The unmanned aerial vehicle (UAV) assisted transmission can significantly improve the spectrum efficiency and coverage of the wireless communication network. The paper proposed a cooperative secrecy transmission mechanism, which can take advantage of transmitter signal of primary user (PU) as a dedicated radio frequency (RF) source with decode and forward (DF) UAV selection and energy harvesting (EH) under cognitive network. Specifically, the transmitting signal of PU in the cognitive network can be viewed and used as energy to drive secondary user (SU) nodes and multi-relay nodes to send signals. It was worth noting that the DF UAV relay may bring about the unexpected noise and decrease the received signal to noise ratio (SNR), therefore, the multiple UAV assisted relays selected with secrecy capacity maximization criteria under cognitive network was proposed and investigated in detail, where the destination node equipped with multiple antennas and adopted the optimal antenna selection receiver. Moreover, By using the function analysis method, the closed expressions of the system secrecy outage probability (SOP) and the probability of non-zero secrecy capacity were calculated and derived accurately. Furthermore, the asymptotic expressions of the UAV assisted relay system under the large SNR cases were analyzed in detail, where the expressions of secrecy diversity order and secrecy diversity gain of the UAV assisted relay cognitive system were obtained thoroughly. Simulation results verified the effectiveness of the proposed mechanism and the correctness of the calculation.

Taxi demand prediction plays a significant role in assisting the pre-allocation of taxi resources to avoid mismatches between demand and service, particularly in the era of the sharing economy and autonomous driving. However, most studies have only tried to figure out the complex spatial-temporal pattern of taxi demand from historical taxi demand series, neglecting the intrinsic influences of regional functions, and failing to effectively capture the dynamic long-term periodicity. In this paper, we make two important observations: (1) taxi demand pattern varies significantly between different functional regions; and (2) taxi demand follows a dynamic daily and weekly pattern. To address these two issues, we adopt Points of Interest (POIs) to identify regional functions, and propose a novel BERT-based Deep Spatial-Temporal Network (BDSTN) to model the complex spatial-temporal relations from heterogeneous local and global features. In BDSTN, a Spatiotemporal Pattern Matching module is introduced to capture the complex spatiotemporal pattern of taxi demand while considering its dynamic temporal periodicity, and a Functional Similarity Embedding module is adopted to learn the functional similarity among all regions via POIs. To the best of our knowledge, this is the first work to use BERT-based architecture to learn taxi demand patterns, and is also the first to take functional similarity represented by POIs into consideration. Our experimental results on real-world traffic datasets in New York City demonstrate that the effectiveness of the proposed method outperforms the state-of-the-art methods, and that the efficiency of our proposed model is higher than other deep learning methods.

Channel state information based activity recognition has gathered immense attention over recent years. Many existing works achieved desirable performance in various applications, including healthcare, security and Internet of Things, with different machine learning algorithms. However, they usually fail to consider the availability of enough samples to be trained. Besides, many applications only focus on the scenario where only single subject presents. To address these challenges, in this paper, we propose a three-phase system Wi-multi that targets at recognizing multiple human activities in a wireless environment. Different system phases are applied according to the size of available collected samples. Specifically, distance-based classification using Dynamic Time Warping is applied when there are few samples in the profile. Then, Support Vector Machine is employed when representative features can be extracted from training samples. Lastly, recurrent neural networks is exploited when a large number of samples are available. Extensive experiments results show that Wi-multi achieves an accuracy of 96.1% on average. It is also able to achieve a desirable tradeoff between accuracy and efficiency in different phases.

In the vehicular networks (VNs) assisted by the integration of sensing and communication (ISAC), rapid processing of data from sensors is a necessary condition to ensure safe driving and enhance user experience. Utilizing the computational resources of the roadside unit (RSU) can effectively reduce the task processing delay. However, in some areas of the road, uneven distribution of task-vehicles can lead to severe load imbalance in neighboring RSUs, and these tasks often have different delay requirements. The tasks in the high-load area can be offloaded to the low-load area to balance the load. We use the idle-vehicles in the low-load RSU area that are close to the task-vehicles as relays to hop and offload the tasks to the low-load RSUs. On the other hand, in order to satisfy the delay requirements of the heterogeneous tasks, this article proposes the priority ordering of the heterogeneous tasks, the more delay-sensitive tasks require more resources to meet their delay requirements, i.e., the higher the priority. In order to both satisfy the delay requirements of heterogeneous tasks and maintain a small average system delay, we establish the optimization problem of minimizing the weighted average system delay and solve it by using the relay hopping and differentiated task prioritization (RHATP) algorithm. Simulation results show that under the condition of guaranteeing the delay requirement of high-priority tasks, the strategy can achieve lower system delay and effectively reduce the processing delay in high-load areas. And it still maintains stable performance in different scenarios.