AI-Enabled Future Wireless Networks: Challenges, Opportunities, and Open Issues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An expected plethora of demanding services and use cases mandates a revolutionary shift in the way future wireless network resources are managed. Indeed, when application requirements for tight quality of service (QoS) are combined with increased network complexity, legacy network-management routines will become untenable in 6G. Artificial intelligence (AI) is emerging as a fundamental enabler to orchestrate network resources from bottom to top. AIenabled radio access and core will open up new opportunities for automated 6G configurations. At the same time, many challenges in AI-enabled networks need to be addressed. Long convergence times, memory complexity, and the intricate behavior of machine-learning algorithms under uncertainty and the network's highly dynamic channel, traffic, and mobility conditions contribute to the challenges. In this article, we survey state-of-the-art research on using machine-learning techniques to improve the performance of wireless networks. In addition, we identify challenges and open issues to provide a roadmap for researchers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it