Research on anti-jamming transmission mechanism and intelligent modulation recognition algorithm of data link for complex battlefield environment
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Bibliographic record
Abstract
Under the background of multi-domain joint operations in modern warfare, data link, as the "nerve center" of the battlefield, faces the severe challenge of high-density and multi-dimensional dynamic electromagnetic interference. The traditional data link system has an "adaptive dilemma" because it adopts a fixed anti-jamming strategy, and modulation recognition is limited by the performance bottleneck under low signal-to-noise ratio (SNR) and the "intelligent bottleneck" of insufficient generalization ability of deep learning model. Aiming at the above problems, this study constructs a closed-loop framework of "perception-decision-execution", and proposes a cross-layer cooperative anti-interference transmission mechanism and an intelligent modulation recognition algorithm. The anti-jamming transmission mechanism perceives the channel and interference characteristics in real time through lightweight convolutional neural network (CNN), and dynamically optimizes frequency hopping, direct sequence spread spectrum and power control strategies based on Dueling Double DQN (DDQN) algorithm to realize adaptive resource allocation. The design of the intelligent modulation recognition algorithm TFSC-Net (Time-Frequency-Symbolic Joint Network) dual-channel feature fusion model jointly extracts features from the time-frequency domain and symbol domain of the signal, and improves the recognition accuracy and generalization capability under low SNR by combining an enhanced loss function. Experimental results demonstrate that the proposed scheme achieves a low bit error rate of 8.7×10⁻⁵ and a throughput of 34.9 Mbps in a strong interference environment (JSR=20dB). TFSC-Net achieves a recognition rate of 89.4% at 0dB SNR and 97.3% at 10dB SNR, with a latency controlled at 13.8ms, balancing interference resistance, recognition accuracy, and real-time performance. The research findings provide technical support for enhancing the survivability of battlefield communications, strengthening electronic warfare advantages, and promoting the development of the next-generation data link system.
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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 | 0.001 | 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) | 0.000 | 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