Mutual Interference Measurement for Millimeter-Wave D2D Communications in Indoor Office Environment
Bibliographic record
Abstract
Millimeter wave (mmWave) Device-to-device (D2D) communication is an attractive add-on component in 5G networks, since it enables to share the network load and improve spectrum efficiency by direct communication between nearby mobile devices. Mutual interference across different networks and different users is a crucial factor which affects the system performance necessitating to be properly considered in the system design. To better understand the effects of mutual interference, in this paper, we show mutual interference measurement results in an indoor office environment with D2D network deployment at the 60 GHz frequency band. Based on our measurements, some key impact factors, including transmitter and receiver beamwidths and positions as well as the number of active links are investigated. The results demonstrate that a decrease in the beamwidths of transmitter and receiver or a decrease in the number of active links can alleviate the mutual interference. Furthermore, it is exhibited that the interference in such D2D deployment environment is mainly dependent on the number of active links and a few active links among all active links dominate the mutual interference.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".