In recent years, unmanned aerial vehicles (UAVs) have received considerable attention from regulators, industry and research community, due to rapid growth in a broad range of applications. Particularly, UAVs are being used to provide a promising solution to reliable and cost-effective wireless communications from the sky. The deployment of UAVs has been regarded as an alternative complement of existing cellular systems, to achieve higher transmission efficiency with enhanced coverage and capacity. However, heavily utilized microwave spectrum bands below 6 GHz utilized by legacy wireless systems are insufficient to attain remarkable data rate enhancement for numerous emerging applications. To resolve the spectrum crunch crisis and satisfy the requirements of 5G and beyond mobile communications, one potential solution is to use the abundance of unoccupied bandwidth available at millimeter wave (mmWave) frequencies. Inspired by the technique potentials, mmWave communications have also paved the way into the widespread use of UAVs to assist wireless networks for future 5G and beyond wireless applications. In this paper, we provide a comprehensive survey on current achievements in the integration of 5G mmWave communications into UAV-assisted wireless networks. More precisely, a taxonomy to classify the existing research issues is presented, by considering seven cutting-edge solutions. Subsequently, we provide a brief overview of 5G mmWave communications for UAV-assisted wireless networks from two aspects, i.e., key technical advantages and challenges as well as potential applications. Based on the proposed taxonomy, we further discuss in detail the state-of-the-art issues, solutions, and open challenges for this newly emerging area. Lastly, we complete this survey by pointing out open issues and shedding new light on future directions for further research on this area. INDEX TERMSMillimeter wave (mmWave) communications, unmanned aerial vehicle (UAV), mmWave UAV communications, UAV-assisted wireless networks, 5G and beyond.HAITAO XU received the B.E. degree in communication engineering from Sun Yat-sen University, Guangzhou, China, in 2007, the M.E. degree in communication system and signal processing from the University of Bristol, U.K., in 2009, and the Ph.D. degree in communication and information systems from the

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The comprehensiveness of the raw input data and the effectiveness of feature engineering are two key factors affecting the performance of machine learning (ML). To improve the data comprehensiveness for Global Navigation Satellite System Reflectometry (GNSS-R) ocean wind speed retrieval, this paper introduces a new input data structure, which is composed of Delay-Doppler Maps (DDM) and all satellite receiver status (SRS) parameters. Then, to overcome the difficulty of handcrafted feature engineering and effectively fusion the information of DDM and SRS, we presented a heterogeneous multimodal deep learning (HMDL) method to retrieve the wind speed according to the heterogeneity of the input data. The proposed model is verified by the performance evaluation of realistic data sets obtained from TDS-1. The new input data structure improves the prediction accuracy at 13.5% to 30.7% on mean absolute error (MAE) at 10.6% to 29.5% on the root mean square error (RMSE). The HMDL improves the prediction accuracy at 7.7% on MAE and 7.1% on RMSE. The whole proposed solution improves the prediction accuracy at 36.3% on MAE and 36.8% on RMSE, comparing with the traditional neural network (NN) based solution. The results clearly show that both the introduction of the new input data structure and HMDL effectively improve the accuracy and robustness of GNSS-R wind speed retrieval.

Because the node of vehicular ad-hoc networks has the characteristics of high mobility and encounter temporary, a trust management between the nodes in the routing process becomes more difficult. To solve this problem, this paper proposes a new trusted routing protocol in VANET based on GeoDTN+Nav by using trust management model of Bayesian and the three opportunistic routing forwarding models, which includes four steps of the routing initialization, the routing discovery, the trusted routing establishment and the routing deletion. The proposed protocol not only improves the security of routing, but also has the lower time complexity. Besides, experimental results and analysis show that the protocol has achieved good performance in the removal ratio of malicious nodes, correct reception ratio of packet and the message payload.

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