Hybrid Amplification: An Efficient Scheme for Energy Saving in MIMO Systems
Bibliographic record
Abstract
In this paper, a new amplification scheme for adaptive MIMO systems is proposed and tested. In this ‘hybrid amplification’ configuration, different amplifiers with different peak powers are used. In this way, each transmitter RF chain has a different DC-power consumption behavior. The adaptation algorithm, which chooses power and rate for each transmitter, uses these different amplifier behaviors to minimize the overall consumed energy. Several MIMO configurations designed for constant capacity applications have been simulated with different amplification schemes. Realistic amplifier models based on measured data are used. The difference between the amplifiers’ RF powers is set in order to keep optimal system performances. Then, it is shown that energy savings higher than 10% can be obtained with the hybrid amplification. The different tests are done for MQAM constellations in uncorrelated Rayleigh fading channels detected with a VBLAST ZF-SIC algorithm but can easily be extended to other correlated channels, detection algorithms or constellations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".