Cellular OFDMA Cognitive Radio Networks: Generalized Spectral Footprint Minimization
Bibliographic record
Abstract
We consider the problem of joint subchannel and power allocation for an orthogonal frequency-division multiple-access (OFDMA)-based cognitive radio network (CRN). We formulate the downlink resource-allocation problem as a generalized spectral footprint (SF) (bandwidth-power product) minimization problem under the interference threshold at the primary users (PUs), as well as the total power and quality-of-service constraints. A cognitive base station (BS) solves this nonconvex mixed-integer programming problem iteratively by dividing it into a subchannel-allocation master problem and a power-allocation subproblem. The subchannel assignment problem for secondary users (SUs) is solved by applying a modified Hungarian algorithm, whereas the power-allocation subproblem is solved by using a Lagrangian technique. Specifically, we propose a low-complexity modified Hungarian algorithm for subchannel allocation that exploits the local information in the cost matrix. To apply the modified Hungarian algorithm, we require knowledge of the exact number of subchannel requirements of each user in every iteration. Hence, we develop an algorithm to update the number of subchannels required by each user in each iteration based on the SF difference of each user. An asymptotic analysis is carried out for the single SU case, and a closed-form expression is derived for the optimal number of subchannels that minimizes the SF. The performance of our generalized SF minimization technique is compared with the water-filling power-allocation scheme and a scheme based on brute-force search. In addition, several applications of the proposed algorithms are outlined.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".