Multiplexing B5G/6G Services Over Aerial VLC Networks: A Comprehensive Radio Resource Management Framework

Downlink transmission of a nonorthogonal visible light communication (VLC) system, empowered by autonomous aerial vehicles (AAVs), is studied for coexisting enhanced mobile broadband (eMBB), ultrareliable low-latency communication (URLLC), and massive machine-type communication (mMTC) services. A joint resource allocation problem involving user association, transmit power, and flight trajectory of AAVs is formulated, with the goal of characterizing a multiobjective tradeoff as a weighted sum of the power consumption of each AAV and the perceived Quality of Experience (QoE) of its associated eMBB users, while ensuring the service-specific requirements for eMBB, mMTC, and URLLC are met. Assuming the imperfection of channel state information (CSI), we invoke a generalized Benders decomposition (GBD) methodology, leveraging tools from convex optimization and multiagent deep reinforcement learning to address this problem. We further analytically derive the upper and lower bounds on the reward function for each AAV as a learning agent. Extensive simulations confirm that our proposed method outperforms the single-agent counterpart in the literature, with up to a 22% reduction in power consumption and a 13% gain in perceived QoE. Additionally, compared to the globally optimal brute-force method for AAV-user association, our proposed method experiences only a trivial performance loss in a small-scale scenario.