Reliable Millimeter Wave Communication for IoT Devices
Bibliographic record
Abstract
In this paper, we propose a nearest line-of-sight relay (NLR) selection technique for internet of things (IoT) devices in millimeter wave (mmWave) relaying systems. We present a tractable analytical framework to characterize the network connectivity for the proposed technique using tools from stochastic geometry. Moreover, we investigate the impact of the relay-selected region and the distance between the base station and IoT device on the network connectivity of mmWave relaying systems. The analytical results unveil a high degree of accuracy which is confirmed by extensive simulations at different relay densities, blockage densities, and signal-to-noise ratio (SNR) thresholds. Results obtained via both simulations and analyses reveal the trade-off between the network connectivity and the energy consumption of IoT devices. Results also reveal a significant impact of blockage density and controlling the relay-selected region on the network connectivity and energy consumption.
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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".