Linear Precoding Game for MIMO MAC With Dynamic Access Point Selection
Bibliographic record
Abstract
This paper examines a non-cooperative game in precoding design for MIMO multiple-access channels with dynamic access point (AP) selection. This game is first shown to be a potential game, where the potential function is the sum rate achieved by successive interference cancellation. Due to the mixed-integer nature of the optimization variable, it is challenging to directly characterize the maximum of the potential function, which are closely related to the Nash equilibrium (NE) of the game. Instead, we establish the existence and achievability of the maximum through non-decreasing and upperbounded properties of the potential function as a direct result of our proposed update scheme. A distributed algorithm is designed where each player selfishly optimizes its AP selection and linear precoding strategy in a sequential manner. Convergence is a by-product of the established properties of the potential function which are materialized by an iterative waterfilling algorithm. Numerical results show that the algorithm is able to reach fast convergence and provides a system sum rate nearing that of the optimal centralized solution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".