On the Performance of the Golden Code in the Presence of Channel Estimation Error in Correlated MIMO Rayleigh Fading Channels
Bibliographic record
Abstract
Abstract The Golden Code (GC) is a full-rate and full-diversity, space-time code for 2 × 2 multiple-input multiple-output (MIMO) systems with non-vanishing minimum determinant. Thanks to its algebraic construction, the GC achieves diversity-multiplexing gain trade-off and preserves the mutual information. The zero-forcing (ZF) detection is one of the simplest detection techniques that has a low computational complexity and provides a suboptimal performance. The purpose of this work is to investigate the effect of channel estimation error on the performance of the MIMO-ZF receiver for golden coded systems in spatio-temporally correlated Rayleigh fading channels. An upper and a lower bounds of the error probability are delivered and compared to the Monte Carlo simulation results. We quantify the capacity reduction due to the channel estimation error and to the spatio-temporally correlation. A tight lower bound of the ergodic capacity is provided. Numerical results show an excellent agreement between analytic bounds and Monte Carlo simulation curves. Moreover, the performances of the GC, in terms of bit error rate (BER) and channel capacity, are compared with those of the Alamouti 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 | 0.003 | 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 | 0.002 |
| 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".