Abstract
Unmanned aerial vehicle- (UAV-) assisted communication has great potential to provide on-demand wireless services and improve the outdoor link throughput. In this paper, a UAV-based cognitive radio network (CRN) is investigated in which the UAV works as a secondary user (SU). Considering the overlay spectrum sensing mode, the UAV can operate on the licensed spectrum bands of primary user (PU) only when PU is idle. In each working frame structure, both sensing time slot and transmission time slot are analysed in radians. Specifically, our objective is to maximize the spectrum efficiency (SE) of the UAV by jointly optimizing the sensing radian and the number of radians. For the single-radian and multiradian schemes, the dichotomy and alternative iterative optimization (AIO) algorithm are proposed to solve the SE optimization problem. Simulation results show that the proposed multiradian cooperative spectrum sensing (CSS) scheme can achieve better performance on ensuring the quality-of-service (QoS) of the PU, and it can significantly enhance the SE of the UAV especially in the severe channel environments.
Highlights
Unmanned aerial vehicles (UAVs) have remarkably gained much popularity in various communication scenarios due to their advantages of high maneuverability and flexibility [1]
Compared to the channel coherence time, the transmission time is relatively long, so we focus on the average statistics of the channel
Two kinds of sensing scenarios for the unmanned aerial vehicles (UAVs) are considered when investigating the optimal sensing radian: Scenario 1: in the case of static sensing, the effect of changing dSP on c can be ignored during the UAV flight, and c is considered to be constant during the sensing process because of the small sensing radian of the UAV. erefore, the sensing process can be considered as static sensing in one frame [23]
Summary
Unmanned aerial vehicles (UAVs) have remarkably gained much popularity in various communication scenarios due to their advantages of high maneuverability and flexibility [1]. UAVs are initially designed for military operations, tracking and surveillance, fire control, and other applications [2, 3], most of which are dangerous and even impossible for human operators. Due to the gradual enrichment of their functions and the continuous development of sensor technology, the UAVs equipped with sensors and communication equipment can be applied in various fields. The application of UAV communication faces many challenges, and spectrum shortage is one of the key issues. E UAV generally operates on IEEE S-band, IEEE L-band, and industrial, scientific, and medical (ISM) frequency bands, and these spectrum resources have been occupied by many wireless networks (such as Wi-Fi, Bluetooth, and IEEE 802.15.4 network) [8] Due to the rapid development of 5G network [5], device-to-device (D2D) technology [6], and Internet of ings (IoT) [7], the spectrum demand is growing rapidly. e UAV generally operates on IEEE S-band, IEEE L-band, and industrial, scientific, and medical (ISM) frequency bands, and these spectrum resources have been occupied by many wireless networks (such as Wi-Fi, Bluetooth, and IEEE 802.15.4 network) [8]
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