Intelligent Resource Allocation for UAV-Based Cognitive NOMA Networks: An Active Inference Approach

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.

Active Inference for Sum Rate Maximization in UAV-Assisted Cognitive NOMA Networks

Given the surge in wireless data traffic driven by the emerging Internet of Things (IoT), unmanned aerial vehicles (UAVs), cognitive radio (CR), and non-orthogonal multiple access (NOMA) have been recognized as promising techniques to overcome massive connectivity issues. As a result, there is an increasing need to intelligently improve the channel capacity of future wireless networks. Motivated by active inference from cognitive neuroscience, this paper investigates joint subchannel and power allocation for an uplink UAV-assisted cognitive NOMA network. Maximizing the sum rate is often a highly challenging optimization problem due to dynamic network conditions and power constraints. To address this challenge, we propose an active inference-based algorithm. We transform the sum rate maximization problem into abnormality minimization by utilizing a generalized state-space model to characterize the time-changing network environment. The problem is then solved using an Active Generalized Dynamic Bayesian Network (Active-GDBN). The proposed framework consists of an offline perception stage, in which a UAV employs a hierarchical GDBN structure to learn an optimal generative model of discrete subchannels and continuous power allocation. In the online active inference stage, the UAV dynamically selects discrete subchannels and continuous power to maximize the sum rate of secondary users. By leveraging the errors in each episode, the UAV can adapt its resource allocation policies and belief updating to improve its performance over time. Simulation results demonstrate the effectiveness of our proposed algorithm in terms of cumulative sum rate compared to benchmark schemes.

Dynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing

In multi-access edge computing (MEC), most existing task software caching works focus on statically caching data at the network edge, which may hardly preserve high reusability due to the time-varying user requests in practice. To this end, this work considers dynamic task software caching at the MEC server to assist users' task execution. Specifically, we formulate a joint task software caching update (TSCU) and computation offloading (COMO) problem to minimize users' energy consumption while guaranteeing delay constraints, where the limited cache size and computation capability of the MEC server, as well as the time-varying task demand of users are investigated. This problem is proved to be non-deterministic polynomial-time hard, so we transform it into two sub-problems according to their temporal correlations, i.e., the real-time COMO problem and the Markov decision process-based TSCU problem. We first model the COMO problem as a multi-user game and propose a decentralized algorithm to address its Nash equilibrium solution. We then propose a double deep Q-network (DDQN)-based method to solve the TSCU policy. To reduce the computation complexity and convergence time, we provide a new design for the deep neural network (DNN) in DDQN, named state coding and action aggregation (SCAA). In SCAA-DNN, we introduce a dropout mechanism in the input layer to code users' activity states. Additionally, at the output layer, we devise a two-layer architecture to dynamically aggregate caching actions, which is able to solve the huge state-action space problem. Simulation results show that the proposed solution outperforms existing schemes, saving over 12% energy, and converges with fewer training episodes.

A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach

This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference ($\textit{AIn}$), and a cognitive-UAV is employed as a case study. An Active Generalized Dynamic Bayesian Network (Active-GDBN) is proposed to represent the external environment that jointly encodes the physical signal dynamics and the dynamic interaction between UAV and jammer in the spectrum. We cast the action and planning as a Bayesian inference problem that can be solved by avoiding surprising states (minimizing abnormality) during online learning. Simulation results verify the effectiveness of the proposed $\textit{AIn}$ approach in minimizing abnormalities (maximizing rewards) and has a high convergence speed by comparing it with the conventional Frequency Hopping and Q-learning.