Joint Channel and Phase Noise Estimation and Data Detection for GFDM
Bibliographic record
Abstract
Generalized frequency division multiplexing (GFDM), an enabler of beyond-5G wireless networks, can be critically impaired due to radio frequency (RF) phase noise. However, joint channel estimation and phase noise compensation for GFDM systems have not been addressed before. Hence, we tackle this problem. To this end, we propose an iterative algorithm for joint channel and phase noise estimation and two algorithms for joint data detection and phase noise compensation. These algorithms use linear and non-linear least-squares (NLS) methods and employ block-type and comb-type pilots. The complexity of these algorithms is also analyzed. Moreover, to reduce their complexity, interpolation techniques are deployed to decrease the number of unknowns. We also analyze the signal-to-interference-plus noise ratio (SINR) and sum-rate of GFDM contaminated with phase noise. Furthermore, the accuracy of the channel and phase noise estimates is established via Cramér-Rao lower bounds (CRLBs). The simulation results illustrate that the mean-squared error (MSE) performance of the proposed joint channel and phase noise estimator reaches the CRLB. Moreover, the proposed joint data symbol detection and phase noise compensation algorithms nearly eliminate the impacts of phase noise in GFDM systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".