scheme over the benchmark.

Point clouds from 3D light detection and ranging (LiDAR) are widely used. Noise caused by falling snow reduces the availability of point clouds. Due to the sparseness of LiDAR point clouds and the fact that the snow point clouds are easily affected by multi factors such as wind or snowfall conditions, it is difficult to accurately remove the snow while preserving the details of the point clouds. To solve the problem, this paper presents a de-snowing approach combining the intensity and spatial-temporal features. An intensity-based filter firstly removes the snow. Then a repairing method restores the non-snow points based on the spatial-temporal features. Experimental results demonstrate that our approach outperforms existing work in the literature and performs the least damage to the point clouds in different snowfall scenarios.

Detection of the navigable regions for the unmanned surface vehicles (USVs) sailing on the narrow rivers is very important. Existing detection methods mostly depend on the cameras, which is sensitive to environments and cannot provide reliable navigable regions for sailing. In this paper, we propose a scheme to process 3D LiDAR data to achieve an accurate and robust navigable regions detection. We conduct field experiments in a narrow river in different scenarios to prove the performance of the proposed scheme, which reaches on average 93.8% precision and 92.7% recall.