Signal Detection in Space-Time Coded Communication Systems with Imperfect Channel Estimation and Carrier Frequency Offset
Bibliographic record
Abstract
In multi-antenna communication systems, signal detection is significantly affected by the presence of channel fading and the introduction of Carrier Frequency Offset (CFO) during signal demodulation. The conventional solution is to estimate the Channel State Information (CSI) and CFO and apply estimates in a detector metric that assumes perfect knowledge of CSI and CFO. This thesis proposes new metrics for Space-Time Block decoding with noisy CSI and CFO estimates by including the error variance of CSI and CFO estimates in the metric derivation.The BER performance of the conventional metric and proposed metrics, both using Joint Maximum A Posteriori (MAP) CSI/CFO estimates shows that the former slightly outperforms the latter and their performances converge at high SNR values. However, under worse-case scenarios, the proposed metrics outperform the conventional metric.We conclude that the joint MAP estimator/conventional metric combination is more appropriate for signal detection due to its relatively low complexity.
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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 | 0.002 | 0.015 |
| 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 | 0.002 |
| 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".