Stochastic Geometry-Based Modeling and Analysis of Massive MIMO-Enabled Millimeter Wave Cellular Networks
Bibliographic record
Abstract
Massive multiple-input multiple-output (MIMO) systems operating within the millimeter wave frequency range offer exciting opportunities for the future fifth-generation (5G) wireless networks. While the increased bandwidth and spectral efficiency are attractive, transitioning to millimeter massive MIMO presents significant challenges with respect to blockages, high attenuation, and channel estimation errors. To address these challenges, this paper evaluates the outage performance of a millimeter wave cellular network using massive MIMO under a stochastic set-up subject to pilot contamination and matched-filter precoding. We model the cellular users and base stations with Poisson point processes. Furthermore, we consider blockages from random objects, and employ different path loss and fading models for the line-of-sight (LOS) and non-line-of-sight scenarios. Moreover, both fixed power transmissions and path loss inversion-based power control are considered along with the sectored antenna patterns. Using stochastic geometry, we derive the moment generating function of the interference experienced by a typical cellular user and its outage probability. It is observed that the environments with different path loss exponents have varying behaviour for similar blockage sizes and densities. In addition, the ratio of the number of cellular users to that of base stations, and the antenna beamwidth are critical parameters affecting the outage performance.
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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 | 0.001 |
| 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 | 0.001 | 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".