Exploring Active Inference for Efficient Resource Allocation in UAV-Enabled Cognitive NOMA Uplink

The integration of unmanned aerial vehicles (UAVs), cognitive radio (CR), and non-orthogonal multiple access (NOMA) presents a promising solution to significantly enhance the performance of future wireless networks. Achieving this integration requires cognitive self-awareness for intelligent resource allocation. In this paper, we address the problem of sum rate maximization in UAV-enabled cognitive NOMA uplink systems through the joint optimization of subchannel assignment and power allocation, while considering the UAV's mobility. The traditional approach to finding the optimal solution requires an iterative or exhaustive search across all possible combinations of subchannel assignment, power allocation, and UAV position at each time slot, leading to excessive computational complexity. Furthermore, machine learning models, often trained on datasets that do not fully capture the complexity of real-world scenarios, struggle to handle non-stationary events effectively. To solve this nonconvex optimization challenge, we draw inspiration from active inference in cognitive neuroscience and propose a novel data-driven approach called the Active Generalized Dynamic Bayesian Network (Active-GDBN). The main idea is to process the unknown nonlinear input of an exhaustive search optimization algorithm using an Active-GDBN framework. This framework leverages a probabilistic generative model to learn the complex relationships and dependencies among subchannel assignments, power distributions, and the UAV's mobility. The model is facilitated by continuous neuronal message passing in both discrete and continuous states to predict the optimal configuration. Numerical results show that the proposed approach achieves sum rate performance near the optimal exhaustive search and surpasses other baseline approaches.