Quantifying Network Level Improvement due to Beamforming on the Performance of Large-Scale Dense Urban IoT Networks
Bibliographic record
Abstract
There have been many papers that show the physical layer advantages of beamforming. However, to the best of our knowledge, none have dealt with the large-scale network level performance evaluation. While it is clear that beamforming brings a positive effect on network performance, that improvement has not been quantified. This is important given that, despite its advantages, large-scale beamforming deployment also implies additional CAPEX and OPEX costs for service providers. This paper presents a large-scale network layer simulation study that aims at quantifying some network layer Key Performance Indicators improvement achieved by the massive introduction of beamforming in the network. We first show how we adapted a simulation-efficient packet scheduling procedure to our large-scale PIoT simulator engine. Then, we perform extensive simulations on a real network of the city of Montréal with 30000 UEs (User Equipment) and 3019 antennas. Among many results it was found that, on average, more than 30 ms of waiting delay reduction can be achieved with beamforming. Also, for applications with very short packet inter arrival time, the total traffic serviced more than doubled. Simulation results also show that while the increase in the number of beams improves performance, there is also a saturation effect that can be perceived at the network level. It was also found that when the traffic arrival is Poisson, the benefits of beamforming are less striking, which suggests that the type of application traffic may have an influence in the beneficial impact of beamforming at the network level, opening the path for further investigation. Finally, given that the simulator front end is publicly available at http://piotsimulation.com , the paper also shows the potentiality for the community to perform what-if cross layer analysis in a real-sized urban network.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".