MD-GAN-Based UAV Trajectory and Power Optimization for Cognitive Covert Communications

Abstract

This paper investigates the covert performance for an unmanned aerial vehicle (UAV) jammer assisted cognitive radio network. In particular, the covert transmission of secondary users can be effectively protected by UAV jamming against the eavesdropping. For practical consideration, the UAV is assumed to only know certain partial channel distribution information (CDI), whereas not to know the detection threshold of eavesdropper. For this sake, we propose a model-driven generative adversarial network (MD-GAN) assisted optimization framework, consisting of a generator and a discriminator, where the unknown channel information and the detection threshold are learned weights. Then a GAN based joint trajectory and power optimization (GAN-JTP) algorithm is developed to train the MD-GAN optimization framework for covert communication, which results in the joint solution of UAV's trajectory and transmit power to maximize the covert rate and the probability of detection errors. Our simulation results show that, the proposed GAN-JTP with a rapid convergence speed can attain near-optimal solutions of UAV's trajectory and transmit power for the covert communication.

Security Threats and Mitigation Techniques in UAV Communications: A Comprehensive Survey

“…They have proposed a joint optimization model for maximizing the probability of error detection and covert rate by optimizing the UAV's power and trajectory tackled by a generative adversarial network. Aiming to maximize the secrecy energy efficiency of the FD UAV-enabled WSN, [165] has studied the system model in which a UAV acquires the information in the uplink and transmits jamming signals to confuse the terrestrial eavesdropper. allocation for security and data transmission based on the application scenario.…”

Covert Communications in Satellite Internet: A Survey

“…In addition, Huang et al proposed a scheme that enabled a transmitter to covertly communicate with multiple receivers by sending AN signals through a friendly jammer [53]. Li et al further analyzed the covert performance of the UAV jamming node-assisted cognitive radio network and proposed a model-driven generative adversarial network optimization framework to jointly optimize the UAV flight trajectory and transmit power to maximize the covert communication rate [54]. Recently, Wang et al investigated the covertness performance of Beidou navigation satellite system with a terrestrial jammer [55].…”

Machine Learning in Beyond 5G/6G Networks—State-of-the-Art and Future Trends

“…For example, the authors in [138] employ a GAN approach to pre-train a deep-RL framework to provide resource allocation for ultra reliable low latency communication (URLLC) in the downlink of a 6G wireless network, with results showing near-optimal performance within the rate-reliability-latency region, depending on the network and service requirements. Furthermore, the authors in [139] proposea GAN based joint trajectory and power optimization (GAN-JTP) algorithm for a UAV trajectory prediction and power optimization, with results being close to optimal with high convergence speed. In the context of a complex 6G network system, the development of GANs seems crucial for the upcoming challenges.…”