Cognitive spectrum access in device-to-device-enabled cellular networks
Bibliographic record
Abstract
Cognitive spectrum access (CSA) in in-band D2D-enabled cellular networks is a potential feature that can promote efficient resource utilization and interference management among coexisting cellular and D2D users. In this article, we first outline the challenges in resource allocation posed by the coexistence of cellular and D2D users. Next, we provide a qualitative overview of the existing resource allocation and interference management policies for in-band D2D-enabled cellular networks. We then demonstrate how cognition along with limited information exchange between D2D users and the core network can be used to mitigate interference and enhance spectral efficiency of both cellular and D2D users. In particular, we propose a CSA scheme that exploits channel sensing and interference- aware decision making at the D2D terminals. This CSA scheme at the D2D terminals is complemented by a D2D-aware channel access method at the cellular BSs. The performance gains of the proposed CSA scheme are characterized in terms of channel access probability for a typical D2D transmitter and spectral efficiencies for both cellular and D2D transmissions. Finally, potential research issues that require further investigation are highlighted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".