Bibliographic record
Abstract
Introduction Spread spectrum communications was originally used in the military for the purpose of interference rejection and enciphering. In digital cellular communications, spread spectrum modulation is used as a multiple-access technique. Spectrum spreading is mainly performed by one of the following three schemes. Direct sequence (DS) : Data is spread and the carrier frequency is fixed. Frequency hopping (FH) : Data is directly modulated and the carrier frequency is spread by channel hopping. Time hopping (TH) : Signal transmission is randomized in time. The first two schemes are known as spectral spreading , and are introduced in this chapter. Time hopping is known as temporal spreading , and will be introduced in Chapter 20. Spectrum spreading provides frequency diversity, low PSD of the transmitted signal, and reduced band-limited interference, while temporal spreading has the advantage of time diversity, low instantaneous power of the transmitted signals, and reduced impulse interference. CDMA is a spread spectrum modulation technology in which all users occupy the same time and frequency, and they can be separated by their specific codes. For DS-CDMA systems, at the BS, the baseband bitstream for each MS is first mapped onto M -ary symbols such as QPSK symbols; each of the I and Q signals is then spread by multiplying a spreading code and then a scrambling code. The spread signals for all MSs are then amplified to their respective power, summed, modulated to the specified band, and then transmitted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.093 |
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".