Performance Evaluation of Linear Turbo Receivers Using Analytical Extrinsic Information Transfer Functions
Bibliographic record
Abstract
Turbo-receivers reduce the effect of the interference-limited propagation channels through the iterative exchange of information between the front-end receiver and the channel decoder. Such an iterative (turbo) process is difficult to describe in a closed form so the performance evaluation is often done by means of extensive numerical simulations. Analytical methods for performance evaluation have also been proposed in the literature, based on Gaussian approximation of the output of the linear signal combiner. In this paper, we propose to use mutual information to parameterize the logarithmic-likelihood ratios (LLRs) at the input/output of the decoder, casting our approach into the framework of extrinsic information transfer (EXIT) analysis. We find the EXIT functions of the front-end (FE) receiver analytically, that is, using solely the information about the channel state. This is done, decomposing the FE receiver into elementary blocks described independently. Our method gives an insight into the principle of functioning of the linear turbo-receivers, allows for an accurate calculation of the expected bit error rate in each iteration, and is more flexible than the one previously used in the literature, allowing us to analyze the performance for various FE structures. We compare the proposed analytical method with the results of simulated data transmission in case of multiple antennas transceivers.
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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 | 0.014 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".