Felix Obite

Future wireless networks demand intelligent, data-intensive services with high reliability and low latency, motivating semantic-aware communication as a transformative paradigm. In this paper, we address the NP-hard problem of resource allocation in UAV-based NOMA networks by proposing a novel framework named Active Semantic Generalized Dynamic Bayesian Network (Active-SGDBN). Unlike conventional optimization or data-driven methods, our approach integrates expert domain knowledge, active inference, and semantic context-aware reasoning to enable real-time, interpretable decision-making. Specifically, we optimize power allocation based on UAV mobility to maximize system sum-rate while encoding semantic structures through unsupervised clustering and Bayesian inference. Simulation results demonstrate that Active-SGDBN achieves near-optimal performance and surpasses benchmark schemes in both sum-rate and bit error rate (BER), validating its effectiveness and generalization capability.

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.

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 upper bound and surpasses other baseline approaches.

As the scaling down of silicon MOSFET is approaching its utmost limit, different materials are effectively being examined in order to keep the scaling trend. Among these, carbon nanotubes (CNTs) have emerged as one of the most extensively studied materials due to their excellent performance properties such as minimal short channel effects, high mobility, and high normalized drive currents. CNTs are the backbone of carbon nanotube field effect transistor, which is considered as the most preferred candidate for the replacement of silicon transistors. Despite their practical significance, a well-organized framework, and consistent review are still lacking. To this end, this paper presents an intensive review in order to define the state of the art in this field from a fresh and unifying viewpoint while elucidating fruitful insights into recent advances and future trends. In particular, we review material properties and structures. Specifically, we emphasize on the most relevant device fabrication and current modeling concepts. Furthermore, we distill key insights into recent advances and challenges that may sustain or expand future applications. The future research directions are also carefully analyzed.

Cognitive radio (CR) technology has emerged as a realistic solution to the spectrum scarcity problem in present day wireless networks. A major challenge in CR radio networks is the hidden node problem, which is the inability of the CR nodes to detect the primary user. This paper proposes energy detector-based distributed sequential cooperative spectrum sensing over Nakagami-m fading, as a tool to solve the hidden node problem. The derivation of energy detection performance over Nakagami-m fading channel is presented. Since the observation represents a random variable, likelihood ratio test (LRT) is known to be optimal in this type of detection problem. The LRT is implemented using the Neyman-Pearson Criterion (maximizing the probability of detection but at a constraint of false alarm probability). The performance of the proposed method has been evaluated both by numerical analysis and simulations. The effect of cooperation among a group of CR nodes and system parameters such as SNR, detection threshold and number of samples per CR nodes is investigated. Results show improved detection performance by implementing the proposed model.

In the last few years, High Altitude Platforms (HAPs) have attracted considerable effort due to their ability to exploit the advantages of satellite and terrestrial-based systems. Rain attenuation is the most dominant atmospheric impairment, especially at such frequency band. This paper addresses the modelling of rain attenuation and describes a propagation channel model for HAPs at Ka-band to provide efficient and robust wireless access for tropical regions. The attenuation due to rain is modeled based on three years measured data for Johor Bahru to estimate the actual effect of rain on signals at Ka band. The radio propagation channel is usually characterized as a random multipath channel. Specifically, a statistical derivation of probability distribution function for Rayleigh and Rician fading channels are presented. The model consists of multiple path scattering effects, time dispersion, and Doppler shifts acting on the HAPs communication link. Simulation results represent the fading signal level variations. Results show perfect agreement between simulation and theoretical, thereby conforming to the multipath structures. The information obtained will be useful to system engineers for HAPs link budget analysis in order to obtain the required fade margin for optimal system performance in tropical regions.

Carbon nanotubes (CNTs) have emerged as exceptional materials, exhibiting promising potentials as sensing elements in electromechanical sensors. In this review, the fascinating properties of CNTs that make them attractive materials for biosensors are investigated from new perspectives, eliciting scenarios that point to practical applications. The review also highlights the design architectures for CNT-based biosensors and their employment in biomolecular detection. We further detail novel developments in the field of chemically sensitive field effect transistors (ChemFETs), including CNT-based chemiresistors. After presenting general and specific design and application challenges, the review concludes with scientific advance and new insight into possible future applications.