Integrating multi-modal environment sensing and wireless channel prediction provides an innovative unmanned aerial vehicle (UAV) channel modeling solution, which can serve as a foundation for future UAV communication system design and network optimization. This paper proposes a novel UAV channel predictive model using multi-modal environment sensing information and measured channel data. To enable comprehensive understanding of the complex and dynamic UAV communication surroundings, the Global position system (GPS) location data, inertial measurement unit (IMU) data, channel data, and environment information are fused as the model’s input. Within a generative adversarial network (GAN) framework, the model enhances the prediction accuracy of channel data in complex environments through adversarial training of the generator and discriminator, producing channel data as the model’s output that closely approximates real-world measurements. Furthermore, visual perception data from UAVs is incorporated into the prediction process, allowing the model to abstract scatterers by capturing changes in environment obstacles. During channel prediction, an attention mechanism is also incorporated into the multi-modal data fusion process, dynamically adjusting the importance of each modality to ensure the model concentrate on the most crucial features. Results from the experiments indicate that the proposed method facilitates real-time predictions of ground channel data across a range of flight altitudes and communication frequencies. This advancement contributes important knowledge to UAV communication studies and significantly boosts the effectiveness and reliability of intelligent air-to-ground communication networks.

A channel prediction modeling method based on multimodal fusion perception is proposed for complex environments in unmanned aerial vehicle (UAV) air-to-ground (A2G) communications. Two-dimensional (2D) environmental information and three-dimensional (3D) point cloud data are fused to enhance the model’s ability to capture environment blockage, reflection, and multipath effects. The 2D information provides target objects’ planar distribution and texture features, while the 3D point clouds supplement spatial position, size, and height information. These complementary modalities comprehensively describe the geometric structures present in complex environments. A multimodal modeling network is constructed to explore the nonlinear mapping between environmental perception data and channel data. The network takes 2D building distribution, 3D point cloud data, global image features, UAV and receiver positions, and communication parameters as joint inputs. Feature extraction and fusion modules achieve effective joint encoding of heterogeneous multimodal features. A spatial feature decoupling (SFD) module is designed to address interference caused by coupled features. It separates the data distributions corresponding to different channel characteristics, improving the accuracy of channel impulse response (CIR) prediction. Experimental results demonstrate that the proposed method significantly improves the reliability and adaptability of UAV channel modeling in complex urban scenarios.

Unmanned-aerial-vehicle (UAV) communications, as a critical application scenario in the sixth generation (6G) wireless communication field, has garnered widespread attention. During UAV-to-ground communication, channel data plays a pivotal role. Analyzing channel data enables an understanding of communication environments’ diversity and temporal variability, thereby facilitating the construction of more efficient communication systems. This article proposes a novel UAV-to-ground channel prediction method based on multimodal fusion. The method aims to achieve real-time and precise prediction of UAV-to-ground channel data from UAVs in the 3-D airspace by integrating various sources of information, including UAV-captured images, location data of transmitters and receivers, and communication settings. The network uses a fused architecture combining convolutional neural network (CNN) and Transformer architecture to extract and integrate features from diverse information sources. This fusion strategy significantly enhances the accuracy of UAV-to-ground channel prediction. Incorporating image information enables the network better to comprehend the complexity and dynamics of communication environments, thereby assisting in achieving more precise UAV-to-ground channel prediction. Experimental results demonstrate that the proposed method achieves real-time prediction of ground channels across various flight altitudes and communication frequency bands. This provides robust technical support for advancing UAV communication and offers new insights for optimizing and upgrading future wireless communication systems.

Transit signal priority (TSP) is a typical strategy used to reduce the delay in public transit and it has been used extensively to improve the transit operations in urban traffic systems. This paper proposes a transit signal priority controlling method considering both the non-transit traffic benefits and the coordinated phase states for multi-ring time plan. The integer linear programming method is used to build the green time extension and red time truncation models. In the proposed model, the optimization object includes two levels: the maximum priority effect for the first level and minimum time deviations for non-transit phases for the second level. Based on this, the semi-ring structure and the backward/forward migration time parameters of coordinated phases are further presented for establishing the constraints. The overall scheme is thoroughly tested and demonstrated in a realistic experimental scenario. Compared with the TSP scheme which takes the coordinated phase as the deadline for adjusting phases, the proposed method can maximize the priority effect by taking full advantage of the migration state of coordinated phases. Moreover, it also reduces the effects to non-transit phases by compensation and traffic-state-based compression mechanisms.

With the increasing in complexion of large-scale activities in business process, workflow model has been focused on highly than before. In this research, a new workflow model (WfM) with two layers (step and task layers) was proposed, which leads to process logic and concrete affairs couple loose, and deals with problems of simple logic of directed graph WfM and complex realization of net structure WfM. With the half structure model, process logic is defined during design, and real tasks route is chosen in practice, which enhances the flexibility of WfM. Besides, the notion, document-driven workflow, was introduced in that WfM. The parsing process of documents, the design format of documents and the delivery technology were also given. In order to accomplish a complete WfM, we integrated WfM and organization model in both static and dynamic ways. Finally we delineate the realization of S-T platform based on that WfM in this paper

The research of workflow technology has been an active area for many years. Modeling of workflow is arguably the most important issue. In this paper, a workflow model with two layers named as S-T workflow model is created which is based on directed cyclic graph. We separate control node and task node in two layers. Step layer describes process control logic and task layer parallels concrete affairs to make the workflow clear. We address the modeling strategy at first, and then depict the features of S-T workflow model in detail, and propose arithmetic to verify the soundness of modeling structure. Besides, the parsing process of documents, and the design format of documents are also given in this paper