Bibliographic record
Abstract
The purpose of this thesis, is to derive and evaluate the performance of noncoherent, maximum likelihood receivers with improved performance, for trellis coded PSK and QAM type signals, transmitted over Rician, correlated, fast, frequency non-selective and frequency selective fading channels, with and without diversity. First we derive the optimal, in the maximum likelihood detection sense, receiver structure for frequency non-selective Rician fading channels, employing diversity reception. In order to reduce the complexity of the optimal receiver, we propose and evaluate the performance of suboptimal receiver structures, which show significant performance improvements as compared to conventional techniques. Investigation of the effects on performance of the proposed algorithms, due to imperfect statistical knowledge of the fading channel typical for a real life environment, demonstrates very small sensitivity even to large errors in estimates of channel parameters. Complementing our work in frequency non-selective fading, we derive the optimal, in the maximum likelihood detection sense, receiver, for the correlated, fast, frequency selective Rician fading channel. In the interest of system simplicity, we propose and evaluate reduced complexity versions of the decoding algorithms. The impact of simplifying assumptions in the theoretical derivation, as well as the receiver sensitivity to non ideal channel knowledge, is investigated. The results show significant performance improvements over the fastest known channel equalization technique, accompanied by small sensitivity to imperfections. Last, we derive analytical performance bounds for simplified versions of the optimal diversity receiver, for frequency non-selective, Rician fading channels. The tightness and accuracy of the bounds is verified, through the excellent agreement between computer simulation results, and bound calculation. Performance evaluation demonstrates significant improvements, approaching the effectiveness of coherent detection in AWGN, even with a relatively small diversity order, for Rician, as well as shadowed EHF fading channels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".