Signal detection in optical orthogonal time space modulation for efficient VLC application
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper presents a detailed analysis of the bit error rate (BER) performance of a proposed maximum likelihood (ML) detection scheme for optical orthogonal time space modulation (OTSM) systems using 64-QPSK, considering practical optical impairments and diverse channel conditions. The evaluation covers scenarios with 5 % and 10 % channel estimation errors, as well as Rayleigh and Rician fading environments. Simulation results confirm that the proposed machine learning (ML) detector consistently outperforms conventional methods – including OTSM, zero-forcing equalization (ZFE), minimum mean square error (MMSE), and conventional ML – by delivering substantial SNR gains. For instance, under 10 % and 5 % estimation errors, the target BER of 10 −3 is achieved at 12.2 dB and 10.8 dB, respectively, providing up to 6 dB improvement over baselines. In Rayleigh fading, the same BER is attained at 9.6 dB with a gain of 7.7 dB, while in Rician fading, the detector achieves optimal performance at only 6 dB, outperforming others by as much as 9.5 dB. These results underscore the robustness of the proposed ML approach against estimation inaccuracies and fading, making it well-suited for low-power, high-reliability applications in 6G, Internet of things (IoT), vehicular networks, and satellite communications.
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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 | 0.001 | 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 | 0.000 |
| 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