Data · dataset · 2026
Outage performance and calibration of ML-assisted resource allocation for next-generation wireless systems
Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.17034/32826350.v1
Next-generation wireless networks, including 6G systems, require intelligent, adaptive strategies to mitigate link failures under dynamic channel conditions.
Description
While machine learning (ML) is a key enabler for such strategies, conventional ML approaches often yield limited performance gains and may fail to meet stringent reliability demands. This thesis develops novel ML solutions for intelligent resource allocation by predicting and avoiding link deterioration caused by random channel fluctuations.
An ML-assisted resource allocation system is studied to anticipate reliability degradation in time-varying wireless environments, with outage probability (OP) serving as the metric for resource allocation error. As part of this framework, the predictor is trained using an outage loss function (OLF) specifically designed for this system. This work first analyses the outage performance of the system over Rayleigh fading channels, proposing closed-form analytical approximations and deriving conditions for optimal classifier performance.
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Results show that models trained via the OLF significantly outperform those trained with conventional binary cross-entropy. The thesis further investigates the calibration of the ML-based outage predictor, evaluating the reliability of its probability estimates. Histogram-based reliability diagrams with logarithmic binning are proposed to better characterise calibration in the low probability region relevant to high-reliability operation.
Theoretical properties under perfect calibration are derived to guide threshold selection for meeting specific reliability requirements. Finally, the analysis is extended to Rician fading channels, which incorporate a dominant line-of-sight component representative of high-frequency wireless environments. The impact of the Rician K-factor on outage behaviour and calibration performance is examined, alongside tractable OP approximations.
The thesis concludes by summarising the main contributions and outlining directions for future research.<br><br><i>Thesis is embargoed until 31 July 2027.</i>
Links
Where it is published
- DOI doi.org/10.17034/32826350.v1 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Artificial intelligence · Artificial intelligence · Artificial intelligence · Computer Science & AI · Computer Science & AI · Computer Science & AI · Deep learning · Deep learning · Deep learning · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Machine learning · Machine learning · Machine learning · Mathematics & Statistics · Mathematics & Statistics · Mathematics & Statistics
Provenance · 3 source records, 23 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/32826350 | 6 d ago | JSON v1 |
| Deakin Research Online | oai:figshare.com:article/32826350 | 6 d ago | JSON v1 |
| DMU Figshare | oai:figshare.com:article/32826350 | 6 d ago | JSON v1 |
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|---|---|---|---|
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