Performance enhancement techniques for variable spreading factor OFCDM systems
Bibliographic record
Abstract
n this thesis we investigate the effect of Carrier Frequency Offset (CFO) on the performance of downlink Variable Spreading Factor (VSF) OFCDM systems when subcarrier grouping is used. An analytic expression of the SINR is derived for downlink VSF-OFCDM with CFO for the case of maximal ratio combining receiver. Numerical results show that, when the total spreading factor is fixed, the VSF-OFCDM system with higher frequency domain spreading factor is more sensitive to CFO than that with lower frequency domain spreading factor. Due to the adverse impact of the CFO on VSF-OFCDM systems, we propose a correction scheme based on the maximum likelihood principle. We derive the likelihood function for VSF-OFCDM system with CFO and use a gradient algorithm to estimate and minimize the effect of CFO in a tracking mode. Our results show that the BER performance in the low SNR environment can be improved significantly with few number of iterations for different spreading factors. We also propose a threshold-based group-adaptive modulation algorithm used with an adaptive subcarrier allocation technique for downlink VSF-OFCDM to increase the spectral efficiency for a given target BER. The proposed algorithm provides an increase in spectral efficiency without increasing the total transmit power for different spreading factors with and without coding.
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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) | 0.000 | 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) | 0.001 | 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".