Nonconvex and game theory optimization for resource allocation in wireless communications
Bibliographic record
Abstract
Designing wireless communication systems that efficiently utilize the resources frequency spectrum and electric power, leads to problems in mathematical optimization. Most of these optimization problems are difficult to solve because the objective functions are nonconvex. While some problems remain unsolved, the solutions proposed in the literature for the others are of somewhat limited use because the algorithms are either unstable or have too high a computational complexity. This dissertation presents several stable algorithms, most of which have polynomial complexity, that solve five different nonconvex optimization problems in wireless communication. Two centralized and two distributed algorithms deal with the power allocation that maximizes the throughput in the Gaussian interference channel (GIC)with various constraints. The most valuable of these algorithms, the one with the minimum rate constraints became possible after a significant theoretical development in the dissertation that proves that the throughput of the GIC has a new generalized convex structure called invexity. The fifth algorithm has linear complexity, and finds the power allocation that maximizes the energy efficiency (EE) of OFDMA transmissions, for a given subchannel assignment. Some fundamental results regarding the power allocation are then used in the genetic algorithm for determining the subchannel allocation that maximizes the EE. Pricing for channel subleasing for ad-hoc wireless networks is considered next. This involves the simultaneous optimization of many functions that are interconnected through the variables involved. A composite game, a strategic game within a Stackelberg game, is used to solve this optimization problem with polynomial complexity. For each optimization problem solved, numerical results obtained using simulations that support the analysis and demonstrate the performance of the algorithms are presented.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".