Performance Analysis for BICM Transmission over Gaussian Mixture Noise Fading Channels
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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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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it