Resource Allocation for OFDM-Based Cognitive Radio Multicast Networks
Bibliographic record
Abstract
In cognitive radio networks with the coexistence of primary and secondary users, the problem of how to optimally allocate available resources (e.g., bandwidth and power) to multicast groups of secondary users that use orthogonal frequency division multiplexing (OFDM) is important. Taking the maximization of the weighted sum rate of such groups as the design objective, we propose a practically optimal subcarrier and power allocation scheme under constraints on the tolerable interference thresholds at individual primary user's frequency bands. Specifically, the optimization problem is solved via the dual method, where subcarriers are assigned in a per-tone basis and power is distributed in a water-filling fashion. As the number of subcarriers becomes large, the dual-domain solution becomes the global optimum of the primal problem with the duality gap vanishing to zero. The proposed design is valid for both unicast and multicast transmissions, and its computational complexity is only linear in the number of subcarriers. The effects of adjacent subcarrier nulling technique, which is to reduce mutual interference between primary and secondary frequency bands, on the proposed scheme are also examined. The superiority of the dual approach is confirmed by numerical results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".