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Aerial base stations (ABSs) are expected to be important supplementary components for the 5G-and-beyond communication systems to achieve global Internet of Everything. To fully exploit the advantages of the ABSs seamless connection, in this paper, we propose a high altitude platform (HAP) and low altitude platforms (LAPs) cooperated network architecture. Specifically, the HAP can extend the network coverage and LAPs can act as relays to improve the transmission performance for hot spots. In the considered network, we investigate the sum rate maximization problem by optimizing the downlink and the uplink transmissions, respectively. In the downlink, we adopt the orthogonal frequency division multiple access technique and take the basic rate requirement into account. In addition, we optimize the height of LAPs and the spectrum allocation between the HAP and LAPs, and between the LAP and users. Then, we decouple the downlink sum rate maximation problem as three subproblems. An alternating optimization framework is designed to deal with these non-convex optimization problems, where the height and spectrum allocation are tackled in turn. In the uplink, we adopt the non-orthogonal multiple access technique, and consider the decoding threshold of the successive interference cancellation technique. Afterward, we optimize the power allocation of each user, and the spectrum allocation between users and the LAP, and between LAPs and the HAP. Finally, simulation results show the effects of spectrum allocation, height optimization and power allocation on network performance, which verify that the proposed scheme can achieve higher sum rate on both downlink and uplink in comparison with the current works. [ABSTRACT FROM AUTHOR]

This letter investigates the application of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) in air base station (ABS)-assisted vehicular ad hoc networks (VANETs). A tethered ABS acts as the server to provide MEC services for vehicles. Vehicles adopt NOMA to offload their tasks to the ABS. For the considered network, we formulate an average task processing ratio maximization problem by jointly optimizing the offloading decision, computation resource allocation, and power allocation. For tackling this non-convex problem, we first obtain the offloading decision and the computation resource allocation with the aid of the relax-and-round method and the convex problem solver. Then, the optimal transmission power allocation of vehicles is derived by considering the successive interference cancellation decoding threshold. Finally, the simulation results show that the proposed algorithm has a significant performance improvement in the average task processing ratio compared with the current works in different urban scenarios. [ABSTRACT FROM AUTHOR]

This paper investigates the delay sensitive secure transmission problem in unmanned aerial vehicle (UAV)-relayed vehicular ad hoc networks (VANETs). Specifically, we take the security assurance into account and formulate this problem as a total information delay minimization problem by optimizing the UAV relay trajectory and channel allocation. To tackle this optimization problem, we propose an alternating optimization framework, where the UAV relay trajectory is solved by the Newton method, and the channel allocation is obtained by relax-and-round and sequential convex approximation methods. Finally, simulation results show the performance improvement of our algorithm compared with the current works in terms of the total information delay. [ABSTRACT FROM AUTHOR]