A cheat-proof power control policy for self-organizing full-duplex small cells
Bibliographic record
Abstract
Distributed resource allocation is considered as a significant feature of future self-organizing wireless networks. On the other hand, full-duplex transmission is an emerging technology which can theoretically double the data rate. and hence enhance the performance of wireless networks significantly. In this work, we address the problem of distributed power control in a two-tier network with full-duplex small cells underlaying one macro cell in a co-channel deployment scenario. We first formulate the corresponding distributed power control problem as a non-cooperative game and then extend it to a repeated game with imperfect public monitoring. The repeated game setting is used as it can withstand cheating. We show the existence and uniqueness of the Nash equilibrium of the formulated non-cooperative game and characterize the set of perfect public equilibrium for the repeated game. A two phase distributed algorithm is then proposed to achieve a power profile which is also a perfect public equilibrium. The power control algorithm is cheat-proof and needs only a small amount of information exchange among network nodes. The effectiveness of the algorithm is shown using numerical results. Our proposed algorithm and analysis are also valid for a half-duplex system as it is a special case of the full-duplex model presented in the paper.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".