Toward Smart and Flexible Spectrum Usage: Application of Horizontal Model Algorithm in Cognitive Radio Systems
Bibliographic record
Abstract
The growing need for wireless communication services has resulted in a pressing need for more enhanced exploitation of available spectrum bandwidth.Cognitive Radio (CR) technology emerges as a potential solution, facilitating secondary users, in their ability to opportunistically access underutilized spectral resources without causing harmful interference to primary users.In this paper, we propose and analyze a horizontal spectrum-sharing model for CR systems operating in wideband environments.Unlike traditional orthogonal access schemes, the horizontal model enforces exclusive subband transmission per user in the Time-Division Duplex (TDD) framework, combined with the water-filling power allocation strategy to maximize spectral efficiency.We develop an analytical framework to characterize the spectral efficiency and capacity achievable by the proposed model and assess its asymptotic behavior under Rayleigh fading channels.Extensive simulations are conducted to validate the theoretical findings, comparing the horizontal model to the classical orthogonal model across various Signal-to-Noise Ratio (SNR) regimes.Results demonstrate that the horizontal sharing approach significantly improves spectral efficiency, especially in low-SNR conditions, and converges to the orthogonal performance in high-SNR environments.This work provides valuable insights into the design of future CR systems, highlighting the advantages of opportunistic spectrum pooling strategies for enhancing overall network performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".