Analysis and outage performance evaluation of a fair scheduling for independent non‐identically distributed users in a cognitive radio using OSTBC with equally correlated transmit antennas
Bibliographic record
Abstract
In this study, a fair user‐scheduling method is considered for an underlay cognitive radio system, in which the secondary users (SUs) are sharing the licenced frequency spectrum of a single primary user (PU), where the PU and SUs utilise Alamouti orthogonal space‐time block coding (OSTBC). The impacts of some important practical issues are investigated in the system. First, it is assumed that the signal‐to‐noise‐ratios of the SUs are independent non‐identically distributed. Second, the transmit antennas for OSTBC corresponding to the SUs are assumed to be equally correlated. Third, the interference from the PU to SUs as well as the interferences from SUs to the PU are taken into consideration. To investigate the outage performance of the SUs, a closed‐form expression for the cumulative distribution function of the SUs signal‐to‐interference‐noise‐ratio is obtained and then used to derive an expression for evaluating the secondary system outage performance. The numerically evaluated results, validated by computer simulations, provide insights about system outage performance under various practical situations on the impacts of the spatial antennas correlation and the PU interference on to SUs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.001 |
| 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".