Adaptive equalization for a multipath fading environment with interference and noise
Bibliographic record
Abstract
Investigates the average bit error rate (EER) and outage performance of a TDMA indoor wireless cellular system employing an adaptive fractionally spaced decision feedback equalizer (DFE) in the presence of adjacent and co-channel interference (ACI and CCI) and additive Gaussian noise. There exist performance gains in the presence of strong interference and negligible ambient noise relative to the equivalent stationary noise case. Moreover, a directly adapted RLS DFE performs better than a computed MMSE DFE which employs estimates of the channel impulse response (CIR) and interference plus noise autocorrelation. However, these comparative performance gains are compromised by the presence of appreciable noise power and/or more interferers than the receiver can process effectively, The use of a wide receiver bandwidth yields a performance improvement for channel spacings which allow for sufficient spectral overlap of the ACI with the desired signal bandwidth. Thus, a reduction in channel spacing increases the radio capacity while maintaining a desired average EER or outage performance.>
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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 | 0.001 |
| 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".