A High Speed Underwater Wireless Communication Through a Novel Hybrid Opto-Acoustic Modem Using MIMO-OFDM
Bibliographic record
Abstract
For efficient underwater opto/acoustic communication, this research proposes the use of MIMO in conjunction with OFDM. OFDM (Orthogonal Frequency-Division Multiplexing) and MIMO (Multiple Input Multiple Output) systems may be widely used in wireless networks to provide high data transfer rates, resistance to multipath fading, and an increase in the channel's Spatial Multiplexing and Spatial Diversity Gain. Transmission speed can be increased by altering bandwidth or spectral efficiency (or both) in wireless data transmission systems. Systems that use Multi-Input Multi-Output (MIMO) technologies have the potential to improve spectral efficiency by employing several transmitters and receivers in tandem. To maximize spectrum efficiency and minimize inter-symbol interference, Orthogonal Frequency Division Multiplexing (OFDM) divides signals into a number of narrow band channels (ISI). In other words, combining the benefits of MIMO with OFDM will boost spectral efficiency while also increasing the link's dependability and spectral gain. MIMO and OFDM approaches are integrated in this research to increase opto-acoustic modem performance. MATLAB Simulink tool was used to design and simulate the proposed hybrid opto-acoustic modem with MIMO-OFDM for optical and acoustic (EM) signal transmission and reception. The simulation results verify the viability of the proposed method, and the measured bit-error rate (BER) for acoustic (EM) signal is 0.4958 and optical signal is 0.5101. The overall bandwidth of the system is from -150 MHz to +150 MHz.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".