Cyclostationary-based diversity combining for blind channel equalization using multiple receive antennas
Bibliographic record
Abstract
At high data rates, radio channels are characterized by severe intersymbol interference (ISI) and deep fades in the received signal levels. This paper develops an integrated approach for the mitigation of these effects using diversity combining and channel equalization in radio systems with multiple receive antennas. To accommodate higher data rates exceeding the channel coherence bandwidth, frequency selective channels are compensated utilizing blind equalization algorithms that exploit the cyclostationary signal structure inherent in communication signals. To mitigate the effects of flat fading, diversity combining is deployed which improves the bit error rate (BER) performance by merging distorted replicas of the transmitted signal in an intelligent fashion. This paper represents an effort in building on the strengths of these two distortion mitigation schemes so as to achieve additional benefit of compensating for channels that could not otherwise be compensated. The proposed combining algorithms are collectively referred to as cyclostationary-based diversity combining (CSDC). Both pre-equalization and post-equalization CSDC schemes are discussed in this paper. Simulation results for the performance of CSDC algorithms are presented.
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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".