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.

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 low-power wide-area networks (LPWANs) such as Long Range (LoRa) enable the access point (AP) to optimize the uplink power and channel of user equipment (UE), but the energy efficiency and quality of service (QoS) are low due to frequent downlink feedback against smart jamming. In this paper, we propose an RL-based anti-jamming energy-efficient wide-area communication scheme to optimize the transmit power, channel and downlink feedback policy with QoS guarantee. The similarities of channel states and UE communication tasks are evaluated to update learning parameters and accelerate policy selection under low duty cycles in LPWANs. We further propose a deep RL-based scheme that exploits neural networks to address the quantization error of the received power at AP and UE battery level and update weights according to prioritized experience replay to improve learning efficiency. The computational complexity and the performance bound in terms of signal-to-noise ratio and UE energy consumption are provided based on the Stackelberg equilibrium of the anti-jamming game. Our proposed schemes are implemented based on Raspberry Pi and BME280 sensors to support the LoRa-based transmission of temperature, humidity and pressure data. Experimental results on campus show the performance gain with less packet loss rate and UE energy consumption over the benchmark against smart jamming.

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.

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.

False data injection (FDI) attacks construct attack vectors to inject false data into tampered meters with the goal of falsifying state estimation, but resulting in low successful attack rate with high attack costs in terms of the number of tampered meters in large-scale smart grids, because the bad data detection at the control center chooses the dynamic detection thresholds to identify the modified meter measurements. In this article, we propose a reinforcement learning-based FDI attack scheme that optimizes both the tampered meters and the false data to enhance the success attack rate and injected errors while reducing attack costs. Based on meter measurements and previous performance, the attack vector is constructed to induce more errors in state estimation and bypass bad data detection. The performance bounds regarding the successful attack rate and the injected error are derived in terms of the number of bus phase angles, the susceptance of the transmission line, and the maximum false data based on the Nash equilibrium of the FDI game. Simulations performed on both the IEEE 14-bus and IEEE 118-bus systems demonstrate the performance gain over the benchmarks.

In this paper, we develop a non-coherent differential chaos shift keying based index modulation by using initial value index (IVI-DCSK) to convey additional information for wireless communications. In the proposed scheme,mcmapped bits are carried by 2mcchaotic sequences by exploiting the quasi-orthogonality of different chaotic signals, while the modulated bit is carried by DCSK. To diminish the multiuser interference, the references allocated to different users are sent in individual time slots, while the information-bearing sequences for the mapped bits of users are sent simultaneously. We then derive the bit error rate (BER) expression of multi-user IVI-DCSK over multipath Rayleigh fading channels. The theoretical and consistent simulation results show that the proposed IVI-DCSK achieves significant gains over the conventional chaotic-based index modulations, i.e., permutation index DCSK (PI-DCSK) and code index modulation DCSK (CIM-DCSK). This gain can be more than 4 dB in fading channels with high multipath delay. In addition, it achieves higher energy and spectral efficiencies over the latter ones. The superiority of the proposed scheme is further verified in practical ultra-wideband (UWB) communications. Thus, this proposed scheme is efficient and promising for chaotic-based low-complexity communications, such as in wireless local area network (WLAN) and indoor applications.

Based on the network observations and learning parameters shared by the neighboring learning agents, multi-agent reinforcement learning (RL) has to enhance the performance over adversarial communications, in which spoofing attackers send fake learning messages to fool the learning agent and thus degrade the performance of wireless applications. In this paper, we propose a safe multi-agent RL algorithm for wireless applications against adversarial communications, in which each learning agent chooses the cooperative agents to share the learning information and authenticates the received learning messages before integrating them into the RL state formulation and the learning parameter update. The communication policy distribution for the cooperative agent selection is formulated based on the long-term discounted reward and the sharing reputation for each neighboring agent, which is updated based on the authentication results to indicate the probability as a spoofing attacker. Neural networks are designed to estimate the long-term discounted reward and the sharing reputation for the learning agent with sufficient computational resources in large-scale wireless networks to enhance the agent selection security. As a case study, our proposed algorithm is implemented in the unmanned aerial vehicle swarm anti-jamming video transmission against spoofing attackers that send fake received jamming power as well as Q-values and neural network weights in the anti-jamming transmission policy learning. Both simulation and experimental results are provided to verify the performance gain over the benchmark.

The differential chaos shift keying with code index modulation (CIM-DCSK) using Walsh codes can increase the data rate, but error rate performance degrades over practical multipath channel with high delay spread. To tackle this drawback, this paper proposes a parallel CIM-DCSK (PT-CIM-DCSK), where the first time slot is used to transmit the chaotic reference sequence, and the second slot is used to transmit the multiple sequences carrying data bits. In particular, multiple$q$chaotic sequences are added for data transmission in parallel, and each sequence is modulated by CIM-DCSK with$m_{c}$bits. By exploiting the quasi-orthogonal characteristics of permuted chaotic signals, the interference between$q$sequences can be remarkably suppressed. In this way, given the data rate, PT-CIM-DCSK can achieve a larger length of Walsh segment than the CIM-DCSK, since its parallel transmission compensates the rate. In addition, the error performance of the proposed scheme over the multipath Rayleigh fading channel is theoretically analyzed. The theoretical and consistent simulation results demonstrate that the PT-CIM-DCSK can obtain significant performance gains over the conventional CIM-DCSK, and the gain can be up to 5 dB in multipath fading channel with high delay. This is because the larger Walsh segment of PT-CIM-DCSK provides the enhanced robustness against multipath effect. The performance superiority of the proposed scheme is further verified in practical ultra-wideband (UWB) wireless industrial scenarios, which suggests promising for low-cost and low-complexity wireless communication applications.