Accurate BCJR-based synchronization algorithm for single carrier channels with extremely high order modulations
Bibliographic record
Abstract
Microwave transmission is a predominant technology in telecommunication networks. As capacity requirement for mobile networks rises every day, microwave backhauls links are forced to constantly improve their spectral efficiency over limited allocated bandwidths. Moving toward higher order modulation is a cost-efficient solution to increase capacity in bandwidth-limited channels. Complex higher order modulations may be practical if a well-defined, high performance, carrier synchronization algorithm is provided to detect and remove the phase noise from the desired signal. The importance of such a precise carrier recovery algorithm is stressed with the transition from legacy, GaAs VCO frequency sources, to integrated, low-cost CMOS based VCOs that suffer from a significantly elevated phase noise level. Contribution of this paper is proposing a novel carrier synchronization algorithm that handles phase noise challenges effectively and allows complicated extremely high QAMs at an acceptable complexity level. Our proposed algorithm is pilot-aided and employs a BCJR-based sequence estimator to detect fast-varying phase noise symbol-by-symbol. We verify and validate performance of our proposed algorithm on VC707 XILINX board and emulation results confirm zero BER performance for complex modulation orders as high as 4K-QAM.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".