A Predictive Integrated Sensing, Communication, and Computation Over-the-Air Approach for IoV: Optimization and Trade-Off Analysis
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".