Bibliographic record
Abstract
With the increasing requirements for future wireless applications, OFDM, MC-CDMA, and MC-DS-CDMA have all been considered for 4G wireless systems. These systems have the ability to incorporate very large band widths without sacrificing equalization complexity. The long symbol duration is effective at mitigating ISI, and adaptive modulation or frequency diversity can be used to provide protection against destructive fades. The benefit of MC CDMA is that it experiences frequency diversity because each bit is transmitted over several independently faded subcarriers. If some subcarriers experience destructive fades, diversity combining can be used at the receiver to recover the data. This improves the BER performance over OFDM, and this improvement is more significant as the number of subcarriers is increased. The draw back of MC-CDMA is that it may experience high levels of multiuser access interference (MAI) when the channel is heavily loaded. This occurs because each chip of the PN sequence experiences independent fading, which tends to destroy the orthogonality between spreading sequences. This increases the MAI and degrades the BER performance. Although OFDM, MC-CDMA, and MC-DS-CDMA signals experience a high PAPR, synchronization issues, and ICI, the benefits greatly outweigh these disadvantages.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".