Performance Analysis for BICM Transmission over Gaussian Mixture Noise Fading Channels
Bibliographic record
Abstract
Bit-interleaved coded modulation (BICM) has been adopted in many systems and standards for spectrally efficient coded transmission. The analytical evaluation of BICM performance parameters, in particular bit-error rate (BER), has received considerable attention in the recent past. In this paper, we derive BER approximations for BICM transmission over general fading channels impaired by Gaussian mixture noise (GMN). To this end, we build upon the saddlepoint approximation of the pairwise error probability (PEP) and a recently established approximation for the probability density function (PDF) of bit-wise reliability metrics for nonfading additive white Gaussian noise (AWGN) channels. We extend this PDF approximation to the case of GMN, and obtain closed-form expressions for its Laplace transform for fading GMN channels. The latter allows us to express the PEP and thus BER via the saddlepoint approximation. For the special case of fading AWGN channels the presented approximations are closed form, since the saddlepoint is well approximated by 1/2 for BICM decoding. Furthermore, we derive closed-form PEP expressions also for GMN channels in the high signal-to-noise ratio regime and establish the diversity and coding gain for BICM transmission over fading GMN channels. Selected numerical results for the BER of convolutional coded BICM highlight the usefulness of the proposed approximations and the differences between AWGN and GMN channels.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".