Energy and Spectral Efficiency Analysis for a Device-to-Device-Enabled Millimeter-Wave OFDMA Cellular Network
Bibliographic record
Abstract
The spectral efficiency (SE) and energy efficiency (EE) performance of a millimeter-wave (mmWave) cellular network is studied where a user device can associate with a base station (BS) or another user for device-to-device (D2D) communication based on an interference-aware D2D distance threshold. Using the tools of stochastic geometry, the mean interference, coverage probability, area SE, and network EE are derived under the proposed association scheme. Performance of the proposed scheme is compared with that of the minimum path loss (Min PL)-based and maximum biased-received-power (Max BRP)-based association schemes. The proposed scheme is shown to give the best coverage probability performance in noise-limited networks, while the three schemes converge in performance in interference-limited networks in the high coverage threshold regime (>20 dB). Further, the proposed scheme achieves up to 60% increase in the area SE and EE, compared to the Min PL-based scheme that gives the next best performance. Lastly, the paper proposed a goal attainment algorithm that achieves up to a seven-fold decrease in the mean deviation from a preset SE objective and 50% savings in EE, compared to the achievable performance under a constant transmit power and bandwidth allocation scheme.
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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 | 0.001 | 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".