Cognitive Waveform Design for Radar-Communication Transceiver Networks
Bibliographic record
Abstract
The system architecture for cognitive radar-communication (CRC) transceiver is proposed. A cognitive waveforms design approach, which is suitable for simultaneously performing both data communication and target detection, is presented. This approach aims at estimating target scattering coefficient (TSC) from the radar scene and facilitating high data rate communications. In order to minimize the mean square error (MSE) of the TSC, a convex cost function is established. The peak to average power ratio- (PAPR-) constrained optimal solution is achieved by applying the Kalman filtering-based strategy to design the set of ultra-wideband (UWB) transmission pulses and embed into them the information data with the M-ary position phase shift keying modulation technique. In addition to theoretical considerations, the simulation results show an improvement in target scattering coefficient (TSC) estimation and target detection probability as the number of iterations increases, while still transmitting data rates in the range of several Mbps with low bit error rates between CRC transceivers.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".