Photonic-Assisted Joint Radar and Communication System Using Overlapped Spectrum for High-Resolution Ranging and High-Speed Data Transmission
Bibliographic record
Abstract
We propose and demonstrate a photonic-assisted joint radar and communication (JRC) system using a JRC signal with overlapped spectrum to support simultaneous wideband ranging and high-speed communications. For radar ranging, an electrical linearly-frequency-modulated (LFM) waveform is modulated on an optical carrier at a Mach-Zehnder modulator (MZM) biased at the null point to generate odd-order sidebands. By filtering out the first-order sidebands, two third-order sidebands are obtained. By beating the two sidebands at a photodetector (PD), a frequency-sextupled electrical LFM with sextupled bandwidth is generated. For wireless communications, a binary phase-shift keying (BPSK) or quadrature phase-shift keying (QPSK) signal is modulated on one of the third-order sidebands. By beating the modulated sideband with the other unmodulated sideband, a frequency upconverted wireless signal is generated. The JRC signals are radiated to free space. At the radar receiver, the echo signal is applied to another MZM where the signal is optically dechirped to extract the target information. At the communication receiver, the JRC signal is received and applied to an MZM, where the radar and the communication signals beat to generate the original communication signal. Since no local oscillator (LO) signal is employed, the system is greatly simplified. The dual functions of the system are evaluated by an experiment. For radar ranging, a wideband LFM radar signal with a 9.6-GHz bandwidth is generated, and radar ranging with a high range resolution of 2.5 cm is obtained. For wireless communication, a 4-GBaud/s BPSK and a 2-GBaud/s QPSK signal are transmitted over 20 m, with the bit error rates (BERs) of$1.82\times 10^{-5}$and$1.96\times 10^{-5}$, respectively, which are below the forward error correction (FEC) limit for error free communications.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".