SINR maximization approach for partial equalization in OFDM systems
Bibliographic record
Abstract
In orthogonal frequency division multiplexing (OFDM) system, a cyclic prefix (CP) as redundant information is appended to each OFDM symbol in order to mitigate the impact of intersymbol interference (ISI) on data transmission over multipath channels. On the one hand, the CP interval should not be shorter than the channel impulse response (CIR) length in order to eliminate the ISI completely and on the other hand, appending the CP decreases the bandwidth efficiency of the OFDM systems. In this paper a new TEQ design method based on maximizing signal-to-interference plus noise ratio (SINR) is proposed in order to equalize the received signal partially and eliminate a potion of the ISI when the appended CP interval is shorter than the CIR length. The true SINR for the OFDM system is formulated based on a relative delay, D, between the starting point and the effective point of overall impulse response (OIR) (convolution of the CIR and the equalizer impulse response). The proposed method called maximum SINR time-domain equalization (MSINR-TEQ) estimates the tap coefficients of the equalizer by maximizing the SINR at the output of the equalizer based on the value of the relative delay, D. The MSINR performance is evaluated for data transmission over multipath fading channels with additive white Gaussian noise. Computer simulations show that when the CIR interval is longer than the CP interval, the TEQ designed based on the MSINR method improves the performance of the OFDM system significantly in comparison with the system without an equalizer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".