Bibliographic record
Abstract
Polar codes, ever since their introduction, have been shown to be very effective for various wireless communication channels.This, together with their relatively low implementation complexity, has made polar codes an attractive coding scheme for wireless communications.On the other hand, within the realm of non-coherent wireless MIMO communication, Grassmannian signalling has been shown to approach the ergodic capacity of frequency-flat block fading channels at high SNR.In this paper, we combine these two components together using multilevel polar coding to create a system that produces the best known performance for a coded non-coherent channel.This combination requires that the signal constellation be set-partition labelled, so, a novel set partitioning algorithm, which works for regular and irregular multidimensional constellations, such as the Grassmannian constellation, is proposed.Finally, we develop a methodology for designing polar codes for a noisy channel and this is used to further improve the performance of our system.In simulation, we compare the error rate performance of our design with that of existing schemes and show that significant gains are possible over the previously known best technique, which is based on turbo codes.We then provide a complexity analysis of our receiver in comparison with other existing methods and show that it is able to provide these gains at a considerably lower complexity.Prof. Halim Yanikomeroglu, thank you very much for all the encouragement you gave to me, financial and otherwise, and thank you for providing a comfortable working space.To Dr. Ramy Gohary, I appreciate all the time you spent explaining the Grassmannian concepts to me and all the resources you provided to improve my understanding.I thank you all for believing in me, I could not have asked for a better team of supervisors.I would also like to thank my research colleagues for all the support, encouragement and advice they gave me throughout my research process.I would like to thank my family
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".