Deep-Learning Based Detectors for SISO and SIMO IM/DD FSO Systems
Bibliographic record
Abstract
Free space optical (FSO) communication has the potential to develop ultra-fast data links that can be used in a vari-ety of sixth-generation (6G) applications, including heterogeneous networks with enormous connectivity and wireless backhauls for cellular systems. This paper proposes several low-complexity deep neural networks designed to detect received symbols in FSO systems, eliminating the need for channel state information (CSI). We consider different intensity modulation (IM) schemes (e.g., on-off shift keying (OOK) and pulse amplitude modulation (PAM)) and different FSO systems (e.g., single-input single-output (SISO) and single-input multiple-output (SIMO)). We design the neural network to be able to exploit the temporal correlation of the channels and the common information received at every single photodetector (the case of SIMO). The convolutional neural network (CNN) is designed in a way that it can learn the channels without using pilots and also can jointly combine the received signals and detect the symbols for a wide range of signal-to-noise ratio (SNR) values and different turbulence levels. Numerical results show that the proposed CNN demonstrates a better performance compared to the maximum likelihood approach that utilizes imperfect channel information for symbol detection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".