Energy-Efficient Power Allocation Maximization for Multi-User MIMO Broadcast Channel
Bibliographic record
Abstract
This paper proposes an${i}$terative${w}$ater-${f}$illing algorithm (IWF) for the${e}$nergy-${e}$fficiency (EE) maximization problem of the multi-user${m}$ultiple-${i}$nput and${m}$ultiple-${o}$utput (MIMO)${b}$roadcast${c}$hannel (BC). This algorithm is termed as IWF-EE-BC and has two levels of operations. The inner level computes solutions, by an algorithm, is named as${w}$ater-${f}$illing for the EE of the BC, which is implemented within a single iteration, with the short name: WF-EE-BC1. The solutions by WF-EE-BC1 are the optimal solutions of the auxiliary energy-efficiency maximization problems. Each term of the added throughput part in these auxiliary problems is decoupled in power variables for all users. Then the outer level determines when to output a good solution to the considered problem, based on the results obtained by the inner level. The considered problem has complex-valued matrix optimization variables, beyond the range of the optimization problems whose optimization variables are often real-valued variables. Particularly, it is a${s}$emi-${d}$efinite${o}$ptimization problem (SDO) with a more complicated form of the objective function, over the field of complex numbers. Since existing results on optimization algorithms, including SDO ones, cannot guarantee convergence of IWF-EE-BC, the novel fixed point method is designed and used. Overcoming these difficulties, this paper obtains convergence of IWF-EE-BC, with efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 0.002 |
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".