Joint CoMP Transmission for UAV-Aided Cognitive Satellite Terrestrial Networks

Abstract

This paper investigates a novel architecture of coordinated multi-point (CoMP) transmission in the cognitive satellite-terrestrial network associated with an unmanned aerial vehicle (UAV). We consider the downlink communication where the UAV and base station (BS) cooperatively serve the terrestrial user by sharing the licensed satellite network spectrum. The goal of this paper is to maximize the achievable rate of the terrestrial user by jointly optimizing BS/UAV transmit power allocation and UAV trajectory, subjecting to the interference temperature threshold imposed on satellite network as well as UAV mobility constraint. The formulated problem is shown in a complicated non-convex form, which is hard to tackle. To this end, we decompose the original problem into two sub-problems, and the block coordinate descent method is employed to solve the two sub-problems alternately. Specifically, for the first sub-problem, the optimal BS/UAV transmit power is obtained with a given UAV trajectory by using the Lagrangian dual method. For the second sub-problem, the UAV trajectory is attained with a given BS/UAV power allocation by using the successive convex approximation technique. Simulation results show that our proposed joint CoMP transmission scheme significantly improves the terrestrial network throughput compared with other benchmark schemes.INDEX TERMS Unmanned aerial vehicle, satellite-terrestrial networks, UAV trajectory, coordinated multi-point.

Efficient Task Allocation Protocol for a Hybrid-Hierarchical Spatial-Aerial-Terrestrial Edge-Centric IoT Architecture

“…Different research efforts have been devoted to investigating the effects of inter-operation of UAVs and satellites on improving the performance of edge-enabled IoT networks. For example, Hua et al [27] consider a system in which the TNs can process the tasks locally or offload them to the higher layers. UAVs and satellites are leveraged to offload the tasks to the edge servers and the cloud servers, respectively.…”

“…The channel model between a UAV and the corresponding LEO satellite follows the shadowed Rician (SR) [27,42]. Similar to the TNs-UAVs pair, the LoS propagation is established between each UAV-LEO pair.…”

Robust Trajectory and Transmit Power Optimization for Secure UAV-Enabled Cognitive Radio Networks

“…In [31], a UAV-based CR system was proposed to improve the detection performance of spectrum sensing and access the idle spectrum. In [32], a UAV-aided CR satellite terrestrial network was investigated. By jointly optimizing the base station/UAV transmit power and the UAV's trajectory, the achievable rate of the ground user can be maximized.…”

Cooperative Resource Management for Cognitive Satellite-Aerial-Terrestrial Integrated Networks Towards IoT

“…Considering the coexistence between the UAV and underlaid device-to-device (D2D) users, the coverage probability and system rate were investigated in [31]. Taking the constraints of interference and the mobility of UAV into account, the authors in [32] optimized the trajectory of UAV and transmit power of both terrestrial BS and UAV to maximize the achievable rate of terrestrial users. For the scenario where UAV and terrestrial users are served by the cellular network, the transmit rate of UAV and power allocation were optimized to maximize the weighted sum-rate of the UAV and terrestrial users [33].…”