Towards Efficient and Fair Radio Resource Allocation Schemes for Interference-Limited Celluar Networks
Bibliographic record
Abstract
The focus of this thesis is on studying the tradeoff between efficiency and fairness in interference-limited cellular networks.We start by characterizing the optimal tradeoff between efficiency and fairness in general resource allocation problems, including those encountered in cellular networks, where efficiency is measured by the sum-rate and fairness is measured by the Jain's fairness index.Among the commonly-used methods to approach these problems is the one based on the α-fair policy.Analyzing this policy, we show that it does not necessarily achieve the optimal Efficiency-Jain Tradeoff (EJT) except for the case of two users.When the number of users is greater than two, we prove that the gap between the efficiency achieved by the α-fair policy and that achieved by the optimal EJT policy for the same Jain's index can be unbounded.Finding the optimal EJT corresponds to solving potentially difficult non-convex optimization problems.To alleviate this difficulty, we derive sufficient conditions, which are shown to be sharp and naturally satisfied in various radio resource allocation problems.These conditions provide us with a means for identifying cases in which finding the optimal EJT can be reformulated as convex optimization problems.The new formulations are used to devise computationally-efficient resource schedulers that achieve the optimal EJT and surpass the baseline schedulers in terms of sum-rate efficiency, Jain's fairness index, median rate, and user satisfaction, without incurring additional complexity.Applying the proposed optimal EJT schedulers in interference-limited cellular iii and brothers, thanks for all your support during this research.To Dr.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".