A Microwave Photonic Neural Network Using Recurrent Multi-Frequency Single-Sideband Modulation in an Optical Fiber Loop
Bibliographic record
Abstract
Taking advantage of the high speed and wide bandwidth of photonics, microwave photonics (MWP) is well-suited for high-speed signal processing. In this paper, we propose and experimentally demonstrate a novel microwave photonic neural network based on recurrent carrier-suppressed single sideband (CS-SSB) modulation implemented using an amplified optical fiber loop incorporating a dual-parallel Mach-Zehnder modulator (DP-MZM). We show that the matrix-vector multiplication (MVM) between the neurons and weights can be implemented by multi-tone CS-SSB modulation on multiple optical carriers. By using a programmable optical filter to introduce attenuations and control the powers of the sidebands, it is also possible to achieve the desired neuron biases. Multiple layers in a deep neural network are realized by recurrent CS-SSB modulation and programmable filtering in the optical fiber loop. The approach is evaluated experimentally. A 2-2-4 feedforward neural network at a computational speed of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$30\times 10^{6}\text{MAC}/\mathrm{s}$</tex> (multiply-accumulate operations per second) is implemented to successfully tackle the task of 2-bit binary number recognition.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".