
Optimization of Age of Information in Adaptive FD/HD Cooperative SWIPT NOMA/OMA System
Author(s) -
Simon Kaboyo,
Mohammed Abo-Zahhad,
Osamu Muta,
Ahmed H. Abd El-Malek,
Maha M. Elsabrouty
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3598362
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The Age of Information (AoI) is a key metric in monitoring and control applications of Internet of Things (IoT) networks, where real-time decision-making relies on the freshness of the information. This paper presents an innovative approach to optimizing the AoI in downlink NOMA/OMA systems. We propose an AoI minimization-oriented adaptive framework that selects the optimal transmission mode from four operational strategies: full-duplex (FD) cooperative simultaneous wireless information and power transfer (SWIPT) non-orthogonal multiple access (NOMA), half-duplex (HD) cooperative SWIPT NOMA, regular NOMA, and orthogonal multiple access (OMA). Since the FD cooperative SWIPT NOMA requires the near user to be equipped with two antennas to enable FD operation, we propose enhancing the HD, OMA, and regular NOMA modes of operation by applying beamforming diversity. The proposed work analytically evaluates the system error performance by deriving closed-form expressions of the average block error rate (BLER) for the four operating modes and validates the results through Monte Carlo simulations. Based on the average BLER, the AoI for each operational mode is analysed, illustrating the necessity of the adaptive system. Furthermore, we utilize the finite state Markov decision process to devise an optimal adaptive policy that selects the best transmission strategy based on the AoI in the transmission time slot. Finally, we present a suboptimal policy using the drift-plus-penalty algorithm to reduce system complexity while maintaining near-optimal performance. The results demonstrate that the proposed approach minimizes the AoI, providing valuable insights into system design.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom