Dynamic spectrum access analysis in a multi-user cognitive radio network using Markov chains
Bibliographic record
Abstract
Spectrum sharing between license holders (primary users) and unlicensed secondary users has been proposed as a solution to frequency wastage. Anyhow, the coexistence among these two types of users should not affect the quality of service of primary users. In this paper, we propose an interference control approach when one primary and multiple secondary users are simultaneously transmitting on the same frequency band. We first evaluate the Signal to Interference-plus-Noise Ratio (SINR) of the primary user in the presence of multiple secondary transmissions. Assuming a constraint on the primary SINR, we explicitly compute the probability that this SINR exceeds a certain threshold in the case of cohabitation. These precise probabilities are afterwards used in the transitions between different states of the Continuous Time Markov Chain that we proposed to model the system. A new model with additional outage probabilities is then built, leading to do a cross layer design of dynamic spectrum access, by considering both the traffic queuing problem and the channel outage in physical layer. Finally, some performance metrics of the system are analyzed, namely the primary throughput, the mean number of secondary users accessing the spectrum, and the secondary deprivation rate.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".