Hongmei Zhao

To address time-frequency aliasing in radio frequency (RF) signals from multiple UAVs and co-channel interference from IoT devices (e.g., WiFi, Bluetooth)—both of which hinder the detection of multiple UAV RF signal targets—this paper proposes a lightweight detection model named YOLOv11-WDMA. First, Wavelet Transform Convolution (WTConv) is introduced into the C3k2 module of the backbone network. By multi-level frequency decomposition, WTConv provides multi-scale receptive fields and enhances the extraction of high-frequency detail features of small targets such as Bluetooth and remote control signals. Second, a Dual-Path Modulated Feature Enhancement with Learnable Fusion (DPMA-LF) module is proposed and embedded in the detection head. This module applies cascaded spatial and channel modulation to two parallel feature streams, then adaptively fuses the modulated features with the original neck features via learnable weights, amplifying feature-space differences between aliased signals and background noise. Experiments on a simulated multi-UAV time-frequency aliasing dataset constructed from the public DroneRFa dataset show that, within the signal-to-noise ratio (SNR) range of [-10, 10] dB, YOLOv11-WDMA achieves 96% mAP@0.5 and 87.4% mAP@0.5:0.95, surpassing the YOLOv11n baseline by 13.2% and 17.4%, respectively. The model has only 2.97M parameters and 6.9 GFLOPs, while outperforming all existing nano-scale YOLO models and achieving detection accuracy comparable to RT-DETR and small-scale YOLO models, making it well suited for deployment on edge computing terminals.

Ultrawideband (UWB) signals offer strong penetration capabilities and high transmission rates, playing an important role in precise indoor positioning. However, the complex and dynamic nature of indoor environments often leads to nonline-of-sight (NLOS) propagation and severe signal attenuation, resulting in significant ranging and positioning errors. Currently, convolutional neural network (CNN)-based approaches have made some progress in NLOS identification tasks. Nevertheless, traditional CNNs suffer from performance bottlenecks owing to their lack of temporal feature modeling capabilities and limited representational diversity. To address this issue, this article introduces the concept of feature fusion and proposes a multifeature fusion CNN model that incorporates Gramian angular field (GAF) encoding. By applying GAF encoding, it converts a 1-D channel impulse response (CIR) signal into a 2-D image, capturing the temporal correlations within the signals. During model training, different CNN architectures are trained using these GAF images, and the best-performing network is selected to construct a dual stream CNN that extracts both the time domain features of the original CIR signals and the frequency domain features of the GAF images. Meanwhile, physical layer parameters such as the maximum growth amplitude of the received CIR signals (maxGrowthCIR), the amplitudes of the first and second paths near the first path (FP_Ampl1 and FP_Ampl2), and UWB signal noise are incorporated to build a multidimensional auxiliary feature set. These auxiliary features enhance the ability to distinguish NLOS scenarios that are difficult to identify using CIR features alone, further reducing NLOS errors. In addition, a hybrid fusion method is used to capture correlations among channel features, mitigating the risk of data drowning caused by differences in input feature dimensions. Simulation results demonstrate that the proposed method achieves outstanding classification performance in both binary and multiclass NLOS tasks, with accuracy exceeding 95%. Compared with traditional algorithms, the proposed method reduces the mean absolute error (MAE) and root mean square error (RMSE) of distance estimation by 47.14% and 36.08%, respectively, achieving a coefficient of determination ( $R^{2}$ ) of 0.9493. Furthermore, based on the recognition results, the proposed method can reduce the RMSE fraction and the MAE of the distance error by 43.78% and 41.67%, respectively, compared with the existing methods. In the actual indoor positioning experiments, this method improves positioning accuracy by more than 83%, fully verifying its engineering application potential in complex indoor localization scenarios.

Motivated by the development of vehicular technologies and applications, the vehicle-to-everything (V2X) communications are realized by the dedicated short-range communications (DSRC) and the cellular V2X (C-V2X) which are undergoing continuous and widespread evolution. As the first standardized DSRC technology, IEEE 802.11p, that has been studied with large-scale field trials performed worldwide, is more robust and mature than the other V2X technologies. The main contributions of the proposed work over the previous work are as follows. Firstly, extensive physical layer (PHY) metrics, containing the packet error rate (PER), packet reception ratio (PRR), output packet inter-arrival time (IAT), and output effective data rate, are sufficiently adopted to accomplish the thorough PHY evaluation which can avoid the limitation brought by the partial PHY metrics. Secondly, various multi-antenna configurations, including the multiple-input multiple-output (MIMO), single-input multiple-output (SIMO), and multiple-input single-output (MISO) systems, are added to remedy the incomplete analysis on antenna configurations induced by the only single-input single-output (SISO) configuration. Finally, considerably different packet sizes and modulation and coding schemes (MCSs) are discussed under the urban non-line-of-sight (NLOS) and highway NLOS scenarios to uncover the impact of each parameter on the PHY performance which cannot be found in the fixed parameter or slightly different parameters. Some important conclusions obtained from a complete MATLAB-based PHY simulation are as follows. Firstly, the multi-antenna systems are more advantageous in reducing the PER, increasing the PRR and transmission coverage, decreasing the output packet IAT, and elevating the output effective data rate, compared to the SISO system, above the distance threshold and below the signal-to-noise ratio (SNR) threshold. Secondly, the packet size and the MCS should be adjusted simultaneously in different applications to accommodate their high-reliability, low-latency, or high-throughput requirement. Finally, the urban NLOS scenario with the lower Doppler effect is more tolerant than the highway NLOS scenario in the vehicle-to-vehicle (V2V) communications due to its lower PER, larger PRR and transmission coverage, smaller output packet IAT, and higher output effective data rate.

This paper proposes a novel tri-band dual-sense circularly polarized (CP) metasurface (MTS) microstrip antenna based on characteristic mode analysis (CMA). The antenna consists of modified 3×2 MTS units, orthogonally feeding slots, and a T-shaped power divider as the feeding network. CMA is initially conducted on the MTS units to elucidate the operational principles of the proposed antenna. Rectangular patches are replaced by curved rings with lower radiation, and adjustments are made to the corners of the units to achieve CP and mitigate reverse current in resonant modes. Subsequently, CMA is performed on the modified MTS units with the feeding slots to excite resonant modes. The results show that three pairs of orthogonal modes excited by the feeding slots are achieved at 7.75GHz, 8.8GHz, and 9.8GHz, respectively forming CP. Finally, the inclusion of a T-shaped power divider is simulated to validate the antenna's performance. A prototype experiment verifies the principle and design methodology, with measurements agreeing well with simulation results.

To enable vehicle-to-everything (V2X) communications, both the dedicated short-range communications (DSRC) and the cellular V2X (C-V2X) involved in the radio access technologies (RATs) are experiencing extensive development to support advanced vehicular applications and scenarios. Compared with the C-V2X whose effective reliability and scalability are still to be completely verified, the DSRC maintains its superiority due to the extensive safety-related field trials performed worldwide. Furthermore, IEEE 802.11bd, the next-generation V2X (NGV) standard for the vehicular ad hoc networks (VANETs) in the DSRC, is expected to greatly improve the performance compared to IEEE 802.11p with new physical layer (PHY) and medium access control (MAC) technologies. Hence, aimed to obtain the complete PHY evaluation on IEEE 802.11bd in terms of the packet error rate (PER), the packet reception ratio (PRR), the effective data rate, and the packet inter-arrival time (IAT), the various antenna configurations, vehicle-to-vehicle (V2V) scenarios, packet sizes, and modulation and coding schemes (MCSs) are investigated and compared in a full PHY simulation. The results indicate that the multiple-input multiple-output (MIMO) configuration is the most advantageous technique in decreasing the PER, increasing the PRR and the transmission coverage, elevating the output effective data rate, and reducing the output packet IAT in contrast with the single-input multiple-output (SIMO), multiple-input single-output (MISO), and single-input single-output (SISO) configurations at the farther distance. The urban non-line-of-sight (NLOS) scenario experiences the slightly better quality of communication and is more robust than the highway NLOS scenario. Both the packet size and the MCS need to be selected properly to satisfy the high-reliability, high-throughput, or low-latency requirements.