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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".