Bibliographic record
Abstract
Passive Intermodulation (PIM) is a well-known phenomenon that poses a significant challenge to modern wireless communication systems.Despite efforts to reduce or mitigate PIM through hardware design, frequency planning, and band separation techniques, the impact of PIM on radio performance remains substantial.Particularly, radio transceivers operating in the frequency division duplex mode are known to be susceptible to PIM problems.Moreover, with the adoption of carrier aggregation and advanced multi-antenna technology, PIM is increasingly becoming a major issue in multi-band multi-standard radio systems.Therefore, innovative approaches that exploit PIM detection, avoidance, and cancellation techniques are required to effectively mitigate or reduce its impact.In response to these challenges, this thesis highlights various PIM interference mechanisms and their interactions that can potentially lead to sensitivity degradation.The thesis continues with a discussion of PIM mitigation approaches and underscores the potential of digital PIM cancellation (PIMC) solutions to offer a hardware-independent resolution for reducing PIM impact in multi-band radios in the 5G era and beyond.Two key contributions are presented as follows:Received baseband UL signal θ p Model impulse response
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".