Impact of Receiver Antenna Polarization and Resource Scheduler on the Downlink Performance of High Velocity Users in 5G Millimeter Wave Small Cell Technology
Bibliographic record
Abstract
With the occupancy of the existing frequency spectrum the demands of the skyrocketing data traffic paved the path for 5G millimeter Wave (mmWave) technology. The wide frequency spectrum and high directivity can elevate the mean data rate. This performance gain comes at the cost of a higher pathloss which limits the use of mmWave technology to small cells. Also at such high frequencies the multipath components and blockages appear as persistent barriers leading to alteration of antenna polarization, affecting resource scheduler performance and throughput degradation. The impact of these barriers for high velocity user equipments (UEs) in mmWave network is still left to be studied. Using closed loop spatial multiplexing transmission scheme this study analyses the effect of receiver antenna polarization and types of resource scheduler used in base station on the downlink performance of mobile users (0-120kmph) in the mmWave small cell network. Thorough investigation has been conducted to infer which antenna combination should be advantageous under different scheduling algorithms for high mobility UEs. Our results indicate that the scheduler performance is complimented by the receiver antenna polarization, and by appropriate selection a better downlink performance can be sustained. Also reception under linear polarization performs better over circular polarization for high velocity UEs.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".