Energy Efficiency–Spectral Efficiency Tradeoff: A Multiobjective Optimization Approach
Bibliographic record
Abstract
In this paper, we consider the resource-allocation problem for energy efficiency (EE)-spectral efficiency (SE) tradeoff. Unlike traditional research that uses the EE as an objective function and imposes constraints either on the SE or on the achievable rate, we propound a multiobjective optimization approach that can flexibly switch between the EE and SE functions or change the priority level of each function using a tradeoff parameter. Our dynamic approach is more tractable than the conventional approaches and more convenient to realistic communication applications and scenarios. We prove that the multiobjective optimization of the EE and SE is equivalent to a simple problem that maximizes the achievable rate/SE and minimizes the total power consumption. Then, we apply the generalized framework of the resource allocation for the EE-SE tradeoff to optimally allocate the subcarriers' power for orthogonal frequency-division multiplexing (OFDM) with imperfect channel estimation. Finally, we use numerical results to discuss the choice of the tradeoff parameter and study the effect of the estimation error, transmission power budget, and channel-to-noise ratio on the multiobjective optimization.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".