UAV-Aided Jamming for Secure Ground Communication With Unknown Eavesdropper Location

Abstract

This paper investigates unmanned aerial vehicle (UAV)-aided jamming technique for enabling physical layer keyless security in scenarios where the exact eavesdropper location is unknown. We assume that the unknown eavesdropper location is within an ellipse characterizing the coverage region of the transmitter. By sequentially optimizing the transmit power, the flight path of the UAV and its jamming power, we aim at maximizing the average secrecy rate with arbitrary eavesdropper location. Simulation results demonstrate that the optimal flight path obtains better secrecy rate performance compared to that using direct UAV flight path encasing the transmitter and the legitimate receiver. Most importantly, even with the unknown eavesdropper location, we obtained a secrecy rate that is comparable to a scenario when the eavesdropper's location is known. However, the average secrecy rate with the unknown eavesdropper location varies depending on the proximity of the eavesdropper to the known location of the transmitter. We also observe that due to the UAV-aided jamming, the average secrecy rate stabilizes at some point even though the average received envelope power of the eavesdropper increases. This essentially demonstrates the effectiveness of the proposed scheme.

Adaptive UAV-Trajectory Optimization Under Quality of Service Constraints: A Model-Free Solution

“…Furthermore, with successive convex approximation (SCA) techniques, an intelligent reflecting surface aided UAV communication system was investigated in [14] by designing the beamforming and trajectory of the UAV. In [15], a SCA based power and trajectory optimization approach was proposed for the an UAV-aided secure communication network.…”

“…From ( 13), we can express the state-transition probabilities for the UAV from s to s by taking action a, which is given by P(s, a, s ) = P(s |s, a) = Pr{S(t) = s |S(t − 1) = s, A(t − 1) = a} = r∈R P(s , r|s, a), (15) where the expected rewards for the state-action pair can be expressed as a two-argument function r : S × A ∈ R, i.e.,…”

Dual-UAV Enabled Secure Data Collection With Propulsion Limitation

“…Different from traditional methods to enhance the security in terrestrial communication systems, UAV can utilize its high mobility and flexibility to avoid being eavesdropped via adjusting its flight trajectory. For instance, the UAV can fly close to the legitimate user or fly away from the eavesdropper via trajectory optimization, and thus the average secrecy rate can be improved in the presence of a single eavesdropper [27], multiple eavesdroppers [28], or eavesdroppers with unknown locations [29]. Furthermore, adding artificial noise was an effective method to further improve the security of the UAV communication [30]- [34].…”

Joint Location and Transmit Power Optimization for NOMA-UAV Networks via Updating Decoding Order

“…Recently, unmanned aerial vehicles (UAVs) have been widely used as carrying platforms of base stations in wireless communications [1], [2]. Many recent studies have been dedicated to UAV communications [3]- [7]. In [3], the joint optimization of trajectory and transmit power was studied by Wu et al to maximize the sum rate in multi-UAV networks.…”

“…a UAV-assisted cellular network, applying the superimposed training sequence with imperfect channel statistics. UAV-aided jamming for secure communication with unknown location of the eavesdropper was investigated in [7] by Nnamani et al On the other hand, to improve the spectrum efficiency, non-orthogonal multiple access (NOMA) is emerging as a crucial technique for future wireless networks [8]- [10]. In [8], Chen et al have proved that NOMA has a better performance than orthogonal multiple access (OMA).…”