Experimental Study of Radiated and Conducted UWB Interference and its Impact on the Throughput of 5-GHz WLAN Receivers
Bibliographic record
Abstract
This paper discusses and reports the measured throughput of a 5-GHz WLAN receiver in the presence of ultra-wideband (UWB) interference signals. Two different experimental scenarios are considered: 1) conducted interference where the UWB signal was injected directly into the victim receiver, and 2) radiated interference where the UWB signal was radiated from an antenna in close proximity to the victim receiver. In each scenario, the performance of the victim receiver was evaluated by measuring the average throughput (average over sending a file 10 times). The measured throughput as a function of the signal-to-interference ratio SIR indicates that, a throughput of 7.8 Mbps can be achieved with SIR of 3.5 dB at the input of the receiver. However, this achieved throughput represents 50% performance degradation. The results also indicate that, having two UWB interference sources as close as 20 cm to the victim receiver could degrade the receiver performance significantly depending on the received signal level. A separation of at least 0.5 meter between the antennas of the victim and offending devices would satisfy the system requirement for throughput greater than 7 Mbps
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".