Trade-Off between Spectral and Energy Efficiencies in a Fading Communication Link
Bibliographic record
Abstract
Spectral efficiency (SE) is one of the key performance indicators of wireless communications, and energy efficiency (EE) is an urgent need to tackle the challenges raised by the high demands of wireless traffics and energy consumption. However, these two important design criteria conflict with each other and a careful study of their trade-off is mandatory for designing future wireless communication systems. In this paper, we introduce an optimization problem to maximize the ergodic SE of a point-to-point communication link with a constraint on its minimum ergodic EE. We prove that, at optimality, the constraint on minimum EE is met with equality, and use it to provide a closed-form expression for finding the optimal water-filling level in a Nakagami-m fading channel with integer values of m. We exploit this formulation to investigate the relationship between SE and EE as a function of circuit power, power amplifier (PA) efficiency and channel power gain. We observe that the SE and EE always contradict with each other, however, the trade-off curve is non-linear. The curve is steeper at the extremities as compared to the middle region. Hence, a small sacrifice in EE from its maximum value may map into a significant gain in SE. Our simulations show that this gain in SE is a decreasing function of the circuit power and channel power gain while an increasing function of the PA 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 | 0.004 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".