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

Downlink transmission of a non-orthogonal visible light communication system, empowered by unmanned aerial vehicles (UAVs), is studied for coexisting enhanced mobile broadband (eMBB), ultra-reliable 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 UAVs is formulated, with the goal of characterizing a multi-objective trade-off as a weighted sum of the power consumption of each UAV 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, we invoke a generalized Benders decomposition (GBD) methodology, leveraging tools from convex optimization and multi-agent deep reinforcement learning to address this problem. We further analytically derive the upper and lower bounds on the reward function for each UAV 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 UAV-user association, our proposed method experiences only a trivial performance loss in a small-scale scenario.