While physical layer authentication can mitigate wireless channel vulnerabilities, its reliability is often compromised by inherent noise and variability of observed physical layer attributes. As a solution, collaborative physical layer authentication (CPLA) introduces multiple nodes to enhance performance, but incurs additional computational and communication costs for collaborators. Without incentive, desired collaborators may act selfishly and withdraw, and involving unreliable collaborators could degrade performance. Therefore, this paper proposes an incentive mechanism with a new centralized governance approach to coordinate CPLA, engaging reliable collaborators to optimize authentication accuracy. Specifically, we model the interaction between the center and collaborators as a Stackelberg game to establish. To reduce redundant computations in equilibrium solving, we first construct a candidate pool containing potential trainable combinations. Subsequently, we design incentive and training schemes for each candidate combination. Moreover, a quality-driven combination selection scheme is proposed to maximize incentive effectiveness. Based on the candidate pool and strategies, it integrates a deep Q-network as collaborator quality manager and a combination-level evaluation module, and via “filter-then-verify” identifies optimal incentive targets with low complexity while improving authentication accuracy. Simulations demonstrate that the proposed scheme successfully incentivizes selfish collaborators and achieves 99% authentication accuracy in unreliable collaborative environments.

Physical layer authentication (PLA) as a promising solution has gained widespread attention due to its high security and lightweight deployment. A fixed authentication center is vulnerable to attacks, making both centralized PLA (CPLA) with a single authentication center and collaborative PLA with multiple collaboration nodes at risk of a single point of failure. In this paper, we propose a leader election algorithm for distributed physical layer authentication, where multiple receivers are col-laboratively trained under a leader and complete authentication independently. Specifically, the one with the strongest model generalization ability is elected as the leader by individual receivers voting based on data quality. The role of the leader is to filter out the underperforming receivers, assign reasonable weights to local models, and construct an authentication white list to achieve better authentication performance. Simulation results show that the proposed scheme outperforms the randomly selected leader and the traditional PLA schemes.

The uncertainty of water cloud feedback on radiative forcing is one of the largest obstacles to producing confident projections of the global climate. Sufficient measurements of water clouds are crucial to addressing this issue. However, existing techniques based on remote sensing or in situ instruments face limitations in data capacity attributed to the short lifetime, high temporal variability, and complex vertical structure of water clouds. In this study, taking advantage of a dual-field-of-view (dual-FOV) high-spectral-resolution lidar (HSRL), we developed a novel algorithm to obtain diurnal simultaneous profiles of water cloud optical and microphysical properties with high temporal-spatial resolution. This technique does not rely on the widely used subadiabatic assumption about the vertical structure of water clouds. The retrieval algorithm, validated by simulations and cloud radar measurements, was applied to field experiment data collected at the Beijing and Hangzhou sites in China. The relationship functions between water cloud properties are presented to enhance our understanding of the underlying processes. Furthermore, the vertical distributions of retrieved properties are compared to the subadiabatic assumption. The dual-FOV HSRL technique enables comprehensive observations, enhancing our understanding of water clouds and providing significant insights into the interactions among clouds, aerosols, precipitation, and radiation.

Collaborative physical layer authentication (CPLA) is a promising alternative, addressing common single-point failure issues in centralized authentication systems through its unique architecture. However, the necessary involvement of multiple parties increases the risk to collaborative systems, particularly from hostile cooperators, significantly impacting the performance of CPLA. In existing CPLA approaches, the most common strategy to combat malicious cooperators attacks is to select the best collaborative combination. This strategy achieves the customization goal by excluding hostile-minded devices. However, processing a non-fixed search space typically demands a substantial investment of time and resources. As a remedy, we propose a decision-level-based CPLA scheme with a weighted voting mechanism. Our scheme aims to implement streamlined and effective dynamic management of cooperators to ensure that multi-directional information provides positive effects on authentication. Specifically, we conduct a two-stage performance appraisal of all cooperators. To measure the trustworthiness of cooperators, an impression-driven reliability evaluation scheme is developed. We analyze the riskiness of individual cooperators to prevent centers from falling into cognitive blind spots. Finally, we validate the feasibility of the scheme. The results demonstrate that, in a scenario where 50% of participants are malicious, our approach achieves an accuracy improvement of 2.96% to 3% compared to other dynamic weighted voting schemes. The robustness and stability of the proposed CPLA scheme outperform the benchmark schemes.

Collaborative physical layer authentication (CPLA), which leverages spatial diversity, holds promise for enhancing the performance of feature-based physical layer authentication. However, some existing CPLA schemes simply aggregate the local information of collaborators to make final judgments and rarely consider the involvement of malicious collaborators. In this paper, we propose an impression-weighted based local decision aggregation scheme for detecting spoofing attacks in the presence of malicious collaborators. Specifically, the authenticator continually evaluates the authentication capabilities of collaborators by verifying the accuracy of local decisions and then synthesizes their long-term capabilities into impressions using a fuzzy membership function. These impression values will be dynamically updated upon completion of each authentication task. Moreover, a reinforcement learning scheme is employed to find the optimal threshold for authentication in a dynamic environment. Simulation results validate the high robustness and effectiveness of our proposed approach, guaranteeing the CPLA system's reliable operation.

Detecting the vertical profile of optical properties is an important task in the remote sensing of the upper ocean, especially for 3-D reconstruction. Ocean color remote sensing can only provide surface information, while the light detection and ranging (lidar) technique can provide depth-resolved data. Lidar can provide global-scale observations of the upper ocean for days and nights with minimal atmospheric correction errors. Unfortunately, due to the strong multiple scattering effects that occur when light propagates in seawater, the simple lidar equation may cause some deviations between the actual measurements and the simulation of the lidar signals. In this paper, we present a shipborne oceanic lidar, which was developed to detect the optical properties of seawater. For evaluating the performance of the lidar system, a Monte Carlo (MC) model was established to simulate lidar signals based on the simultaneous in situ inherent optical properties of seawater. The lidar measurements and the MC simulation can provide both the lidar signals and the retrieved lidar attenuation coefficient α. The results of the comparison indicate that the lidar-measured signals correspond well with the MC-simulated signals at different experiment stations in the Yellow Sea and at various receiving fields of view (FOVs). We also observed strong correlations between the lidar-measured α and MC-simulated α at different stations (r = 0.95) and at various FOVs (r = 0.96). The results indicate the reliability of the developed lidar system.