Bibliographic record
Abstract
Coordinated power control and beamforming is essential in improving service in, and enabling new applications for, future generations of wireless networks. However, the associated optimization problems are often mathematically intractable, requiring the use of novel techniques to solve them. This thesis studies resource allocation optimization in two parts. In the first part, we focus on solving classical resource allocation problems by developing novel optimization-theoretic and deep learning techniques to overcome the limitations of conventional resource allocation algorithms. To this end, we first consider sum-rate optimization via power control in a multi-user cellular network. Our key contribution is the development of centralized and distributed deep reinforcement learning algorithms which outperform state-of-the-art optimization techniques while generating solutions orders of magnitude faster and requiring substantially less channel state information exchange between base stations. Next, we consider the classic long-term average proportional fair throughput optimization problem. Contrary to prior works which solve the non-convex and NP-hard proxy weighted sum-rate maximization problem, we prove that the original problem can be recast in convex form and efficiently solved to optimality. In the second part of this thesis, we introduce a novel class of problems called percentile programs in which we seek to optimize a function of a set of variables at a desired percentile. Our primary emphasis lies in developing effective power control and beamforming strategies to improve throughput for cell-edge (i.e., lower-percentile) users in wireless networks. We rigorously establish the computational complexity status of the broader class of percentile throughput optimization problems, and extend the proposed techniques to long-term average utility optimization. Moreover, we showcase that the proposed techniques can be readily extended to optimize all concave nondecreasing utility functions, allowing us to combine percentile and conventional utility functions to construct novel 'hybrid' utility functions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".