Effect of Imperfect Spectrum Sensing on Slotted Secondary Transmission: Energy Efficiency and Queuing Performance
Bibliographic record
Abstract
In cognitive radio communication system, unlicensed secondary user (SU) can opportunistically transmit over the under-utilized spectrum of primary user. With interweave implementation, SU performs spectrum sensing on the target frequency band to detect transmission opportunity. Sensing errors can greatly affect the performance of secondary transmission. In this paper, we propose a discrete-time Markov model to characterize slotted secondary transmission process with imperfect spectrum sensing. The stationary distribution is then applied to total collision probability evaluation and energy efficiency optimization for secondary transmission. Assuming that SU adopts adaptive transmission, we also evaluate the queuing performance of slotted secondary transmission, based on a 2-D finite-state Markov chain. Selected numerical results are presented to illustrate the mathematical formulation and to validate our analytical results. We show that false alarm has significant effect on the secondary throughput, whereas miss detection only notably reduces the secondary throughput when the traffic intensity is low.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".