Self-Interference Mitigation in In-Band Full-Duplex Systems Using 180° Hybrid Coupler for 5G Application
Bibliographic record
Abstract
In-band full duplex (IBFD) system is an evolving technology in communications, however its implementation is often susceptible to interference. Therefore, this paper proposes to enhance the performance of IBFD system, particularly in the context of fifth-generation (5G) application which is operating within the frequency of 3.5 GHz. By utilizing a Self-Interference Cancellation (SIC) technique, the capability in interference mitigation is investigated by implementing a 180° hybrid coupler on an IBFD antenna integrated with a proximity coupling method. The results show a good performance of refection coefficient on Port $1\left(\mathrm{~S}_{11}\right)$ at the desirable frequency, satisfying predefined standard with the value of less than –10 dB. Moreover, the performance of reflection coefficient on Port $2\left(\mathbf{S}_{22}\right)$ adheres closely to the specified requirement, surpassing the measured value which exceeds $\mathbf{- 1 0} \mathrm{dB}$. In addition, the results also highlight the effective isolation which is characterized by polarization tendencies converging towards directional behavior, thus affirming its suitability for 5G application.
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 | 0.000 | 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".