Millimeter Wave-based Fronthaul Network for Cell-free Massive MIMO
Bibliographic record
Abstract
One of the major technological breakthroughs to support unprecedented demands of the future generations of wireless communication networks is cell-free (CF) massive multiple-input multiple-output (mMIMO). However, a fronthaul network with high and reliable capacity links is a prerequisite to realize the full potential of the CF mMIMO. Aiming at deploying a cost-efficient fronthaul network, this paper proposes a millimeter wave (mmWave)-based fronthaul network for the CF mMIMO system operation thanks to the broad bandwidth in the mmWave frequency band. Stochastic geometry tools have been exploited to reflect the impact of the considered fronthaul network on the uplink (UL) performance of CF mMIMO systems. Results reveal that increasing the blockage density deteriorates the average UL data rates, however, increasing the density of CPUs can limit the blockages effect on the system performance. Besides, while it is more preferable to deploy a large number of antennas per access points (AP)s under low blockage densities, having a large number of APs, provided with a small number of antennas leads to superior UL data rates at high blockage densities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".