Smart grids are being expanded in scale with the increasing complexity of the equipment. Edge computing is gradually replacing conventional cloud computing due to its low latency, low power consumption, and high reliability. The CORDIC algorithm has the characteristics of high-speed real-time processing and is very suitable for hardware accelerators in edge computing devices. The iterative calculation method of the CORDIC algorithm yet leads to problems such as complex structure and high consumption of hardware resource. In this paper, we propose an RDP-CORDIC algorithm which pre-computes all micro-rotation directions and transforms the conventional single-stage iterative structure into a three-stage and multi-stage combined iterative structure, thereby enabling it to solve the problems of the conventional CORDIC algorithm with many iterations and high consumption. An accuracy compensation algorithm for the direction prediction constant is also proposed to solve the problem of high ROM consumption in the high precision implementation of the RDP-CORDIC algorithm. The experimental results showed that the RDP-CORDIC algorithm had faster computation speed and lower resource consumption with higher guaranteed accuracy than other CORDIC algorithms. Therefore, the RDP-CORDIC algorithm proposed in this paper may effectively increase computation performance while reducing the power and resource consumption of edge computing devices in smart grid systems.

Unmanned aerial vehicles (UAVs) equipped with communication devices are easy to deploy and can serve as temporary mobile communication stations, covering dynamic ground users and establishing communication links. The proper planning of UAV paths can improve user coverage while maintaining communication quality. The traditional particle swarm optimization (PSO) algorithm exhibits strong global search capabilities; however, it suffers from challenges such as uneven search coverage owing to the random distribution of the initial particle positions. Additionally, improper parameter selection often leads to premature convergence to local optima. This study proposes a method for multi-UAV coverage of dynamic users by combining Tent chaotic mapping with PSO (Tent–PSO). By using Tent chaotic mapping to adjust a UAV’s speed and position, the method ensures that particles are evenly distributed in the search space. Simulation results demonstrate that Tent–PSO improves the coverage of the global optimal solution by 10% and increases throughput by at least 37%, confirming its effectiveness in covering dynamic user scenarios.

Spectrum sensing is the necessary premise for implementing cognitive radio technology. The conventional wideband spectrum sensing methods mainly work with sweeping frequency and still face major challenges in performance and efficiency. This paper introduces a new wideband spectrum sensing method based on channels clustering and prediction. This method counts on the division of the wideband spectrum into uniform sub-channels, and employs a density-based clustering algorithm called Ordering Points to Identify Clustering Structure (OPTICS) to cluster the channels in view of the correlation between the channels. The detection channel (DC) is selected and detected for each cluster, and states of other channels (estimated channels, ECs) in the cluster are then predicted with Hidden Markov Model (HMM), so that all channels states of the wideband spectrum are finally obtained. The simulation results show that the proposed method could effectively improve the wideband spectrum sensing performance.

As the threat from malicious UAVs continues to intensify, accurate identification of individual UAVs has become a critical challenge in regulatory and security domains. Existing single-signal analysis methods suffer from limited recognition accuracy. To address this issue, this paper proposes a high-precision individual identification method for UAVs based on FFS-SPWVD and DIR-YOLOv11. The proposed method first employs a frame-by-frame search strategy combined with the smoothing pseudo-Wigner–Ville distribution (SPWVD) algorithm to obtain effective time–frequency feature representations of flight control signals. Building on this foundation, the YOLOv11n network is adopted as the baseline architecture. To enhance the extraction of time–frequency texture features from UAV signals in complex environments, a Multi-Branch Auxiliary Multi-Scale Fusion Network is incorporated into the neck network. Meanwhile, partial space–frequency selective convolutions are introduced into selected C3k2 modules to alleviate the increased computational burden caused by architectural modifications and to reduce the overall number of model parameters. Experimental results on the public DroneRFb-DIR dataset demonstrate that the proposed method effectively extracts flight control frames and performs high-resolution time–frequency analysis. In individual UAV identification tasks, the proposed approach achieves 96.17% accuracy, 97.82% mAP50, and 95.29% recall, outperforming YOLOv11, YOLOv12, and YOLOv13. This study demonstrates that the proposed method achieves both high accuracy and computational efficiency in individual UAV recognition, providing a practical technical solution for whitelist identification and group size estimation in application scenarios such as border patrol, traffic control, and large-scale events.

In order to ease the conflict between the bandwidth demand of high-rate wireless communication and the shortage of spectrum resources, a wideband spectrum sensing method based on reconfigurable filter bank (RFB) with adjustable resolution is presented. The wideband signals are uniformly divided into multi-narrowband signals by RFB, which is designed by polyphase uniform Discrete Fourier Transform (DFT) modulation, and each sub-band is sensed by energy detection. According to the idle proportion of detected sub-bands, the number of RFB sub-bands is reset in next spectrum-sensing time. By simulating with collected wideband dataset, the influence of filter bank sub-bands number and idle state proportion on the sensing results is analyzed, and then on the basis of the trade-off between spectrum-sensing resolution and computational complexity, the optimal sub-bands number of filter bank is selected, so as to improve the detection performance and save resources.

By leveraging the complementary advantages of free-space optical (FSO) and radio frequency (RF) links, hybrid FSO/RF systems exhibit broad application prospects. However, maintaining robustness while performing trade-off optimization between reliability and transmission efficiency under dynamic conditions with power constraints remains challenging. To address this, we propose a weather-aware multi-objective adaptive power allocation approach for hybrid FSO/RF systems based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a deep neural network (DNN). Closed-form expressions for the average bit-error rate (ABER) and average channel capacity (ACAP) are derived and used to evaluate NSGA-II objectives, generating a labeled optimal allocation dataset across diverse scenarios. A DNN is then trained on the dataset to learn adaptive power allocation strategies for dynamic environments. Numerical results demonstrate that the proposed scheme effectively achieves adaptive power allocation and significantly outperforms existing benchmark schemes. In dynamic scenarios, it reduces the ABER by 1–2 orders of magnitude and substantially lowers the outage probability (OP), while improving the overall ACAP by more than 0.5 Gbps under the same transmit power.