Iterative frequency domain channel estimation for dft-precoded ofdm systems using in-band pilots
Bibliographic record
Abstract
We consider two techniques of in-band frequency domain multiplexed (FDM) pilots using interleaved frequency domain multiple access (IFDMA) signal with a Chu sequence for DFT-precoded OFDM (or single-carrier (SC)) systems. One, called frequency domain superimposed pilot technique (FDSPT), superimposes pilot tones onto scaled or deleted data tones, which preserves spectral efficiency at the expense of a slight performance loss. The other, called frequency expanding technique (FET), multiplexes pilot tones by displacing data tones, which slightly reduces spectral efficiency. Using FDM pilots in SC systems facilitates flexible and efficient assignment of signals to available spectrum. We propose an iterative frequency domain decision-directed interference cancellation technique to reduce the intersymbol interference level of SC signals with FDSPT pilots (resulting from the suppression of data tones). Moreover, we propose a low complexity frequency domain iterative decision-directed channel estimation (IDDCE) technique for SC systems using FDM pilots. Using IDDCE, the frame error rate (FER) performance for coded SC systems using FET and FDSPT pilots with interference cancellation is found to be about 0.2 dB and about 0.5 dB, respectively, away from the FER performance with known channel frequency response at FER=10-2. FDSPT pilots can also be used for OFDM systems with channel coding. It is found that an extra 1 dB of SNR is required at FER=10-2,compared with that using the conventional FET pilots for OFDM systems.
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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 | 0.000 |
| 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".