Performance Enhancement of High Order LDPC Coded Modulation Systems with Application in Mobile Backhaul Networks
Bibliographic record
Abstract
Wireless backhaul communication systems play a crucial role in the infrastructure of current cellular networks.In light of the expensive costs of fiber deployment, the importance of these links is anticipated to continue over the next few decades.Point-to-point microwave radio links (PtPMRLs) are the backbone of every wireless backhaul system.To achieve higher spectral efficiencies, the PtPMRL are designed based on the concurrent exploitation of high-order modulations and powerful forward error correcting codes.An example of such design paradigm is the high-order lowdensity-parity-check coded modulation systems (LDPC-CMSs).In this thesis two key challenges in the high-order LDPC-CMSs are overcome.In our first contribution, we consider the problem of oscillator phase hit (PH) in microwave backhaul communication links.PH results in a temporary link loss in the communication system and bears an expensive cost on the operators.In this work, we propose a two-stage solution to mitigate PH.In the first stage, we use Neyman-Pearson binary hypothesis testing to develop a low cost PH detection algorithm.In this test, a likelihood ratio test is designed and the optimal detection threshold is analytically calculated.The proposed PH detection scheme entails small real-time computations while using the existing pilot symbols in the system; thus, no extra pilot overhead is required.In the second stage, we use maximum likelihood estimation to develop a PH correction scheme.In particular, we formulate the joint estimation of phase noise (PN) and PH as a maximum likelihood estimation problem.Solving this problem results in the estimation of PN with the location and magnitude of PH.By applying the proposed correction scheme, the number of affected symbols by PH is significantly reduced.In particular, the number of remaining erroneous symbols due to PH (if any) is within the error correction capability of modern LDPC codes used iii I would like to express my sincere gratitude to my supervisor, Professor Ramy Gohary for his tremendous support.Working with Prof. Gohary gave me the opportunity of learning technical writing and critical thinking at the same time.I am truly grateful for all the amazing time we spent together working on and discussing every detail of my research.I know that such an opportunity does not exist for many students, and I did not take having that for granted.I would also like to thank him for his friendship, empathy, and great sense of humor.Foremost, I am extremely grateful to my wife, Neda, for her love, caring, and continuous support to complete my PhD
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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".