  Future wireless networks will need to improve adaptive resource allocation
and decision-making to handle the increasing number of intelligent devices.
Unmanned aerial vehicles (UAVs) are being explored for their potential in
real-time decision-making. Moreover, cognitive non-orthogonal multiple access
(Cognitive-NOMA) is envisioned as a remedy to address spectrum scarcity and
enable massive connectivity. This paper investigates the design of joint
subchannel and power allocation in an uplink UAV-based cognitive NOMA network.
We aim to maximize the cumulative sum rate by jointly optimizing the subchannel
and power allocation based on the UAV's mobility at each time step. This is
often formulated as an optimization problem with random variables. However,
conventional optimization algorithms normally introduce significant complexity,
and machine learning methods often rely on large but partially representative
datasets to build solution models, assuming stationary testing data.
Consequently, inference strategies for non stationary events are often
overlooked. In this study, we introduce a novel active inference-based learning
approach, rooted in cognitive neuroscience, to solve this complex problem. The
framework involves creating a training dataset using random or iterative
methods to find suboptimal resource allocations. This dataset trains a mobile
UAV offline, enabling it to learn a generative model of discrete subchannels
and continuous power allocation. The UAV then uses this model for online
inference. The method incrementally derives new generative models from training
data by identifying dynamic equilibrium conditions between required actions and
variables, represented within a unique dynamic Bayesian network. The proposed
approach is validated through numerical simulations, showing efficient
performance compared to suboptimal baseline schemes.
