Coverage Analysis of Millimeter Wave Decode-and-Forward Networks With Best Relay Selection
Bibliographic record
Abstract
In this paper, we investigate the coverage probability improvement of a millimeter wave network due to the deployment of spatially random decode-and-forward (DF) relays. The source and receiver are located at a fixed distance and all the relay nodes are distributed as a 2-D homogeneous Poisson point process (PPP). We first derive the spatial distribution of the set of decoding relays whose received signal-to-noise ratio (SNR) are above the minimum SNR threshold. This set is a 2-D inhomogeneous PPP. From this set, we select a relay that has minimum path loss to the receiver and derive the achievable coverage due to this selection. The analysis is developed using tools from stochastic geometry and is verified using Monte-Carlo simulation. The coverage probabilities of the direct link without relaying, a randomly chosen relay link, and the selected relay link are compared to show the significant performance gain when relay selection is used. We also analyze the effects of beam misalignment and different power allocations at the source and relay on coverage probability. In addition, rate coverage and spectral efficiency are compared for direct and selected relay links to show impressive performance gains with relaying.
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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 | 0.006 |
| 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 | 0.001 | 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".