Universal-filtered multi-carrier technique for wireless systems beyond LTE
Bibliographic record
Abstract
In this paper, we propose a multi-carrier transmission scheme to overcome the problem of intercarrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) systems. In the proposed scheme, called universal-filtered multi-carrier (UFMC), a filtering operation is applied to a group of consecutive subcarriers (e.g. a given allocation of a single user) in order to reduce out-of-band sidelobe levels and subsequently minimize the potential ICI between adjacent users in case of asynchronous transmissions. We consider a coordinated multi-point (CoMP) reception technique, where a number of base stations (BSs) send the received signals from user equipments (UEs) to a CoMP central unit (CCU) for joint detection and processing. We examine the impact of carrier frequency offset (CFO) on the performance of the proposed scheme and compare the results with the performance of cyclic prefix based orthogonal frequency division multiplexing (CP-OFDM) systems. We use computer experiments to illustrate the efficiency of the proposed multi-carrier scheme. The results indicate that the UFMC scheme outperforms the OFDM for both perfect and non-perfect frequency synchronization between the UEs and BSs.
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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) | 0.000 | 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 | 0.001 |
| 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".