Biased Stackelberg game‐based UAV relay anti‐jamming communications: Exploiting trajectory optimization and transmission mode selection

• Published in: IET communications Vol. 16; no. 20; pp. 2467 - 2478
• Main Authors: Su, Zhe, Wu, Qihui, Qi, Nan, Jia, Luliang, Du, Zhiyong
• Format: Journal Article
• Language: English
• Published: Stevenage John Wiley & Sons, Inc 01.12.2022; Wiley
• Subjects: Algorithms; Cost analysis; Costs; Distance learning; Game theory; Jammers; Jamming; Markov analysis; Modal choice; Optimization; Power; Rationality; Relay; Switching; Trajectory optimization; Unmanned aerial vehicles; Wireless communications
• ISSN: 1751-8628; 1751-8636
• DOI: 10.1049/cmu2.12502

Although unmanned aerial vehicle (UAV) relay can provide auxiliary communication due to its flexible mobility, it is vulnerable to jamming attacks. This paper considers the UAV relay anti‐jamming communication issue under the threat of a malicious jammer with beam‐forming jamming capability. To prevent the relay link from deteriorating, UAV trajectory adjustment and transmission mode switching between half‐duplex and full‐duplex are two available schemes, while they will incur the additional flying costs and continuous mode switching, respectively. To balance the trade‐off between trajectory optimization and mode selection, this paper investigates the joint trajectory optimization and mode selection anti‐jamming approach. First, an anti‐jamming utility considering the cost‐efficient and end‐to‐end capacity gains is designed. Second, to model the bounded rationality of both the UAV relay and the jammer due to the adversarial context, a biased Stackelberg game to analyse the competitive system interactions is proposed. Moreover, the existence of Stackelberg equilibrium (SE) in the problem is proved. Finally, a joint mode selection and trajectory optimization (JMSTO) algorithm based on the multi‐armed bandit is proposed to obtain the SE. It is further demonstrated that the JMSTO algorithm has a logarithmic regret. The results show that our proposed JMSTO algorithm is superior to non‐joint optimization methods.