Effects of imperfect channel estimation on space–time coding performance
Bibliographic record
Abstract
The effect of imperfect channel estimation on the bit error rate (BER) of multiple-input multiple-output communication systems utilising space–time coding is investigated. A multipath channel is considered and a given level for the channel estimation error is assumed. The receiver employs an equaliser to reduce the ISI in the received signal. A closed-form expression for the SNR at the output of the equaliser that employs the results of the channel estimation is derived. The theoretical SNR derived is used as a basis to assess the performance of the system. Results are applicable to any channel estimation technique. Analysis shows that the deterioration of performance in the multiple transmit antenna scheme outweighs the benefits achieved over the single antenna case when the SNR and channel estimation error are large. The degradation in the transmit diversity scheme exceeds 8 dB to achieve a BER of 10−4 when the channel estimation error is 5% relative to the perfect channel estimation case.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".