Abstract
In the sixth generation (6G) of mobile communication networks, radio resource allocation management (RRAM) systems must offer high levels of customization and sustainability in their operations. For this reason, this article proposes a framework that maximizes the quality of experience (QoE) of users of a 6G network that implements the delta-orthogonal multiple access (D-OMA) scheme as well as the system's energy efficiency (EE), considering imperfections in successive interference cancellation (SIC) activities. The framework has two phases: in phase (a), a low-complexity heuristic was implemented in order to carry out the selection of base stations (BSs) and distribution of the electromagnetic spectrum slices to each communication device (CD); and in phase (b), a new bi-objective selection hyper-heuristic (SHH) performs the transmission power levels allocation. To evaluate the framework's performance, we performed two tests: First, we evaluated the generalization capacity of our SHH with the isolated application of its constituent metaheuristics. Second, our hyper-heuristic was compared to other state-of-the-art transmit power allocation methods (TPLAMs) developed for non-orthogonal multiple access (NOMA) environments. For a fair comparison, all TPLAMs used the same heuristic for phase (a). As a result, the framework, in addition to providing high levels of QoE to network users, also presented low levels of failure probability as well as improved the average EE of the system.
| Original language | English |
|---|---|
| Article number | 105830 |
| Journal | Engineering Applications of Artificial Intelligence |
| Volume | 119 |
| DOIs | |
| State | Published - Mar 2023 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2023 Elsevier Ltd
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 17 Partnerships for the Goals
Keywords
- 6G
- Bi-objective selection hyper-heuristic
- D-OMA
- Imperfect SIC
- QoE
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Artificial Intelligence
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