Optimal symbol timing for OFDM wireless communications
Bibliographic record
Abstract
Orthogonal frequency division multiplexing (OFDM) has become a promising physical layer modulation technology for beyond 3G or 4G wireless communications due to effective inter-symbol interference mitigation for high speed data transmission. However, the timing of the OFDM symbol, i.e., the placement of the DFT collection window in a multi-path time dispersive channel remains an important and challenging issue in OFDM receiver design. An erroneous timing decision creates inter-symbol interference (ISI), inter-carrier interference (ICI), channel attenuation, and channel estimation error, which leads to a penalty on the collected OFDM symbol signal to noise ratio (SNR) resulting in an irreducible error floor. In this paper we quantify such effects and derive an optimal OFDM symbol timing solution in the sense of maximizing the signal to interference ratio (SIR) of the collected OFDM symbol. A practical timing algorithm, referred to as the equilibrium algorithm, is then developed to approximate the optimal timing decision. Compared with existing schemes in the literature, the proposed approach does not rely on explicit detection of individual channel paths or the delay spread boundary and therefore greatly reduces timing complexity. The equilibrium algorithm performs nearly as well as the optimal solution over a variety of channel delay spreads, is simple to implement, and is robust to channel estimation errors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".