Maximizing the Secrecy Energy Efficiency of the Cooperative Rate-Splitting Aided Downlink in Multi-Carrier UAV Networks

Abstract

Although Unmanned Aerial Vehicles (UAVs) are capable of significantly improving the information security by detecting the eavesdropper's location, their limited energy motivates our research to propose a secure and energy efficient scheme. Thanks to the common-message philosophy introduced by Rate-Splitting (RS), we no longer have to allocate a portion of the transmit power to radiate Artificial Noise (AN), and yet both the Energy Efficiency (EE) and secrecy can be improved. Hence we define and study the Secrecy Energy Efficiency (SEE) of a multi-carrier multi-UAV network, in which Cooperative Rate-Splitting (CRS) is employed by each multi-antenna UAV Base-Station (UAV-BS) for protecting their downlink transmissions against an external eavesdropper ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Eve$</tex-math></inline-formula> ). Furthermore, we consider the challenging scenario in which CRS is employed by each multi-antenna UAV-BS to protect their corresponding downlink transmissions against an external <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Eve$</tex-math></inline-formula> . We further consider a difficult scenario in terms of security in which only imperfect channel state information of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Eve$</tex-math></inline-formula> is available at the Tx. Accordingly, we conceive a robust secure resource allocation algorithm, which maximizes the SEE by jointly optimizing both the user association matrix and the network parameter allocation problem, including the RS precoders, time slot sharing and power allocation. Due to the non-convexity of the problem, it is decoupled into a pair of convex sub-problems. Firstly, new two-tier intra-cell optimization problems are formulated for achieving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\xi$</tex-math></inline-formula> -optimal solutions by iterative block coordinate decent programming. Then, the power of each sub-channel is optimized by formulating the associated power control problem.