Bibliographic record
Abstract
In wireless channels, the signal quality degrades mainly due to the additive noise and the random variation of attenuation of the signal, known as fading.The additive noise can be compensated to some extent using forward error correction (FEC) coding and automatic repeat request (ARQ).The fading can be compensated not only with FEC codes and ARQ schemes but also using spatial diversity and multiplexing achieved by employing multiple antenna systems, known as multiple-input multiple-output (MIMO) systems.MIMO schemes fall into different categories based on system requirements, e.g., space-time block codes (STBCs) and limited feedback schemes.FEC codes, as a method of substantial performance improvement, are employed in most modern communication systems.However, the optimal design of the concatenation of FEC codes and modulation schemes for different applications is an open problem.Polar codes are a new class of FEC codes that benefit from simple rate matching and a variety of low-complexity decoders which facilitate the design of efficient systems.Multilevel coding with multistage decoding (MLC/MSD) is a low-complexity capacity achieving coded-modulation technique that can be designed efficiently for polar codes due to the conceptual similarity.In this thesis, in order to achieve low-arithmetic-complexity/high-performance coded-modulation schemes for wireless channels, multilevel polar coded-modulation First and foremost, I want to thank my co-supervisors Professor Halim Yanikomeroglu and Professor Ian Marsland.I am thankful to Professor Yanikomeroglu for the encouragement and the endless support throughout my Ph.D. studies.His advice on both research as well as on my career have been invaluable.I would also like to appreciate Professor Marsland for the enormous contribution of time and ideas to increase the productivity of my thesis.During my studies, we had numerous meetings and discussions on a variety of research issues; throughout them, he has taught me many aspects of communication systems.Completing my Ph.D. would have been difficult without his excellent support and patience.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".