Danyang Chen

In this work, a resource allocation strategy integrating time switching and power splitting (TS-PS) is presented to address the energy efficiency optimization problem in self-powered visible light communication and positioning (SPVLCP) systems. Specifically, for the SPVLCP system employing filter-bank multicarrier (FBMC) modulation, the proposed strategy jointly accounts for energy harvesting requirements, positioning accuracy demands, and transmit-power constraints, formulating the energy efficiency (EE) maximization task as a constrained non-convex optimization problem. To tackle this problem, an iterative algorithm is developed by integrating the concave-convex procedure (CCCP) with successive convex approximation (SCA). Leveraging equivalence transformations and convex surrogate approximations, the original problem is transformed into a series of convex subproblems, which can then be efficiently solved using standard convex optimization solvers. Numerical simulations demonstrate that the proposed strategy significantly enhances the energy efficiency of the SPVLCP system compared with conventional power-splitting (PS) and time-switching (TS) schemes. The performance improvement is especially notable in scenarios with high energy harvesting demands and stringent positioning accuracy requirements. In addition, comparisons with the multi-verse optimizer (MVO) show that the proposed algorithm achieves superior performance and computational efficiency.

Phase-sensitive optical time domain reflectometry ( φ -OTDR) systems frequently encounter a bottleneck where high-accuracy event recognition compromises real-time edge processing capabilities due to the computational demands of complex deep learning models. This paper presents an end-to-end classification framework that resolves this trade-off by integrating discrete first-order difference preprocessing with a lightweight 1D convolutional neural network (1D-CNN). By applying a differential operator to suppress non-stationary baseline drift and Z-score standardization for distributional stability, the proposed method extracts high-frequency transient features from raw time-series data. The subsequent 1D-CNN architecture, characterized by a pyramidal channel expansion and global adaptive average pooling, captures temporal dependencies with minimal structural redundancy. Experimental validation on a public benchmark dataset demonstrates that the model achieves an average accuracy of 99.84%, surpassing state-of-the-art vision transformer (ViT) and MobileNetV2 architectures. Furthermore, the model maintains a parameter footprint of only 42k and achieves an end-to-end inference latency of 0.517 ms on GPU. These results demonstrate that the proposed architecture offers an effective balance between precision and efficiency, enabling robust real-time monitoring on resource-constrained edge devices.

In this work, an integrated ultraviolet (UV) communication– positioning with energy harvesting (UV-CPEH) system in non-line-of-sight (NLOS) channels is considered. Under constraints on positioning error, total transmit power, and harvested energy, a joint optimization model based on the time-switching and power-splitting (TS–PS) mechanism is formulated to enhance the achievable system rate. To address the inherent non-convexity, an efficient iterative algorithm leveraging successive convex approximation (SCA) is proposed to obtain a convergent solution. Numerical results show that the proposed time–power optimization strategy effectively balances the trade-off among communication, positioning, and energy harvesting.

Free space optical (FSO) wireless technology has emerged as a favorable solution for high-speed wireless communication links that can accomplish large data rates due to its broad bandwidth, unlicensed spectrum, and aversion to radio frequency interference. However, its operation is vigorously affected by scintillation in the atmosphere and misalignment errors owing to beam wander or jitter. To determine a more practical statistical representation of such impairments, the Málaga ( M ) turbulence model is applied, which helps to model all kinds of turbulence regimes along with the consequences of pointing errors. Traditional and asymptotic closed-form analytical expressions are developed to evaluate the probability of outage, average bit error rate (BER), and ergodic capacity of a MISO optical wireless system with (1, 2, …, J ) transmitters by using both heterodyne detection and intensity modulation direct detection (IM/DD) techniques. This research analyzes these two methods of the proposed optical wireless system through the use of Monte Carlo Simulations. The simulation results strongly verify the closed-form results under varied turbulence conditions with pointing errors. The outcomes reveal that the detection methods, turbulence intensity, and pointing errors have a significant impact on the system performance. Heterodyne detection is flexible in both moderate and high turbulence regimes, whereas IM/DD is quite useful in the weak turbulence regions because it is less complex and economically viable. The results of this study will be suitable for the modeling and optimization of future FSO systems, especially in scenarios where atmospheric scintillation and misalignments are difficult to control.

This letter presents a reinforcement learning (RL)-based trajectory optimization scheme for autonomous underwater vehicles (AUVs) in underwater acoustic-optical hybrid communication systems, aiming to minimize the average age of information (AoI) and enhance AoI fairness. By modeling the AUV trajectory optimization problem as a continuousstate Markov Decision Process (MDP), the twin delayed deep deterministic policy gradient (TD3) and soft actor-critic (SAC) are employed to learn adaptive trajectory optimization strategies. The simulation results demonstrate that the proposed scheme performs better than traditional schemes in convergence speed, average AoI, and AoI fairness. Compared to the baseline without fairness, the SAC algorithm enhances AoI fairness by 38%, while the TD3 algorithm improves it by 14%.

This letter investigates a demodulation scheme of ultraviolet communication (UVC) systems in mobile scenarios. Firstly, a channel impulse response (CIR) model is proposed to describe the macroscopic motion trend of UVC channel gain and microscopic impulse broadening phenomenon in a unified way. Then, a demodulation scheme based on modified temporal-convolution feature fusion network (m-TCFFN) is proposed for the time-varying characteristics of the UVC channel in mobile scenarios. Simulation results firstly verify the accuracy of the proposed CIR model. In addition, the reliable communication rate of m-TCFFN reaches 10 Mbps, 8 Mbps, 4 Mbps and 3 Mbps in four scenarios of whether the transceivers are coplanar or not at the initial moment and whether the motion orientation is coplanar or not, respectively. The results show that the m-TCFFN improves the anti-interference capability and communication rate of the UVC system, and provide a new solution for the communication needs of unmanned clusters in special environments.

Based on the scattering characteristics of ultraviolet light, the existing studies on ultraviolet communication (UVC) channels mainly focus on non-line-of-sight (NLOS) links, but lack research on line-of-sight (LOS) links. In this paper, a single-scatter sphere-threshold-based NLOS/LOS UVC channel model is proposed by introducing an imaginary threshold ball. The analytical expressions for the path loss (PL) of the LOS link and the whole link are derived. The simulation results demonstrate that the proposed model aligns closely with the Monte Carlo model, and in the presence of LOS links, its root mean square error (RMSE) is only 0.1138 dB. Further, the expressions for the PL and the channel impulse response (CIR) when the receiver is moving are derived, and the impact of parameters such as motion speed and motion direction is systematically analyzed. The results show that the PL and the CIR are more sensitive to the coplanar-noncoplanar transition (CNT) when the transmitter and receiver are close to a coplanar state at the initial time. In addition, the distance between the transmitter and receiver is the dominant factor affecting channel performance in all scenarios.