Performance analysis of hexagonal cellular networks in fading channels
Bibliographic record
Abstract
This paper analyzes the location-dependent performance metrics of coverage probability and spectral efficiency in hexagonal cellular networks under Rayleigh fading with a general distribution for shadowing and also including two special cases of no shadowing and lognormal shadowing. The effects of system parameters such as frequency reuse factor, transmission probability of base stations, and signal-to-interference-plus-noise ratio gap from Shannon capacity are accurately characterized. The proposed approach is applied to fractional frequency reuse FFR scheme where the impact of FFR on spectral efficiency is evaluated. Numerical results show that i in a lognormal-shadowed Rayleigh fading channel with the shadowing standard deviation of 12dB, the cell area wide spectral efficiency is degraded by approximately 40% compared with when there is Rayleigh fading without shadowing; ii the improvement in spectral efficiency achieved by FFR over the universal frequency reuse increases as the transmission probability increases and the shadowing becomes less severe; and iii in Rayleigh fading without shadowing environment where all the base stations are actively transmitting, FFR achieves approximately 20% improvement in spectral efficiency in the cell edge area. Interestingly, this improvement increases to about 30% if a 3-dB signal-to-interference-plus-noise ratio gap from Shannon capacity is further accounted. Copyright © 2015 JohnWiley & Sons
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".