Analysing bit-interleaved coded modulation in multiple-input multiple-output systems with channel estimation error
Bibliographic record
Abstract
The performance of bit-interleaved coded modulation in multiple-input multiple-output (BICM-MIMO) systems using an iterative channel estimator is analysed. In a conventional iterative channel estimator, after initialisation with the training phase, the channel estimator switches to the data phase. However, such a conventional iterative channel estimator does not always improve the performance of the receiver. In order to guarantee the performance improvement, a condition on when the output of the decoder should be used by the estimator is determined. Such a condition is related to the reliability of the soft information utilised by the channel estimator. The key in establishing this relationship is to use the mutual information (MI) that the observation vector has about the channel gains given the output of the decoder at each iteration. In this switch-augmented conventional iterative channel estimator, referred to as SAICE, the condition is theoretically found and indicates the needed reliability of the soft information for the channel estimator at the switching time. The switch-augmented scheme guarantees performance improvement of the iterative receiver with each iteration, however, it might need many iterations to converge for moderate to low signal-to-noise ratios (SNRs). A less computationally intensive approach is to use both the training and data segments of the observation. This approach produces a combined iterative channel estimator (CICE) for BICM-MIMO systems. The performance behaviour of the BICM-MIMO system is illustrated through the extrinsic information transfer (EXIT) chart with imperfect channel state information (CSI). Analytical results are verified with computer simulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".