In this paper, we present a novel multi-hop link scheduling framework that utilizes the vision perception from cameras of the road-side unit (RSU) as well as cameras of the vehicle to support the large-capacity and reliable transmission of high-speed dynamic vehicle network. Specifically, we propose a vision based link state identification method to determine whether the communications links among RSU and different vehicles are blocked or connected. We firstly utilize the 3D detection technique to obtain the vehicle spatial distribution in surrounding environment. Then, the geometric calculation is adopted to accurately analyze the link states between RSU and different vehicles. Moreover, we design an environmental statistical information based low-complexity link scheduling method, and utilize the joint statistical distribution of the residual transmission distance and the residual multi-hop latency to optimize the total transmission latency. Simulation results show that the proposed vision based link state identification method significantly outperforms the exiting methods, and the proposed link scheduling method can approximately achieve the optimal performance as that from the exhaustive search method but with much less computation overhead.

In this paper, we present a novel multi-hop link scheduling framework that utilizes the vision perception from cameras of the road-side unit (RSU) to support the large-capacity and reliable transmission of the high-speed dynamic vehicle network. Specifically, we propose a vision based link state identification method to determine whether the communication links between RSU and different vehicles are blocked or connected. The 3D detection technique is firstly used to obtain the vehicle spatial distribution in surrounding environment. Then, the geometric calculation is adopted to accurately analyze the link states between RSU and different vehicles. Moreover, we design an environmental statistical information based low-complexity link scheduling method. The joint statistical distribution of the residual transmission distance and the residual multi-hop latency is used to optimize the total multi-hop latency. Simulation results show that the proposed vision based link state identification method can significantly outperform the exiting methods, and the proposed link scheduling method can approximately achieve the optimal performance as that from the exhaustive search method but with much less computation overhead.

Visual perception is an effective way to obtain the spatial characteristics of wireless channels and to reduce the overhead for communications system. A critical problem for the visual assistance is that the communications system needs to match the radio signal with the visual information of the corresponding user, i.e., to identify the visual user that corresponds to the target radio signal from all the environmental objects. In this paper, we propose a user matching method for environment with a variable number of objects. Specifically, we apply 3D detection to extract all the environmental objects from the images taken by multiple cameras. Then, we design a deep neural network (DNN) to estimate the location distribution of users by the images and beam pairs at multiple moments, and thereby identify the users from all the extracted environmental objects. Moreover, we present a resource allocation method based on the taken images to reduce the time and spectrum overhead compared to traditional resource allocation methods. Simulation results show that the proposed user matching method outperforms the existing methods, and the proposed resource allocation method can achieve 92% transmission rate of the traditional resource allocation method but with the time and spectrum overhead significantly reduced.

Visual information, captured for example by cameras, can effectively reflect the sizes and locations of the environmental scattering objects, and thereby can be used to infer communications parameters like propagation directions, receiver powers, as well as the blockage status. In this paper, we propose a novel beam alignment framework that leverages images taken by cameras installed at the mobile user. Specifically, we utilize 3D object detection techniques to extract the size and location information of the dynamic vehicles around the mobile user, and design a deep neural network (DNN) to infer the optimal beam pair for transceivers without any pilot signal overhead. Moreover, to avoid performing beam alignment too frequently or too slowly, a beam coherence time (BCT) prediction method is developed based on the vision information. This can effectively improve the transmission rate compared with the beam alignment approach with the fixed BCT. Simulation results show that the proposed vision based beam alignment methods outperform the existing LIDAR and vision based solutions, and demand for much lower hardware cost and communication overhead.

Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user’s movement, we design a deep neural network (DNN) to predict CCM from user location and user speed, and the corresponding estimation method is named as ULCCME. A location denoising method is further developed to reduce the positioning error and improve the robustness of ULCCME. For cases when user location information is not available, we propose an interesting way that uses the environmental 3D images to predict the CCM, and the corresponding estimation method is named as SICCME. Simulation results show that both the proposed methods are effective and will benefit the subsequent channel estimation.