Truthful Double Auction for Joint Internet of Energy and Profit Optimization in Cognitive Radio Networks
Bibliographic record
Abstract
With the development of cognitive radio networks in recent years, spectrum utilization has been enhanced, as secondary users can lease under-utilized spectrum from the spectrum owners. Spectrum is allocated through auctions in wireless communication networks. The auction can provide benefits for both primary users and secondary users. Existing auction mechanisms for spectrum are mainly based on interference graphs and consider the heterogeneity of spectrums only to a minimal degree. The economic efficiency of the auction is usually neglected due to the focus on improving spectrum utilization. In this paper, we consider a signal-to-interference-plus-noise ratio (SINR) constrained interference model; this model is more realistic as users can simultaneously communicate as long as their requirements SINRs are satisfied. We propose a truthful profit maximization double auction mechanism to improve the benefit of networks with low energy. At the same time, security concerns are guaranteed because buyers and sellers make their true critical decision, i.e., they cannot improve their utility by misreporting their asks and bids. Moreover, our proposed novel auction mechanism is individually rational and budget-balanced. The experiments demonstrate that our auction mechanism efficiently increases the number of winners and improves the auctioneer's profit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".