Outage Probability Minimization in Secure NOMA Cognitive Radio Systems With UAV Relay: A Machine Learning Approach

This paper considers a multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) cognitive radio (CR) system with an unmanned aerial vehicle relay (UR). In this system, a secondary transmitter (ST) uses licensed spectrum from the primary network to transmit signals to its secondary...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on cognitive communications and networking 2023-04, Vol.9 (2), p.435-451
Main Authors: Vo, Van Nhan, Nguyen, Le-Mai-Duyen, Tran, Hung, Dang, Viet-Hung, Niyato, Dusit, Cuong, Dang Ngoc, Luong, Nguyen Cong, So-In, Chakchai
Format: Article
Language:English
Subjects:
Autonomous aerial vehicles
Cognitive radio
constrained continuous genetic algorithm
Constraints
Eavesdropping
Genetic algorithms
Machine learning
MIMO communication
NOMA
non-orthogonal multiple access
Nonorthogonal multiple access
Optimization
Outages
Power system reliability
Probability
Relay
Relays
unmanned aerial vehicle
Unmanned aerial vehicles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper considers a multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) cognitive radio (CR) system with an unmanned aerial vehicle relay (UR). In this system, a secondary transmitter (ST) uses licensed spectrum from the primary network to transmit signals to its secondary receivers (SRs) based on NOMA. The UR is used as a relay to forward the signals from the ST to the SRs. As a result, the system can achieve significant improvements in spectral efficiency and network capacity. However, such a MIMO NOMA CR system faces issues of interference and security, i.e., eavesdropping attacks, due to the shared spectrum use and the UR. Therefore, we aim to minimize the outage probability of the secondary network, subject to constraints on the outage probability of the primary network and the intercept probabilities of eavesdroppers. Then, we attempt to optimize the transmit power of the UR, the coordinates of the UR, and the power allocation factors for NOMA. We further derive closed-form expressions for the outage probabilities of the secondary and primary networks and the intercept probabilities at the eavesdroppers. We propose using a machine learning algorithm based on a constrained continuous genetic algorithm to solve the optimization problem.
ISSN:2332-7731
2332-7731
DOI:10.1109/TCCN.2022.3226184