Hybrid Beamforming for C-NOMA-Enabled Multi-UAVs in 6G IoT Networks

The advent of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{6 G}$</tex> Internet of Things (IoT) networks demands ultra-reliable, energy-efficient communications to support the massive integration of connected devices. Non-orthogonal multiple access (NOMA) has emerged as a key technology to improve spectral efficiency (SE) by allowing multiple IoT devices (IDs) to share the same frequency resource block through power-domain multiplexing. However, as the number of IDs increases, NOMA faces significant interference challenges, which limit its scalability and degrade system performance. To address these limitations, we propose a clustered NOMA (C-NOMA) framework, which organizes IDs into clusters and applies NOMA within each cluster, reducing interference and improving the effectiveness of successive interference cancellation (SIC). Additionally, we integrate hybrid beamforming (HBF) with C-NOMA, where unmanned aerial vehicles (UAVs) serve as mobile base stations using 2D uniform planar array (UPA) antennas for beam steering. This enables multiple IDs to be served within each cluster with fewer RF chains, further enhancing SE through spatial multiplexing and beam steering. In order to solve the energy efficiency (EE) maximization problem, we reformulate the optimization problem of HBF and power allocation (PA) as a Markov decision process (MDP) and solve it using multi-agent reinforcement learning (MARL). Simulation results show that our proposed Full-RL algorithm achieves up to 37.7 % higher EE compared to the baseline RL-based PA method. This baseline only uses RL for PA and relies on the averaged phase for analog beamforming without further optimization.