On the Physical Layer Security of the Cooperative Rate-Splitting-Aided Downlink in UAV Networks

Unmanned Aerial Vehicles (UAVs) have found compelling applications in intelligent logistics, search and rescue as well as in air-borne Base Station (BS). However, their communications are prone to both channel errors and eavesdropping. Hence, we investigate the max-min secrecy fairness of UAV-aided cellular networks, in which Cooperative Rate-Splitting (CRS) aided downlink transmissions are employed by each multi-antenna UAV Base Station (UAV-BS) to safeguard the downlink of a two-user Multi-Input Single-Output (MISO) system against an external multi-antenna Eavesdropper (
$Eve$
). Realistically, only Imperfect Channel State Information (ICSI) is assumed to be available at the transmitter. Additionally, we consider a realistic total power constraint and guarantee the specific Quality of Service (QoS) requirements of the legitimate users. To handle the worst-case channel uncertainty of the legitimate users and an external
$Eve$
, we conceive a robust secure resource allocation algorithm, which maximizes the minimum worst-case secrecy rate of the legitimate users. Based on the CRS principle, the transmitter splits and encodes the messages of legitimate users into common as well as private streams and the user having stronger CSI is asked to help the cell-edge user by opportunistically forwarding its decoded common message. In contrast to the existing schemes adopted in the literature for ensuring secure transmission of the first cooperative phase only, in our proposed solution the common message has a twin-fold mission. Explicitly, apart from serving as the desired message, it also acts as Artificial Noise (AN) for drowning out
$Eve$
without consuming extra power. This is in stark contrast to the conventional AN designs. In the second phase, the pure AN is directed towards the
$Eve$
, deploying a robust Maximum Ratio Transmitter (MRT) beamformer at the UAV-BS. To solve the resultant non-convex optimization problem we resort to the Sequential Parametric Convex Approximation (SPCA) method together with a bespoke initialization algorithm to avoid any failure due to infeasibility. Our simulation results confirm that the proposed secure transmission scheme outperforms the existing cooperative benchmarkers.