Interference Avoidance Position Planning in UAV-Assisted Wireless Communication

Abstract

We consider unmanned aerial vehicle (UAV)-assisted wireless communication employing UAVs as relay nodes to increase the throughput between a pair of transmitter and receiver. We focus on developing effective methods to position the UAV(s) in the sky in the presence of a major source of interference, the existence of which makes the problem non-trivial. First, we consider utilizing a single UAV, for which we develop a theoretical framework to determine its optimal position aiming to maximize the SIR of the system. To this end, we investigate the problem for three practical scenarios, in which the position of the UAV is: (i) vertically fixed, horizontally adjustable; (ii) horizontally fixed, vertically adjustable; (iii) both horizontally and vertically adjustable. Afterward, we consider employing multiple UAVs, for which we propose a cost-effective method that simultaneously minimizes the number of required UAVs and determines their optimal positions so as to guarantee a certain SIR of the system. We further develop a distributed placement algorithm, which can increase the SIR of the system given an arbitrary number of UAVs. Numerical simulations are provided to evaluate the performance of our proposed methods.

Soft Frequency Reuse With Allocation of Resource Plans Based on Machine Learning in the Networks With Flying Base Stations

“…Nevertheless, the perfect synchronization might be impossible to guarantee in practical applications. In [8], [9], the FlyBS is deployed between a pair of a transmitter and a receiver to manage interference from a macro base station. The FlyBS acts as a relay and the interference is suppressed by an adjustment of the FlyBS's position.…”

“…However, the CoMP requires a significant number of signaling exchange among the FlyBSs and synchronization among different FlyBSs for CoMP operation. Some techniques optimize the positions of the FlyBSs [8]- [10], and transmission power [11]. Such techniques have, however, a high computational overhead due to the iterative nature of the optimization algorithms.…”

Optimal Jammer Placement in UAV-assisted Relay Networks

“…For the link between a UAV and the TRs or the MSI (air-toground and ground-to-air), we denote the path loss between a UAV i and terrestrial node j by η NLoS d 2 ij (for more information on η NLoS please refer to [10] and references therein). Due to the geographical limitations, direct communication between the TRs is not considered.…”

“…In the context of UAV relay networks, we were among the first to study the placement optimization and trajectory design for UAV relays to evade the interference caused by the jammers [9]- [12]. Considering a major source of interference (MSI), the optimal placement of the UAV relays along with identifying the minimum number of required UAVs to satisfy a desired communication quality are studied in [9], [10]. A joint power allocation and trajectory design is proposed in [11], [12] to evade the interference caused by another established wireless network.…”

Interference Avoidance Position Planning in Dual-hop and Multi-hop UAV Relay Networks

“…is the excessive path loss factor incurred by shadowing, scattering, etc., in the LoS (NLoS) link, f c is the carrier frequency, c is the speed of light, α = 2 is the path-loss exponent 1 , and d i, j is the Euclidean distance between node i and node j. The link between two UAVs (air-to-air) is modeled using the LoS model, while the link between the MSI and the Rx (ground-to-ground) is modeled based on the NLoS model.…”