Coverage Analysis of Decode-and-Forward Relaying in Millimeter Wave Networks
Bibliographic record
Abstract
In this paper, we demonstrate the coverage probability improvement of a millimeter wave (mmWave) network due to the deployment of spatially random decode-and-forward (DF) relays. We assume the transmitter and receiver are located at a fixed distance and that the potential relay nodes are spatially distributed as a two dimensional homogeneous Poisson point process (PPP). We first derive the spatial distribution of decoding set of relays that meet the required signal-to-noise ratio (SNR) threshold. From this set, we select a relay that has minimum path- loss from the receiver and derive the coverage probability achievable due to this selection. The analysis is based on stochastic geometry and is verified via Monte-Carlo simulation. The coverage probabilities of (a) direct link without relaying and (b) relayed link are compared to show that relaying provides significant coverage improvements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".