Energy-Efficient Adaptive Power Allocation for Incremental MIMO Systems
Bibliographic record
Abstract
We consider energy-efficient adaptive power allocation for three incremental multiple-input-multiple-output (IMIMO) systems employing automatic repeat request (ARQ), hybrid ARQ (HARQ) with Chase combining (CC), and HARQ with incremental redundancy (IR) to minimize their rate-outage probability (or equivalently packet drop rate) under a constraint on average energy consumption per data packet. We first provide the rate-outage probability expressions for the three IMIMO systems and then propose methods to convert them into a tractable form and formulate the corresponding nonconvex optimization problems that can be solved by an interior-point algorithm for finding a local optimum. To reduce further the solution complexity, using an asymptotically equivalent approximation of the rate-outage probability expressions, we approximate the nonconvex optimization problems as a unified geometric programming problem (GPP), for which we derive the closed-form solution. Illustrative results indicate that the proposed power allocation (PPA) offers significant gains in energy savings as compared with the equal power allocation (EPA), and the simple closed-form GPP solution can provide closer performance to the exact method at lower values of rate-outage probability for the three IMIMO systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".