Channel Inversion Power Control-Aided Multi-User Secret and Covert UAV Communications

To satisfy diverse security requirements of ground users in uncrewed aerial vehicle (UAV) networks, we propose a channel inversion power control (CIPC) aided multi-user collaborative secret and covert uplink transmission strategy for UAV secure communication. Specifically, using the non-orthogonal multiple access (NOMA) technology, multiple ground covert users named Carlo, hide their weak covert signals in the strong secret signal from a secret user named Bob, and transmit to the UAV named Alice. An adversary Willie attempts to eavesdrop Bob’s confidential message and detect whether Carlo is transmitting or not. To evaluate the link reliability and security of secret and covert transmissions, we first derive closed-form expressions of the secret connection probability (SCP), secrecy outage probability (SOP), covert connection probability (CCP), and detection error probability (DEP) under perfect channel state information while accounting for the uncertainty of the adversary’s noise power. We then further incorporate the legitimate-link channel uncertainty into the analysis and characterize its impact on the key performance metrics, particularly the average values of SCP, SOP, and CCP. To characterize the theoretical benchmark of the proposed transmission strategy, we investigate the performance in both rotary-wing and fixed-wing UAV scenarios. Particularly, in the rotary-wing UAV scenario, we formulate an optimization problem to maximize the average effective sum covert rate subject to constraints of SCP, SOP, DEP, CIPC parameter, ground user’s transmission power, and the UAV’s altitude. Subsequently, we provide an optimal and a sub-optimal solution to the optimization problem. In the fixed-wing UAV scenario, we formulate an optimization problem to maximize the average covert rate subject to the constraints of SCP, SOP, DEP, CIPC parameter, user scheduling, and the UAV’s flight parameters. Furthermore, using the successive convex approximation (SCA) method, we propose an alternating optimization (AO) algorithm to obtain a high-quality feasible solution. Finally, our results reveal the influence of key parameters on the system performance, analytically and numerically.