Subcarriers assignment scheme for multiple secondary users in OFDMA‐based IEEE 802.22 WRAN: A game theoretic approach
Bibliographic record
Abstract
Abstract In this work, cognitive radio and orthogonal frequency division multiple access–based IEEE 802.22 wireless regional area networks is considered. Generally, subchannel is assigned to the user having the best channel gain to that subchannel and problem is known to be NP‐hard. This assignment sometimes results in the unfair allocation where user with best channel gain is allocated more carriers as compared with the user with worst channel condition. In this work, a suboptimal algorithm is developed in which initially optimal number of subcarriers are found considering the equal power distribution. The problem is formulated as an oligopoly market competition and a noncooperative Cournot game is used in which different unlicensed secondary users (SUs) compete for the number of subcarriers based upon the data rate they are getting from current channel condition. The fair distribution of subcarriers is ensured by finding the Nash equilibrium. After subchannels are assigned to the SUs, power allocation is performed for each user with the water‐filling algorithm. Simulation results show that the proposed approach can attain superior performance over considered benchmark scheme in the literature in terms of minimum data rate and fairness achieved by the SU. Results validating fair allocation of subcarriers is also shown.
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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 | 0.001 | 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".