Secure and robust resource allocation for UAV-assisted anti-jamming spectrum sharing under uncertain jamming in airborne maneuvering networks

Airborne Maneuvering Network (AMN) has attracted great attention in diverse practical scenarios. Low-altitude maneuvering UAV plays as the operational backbone of AMN to promote the development of an efficient, secure, and low-latency AMN by providing new airborne wireless nerve tracts. However, the widespread adoption of AMNs witnesses a drastic increase of mobile users and data-intensive applications, which makes it suffer from the intense spectrum competition. Spectrum sharing shows promise in alleviating the severe spectrum scarcity of AMNs. Moreover, due to the broadcast nature of wireless channels and the increasing probability of the line-of-sight transmission, the AMN is vulnerable to malicious jamming, especially encountering the coupled uncertainty and dynamic jamming. To solve these problems, UAV-assisted anti-jamming spectrum sharing in AMNs is investigated. We propose a joint space-power-frequency domain optimization approach to simultaneously mitigate both external malicious jamming and internal sharing interference. The sum rate maximization of the secondary network is studied by jointly optimizing the UAV transmit power, sub-band allocation, and trajectory. To tackle the formulated intractable non-convex problem, we propose a computationally efficient iterative algorithm based on alternating optimization, integrating the S-procedure to handle bounded uncertainties and the successive convex approximation to obtain near-optimal convex solutions. Extensive simulation results show that our proposed scheme can significantly increase the sum transmission rate. Moreover, it is shown that our proposed scheme is the best robust among all benchmark schemes against jammer location and power uncertainties, confirming the practicality for the next-generation UAV based airborne networks.