Analysis of the effects of Nyquist pulse‐shaping on the performance of OFDM systems with carrier frequency offset
Bibliographic record
Abstract
Abstract An exact method for calculating the bit error rate (BER) of an uncoded orthogonal frequency‐division multiplexing (OFDM) system with transmitter Nyquist pulse‐shaping over AWGN channels in the presence of frequency offset is derived. This method represents a unified way to calculate the BER of this system with different one‐ and two‐dimensional subcarrier modulation formats. The precise BER expressions are obtained using a characteristic function method. The effects of several widely referenced Nyquist pulse‐shapings, including the Franks pulse, the raised‐cosine pulse, the ‘better than’ raised‐cosine (BTRC) pulse, the second‐order continuous window (SOCW), the double‐jump pulse and the polynomial pulse on intercarrier interference (ICI) reduction and BER improvement of the system with carrier frequency offset are examined in the AWGN channel. The dependence of the BER on the roll‐off factor of the pulse employed for a specific system in the presence of frequency offset is investigated. Analysis and numerical results show that the Franks pulse exhibits the best performance among the Nyquist pulses considered in most cases. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.004 |
| 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".