Millimeter-Wave Joint Sensing and Communication System Design for Connected Automated Vehicles
Bibliographic record
Abstract
This paper introduces a novel mmWave joint radar and communication (JRC) system operating in the 24 GHz ISM band, specifically designed for automated connected vehicles. The system utilizes a single transceiver platform featuring a frequency-shift keying modulation scheme that enables simultaneous operation in the radar and communication modes. The proposed JRC system boasts several distinctive features. Firstly, its simultaneous sensing and communication capability, a departure from the conventional sequential arrangement in the time domain. Secondly, the system is characterized by its simplicity, offering ease of implementation and operation. The third feature addresses synchronization challenges through an innovative approach that eliminates the dependence on a shared clock or timing reference, enhancing the system flexibility and adaptability. Lastly, the system stands out for its cost-effectiveness. Results from simulations and experiments collectively showcase the system’s promising performance. In particular, the JRC system demonstrates robust radio communication capabilities with adjustable data rate based on the instantaneous measured Doppler frequency, while also exhibiting promising radar capabilities. This dual functionality underscores the system’s versatility and potential for practical applications in smart environments. Notably, throughout the transmission of vital information, no errors were detected in the communication mode, affirming the reliability of the system.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".