Resource optimization for energy efficiency in multi-cell massive MIMO with MRC detectors
Bibliographic record
Abstract
In this paper, resource allocation for energy-efficient communications in a pilot-contaminated uplink multi-cell massive MIMO system with MRC detectors is investigated. The problem of maximizing energy efficiency (EE) of data transmissions in the system is studied by optimizing the number of antennas per BS, the pilot signal power, and the data signal power. The considered optimization problem takes into account the circuit power consumption, pilot contamination, and budget constraints in the number of antennas per Base Station (BS) and the average transmission power per symbol. The resulting optimization problem has a non-convex fractional objective function which is difficult to solve in its original form. Therefore, principles from fractional programming are used to first transform the problem into an equivalent parametric form and then to derive an iterative resource allocation algorithm. In each iteration, an alternating optimization technique is used to solve the objective function by decomposing it into a sequence of solvable difference of convex (D.C) programming subproblems. Simulation results show that higher EE levels can be achieved by optimizing the pilot and data powers separately. Also, increasing the number of antennas per BS with the power budget may or may not be energy-efficient, depending on the range of operation.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".