Adaptive power allocation for chase combining HARQ based low-complexity MIMO systems
Bibliographic record
Abstract
This paper deals with energy-efficient adaptive power allocation for an incremental multiple-input multiple-output (IMIMO) system employing hybrid automatic repeat request (HARQ) with Chase combining (CC), to minimize its rate-outage probability under a constraint on average energy consumption per data packet. We first provide the rate-outage probability expressions for the considered IMIMO system, and use Gauss-Legendre approximation to convert them into a tractable form and formulate a non-convex optimization problem that can be solved by an interior-point algorithm for finding a local optimum. Next, to further reduce the solution complexity, using an asymptotically equivalent approximation of the rate-outage probability expression, we approximate the non-convex optimization problem as a geometric programming problem (GPP), for which a solution can be obtained using convex optimization algorithms. Illustrative results indicate that the proposed power allocation (PPA) offers significant gains in energy savings as compared to the equal-power allocation (EPA), and the less complex GPP approach can provide a closer performance to the exact method at lower values of rate-outage probability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".