Accuracy-Aware Low-Complexity Deep Learning Models for Automatic Modulation Recognition
Why this work is in the frame
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Bibliographic record
Abstract
This paper explores the design of Deep Learning (DL) models for Automatic Modulation Recognition (AMR) in wireless communications. The primary goal is to enhance the efficiency and hardware compatibility of convolutional neural networks (CNNs) for AMR through hyperparameter tuning and model compression. The paper first examines the effectiveness of applying quantization and pruning on the accuracy and compu-tational cost of two prominent CNN models from the literature. It then introduces a new CNN model that achieves superior accu-racy with lower computational complexity compared to previous work. The design flow integrates TensorFlow Lite for pruning and quantization, and NVIDIA TensorRT for benchmarking on GPUs specialized for machine learning computing. Experimental results show significant reductions in model size and computational complexity while maintaining accuracy, rendering the proposed DL models suitable for real-time applications on edge devices.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it