A Predictive Integrated Sensing, Communication, and Computation Over-the-Air Approach for IoV: Optimization and Trade-Off Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Integrated Sensing, Communication, and Computation (ISCC) has the potential to meet diverse requirements of Internet of Vehicles (IoV), such as high reliability and low power consumption. However, existing works have not fully considered the problems of unreliable communication links and inefficient data processing under resource constraints in non-ideal environments. To address these issues, this paper proposes a predictive Integrated Sensing, Communication and Computation Over-the-Air (ISCCO) approach based on Orthogonal Time Frequency Space (OTFS) modulation. It takes high Doppler shifts, network dynamics, and resource constraints into account. In particular, the Road Side Unit (RSU) performs target tracking while communicating with the downlink users through Space Division Multiplexing (SDM), and receives the transmission results of the uplink. For the downlink, a predictive beamforming approach based on Extended Kalman Filtering (EKF) is employed, while Over-the-Air computation (AirComp) is utilized for the uplink. The transmit power and receive beamformer at the RSU, along with the transmit power of the uplink users, are jointly optimized through two formulated optimization problems: sensing performance maximization and power consumption minimization. To solve these problems, we adopt an Alternating Optimization (AO)-based algorithm for finding the local optimal solution. Simulation results validate the effectiveness of the AO-based algorithm, and the analysis of the trade-offs between multi-dimensional performance of ISCC and power consumption is conducted.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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