Real-time ray-based channel generation and emulation for UAV communications

A real-time ray-based hardware emulator for Unmanned Aerial Vehicle(UAV) communication channels which suits for the Three-Dimensional(3D) dynamic scenarios and considers the movements of both terminals is developed in this paper. The time-variant channel parameters, i.e.,ray delay, ray gain, and ray Doppler frequency are precalculated in the host by using the Ray Tracing(RT) method. Meanwhile, RT simulation dramatically increases the number of valid rays. To address the problem of resource limitation and huge computational burden in the implementation,an efficient ray coefficients generation method based on iteration is proposed and implemented.With the advantages of low cost and high flexibility, a Software Defined Radio(SDR) hardware platform is used to emulate the ray-based UAV channels by utilizing the compact architecture including the Time-Division(TD) scheme and Tapped-Delay Line(TDL) for channel convolution.Finally, hardware measurement results demonstrate that the properties of emulated channel, i.e.,Power Delay Profile(PDP) and Doppler Power Spectrum Density(DPSD) consist with the simulated ones, which verifies the correctness of hardware implementation. The proposed channel emulator provides an efficient way for optimization, verification, and evaluation of UAV communication systems.

3D Model Retrieval Method Based on Affinity Propagation Clustering

In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature extraction efficiency of 3D models. Based on the relationship between model and its projection,the intersection in 3D space is transformed into intersection in 2D space,which reduces the number of intersection and improves the efficiency of the extraction algorithm. In feature extraction,multi-layer spheres method is analyzed. The two-layer spheres method makes the feature vector more accurate and improves retrieval precision. Secondly,Semi-supervised Affinity Propagation ( S-AP) clustering is utilized because it can be applied to different cluster structures. The S-AP algorithm is adopted to find the center models and then the center model collection is built. During retrieval process,the collection is utilized to classify the query model into corresponding model base and then the most similar model is retrieved in the model base. Finally,75 sample models from Princeton library are selected to do the experiment and then 36 models are used for retrieval test. The results validate that the proposed method outperforms the original method and the retrieval precision and recall ratios are improved effectively.