WaveNet: Toward Waveform Classification in Integrated Radar–Communication Systems With Improved Accuracy and Reduced Complexity
Bibliographic record
Abstract
The integration of radar and communication systems in 6G networks has led to a significant challenge of spectrum congestion. To address this issue, we propose a deep learning-based method for efficient waveform-based signal classification. Our method is designed to handle large and impaired radar and communication signals, and is crucial for the implementation of resource-limited cognitive radio-enabled Internet-of-Things (CR-IoT) devices. We introduce WaveNet, a cost-efficient deep convolutional neural network that can aptly learn underlying radio features from time-frequency images transformed by a smooth pseudo Wigner-Ville distribution. WaveNet incorporates several innovative modules, including cost-efficient feature awareness, which integrates two well-designed structural blocks: grouped-of-kernel-wise residual connections and dual asymmetric channel attention. These enhancements significantly reduce network size without compromising classification accuracy. Based on various simulations experimented on an impaired signal dataset containing eight radar and communication waveform types, the results demonstrate the effectiveness and robustness of WaveNet, achieving an overall classification accuracy of 92.02%. Compared to the current state-of-the-art deep models, WaveNet has the lowest architectural complexity, with a network size five times smaller, while still outperforming them by approximately 0.5 – 1.69%. Consequently, WaveNet emerges as a valuable solution for waveform classification in integrated radar-communication 6G systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".