Opportunistic spectrum sharing in Poisson femtocell networks
Bibliographic record
Abstract
In this paper, we propose a cognitive-based opportunistic spectrum sharing strategy for performance enhancement of a femtocell networks. This cognitive-based opportunistic spectrum access strategy aims to achieve better spectrum utilization with quality of service (QoS) protection for macrocell user equipments (MUEs) since the macrocell tier can be over-allocated spectrum resources. We analyze the performance of the two-tier network where the success probability with respect to the received signal to interference ratio (SIR) at each user and the total network throughput are derived under both closed and open access policies. In addition, we describe how to optimally choose the SIR threshold Q to maximize the total network throughput subject to QoS constraints for macrocell and femtocell users in terms of success probability. Via numerical studies, we show that our proposed opportunistic spectrum access scheme can achieve significant throughput gain compared to the conventional spectrum partitioning strategy.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".