Rate–Interference Tradeoff in OFDM-Based Cognitive Radio Systems
Bibliographic record
Abstract
In this paper, we investigate the tradeoff between increasing the secondary users' (SUs) transmission rate and reducing the interference levels at the primary users (PUs) for orthogonal-frequency-division-multiplexing-based cognitive radio systems. To achieve this target, we formulate a generalized multiobjective optimization (MOOP) problem that jointly maximizes the transmission rate of the SU and minimizes the cochannel interference (CCI) and adjacent channel interference (ACI) to existing PUs. We additionally constrain the allowed CCI and ACI to the PUs to guarantee the PUs' protection from harmful interference. The MOOP problem is solved by linearly combining the normalized competing objective functions - through weighting coefficients - into a single objective function. Prior work in the literature that maximizes the SU transmission rate can be considered as a special case of the generalized MOOP problem by setting the weighting coefficients associated with interference minimization to zero. Since estimating the full channel state information (CSI) of the links between the SU transmitter and the PU receivers is practically challenging, we assume only partial CSI knowledge of these links. Simulation results illustrate the performance of the proposed algorithm and quantify the SU performance loss due to incomplete CSI knowledge. Furthermore, the proposed algorithm is compared with state-of-the-art techniques, and our performance results show that the proposed algorithm is more energy aware, yet with reduced complexity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".