Auction-Based Relay Selection and Power Allocation in Green Relay-Assisted Cellular Networks
Bibliographic record
Abstract
Nowadays, wireless communication has become a fabric of our daily life, and it has been a universal demand for higher capacity and longer battery lifetime. For the sake of solving these problems, this paper considers taking the advantage of cooperative communication with the assistance of green relays. So, in this paper, we design an auction market that is composed of one base station (acts as the auctioneer), multiple green relays (act as sellers), and multiple mobile terminals (act as buyers). The terminals need to pay for the cooperative service, and the relays sell it for revenue. In addition, the terminals can raise their bid according to their residual energy; thus the terminals that lack energy have more opportunities to get cooperative service to avoid energy exhaustion. At the same time, the relays can reduce the price based on their instantaneous energy harvesting amount, and, hence, the relays can serve more terminals when they can harvest more energy from the environment. This paper also proposes three different auction rules to show the effect of relay selection. Furthermore, power allocation among relays and terminals is adopted to minimize the power consumption of terminals under the SNR requirement. Simulation results show that the three relay selection rules perform diversely and have their own advantages and disadvantages, but all of them can improve system capacity and prolong the lifetime of the mobile terminals effectively. And, with power allocation, the terminal can utilize the least energy to achieve its SNR requirement.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".